From 28a911aac55fcedbc1934fde2629ac961026df1f Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 10:55:10 +0200 Subject: [PATCH 01/12] docs(design): demonstrate the inhomogeneous-file cases of zarr-specs#399 A script writes small netCDF4 files that differ in codec, chunk length, inner chunking, CF scaling and dtype, virtualizes each with VirtualiZarr, and tries to combine them along time: four cases fail, and the per-file scale_factor case combines and reads silently wrong values. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 27 +++ design/experiments/inhomogeneous/demo.py | 179 ++++++++++++++++++++ design/experiments/inhomogeneous/output.txt | 56 ++++++ 3 files changed, 262 insertions(+) create mode 100644 design/experiments/inhomogeneous/README.md create mode 100644 design/experiments/inhomogeneous/demo.py create mode 100644 design/experiments/inhomogeneous/output.txt diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md new file mode 100644 index 00000000..1e48b8c6 --- /dev/null +++ b/design/experiments/inhomogeneous/README.md @@ -0,0 +1,27 @@ +# Inhomogeneous files: where virtualization falls off the cliff + +An executable companion to +[zarr-developers/zarr-specs#399](https://github.com/zarr-developers/zarr-specs/issues/399) +("Varying codec parameters per-chunk"). [demo.py](demo.py) writes small netCDF4 +files that are each valid and consistent on their own, virtualizes each one with +VirtualiZarr (which works), and then tries to combine them along time into one +logical Zarr array, as you would a real archive. + +```bash +uv run python design/experiments/inhomogeneous/demo.py +``` + +| case | the files | what happens | +|---|---|---| +| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | **fails**: a Zarr array has one codec chain | +| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | **fails**: needs variable-length chunks | +| 3. ragged inner chunks (tiled GeoTIFF / netCDF cubes) | 10 steps each, chunked by 4: 4, 4, 2 | **fails**: the short last chunk breaks the regular grid | +| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16 packed with each file's own scaling | **combines, then reads silently wrong values**: 48 of 72 values differ, because the first file's scaling is applied to every file | +| 5. varying dtype | int16, int16, float32 | **fails**: one array, one dtype | + +Case 4 is the dangerous one: nothing errors. The CF attributes are array-level +metadata, so concatenating keeps the first file's `scale_factor` and `add_offset` +and decodes the other files' integers with them. + +Versions: VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. The full +output of a run is in [output.txt](output.txt). diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py new file mode 100644 index 00000000..5969f4c7 --- /dev/null +++ b/design/experiments/inhomogeneous/demo.py @@ -0,0 +1,179 @@ +"""Why many-file datasets fall off the virtualization "complexity cliff". + +An executable companion to zarr-developers/zarr-specs#399 ("Varying codec parameters +per-chunk"). Each case writes a few small netCDF4 files that are each perfectly valid, +virtualizes every file with VirtualiZarr, and then tries to combine them into one +logical Zarr array along time, the way you would a real archive. Each case reports +what happens: an error, or a combined array whose values are compared with the +truth read straight from the files. + + uv run python design/experiments/inhomogeneous/demo.py + +Cases (the use cases of the issue): + +1. codec eras: the compression changes partway through the record (GOES-16) +2. rectilinear: files of different lengths along time (monthly files) +3. ragged inner chunks: each file is chunked internally and its last chunk is short +4. per-file scale_factor / add_offset (BOM BARPA) +5. varying dtype: the same variable stored as int16 in some files and float32 in others +""" + +from __future__ import annotations + +import tempfile +import warnings +from pathlib import Path + +import h5py +import numpy as np +import virtualizarr as vz +import xarray as xr +from obspec_utils.registry import ObjectStoreRegistry +from obstore.store import LocalStore +from virtualizarr.parsers import HDFParser + +warnings.filterwarnings("ignore") +NX = 6 # a small spatial axis, so every case runs in a second + + +def write(path: Path, data: np.ndarray, *, t0: int, chunks=None, compression=None, + compression_opts=None, shuffle=False, attrs=None) -> None: + """One netCDF4 file: a variable `v(time, x)`, its `time` coordinate starting at t0.""" + with h5py.File(path, "w") as f: + nt = data.shape[0] + t = f.create_dataset("time", data=np.arange(t0, t0 + nt, dtype="int64")) + t.make_scale("time") + t.attrs["units"] = "days since 2000-01-01" + x = f.create_dataset("x", data=np.arange(NX, dtype="int64")) + x.make_scale("x") + v = f.create_dataset("v", data=data, chunks=chunks, compression=compression, + compression_opts=compression_opts, shuffle=shuffle) + v.dims[0].attach_scale(t) + v.dims[1].attach_scale(x) + for k, val in (attrs or {}).items(): + v.attrs[k] = val + + +def virtualize(paths: list[Path], root: Path) -> list[xr.Dataset]: + """Each file as a virtual dataset (only its metadata is read).""" + registry = ObjectStoreRegistry({f"file://{root}": LocalStore(prefix=root)}) + return [vz.open_virtual_dataset(f"file://{p}", registry=registry, parser=HDFParser(), + loadable_variables=["time", "x"]) for p in paths] + + +def combine(vdss: list[xr.Dataset]) -> xr.Dataset: + """Concatenate along time, as open_virtual_mfdataset would.""" + return xr.concat(vdss, dim="time", coords="minimal", compat="override", combine_attrs="override") + + +def truth(paths: list[Path]) -> np.ndarray: + """What a user expects: every file decoded on its own (CF scaling applied), then stacked.""" + return np.concatenate([xr.open_dataset(p, engine="h5netcdf")["v"].values for p in paths]) + + +def read_back(vds: xr.Dataset) -> np.ndarray: + """Read the combined virtual array through Zarr, as a user of the virtual store would: + via refkv's in-memory reference store, decoding CF attributes the way xarray does.""" + import refkv + import refkv.zarr + + store = refkv.ReferenceStore.from_virtualizarr(vds) + return xr.open_zarr(refkv.zarr.store(store), consolidated=False)["v"].values + + +def run(title: str, issue: str, make) -> None: + print(f"\n{'=' * 78}\n{title}\n {issue}\n{'-' * 78}") + with tempfile.TemporaryDirectory() as d: + root = Path(d) + paths = make(root) + for p in paths: + with h5py.File(p) as f: + v = f["v"] + filters = {k: v for k, v in (("compression", v.compression), ("opts", v.compression_opts), + ("shuffle", v.shuffle)) if v} + cf = {k: float(v.attrs[k]) for k in ("scale_factor", "add_offset") if k in v.attrs} + print(f" {p.name}: shape={v.shape} chunks={v.chunks} dtype={v.dtype} " + f"filters={filters or 'none'}" + (f" {cf}" if cf else "")) + vdss = virtualize(paths, root) # each file alone virtualizes fine + print(f" -> each file virtualizes on its own: {len(vdss)} virtual datasets") + try: + combined = combine(vdss) + except Exception as e: # the cliff: no single Zarr array can describe these files + print(f" -> combining them fails: {type(e).__name__}: {str(e).splitlines()[0][:220]}") + return + print(f" -> combined: v{tuple(combined['v'].shape)}, codecs/encoding of the FIRST file used for all") + try: + got, want = read_back(combined), truth(paths) + except Exception as e: + print(f" -> reading the combined array fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") + return + if got.shape == want.shape and np.allclose(got, want, equal_nan=True): + print(" -> values match the files") + else: + bad = int(np.sum(~np.isclose(got, want, equal_nan=True))) if got.shape == want.shape else "all" + print(f" -> SILENTLY WRONG: {bad} of {want.size} values differ from the files") + print(f" expected {np.round(want[:, 0], 3)}\n got {np.round(got[:, 0], 3)}") + + +rng = np.random.default_rng(0) + + +def era_files(root: Path) -> list[Path]: + paths = [] + # gzip, then gzip behind a shuffle filter, then no compression at all + for i, (comp, opts, shuf) in enumerate([("gzip", 4, False), ("gzip", 4, False), + ("gzip", 4, True), (None, None, False)]): + p = root / f"era_{i}.nc" + write(p, rng.random((4, NX)).astype("float32"), t0=4 * i, chunks=(4, NX), + compression=comp, compression_opts=opts, shuffle=shuf) + paths.append(p) + return paths + + +def rectilinear_files(root: Path) -> list[Path]: + paths, t0 = [], 0 + for i, nt in enumerate([31, 28, 31]): # January, February, March + p = root / f"month_{i}.nc" + write(p, rng.random((nt, NX)).astype("float32"), t0=t0, chunks=(nt, NX), compression="gzip") + paths.append(p) + t0 += nt + return paths + + +def ragged_files(root: Path) -> list[Path]: + paths = [] + for i in range(3): # 10 steps per file, chunked by 4: chunks of 4, 4, 2 + p = root / f"ragged_{i}.nc" + write(p, rng.random((10, NX)).astype("float32"), t0=10 * i, chunks=(4, NX), compression="gzip") + paths.append(p) + return paths + + +def scaled_files(root: Path) -> list[Path]: + paths = [] + for i, (scale, offset) in enumerate([(0.01, 280.0), (0.02, 270.0), (0.005, 290.0)]): + p = root / f"scaled_{i}.nc" + physical = 285.0 + 5 * rng.random((4, NX)) + packed = np.round((physical - offset) / scale).astype("int16") + write(p, packed, t0=4 * i, chunks=(4, NX), compression="gzip", + attrs={"scale_factor": np.float32(scale), "add_offset": np.float32(offset)}) + paths.append(p) + return paths + + +def dtype_files(root: Path) -> list[Path]: + paths = [] + for i, dtype in enumerate(["int16", "int16", "float32"]): + p = root / f"dtype_{i}.nc" + write(p, (100 * rng.random((4, NX))).astype(dtype), t0=4 * i, chunks=(4, NX), compression="gzip") + paths.append(p) + return paths + + +if __name__ == "__main__": + run("1. codec eras", "the compression changes partway through the record (GOES-16)", era_files) + run("2. rectilinear chunks", "monthly files: 31, 28 and 31 days, one chunk each", rectilinear_files) + run("3. ragged inner chunks", "each file chunked by 4 along time, 10 steps: chunks 4, 4, 2", ragged_files) + run("4. per-file scale_factor / add_offset", "each file packs int16 with its own CF scaling (BOM BARPA)", + scaled_files) + run("5. varying dtype", "the same variable is int16 in two files and float32 in the third", dtype_files) diff --git a/design/experiments/inhomogeneous/output.txt b/design/experiments/inhomogeneous/output.txt new file mode 100644 index 00000000..8834cfad --- /dev/null +++ b/design/experiments/inhomogeneous/output.txt @@ -0,0 +1,56 @@ + +============================================================================== +1. codec eras + the compression changes partway through the record (GOES-16) +------------------------------------------------------------------------------ + era_0.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + era_1.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + era_2.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4, 'shuffle': True} + era_3.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters=none + -> each file virtualizes on its own: 4 virtual datasets + -> combining them fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numcodecs.zlib', codec_config={'level': 4})) vs (BytesCodec(endi + +============================================================================== +2. rectilinear chunks + monthly files: 31, 28 and 31 days, one chunk each +------------------------------------------------------------------------------ + month_0.nc: shape=(31, 6) chunks=(31, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + month_1.nc: shape=(28, 6) chunks=(28, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + month_2.nc: shape=(31, 6) chunks=(31, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + -> each file virtualizes on its own: 3 virtual datasets + -> combining them fails: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). + +============================================================================== +3. ragged inner chunks + each file chunked by 4 along time, 10 steps: chunks 4, 4, 2 +------------------------------------------------------------------------------ + ragged_0.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + ragged_1.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + ragged_2.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + -> each file virtualizes on its own: 3 virtual datasets + -> combining them fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenation axis which is not evenly divisible by chunk length 4. + +============================================================================== +4. per-file scale_factor / add_offset + each file packs int16 with its own CF scaling (BOM BARPA) +------------------------------------------------------------------------------ + scaled_0.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.009999999776482582, 'add_offset': 280.0} + scaled_1.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.019999999552965164, 'add_offset': 270.0} + scaled_2.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.004999999888241291, 'add_offset': 290.0} + -> each file virtualizes on its own: 3 virtual datasets + -> combined: v(12, 6), codecs/encoding of the FIRST file used for all + -> SILENTLY WRONG: 48 of 72 values differ from the files + expected [287.03 286.26 289.55 285.9 287.4 289.54 288.84 288.14 289.43 289.17 + 289.47 288.41] + got [287.03 286.26 289.55 285.9 288.7 289.77 289.42 289.07 278.86 278.34 + 278.94 276.82] + +============================================================================== +5. varying dtype + the same variable is int16 in two files and float32 in the third +------------------------------------------------------------------------------ + dtype_0.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} + dtype_1.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} + dtype_2.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} + -> each file virtualizes on its own: 3 virtual datasets + -> combining them fails: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype(' Date: Wed, 14 Oct 2026 11:04:52 +0200 Subject: [PATCH 02/12] docs(design): combine the zarr-specs#399 files into one array with refkv variant.py builds one array from VirtualiZarr's per-file manifests: a chunk_variant codec whose configuration lists how chunks are stored, and refkv reference values that prefix each chunk with its variant's index as a literal byte before the file's own bytes. With the rectilinear chunk grid, all five cases (codec eras, month-length files, ragged inner chunks, per-file CF scaling, varying dtype) read back equal to the files, where VirtualiZarr fails four and silently misreads the fifth. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 81 ++++++-- design/experiments/inhomogeneous/demo.py | 67 +++++-- design/experiments/inhomogeneous/output.txt | 21 ++- design/experiments/inhomogeneous/variant.py | 193 ++++++++++++++++++++ 4 files changed, 323 insertions(+), 39 deletions(-) create mode 100644 design/experiments/inhomogeneous/variant.py diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 1e48b8c6..a8713a7f 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -1,27 +1,76 @@ -# Inhomogeneous files: where virtualization falls off the cliff +# Inhomogeneous files: one array with refkv, where VirtualiZarr can't combine them An executable companion to [zarr-developers/zarr-specs#399](https://github.com/zarr-developers/zarr-specs/issues/399) ("Varying codec parameters per-chunk"). [demo.py](demo.py) writes small netCDF4 -files that are each valid and consistent on their own, virtualizes each one with -VirtualiZarr (which works), and then tries to combine them along time into one -logical Zarr array, as you would a real archive. +files, each valid and consistent on its own, in the five shapes the issue +describes. It virtualizes each file with VirtualiZarr (which works), and then +combines them along time into one logical array twice: + +- **VirtualiZarr:** by concatenating the files' ManifestArrays, as + `open_virtual_mfdataset` does. +- **refkv:** from the same per-file manifests, with [variant.py](variant.py). The + result is saved as a vzip archive, reopened, and read through zarr-python and + xarray. + +Every result is compared with the files read one by one. ```bash uv run python design/experiments/inhomogeneous/demo.py ``` -| case | the files | what happens | -|---|---|---| -| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | **fails**: a Zarr array has one codec chain | -| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | **fails**: needs variable-length chunks | -| 3. ragged inner chunks (tiled GeoTIFF / netCDF cubes) | 10 steps each, chunked by 4: 4, 4, 2 | **fails**: the short last chunk breaks the regular grid | -| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16 packed with each file's own scaling | **combines, then reads silently wrong values**: 48 of 72 values differ, because the first file's scaling is applied to every file | -| 5. varying dtype | int16, int16, float32 | **fails**: one array, one dtype | +| case | the files | VirtualiZarr | refkv | +|---|---|---|---| +| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ one array, 3 chunk variants | +| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | fails: needs variable-length chunks | ✅ rectilinear grid | +| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | fails: partial chunks break the regular grid | ✅ rectilinear grid, edge chunks cropped | +| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | **combines, then 48 of 72 values are silently wrong** (the first file's scaling is applied to all) | ✅ each chunk decoded with its own file's scaling | +| 5. varying dtype | int16, int16, float32 | fails: one array, one dtype | ✅ int16 chunks cast to the array's float32 | + +In every refkv case, every value equals the files' values. The archive holds a few +kilobytes of metadata, and the chunks are read in place from the netCDF files. +[output.txt](output.txt) is a full run. + +## How + +Two pieces, and no data is copied: + +1. **`chunk_variant`** is an array-to-bytes codec. Its configuration lists the ways a + chunk is stored ("variants"). Each variant has a codec chain (numcodecs + configurations), a stored dtype and byte order, the stored chunk shape, and + optionally a CF `scale_factor` and `add_offset`. A chunk's bytes start with a + varint, the index of its variant. The codec decodes the rest with that variant, + crops it to the chunk's cell (HDF5 stores edge chunks whole), applies the scaling, + and casts the result to the array's dtype. This is design 2.1 of the issue: the + per-chunk context injected into the chunk bytes. +2. **refkv's reference values carry the varint.** Each chunk's value is + `Concat(Range(data=), Range(source=, offset, length))`: one literal + byte stored in the archive, then the file's own bytes. Ragged and month-length + files use the zarr-extensions + [`rectilinear`](https://github.com/zarr-developers/zarr-extensions/tree/main/chunk-grids/rectilinear) + chunk grid, which zarr-python 3.4 reads with + `zarr.config.set({"array.rectilinear_chunks": True})`. + +This answers the issue's objections to design 2.1: + +- **No store does anything at read time.** The context lives in the reference, so + the archive is an ordinary key-value store whose values happen to start with a + byte. Arrays that don't use the codec aren't affected. +- **No codec does I/O.** The variant table is in `zarr.json`. Each chunk carries + only an index into it: one byte here, a varint in general. +- **It scales to the pathological case.** One variant per file in case 4 costs one + table entry per file plus one byte per chunk. A run of files that share a variant, + such as GOES-16's four eras, shares one entry. +- **It fails loudly.** A reader without the codec refuses the array as an unknown + codec. It does not return wrong values. -Case 4 is the dangerous one: nothing errors. The CF attributes are array-level -metadata, so concatenating keeps the first file's `scale_factor` and `add_offset` -and decodes the other files' integers with them. +## Limits -Versions: VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. The full -output of a run is in [output.txt](output.txt). +- `chunk_variant` is defined here, not registered as a zarr extension. A reader + needs this codec (as refkv's readers need `n5_default` and the image codecs). +- Each variant decodes with numcodecs, not with a nested zarr codec pipeline, which + keeps the sketch short. A zarr extension would nest zarr codec configurations. +- Concatenating archives (VirtualiZarr's manifest-level operations) needs the variant + tables merged and the chunks' indexes renumbered. That is a literal byte per chunk, + but tooling would have to do it. +- Versions: VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 5969f4c7..26ff8312 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -20,6 +20,8 @@ from __future__ import annotations +import json +import sys import tempfile import warnings from pathlib import Path @@ -33,6 +35,7 @@ from virtualizarr.parsers import HDFParser warnings.filterwarnings("ignore") +sys.path.insert(0, str(Path(__file__).parent)) NX = 6 # a small spatial axis, so every case runs in a second @@ -96,23 +99,53 @@ def run(title: str, issue: str, make) -> None: f"filters={filters or 'none'}" + (f" {cf}" if cf else "")) vdss = virtualize(paths, root) # each file alone virtualizes fine print(f" -> each file virtualizes on its own: {len(vdss)} virtual datasets") - try: - combined = combine(vdss) - except Exception as e: # the cliff: no single Zarr array can describe these files - print(f" -> combining them fails: {type(e).__name__}: {str(e).splitlines()[0][:220]}") - return - print(f" -> combined: v{tuple(combined['v'].shape)}, codecs/encoding of the FIRST file used for all") - try: - got, want = read_back(combined), truth(paths) - except Exception as e: - print(f" -> reading the combined array fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") - return - if got.shape == want.shape and np.allclose(got, want, equal_nan=True): - print(" -> values match the files") - else: - bad = int(np.sum(~np.isclose(got, want, equal_nan=True))) if got.shape == want.shape else "all" - print(f" -> SILENTLY WRONG: {bad} of {want.size} values differ from the files") - print(f" expected {np.round(want[:, 0], 3)}\n got {np.round(got[:, 0], 3)}") + want = truth(paths) + virtualizarr_result(vdss, want) + refkv_result(vdss, want, root) + + +def virtualizarr_result(vdss: list[xr.Dataset], want: np.ndarray) -> None: + """VirtualiZarr: concatenate the ManifestArrays, then read through Zarr.""" + try: + combined = combine(vdss) + except Exception as e: # the cliff: no single Zarr array can describe these files + print(f" VirtualiZarr: combining fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") + return + try: + got = read_back(combined) + except Exception as e: + print(f" VirtualiZarr: combined, but reading fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") + return + report("VirtualiZarr: combined (one codec chain and the first file's attributes for all)", got, want) + +def report(label: str, got: np.ndarray, want: np.ndarray) -> None: + if got.shape == want.shape and np.allclose(got, want, equal_nan=True): + print(f" {label}: v{got.shape} {got.dtype}, every value matches the files") + else: + bad = int(np.sum(~np.isclose(got, want, equal_nan=True))) if got.shape == want.shape else "all" + print(f" {label}: SILENTLY WRONG, {bad} of {want.size} values differ from the files") + print(f" expected {np.round(want[:, 0], 2)}\n got {np.round(got[:, 0], 2)}") + + +def refkv_result(vdss: list[xr.Dataset], want: np.ndarray, root: Path) -> None: + """refkv: one array from the same per-file manifests (variant.py), saved as a vzip + archive and read back through Zarr.""" + import refkv + import variant + + try: + store = variant.combine(vdss) + archive = root / "combined.vzip" + store.save(str(archive)) + got = variant.read(refkv.open(str(archive))) + except Exception as e: + print(f" refkv: fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") + return + n = len(json.loads(store.get("v/zarr.json"))["codecs"][0]["configuration"]["variants"]) + report(f"refkv: one array, {n} chunk variant{'s' * (n != 1)}, saved and reopened", got, want) + data = sum(p.stat().st_size for p in root.glob("*.nc")) + print(f" the archive is {archive.stat().st_size:,} bytes; the chunks stay in the " + f"{len(store.sources)} files ({data:,} bytes), read in place") rng = np.random.default_rng(0) diff --git a/design/experiments/inhomogeneous/output.txt b/design/experiments/inhomogeneous/output.txt index 8834cfad..7b4e22f6 100644 --- a/design/experiments/inhomogeneous/output.txt +++ b/design/experiments/inhomogeneous/output.txt @@ -8,7 +8,9 @@ era_2.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4, 'shuffle': True} era_3.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters=none -> each file virtualizes on its own: 4 virtual datasets - -> combining them fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numcodecs.zlib', codec_config={'level': 4})) vs (BytesCodec(endi + VirtualiZarr: combining fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numcodecs.zlib', codec_config={'level': 4})) + refkv: one array, 3 chunk variants, saved and reopened: v(16, 6) float32, every value matches the files + the archive is 3,048 bytes; the chunks stay in the 4 files (43,964 bytes), read in place ============================================================================== 2. rectilinear chunks @@ -18,7 +20,9 @@ month_1.nc: shape=(28, 6) chunks=(28, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} month_2.nc: shape=(31, 6) chunks=(31, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} -> each file virtualizes on its own: 3 virtual datasets - -> combining them fails: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). + VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). + refkv: one array, 2 chunk variants, saved and reopened: v(90, 6) float32, every value matches the files + the archive is 3,416 bytes; the chunks stay in the 3 files (35,389 bytes), read in place ============================================================================== 3. ragged inner chunks @@ -28,7 +32,9 @@ ragged_1.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} ragged_2.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} -> each file virtualizes on its own: 3 virtual datasets - -> combining them fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenation axis which is not evenly divisible by chunk length 4. + VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenation axis which is not evenly divisible by + refkv: one array, 1 chunk variant, saved and reopened: v(30, 6) float32, every value matches the files + the archive is 3,570 bytes; the chunks stay in the 3 files (33,631 bytes), read in place ============================================================================== 4. per-file scale_factor / add_offset @@ -38,12 +44,13 @@ scaled_1.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.019999999552965164, 'add_offset': 270.0} scaled_2.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.004999999888241291, 'add_offset': 290.0} -> each file virtualizes on its own: 3 virtual datasets - -> combined: v(12, 6), codecs/encoding of the FIRST file used for all - -> SILENTLY WRONG: 48 of 72 values differ from the files + VirtualiZarr: combined (one codec chain and the first file's attributes for all): SILENTLY WRONG, 48 of 72 values differ from the files expected [287.03 286.26 289.55 285.9 287.4 289.54 288.84 288.14 289.43 289.17 289.47 288.41] got [287.03 286.26 289.55 285.9 288.7 289.77 289.42 289.07 278.86 278.34 278.94 276.82] + refkv: one array, 3 chunk variants, saved and reopened: v(12, 6) float32, every value matches the files + the archive is 3,057 bytes; the chunks stay in the 3 files (32,836 bytes), read in place ============================================================================== 5. varying dtype @@ -53,4 +60,6 @@ dtype_1.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} dtype_2.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} -> each file virtualizes on its own: 3 virtual datasets - -> combining them fails: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype('), Range(source=, offset, length))`: a few literal + bytes stored in the archive, followed by the file's own bytes, read in place. The + context travels with the reference, so no store has to inject it at read time, + no codec does I/O, and the result is an ordinary archive any refkv reader opens. + +Ragged and month-length files use the zarr-extensions `rectilinear` chunk grid, which +zarr-python 3.4 reads with `zarr.config.set({"array.rectilinear_chunks": True})`. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from typing import Self + +import numcodecs +import numpy as np +import xarray as xr +import zarr +from zarr.abc.codec import ArrayBytesCodec +from zarr.core.array_spec import ArraySpec +from zarr.core.buffer import Buffer, NDBuffer +from zarr.registry import register_codec + +import refkv +import refkv.zarr +from refkv.pb import Concat, Range, Source + + +def _varint(n: int) -> bytes: + out = bytearray() + while True: + b, n = n & 0x7F, n >> 7 + out.append(b | (0x80 if n else 0)) + if not n: + return bytes(out) + + +def _read_varint(buf: bytes) -> tuple[int, int]: + n = shift = pos = 0 + while True: + b = buf[pos] + pos += 1 + n |= (b & 0x7F) << shift + shift += 7 + if not b & 0x80: + return n, pos + + +@dataclass(frozen=True) +class ChunkVariantCodec(ArrayBytesCodec): + """`chunk_variant`: `{"variants": [{"data_type", "endian", "shape", "codecs", + "scale_factor", "add_offset"}, ...]}`; `codecs` are numcodecs configurations in + encode order (the decoder applies them in reverse).""" + + variants: tuple = () + is_fixed_size = False + + @classmethod + def from_dict(cls, data: dict) -> Self: + return cls(variants=tuple(json.dumps(v, sort_keys=True) for v in data["configuration"]["variants"])) + + def to_dict(self) -> dict: + return {"name": "chunk_variant", "configuration": {"variants": [json.loads(v) for v in self.variants]}} + + async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: + raw = chunk_bytes.to_bytes() + index, pos = _read_varint(raw) + v = json.loads(self.variants[index]) + data = raw[pos:] + for config in reversed(v["codecs"]): + data = numcodecs.get_codec(dict(config)).decode(data) + stored = np.dtype(v["data_type"]).newbyteorder("<" if v["endian"] == "little" else ">") + arr = np.frombuffer(bytes(data), dtype=stored).reshape(v["shape"]) + arr = arr[tuple(slice(0, n) for n in chunk_spec.shape)] # an edge chunk stored whole + out_dtype = chunk_spec.dtype.to_native_dtype() + if "scale_factor" in v or "add_offset" in v: + arr = arr * np.asarray(v.get("scale_factor", 1), out_dtype) + np.asarray(v.get("add_offset", 0), out_dtype) + return chunk_spec.prototype.nd_buffer.from_ndarray_like(np.ascontiguousarray(arr, dtype=out_dtype)) + + async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> Buffer: + raise NotImplementedError("chunk_variant decodes virtual chunks only") + + def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: + raise NotImplementedError + + +register_codec("chunk_variant", ChunkVariantCodec) + + +def _variant(ma, attrs) -> dict: + """How one file's chunks are stored, from its VirtualiZarr ManifestArray.""" + m = ma.metadata + native = m.data_type.to_native_dtype() + endian = "little" + codecs = [] + for c in m.codecs: + d = c.to_dict() + if d["name"] == "bytes": + endian = (d.get("configuration") or {}).get("endian", "little") + else: # a numcodecs codec, named "numcodecs." + codecs.append({"id": d["name"].split(".", 1)[1], **(d.get("configuration") or {})}) + v = {"data_type": native.str.lstrip("<>|="), "endian": endian, "shape": list(m.chunks), "codecs": codecs} + for k in ("scale_factor", "add_offset"): + if k in attrs: + v[k] = float(attrs[k]) + return v + + +def _lengths(n: int, c: int) -> list[int]: + return [c] * (n // c) + ([n % c] if n % c else []) + + +def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv.ReferenceStore: + """One reference store holding `name` concatenated along `dim` (axis 0) across the + files' virtual datasets, and the concatenated `dim` and other coordinates.""" + variants, keys = [], {} + sources, source_of = [], {} + mapping: dict = {} + time_chunks: list[int] = [] + other_chunks = None + scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) + out_dtype = np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() + for v in vdss]) + for vds in vdss: + ma = vds[name].data + variant = _variant(ma, vds[name].attrs) + key = json.dumps(variant, sort_keys=True) + if key not in keys: + keys[key] = len(variants) + variants.append(variant) + prefix = Range(data=_varint(keys[key])) + c0, *crest = ma.metadata.chunks + rest = [_lengths(n, c) for n, c in zip(ma.shape[1:], crest)] + if other_chunks is None: + other_chunks = rest + elif rest != other_chunks: + raise ValueError("the files are chunked differently along the other axes") + base = len(time_chunks) + time_chunks += _lengths(ma.shape[0], c0) + for idx, entry in ma.manifest.dict().items(): + i, *js = (int(x) for x in idx.split(".")) + s = source_of.setdefault(entry["path"], len(source_of)) + if s == len(sources): + sources.append(Source(url=entry["path"])) + mapping[f"{name}/c/" + "/".join(str(x) for x in (base + i, *js))] = Concat( + (prefix, Range(source=s, offset=entry["offset"], length=entry["length"]))) + shape = [sum(time_chunks), *[sum(r) for r in other_chunks]] + mapping["zarr.json"] = json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode() + mapping[f"{name}/zarr.json"] = json.dumps({ + "zarr_format": 3, "node_type": "array", "shape": shape, "data_type": np.dtype(out_dtype).name, + "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", + "chunk_shapes": [time_chunks, *other_chunks]}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": "NaN", "codecs": [{"name": "chunk_variant", "configuration": {"variants": variants}}], + "dimension_names": list(vdss[0][name].dims), "attributes": {}, + }).encode() + for coord in vdss[0][name].dims: # the coordinates, small: inline bytes + values = (np.concatenate([v[coord].values for v in vdss]) if coord == dim else vdss[0][coord].values) + attrs = {} + if values.dtype.kind == "M": # decoded CF times: back to numbers with their units + epoch = np.datetime64("2000-01-01", "ns") + values = ((values - epoch) // np.timedelta64(1, "D")).astype("int64") + attrs = {"units": "days since 2000-01-01"} + values = np.ascontiguousarray(values).astype(values.dtype.newbyteorder("<")) + mapping[f"{coord}/zarr.json"] = json.dumps({ + "zarr_format": 3, "node_type": "array", "shape": [len(values)], "data_type": values.dtype.name, + "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": [len(values)]}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": 0, "codecs": [{"name": "bytes", "configuration": {"endian": "little"}}], + "dimension_names": [coord], "attributes": attrs, + }).encode() + mapping[f"{coord}/c/0"] = values.tobytes() + return refkv.ReferenceStore(mapping, sources) + + +def read(store: refkv.ReferenceStore, name: str = "v") -> np.ndarray: + with zarr.config.set({"array.rectilinear_chunks": True}): + return xr.open_zarr(refkv.zarr.store(store), consolidated=False)[name].values From 0bd2947439e8ff496be7b3b207329594a2eafe0c Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 11:15:57 +0200 Subject: [PATCH 03/12] docs(design): combine the zarr-specs#399 cases with a generic switch codec Each per-file difference is its own codec: the file's own compression, zarr-extensions cast_value and scale_offset, and a new crop for whole-stored edge chunks. A generic switch codec chooses, per chunk, which chain applies, by a varint index that refkv's reference values prefix to the file's bytes. The README explains why the chooser stays format-agnostic, how it composes, and its limits. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 162 +++++++++----- design/experiments/inhomogeneous/demo.py | 8 +- design/experiments/inhomogeneous/output.txt | 20 +- design/experiments/inhomogeneous/switch.py | 224 ++++++++++++++++++++ design/experiments/inhomogeneous/variant.py | 193 ----------------- 5 files changed, 347 insertions(+), 260 deletions(-) create mode 100644 design/experiments/inhomogeneous/switch.py delete mode 100644 design/experiments/inhomogeneous/variant.py diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index a8713a7f..7ef31225 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -4,73 +4,129 @@ An executable companion to [zarr-developers/zarr-specs#399](https://github.com/zarr-developers/zarr-specs/issues/399) ("Varying codec parameters per-chunk"). [demo.py](demo.py) writes small netCDF4 files, each valid and consistent on its own, in the five shapes the issue -describes. It virtualizes each file with VirtualiZarr (which works), and then +describes. It virtualizes each file with VirtualiZarr (which works). It then combines them along time into one logical array twice: -- **VirtualiZarr:** by concatenating the files' ManifestArrays, as +- **VirtualiZarr:** concatenating the files' ManifestArrays, as `open_virtual_mfdataset` does. -- **refkv:** from the same per-file manifests, with [variant.py](variant.py). The - result is saved as a vzip archive, reopened, and read through zarr-python and - xarray. +- **refkv:** building one array from the same per-file manifests, with + [switch.py](switch.py). The result is saved as a vzip archive, reopened, and read + through zarr-python and xarray. -Every result is compared with the files read one by one. +Both are compared with the files read one by one. ```bash -uv run python design/experiments/inhomogeneous/demo.py +uv run --with "cast-value-rs>=0.4.2" python design/experiments/inhomogeneous/demo.py ``` | case | the files | VirtualiZarr | refkv | |---|---|---|---| -| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ one array, 3 chunk variants | +| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ 3 cases | | 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | fails: needs variable-length chunks | ✅ rectilinear grid | -| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | fails: partial chunks break the regular grid | ✅ rectilinear grid, edge chunks cropped | -| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | **combines, then 48 of 72 values are silently wrong** (the first file's scaling is applied to all) | ✅ each chunk decoded with its own file's scaling | -| 5. varying dtype | int16, int16, float32 | fails: one array, one dtype | ✅ int16 chunks cast to the array's float32 | +| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | fails: partial chunks break the regular grid | ✅ rectilinear grid, `crop` | +| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | **combines, then 48 of 72 values are silently wrong** | ✅ 3 cases | +| 5. varying dtype | int16, int16, float32 | fails: one array, one dtype | ✅ 2 cases | -In every refkv case, every value equals the files' values. The archive holds a few -kilobytes of metadata, and the chunks are read in place from the netCDF files. +In every refkv case, every value equals the files' values. The archive holds a +few kilobytes of metadata; the chunks are read in place from the netCDF files. [output.txt](output.txt) is a full run. -## How - -Two pieces, and no data is copied: - -1. **`chunk_variant`** is an array-to-bytes codec. Its configuration lists the ways a - chunk is stored ("variants"). Each variant has a codec chain (numcodecs - configurations), a stored dtype and byte order, the stored chunk shape, and - optionally a CF `scale_factor` and `add_offset`. A chunk's bytes start with a - varint, the index of its variant. The codec decodes the rest with that variant, - crops it to the chunk's cell (HDF5 stores edge chunks whole), applies the scaling, - and casts the result to the array's dtype. This is design 2.1 of the issue: the - per-chunk context injected into the chunk bytes. -2. **refkv's reference values carry the varint.** Each chunk's value is - `Concat(Range(data=), Range(source=, offset, length))`: one literal - byte stored in the archive, then the file's own bytes. Ragged and month-length - files use the zarr-extensions - [`rectilinear`](https://github.com/zarr-developers/zarr-extensions/tree/main/chunk-grids/rectilinear) - chunk grid, which zarr-python 3.4 reads with - `zarr.config.set({"array.rectilinear_chunks": True})`. - -This answers the issue's objections to design 2.1: - -- **No store does anything at read time.** The context lives in the reference, so - the archive is an ordinary key-value store whose values happen to start with a - byte. Arrays that don't use the codec aren't affected. -- **No codec does I/O.** The variant table is in `zarr.json`. Each chunk carries - only an index into it: one byte here, a varint in general. -- **It scales to the pathological case.** One variant per file in case 4 costs one - table entry per file plus one byte per chunk. A run of files that share a variant, - such as GOES-16's four eras, shares one entry. -- **It fails loudly.** A reader without the codec refuses the array as an unknown - codec. It does not return wrong values. +## How: one codec per transformation, and one that chooses + +Each thing that varies between the files is its own, independent codec: + +| what varies per file | codec | +|---|---| +| compression, shuffle | the file's own codecs, as VirtualiZarr reports them (`numcodecs.zlib`, `numcodecs.shuffle`) | +| stored dtype | [`cast_value`](https://github.com/zarr-developers/zarr-extensions/tree/main/codecs/cast_value) (zarr-extensions, in zarr-python 3.4) | +| CF `scale_factor` / `add_offset` | [`scale_offset`](https://github.com/zarr-developers/zarr-extensions/tree/main/codecs/scale_offset) (zarr-extensions, in zarr-python 3.4) | +| stored chunk shape (HDF5 stores edge chunks whole) | `crop` (new: decodes by keeping the cell, encodes by padding) | + +One codec, `switch`, chooses per chunk which composition of them applies. + +- **What `switch` is.** It is an array-to-bytes codec, `{"cases": [chain, ...]}`, + where each case is an ordinary Zarr codec chain. A chunk's bytes start with a + varint, the index of its case. `switch` decodes the rest with that chain, the way + `sharding_indexed` decodes inner chunks with its inner chain. +- **Where the index comes from.** refkv's reference values carry it. A chunk is + `Concat(Range(data=), Range(source=, offset, length))`: one literal + byte in the archive, then the file's own bytes. +- **Grids.** Month-length and ragged files use the zarr-extensions + [`rectilinear`](https://github.com/zarr-developers/zarr-extensions/tree/main/chunk-grids/rectilinear) + chunk grid. + +Case 4's array, with two of its three cases: + +```json +"data_type": "float32", +"chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [[4, 4, 4], [6]]}}, +"codecs": [{"name": "switch", "configuration": {"cases": [ + [{"name": "scale_offset", "configuration": {"offset": 280.0, "scale": 100.00000223517424}}, + {"name": "cast_value", "configuration": {"data_type": "int16", "out_of_range": "clamp"}}, + {"name": "crop", "configuration": {"shape": [4, 6]}}, + {"name": "bytes", "configuration": {"endian": "little"}}, + {"name": "numcodecs.zlib", "configuration": {"level": 4}}], + [{"name": "scale_offset", "configuration": {"offset": 270.0, "scale": 50.00000111758712}}, + ...]]}}] +``` + +## Why the chooser is generic, and the formats live in the transformations + +The array-to-bytes codec could be scoped to the source product instead: a +netCDF-4/HDF5 codec whose variants are written in HDF5's own terms (datatype, +filter pipeline, chunk dimensions, CF packing), decoding with HDF5's semantics. +The design here splits that apart: + +- **The chooser carries no format knowledge.** `switch` only selects a chain. The + same codec combines TIFF tiles with different compressions, GRIB messages with + different packing, or HDF5 files with different filters. One codec, not one per + product. +- **Format knowledge goes where it varies: in single-purpose codecs** that any case + can use. HDF5's deflate and shuffle are numcodecs codecs already. CF packing is + `scale_offset`, dtypes are `cast_value`, and HDF5's whole edge chunks are `crop`. + A product transformation with no Zarr equivalent (HDF5's Fletcher-32 checksum, its + N-bit or scale-offset filters, SZIP) becomes one more single-purpose codec, usable + in any case and outside `switch` too, rather than a branch inside a per-product + codec. +- **Each piece is reviewable on its own.** A reader implements `switch` once. + Everything inside a case is a codec it either knows or rejects as unknown. + +A product-scoped codec would be the better choice only where a product's chunks +can't be described as a chain of independent transformations. That happens when a +per-chunk header has to be parsed: refkv's `n5_default` exists for exactly that +reason. + +## Composition + +- **Codecs outside `switch`.** + - Array-to-array codecs listed before it (`transpose`, a uniform `scale_offset`) + apply to every chunk, whatever its case. + - Bytes-to-bytes codecs after it would apply to the index *and* the source's bytes, + so a virtual array has none. A materialized array may. +- **Nesting.** A case is a full chain, so it may end in another `switch`. The chunk + then starts with the outer index, then the inner one. This composes independent + axes of variation, for example a codec era (outer) and a per-file scaling (inner), + at the cost of repeating the inner table in each outer case. +- **Sharding.** A case may be `sharding_indexed` (a source stored as shards), and a + shard's inner chain may use `switch` (inner chunks that vary). +- **Precedent:** [`conditional`](https://github.com/zarr-developers/zarr-extensions/tree/main/codecs/conditional) + (zarr-extensions) already puts a per-chunk header in the chunk bytes, to turn codecs + of one chain on or off. `switch` chooses among chains, so it also covers what + `conditional` can't: a different dtype, scaling, or stored shape per chunk. +- **Concatenating two such arrays** (VirtualiZarr's manifest-level operations) means + joining their case lists and renumbering each chunk's index: one literal byte per + chunk, rewritten by tooling. No source bytes change. ## Limits -- `chunk_variant` is defined here, not registered as a zarr extension. A reader - needs this codec (as refkv's readers need `n5_default` and the image codecs). -- Each variant decodes with numcodecs, not with a nested zarr codec pipeline, which - keeps the sketch short. A zarr extension would nest zarr codec configurations. -- Concatenating archives (VirtualiZarr's manifest-level operations) needs the variant - tables merged and the chunks' indexes renumbered. That is a literal byte per chunk, - but tooling would have to do it. -- Versions: VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. +- **`switch` and `crop` are defined here**, not registered as zarr extensions. +- **`scale_offset` stores the encode multiplier, CF the decode one.** CF's + `scale_factor` of 0.01 becomes `scale` 100.00000223517424, the reciprocal of + float32 0.01. Decoding through `x / scale` matches `x * scale_factor` to within an + ulp, not bit for bit. Losing no information would need `scale_offset` to accept the + factor in either direction. +- **The fill value must survive every case's encode direction.** `cast_value` refuses + a NaN fill for an int16 case, so the arrays use 0, with `out_of_range: "clamp"`. + Clamping only affects encoding; decoding int16 to float32 is exact. +- **zarr-python's `cast_value` needs the `cast-value-rs` package.** +- **Versions:** VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 26ff8312..5c1974ad 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -7,7 +7,7 @@ what happens: an error, or a combined array whose values are compared with the truth read straight from the files. - uv run python design/experiments/inhomogeneous/demo.py + uv run --with "cast-value-rs>=0.4.2" python design/experiments/inhomogeneous/demo.py Cases (the use cases of the issue): @@ -131,7 +131,7 @@ def refkv_result(vdss: list[xr.Dataset], want: np.ndarray, root: Path) -> None: """refkv: one array from the same per-file manifests (variant.py), saved as a vzip archive and read back through Zarr.""" import refkv - import variant + import switch as variant try: store = variant.combine(vdss) @@ -141,8 +141,8 @@ def refkv_result(vdss: list[xr.Dataset], want: np.ndarray, root: Path) -> None: except Exception as e: print(f" refkv: fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") return - n = len(json.loads(store.get("v/zarr.json"))["codecs"][0]["configuration"]["variants"]) - report(f"refkv: one array, {n} chunk variant{'s' * (n != 1)}, saved and reopened", got, want) + n = len(json.loads(store.get("v/zarr.json"))["codecs"][0]["configuration"]["cases"]) + report(f"refkv: one array, {n} switch case{'s' * (n != 1)}, saved and reopened", got, want) data = sum(p.stat().st_size for p in root.glob("*.nc")) print(f" the archive is {archive.stat().st_size:,} bytes; the chunks stay in the " f"{len(store.sources)} files ({data:,} bytes), read in place") diff --git a/design/experiments/inhomogeneous/output.txt b/design/experiments/inhomogeneous/output.txt index 7b4e22f6..22ea87a0 100644 --- a/design/experiments/inhomogeneous/output.txt +++ b/design/experiments/inhomogeneous/output.txt @@ -9,8 +9,8 @@ era_3.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters=none -> each file virtualizes on its own: 4 virtual datasets VirtualiZarr: combining fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numcodecs.zlib', codec_config={'level': 4})) - refkv: one array, 3 chunk variants, saved and reopened: v(16, 6) float32, every value matches the files - the archive is 3,048 bytes; the chunks stay in the 4 files (43,964 bytes), read in place + refkv: one array, 3 switch cases, saved and reopened: v(16, 6) float32, every value matches the files + the archive is 3,259 bytes; the chunks stay in the 4 files (43,964 bytes), read in place ============================================================================== 2. rectilinear chunks @@ -21,8 +21,8 @@ month_2.nc: shape=(31, 6) chunks=(31, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} -> each file virtualizes on its own: 3 virtual datasets VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). - refkv: one array, 2 chunk variants, saved and reopened: v(90, 6) float32, every value matches the files - the archive is 3,416 bytes; the chunks stay in the 3 files (35,389 bytes), read in place + refkv: one array, 2 switch cases, saved and reopened: v(90, 6) float32, every value matches the files + the archive is 3,554 bytes; the chunks stay in the 3 files (35,389 bytes), read in place ============================================================================== 3. ragged inner chunks @@ -33,8 +33,8 @@ ragged_2.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} -> each file virtualizes on its own: 3 virtual datasets VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenation axis which is not evenly divisible by - refkv: one array, 1 chunk variant, saved and reopened: v(30, 6) float32, every value matches the files - the archive is 3,570 bytes; the chunks stay in the 3 files (33,631 bytes), read in place + refkv: one array, 1 switch case, saved and reopened: v(30, 6) float32, every value matches the files + the archive is 3,633 bytes; the chunks stay in the 3 files (33,631 bytes), read in place ============================================================================== 4. per-file scale_factor / add_offset @@ -49,8 +49,8 @@ 289.47 288.41] got [287.03 286.26 289.55 285.9 288.7 289.77 289.42 289.07 278.86 278.34 278.94 276.82] - refkv: one array, 3 chunk variants, saved and reopened: v(12, 6) float32, every value matches the files - the archive is 3,057 bytes; the chunks stay in the 3 files (32,836 bytes), read in place + refkv: one array, 3 switch cases, saved and reopened: v(12, 6) float32, every value matches the files + the archive is 3,635 bytes; the chunks stay in the 3 files (32,836 bytes), read in place ============================================================================== 5. varying dtype @@ -61,5 +61,5 @@ dtype_2.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} -> each file virtualizes on its own: 3 virtual datasets VirtualiZarr: combining fails: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype('), Range(source=, offset, length))`, a literal byte in +the archive, then the file's own bytes read in place. No store injects anything at read +time and no codec does I/O. + +Ragged and month-length files use the zarr-extensions `rectilinear` chunk grid, which +zarr-python 3.4 reads with `zarr.config.set({"array.rectilinear_chunks": True})`. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, replace +from typing import Self + +import numpy as np +import xarray as xr +import zarr +from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec +from zarr.core.array_spec import ArraySpec +from zarr.core.buffer import Buffer, NDBuffer +from zarr.core.metadata.v3 import parse_codecs +from zarr.registry import get_pipeline_class, register_codec + +import refkv +import refkv.zarr +from refkv.pb import Concat, Range, Source + + +def varint(n: int) -> bytes: + out = bytearray() + while True: + b, n = n & 0x7F, n >> 7 + out.append(b | (0x80 if n else 0)) + if not n: + return bytes(out) + + +def read_varint(buf: bytes) -> tuple[int, int]: + n = shift = pos = 0 + while True: + b = buf[pos] + pos += 1 + n |= (b & 0x7F) << shift + shift += 7 + if not b & 0x80: + return n, pos + + +@dataclass(frozen=True) +class CropCodec(ArrayArrayCodec): + """`crop`: the stored chunk has `shape`, at least the chunk's cell in every + dimension; decoding keeps its leading corner, the cell.""" + + shape: tuple[int, ...] = () + is_fixed_size = True + + @classmethod + def from_dict(cls, data: dict) -> Self: + return cls(shape=tuple(data["configuration"]["shape"])) + + def to_dict(self) -> dict: + return {"name": "crop", "configuration": {"shape": list(self.shape)}} + + def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec: + if len(self.shape) != len(chunk_spec.shape) or any(s < c for s, c in zip(self.shape, chunk_spec.shape)): + raise ValueError(f"crop: stored shape {self.shape} is smaller than the chunk {chunk_spec.shape}") + return replace(chunk_spec, shape=self.shape) + + async def _decode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> NDBuffer: + return chunk_array[tuple(slice(0, n) for n in chunk_spec.shape)] + + async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> NDBuffer: + a = chunk_array.as_numpy_array() + out = np.full(self.shape, chunk_spec.fill_value, dtype=a.dtype) + out[tuple(slice(0, n) for n in a.shape)] = a + return chunk_spec.prototype.nd_buffer.from_ndarray_like(out) + + def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: + return input_byte_length // int(np.prod(chunk_spec.shape)) * int(np.prod(self.shape)) + + +register_codec("crop", CropCodec) + + +@dataclass(frozen=True) +class SwitchCodec(ArrayBytesCodec): + """`switch`: `{"cases": [chain, ...]}`; a chunk is a varint case index, then the + bytes that case's chain decodes.""" + + cases: tuple = () # each a tuple of parsed codecs + is_fixed_size = False + + @classmethod + def from_dict(cls, data: dict) -> Self: + return cls(cases=tuple(tuple(parse_codecs(chain)) for chain in data["configuration"]["cases"])) + + def to_dict(self) -> dict: + return {"name": "switch", "configuration": {"cases": [[c.to_dict() for c in chain] for chain in self.cases]}} + + async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: + raw = chunk_bytes.to_bytes() + index, pos = read_varint(raw) + pipeline = get_pipeline_class().from_codecs(self.cases[index]).evolve_from_array_spec(chunk_spec) + (out,) = await pipeline.decode([(chunk_spec.prototype.buffer.from_bytes(raw[pos:]), chunk_spec)]) + return out + + async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> Buffer: + raise NotImplementedError("this sketch decodes virtual chunks only; a writer would choose a case") + + def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: + raise NotImplementedError + + +register_codec("switch", SwitchCodec) + + +def case(ma, attrs, out_dtype: np.dtype) -> list[dict]: + """The codec chain that decodes one file's chunks to the combined array's dtype: + the independent transforms its file needs, then the file's own codecs.""" + m = ma.metadata + stored = m.data_type.to_native_dtype() + chain: list[dict] = [] + if "scale_factor" in attrs or "add_offset" in attrs: # CF: physical = packed * scale_factor + add_offset + chain.append({"name": "scale_offset", "configuration": { + "offset": float(attrs.get("add_offset", 0)), "scale": 1 / float(attrs.get("scale_factor", 1))}}) + if stored != out_dtype: + # decoding stored -> out_dtype is exact; out_of_range only governs encoding, here the + # fill value, which every case's chain must be able to encode + chain.append({"name": "cast_value", "configuration": {"data_type": stored.name, "out_of_range": "clamp"}}) + chain.append({"name": "crop", "configuration": {"shape": list(m.chunks)}}) + return chain + [c.to_dict() for c in m.codecs] + + +def lengths(n: int, c: int) -> list[int]: + return [c] * (n // c) + ([n % c] if n % c else []) + + +def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv.ReferenceStore: + """One reference store holding `name` concatenated along `dim` (axis 0) across the + files' virtual datasets, with the concatenated coordinates.""" + scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) + out_dtype = np.dtype(np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() + for v in vdss])) + cases: list[list[dict]] = [] + index: dict[str, int] = {} + sources, source_of, mapping = [], {}, {} + time_chunks: list[int] = [] + other = None + for vds in vdss: + ma = vds[name].data + chain = case(ma, vds[name].attrs, out_dtype) + k = index.setdefault(json.dumps(chain, sort_keys=True), len(cases)) + if k == len(cases): + cases.append(chain) + c0, *crest = ma.metadata.chunks + rest = [lengths(n, c) for n, c in zip(ma.shape[1:], crest)] + if other is None: + other = rest + elif rest != other: + raise ValueError("the files are chunked differently along the other axes") + base = len(time_chunks) + time_chunks += lengths(ma.shape[0], c0) + for idx, e in ma.manifest.dict().items(): + i, *js = (int(x) for x in idx.split(".")) + s = source_of.setdefault(e["path"], len(source_of)) + if s == len(sources): + sources.append(Source(url=e["path"])) + mapping[f"{name}/c/" + "/".join(map(str, (base + i, *js)))] = Concat( + (Range(data=varint(k)), Range(source=s, offset=e["offset"], length=e["length"]))) + mapping["zarr.json"] = json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode() + mapping[f"{name}/zarr.json"] = json.dumps({ + "zarr_format": 3, "node_type": "array", + "shape": [sum(time_chunks), *map(sum, other)], "data_type": out_dtype.name, + "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [time_chunks, *other]}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": 0.0, "codecs": [{"name": "switch", "configuration": {"cases": cases}}], + "dimension_names": list(vdss[0][name].dims), "attributes": {}, + }).encode() + for coord in vdss[0][name].dims: # the coordinates, small: inline bytes + values = np.concatenate([v[coord].values for v in vdss]) if coord == dim else vdss[0][coord].values + attrs = {} + if values.dtype.kind == "M": # decoded CF times: back to numbers with their units + values = ((values - np.datetime64("2000-01-01", "ns")) // np.timedelta64(1, "D")).astype("int64") + attrs = {"units": "days since 2000-01-01"} + values = np.ascontiguousarray(values).astype(values.dtype.newbyteorder("<")) + mapping[f"{coord}/zarr.json"] = json.dumps({ + "zarr_format": 3, "node_type": "array", "shape": [len(values)], "data_type": values.dtype.name, + "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": [len(values)]}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": 0, "codecs": [{"name": "bytes", "configuration": {"endian": "little"}}], + "dimension_names": [coord], "attributes": attrs, + }).encode() + mapping[f"{coord}/c/0"] = values.tobytes() + return refkv.ReferenceStore(mapping, sources) + + +def read(store, name: str = "v") -> np.ndarray: + with zarr.config.set({"array.rectilinear_chunks": True}): + return xr.open_zarr(refkv.zarr.store(store), consolidated=False)[name].values diff --git a/design/experiments/inhomogeneous/variant.py b/design/experiments/inhomogeneous/variant.py deleted file mode 100644 index d93c79d6..00000000 --- a/design/experiments/inhomogeneous/variant.py +++ /dev/null @@ -1,193 +0,0 @@ -"""Combining inhomogeneous files into one Zarr array with refkv. - -Each file still virtualizes on its own (VirtualiZarr does this well). What no single -Zarr array can express is that the files' chunks decode differently: another codec -chain, another dtype, another CF scaling, another stored chunk shape. Two pieces fix -that without copying a byte of data: - -1. `chunk_variant`, an array-to-bytes codec whose configuration lists the ways a chunk - can be stored (its "variants": codec chain, stored dtype and chunk shape, CF scale - and offset). A chunk's bytes start with a varint, the index of its variant; the codec - decodes the rest with that variant, crops it to the chunk's cell and casts it to the - array's dtype. This is design 2.1 of zarr-specs#399 ("inject context into the chunk - bytes"). -2. refkv's reference values carry that varint. A chunk is - `Concat(Range(data=), Range(source=, offset, length))`: a few literal - bytes stored in the archive, followed by the file's own bytes, read in place. The - context travels with the reference, so no store has to inject it at read time, - no codec does I/O, and the result is an ordinary archive any refkv reader opens. - -Ragged and month-length files use the zarr-extensions `rectilinear` chunk grid, which -zarr-python 3.4 reads with `zarr.config.set({"array.rectilinear_chunks": True})`. -""" - -from __future__ import annotations - -import json -from dataclasses import dataclass -from typing import Self - -import numcodecs -import numpy as np -import xarray as xr -import zarr -from zarr.abc.codec import ArrayBytesCodec -from zarr.core.array_spec import ArraySpec -from zarr.core.buffer import Buffer, NDBuffer -from zarr.registry import register_codec - -import refkv -import refkv.zarr -from refkv.pb import Concat, Range, Source - - -def _varint(n: int) -> bytes: - out = bytearray() - while True: - b, n = n & 0x7F, n >> 7 - out.append(b | (0x80 if n else 0)) - if not n: - return bytes(out) - - -def _read_varint(buf: bytes) -> tuple[int, int]: - n = shift = pos = 0 - while True: - b = buf[pos] - pos += 1 - n |= (b & 0x7F) << shift - shift += 7 - if not b & 0x80: - return n, pos - - -@dataclass(frozen=True) -class ChunkVariantCodec(ArrayBytesCodec): - """`chunk_variant`: `{"variants": [{"data_type", "endian", "shape", "codecs", - "scale_factor", "add_offset"}, ...]}`; `codecs` are numcodecs configurations in - encode order (the decoder applies them in reverse).""" - - variants: tuple = () - is_fixed_size = False - - @classmethod - def from_dict(cls, data: dict) -> Self: - return cls(variants=tuple(json.dumps(v, sort_keys=True) for v in data["configuration"]["variants"])) - - def to_dict(self) -> dict: - return {"name": "chunk_variant", "configuration": {"variants": [json.loads(v) for v in self.variants]}} - - async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: - raw = chunk_bytes.to_bytes() - index, pos = _read_varint(raw) - v = json.loads(self.variants[index]) - data = raw[pos:] - for config in reversed(v["codecs"]): - data = numcodecs.get_codec(dict(config)).decode(data) - stored = np.dtype(v["data_type"]).newbyteorder("<" if v["endian"] == "little" else ">") - arr = np.frombuffer(bytes(data), dtype=stored).reshape(v["shape"]) - arr = arr[tuple(slice(0, n) for n in chunk_spec.shape)] # an edge chunk stored whole - out_dtype = chunk_spec.dtype.to_native_dtype() - if "scale_factor" in v or "add_offset" in v: - arr = arr * np.asarray(v.get("scale_factor", 1), out_dtype) + np.asarray(v.get("add_offset", 0), out_dtype) - return chunk_spec.prototype.nd_buffer.from_ndarray_like(np.ascontiguousarray(arr, dtype=out_dtype)) - - async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> Buffer: - raise NotImplementedError("chunk_variant decodes virtual chunks only") - - def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: - raise NotImplementedError - - -register_codec("chunk_variant", ChunkVariantCodec) - - -def _variant(ma, attrs) -> dict: - """How one file's chunks are stored, from its VirtualiZarr ManifestArray.""" - m = ma.metadata - native = m.data_type.to_native_dtype() - endian = "little" - codecs = [] - for c in m.codecs: - d = c.to_dict() - if d["name"] == "bytes": - endian = (d.get("configuration") or {}).get("endian", "little") - else: # a numcodecs codec, named "numcodecs." - codecs.append({"id": d["name"].split(".", 1)[1], **(d.get("configuration") or {})}) - v = {"data_type": native.str.lstrip("<>|="), "endian": endian, "shape": list(m.chunks), "codecs": codecs} - for k in ("scale_factor", "add_offset"): - if k in attrs: - v[k] = float(attrs[k]) - return v - - -def _lengths(n: int, c: int) -> list[int]: - return [c] * (n // c) + ([n % c] if n % c else []) - - -def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv.ReferenceStore: - """One reference store holding `name` concatenated along `dim` (axis 0) across the - files' virtual datasets, and the concatenated `dim` and other coordinates.""" - variants, keys = [], {} - sources, source_of = [], {} - mapping: dict = {} - time_chunks: list[int] = [] - other_chunks = None - scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) - out_dtype = np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() - for v in vdss]) - for vds in vdss: - ma = vds[name].data - variant = _variant(ma, vds[name].attrs) - key = json.dumps(variant, sort_keys=True) - if key not in keys: - keys[key] = len(variants) - variants.append(variant) - prefix = Range(data=_varint(keys[key])) - c0, *crest = ma.metadata.chunks - rest = [_lengths(n, c) for n, c in zip(ma.shape[1:], crest)] - if other_chunks is None: - other_chunks = rest - elif rest != other_chunks: - raise ValueError("the files are chunked differently along the other axes") - base = len(time_chunks) - time_chunks += _lengths(ma.shape[0], c0) - for idx, entry in ma.manifest.dict().items(): - i, *js = (int(x) for x in idx.split(".")) - s = source_of.setdefault(entry["path"], len(source_of)) - if s == len(sources): - sources.append(Source(url=entry["path"])) - mapping[f"{name}/c/" + "/".join(str(x) for x in (base + i, *js))] = Concat( - (prefix, Range(source=s, offset=entry["offset"], length=entry["length"]))) - shape = [sum(time_chunks), *[sum(r) for r in other_chunks]] - mapping["zarr.json"] = json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode() - mapping[f"{name}/zarr.json"] = json.dumps({ - "zarr_format": 3, "node_type": "array", "shape": shape, "data_type": np.dtype(out_dtype).name, - "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", - "chunk_shapes": [time_chunks, *other_chunks]}}, - "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, - "fill_value": "NaN", "codecs": [{"name": "chunk_variant", "configuration": {"variants": variants}}], - "dimension_names": list(vdss[0][name].dims), "attributes": {}, - }).encode() - for coord in vdss[0][name].dims: # the coordinates, small: inline bytes - values = (np.concatenate([v[coord].values for v in vdss]) if coord == dim else vdss[0][coord].values) - attrs = {} - if values.dtype.kind == "M": # decoded CF times: back to numbers with their units - epoch = np.datetime64("2000-01-01", "ns") - values = ((values - epoch) // np.timedelta64(1, "D")).astype("int64") - attrs = {"units": "days since 2000-01-01"} - values = np.ascontiguousarray(values).astype(values.dtype.newbyteorder("<")) - mapping[f"{coord}/zarr.json"] = json.dumps({ - "zarr_format": 3, "node_type": "array", "shape": [len(values)], "data_type": values.dtype.name, - "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": [len(values)]}}, - "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, - "fill_value": 0, "codecs": [{"name": "bytes", "configuration": {"endian": "little"}}], - "dimension_names": [coord], "attributes": attrs, - }).encode() - mapping[f"{coord}/c/0"] = values.tobytes() - return refkv.ReferenceStore(mapping, sources) - - -def read(store: refkv.ReferenceStore, name: str = "v") -> np.ndarray: - with zarr.config.set({"array.rectilinear_chunks": True}): - return xr.open_zarr(refkv.zarr.store(store), consolidated=False)[name].values From 65fd21055422ffa69da14b3cd741ee0010a799ef Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 11:17:26 +0200 Subject: [PATCH 04/12] docs(design): map NaN to the file's _FillValue in cast_value, rather than clamping Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 15 +++++++++++---- design/experiments/inhomogeneous/demo.py | 6 ++++-- design/experiments/inhomogeneous/output.txt | 10 +++++----- design/experiments/inhomogeneous/switch.py | 14 ++++++++++---- 4 files changed, 30 insertions(+), 15 deletions(-) diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 7ef31225..4ccdaf61 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -62,7 +62,8 @@ Case 4's array, with two of its three cases: "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [[4, 4, 4], [6]]}}, "codecs": [{"name": "switch", "configuration": {"cases": [ [{"name": "scale_offset", "configuration": {"offset": 280.0, "scale": 100.00000223517424}}, - {"name": "cast_value", "configuration": {"data_type": "int16", "out_of_range": "clamp"}}, + {"name": "cast_value", "configuration": {"data_type": "int16", + "scalar_map": {"encode": [["NaN", -32767]], "decode": [[-32767, "NaN"]]}}}, {"name": "crop", "configuration": {"shape": [4, 6]}}, {"name": "bytes", "configuration": {"endian": "little"}}, {"name": "numcodecs.zlib", "configuration": {"level": 4}}], @@ -125,8 +126,14 @@ reason. float32 0.01. Decoding through `x / scale` matches `x * scale_factor` to within an ulp, not bit for bit. Losing no information would need `scale_offset` to accept the factor in either direction. -- **The fill value must survive every case's encode direction.** `cast_value` refuses - a NaN fill for an int16 case, so the arrays use 0, with `out_of_range: "clamp"`. - Clamping only affects encoding; decoding int16 to float32 is exact. +- **The fill value must round-trip through every case.** The array's fill value is + NaN, and an int16 case can't hold NaN. Each such case's `cast_value` therefore maps + NaN to the file's CF `_FillValue` and back (`scalar_map`), which is what + `_FillValue` means. A file without `_FillValue` has no lossless choice, so the demo + refuses it. An earlier version used `out_of_range: "clamp"` with a fill of 0, which + can't round-trip either; zarr-python 3.4 accepted it anyway. That is a zarr-python + bug against the cast_value spec ("If the fill value cannot survive a round-trip + cast, implementations MUST treat this as an error"), and it is being fixed + separately. - **zarr-python's `cast_value` needs the `cast-value-rs` package.** - **Versions:** VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 5c1974ad..6e61be69 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -189,7 +189,8 @@ def scaled_files(root: Path) -> list[Path]: physical = 285.0 + 5 * rng.random((4, NX)) packed = np.round((physical - offset) / scale).astype("int16") write(p, packed, t0=4 * i, chunks=(4, NX), compression="gzip", - attrs={"scale_factor": np.float32(scale), "add_offset": np.float32(offset)}) + attrs={"scale_factor": np.float32(scale), "add_offset": np.float32(offset), + "_FillValue": np.int16(-32767)}) paths.append(p) return paths @@ -198,7 +199,8 @@ def dtype_files(root: Path) -> list[Path]: paths = [] for i, dtype in enumerate(["int16", "int16", "float32"]): p = root / f"dtype_{i}.nc" - write(p, (100 * rng.random((4, NX))).astype(dtype), t0=4 * i, chunks=(4, NX), compression="gzip") + write(p, (100 * rng.random((4, NX))).astype(dtype), t0=4 * i, chunks=(4, NX), compression="gzip", + attrs={"_FillValue": np.int16(-32767)} if dtype == "int16" else None) paths.append(p) return paths diff --git a/design/experiments/inhomogeneous/output.txt b/design/experiments/inhomogeneous/output.txt index 22ea87a0..1acd0c82 100644 --- a/design/experiments/inhomogeneous/output.txt +++ b/design/experiments/inhomogeneous/output.txt @@ -10,7 +10,7 @@ -> each file virtualizes on its own: 4 virtual datasets VirtualiZarr: combining fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numcodecs.zlib', codec_config={'level': 4})) refkv: one array, 3 switch cases, saved and reopened: v(16, 6) float32, every value matches the files - the archive is 3,259 bytes; the chunks stay in the 4 files (43,964 bytes), read in place + the archive is 3,261 bytes; the chunks stay in the 4 files (43,964 bytes), read in place ============================================================================== 2. rectilinear chunks @@ -22,7 +22,7 @@ -> each file virtualizes on its own: 3 virtual datasets VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). refkv: one array, 2 switch cases, saved and reopened: v(90, 6) float32, every value matches the files - the archive is 3,554 bytes; the chunks stay in the 3 files (35,389 bytes), read in place + the archive is 3,556 bytes; the chunks stay in the 3 files (35,389 bytes), read in place ============================================================================== 3. ragged inner chunks @@ -34,7 +34,7 @@ -> each file virtualizes on its own: 3 virtual datasets VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenation axis which is not evenly divisible by refkv: one array, 1 switch case, saved and reopened: v(30, 6) float32, every value matches the files - the archive is 3,633 bytes; the chunks stay in the 3 files (33,631 bytes), read in place + the archive is 3,635 bytes; the chunks stay in the 3 files (33,631 bytes), read in place ============================================================================== 4. per-file scale_factor / add_offset @@ -50,7 +50,7 @@ got [287.03 286.26 289.55 285.9 288.7 289.77 289.42 289.07 278.86 278.34 278.94 276.82] refkv: one array, 3 switch cases, saved and reopened: v(12, 6) float32, every value matches the files - the archive is 3,635 bytes; the chunks stay in the 3 files (32,836 bytes), read in place + the archive is 3,784 bytes; the chunks stay in the 3 files (32,836 bytes), read in place ============================================================================== 5. varying dtype @@ -62,4 +62,4 @@ -> each file virtualizes on its own: 3 virtual datasets VirtualiZarr: combining fails: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype(' list[dict]: chain.append({"name": "scale_offset", "configuration": { "offset": float(attrs.get("add_offset", 0)), "scale": 1 / float(attrs.get("scale_factor", 1))}}) if stored != out_dtype: - # decoding stored -> out_dtype is exact; out_of_range only governs encoding, here the - # fill value, which every case's chain must be able to encode - chain.append({"name": "cast_value", "configuration": {"data_type": stored.name, "out_of_range": "clamp"}}) + config = {"data_type": stored.name} + if stored.kind in "iu": + # the array's fill value, NaN, has to round-trip through the integer type + # (cast_value, "data_type"): CF's _FillValue is the integer that stands for it + fill = attrs.get("_FillValue") + if fill is None: + raise ValueError(f"an {stored} file without _FillValue: no integer can stand for NaN") + config["scalar_map"] = {"encode": [["NaN", int(fill)]], "decode": [[int(fill), "NaN"]]} + chain.append({"name": "cast_value", "configuration": config}) chain.append({"name": "crop", "configuration": {"shape": list(m.chunks)}}) return chain + [c.to_dict() for c in m.codecs] @@ -198,7 +204,7 @@ def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv "shape": [sum(time_chunks), *map(sum, other)], "data_type": out_dtype.name, "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [time_chunks, *other]}}, "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, - "fill_value": 0.0, "codecs": [{"name": "switch", "configuration": {"cases": cases}}], + "fill_value": "NaN", "codecs": [{"name": "switch", "configuration": {"cases": cases}}], "dimension_names": list(vdss[0][name].dims), "attributes": {}, }).encode() for coord in vdss[0][name].dims: # the coordinates, small: inline bytes From fe2b17bed188f39e235cc6c7db4d6eb1f6f82935 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 11:49:32 +0200 Subject: [PATCH 05/12] docs(design): check the zarr-specs#399 demo bit for bit, and plot every case Each case's files now hold one smooth field that continues across them, so a misdecoded file shows as a break at its boundary. Results are compared with the files bit for bit (or the largest difference is reported), and figures/ gets a plot per case: the files, refkv and VirtualiZarr along time, and refkv's whole field. Every refkv case is bit-identical; VirtualiZarr's per-file-scaling case is off by up to 19.6. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 61 ++++- design/experiments/inhomogeneous/demo.py | 248 +++++++++++------- .../inhomogeneous/figures/case1.png | Bin 0 -> 85561 bytes .../inhomogeneous/figures/case2.png | Bin 0 -> 91585 bytes .../inhomogeneous/figures/case3.png | Bin 0 -> 79247 bytes .../inhomogeneous/figures/case4.png | Bin 0 -> 77735 bytes .../inhomogeneous/figures/case5.png | Bin 0 -> 73193 bytes design/experiments/inhomogeneous/output.txt | 84 +++--- 8 files changed, 242 insertions(+), 151 deletions(-) create mode 100644 design/experiments/inhomogeneous/figures/case1.png create mode 100644 design/experiments/inhomogeneous/figures/case2.png create mode 100644 design/experiments/inhomogeneous/figures/case3.png create mode 100644 design/experiments/inhomogeneous/figures/case4.png create mode 100644 design/experiments/inhomogeneous/figures/case5.png diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 4ccdaf61..180f28aa 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -16,20 +16,52 @@ combines them along time into one logical array twice: Both are compared with the files read one by one. ```bash -uv run --with "cast-value-rs>=0.4.2" python design/experiments/inhomogeneous/demo.py +uv run --with "cast-value-rs>=0.4.2" --with matplotlib python design/experiments/inhomogeneous/demo.py ``` | case | the files | VirtualiZarr | refkv | |---|---|---|---| -| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ 3 cases | -| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | fails: needs variable-length chunks | ✅ rectilinear grid | -| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | fails: partial chunks break the regular grid | ✅ rectilinear grid, `crop` | -| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | **combines, then 48 of 72 values are silently wrong** | ✅ 3 cases | -| 5. varying dtype | int16, int16, float32 | fails: one array, one dtype | ✅ 2 cases | - -In every refkv case, every value equals the files' values. The archive holds a -few kilobytes of metadata; the chunks are read in place from the netCDF files. -[output.txt](output.txt) is a full run. +| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ bit for bit, 3 cases | +| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | fails: needs variable-length chunks | ✅ bit for bit, rectilinear grid | +| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | fails: partial chunks break the regular grid | ✅ bit for bit, rectilinear grid + `crop` | +| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | **combines, then 120 of 180 values are wrong, by up to 19.6** | ✅ bit for bit, 3 cases | +| 5. varying dtype | int16, int16, float32 | fails: one array, one dtype | ✅ bit for bit, 2 cases | + +## How the results are checked + +- **The reference.** Each file is read on its own by xarray with h5netcdf, which + applies HDF5's own filters (deflate, shuffle) and xarray's CF decoding + (`scale_factor`, `add_offset`, `_FillValue`). The decoded arrays are concatenated + along time. +- **The combined arrays.** VirtualiZarr's result is read through Zarr, using refkv's + in-memory store for its manifest. refkv's is saved as a vzip archive, reopened, and + read through zarr-python and xarray. +- **The comparison.** "Bit for bit" means `np.array_equal(..., equal_nan=True)`, with + no tolerance. Otherwise the run reports the largest absolute difference, and how + many values are off by more than 0.001. +- **The data.** Each case's files hold one smooth field, `v(time, x)`, that continues + across the files. A file decoded with another file's codecs, dtype or scaling shows + as a break at its boundary. + +[`figures/`](figures/) has one plot per case: + +- **Top panel:** `v` along time at `x = 0`, for the files, refkv, and VirtualiZarr + where it combines. The dotted lines are the file boundaries; each file's encoding is + labeled above its span. +- **Bottom panel:** all of refkv's `v(time, x)`. + +Case 4 is the one where VirtualiZarr produces an array: the second file drifts, and +the third drops by about 20. + +![case 4](figures/case4.png) + +| | | | +|---|---|---| +| [![case 1](figures/case1.png)](figures/case1.png) | [![case 2](figures/case2.png)](figures/case2.png) | [![case 3](figures/case3.png)](figures/case3.png) | +| [![case 5](figures/case5.png)](figures/case5.png) | | | + +The archives hold a few kilobytes of metadata; the chunks are read in place from +the netCDF files. [output.txt](output.txt) is a full run. ## How: one codec per transformation, and one that chooses @@ -121,10 +153,11 @@ reason. ## Limits - **`switch` and `crop` are defined here**, not registered as zarr extensions. -- **`scale_offset` stores the encode multiplier, CF the decode one.** CF's - `scale_factor` of 0.01 becomes `scale` 100.00000223517424, the reciprocal of - float32 0.01. Decoding through `x / scale` matches `x * scale_factor` to within an - ulp, not bit for bit. Losing no information would need `scale_offset` to accept the +- **`scale_offset` stores the encode multiplier; CF stores the decode one.** CF's + `scale_factor` of 0.01 becomes `scale` 100.00000223517424, the reciprocal of float32 + 0.01. Here decoding through `x / scale` gives exactly what `x * scale_factor` gives, + bit for bit, but that isn't guaranteed for every factor and value: in general the + two agree to within an ulp. A guarantee would need `scale_offset` to accept the factor in either direction. - **The fill value must round-trip through every case.** The array's fill value is NaN, and an int16 case can't hold NaN. Each such case's `cast_value` therefore maps diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 6e61be69..48a9dec7 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -1,13 +1,22 @@ -"""Why many-file datasets fall off the virtualization "complexity cliff". +"""Why many-file datasets fall off the virtualization "complexity cliff", and how +refkv gets them into one array. An executable companion to zarr-developers/zarr-specs#399 ("Varying codec parameters -per-chunk"). Each case writes a few small netCDF4 files that are each perfectly valid, -virtualizes every file with VirtualiZarr, and then tries to combine them into one -logical Zarr array along time, the way you would a real archive. Each case reports -what happens: an error, or a combined array whose values are compared with the -truth read straight from the files. - - uv run --with "cast-value-rs>=0.4.2" python design/experiments/inhomogeneous/demo.py +per-chunk"). Each case writes a few small netCDF4 files, each valid and consistent on +its own, holding one smooth field `v(time, x)` that continues across the files. It +virtualizes every file with VirtualiZarr, then combines them into one array along time +twice: with VirtualiZarr (concatenating its ManifestArrays) and with refkv +(switch.py). Each result is read back through Zarr and compared with the files read +one by one: + +- the reference is each file decoded by xarray + h5netcdf (HDF5's own filters, CF + scaling and _FillValue), concatenated; +- "identical" means equal bit for bit (NaN equal to NaN); otherwise the largest + absolute difference is reported; +- figures/ gets one plot per case: the files, refkv and VirtualiZarr along time at + x = 0, with the files' boundaries, and refkv minus the files over (time, x). + + uv run --with "cast-value-rs>=0.4.2" --with matplotlib python design/experiments/inhomogeneous/demo.py Cases (the use cases of the issue): @@ -24,6 +33,7 @@ import sys import tempfile import warnings +from dataclasses import dataclass, field from pathlib import Path import h5py @@ -36,9 +46,17 @@ warnings.filterwarnings("ignore") sys.path.insert(0, str(Path(__file__).parent)) +HERE = Path(__file__).parent NX = 6 # a small spatial axis, so every case runs in a second +def smooth(t: np.ndarray, base: float = 285.0, amp: float = 4.0) -> np.ndarray: + """One smooth field over (time, x), continuous across files: a misdecoded file + shows as a jump at its boundary.""" + x = np.arange(NX) + return base + amp * np.sin(2 * np.pi * t[:, None] / 37.0) + 0.6 * np.cos(2 * np.pi * x[None, :] / NX) + + def write(path: Path, data: np.ndarray, *, t0: int, chunks=None, compression=None, compression_opts=None, shuffle=False, attrs=None) -> None: """One netCDF4 file: a variable `v(time, x)`, its `time` coordinate starting at t0.""" @@ -64,88 +82,131 @@ def virtualize(paths: list[Path], root: Path) -> list[xr.Dataset]: loadable_variables=["time", "x"]) for p in paths] -def combine(vdss: list[xr.Dataset]) -> xr.Dataset: - """Concatenate along time, as open_virtual_mfdataset would.""" - return xr.concat(vdss, dim="time", coords="minimal", compat="override", combine_attrs="override") - - def truth(paths: list[Path]) -> np.ndarray: - """What a user expects: every file decoded on its own (CF scaling applied), then stacked.""" + """What a user expects: every file decoded on its own, then stacked.""" return np.concatenate([xr.open_dataset(p, engine="h5netcdf")["v"].values for p in paths]) -def read_back(vds: xr.Dataset) -> np.ndarray: - """Read the combined virtual array through Zarr, as a user of the virtual store would: - via refkv's in-memory reference store, decoding CF attributes the way xarray does.""" - import refkv - import refkv.zarr +@dataclass +class Result: + title: str + files: list[str] + boundaries: list[int] # the time index where each file starts + want: np.ndarray + vz: np.ndarray | None = None + vz_note: str = "" + rk: np.ndarray | None = None + rk_note: str = "" + extra: dict = field(default_factory=dict) - store = refkv.ReferenceStore.from_virtualizarr(vds) - return xr.open_zarr(refkv.zarr.store(store), consolidated=False)["v"].values - -def run(title: str, issue: str, make) -> None: - print(f"\n{'=' * 78}\n{title}\n {issue}\n{'-' * 78}") - with tempfile.TemporaryDirectory() as d: - root = Path(d) - paths = make(root) - for p in paths: - with h5py.File(p) as f: - v = f["v"] - filters = {k: v for k, v in (("compression", v.compression), ("opts", v.compression_opts), - ("shuffle", v.shuffle)) if v} - cf = {k: float(v.attrs[k]) for k in ("scale_factor", "add_offset") if k in v.attrs} - print(f" {p.name}: shape={v.shape} chunks={v.chunks} dtype={v.dtype} " - f"filters={filters or 'none'}" + (f" {cf}" if cf else "")) - vdss = virtualize(paths, root) # each file alone virtualizes fine - print(f" -> each file virtualizes on its own: {len(vdss)} virtual datasets") - want = truth(paths) - virtualizarr_result(vdss, want) - refkv_result(vdss, want, root) +def compare(got: np.ndarray, want: np.ndarray) -> str: + if got.shape != want.shape: + return f"shape {got.shape}, not {want.shape}" + if np.array_equal(got, want, equal_nan=True): + return "identical to the files (bit for bit)" + d = np.abs(got.astype("float64") - want.astype("float64")) + bad = int(np.sum(~np.isclose(got, want, equal_nan=True, rtol=0, atol=1e-3))) + return (f"max |difference| {np.nanmax(d):.3g}; {bad} of {want.size} values off by more than 0.001" + if bad else f"max |difference| {np.nanmax(d):.3g} (float32 rounding)") -def virtualizarr_result(vdss: list[xr.Dataset], want: np.ndarray) -> None: +def virtualizarr_result(r: Result, vdss: list[xr.Dataset]) -> None: """VirtualiZarr: concatenate the ManifestArrays, then read through Zarr.""" + import refkv + import refkv.zarr + try: - combined = combine(vdss) + combined = xr.concat(vdss, dim="time", coords="minimal", compat="override", combine_attrs="override") except Exception as e: # the cliff: no single Zarr array can describe these files - print(f" VirtualiZarr: combining fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") - return - try: - got = read_back(combined) - except Exception as e: - print(f" VirtualiZarr: combined, but reading fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") + r.vz_note = f"combining fails: {type(e).__name__}: {str(e).splitlines()[0][:160]}" return - report("VirtualiZarr: combined (one codec chain and the first file's attributes for all)", got, want) - -def report(label: str, got: np.ndarray, want: np.ndarray) -> None: - if got.shape == want.shape and np.allclose(got, want, equal_nan=True): - print(f" {label}: v{got.shape} {got.dtype}, every value matches the files") - else: - bad = int(np.sum(~np.isclose(got, want, equal_nan=True))) if got.shape == want.shape else "all" - print(f" {label}: SILENTLY WRONG, {bad} of {want.size} values differ from the files") - print(f" expected {np.round(want[:, 0], 2)}\n got {np.round(got[:, 0], 2)}") + store = refkv.ReferenceStore.from_virtualizarr(combined) + r.vz = xr.open_zarr(refkv.zarr.store(store), consolidated=False)["v"].values + r.vz_note = "combined: " + compare(r.vz, r.want) -def refkv_result(vdss: list[xr.Dataset], want: np.ndarray, root: Path) -> None: - """refkv: one array from the same per-file manifests (variant.py), saved as a vzip - archive and read back through Zarr.""" +def refkv_result(r: Result, vdss: list[xr.Dataset], root: Path) -> None: + """refkv: one array from the same per-file manifests (switch.py), saved as a vzip + archive, reopened, and read through Zarr.""" import refkv - import switch as variant + import switch - try: - store = variant.combine(vdss) - archive = root / "combined.vzip" - store.save(str(archive)) - got = variant.read(refkv.open(str(archive))) - except Exception as e: - print(f" refkv: fails: {type(e).__name__}: {str(e).splitlines()[0][:200]}") - return + store = switch.combine(vdss) + archive = root / "combined.vzip" + store.save(str(archive)) + r.rk = switch.read(refkv.open(str(archive))) n = len(json.loads(store.get("v/zarr.json"))["codecs"][0]["configuration"]["cases"]) - report(f"refkv: one array, {n} switch case{'s' * (n != 1)}, saved and reopened", got, want) data = sum(p.stat().st_size for p in root.glob("*.nc")) - print(f" the archive is {archive.stat().st_size:,} bytes; the chunks stay in the " - f"{len(store.sources)} files ({data:,} bytes), read in place") + r.rk_note = f"one array, {n} switch case{'s' * (n != 1)}: " + compare(r.rk, r.want) + r.extra["archive"] = (f"the archive is {archive.stat().st_size:,} bytes; the chunks stay in " + f"the {len(store.sources)} files ({data:,} bytes), read in place") + + +def run(title: str, make) -> Result: + print(f"\n{'=' * 78}\n{title}\n{'-' * 78}") + with tempfile.TemporaryDirectory() as d: + root = Path(d) + paths = make(root) + files, starts, t = [], [], 0 + for p in paths: + with h5py.File(p) as f: + v = f["v"] + filters = [s for s in (v.compression and f"{v.compression}", v.shuffle and "shuffle") if s] + cf = "" + if "scale_factor" in v.attrs: + cf = f"\n×{float(v.attrs['scale_factor']):g} +{float(v.attrs['add_offset']):g}" + label = f"{v.dtype}\n" + ("+".join(filters) or "uncompressed") + cf + print(f" {p.name}: shape={v.shape} chunks={v.chunks} {label.replace(chr(10), ' ')}") + files.append(label) + starts.append(t) + t += v.shape[0] + r = Result(title, files, starts, truth(paths)) + vdss = virtualize(paths, root) # each file alone virtualizes fine + virtualizarr_result(r, vdss) + refkv_result(r, vdss, root) + print(f" VirtualiZarr: {r.vz_note}\n refkv: {r.rk_note}\n {r.extra['archive']}") + return r + + +def plot(r: Result, path: Path) -> None: + import textwrap + + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + fig, (ax, ax2) = plt.subplots(2, 1, figsize=(9, 6.5), height_ratios=[3, 2], constrained_layout=True) + fig.suptitle(r.title, fontsize=10, x=0.01, ha="left") + n = r.want.shape[0] + t = np.arange(n) + ax.plot(t, r.want[:, 0], color="0.75", lw=6, label="the files, each read on its own") + if r.vz is not None: + ax.plot(t, r.vz[:, 0], color="tab:red", lw=1.5, marker="x", ms=4, label="VirtualiZarr, combined") + if r.rk is not None: + ax.plot(t, r.rk[:, 0], color="k", lw=1.2, ls="--", label="refkv, combined") + ends = r.boundaries[1:] + [n] + for b in r.boundaries[1:]: + ax.axvline(b - 0.5, color="0.5", lw=0.8, ls=":") + top = ax.secondary_xaxis("top") # each file's encoding, over its span + top.set_xticks([(b + e - 1) / 2 for b, e in zip(r.boundaries, ends)], r.files, fontsize=7) + top.tick_params(length=0) + ax.set_xlim(-0.5, n - 0.5) + ax.set_xlabel("time index") + ax.set_ylabel("v at x = 0") + vz = r.vz_note if r.vz is not None else "\n".join(textwrap.wrap(r.vz_note, 120)) + ax.set_title(f"VirtualiZarr: {vz}", fontsize=8, loc="left", color="tab:red") + ax.legend(fontsize=8, loc="best") + im = ax2.imshow(r.rk.T, aspect="auto", cmap="viridis", interpolation="nearest", + extent=(-0.5, n - 0.5, NX - 0.5, -0.5)) + for b in r.boundaries[1:]: + ax2.axvline(b - 0.5, color="w", lw=0.8, ls=":") + ax2.set_xlabel("time index") + ax2.set_ylabel("x") + ax2.set_title(f"refkv, all of v(time, x): {r.rk_note}", fontsize=8, loc="left") + fig.colorbar(im, ax=ax2, label="v") + fig.savefig(path, dpi=110) + plt.close(fig) rng = np.random.default_rng(0) @@ -153,12 +214,11 @@ def refkv_result(vdss: list[xr.Dataset], want: np.ndarray, root: Path) -> None: def era_files(root: Path) -> list[Path]: paths = [] - # gzip, then gzip behind a shuffle filter, then no compression at all - for i, (comp, opts, shuf) in enumerate([("gzip", 4, False), ("gzip", 4, False), - ("gzip", 4, True), (None, None, False)]): + # gzip, gzip, then gzip behind a shuffle filter, then no compression at all + for i, (comp, shuf) in enumerate([("gzip", False), ("gzip", False), ("gzip", True), (None, False)]): p = root / f"era_{i}.nc" - write(p, rng.random((4, NX)).astype("float32"), t0=4 * i, chunks=(4, NX), - compression=comp, compression_opts=opts, shuffle=shuf) + write(p, smooth(np.arange(8 * i, 8 * i + 8)).astype("float32"), t0=8 * i, chunks=(4, NX), + compression=comp, shuffle=shuf) paths.append(p) return paths @@ -167,7 +227,7 @@ def rectilinear_files(root: Path) -> list[Path]: paths, t0 = [], 0 for i, nt in enumerate([31, 28, 31]): # January, February, March p = root / f"month_{i}.nc" - write(p, rng.random((nt, NX)).astype("float32"), t0=t0, chunks=(nt, NX), compression="gzip") + write(p, smooth(np.arange(t0, t0 + nt)).astype("float32"), t0=t0, chunks=(nt, NX), compression="gzip") paths.append(p) t0 += nt return paths @@ -177,7 +237,8 @@ def ragged_files(root: Path) -> list[Path]: paths = [] for i in range(3): # 10 steps per file, chunked by 4: chunks of 4, 4, 2 p = root / f"ragged_{i}.nc" - write(p, rng.random((10, NX)).astype("float32"), t0=10 * i, chunks=(4, NX), compression="gzip") + write(p, smooth(np.arange(10 * i, 10 * i + 10)).astype("float32"), t0=10 * i, chunks=(4, NX), + compression="gzip") paths.append(p) return paths @@ -186,9 +247,9 @@ def scaled_files(root: Path) -> list[Path]: paths = [] for i, (scale, offset) in enumerate([(0.01, 280.0), (0.02, 270.0), (0.005, 290.0)]): p = root / f"scaled_{i}.nc" - physical = 285.0 + 5 * rng.random((4, NX)) + physical = smooth(np.arange(10 * i, 10 * i + 10)) packed = np.round((physical - offset) / scale).astype("int16") - write(p, packed, t0=4 * i, chunks=(4, NX), compression="gzip", + write(p, packed, t0=10 * i, chunks=(5, NX), compression="gzip", attrs={"scale_factor": np.float32(scale), "add_offset": np.float32(offset), "_FillValue": np.int16(-32767)}) paths.append(p) @@ -199,16 +260,25 @@ def dtype_files(root: Path) -> list[Path]: paths = [] for i, dtype in enumerate(["int16", "int16", "float32"]): p = root / f"dtype_{i}.nc" - write(p, (100 * rng.random((4, NX))).astype(dtype), t0=4 * i, chunks=(4, NX), compression="gzip", - attrs={"_FillValue": np.int16(-32767)} if dtype == "int16" else None) + values = smooth(np.arange(10 * i, 10 * i + 10), base=50, amp=40) + write(p, (np.round(values) if dtype == "int16" else values).astype(dtype), t0=10 * i, chunks=(5, NX), + compression="gzip", attrs={"_FillValue": np.int16(-32767)} if dtype == "int16" else None) paths.append(p) return paths +CASES = [ + ("1. codec eras: gzip, gzip, shuffle + gzip, uncompressed (GOES-16)", era_files), + ("2. rectilinear: monthly files of 31, 28 and 31 days, one chunk each", rectilinear_files), + ("3. ragged inner chunks: 10 steps per file chunked by 4 (4, 4, 2)", ragged_files), + ("4. per-file scale_factor / add_offset on int16 (BOM BARPA)", scaled_files), + ("5. varying dtype: int16, int16, float32", dtype_files), +] + if __name__ == "__main__": - run("1. codec eras", "the compression changes partway through the record (GOES-16)", era_files) - run("2. rectilinear chunks", "monthly files: 31, 28 and 31 days, one chunk each", rectilinear_files) - run("3. ragged inner chunks", "each file chunked by 4 along time, 10 steps: chunks 4, 4, 2", ragged_files) - run("4. per-file scale_factor / add_offset", "each file packs int16 with its own CF scaling (BOM BARPA)", - scaled_files) - run("5. varying dtype", "the same variable is int16 in two files and float32 in the third", dtype_files) + figures = HERE / "figures" + figures.mkdir(exist_ok=True) + for i, (title, make) in enumerate(CASES, 1): + r = run(title, make) + plot(r, figures / f"case{i}.png") + print(f"\nfigures: {figures}") diff --git a/design/experiments/inhomogeneous/figures/case1.png b/design/experiments/inhomogeneous/figures/case1.png new file mode 100644 index 0000000000000000000000000000000000000000..a578f561d015a50405ef1aaac2a73098797ba1e0 GIT binary patch literal 85561 zcmZs?cQl)C_&?sEtx>gUt0;=LR#j2ERjX8W+qvbhQxNpSCexCivkt3XUjrAWL zIdYtMZfwJ|JZU zB_)OHau;6&1o(nA6cxSy?;Q$0{_cveQYpY9N7#Igt-wc)aIgIPJNn@L->@S`j~uzH zfBQ*r-fI4d27XuJc-o z|6bZ`{_iKw&({V2_r7XwHsb$Y>w|ca-^_9sK?t%cw$|e@w$wr~TX=UN+rjpov>Cp{ zEZ4v3%H5uFIB}VR+`ONS`?VBg4%(ZM@dp98k9F&yptySlG@Ork-D*Q3%3xH8Qao7fzdSr$looRL(_g z4yrw*FF=J@ZShwhD|br@wU z_{JI>GNdL~H?pNa)eGM+Y*wJ9K0AANRcv!{5PX88WToovub}Gjsg`hPi3kL4?WhotcCnq;Z*T<4At+Y$7a(JXunE)vl@b 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zBg$r%^#2>Q_&>+~@e0UTR3``;AD6%Z$rk){|1p(Yw{BHDV1g0M3}6LdnFM69iY%i4dWp+ zz*m}Rn-uJa%0oRGp96>2zJ2@DL~IHn2)7=<4;~0Zc?B_!H!IhnBnaQydLW5BOV9@| z!?vITXB3);G`R}LUex+@D@;W@H@)_AN^C49DvI}l=i1?7>7NzfujATiiwMS#SLWvC z5-!2?GAjTegWI;i11(eY;sLQW=7VbIjn)UZD5iC9oYW=FZY);$<@;BT7Y0|<^m zgNLHo@DL_2Sqh`tZXz2~XVY&ueuH6Qe303J)mD$9iXVY@$H=kNh>PAEtW@gQB?#VU zybpT#b>!1pAqLfs1%u0>9!edXUAP1HwV~93te97@x90RO@8SCd78efGKdFz42n(yx zKS0rj0HO2xWJAnX%lR={q~t)L^w(@-J|Hjq1>;X<8$p~Y9;%?|>fcaTh+le(LvqzL zA$VX|;xlL?d=2uM zL79WwHI8PlLL6hs7@a$?XyZ^zhCy|w36K4t5N`*W>iiTZXYIFHu_BN$*M z#;g&p*AB6N^(s9@wA)@CVxh+H^5WVG;JB}cc-Cgmn<(BgIod9LSyD_OLfpA$WE%=c z0>q`Y+swi*$i{)z+jmm|IX;Z6$PicmVd)?4^Cz>K3kyq&`}1&t`l7#9SJ~IwpD!b~ z1QcUNH8>>*!?GS;gKF>YX0$bw=-31wr!1G~{!gz-e z9xm29WzF^g^u*B=4O`Sk+GWNpBbgU1+p-2aydhD=%VlC^^Lz;2vdaEX3?k_P;b(`i zoO8|X010e*2%y`{LSE2sydoj3Mx)bS|F_(4XgjY3$t?TTGaNf|9H6%38&3I9(h@(P1LZu|ub&4pC!IhDjphYs zhz@=A)p_i3z$*U1vNCwr2=Ko=r7g48qB`(zA?>!4`|Q2>;;hcN8O*8-X%(ARz|bis z*aNw2CZru?VVF;a7|LkPE-|d<;aAva?w4ftx4hVeHciax`Tv8d{{Na_tMqB(Qpzml UwJo~>5A/design/experiments/inhomogeneous/figures From b3310d16d6789bfd17c01a2c116e299afc1ef8d1 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 12:10:57 +0200 Subject: [PATCH 08/12] style(design): import at module level in the zarr-specs#399 demo Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/demo.py | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index d212625e..533749ea 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -55,13 +55,14 @@ from __future__ import annotations import json -import sys import tempfile +import textwrap import warnings from dataclasses import dataclass, field from pathlib import Path import h5py +import matplotlib import numpy as np import virtualizarr as vz import xarray as xr @@ -69,8 +70,14 @@ from obstore.store import LocalStore from virtualizarr.parsers import HDFParser +import refkv +import refkv.zarr +import switch # next to this script, which Python puts on sys.path + +matplotlib.use("Agg") # files only, no display +import matplotlib.pyplot as plt # noqa: E402 - after choosing the backend + warnings.filterwarnings("ignore") -sys.path.insert(0, str(Path(__file__).parent)) HERE = Path(__file__).parent NX = 6 # a small spatial axis, so every case runs in a second @@ -139,9 +146,6 @@ def compare(got: np.ndarray, want: np.ndarray) -> str: def virtualizarr_result(r: Result, vdss: list[xr.Dataset]) -> None: """One Zarr array today: concatenate the virtual datasets (xarray's default combine_attrs="override", as open_virtual_mfdataset uses), then read through Zarr.""" - import refkv - import refkv.zarr - try: combined = xr.concat(vdss, dim="time", coords="minimal", compat="override", combine_attrs="override") except Exception as e: # what one Zarr array can't express, as VirtualiZarr reports it @@ -156,9 +160,6 @@ def virtualizarr_result(r: Result, vdss: list[xr.Dataset]) -> None: def refkv_result(r: Result, vdss: list[xr.Dataset], root: Path) -> None: """refkv: one array from the same per-file manifests (switch.py), saved as a vzip archive, reopened, and read through Zarr.""" - import refkv - import switch - store = switch.combine(vdss) archive = root / "combined.vzip" store.save(str(archive)) @@ -198,12 +199,6 @@ def run(title: str, make) -> Result: def plot(r: Result, path: Path) -> None: - import textwrap - - import matplotlib - matplotlib.use("Agg") - import matplotlib.pyplot as plt - fig, (ax, ax2) = plt.subplots(2, 1, figsize=(9, 6.5), height_ratios=[3, 2], constrained_layout=True) fig.suptitle(r.title, fontsize=10, x=0.01, ha="left") n = r.want.shape[0] From 2c27e2180774962f7bedf7c4d9438f3f29ff4eca Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 12:20:08 +0200 Subject: [PATCH 09/12] docs(design): make switch cases templates with per-chunk parameters, and measure scaling A switch case is now a codec chain that may leave numbers open ({"parameter": k}); a chunk's header carries its case index and those numbers. zarr.json holds the kinds of encoding, and per-chunk numbers travel in each chunk's reference: case 4 needs one case instead of three. scaling.py packs every chunk differently: at 100,000 chunks, one case per chunk is a 37.7 MB zarr.json and 2.3 s to open, against 0.8 kB and 0.3 s with one parameterized case; both read bit for bit. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 91 +++++++++--- .../inhomogeneous/figures/case4.png | Bin 79742 -> 79663 bytes .../inhomogeneous/figures/case5.png | Bin 72348 -> 72412 bytes design/experiments/inhomogeneous/output.txt | 17 +-- design/experiments/inhomogeneous/scaling.py | 135 ++++++++++++++++++ .../inhomogeneous/scaling_output.txt | 7 + design/experiments/inhomogeneous/switch.py | 94 +++++++++--- 7 files changed, 293 insertions(+), 51 deletions(-) create mode 100644 design/experiments/inhomogeneous/scaling.py create mode 100644 design/experiments/inhomogeneous/scaling_output.txt diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 59b2b612..e9c98d3d 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -97,33 +97,80 @@ Each thing that varies between the files is its own, independent codec: One codec, `switch`, chooses per chunk which composition of them applies. -- **What `switch` is.** It is an array-to-bytes codec, `{"cases": [chain, ...]}`, - where each case is an ordinary Zarr codec chain. A chunk's bytes start with a - varint, the index of its case. `switch` decodes the rest with that chain, the way - `sharding_indexed` decodes inner chunks with its inner chain. -- **Where the index comes from.** refkv's reference values carry it. A chunk is - `Concat(Range(data=), Range(source=, offset, length))`: one literal - byte in the archive, then the file's own bytes. +- **What `switch` is.** It is an array-to-bytes codec, + `{"cases": [{"codecs": chain, "parameters": [data_type, ...]}, ...]}`. + - **Each case is an ordinary Zarr codec chain that may leave numbers open.** + `{"parameter": k}` anywhere in a codec's configuration stands for the chunk's + k-th parameter. + - **A chunk's bytes start with a header:** a varint case index, then that case's + parameters, each little-endian in its declared data type. + - **`switch` fills in the case with those values and decodes the rest with it,** the + way `sharding_indexed` decodes inner chunks with its inner chain. +- **Where the header comes from.** refkv's reference values carry it. A chunk is + `Concat(Range(data=

), Range(source=, offset, length))`: a few literal + bytes in the archive's index, then the file's own bytes. +- **What goes where.** `zarr.json` holds the *kinds* of encoding: in case 4, one case + for all three files. Each file's `add_offset` and the reciprocal of its + `scale_factor` travel in its chunks' headers, 17 bytes each. Chunks that differ only + in numbers share a case. - **Grids.** Month-length and ragged files use the zarr-extensions [`rectilinear`](https://github.com/zarr-developers/zarr-extensions/tree/main/chunk-grids/rectilinear) chunk grid. -Case 4's array, with two of its three cases: +Case 4's array, with its one case: ```json "data_type": "float32", -"chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [[4, 4, 4], [6]]}}, -"codecs": [{"name": "switch", "configuration": {"cases": [ - [{"name": "scale_offset", "configuration": {"offset": 280.0, "scale": 100.00000223517424}}, - {"name": "cast_value", "configuration": {"data_type": "int16", - "scalar_map": {"encode": [["NaN", -32767]], "decode": [[-32767, "NaN"]]}}}, - {"name": "crop", "configuration": {"shape": [4, 6]}}, - {"name": "bytes", "configuration": {"endian": "little"}}, - {"name": "numcodecs.zlib", "configuration": {"level": 4}}], - [{"name": "scale_offset", "configuration": {"offset": 270.0, "scale": 50.00000111758712}}, - ...]]}}] +"chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [[5, 5, 5, 5, 5, 5], [6]]}}, +"codecs": [{"name": "switch", "configuration": {"cases": [{ + "codecs": [ + {"name": "scale_offset", "configuration": {"offset": {"parameter": 0}, "scale": {"parameter": 1}}}, + {"name": "cast_value", "configuration": {"data_type": "int16", + "scalar_map": {"encode": [["NaN", -32767]], "decode": [[-32767, "NaN"]]}}}, + {"name": "crop", "configuration": {"shape": [5, 6]}}, + {"name": "bytes", "configuration": {"endian": "little"}}, + {"name": "numcodecs.zlib", "configuration": {"level": 4}}], + "parameters": ["float64", "float64"]}]}}] ``` +## Scaling: when every chunk is encoded differently + +A table of whole chains grows with the number of distinct encodings, and every reader +reads the whole table before its first chunk. [scaling.py](scaling.py) measures the +worst case: N chunks, each packed with its own CF scale and offset (as GRIB packs each +message), in one source file. It describes the same array two ways: + +- **One case per chunk:** each chunk's numbers are written into its own case. +- **One case, two parameters:** each chunk's numbers are in its header. + +```bash +uv run scaling.py +``` + +| chunks | design | `zarr.json` | archive | open the array | read one chunk | +|---|---|---|---|---|---| +| 1,000 | one case per chunk | 378 kB | 0.50 MB | 0.02 s | 2.0 ms | +| 1,000 | one case, two parameters | 0.8 kB | 0.16 MB | 0.00 s | 1.4 ms | +| 10,000 | one case per chunk | 3.8 MB | 5.05 MB | 0.17 s | 1.9 ms | +| 10,000 | one case, two parameters | 0.8 kB | 1.58 MB | 0.02 s | 1.7 ms | +| 100,000 | one case per chunk | 37.7 MB | 51.0 MB | 2.26 s | 1.8 ms | +| 100,000 | one case, two parameters | 0.8 kB | 16.1 MB | 0.28 s | 1.4 ms | + +Both designs read 200 chunks spread over the array bit for bit +([scaling_output.txt](scaling_output.txt)). + +- **One case per chunk.** `zarr.json` costs about 380 bytes per chunk, all read and + parsed before the first chunk. +- **One case, two parameters.** `zarr.json` stays the same size whatever the chunk + count. The parameters cost 17 bytes per chunk, in the reference each read already + fetches. What still grows with N, about 160 bytes per chunk and 0.28 s to open at + 100,000, is the archive's own index of 100,000 keys. Any archive of that many chunks + pays it. + +That is the split in point 1 of the issue. Context chunks share goes in the metadata; +context per chunk goes in the manifest, which already exists per chunk. With +references that carry literal bytes, the second half needs no new mechanism. + ## Why the chooser is generic, and the formats live in the transformations The array-to-bytes codec could be scoped to the source product instead: a @@ -167,9 +214,11 @@ reason. (zarr-extensions) already puts a per-chunk header in the chunk bytes, to turn codecs of one chain on or off. `switch` chooses among chains, so it also covers what `conditional` can't: a different dtype, scaling, or stored shape per chunk. -- **Concatenating two such arrays** (manifest-level operations, as VirtualiZarr does them) means - joining their case lists and renumbering each chunk's index: one literal byte per - chunk, rewritten by tooling. No source bytes change. +- **Concatenating two such arrays** (manifest-level operations, as VirtualiZarr does + them) means joining their case lists and renumbering the case index at the start of + each chunk's header. The parameters travel with the chunk unchanged, and so do the + source bytes. Identifying cases by a hash of their canonical JSON instead of by + position would make the join a union, with no renumbering. ## Limits diff --git a/design/experiments/inhomogeneous/figures/case4.png b/design/experiments/inhomogeneous/figures/case4.png index c76114c8af7440e95db6aec45da7eab9e52eebd1..5e6b205d99cb98a53d6d3185b39574a714ae1d8a 100644 GIT binary patch delta 32054 zcmYJbcRZEvA3uIXXlP4hl#&q2EW45|vbV~LlM#-6+$2egWQDBkT^yTZypuf-PFY8p z$FVsMj&siMI-l?N@%{Cer0#QH*Xw${p6iOb##Fw>^xdxx3UM!T;k!${K7~R#guRxq zFT7GPqpEK%e9Fk^XJnwr8XJ0<@H3LiQ&2v0@hC@a`%wv|NB8wr?!P^H+`D{^syrYR z={$Zz*V%1+0+%#EnUs>9OaFRZmQ=TRZ}F?eU-WE@@^t-b^@MPIx&QV8)zx7LU*Ayb(BvQOAc`EQ&^rFy+ThAJKc@btfl;L8(c<|Y@n$LKfxwj? z5zhCD+*;1Mj*cV>>b8#oQsN1wdIsLDWS() zb43j+(XGi+y5@`5#zq{i3ExD$#d$n(UdHhltVx%?C_xKn>)MVTh^qD>F9?^d(c&I82t)n-GNqV$UBgx6j$))* zowtfKvP|PBHmNi*)elfj*fXUvos!E5%h zHl-+)geFk098J0Bm8Y4ev@`fnw`68rCs*%y zSMJmJ3Yn%WZi=2@bU*ZMsq!Ene~9@~W$ST8HxgR?{Ue*UK0~bx_*{+>_eqs;70$iy 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ztR{^S;`M#9wcbx(%G*jQDk~OWHGpmB;*aP)Z2OP-<5S2WGLTyN=Qfx26(65++(ro` z)OmCanZ(D^zuhRkFjWrN`Z(Adkl3hE%)O_F3M21ExhYHu1g(6!?A}e8R!H8zvu-27 z%Sx+7=r6)~C6_OHO7Pt1ZSjTZB4h#W5ZeMRsA$qIu)JOYQJ6JWtQsU0cJJqVks?fP|w^HI?|*@b2~BI z4wN7exyK>;Gd}&*`>A#o2UgY_Dv~`^eay_v7SLCxCsokRN;AR963Phj8aM`Ei*zzb5DjndF44Koh!yR((NJ9{H$46Pm;CZDz`Qy8EPA6eUzwE7I_g+y}d95C_%hdalj7-JHDIdW88aGfuDPYBd zK~glloNibe015aoFxCf1b+# diff --git a/design/experiments/inhomogeneous/output.txt b/design/experiments/inhomogeneous/output.txt index ca93f960..f0b55f60 100644 --- a/design/experiments/inhomogeneous/output.txt +++ b/design/experiments/inhomogeneous/output.txt @@ -1,6 +1,3 @@ -Downloading refkv (2.0MiB) - Downloaded refkv -Installed 32 packages in 85ms ============================================================================== 1. codec eras: gzip, gzip, shuffle + gzip, uncompressed (GOES-16) @@ -11,7 +8,7 @@ Installed 32 packages in 85ms era_3.nc: shape=(8, 6) chunks=(4, 6) float32 uncompressed one Zarr array today: not one array: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numc switch sketch: one array, 3 switch cases: identical to the files (bit for bit) - the archive is 3,885 bytes; the chunks stay in the 4 files (44,339 bytes), read in place + the archive is 3,975 bytes; the chunks stay in the 4 files (44,339 bytes), read in place ============================================================================== 2. rectilinear: monthly files of 31, 28 and 31 days, one chunk each @@ -21,7 +18,7 @@ Installed 32 packages in 85ms month_2.nc: shape=(31, 6) chunks=(31, 6) float32 gzip one Zarr array today: not one array: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). switch sketch: one array, 2 switch cases: identical to the files (bit for bit) - the archive is 3,556 bytes; the chunks stay in the 3 files (34,740 bytes), read in place + the archive is 3,616 bytes; the chunks stay in the 3 files (34,740 bytes), read in place ============================================================================== 3. ragged inner chunks: 10 steps per file chunked by 4 (4, 4, 2) @@ -31,7 +28,7 @@ Installed 32 packages in 85ms ragged_2.nc: shape=(10, 6) chunks=(4, 6) float32 gzip one Zarr array today: not one array: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenati switch sketch: one array, 1 switch case: identical to the files (bit for bit) - the archive is 3,635 bytes; the chunks stay in the 3 files (33,433 bytes), read in place + the archive is 3,665 bytes; the chunks stay in the 3 files (33,433 bytes), read in place ============================================================================== 4. per-file scale_factor / add_offset on int16 (BOM BARPA) @@ -40,8 +37,8 @@ Installed 32 packages in 85ms scaled_1.nc: shape=(10, 6) chunks=(5, 6) int16 gzip ×0.02 +270 scaled_2.nc: shape=(10, 6) chunks=(5, 6) int16 gzip ×0.005 +290 one Zarr array today: combined, with the first file's attributes for every file (combine_attrs='override'; 'no_conflicts' refuses instead): max |difference| 19.6; 120 of 180 values off by more than 0.001 - switch sketch: one array, 3 switch cases: identical to the files (bit for bit) - the archive is 4,299 bytes; the chunks stay in the 3 files (33,190 bytes), read in place + switch sketch: one array, 1 switch case: identical to the files (bit for bit) + the archive is 3,745 bytes; the chunks stay in the 3 files (33,190 bytes), read in place ============================================================================== 5. varying dtype: int16, int16, float32 @@ -49,8 +46,8 @@ Installed 32 packages in 85ms dtype_0.nc: shape=(10, 6) chunks=(5, 6) int16 gzip dtype_1.nc: shape=(10, 6) chunks=(5, 6) int16 gzip dtype_2.nc: shape=(10, 6) chunks=(5, 6) float32 gzip - one Zarr array today: not one array: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype('/design/experiments/inhomogeneous/figures diff --git a/design/experiments/inhomogeneous/scaling.py b/design/experiments/inhomogeneous/scaling.py new file mode 100644 index 00000000..2e2d0d08 --- /dev/null +++ b/design/experiments/inhomogeneous/scaling.py @@ -0,0 +1,135 @@ +# /// script +# requires-python = ">=3.12" +# dependencies = [ +# "refkv[virtualizarr]>=0.1.0", +# "obstore>=0.11.1", +# "zarr>=3.4.0", +# "cast-value-rs>=0.4.2", +# "xarray>=2026.9.0", +# ] +# /// +"""How `switch` scales when every chunk is encoded differently. + +The worst case for a table of codec chains: N chunks, each packed with its own CF +scale and offset (as GRIB packs each message). One source file holds the N chunks, +each zlib-compressed int16. Two archives describe the same array: + +- **one case per chunk**: N cases in `zarr.json`, each a full chain with its numbers; + a chunk's header is its case index. +- **one case, two parameters**: one chain in `zarr.json` with `scale_offset`'s + `offset` and `scale` left open; a chunk's header is the case index and its two + float64 numbers, a literal in its reference in the archive's paged index. + +For each, the run reports `zarr.json`, the archive, the time to open the array and to +read one chunk, and checks 200 chunks spread over the array against the physical +values, bit for bit. + + uv run scaling.py +""" + +from __future__ import annotations + +import json +import tempfile +import time +import zlib +from pathlib import Path + +import numpy as np +import zarr + +import refkv +import refkv.zarr +import switch # next to this script, which Python puts on sys.path +from refkv.pb import Concat, Range, Source + +CHUNK = (4, 6) +FILL = -32767 + + +def packing(i: int) -> tuple[float, float]: + """Chunk i's CF scale_factor and add_offset: a different pair for every chunk.""" + return 0.01 * (1 + (i % 97) / 97), 280.0 + (i % 101) * 0.1 + + +def physical(i: int) -> np.ndarray: + t = np.arange(CHUNK[0] * i, CHUNK[0] * (i + 1))[:, None] + return 285 + 4 * np.sin(2 * np.pi * t / 37.0) + 0.6 * np.cos(2 * np.pi * np.arange(CHUNK[1])[None, :] / CHUNK[1]) + + +def write_source(path: Path, n: int) -> list[tuple[int, int]]: + """The N packed chunks, back to back; each one's (offset, length).""" + ranges, pos = [], 0 + with open(path, "wb") as f: + for i in range(n): + scale, offset = packing(i) + packed = np.round((physical(i) - offset) / scale).astype(" list[dict]: + return [{"name": "scale_offset", "configuration": {"offset": offset, "scale": scale}}, + {"name": "cast_value", "configuration": {"data_type": "int16", + "scalar_map": {"encode": [["NaN", FILL]], "decode": [[FILL, "NaN"]]}}}, + {"name": "bytes", "configuration": {"endian": "little"}}, + {"name": "numcodecs.zlib", "configuration": {"level": 4}}] + + +def archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool) -> int: + n = len(ranges) + if templated: + cases = [{"codecs": chain({"parameter": 1}, {"parameter": 0}), "parameters": ["float64", "float64"]}] + else: + cases = [{"codecs": chain(1 / packing(i)[0], packing(i)[1]), "parameters": []} for i in range(n)] + meta = {"zarr_format": 3, "node_type": "array", "shape": [CHUNK[0] * n, CHUNK[1]], "data_type": "float32", + "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": list(CHUNK)}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": "NaN", "codecs": [{"name": "switch", "configuration": {"cases": cases}}], + "attributes": {}} + doc = json.dumps(meta).encode() + mapping = {"zarr.json": json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode(), + "v/zarr.json": doc} + for i, (off, length) in enumerate(ranges): + scale, offset = packing(i) + head = (switch.header(0, ["float64", "float64"], [offset, 1 / scale]) if templated + else switch.varint(i)) + mapping[f"v/c/{i}/0"] = Concat((Range(data=head), Range(source=0, offset=off, length=length))) + refkv.ReferenceStore(mapping, [Source(url=url)]).save(str(path)) + return len(doc) + + +def measure(path: Path, n: int) -> tuple[float, float, int]: + switch._completed.cache_clear() + t0 = time.perf_counter() + a = zarr.open_array(refkv.zarr.store(refkv.open(str(path))), path="v", mode="r") + t1 = time.perf_counter() + a[CHUNK[0] * (n - 1):CHUNK[0] * n] + t2 = time.perf_counter() + wrong = 0 + for i in np.linspace(0, n - 1, 200).astype(int): + scale, offset = packing(i) + packed = np.round((physical(i) - offset) / scale).astype("int16") + want = packed.astype("float32") / np.float32(1 / scale) + np.float32(offset) + got = a[CHUNK[0] * i:CHUNK[0] * (i + 1)] + wrong += not np.array_equal(got, want.astype("float32")) + return t1 - t0, t2 - t1, wrong + + +if __name__ == "__main__": + print(f"{'chunks':>8} {'design':<26}{'zarr.json':>12}{'archive':>11}{'open':>9}{'read 1':>9} checked") + with tempfile.TemporaryDirectory() as d: + root = Path(d) + for n in (1_000, 10_000, 100_000): + src = root / f"source_{n}.bin" + ranges = write_source(src, n) + for templated, label in ((False, "one case per chunk"), (True, "one case, two parameters")): + path = root / f"{n}_{templated}.vzip" + doc = archive(path, src.as_uri(), ranges, templated) + t_open, t_read, wrong = measure(path, n) + print(f"{n:>8,} {label:<26}{doc / 1e3:>10,.1f}kB{path.stat().st_size / 1e6:>9.2f}MB" + f"{t_open:>8.2f}s{t_read * 1e3:>7.1f}ms " + + ("200 chunks bit for bit" if not wrong else f"{wrong} of 200 chunks differ")) diff --git a/design/experiments/inhomogeneous/scaling_output.txt b/design/experiments/inhomogeneous/scaling_output.txt new file mode 100644 index 00000000..e777f54d --- /dev/null +++ b/design/experiments/inhomogeneous/scaling_output.txt @@ -0,0 +1,7 @@ + chunks design zarr.json archive open read 1 checked + 1,000 one case per chunk 377.8kB 0.50MB 0.02s 2.0ms 200 chunks bit for bit + 1,000 one case, two parameters 0.8kB 0.16MB 0.00s 1.4ms 200 chunks bit for bit + 10,000 one case per chunk 3,775.3kB 5.05MB 0.17s 1.9ms 200 chunks bit for bit + 10,000 one case, two parameters 0.8kB 1.58MB 0.02s 1.7ms 200 chunks bit for bit + 100,000 one case per chunk 37,749.8kB 51.01MB 2.26s 1.8ms 200 chunks bit for bit + 100,000 one case, two parameters 0.8kB 16.10MB 0.28s 1.4ms 200 chunks bit for bit diff --git a/design/experiments/inhomogeneous/switch.py b/design/experiments/inhomogeneous/switch.py index df5f4f68..5918a672 100644 --- a/design/experiments/inhomogeneous/switch.py +++ b/design/experiments/inhomogeneous/switch.py @@ -35,6 +35,7 @@ import json from dataclasses import dataclass, replace +from functools import lru_cache from typing import Self import numpy as np @@ -109,23 +110,40 @@ def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> @dataclass(frozen=True) class SwitchCodec(ArrayBytesCodec): - """`switch`: `{"cases": [chain, ...]}`; a chunk is a varint case index, then the - bytes that case's chain decodes.""" + """`switch`: `{"cases": [{"codecs": chain, "parameters": [data_type, ...]}, ...]}`. - cases: tuple = () # each a tuple of parsed codecs + A case is a codec chain that may leave values open: `{"parameter": k}` anywhere in a + codec's configuration stands for the chunk's k-th parameter. A chunk is a varint + case index, then the case's parameters (each in its declared data type, + little-endian), then the bytes the completed chain decodes. Chunks that differ only + in numbers share one case, so `zarr.json` grows with the kinds of encoding, and the + numbers travel with each chunk. + """ + + cases: tuple = () # (codecs as JSON, parameter dtypes) is_fixed_size = False @classmethod def from_dict(cls, data: dict) -> Self: - return cls(cases=tuple(tuple(parse_codecs(chain)) for chain in data["configuration"]["cases"])) + return cls(cases=tuple((json.dumps(c["codecs"]), tuple(c.get("parameters", ()))) + for c in data["configuration"]["cases"])) def to_dict(self) -> dict: - return {"name": "switch", "configuration": {"cases": [[c.to_dict() for c in chain] for chain in self.cases]}} + return {"name": "switch", "configuration": {"cases": [ + {"codecs": json.loads(codecs), "parameters": list(params)} for codecs, params in self.cases]}} + + def _chain(self, index: int, values: tuple) -> tuple: + return _completed(self.cases[index][0], values) async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: raw = chunk_bytes.to_bytes() index, pos = read_varint(raw) - pipeline = get_pipeline_class().from_codecs(self.cases[index]).evolve_from_array_spec(chunk_spec) + values = [] + for dtype in self.cases[index][1]: + dt = np.dtype(dtype).newbyteorder("<") + values.append(np.frombuffer(raw, dtype=dt, count=1, offset=pos)[0].item()) + pos += dt.itemsize + pipeline = get_pipeline_class().from_codecs(self._chain(index, tuple(values))).evolve_from_array_spec(chunk_spec) (out,) = await pipeline.decode([(chunk_spec.prototype.buffer.from_bytes(raw[pos:]), chunk_spec)]) return out @@ -136,18 +154,46 @@ def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> raise NotImplementedError +def _json_number(v: float | int): + """A parameter as JSON, as Zarr writes fill values: NaN and the infinities as strings.""" + if isinstance(v, float) and not np.isfinite(v): + return "NaN" if np.isnan(v) else ("Infinity" if v > 0 else "-Infinity") + return v + + +@lru_cache(maxsize=4096) +def _completed(codecs: str, values: tuple) -> tuple: + """A case's codecs with its open values filled in, parsed (cached: chunks that + share a case and its numbers share one parse).""" + + def fill(node): + if isinstance(node, dict): + if set(node) == {"parameter"}: + return _json_number(values[node["parameter"]]) + return {k: fill(v) for k, v in node.items()} + if isinstance(node, list): + return [fill(v) for v in node] + return node + + return tuple(parse_codecs(fill(json.loads(codecs)))) + + register_codec("switch", SwitchCodec) -def case(ma, attrs, out_dtype: np.dtype) -> list[dict]: - """The codec chain that decodes one file's chunks to the combined array's dtype: - the independent transforms its file needs, then the file's own codecs.""" +def chain(ma, attrs, out_dtype: np.dtype) -> tuple[list[dict], list[str], list[float]]: + """How one file's chunks decode to the combined array's dtype: a case (the + independent transforms its file needs, then the file's own codecs, with the numbers + that vary between files left open) and the values of those numbers.""" m = ma.metadata stored = m.data_type.to_native_dtype() - chain: list[dict] = [] + codecs: list[dict] = [] + params: list[str] = [] + values: list[float] = [] if "scale_factor" in attrs or "add_offset" in attrs: # CF: physical = packed * scale_factor + add_offset - chain.append({"name": "scale_offset", "configuration": { - "offset": float(attrs.get("add_offset", 0)), "scale": 1 / float(attrs.get("scale_factor", 1))}}) + codecs.append({"name": "scale_offset", "configuration": {"offset": {"parameter": 0}, "scale": {"parameter": 1}}}) + params += ["float64", "float64"] + values += [float(attrs.get("add_offset", 0)), 1 / float(attrs.get("scale_factor", 1))] if stored != out_dtype: config = {"data_type": stored.name} if stored.kind in "iu": @@ -157,9 +203,15 @@ def case(ma, attrs, out_dtype: np.dtype) -> list[dict]: if fill is None: raise ValueError(f"an {stored} file without _FillValue: no integer can stand for NaN") config["scalar_map"] = {"encode": [["NaN", int(fill)]], "decode": [[int(fill), "NaN"]]} - chain.append({"name": "cast_value", "configuration": config}) - chain.append({"name": "crop", "configuration": {"shape": list(m.chunks)}}) - return chain + [c.to_dict() for c in m.codecs] + codecs.append({"name": "cast_value", "configuration": config}) + codecs.append({"name": "crop", "configuration": {"shape": list(m.chunks)}}) + return codecs + [c.to_dict() for c in m.codecs], params, values + + +def header(index: int, params: list[str], values: list[float]) -> bytes: + """A chunk's switch header: its case, then its parameters.""" + return varint(index) + b"".join(np.array([v], dtype=np.dtype(d).newbyteorder("<")).tobytes() + for d, v in zip(params, values)) def lengths(n: int, c: int) -> list[int]: @@ -172,17 +224,19 @@ def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) out_dtype = np.dtype(np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() for v in vdss])) - cases: list[list[dict]] = [] + cases: list[dict] = [] index: dict[str, int] = {} sources, source_of, mapping = [], {}, {} time_chunks: list[int] = [] other = None for vds in vdss: ma = vds[name].data - chain = case(ma, vds[name].attrs, out_dtype) - k = index.setdefault(json.dumps(chain, sort_keys=True), len(cases)) + codecs, params, values = chain(ma, vds[name].attrs, out_dtype) + case = {"codecs": codecs, "parameters": params} + k = index.setdefault(json.dumps(case, sort_keys=True), len(cases)) if k == len(cases): - cases.append(chain) + cases.append(case) + head = header(k, params, values) c0, *crest = ma.metadata.chunks rest = [lengths(n, c) for n, c in zip(ma.shape[1:], crest)] if other is None: @@ -197,7 +251,7 @@ def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv if s == len(sources): sources.append(Source(url=e["path"])) mapping[f"{name}/c/" + "/".join(map(str, (base + i, *js)))] = Concat( - (Range(data=varint(k)), Range(source=s, offset=e["offset"], length=e["length"]))) + (Range(data=head), Range(source=s, offset=e["offset"], length=e["length"]))) mapping["zarr.json"] = json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode() mapping[f"{name}/zarr.json"] = json.dumps({ "zarr_format": 3, "node_type": "array", From daa1f72651ccc097be673089e9a1efde850a0569 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 12:26:46 +0200 Subject: [PATCH 10/12] docs(design): key switch cases by content hash, and concatenate without rewriting chunks A case's key is the first 8 bytes of the SHA-256 of its canonical JSON, and a chunk's header starts with that key. Concatenating two combined arrays merges their case tables by union and copies every chunk header unchanged; case 6 checks the result equals combining all files at once, bit for bit. The scaling run now makes every chunk's packing unique, since content keys merge repeated chains. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 83 +++++---- design/experiments/inhomogeneous/demo.py | 39 +++- .../inhomogeneous/figures/case5.png | Bin 72412 -> 72348 bytes .../inhomogeneous/figures/case6.png | Bin 0 -> 79302 bytes design/experiments/inhomogeneous/output.txt | 20 ++- design/experiments/inhomogeneous/scaling.py | 20 +-- .../inhomogeneous/scaling_output.txt | 12 +- design/experiments/inhomogeneous/switch.py | 166 ++++++++++++------ 8 files changed, 230 insertions(+), 110 deletions(-) create mode 100644 design/experiments/inhomogeneous/figures/case6.png diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index e9c98d3d..ada8b08a 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -35,6 +35,7 @@ files, `demo.py` and [switch.py](switch.py), run from any directory. | 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | not expressible: partial chunks inside a regular grid | bit for bit, rectilinear grid + `crop` | | 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | one array, one set of attributes (see below) | bit for bit, 3 cases | | 5. varying dtype | int16, int16, float32 | not expressible: one dtype per array | bit for bit, 2 cases | +| 6. concatenating two combined arrays | A: gzip, shuffle + gzip; B: gzip, uncompressed | n/a | bit for bit: A's 2 cases + B's 2 cases = 3 (gzip shared); every chunk header copied as is | Case 4 is the subtle one, and it isn't specific to any tool. CF scaling is array-level attributes, and one array has one set, so concatenating the files has to choose: @@ -79,7 +80,7 @@ That's the issue's point: the per-file context has to live somewhere per chunk. | | | | |---|---|---| | [![case 1](figures/case1.png)](figures/case1.png) | [![case 2](figures/case2.png)](figures/case2.png) | [![case 3](figures/case3.png)](figures/case3.png) | -| [![case 5](figures/case5.png)](figures/case5.png) | | | +| [![case 5](figures/case5.png)](figures/case5.png) | [![case 6](figures/case6.png)](figures/case6.png) | | The archives hold a few kilobytes of metadata; the chunks are read in place from the netCDF files. [output.txt](output.txt) is a full run. @@ -98,21 +99,30 @@ Each thing that varies between the files is its own, independent codec: One codec, `switch`, chooses per chunk which composition of them applies. - **What `switch` is.** It is an array-to-bytes codec, - `{"cases": [{"codecs": chain, "parameters": [data_type, ...]}, ...]}`. - - **Each case is an ordinary Zarr codec chain that may leave numbers open.** - `{"parameter": k}` anywhere in a codec's configuration stands for the chunk's - k-th parameter. - - **A chunk's bytes start with a header:** a varint case index, then that case's + `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. + - **Cases are codec chains that may leave numbers open.** Each case is an ordinary + Zarr codec chain; `{"parameter": k}` anywhere in a codec's configuration stands for + the chunk's k-th parameter. + - **A case's key is its content.** The key is the first 8 bytes of the SHA-256 of + the case's canonical JSON, written in hex. + - **The chunk header.** A chunk's bytes start with its case's key, then that case's parameters, each little-endian in its declared data type. - - **`switch` fills in the case with those values and decodes the rest with it,** the - way `sharding_indexed` decodes inner chunks with its inner chain. + - **Decoding.** `switch` looks up the key, fills in the case, and decodes the rest + with it, the way `sharding_indexed` decodes inner chunks with its inner chain. - **Where the header comes from.** refkv's reference values carry it. A chunk is `Concat(Range(data=

), Range(source=, offset, length))`: a few literal bytes in the archive's index, then the file's own bytes. - **What goes where.** `zarr.json` holds the *kinds* of encoding: in case 4, one case for all three files. Each file's `add_offset` and the reciprocal of its - `scale_factor` travel in its chunks' headers, 17 bytes each. Chunks that differ only - in numbers share a case. + `scale_factor` travel in its chunks' headers: 8 key bytes plus 16 bytes of + parameters. Chunks that differ only in numbers share a case. +- **Why keys, not positions.** A key names a case by what it is, not by where it sits + in a list: + - Two arrays that use the same chain use the same key, so concatenating them merges + their case tables by union, and no chunk is rewritten (case 6). + - The same chain listed twice collapses to one entry. + - A reader checks each case against its key, so a table whose entries were edited or + reordered can't silently re-point chunks. - **Grids.** Month-length and ragged files use the zarr-extensions [`rectilinear`](https://github.com/zarr-developers/zarr-extensions/tree/main/chunk-grids/rectilinear) chunk grid. @@ -122,7 +132,7 @@ Case 4's array, with its one case: ```json "data_type": "float32", "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [[5, 5, 5, 5, 5, 5], [6]]}}, -"codecs": [{"name": "switch", "configuration": {"cases": [{ +"codecs": [{"name": "switch", "configuration": {"cases": {"": { "codecs": [ {"name": "scale_offset", "configuration": {"offset": {"parameter": 0}, "scale": {"parameter": 1}}}, {"name": "cast_value", "configuration": {"data_type": "int16", @@ -130,7 +140,7 @@ Case 4's array, with its one case: {"name": "crop", "configuration": {"shape": [5, 6]}}, {"name": "bytes", "configuration": {"endian": "little"}}, {"name": "numcodecs.zlib", "configuration": {"level": 4}}], - "parameters": ["float64", "float64"]}]}}] + "parameters": ["float64", "float64"]}}}}] ``` ## Scaling: when every chunk is encoded differently @@ -149,23 +159,25 @@ uv run scaling.py | chunks | design | `zarr.json` | archive | open the array | read one chunk | |---|---|---|---|---|---| -| 1,000 | one case per chunk | 378 kB | 0.50 MB | 0.02 s | 2.0 ms | -| 1,000 | one case, two parameters | 0.8 kB | 0.16 MB | 0.00 s | 1.4 ms | -| 10,000 | one case per chunk | 3.8 MB | 5.05 MB | 0.17 s | 1.9 ms | -| 10,000 | one case, two parameters | 0.8 kB | 1.58 MB | 0.02 s | 1.7 ms | -| 100,000 | one case per chunk | 37.7 MB | 51.0 MB | 2.26 s | 1.8 ms | -| 100,000 | one case, two parameters | 0.8 kB | 16.1 MB | 0.28 s | 1.4 ms | +| 1,000 | one case per chunk | 402 kB | 0.54 MB | 0.01 s | 1.4 ms | +| 1,000 | one case, two parameters | 0.8 kB | 0.17 MB | 0.00 s | 1.5 ms | +| 10,000 | one case per chunk | 4.0 MB | 5.41 MB | 0.25 s | 1.8 ms | +| 10,000 | one case, two parameters | 0.8 kB | 1.72 MB | 0.02 s | 1.8 ms | +| 100,000 | one case per chunk | 40.1 MB | 54.4 MB | 2.96 s | 1.9 ms | +| 100,000 | one case, two parameters | 0.8 kB | 17.5 MB | 0.27 s | 1.7 ms | Both designs read 200 chunks spread over the array bit for bit ([scaling_output.txt](scaling_output.txt)). -- **One case per chunk.** `zarr.json` costs about 380 bytes per chunk, all read and - parsed before the first chunk. +- **One case per chunk.** `zarr.json` costs about 400 bytes per chunk, all read and + parsed before the first chunk. The keys merge chunks that repeat a chain. An earlier + run whose packing repeated every 9,797 chunks stopped growing at that many cases, so + the run makes every chunk's offset unique to show the worst case. - **One case, two parameters.** `zarr.json` stays the same size whatever the chunk - count. The parameters cost 17 bytes per chunk, in the reference each read already - fetches. What still grows with N, about 160 bytes per chunk and 0.28 s to open at - 100,000, is the archive's own index of 100,000 keys. Any archive of that many chunks - pays it. + count. The header costs 24 bytes per chunk (an 8-byte key and two float64s), in the + reference each read already fetches. What still grows with N, about 175 bytes per + chunk and 0.27 s to open at 100,000, is the archive's own index of 100,000 keys. Any + archive of that many chunks pays it. That is the split in point 1 of the issue. Context chunks share goes in the metadata; context per chunk goes in the manifest, which already exists per chunk. With @@ -202,10 +214,10 @@ reason. - **Codecs outside `switch`.** - Array-to-array codecs listed before it (`transpose`, a uniform `scale_offset`) apply to every chunk, whatever its case. - - Bytes-to-bytes codecs after it would apply to the index *and* the source's bytes, + - Bytes-to-bytes codecs after it would apply to the header *and* the source's bytes, so a virtual array has none. A materialized array may. - **Nesting.** A case is a full chain, so it may end in another `switch`. The chunk - then starts with the outer index, then the inner one. This composes independent + then starts with the outer header, then the inner one. This composes independent axes of variation, for example a codec era (outer) and a per-file scaling (inner), at the cost of repeating the inner table in each outer case. - **Sharding.** A case may be `sharding_indexed` (a source stored as shards), and a @@ -215,14 +227,25 @@ reason. of one chain on or off. `switch` chooses among chains, so it also covers what `conditional` can't: a different dtype, scaling, or stored shape per chunk. - **Concatenating two such arrays** (manifest-level operations, as VirtualiZarr does - them) means joining their case lists and renumbering the case index at the start of - each chunk's header. The parameters travel with the chunk unchanged, and so do the - source bytes. Identifying cases by a hash of their canonical JSON instead of by - position would make the join a union, with no renumbering. + them) is `switch.concatenate`. It works in four steps: + - The case tables merge by union of their keys. + - The second array's chunks move along the axis. + - Their references renumber the sources they name, as any manifest concatenation + must. + - Every chunk's header is copied unchanged. + + Case 6 checks that the result is the same array, with the same `switch` + configuration, as combining all the files at once. ## Limits - **`switch` and `crop` are defined here**, not registered as zarr extensions. +- **Canonical JSON.** Keys hash the case's JSON with sorted keys and no whitespace, + which is enough for this sketch. A standard would name RFC 8785, which also fixes how + numbers are written, so that independent writers produce the same key. +- **Collisions.** Two different chains in one array would need equal 64-bit SHA-256 + prefixes. `concatenate` and the reader both refuse a key whose contents don't match, + so a collision fails loudly instead of decoding the wrong way. - **`scale_offset` stores the encode multiplier; CF stores the decode one.** CF's `scale_factor` of 0.01 becomes `scale` 100.00000223517424, the reciprocal of float32 0.01. Here decoding through `x / scale` gives exactly what `x * scale_factor` gives, diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 533749ea..dc2d4f70 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -219,7 +219,8 @@ def plot(r: Result, path: Path) -> None: ax.set_xlabel("time index") ax.set_ylabel("v at x = 0") vz = r.vz_note if r.vz is not None else "\n".join(textwrap.wrap(r.vz_note, 120)) - ax.set_title(f"one Zarr array today: {vz}", fontsize=8, loc="left", color="0.3") + ax.set_title(vz if r.title.startswith("6.") else f"one Zarr array today: {vz}", fontsize=8, loc="left", + color="0.3") ax.legend(fontsize=8, loc="best") im = ax2.imshow(r.rk.T, aspect="auto", cmap="viridis", interpolation="nearest", extent=(-0.5, n - 0.5, NX - 0.5, -0.5)) @@ -299,10 +300,46 @@ def dtype_files(root: Path) -> list[Path]: ("5. varying dtype: int16, int16, float32", dtype_files), ] +def concatenation(figures: Path) -> None: + """Two arrays combined separately, then concatenated as manifests: the case tables + merge by union of their content keys, and no chunk's header is rewritten.""" + title = "6. concatenating two combined arrays (gzip, shuffle + gzip | gzip, uncompressed)" + print(f"\n{'=' * 78}\n{title}\n{'-' * 78}") + with tempfile.TemporaryDirectory() as d: + root = Path(d) + paths = [] + for i, (comp, shuf) in enumerate([("gzip", False), ("gzip", True), ("gzip", False), (None, False)]): + p = root / f"part_{i}.nc" + write(p, smooth(np.arange(8 * i, 8 * i + 8)).astype("float32"), t0=8 * i, chunks=(4, NX), + compression=comp, shuffle=shuf) + paths.append(p) + vdss = virtualize(paths, root) + a, b = switch.build(vdss[:2]), switch.build(vdss[2:]) + mapping, sources = switch.concatenate(a, b) + keys = [set(json.loads(m["v/zarr.json"])["codecs"][0]["configuration"]["cases"]) for m in (a[0], b[0], mapping)] + moved = all(mapping[f"v/c/{int(k.split('/')[2]) + len(json.loads(a[0]['v/zarr.json'])['chunk_grid']['configuration']['chunk_shapes'][0])}/{k.split('/')[3]}"].parts[0].data + == ref.parts[0].data for k, ref in b[0].items() if k.startswith("v/c/")) + direct = json.loads(switch.build(vdss)[0]["v/zarr.json"])["codecs"] + same = json.loads(mapping["v/zarr.json"])["codecs"] == direct + archive = root / "concatenated.vzip" + refkv.ReferenceStore(mapping, sources).save(str(archive)) + got, want = switch.read(refkv.open(str(archive))), truth(paths) + print(f" array A: {len(keys[0])} cases {sorted(keys[0])}\n array B: {len(keys[1])} cases {sorted(keys[1])}") + print(f" A + B: {len(keys[2])} cases (the shared gzip case once); every chunk header copied unchanged: {moved};" + f" codecs equal to combining all four files at once: {same}") + note = "concatenated, saved and reopened: " + compare(got, want) + print(f" {note}") + r = Result(title, ["float32\ngzip", "float32\ngzip+shuffle", "float32\ngzip", "float32\nuncompressed"], + [0, 8, 16, 24], 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zs|x@){{F-<{OttH0eRCVYTj#m;5^IGG0AyP5CaHK^28AD17aGnZ*K>E!9bk%*-DS| z;q3e!_3ohzdGbjZt55|wqz>Sh$h!cq?^4hHF3#60w-5q@n}_LoD>HH`cCcgPzdzgdI02uV)#dLqM|WIeCdVNw ze7bRZqCE7XyJJbMneB2;y|i8?MdZ@a&KN-~IK%!XHV` XR|!ee?+1wffgf2ZB~;/design/experiments/inhomogeneous/figures diff --git a/design/experiments/inhomogeneous/scaling.py b/design/experiments/inhomogeneous/scaling.py index 2e2d0d08..9673bf4d 100644 --- a/design/experiments/inhomogeneous/scaling.py +++ b/design/experiments/inhomogeneous/scaling.py @@ -15,10 +15,10 @@ each zlib-compressed int16. Two archives describe the same array: - **one case per chunk**: N cases in `zarr.json`, each a full chain with its numbers; - a chunk's header is its case index. + a chunk's header is its case's 8-byte key. - **one case, two parameters**: one chain in `zarr.json` with `scale_offset`'s `offset` and `scale` left open; a chunk's header is the case index and its two - float64 numbers, a literal in its reference in the archive's paged index. + float64 numbers after the key, a literal in its reference in the archive's paged index. For each, the run reports `zarr.json`, the archive, the time to open the array and to read one chunk, and checks 200 chunks spread over the array against the physical @@ -48,8 +48,9 @@ def packing(i: int) -> tuple[float, float]: - """Chunk i's CF scale_factor and add_offset: a different pair for every chunk.""" - return 0.01 * (1 + (i % 97) / 97), 280.0 + (i % 101) * 0.1 + """Chunk i's CF scale_factor and add_offset: a different pair for every chunk (the + offset alone is unique; content keys would merge chunks that repeat a pair).""" + return 0.01 * (1 + (i % 97) / 97), 280.0 + i * 1e-5 def physical(i: int) -> np.ndarray: @@ -81,10 +82,10 @@ def chain(scale, offset) -> list[dict]: def archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool) -> int: n = len(ranges) - if templated: - cases = [{"codecs": chain({"parameter": 1}, {"parameter": 0}), "parameters": ["float64", "float64"]}] - else: - cases = [{"codecs": chain(1 / packing(i)[0], packing(i)[1]), "parameters": []} for i in range(n)] + template = {"codecs": chain({"parameter": 1}, {"parameter": 0}), "parameters": ["float64", "float64"]} + per_chunk = [] if templated else [{"codecs": chain(1 / packing(i)[0], packing(i)[1]), "parameters": []} + for i in range(n)] + cases = {switch.case_key(c).hex(): c for c in ([template] if templated else per_chunk)} meta = {"zarr_format": 3, "node_type": "array", "shape": [CHUNK[0] * n, CHUNK[1]], "data_type": "float32", "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": list(CHUNK)}}, "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, @@ -95,8 +96,7 @@ def archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool "v/zarr.json": doc} for i, (off, length) in enumerate(ranges): scale, offset = packing(i) - head = (switch.header(0, ["float64", "float64"], [offset, 1 / scale]) if templated - else switch.varint(i)) + head = switch.header(template, [offset, 1 / scale]) if templated else switch.header(per_chunk[i], []) mapping[f"v/c/{i}/0"] = Concat((Range(data=head), Range(source=0, offset=off, length=length))) refkv.ReferenceStore(mapping, [Source(url=url)]).save(str(path)) return len(doc) diff --git a/design/experiments/inhomogeneous/scaling_output.txt b/design/experiments/inhomogeneous/scaling_output.txt index e777f54d..5e2b3f64 100644 --- a/design/experiments/inhomogeneous/scaling_output.txt +++ b/design/experiments/inhomogeneous/scaling_output.txt @@ -1,7 +1,7 @@ chunks design zarr.json archive open read 1 checked - 1,000 one case per chunk 377.8kB 0.50MB 0.02s 2.0ms 200 chunks bit for bit - 1,000 one case, two parameters 0.8kB 0.16MB 0.00s 1.4ms 200 chunks bit for bit - 10,000 one case per chunk 3,775.3kB 5.05MB 0.17s 1.9ms 200 chunks bit for bit - 10,000 one case, two parameters 0.8kB 1.58MB 0.02s 1.7ms 200 chunks bit for bit - 100,000 one case per chunk 37,749.8kB 51.01MB 2.26s 1.8ms 200 chunks bit for bit - 100,000 one case, two parameters 0.8kB 16.10MB 0.28s 1.4ms 200 chunks bit for bit + 1,000 one case per chunk 401.7kB 0.54MB 0.01s 1.4ms 200 chunks bit for bit + 1,000 one case, two parameters 0.8kB 0.17MB 0.00s 1.5ms 200 chunks bit for bit + 10,000 one case per chunk 4,014.2kB 5.41MB 0.25s 1.8ms 200 chunks bit for bit + 10,000 one case, two parameters 0.8kB 1.72MB 0.02s 1.8ms 200 chunks bit for bit + 100,000 one case per chunk 40,139.3kB 54.43MB 2.96s 1.9ms 200 chunks bit for bit + 100,000 one case, two parameters 0.8kB 17.50MB 0.27s 1.7ms 200 chunks bit for bit diff --git a/design/experiments/inhomogeneous/switch.py b/design/experiments/inhomogeneous/switch.py index 5918a672..0e61c956 100644 --- a/design/experiments/inhomogeneous/switch.py +++ b/design/experiments/inhomogeneous/switch.py @@ -13,19 +13,23 @@ and one codec, `switch`, chooses per chunk which composition of them applies: -- `switch` is an array-to-bytes codec, `{"cases": [chain, chain, ...]}`, each case an - ordinary Zarr codec chain (array-to-array codecs, one array-to-bytes codec, - bytes-to-bytes codecs). A chunk's bytes start with a varint, the index of its case; - `switch` decodes the rest with that case's chain, as `sharding_indexed` decodes its - inner chunks with its inner chain. +- `switch` is an array-to-bytes codec, + `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. Each case is an + ordinary Zarr codec chain that may leave numbers open (`{"parameter": k}`), keyed by + the first 8 bytes of the SHA-256 of its canonical JSON. A chunk's bytes start with its + case's key and that case's parameters; `switch` fills them in and decodes the rest + with that chain, as `sharding_indexed` decodes its inner chunks with its inner chain. +- Keys name cases by their content: arrays that use the same chain use the same key, + so concatenating arrays merges their case tables by union, and no chunk is rewritten + (`concatenate`). - `crop` is an array-to-array codec, `{"shape": [...]}`: the stored chunk's shape. It decodes by cropping to the chunk's cell (HDF5 stores edge chunks whole) and encodes by padding with the fill value. -refkv's reference values carry each chunk's case index: a chunk is -`Concat(Range(data=), Range(source=, offset, length))`, a literal byte in -the archive, then the file's own bytes read in place. No store injects anything at read -time and no codec does I/O. +refkv's reference values carry each chunk's header: a chunk is +`Concat(Range(data=), Range(source=, offset, length))`, literal +bytes in the archive, then the file's own bytes read in place. No store injects +anything at read time and no codec does I/O. Ragged and month-length files use the zarr-extensions `rectilinear` chunk grid, which zarr-python 3.4 reads with `zarr.config.set({"array.rectilinear_chunks": True})`. @@ -33,6 +37,7 @@ from __future__ import annotations +import hashlib import json from dataclasses import dataclass, replace from functools import lru_cache @@ -52,26 +57,6 @@ from refkv.pb import Concat, Range, Source -def varint(n: int) -> bytes: - out = bytearray() - while True: - b, n = n & 0x7F, n >> 7 - out.append(b | (0x80 if n else 0)) - if not n: - return bytes(out) - - -def read_varint(buf: bytes) -> tuple[int, int]: - n = shift = pos = 0 - while True: - b = buf[pos] - pos += 1 - n |= (b & 0x7F) << shift - shift += 7 - if not b & 0x80: - return n, pos - - @dataclass(frozen=True) class CropCodec(ArrayArrayCodec): """`crop`: the stored chunk has `shape`, at least the chunk's cell in every @@ -108,42 +93,64 @@ def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> register_codec("crop", CropCodec) +KEY_BYTES = 8 # a case's key: the first 8 bytes of the SHA-256 of its canonical JSON + + +def canonical(case: dict) -> bytes: + """A case's canonical JSON: sorted keys, no whitespace, UTF-8. (A standard would + name RFC 8785, which also fixes how numbers are written.)""" + return json.dumps(case, sort_keys=True, separators=(",", ":"), ensure_ascii=False).encode() + + +def case_key(case: dict) -> bytes: + return hashlib.sha256(canonical(case)).digest()[:KEY_BYTES] + + @dataclass(frozen=True) class SwitchCodec(ArrayBytesCodec): - """`switch`: `{"cases": [{"codecs": chain, "parameters": [data_type, ...]}, ...]}`. + """`switch`: `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. A case is a codec chain that may leave values open: `{"parameter": k}` anywhere in a - codec's configuration stands for the chunk's k-th parameter. A chunk is a varint - case index, then the case's parameters (each in its declared data type, - little-endian), then the bytes the completed chain decodes. Chunks that differ only - in numbers share one case, so `zarr.json` grows with the kinds of encoding, and the - numbers travel with each chunk. + codec's configuration stands for the chunk's k-th parameter. A case's key is the + hex of the first 8 bytes of the SHA-256 of its canonical JSON, so it names the case + by its content, not its position: arrays that share a case share its key, and two + case tables merge by union. A chunk is the 8-byte key, then the case's parameters + (each little-endian in its declared data type), then the bytes the completed chain + decodes. """ - cases: tuple = () # (codecs as JSON, parameter dtypes) + cases: tuple = () # (key hex, codecs as JSON, parameter dtypes) is_fixed_size = False + def __post_init__(self) -> None: + object.__setattr__(self, "_by_key", {k: (codecs, params) for k, codecs, params in self.cases}) + @classmethod def from_dict(cls, data: dict) -> Self: - return cls(cases=tuple((json.dumps(c["codecs"]), tuple(c.get("parameters", ()))) - for c in data["configuration"]["cases"])) + cases = [] + for key, case in data["configuration"]["cases"].items(): + if case_key(case).hex() != key: + raise ValueError(f"switch: case {key} is not the key of its contents ({case_key(case).hex()})") + cases.append((key, json.dumps(case["codecs"]), tuple(case.get("parameters", ())))) + return cls(cases=tuple(cases)) def to_dict(self) -> dict: - return {"name": "switch", "configuration": {"cases": [ - {"codecs": json.loads(codecs), "parameters": list(params)} for codecs, params in self.cases]}} - - def _chain(self, index: int, values: tuple) -> tuple: - return _completed(self.cases[index][0], values) + return {"name": "switch", "configuration": {"cases": { + key: {"codecs": json.loads(codecs), "parameters": list(params)} for key, codecs, params in self.cases}}} async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: raw = chunk_bytes.to_bytes() - index, pos = read_varint(raw) + key, pos = raw[:KEY_BYTES].hex(), KEY_BYTES + try: + codecs, params = self._by_key[key] + except KeyError: + raise ValueError(f"switch: the chunk names case {key}, which the array does not list") from None values = [] - for dtype in self.cases[index][1]: + for dtype in params: dt = np.dtype(dtype).newbyteorder("<") values.append(np.frombuffer(raw, dtype=dt, count=1, offset=pos)[0].item()) pos += dt.itemsize - pipeline = get_pipeline_class().from_codecs(self._chain(index, tuple(values))).evolve_from_array_spec(chunk_spec) + pipeline = get_pipeline_class().from_codecs(_completed(codecs, tuple(values))).evolve_from_array_spec(chunk_spec) (out,) = await pipeline.decode([(chunk_spec.prototype.buffer.from_bytes(raw[pos:]), chunk_spec)]) return out @@ -208,10 +215,10 @@ def chain(ma, attrs, out_dtype: np.dtype) -> tuple[list[dict], list[str], list[f return codecs + [c.to_dict() for c in m.codecs], params, values -def header(index: int, params: list[str], values: list[float]) -> bytes: - """A chunk's switch header: its case, then its parameters.""" - return varint(index) + b"".join(np.array([v], dtype=np.dtype(d).newbyteorder("<")).tobytes() - for d, v in zip(params, values)) +def header(case: dict, values: list[float]) -> bytes: + """A chunk's switch header: its case's key, then its parameters.""" + return case_key(case) + b"".join(np.array([v], dtype=np.dtype(d).newbyteorder("<")).tobytes() + for d, v in zip(case["parameters"], values)) def lengths(n: int, c: int) -> list[int]: @@ -221,11 +228,15 @@ def lengths(n: int, c: int) -> list[int]: def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv.ReferenceStore: """One reference store holding `name` concatenated along `dim` (axis 0) across the files' virtual datasets, with the concatenated coordinates.""" + return refkv.ReferenceStore(*build(vdss, name, dim)) + + +def build(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> tuple[dict, list[Source]]: + """`combine`'s mapping and sources.""" scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) out_dtype = np.dtype(np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() for v in vdss])) - cases: list[dict] = [] - index: dict[str, int] = {} + cases: dict[str, dict] = {} sources, source_of, mapping = [], {}, {} time_chunks: list[int] = [] other = None @@ -233,10 +244,8 @@ def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv ma = vds[name].data codecs, params, values = chain(ma, vds[name].attrs, out_dtype) case = {"codecs": codecs, "parameters": params} - k = index.setdefault(json.dumps(case, sort_keys=True), len(cases)) - if k == len(cases): - cases.append(case) - head = header(k, params, values) + cases[case_key(case).hex()] = case + head = header(case, values) c0, *crest = ma.metadata.chunks rest = [lengths(n, c) for n, c in zip(ma.shape[1:], crest)] if other is None: @@ -276,7 +285,50 @@ def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv "dimension_names": [coord], "attributes": attrs, }).encode() mapping[f"{coord}/c/0"] = values.tobytes() - return refkv.ReferenceStore(mapping, sources) + return mapping, sources + + +def concatenate(a: tuple[dict, list[Source]], b: tuple[dict, list[Source]], name: str = "v", + dim: str = "time") -> tuple[dict, list[Source]]: + """`b` appended to `a` along `dim` (axis 0), as manifest operations do: the case + tables merge by union of their keys, `b`'s chunks move along the axis and their + references renumber the sources they name. Every chunk's header (its case key and + parameters) is copied as it is.""" + (ma, sa), (mb, sb) = a, b + meta_a, meta_b = json.loads(ma[f"{name}/zarr.json"]), json.loads(mb[f"{name}/zarr.json"]) + grid_a = meta_a["chunk_grid"]["configuration"]["chunk_shapes"] + grid_b = meta_b["chunk_grid"]["configuration"]["chunk_shapes"] + if meta_a["data_type"] != meta_b["data_type"] or grid_a[1:] != grid_b[1:]: + raise ValueError("the arrays differ in data type or in their chunks along the other axes") + cases = dict(meta_a["codecs"][0]["configuration"]["cases"]) + for key, case in meta_b["codecs"][0]["configuration"]["cases"].items(): + if cases.setdefault(key, case) != case: + raise ValueError(f"case {key} names two different chains") # a key collision + shift, prefix = len(grid_a[0]), f"{name}/c/" + mapping = {k: v for k, v in ma.items() if k.startswith(prefix)} + for k, ref in mb.items(): + if k.startswith(prefix): + i, *rest = k[len(prefix):].split("/") + mapping[prefix + "/".join([str(int(i) + shift), *rest])] = Concat(tuple( + p if p.data is not None else Range(source=p.source + len(sa), offset=p.offset, length=p.length, crc32c=p.crc32c) + for p in ref.parts)) + meta = dict(meta_a, shape=[meta_a["shape"][0] + meta_b["shape"][0], *meta_a["shape"][1:]]) + meta["chunk_grid"] = {"name": "rectilinear", "configuration": {"kind": "inline", + "chunk_shapes": [grid_a[0] + grid_b[0], *grid_a[1:]]}} + meta["codecs"] = [{"name": "switch", "configuration": {"cases": cases}}] + mapping["zarr.json"] = ma["zarr.json"] + mapping[f"{name}/zarr.json"] = json.dumps(meta).encode() + for coord in meta_a["dimension_names"]: + cm = json.loads(ma[f"{coord}/zarr.json"]) + if coord == dim: + dt = np.dtype(cm["data_type"]).newbyteorder("<") + values = np.concatenate([np.frombuffer(ma[f"{coord}/c/0"], dt), np.frombuffer(mb[f"{coord}/c/0"], dt)]) + cm = dict(cm, shape=[len(values)], chunk_grid={"name": "regular", "configuration": {"chunk_shape": [len(values)]}}) + mapping[f"{coord}/c/0"] = values.tobytes() + else: + mapping[f"{coord}/c/0"] = ma[f"{coord}/c/0"] + mapping[f"{coord}/zarr.json"] = json.dumps(cm).encode() + return mapping, [*sa, *sb] def read(store, name: str = "v") -> np.ndarray: From 08e3e573ec33d5875f035aee64a777151a4bd29f Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 12:32:37 +0200 Subject: [PATCH 11/12] refactor(design): fold the switch sketch into demo.py, so the demo is one file Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 8 +- design/experiments/inhomogeneous/demo.py | 356 +++++++++++++++++- .../inhomogeneous/figures/case5.png | Bin 72348 -> 72412 bytes design/experiments/inhomogeneous/output.txt | 2 +- design/experiments/inhomogeneous/scaling.py | 12 +- .../inhomogeneous/scaling_output.txt | 12 +- design/experiments/inhomogeneous/switch.py | 336 ----------------- 7 files changed, 365 insertions(+), 361 deletions(-) delete mode 100644 design/experiments/inhomogeneous/switch.py diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index ada8b08a..7bd4440a 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -15,7 +15,8 @@ The demo looks at the next step: one logical array along time. variable-length chunks). That refusal is the safe behavior; the gap it reports is the one the issue is about. - **A sketch of design 2.1 from the issue.** The same per-file manifests from - VirtualiZarr are combined by [switch.py](switch.py) into one array whose chunks each + VirtualiZarr are combined by the switch sketch (its own section of + [demo.py](demo.py)) into one array whose chunks each select their own codec chain. That array is saved as a vzip archive with refkv, reopened, and read through zarr-python and xarray. @@ -25,8 +26,9 @@ cd design/experiments/inhomogeneous && uv run demo.py `demo.py` declares its dependencies inline ([PEP 723](https://peps.python.org/pep-0723/)), so `uv run` builds an environment for -it alone, with refkv from PyPI. It doesn't use this repository's environment. The two -files, `demo.py` and [switch.py](switch.py), run from any directory. +it alone, with refkv from PyPI. It doesn't use this repository's environment. It is +one file: copy `demo.py` anywhere and run it. [scaling.py](scaling.py) imports the +switch sketch from `demo.py`, so it needs `demo.py` beside it. | case | the files | one Zarr array today | switch sketch | |---|---|---|---| diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index dc2d4f70..4c7ed0eb 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -25,7 +25,7 @@ regular chunk grid and one set of attributes, so VirtualiZarr refuses combinations that would need more, and says what is missing. That refusal protects users; it is the gap the issue describes. -- "switch sketch": the same per-file manifests, combined by switch.py into one array +- "switch sketch": the same per-file manifests, combined by py into one array whose chunks each select their own codec chain (design 2.1 of the issue), saved as a vzip archive with refkv and read back through Zarr. @@ -41,7 +41,7 @@ uv run demo.py The dependencies are declared inline (PEP 723), so `uv run` builds an environment for -this script alone; switch.py, next to it, is the only other file it needs. +this script alone; py, next to it, is the only other file it needs. Cases (the use cases of the issue): @@ -54,25 +54,34 @@ from __future__ import annotations +import hashlib import json import tempfile import textwrap import warnings -from dataclasses import dataclass, field +from dataclasses import dataclass, field, replace +from functools import lru_cache from pathlib import Path +from typing import Self import h5py import matplotlib import numpy as np import virtualizarr as vz import xarray as xr +import zarr from obspec_utils.registry import ObjectStoreRegistry from obstore.store import LocalStore from virtualizarr.parsers import HDFParser +from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec +from zarr.core.array_spec import ArraySpec +from zarr.core.buffer import Buffer, NDBuffer +from zarr.core.metadata.v3 import parse_codecs +from zarr.registry import get_pipeline_class, register_codec import refkv import refkv.zarr -import switch # next to this script, which Python puts on sys.path +from refkv.pb import Concat, Range, Source matplotlib.use("Agg") # files only, no display import matplotlib.pyplot as plt # noqa: E402 - after choosing the backend @@ -82,6 +91,331 @@ NX = 6 # a small spatial axis, so every case runs in a second +# -------------------------------------------------------------------------------------- +# The switch sketch. +# +# Combining inhomogeneous files into one Zarr array with refkv, from independent codecs. +# +# Each file virtualizes on its own (VirtualiZarr does this well). What no single Zarr +# array can express is that the files' chunks decode differently. Here every way a +# chunk can differ is its own, independent codec: +# +# | what varies per file | codec | +# |----------------------------------|-----------------------------------------| +# | compression, shuffle | the file's own codecs (`numcodecs.*`) | +# | stored dtype | `cast_value` (zarr-extensions) | +# | CF `scale_factor` / `add_offset` | `scale_offset` (zarr-extensions) | +# | stored chunk shape (edge chunks) | `crop` (new, below) | +# +# and one codec, `switch`, chooses per chunk which composition of them applies: +# +# - `switch` is an array-to-bytes codec, +# `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. Each case is an +# ordinary Zarr codec chain that may leave numbers open (`{"parameter": k}`), keyed by +# the first 8 bytes of the SHA-256 of its canonical JSON. A chunk's bytes start with its +# case's key and that case's parameters; `switch` fills them in and decodes the rest +# with that chain, as `sharding_indexed` decodes its inner chunks with its inner chain. +# - Keys name cases by their content: arrays that use the same chain use the same key, +# so concatenating arrays merges their case tables by union, and no chunk is rewritten +# (`concatenate`). +# - `crop` is an array-to-array codec, `{"shape": [...]}`: the stored chunk's shape. It +# decodes by cropping to the chunk's cell (HDF5 stores edge chunks whole) and encodes +# by padding with the fill value. +# +# refkv's reference values carry each chunk's header: a chunk is +# `Concat(Range(data=), Range(source=, offset, length))`, literal +# bytes in the archive, then the file's own bytes read in place. No store injects +# anything at read time and no codec does I/O. +# +# Ragged and month-length files use the zarr-extensions `rectilinear` chunk grid, which +# zarr-python 3.4 reads with `zarr.config.set({"array.rectilinear_chunks": True})`. +# -------------------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class CropCodec(ArrayArrayCodec): + """`crop`: the stored chunk has `shape`, at least the chunk's cell in every + dimension; decoding keeps its leading corner, the cell.""" + + shape: tuple[int, ...] = () + is_fixed_size = True + + @classmethod + def from_dict(cls, data: dict) -> Self: + return cls(shape=tuple(data["configuration"]["shape"])) + + def to_dict(self) -> dict: + return {"name": "crop", "configuration": {"shape": list(self.shape)}} + + def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec: + if len(self.shape) != len(chunk_spec.shape) or any(s < c for s, c in zip(self.shape, chunk_spec.shape)): + raise ValueError(f"crop: stored shape {self.shape} is smaller than the chunk {chunk_spec.shape}") + return replace(chunk_spec, shape=self.shape) + + async def _decode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> NDBuffer: + return chunk_array[tuple(slice(0, n) for n in chunk_spec.shape)] + + async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> NDBuffer: + a = chunk_array.as_numpy_array() + out = np.full(self.shape, chunk_spec.fill_value, dtype=a.dtype) + out[tuple(slice(0, n) for n in a.shape)] = a + return chunk_spec.prototype.nd_buffer.from_ndarray_like(out) + + def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: + return input_byte_length // int(np.prod(chunk_spec.shape)) * int(np.prod(self.shape)) + + +register_codec("crop", CropCodec) + + +KEY_BYTES = 8 # a case's key: the first 8 bytes of the SHA-256 of its canonical JSON + + +def canonical(case: dict) -> bytes: + """A case's canonical JSON: sorted keys, no whitespace, UTF-8. (A standard would + name RFC 8785, which also fixes how numbers are written.)""" + return json.dumps(case, sort_keys=True, separators=(",", ":"), ensure_ascii=False).encode() + + +def case_key(case: dict) -> bytes: + return hashlib.sha256(canonical(case)).digest()[:KEY_BYTES] + + +@dataclass(frozen=True) +class SwitchCodec(ArrayBytesCodec): + """`switch`: `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. + + A case is a codec chain that may leave values open: `{"parameter": k}` anywhere in a + codec's configuration stands for the chunk's k-th parameter. A case's key is the + hex of the first 8 bytes of the SHA-256 of its canonical JSON, so it names the case + by its content, not its position: arrays that share a case share its key, and two + case tables merge by union. A chunk is the 8-byte key, then the case's parameters + (each little-endian in its declared data type), then the bytes the completed chain + decodes. + """ + + cases: tuple = () # (key hex, codecs as JSON, parameter dtypes) + is_fixed_size = False + + def __post_init__(self) -> None: + object.__setattr__(self, "_by_key", {k: (codecs, params) for k, codecs, params in self.cases}) + + @classmethod + def from_dict(cls, data: dict) -> Self: + cases = [] + for key, case in data["configuration"]["cases"].items(): + if case_key(case).hex() != key: + raise ValueError(f"switch: case {key} is not the key of its contents ({case_key(case).hex()})") + cases.append((key, json.dumps(case["codecs"]), tuple(case.get("parameters", ())))) + return cls(cases=tuple(cases)) + + def to_dict(self) -> dict: + return {"name": "switch", "configuration": {"cases": { + key: {"codecs": json.loads(codecs), "parameters": list(params)} for key, codecs, params in self.cases}}} + + async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: + raw = chunk_bytes.to_bytes() + key, pos = raw[:KEY_BYTES].hex(), KEY_BYTES + try: + codecs, params = self._by_key[key] + except KeyError: + raise ValueError(f"switch: the chunk names case {key}, which the array does not list") from None + values = [] + for dtype in params: + dt = np.dtype(dtype).newbyteorder("<") + values.append(np.frombuffer(raw, dtype=dt, count=1, offset=pos)[0].item()) + pos += dt.itemsize + pipeline = get_pipeline_class().from_codecs(_completed(codecs, tuple(values))).evolve_from_array_spec(chunk_spec) + (out,) = await pipeline.decode([(chunk_spec.prototype.buffer.from_bytes(raw[pos:]), chunk_spec)]) + return out + + async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> Buffer: + raise NotImplementedError("this sketch decodes virtual chunks only; a writer would choose a case") + + def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: + raise NotImplementedError + + +def _json_number(v: float | int): + """A parameter as JSON, as Zarr writes fill values: NaN and the infinities as strings.""" + if isinstance(v, float) and not np.isfinite(v): + return "NaN" if np.isnan(v) else ("Infinity" if v > 0 else "-Infinity") + return v + + +@lru_cache(maxsize=4096) +def _completed(codecs: str, values: tuple) -> tuple: + """A case's codecs with its open values filled in, parsed (cached: chunks that + share a case and its numbers share one parse).""" + + def fill(node): + if isinstance(node, dict): + if set(node) == {"parameter"}: + return _json_number(values[node["parameter"]]) + return {k: fill(v) for k, v in node.items()} + if isinstance(node, list): + return [fill(v) for v in node] + return node + + return tuple(parse_codecs(fill(json.loads(codecs)))) + + +register_codec("switch", SwitchCodec) + + +def chain(ma, attrs, out_dtype: np.dtype) -> tuple[list[dict], list[str], list[float]]: + """How one file's chunks decode to the combined array's dtype: a case (the + independent transforms its file needs, then the file's own codecs, with the numbers + that vary between files left open) and the values of those numbers.""" + m = ma.metadata + stored = m.data_type.to_native_dtype() + codecs: list[dict] = [] + params: list[str] = [] + values: list[float] = [] + if "scale_factor" in attrs or "add_offset" in attrs: # CF: physical = packed * scale_factor + add_offset + codecs.append({"name": "scale_offset", "configuration": {"offset": {"parameter": 0}, "scale": {"parameter": 1}}}) + params += ["float64", "float64"] + values += [float(attrs.get("add_offset", 0)), 1 / float(attrs.get("scale_factor", 1))] + if stored != out_dtype: + config = {"data_type": stored.name} + if stored.kind in "iu": + # the array's fill value, NaN, has to round-trip through the integer type + # (cast_value, "data_type"): CF's _FillValue is the integer that stands for it + fill = attrs.get("_FillValue") + if fill is None: + raise ValueError(f"an {stored} file without _FillValue: no integer can stand for NaN") + config["scalar_map"] = {"encode": [["NaN", int(fill)]], "decode": [[int(fill), "NaN"]]} + codecs.append({"name": "cast_value", "configuration": config}) + codecs.append({"name": "crop", "configuration": {"shape": list(m.chunks)}}) + return codecs + [c.to_dict() for c in m.codecs], params, values + + +def header(case: dict, values: list[float]) -> bytes: + """A chunk's switch header: its case's key, then its parameters.""" + return case_key(case) + b"".join(np.array([v], dtype=np.dtype(d).newbyteorder("<")).tobytes() + for d, v in zip(case["parameters"], values)) + + +def lengths(n: int, c: int) -> list[int]: + return [c] * (n // c) + ([n % c] if n % c else []) + + +def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv.ReferenceStore: + """One reference store holding `name` concatenated along `dim` (axis 0) across the + files' virtual datasets, with the concatenated coordinates.""" + return refkv.ReferenceStore(*build(vdss, name, dim)) + + +def build(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> tuple[dict, list[Source]]: + """`combine`'s mapping and sources.""" + scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) + out_dtype = np.dtype(np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() + for v in vdss])) + cases: dict[str, dict] = {} + sources, source_of, mapping = [], {}, {} + time_chunks: list[int] = [] + other = None + for vds in vdss: + ma = vds[name].data + codecs, params, values = chain(ma, vds[name].attrs, out_dtype) + case = {"codecs": codecs, "parameters": params} + cases[case_key(case).hex()] = case + head = header(case, values) + c0, *crest = ma.metadata.chunks + rest = [lengths(n, c) for n, c in zip(ma.shape[1:], crest)] + if other is None: + other = rest + elif rest != other: + raise ValueError("the files are chunked differently along the other axes") + base = len(time_chunks) + time_chunks += lengths(ma.shape[0], c0) + for idx, e in ma.manifest.dict().items(): + i, *js = (int(x) for x in idx.split(".")) + s = source_of.setdefault(e["path"], len(source_of)) + if s == len(sources): + sources.append(Source(url=e["path"])) + mapping[f"{name}/c/" + "/".join(map(str, (base + i, *js)))] = Concat( + (Range(data=head), Range(source=s, offset=e["offset"], length=e["length"]))) + mapping["zarr.json"] = json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode() + mapping[f"{name}/zarr.json"] = json.dumps({ + "zarr_format": 3, "node_type": "array", + "shape": [sum(time_chunks), *map(sum, other)], "data_type": out_dtype.name, + "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [time_chunks, *other]}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": "NaN", "codecs": [{"name": "switch", "configuration": {"cases": cases}}], + "dimension_names": list(vdss[0][name].dims), "attributes": {}, + }).encode() + for coord in vdss[0][name].dims: # the coordinates, small: inline bytes + values = np.concatenate([v[coord].values for v in vdss]) if coord == dim else vdss[0][coord].values + attrs = {} + if values.dtype.kind == "M": # decoded CF times: back to numbers with their units + values = ((values - np.datetime64("2000-01-01", "ns")) // np.timedelta64(1, "D")).astype("int64") + attrs = {"units": "days since 2000-01-01"} + values = np.ascontiguousarray(values).astype(values.dtype.newbyteorder("<")) + mapping[f"{coord}/zarr.json"] = json.dumps({ + "zarr_format": 3, "node_type": "array", "shape": [len(values)], "data_type": values.dtype.name, + "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": [len(values)]}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": 0, "codecs": [{"name": "bytes", "configuration": {"endian": "little"}}], + "dimension_names": [coord], "attributes": attrs, + }).encode() + mapping[f"{coord}/c/0"] = values.tobytes() + return mapping, sources + + +def concatenate(a: tuple[dict, list[Source]], b: tuple[dict, list[Source]], name: str = "v", + dim: str = "time") -> tuple[dict, list[Source]]: + """`b` appended to `a` along `dim` (axis 0), as manifest operations do: the case + tables merge by union of their keys, `b`'s chunks move along the axis and their + references renumber the sources they name. Every chunk's header (its case key and + parameters) is copied as it is.""" + (ma, sa), (mb, sb) = a, b + meta_a, meta_b = json.loads(ma[f"{name}/zarr.json"]), json.loads(mb[f"{name}/zarr.json"]) + grid_a = meta_a["chunk_grid"]["configuration"]["chunk_shapes"] + grid_b = meta_b["chunk_grid"]["configuration"]["chunk_shapes"] + if meta_a["data_type"] != meta_b["data_type"] or grid_a[1:] != grid_b[1:]: + raise ValueError("the arrays differ in data type or in their chunks along the other axes") + cases = dict(meta_a["codecs"][0]["configuration"]["cases"]) + for key, case in meta_b["codecs"][0]["configuration"]["cases"].items(): + if cases.setdefault(key, case) != case: + raise ValueError(f"case {key} names two different chains") # a key collision + shift, prefix = len(grid_a[0]), f"{name}/c/" + mapping = {k: v for k, v in ma.items() if k.startswith(prefix)} + for k, ref in mb.items(): + if k.startswith(prefix): + i, *rest = k[len(prefix):].split("/") + mapping[prefix + "/".join([str(int(i) + shift), *rest])] = Concat(tuple( + p if p.data is not None else Range(source=p.source + len(sa), offset=p.offset, length=p.length, crc32c=p.crc32c) + for p in ref.parts)) + meta = dict(meta_a, shape=[meta_a["shape"][0] + meta_b["shape"][0], *meta_a["shape"][1:]]) + meta["chunk_grid"] = {"name": "rectilinear", "configuration": {"kind": "inline", + "chunk_shapes": [grid_a[0] + grid_b[0], *grid_a[1:]]}} + meta["codecs"] = [{"name": "switch", "configuration": {"cases": cases}}] + mapping["zarr.json"] = ma["zarr.json"] + mapping[f"{name}/zarr.json"] = json.dumps(meta).encode() + for coord in meta_a["dimension_names"]: + cm = json.loads(ma[f"{coord}/zarr.json"]) + if coord == dim: + dt = np.dtype(cm["data_type"]).newbyteorder("<") + values = np.concatenate([np.frombuffer(ma[f"{coord}/c/0"], dt), np.frombuffer(mb[f"{coord}/c/0"], dt)]) + cm = dict(cm, shape=[len(values)], chunk_grid={"name": "regular", "configuration": {"chunk_shape": [len(values)]}}) + mapping[f"{coord}/c/0"] = values.tobytes() + else: + mapping[f"{coord}/c/0"] = ma[f"{coord}/c/0"] + mapping[f"{coord}/zarr.json"] = json.dumps(cm).encode() + return mapping, [*sa, *sb] + + +def read(store, name: str = "v") -> np.ndarray: + with zarr.config.set({"array.rectilinear_chunks": True}): + return xr.open_zarr(refkv.zarr.store(store), consolidated=False)[name].values + + +# -------------------------------------------------------------------------------------- +# The demo: the files, the two ways to combine them, the checks and the plots. +# -------------------------------------------------------------------------------------- + + def smooth(t: np.ndarray, base: float = 285.0, amp: float = 4.0) -> np.ndarray: """One smooth field over (time, x), continuous across files: a misdecoded file shows as a jump at its boundary.""" @@ -158,12 +492,12 @@ def virtualizarr_result(r: Result, vdss: list[xr.Dataset]) -> None: def refkv_result(r: Result, vdss: list[xr.Dataset], root: Path) -> None: - """refkv: one array from the same per-file manifests (switch.py), saved as a vzip + """refkv: one array from the same per-file manifests (the switch sketch above), saved as a vzip archive, reopened, and read through Zarr.""" - store = switch.combine(vdss) + store = combine(vdss) archive = root / "combined.vzip" store.save(str(archive)) - r.rk = switch.read(refkv.open(str(archive))) + r.rk = read(refkv.open(str(archive))) n = len(json.loads(store.get("v/zarr.json"))["codecs"][0]["configuration"]["cases"]) data = sum(p.stat().st_size for p in root.glob("*.nc")) r.rk_note = f"one array, {n} switch case{'s' * (n != 1)}: " + compare(r.rk, r.want) @@ -314,16 +648,16 @@ def concatenation(figures: Path) -> None: compression=comp, shuffle=shuf) paths.append(p) vdss = virtualize(paths, root) - a, b = switch.build(vdss[:2]), switch.build(vdss[2:]) - mapping, sources = switch.concatenate(a, b) + a, b = build(vdss[:2]), build(vdss[2:]) + mapping, sources = concatenate(a, b) keys = [set(json.loads(m["v/zarr.json"])["codecs"][0]["configuration"]["cases"]) for m in (a[0], b[0], mapping)] moved = all(mapping[f"v/c/{int(k.split('/')[2]) + len(json.loads(a[0]['v/zarr.json'])['chunk_grid']['configuration']['chunk_shapes'][0])}/{k.split('/')[3]}"].parts[0].data == ref.parts[0].data for k, ref in b[0].items() if k.startswith("v/c/")) - direct = json.loads(switch.build(vdss)[0]["v/zarr.json"])["codecs"] + direct = json.loads(build(vdss)[0]["v/zarr.json"])["codecs"] same = json.loads(mapping["v/zarr.json"])["codecs"] == direct archive = root / "concatenated.vzip" refkv.ReferenceStore(mapping, sources).save(str(archive)) - got, want = switch.read(refkv.open(str(archive))), truth(paths) + got, want = read(refkv.open(str(archive))), truth(paths) print(f" array A: {len(keys[0])} cases {sorted(keys[0])}\n array B: {len(keys[1])} cases {sorted(keys[1])}") print(f" A + B: {len(keys[2])} cases (the shared gzip case once); every chunk header copied unchanged: {moved};" f" codecs 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ztR{^S;`M#9wcbx(%G*jQDk~OWHGpmB;*aP)Z2OP-<5S2WGLTyN=Qfx26(65++(ro` z)OmCanZ(D^zuhRkFjWrN`Z(Adkl3hE%)O_F3M21ExhYHu1g(6!?A}e8R!H8zvu-27 z%Sx+7=r6)~C6_OHO7Pt1ZSjTZB4h#W5ZeMRsA$qIu)JOYQJ6JWtQsU0cJJqVks?fP|w^HI?|*@b2~BI z4wN7exyK>;Gd}&*`>A#o2UgY_Dv~`^eay_v7SLCxCsokRN;AR963Phj8aM`Ei*zzb5DjndF44Koh!yR((NJ9{H$46Pm;CZDz`Qy8EPA6eUzwE7I_g+y}d95C_%hdalj7-JHDIdW88aGfuDPYBd zK~glloNibe015aoFxCf1b+# diff --git a/design/experiments/inhomogeneous/output.txt b/design/experiments/inhomogeneous/output.txt index b021d488..f0dc7c4e 100644 --- a/design/experiments/inhomogeneous/output.txt +++ b/design/experiments/inhomogeneous/output.txt @@ -46,7 +46,7 @@ dtype_0.nc: shape=(10, 6) chunks=(5, 6) int16 gzip dtype_1.nc: shape=(10, 6) chunks=(5, 6) int16 gzip dtype_2.nc: shape=(10, 6) chunks=(5, 6) float32 gzip - one Zarr array today: not one array: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype('=0.1.0", # "obstore>=0.11.1", +# "virtualizarr>=2.7.3", # "zarr>=3.4.0", # "cast-value-rs>=0.4.2", # "xarray>=2026.9.0", +# "h5py>=3.16.0", +# "h5netcdf>=1.8.1", +# "matplotlib>=3.10", # ] # /// """How `switch` scales when every chunk is encoded differently. @@ -40,7 +44,7 @@ import refkv import refkv.zarr -import switch # next to this script, which Python puts on sys.path +from demo import _completed, case_key, header # the switch sketch, in demo.py next to this script from refkv.pb import Concat, Range, Source CHUNK = (4, 6) @@ -85,7 +89,7 @@ def archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool template = {"codecs": chain({"parameter": 1}, {"parameter": 0}), "parameters": ["float64", "float64"]} per_chunk = [] if templated else [{"codecs": chain(1 / packing(i)[0], packing(i)[1]), "parameters": []} for i in range(n)] - cases = {switch.case_key(c).hex(): c for c in ([template] if templated else per_chunk)} + cases = {case_key(c).hex(): c for c in ([template] if templated else per_chunk)} meta = {"zarr_format": 3, "node_type": "array", "shape": [CHUNK[0] * n, CHUNK[1]], "data_type": "float32", "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": list(CHUNK)}}, "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, @@ -96,14 +100,14 @@ def archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool "v/zarr.json": doc} for i, (off, length) in enumerate(ranges): scale, offset = packing(i) - head = switch.header(template, [offset, 1 / scale]) if templated else switch.header(per_chunk[i], []) + head = header(template, [offset, 1 / scale]) if templated else header(per_chunk[i], []) mapping[f"v/c/{i}/0"] = Concat((Range(data=head), Range(source=0, offset=off, length=length))) refkv.ReferenceStore(mapping, [Source(url=url)]).save(str(path)) return len(doc) def measure(path: Path, n: int) -> tuple[float, float, int]: - switch._completed.cache_clear() + _completed.cache_clear() t0 = time.perf_counter() a = zarr.open_array(refkv.zarr.store(refkv.open(str(path))), path="v", mode="r") t1 = time.perf_counter() diff --git a/design/experiments/inhomogeneous/scaling_output.txt b/design/experiments/inhomogeneous/scaling_output.txt index 5e2b3f64..8f5fa773 100644 --- a/design/experiments/inhomogeneous/scaling_output.txt +++ b/design/experiments/inhomogeneous/scaling_output.txt @@ -1,7 +1,7 @@ chunks design zarr.json archive open read 1 checked - 1,000 one case per chunk 401.7kB 0.54MB 0.01s 1.4ms 200 chunks bit for bit - 1,000 one case, two parameters 0.8kB 0.17MB 0.00s 1.5ms 200 chunks bit for bit - 10,000 one case per chunk 4,014.2kB 5.41MB 0.25s 1.8ms 200 chunks bit for bit - 10,000 one case, two parameters 0.8kB 1.72MB 0.02s 1.8ms 200 chunks bit for bit - 100,000 one case per chunk 40,139.3kB 54.43MB 2.96s 1.9ms 200 chunks bit for bit - 100,000 one case, two parameters 0.8kB 17.50MB 0.27s 1.7ms 200 chunks bit for bit + 1,000 one case per chunk 401.7kB 0.54MB 0.03s 2.1ms 200 chunks bit for bit + 1,000 one case, two parameters 0.8kB 0.17MB 0.00s 1.4ms 200 chunks bit for bit + 10,000 one case per chunk 4,014.2kB 5.41MB 0.25s 1.6ms 200 chunks bit for bit + 10,000 one case, two parameters 0.8kB 1.72MB 0.06s 1.9ms 200 chunks bit for bit + 100,000 one case per chunk 40,139.3kB 54.43MB 2.96s 1.8ms 200 chunks bit for bit + 100,000 one case, two parameters 0.8kB 17.50MB 0.25s 1.7ms 200 chunks bit for bit diff --git a/design/experiments/inhomogeneous/switch.py b/design/experiments/inhomogeneous/switch.py deleted file mode 100644 index 0e61c956..00000000 --- a/design/experiments/inhomogeneous/switch.py +++ /dev/null @@ -1,336 +0,0 @@ -"""Combining inhomogeneous files into one Zarr array with refkv, from independent codecs. - -Each file virtualizes on its own (VirtualiZarr does this well). What no single Zarr -array can express is that the files' chunks decode differently. Here every way a -chunk can differ is its own, independent codec: - -| what varies per file | codec | -|----------------------------------|-----------------------------------------| -| compression, shuffle | the file's own codecs (`numcodecs.*`) | -| stored dtype | `cast_value` (zarr-extensions) | -| CF `scale_factor` / `add_offset` | `scale_offset` (zarr-extensions) | -| stored chunk shape (edge chunks) | `crop` (new, below) | - -and one codec, `switch`, chooses per chunk which composition of them applies: - -- `switch` is an array-to-bytes codec, - `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. Each case is an - ordinary Zarr codec chain that may leave numbers open (`{"parameter": k}`), keyed by - the first 8 bytes of the SHA-256 of its canonical JSON. A chunk's bytes start with its - case's key and that case's parameters; `switch` fills them in and decodes the rest - with that chain, as `sharding_indexed` decodes its inner chunks with its inner chain. -- Keys name cases by their content: arrays that use the same chain use the same key, - so concatenating arrays merges their case tables by union, and no chunk is rewritten - (`concatenate`). -- `crop` is an array-to-array codec, `{"shape": [...]}`: the stored chunk's shape. It - decodes by cropping to the chunk's cell (HDF5 stores edge chunks whole) and encodes - by padding with the fill value. - -refkv's reference values carry each chunk's header: a chunk is -`Concat(Range(data=), Range(source=, offset, length))`, literal -bytes in the archive, then the file's own bytes read in place. No store injects -anything at read time and no codec does I/O. - -Ragged and month-length files use the zarr-extensions `rectilinear` chunk grid, which -zarr-python 3.4 reads with `zarr.config.set({"array.rectilinear_chunks": True})`. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass, replace -from functools import lru_cache -from typing import Self - -import numpy as np -import xarray as xr -import zarr -from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec -from zarr.core.array_spec import ArraySpec -from zarr.core.buffer import Buffer, NDBuffer -from zarr.core.metadata.v3 import parse_codecs -from zarr.registry import get_pipeline_class, register_codec - -import refkv -import refkv.zarr -from refkv.pb import Concat, Range, Source - - -@dataclass(frozen=True) -class CropCodec(ArrayArrayCodec): - """`crop`: the stored chunk has `shape`, at least the chunk's cell in every - dimension; decoding keeps its leading corner, the cell.""" - - shape: tuple[int, ...] = () - is_fixed_size = True - - @classmethod - def from_dict(cls, data: dict) -> Self: - return cls(shape=tuple(data["configuration"]["shape"])) - - def to_dict(self) -> dict: - return {"name": "crop", "configuration": {"shape": list(self.shape)}} - - def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec: - if len(self.shape) != len(chunk_spec.shape) or any(s < c for s, c in zip(self.shape, chunk_spec.shape)): - raise ValueError(f"crop: stored shape {self.shape} is smaller than the chunk {chunk_spec.shape}") - return replace(chunk_spec, shape=self.shape) - - async def _decode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> NDBuffer: - return chunk_array[tuple(slice(0, n) for n in chunk_spec.shape)] - - async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> NDBuffer: - a = chunk_array.as_numpy_array() - out = np.full(self.shape, chunk_spec.fill_value, dtype=a.dtype) - out[tuple(slice(0, n) for n in a.shape)] = a - return chunk_spec.prototype.nd_buffer.from_ndarray_like(out) - - def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: - return input_byte_length // int(np.prod(chunk_spec.shape)) * int(np.prod(self.shape)) - - -register_codec("crop", CropCodec) - - -KEY_BYTES = 8 # a case's key: the first 8 bytes of the SHA-256 of its canonical JSON - - -def canonical(case: dict) -> bytes: - """A case's canonical JSON: sorted keys, no whitespace, UTF-8. (A standard would - name RFC 8785, which also fixes how numbers are written.)""" - return json.dumps(case, sort_keys=True, separators=(",", ":"), ensure_ascii=False).encode() - - -def case_key(case: dict) -> bytes: - return hashlib.sha256(canonical(case)).digest()[:KEY_BYTES] - - -@dataclass(frozen=True) -class SwitchCodec(ArrayBytesCodec): - """`switch`: `{"cases": {key: {"codecs": chain, "parameters": [data_type, ...]}}}`. - - A case is a codec chain that may leave values open: `{"parameter": k}` anywhere in a - codec's configuration stands for the chunk's k-th parameter. A case's key is the - hex of the first 8 bytes of the SHA-256 of its canonical JSON, so it names the case - by its content, not its position: arrays that share a case share its key, and two - case tables merge by union. A chunk is the 8-byte key, then the case's parameters - (each little-endian in its declared data type), then the bytes the completed chain - decodes. - """ - - cases: tuple = () # (key hex, codecs as JSON, parameter dtypes) - is_fixed_size = False - - def __post_init__(self) -> None: - object.__setattr__(self, "_by_key", {k: (codecs, params) for k, codecs, params in self.cases}) - - @classmethod - def from_dict(cls, data: dict) -> Self: - cases = [] - for key, case in data["configuration"]["cases"].items(): - if case_key(case).hex() != key: - raise ValueError(f"switch: case {key} is not the key of its contents ({case_key(case).hex()})") - cases.append((key, json.dumps(case["codecs"]), tuple(case.get("parameters", ())))) - return cls(cases=tuple(cases)) - - def to_dict(self) -> dict: - return {"name": "switch", "configuration": {"cases": { - key: {"codecs": json.loads(codecs), "parameters": list(params)} for key, codecs, params in self.cases}}} - - async def _decode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> NDBuffer: - raw = chunk_bytes.to_bytes() - key, pos = raw[:KEY_BYTES].hex(), KEY_BYTES - try: - codecs, params = self._by_key[key] - except KeyError: - raise ValueError(f"switch: the chunk names case {key}, which the array does not list") from None - values = [] - for dtype in params: - dt = np.dtype(dtype).newbyteorder("<") - values.append(np.frombuffer(raw, dtype=dt, count=1, offset=pos)[0].item()) - pos += dt.itemsize - pipeline = get_pipeline_class().from_codecs(_completed(codecs, tuple(values))).evolve_from_array_spec(chunk_spec) - (out,) = await pipeline.decode([(chunk_spec.prototype.buffer.from_bytes(raw[pos:]), chunk_spec)]) - return out - - async def _encode_single(self, chunk_array: NDBuffer, chunk_spec: ArraySpec) -> Buffer: - raise NotImplementedError("this sketch decodes virtual chunks only; a writer would choose a case") - - def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: - raise NotImplementedError - - -def _json_number(v: float | int): - """A parameter as JSON, as Zarr writes fill values: NaN and the infinities as strings.""" - if isinstance(v, float) and not np.isfinite(v): - return "NaN" if np.isnan(v) else ("Infinity" if v > 0 else "-Infinity") - return v - - -@lru_cache(maxsize=4096) -def _completed(codecs: str, values: tuple) -> tuple: - """A case's codecs with its open values filled in, parsed (cached: chunks that - share a case and its numbers share one parse).""" - - def fill(node): - if isinstance(node, dict): - if set(node) == {"parameter"}: - return _json_number(values[node["parameter"]]) - return {k: fill(v) for k, v in node.items()} - if isinstance(node, list): - return [fill(v) for v in node] - return node - - return tuple(parse_codecs(fill(json.loads(codecs)))) - - -register_codec("switch", SwitchCodec) - - -def chain(ma, attrs, out_dtype: np.dtype) -> tuple[list[dict], list[str], list[float]]: - """How one file's chunks decode to the combined array's dtype: a case (the - independent transforms its file needs, then the file's own codecs, with the numbers - that vary between files left open) and the values of those numbers.""" - m = ma.metadata - stored = m.data_type.to_native_dtype() - codecs: list[dict] = [] - params: list[str] = [] - values: list[float] = [] - if "scale_factor" in attrs or "add_offset" in attrs: # CF: physical = packed * scale_factor + add_offset - codecs.append({"name": "scale_offset", "configuration": {"offset": {"parameter": 0}, "scale": {"parameter": 1}}}) - params += ["float64", "float64"] - values += [float(attrs.get("add_offset", 0)), 1 / float(attrs.get("scale_factor", 1))] - if stored != out_dtype: - config = {"data_type": stored.name} - if stored.kind in "iu": - # the array's fill value, NaN, has to round-trip through the integer type - # (cast_value, "data_type"): CF's _FillValue is the integer that stands for it - fill = attrs.get("_FillValue") - if fill is None: - raise ValueError(f"an {stored} file without _FillValue: no integer can stand for NaN") - config["scalar_map"] = {"encode": [["NaN", int(fill)]], "decode": [[int(fill), "NaN"]]} - codecs.append({"name": "cast_value", "configuration": config}) - codecs.append({"name": "crop", "configuration": {"shape": list(m.chunks)}}) - return codecs + [c.to_dict() for c in m.codecs], params, values - - -def header(case: dict, values: list[float]) -> bytes: - """A chunk's switch header: its case's key, then its parameters.""" - return case_key(case) + b"".join(np.array([v], dtype=np.dtype(d).newbyteorder("<")).tobytes() - for d, v in zip(case["parameters"], values)) - - -def lengths(n: int, c: int) -> list[int]: - return [c] * (n // c) + ([n % c] if n % c else []) - - -def combine(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> refkv.ReferenceStore: - """One reference store holding `name` concatenated along `dim` (axis 0) across the - files' virtual datasets, with the concatenated coordinates.""" - return refkv.ReferenceStore(*build(vdss, name, dim)) - - -def build(vdss: list[xr.Dataset], name: str = "v", dim: str = "time") -> tuple[dict, list[Source]]: - """`combine`'s mapping and sources.""" - scaled = any("scale_factor" in v[name].attrs or "add_offset" in v[name].attrs for v in vdss) - out_dtype = np.dtype(np.result_type(*[np.float32 if scaled else v[name].data.metadata.data_type.to_native_dtype() - for v in vdss])) - cases: dict[str, dict] = {} - sources, source_of, mapping = [], {}, {} - time_chunks: list[int] = [] - other = None - for vds in vdss: - ma = vds[name].data - codecs, params, values = chain(ma, vds[name].attrs, out_dtype) - case = {"codecs": codecs, "parameters": params} - cases[case_key(case).hex()] = case - head = header(case, values) - c0, *crest = ma.metadata.chunks - rest = [lengths(n, c) for n, c in zip(ma.shape[1:], crest)] - if other is None: - other = rest - elif rest != other: - raise ValueError("the files are chunked differently along the other axes") - base = len(time_chunks) - time_chunks += lengths(ma.shape[0], c0) - for idx, e in ma.manifest.dict().items(): - i, *js = (int(x) for x in idx.split(".")) - s = source_of.setdefault(e["path"], len(source_of)) - if s == len(sources): - sources.append(Source(url=e["path"])) - mapping[f"{name}/c/" + "/".join(map(str, (base + i, *js)))] = Concat( - (Range(data=head), Range(source=s, offset=e["offset"], length=e["length"]))) - mapping["zarr.json"] = json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode() - mapping[f"{name}/zarr.json"] = json.dumps({ - "zarr_format": 3, "node_type": "array", - "shape": [sum(time_chunks), *map(sum, other)], "data_type": out_dtype.name, - "chunk_grid": {"name": "rectilinear", "configuration": {"kind": "inline", "chunk_shapes": [time_chunks, *other]}}, - "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, - "fill_value": "NaN", "codecs": [{"name": "switch", "configuration": {"cases": cases}}], - "dimension_names": list(vdss[0][name].dims), "attributes": {}, - }).encode() - for coord in vdss[0][name].dims: # the coordinates, small: inline bytes - values = np.concatenate([v[coord].values for v in vdss]) if coord == dim else vdss[0][coord].values - attrs = {} - if values.dtype.kind == "M": # decoded CF times: back to numbers with their units - values = ((values - np.datetime64("2000-01-01", "ns")) // np.timedelta64(1, "D")).astype("int64") - attrs = {"units": "days since 2000-01-01"} - values = np.ascontiguousarray(values).astype(values.dtype.newbyteorder("<")) - mapping[f"{coord}/zarr.json"] = json.dumps({ - "zarr_format": 3, "node_type": "array", "shape": [len(values)], "data_type": values.dtype.name, - "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": [len(values)]}}, - "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, - "fill_value": 0, "codecs": [{"name": "bytes", "configuration": {"endian": "little"}}], - "dimension_names": [coord], "attributes": attrs, - }).encode() - mapping[f"{coord}/c/0"] = values.tobytes() - return mapping, sources - - -def concatenate(a: tuple[dict, list[Source]], b: tuple[dict, list[Source]], name: str = "v", - dim: str = "time") -> tuple[dict, list[Source]]: - """`b` appended to `a` along `dim` (axis 0), as manifest operations do: the case - tables merge by union of their keys, `b`'s chunks move along the axis and their - references renumber the sources they name. Every chunk's header (its case key and - parameters) is copied as it is.""" - (ma, sa), (mb, sb) = a, b - meta_a, meta_b = json.loads(ma[f"{name}/zarr.json"]), json.loads(mb[f"{name}/zarr.json"]) - grid_a = meta_a["chunk_grid"]["configuration"]["chunk_shapes"] - grid_b = meta_b["chunk_grid"]["configuration"]["chunk_shapes"] - if meta_a["data_type"] != meta_b["data_type"] or grid_a[1:] != grid_b[1:]: - raise ValueError("the arrays differ in data type or in their chunks along the other axes") - cases = dict(meta_a["codecs"][0]["configuration"]["cases"]) - for key, case in meta_b["codecs"][0]["configuration"]["cases"].items(): - if cases.setdefault(key, case) != case: - raise ValueError(f"case {key} names two different chains") # a key collision - shift, prefix = len(grid_a[0]), f"{name}/c/" - mapping = {k: v for k, v in ma.items() if k.startswith(prefix)} - for k, ref in mb.items(): - if k.startswith(prefix): - i, *rest = k[len(prefix):].split("/") - mapping[prefix + "/".join([str(int(i) + shift), *rest])] = Concat(tuple( - p if p.data is not None else Range(source=p.source + len(sa), offset=p.offset, length=p.length, crc32c=p.crc32c) - for p in ref.parts)) - meta = dict(meta_a, shape=[meta_a["shape"][0] + meta_b["shape"][0], *meta_a["shape"][1:]]) - meta["chunk_grid"] = {"name": "rectilinear", "configuration": {"kind": "inline", - "chunk_shapes": [grid_a[0] + grid_b[0], *grid_a[1:]]}} - meta["codecs"] = [{"name": "switch", "configuration": {"cases": cases}}] - mapping["zarr.json"] = ma["zarr.json"] - mapping[f"{name}/zarr.json"] = json.dumps(meta).encode() - for coord in meta_a["dimension_names"]: - cm = json.loads(ma[f"{coord}/zarr.json"]) - if coord == dim: - dt = np.dtype(cm["data_type"]).newbyteorder("<") - values = np.concatenate([np.frombuffer(ma[f"{coord}/c/0"], dt), np.frombuffer(mb[f"{coord}/c/0"], dt)]) - cm = dict(cm, shape=[len(values)], chunk_grid={"name": "regular", "configuration": {"chunk_shape": [len(values)]}}) - mapping[f"{coord}/c/0"] = values.tobytes() - else: - mapping[f"{coord}/c/0"] = ma[f"{coord}/c/0"] - mapping[f"{coord}/zarr.json"] = json.dumps(cm).encode() - return mapping, [*sa, *sb] - - -def read(store, name: str = "v") -> np.ndarray: - with zarr.config.set({"array.rectilinear_chunks": True}): - return xr.open_zarr(refkv.zarr.store(store), consolidated=False)[name].values From af9d7b726f7b11d580c1b7a7679b0a81c9098be9 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 13:14:57 +0200 Subject: [PATCH 12/12] refactor(design): fold the scaling run into demo.py, as `uv run demo.py scaling` Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 8 +- design/experiments/inhomogeneous/demo.py | 134 ++++++++++++++++- design/experiments/inhomogeneous/scaling.py | 139 ------------------ .../inhomogeneous/scaling_output.txt | 8 +- 4 files changed, 140 insertions(+), 149 deletions(-) delete mode 100644 design/experiments/inhomogeneous/scaling.py diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 7bd4440a..28fa4bd0 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -27,8 +27,8 @@ cd design/experiments/inhomogeneous && uv run demo.py `demo.py` declares its dependencies inline ([PEP 723](https://peps.python.org/pep-0723/)), so `uv run` builds an environment for it alone, with refkv from PyPI. It doesn't use this repository's environment. It is -one file: copy `demo.py` anywhere and run it. [scaling.py](scaling.py) imports the -switch sketch from `demo.py`, so it needs `demo.py` beside it. +one file: copy `demo.py` anywhere and run it. `uv run demo.py scaling` runs the scaling +comparison below. | case | the files | one Zarr array today | switch sketch | |---|---|---|---| @@ -148,7 +148,7 @@ Case 4's array, with its one case: ## Scaling: when every chunk is encoded differently A table of whole chains grows with the number of distinct encodings, and every reader -reads the whole table before its first chunk. [scaling.py](scaling.py) measures the +reads the whole table before its first chunk. `uv run demo.py scaling` measures the worst case: N chunks, each packed with its own CF scale and offset (as GRIB packs each message), in one source file. It describes the same array two ways: @@ -156,7 +156,7 @@ message), in one source file. It describes the same array two ways: - **One case, two parameters:** each chunk's numbers are in its header. ```bash -uv run scaling.py +uv run demo.py scaling ``` | chunks | design | `zarr.json` | archive | open the array | read one chunk | diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 4c7ed0eb..360b6a6a 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -38,7 +38,8 @@ - figures/ gets one plot per case: the files and the combined arrays along time at x = 0, with the files' boundaries, and the sketch's whole field over (time, x). - uv run demo.py + uv run demo.py # the six cases, and figures/ + uv run demo.py scaling # how switch scales when every chunk is encoded differently The dependencies are declared inline (PEP 723), so `uv run` builds an environment for this script alone; py, next to it, is the only other file it needs. @@ -56,9 +57,12 @@ import hashlib import json +import sys import tempfile import textwrap +import time import warnings +import zlib from dataclasses import dataclass, field, replace from functools import lru_cache from pathlib import Path @@ -669,7 +673,124 @@ def concatenation(figures: Path) -> None: plot(r, figures / "case6.png") -if __name__ == "__main__": +# -------------------------------------------------------------------------------------- +# Scaling: `uv run demo.py scaling`. +# +# How `switch` scales when every chunk is encoded differently. +# +# The worst case for a table of codec chains: N chunks, each packed with its own CF +# scale and offset (as GRIB packs each message). One source file holds the N chunks, +# each zlib-compressed int16. Two archives describe the same array: +# +# - **one case per chunk**: N cases in `zarr.json`, each a full chain with its numbers; +# a chunk's header is its case's 8-byte key. +# - **one case, two parameters**: one chain in `zarr.json` with `scale_offset`'s +# `offset` and `scale` left open; a chunk's header is the case index and its two +# float64 numbers after the key, a literal in its reference in the archive's paged index. +# +# For each, the run reports `zarr.json`, the archive, the time to open the array and to +# read one chunk, and checks 200 chunks spread over the array against the physical +# values, bit for bit. +# +# uv run demo.py scaling +# -------------------------------------------------------------------------------------- + + +SCALE_CHUNK = (4, 6) +SCALE_FILL = -32767 + + +def scale_packing(i: int) -> tuple[float, float]: + """Chunk i's CF scale_factor and add_offset: a different pair for every chunk (the + offset alone is unique; content keys would merge chunks that repeat a pair).""" + return 0.01 * (1 + (i % 97) / 97), 280.0 + i * 1e-5 + + +def scale_physical(i: int) -> np.ndarray: + t = np.arange(SCALE_CHUNK[0] * i, SCALE_CHUNK[0] * (i + 1))[:, None] + return 285 + 4 * np.sin(2 * np.pi * t / 37.0) + 0.6 * np.cos(2 * np.pi * np.arange(SCALE_CHUNK[1])[None, :] / SCALE_CHUNK[1]) + + +def scale_source(path: Path, n: int) -> list[tuple[int, int]]: + """The N packed chunks, back to back; each one's (offset, length).""" + ranges, pos = [], 0 + with open(path, "wb") as f: + for i in range(n): + scale, offset = scale_packing(i) + packed = np.round((scale_physical(i) - offset) / scale).astype(" list[dict]: + return [{"name": "scale_offset", "configuration": {"offset": offset, "scale": scale}}, + {"name": "cast_value", "configuration": {"data_type": "int16", + "scalar_map": {"encode": [["NaN", SCALE_FILL]], "decode": [[SCALE_FILL, "NaN"]]}}}, + {"name": "bytes", "configuration": {"endian": "little"}}, + {"name": "numcodecs.zlib", "configuration": {"level": 4}}] + + +def scale_archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool) -> int: + n = len(ranges) + template = {"codecs": scale_chain({"parameter": 1}, {"parameter": 0}), "parameters": ["float64", "float64"]} + per_chunk = [] if templated else [{"codecs": scale_chain(1 / scale_packing(i)[0], scale_packing(i)[1]), "parameters": []} + for i in range(n)] + cases = {case_key(c).hex(): c for c in ([template] if templated else per_chunk)} + meta = {"zarr_format": 3, "node_type": "array", "shape": [SCALE_CHUNK[0] * n, SCALE_CHUNK[1]], "data_type": "float32", + "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": list(SCALE_CHUNK)}}, + "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, + "fill_value": "NaN", "codecs": [{"name": "switch", "configuration": {"cases": cases}}], + "attributes": {}} + doc = json.dumps(meta).encode() + mapping = {"zarr.json": json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode(), + "v/zarr.json": doc} + for i, (off, length) in enumerate(ranges): + scale, offset = scale_packing(i) + head = header(template, [offset, 1 / scale]) if templated else header(per_chunk[i], []) + mapping[f"v/c/{i}/0"] = Concat((Range(data=head), Range(source=0, offset=off, length=length))) + refkv.ReferenceStore(mapping, [Source(url=url)]).save(str(path)) + return len(doc) + + +def scale_measure(path: Path, n: int) -> tuple[float, float, int]: + _completed.cache_clear() + t0 = time.perf_counter() + a = zarr.open_array(refkv.zarr.store(refkv.open(str(path))), path="v", mode="r") + t1 = time.perf_counter() + a[SCALE_CHUNK[0] * (n - 1):SCALE_CHUNK[0] * n] + t2 = time.perf_counter() + wrong = 0 + for i in np.linspace(0, n - 1, 200).astype(int): + scale, offset = scale_packing(i) + packed = np.round((scale_physical(i) - offset) / scale).astype("int16") + want = packed.astype("float32") / np.float32(1 / scale) + np.float32(offset) + got = a[SCALE_CHUNK[0] * i:SCALE_CHUNK[0] * (i + 1)] + wrong += not np.array_equal(got, want.astype("float32")) + return t1 - t0, t2 - t1, wrong + + +def scaling() -> None: + """The scaling run (`uv run demo.py scaling`).""" + print(f"{'chunks':>8} {'design':<26}{'zarr.json':>12}{'archive':>11}{'open':>9}{'read 1':>9} checked") + with tempfile.TemporaryDirectory() as d: + root = Path(d) + for n in (1_000, 10_000, 100_000): + src = root / f"source_{n}.bin" + ranges = scale_source(src, n) + for templated, label in ((False, "one case per chunk"), (True, "one case, two parameters")): + path = root / f"{n}_{templated}.vzip" + doc = scale_archive(path, src.as_uri(), ranges, templated) + t_open, t_read, wrong = scale_measure(path, n) + print(f"{n:>8,} {label:<26}{doc / 1e3:>10,.1f}kB{path.stat().st_size / 1e6:>9.2f}MB" + f"{t_open:>8.2f}s{t_read * 1e3:>7.1f}ms " + + ("200 chunks bit for bit" if not wrong else f"{wrong} of 200 chunks differ")) + + +def main() -> None: + """The six cases and their figures (`uv run demo.py`).""" figures = HERE / "figures" figures.mkdir(exist_ok=True) for i, (title, make) in enumerate(CASES, 1): @@ -677,3 +798,12 @@ def concatenation(figures: Path) -> None: plot(r, figures / f"case{i}.png") concatenation(figures) print(f"\nfigures: {figures}") + + +if __name__ == "__main__": + if sys.argv[1:] == ["scaling"]: + scaling() + elif sys.argv[1:]: + sys.exit("usage: demo.py [scaling]") + else: + main() diff --git a/design/experiments/inhomogeneous/scaling.py b/design/experiments/inhomogeneous/scaling.py deleted file mode 100644 index 58ab60d3..00000000 --- a/design/experiments/inhomogeneous/scaling.py +++ /dev/null @@ -1,139 +0,0 @@ -# /// script -# requires-python = ">=3.12" -# dependencies = [ -# "refkv[virtualizarr]>=0.1.0", -# "obstore>=0.11.1", -# "virtualizarr>=2.7.3", -# "zarr>=3.4.0", -# "cast-value-rs>=0.4.2", -# "xarray>=2026.9.0", -# "h5py>=3.16.0", -# "h5netcdf>=1.8.1", -# "matplotlib>=3.10", -# ] -# /// -"""How `switch` scales when every chunk is encoded differently. - -The worst case for a table of codec chains: N chunks, each packed with its own CF -scale and offset (as GRIB packs each message). One source file holds the N chunks, -each zlib-compressed int16. Two archives describe the same array: - -- **one case per chunk**: N cases in `zarr.json`, each a full chain with its numbers; - a chunk's header is its case's 8-byte key. -- **one case, two parameters**: one chain in `zarr.json` with `scale_offset`'s - `offset` and `scale` left open; a chunk's header is the case index and its two - float64 numbers after the key, a literal in its reference in the archive's paged index. - -For each, the run reports `zarr.json`, the archive, the time to open the array and to -read one chunk, and checks 200 chunks spread over the array against the physical -values, bit for bit. - - uv run scaling.py -""" - -from __future__ import annotations - -import json -import tempfile -import time -import zlib -from pathlib import Path - -import numpy as np -import zarr - -import refkv -import refkv.zarr -from demo import _completed, case_key, header # the switch sketch, in demo.py next to this script -from refkv.pb import Concat, Range, Source - -CHUNK = (4, 6) -FILL = -32767 - - -def packing(i: int) -> tuple[float, float]: - """Chunk i's CF scale_factor and add_offset: a different pair for every chunk (the - offset alone is unique; content keys would merge chunks that repeat a pair).""" - return 0.01 * (1 + (i % 97) / 97), 280.0 + i * 1e-5 - - -def physical(i: int) -> np.ndarray: - t = np.arange(CHUNK[0] * i, CHUNK[0] * (i + 1))[:, None] - return 285 + 4 * np.sin(2 * np.pi * t / 37.0) + 0.6 * np.cos(2 * np.pi * np.arange(CHUNK[1])[None, :] / CHUNK[1]) - - -def write_source(path: Path, n: int) -> list[tuple[int, int]]: - """The N packed chunks, back to back; each one's (offset, length).""" - ranges, pos = [], 0 - with open(path, "wb") as f: - for i in range(n): - scale, offset = packing(i) - packed = np.round((physical(i) - offset) / scale).astype(" list[dict]: - return [{"name": "scale_offset", "configuration": {"offset": offset, "scale": scale}}, - {"name": "cast_value", "configuration": {"data_type": "int16", - "scalar_map": {"encode": [["NaN", FILL]], "decode": [[FILL, "NaN"]]}}}, - {"name": "bytes", "configuration": {"endian": "little"}}, - {"name": "numcodecs.zlib", "configuration": {"level": 4}}] - - -def archive(path: Path, url: str, ranges: list[tuple[int, int]], templated: bool) -> int: - n = len(ranges) - template = {"codecs": chain({"parameter": 1}, {"parameter": 0}), "parameters": ["float64", "float64"]} - per_chunk = [] if templated else [{"codecs": chain(1 / packing(i)[0], packing(i)[1]), "parameters": []} - for i in range(n)] - cases = {case_key(c).hex(): c for c in ([template] if templated else per_chunk)} - meta = {"zarr_format": 3, "node_type": "array", "shape": [CHUNK[0] * n, CHUNK[1]], "data_type": "float32", - "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": list(CHUNK)}}, - "chunk_key_encoding": {"name": "default", "configuration": {"separator": "/"}}, - "fill_value": "NaN", "codecs": [{"name": "switch", "configuration": {"cases": cases}}], - "attributes": {}} - doc = json.dumps(meta).encode() - mapping = {"zarr.json": json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}}).encode(), - "v/zarr.json": doc} - for i, (off, length) in enumerate(ranges): - scale, offset = packing(i) - head = header(template, [offset, 1 / scale]) if templated else header(per_chunk[i], []) - mapping[f"v/c/{i}/0"] = Concat((Range(data=head), Range(source=0, offset=off, length=length))) - refkv.ReferenceStore(mapping, [Source(url=url)]).save(str(path)) - return len(doc) - - -def measure(path: Path, n: int) -> tuple[float, float, int]: - _completed.cache_clear() - t0 = time.perf_counter() - a = zarr.open_array(refkv.zarr.store(refkv.open(str(path))), path="v", mode="r") - t1 = time.perf_counter() - a[CHUNK[0] * (n - 1):CHUNK[0] * n] - t2 = time.perf_counter() - wrong = 0 - for i in np.linspace(0, n - 1, 200).astype(int): - scale, offset = packing(i) - packed = np.round((physical(i) - offset) / scale).astype("int16") - want = packed.astype("float32") / np.float32(1 / scale) + np.float32(offset) - got = a[CHUNK[0] * i:CHUNK[0] * (i + 1)] - wrong += not np.array_equal(got, want.astype("float32")) - return t1 - t0, t2 - t1, wrong - - -if __name__ == "__main__": - print(f"{'chunks':>8} {'design':<26}{'zarr.json':>12}{'archive':>11}{'open':>9}{'read 1':>9} checked") - with tempfile.TemporaryDirectory() as d: - root = Path(d) - for n in (1_000, 10_000, 100_000): - src = root / f"source_{n}.bin" - ranges = write_source(src, n) - for templated, label in ((False, "one case per chunk"), (True, "one case, two parameters")): - path = root / f"{n}_{templated}.vzip" - doc = archive(path, src.as_uri(), ranges, templated) - t_open, t_read, wrong = measure(path, n) - print(f"{n:>8,} {label:<26}{doc / 1e3:>10,.1f}kB{path.stat().st_size / 1e6:>9.2f}MB" - f"{t_open:>8.2f}s{t_read * 1e3:>7.1f}ms " - + ("200 chunks bit for bit" if not wrong else f"{wrong} of 200 chunks differ")) diff --git a/design/experiments/inhomogeneous/scaling_output.txt b/design/experiments/inhomogeneous/scaling_output.txt index 8f5fa773..74b3939a 100644 --- a/design/experiments/inhomogeneous/scaling_output.txt +++ b/design/experiments/inhomogeneous/scaling_output.txt @@ -1,7 +1,7 @@ chunks design zarr.json archive open read 1 checked - 1,000 one case per chunk 401.7kB 0.54MB 0.03s 2.1ms 200 chunks bit for bit + 1,000 one case per chunk 401.7kB 0.54MB 0.02s 2.6ms 200 chunks bit for bit 1,000 one case, two parameters 0.8kB 0.17MB 0.00s 1.4ms 200 chunks bit for bit - 10,000 one case per chunk 4,014.2kB 5.41MB 0.25s 1.6ms 200 chunks bit for bit - 10,000 one case, two parameters 0.8kB 1.72MB 0.06s 1.9ms 200 chunks bit for bit - 100,000 one case per chunk 40,139.3kB 54.43MB 2.96s 1.8ms 200 chunks bit for bit + 10,000 one case per chunk 4,014.2kB 5.41MB 0.25s 1.8ms 200 chunks bit for bit + 10,000 one case, two parameters 0.8kB 1.72MB 0.07s 1.8ms 200 chunks bit for bit + 100,000 one case per chunk 40,139.3kB 54.43MB 2.98s 1.8ms 200 chunks bit for bit 100,000 one case, two parameters 0.8kB 17.50MB 0.25s 1.7ms 200 chunks bit for bit

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z*aNw2CZru?VVF;a7|LkPE-|d<;aAva?w4ftx4hVeHciax`Tv8d{{Na_tMqB(Qpzml UwJo~>5A each file virtualizes on its own: 4 virtual datasets - VirtualiZarr: combining fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numcodecs.zlib', codec_config={'level': 4})) - refkv: one array, 3 switch cases, saved and reopened: v(16, 6) float32, every value matches the files - the archive is 3,261 bytes; the chunks stay in the 4 files (43,964 bytes), read in place + era_0.nc: shape=(8, 6) chunks=(4, 6) float32 gzip + era_1.nc: shape=(8, 6) chunks=(4, 6) float32 gzip + era_2.nc: shape=(8, 6) chunks=(4, 6) float32 gzip+shuffle + era_3.nc: shape=(8, 6) chunks=(4, 6) float32 uncompressed + VirtualiZarr: combining fails: NotImplementedError: The ManifestArray class cannot concatenate arrays which were stored using different codecs, But found codecs (BytesCodec(endian='little'), Zlib(codec_name='numc + refkv: one array, 3 switch cases: identical to the files (bit for bit) + the archive is 3,885 bytes; the chunks stay in the 4 files (44,339 bytes), read in place ============================================================================== -2. rectilinear chunks - monthly files: 31, 28 and 31 days, one chunk each +2. rectilinear: monthly files of 31, 28 and 31 days, one chunk each ------------------------------------------------------------------------------ - month_0.nc: shape=(31, 6) chunks=(31, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - month_1.nc: shape=(28, 6) chunks=(28, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - month_2.nc: shape=(31, 6) chunks=(31, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - -> each file virtualizes on its own: 3 virtual datasets + month_0.nc: shape=(31, 6) chunks=(31, 6) float32 gzip + month_1.nc: shape=(28, 6) chunks=(28, 6) float32 gzip + month_2.nc: shape=(31, 6) chunks=(31, 6) float32 gzip VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with inconsistent chunk shapes: (28, 6) vs (31, 6) .Requires ZEP003 (Variable-length Chunks). - refkv: one array, 2 switch cases, saved and reopened: v(90, 6) float32, every value matches the files - the archive is 3,556 bytes; the chunks stay in the 3 files (35,389 bytes), read in place + refkv: one array, 2 switch cases: identical to the files (bit for bit) + the archive is 3,556 bytes; the chunks stay in the 3 files (34,740 bytes), read in place ============================================================================== -3. ragged inner chunks - each file chunked by 4 along time, 10 steps: chunks 4, 4, 2 +3. ragged inner chunks: 10 steps per file chunked by 4 (4, 4, 2) ------------------------------------------------------------------------------ - ragged_0.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - ragged_1.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - ragged_2.nc: shape=(10, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - -> each file virtualizes on its own: 3 virtual datasets - VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenation axis which is not evenly divisible by - refkv: one array, 1 switch case, saved and reopened: v(30, 6) float32, every value matches the files - the archive is 3,635 bytes; the chunks stay in the 3 files (33,631 bytes), read in place + ragged_0.nc: shape=(10, 6) chunks=(4, 6) float32 gzip + ragged_1.nc: shape=(10, 6) chunks=(4, 6) float32 gzip + ragged_2.nc: shape=(10, 6) chunks=(4, 6) float32 gzip + VirtualiZarr: combining fails: ValueError: Cannot concatenate arrays with partial chunks because only regular chunk grids are currently supported. Concat input 0 has array length 10 along the concatenati + refkv: one array, 1 switch case: identical to the files (bit for bit) + the archive is 3,635 bytes; the chunks stay in the 3 files (33,433 bytes), read in place ============================================================================== -4. per-file scale_factor / add_offset - each file packs int16 with its own CF scaling (BOM BARPA) +4. per-file scale_factor / add_offset on int16 (BOM BARPA) ------------------------------------------------------------------------------ - scaled_0.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.009999999776482582, 'add_offset': 280.0} - scaled_1.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.019999999552965164, 'add_offset': 270.0} - scaled_2.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} {'scale_factor': 0.004999999888241291, 'add_offset': 290.0} - -> each file virtualizes on its own: 3 virtual datasets - VirtualiZarr: combined (one codec chain and the first file's attributes for all): SILENTLY WRONG, 48 of 72 values differ from the files - expected [287.03 286.26 289.55 285.9 287.4 289.54 288.84 288.14 289.43 289.17 - 289.47 288.41] - got [287.03 286.26 289.55 285.9 288.7 289.77 289.42 289.07 278.86 278.34 - 278.94 276.82] - refkv: one array, 3 switch cases, saved and reopened: v(12, 6) float32, every value matches the files - the archive is 3,784 bytes; the chunks stay in the 3 files (32,836 bytes), read in place + scaled_0.nc: shape=(10, 6) chunks=(5, 6) int16 gzip ×0.01 +280 + scaled_1.nc: shape=(10, 6) chunks=(5, 6) int16 gzip ×0.02 +270 + scaled_2.nc: shape=(10, 6) chunks=(5, 6) int16 gzip ×0.005 +290 + VirtualiZarr: combined: max |difference| 19.6; 120 of 180 values off by more than 0.001 + refkv: one array, 3 switch cases: identical to the files (bit for bit) + the archive is 4,299 bytes; the chunks stay in the 3 files (33,190 bytes), read in place ============================================================================== -5. varying dtype - the same variable is int16 in two files and float32 in the third +5. varying dtype: int16, int16, float32 ------------------------------------------------------------------------------ - dtype_0.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} - dtype_1.nc: shape=(4, 6) chunks=(4, 6) dtype=int16 filters={'compression': 'gzip', 'opts': 4} - dtype_2.nc: shape=(4, 6) chunks=(4, 6) dtype=float32 filters={'compression': 'gzip', 'opts': 4} - -> each file virtualizes on its own: 3 virtual datasets + dtype_0.nc: shape=(10, 6) chunks=(5, 6) int16 gzip + dtype_1.nc: shape=(10, 6) chunks=(5, 6) int16 gzip + dtype_2.nc: shape=(10, 6) chunks=(5, 6) float32 gzip VirtualiZarr: combining fails: ValueError: Cannot combine arrays with inconsistent dtypes, but got 2 distinct dtypes: {dtype('/design/experiments/inhomogeneous/figures From d5cc20a7533636acfa8f1c5c1f42c7b950d4a673 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 12:03:41 +0200 Subject: [PATCH 06/12] docs(design): declare the demo's dependencies inline, so it runs on its own with uv run Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 9 +++++++-- design/experiments/inhomogeneous/demo.py | 19 ++++++++++++++++++- 2 files changed, 25 insertions(+), 3 deletions(-) diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 180f28aa..8d35d27d 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -16,9 +16,14 @@ combines them along time into one logical array twice: Both are compared with the files read one by one. ```bash -uv run --with "cast-value-rs>=0.4.2" --with matplotlib python design/experiments/inhomogeneous/demo.py +cd design/experiments/inhomogeneous && uv run demo.py ``` +`demo.py` declares its dependencies inline +([PEP 723](https://peps.python.org/pep-0723/)), so `uv run` builds an environment for +it alone, with refkv from PyPI. It doesn't use this repository's environment. The two +files, `demo.py` and [switch.py](switch.py), run from any directory. + | case | the files | VirtualiZarr | refkv | |---|---|---|---| | 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ bit for bit, 3 cases | @@ -168,5 +173,5 @@ reason. bug against the cast_value spec ("If the fill value cannot survive a round-trip cast, implementations MUST treat this as an error"), and it is being fixed separately. -- **zarr-python's `cast_value` needs the `cast-value-rs` package.** +- **zarr-python's `cast_value` needs the `cast-value-rs` package**, which the script declares. - **Versions:** VirtualiZarr 2.7.3, zarr 3.4.0, xarray 2026.9.0, h5py 3.16.0. diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 48a9dec7..46aec6ad 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -1,3 +1,17 @@ +# /// script +# requires-python = ">=3.12" +# dependencies = [ +# "refkv[virtualizarr]>=0.1.0", +# "obstore>=0.11.1", +# "virtualizarr>=2.7.3", +# "zarr>=3.4.0", +# "cast-value-rs>=0.4.2", +# "xarray>=2026.9.0", +# "h5py>=3.16.0", +# "h5netcdf>=1.8.1", +# "matplotlib>=3.10", +# ] +# /// """Why many-file datasets fall off the virtualization "complexity cliff", and how refkv gets them into one array. @@ -16,7 +30,10 @@ - figures/ gets one plot per case: the files, refkv and VirtualiZarr along time at x = 0, with the files' boundaries, and refkv minus the files over (time, x). - uv run --with "cast-value-rs>=0.4.2" --with matplotlib python design/experiments/inhomogeneous/demo.py + uv run demo.py + +The dependencies are declared inline (PEP 723), so `uv run` builds an environment for +this script alone; switch.py, next to it, is the only other file it needs. Cases (the use cases of the issue): From ead2801dcd9503961c10ce703f5a7414fd0dbd37 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Oct 2026 12:07:24 +0200 Subject: [PATCH 07/12] docs(design): frame the zarr-specs#399 demo around what one Zarr array can express The concatenated virtual datasets are "one Zarr array today": VirtualiZarr parses every file, and its refusals report what a single array can't hold, which is the gap the issue describes. Case 4's values come from xarray's default combine_attrs="override" keeping the first file's attributes; "no_conflicts" refuses and "drop_conflicts" drops the scaling. The refkv side is a sketch of the issue's design 2.1, built on VirtualiZarr's per-file manifests. Assisted-by: ClaudeCode:claude-opus-5-5 Co-Authored-By: Claude Opus 5.5 --- design/experiments/inhomogeneous/README.md | 78 +++++++++++------- design/experiments/inhomogeneous/demo.py | 52 +++++++----- .../inhomogeneous/figures/case1.png | Bin 85561 -> 83963 bytes .../inhomogeneous/figures/case2.png | Bin 91585 -> 89830 bytes .../inhomogeneous/figures/case3.png | Bin 79247 -> 77888 bytes .../inhomogeneous/figures/case4.png | Bin 77735 -> 79742 bytes .../inhomogeneous/figures/case5.png | Bin 73193 -> 72348 bytes design/experiments/inhomogeneous/output.txt | 33 ++++---- 8 files changed, 97 insertions(+), 66 deletions(-) diff --git a/design/experiments/inhomogeneous/README.md b/design/experiments/inhomogeneous/README.md index 8d35d27d..59b2b612 100644 --- a/design/experiments/inhomogeneous/README.md +++ b/design/experiments/inhomogeneous/README.md @@ -1,19 +1,23 @@ -# Inhomogeneous files: one array with refkv, where VirtualiZarr can't combine them +# Inhomogeneous files: the zarr-specs#399 cases, and a per-chunk codec sketch An executable companion to [zarr-developers/zarr-specs#399](https://github.com/zarr-developers/zarr-specs/issues/399) ("Varying codec parameters per-chunk"). [demo.py](demo.py) writes small netCDF4 -files, each valid and consistent on its own, in the five shapes the issue -describes. It virtualizes each file with VirtualiZarr (which works). It then -combines them along time into one logical array twice: - -- **VirtualiZarr:** concatenating the files' ManifestArrays, as - `open_virtual_mfdataset` does. -- **refkv:** building one array from the same per-file manifests, with - [switch.py](switch.py). The result is saved as a vzip archive, reopened, and read - through zarr-python and xarray. - -Both are compared with the files read one by one. +files in the five shapes the issue describes. Each file is valid and consistent on its +own, and VirtualiZarr parses every one of them without trouble. + +The demo looks at the next step: one logical array along time. + +- **One Zarr array today.** This is `xr.concat` of the virtual datasets, as + `open_virtual_mfdataset` does it. A Zarr array has one codec chain, one dtype, one + regular chunk grid and one set of attributes. So VirtualiZarr refuses combinations + that would need more, and its errors say what's missing (it names ZEP003 for + variable-length chunks). That refusal is the safe behavior; the gap it reports is + the one the issue is about. +- **A sketch of design 2.1 from the issue.** The same per-file manifests from + VirtualiZarr are combined by [switch.py](switch.py) into one array whose chunks each + select their own codec chain. That array is saved as a vzip archive with refkv, + reopened, and read through zarr-python and xarray. ```bash cd design/experiments/inhomogeneous && uv run demo.py @@ -24,13 +28,26 @@ cd design/experiments/inhomogeneous && uv run demo.py it alone, with refkv from PyPI. It doesn't use this repository's environment. The two files, `demo.py` and [switch.py](switch.py), run from any directory. -| case | the files | VirtualiZarr | refkv | +| case | the files | one Zarr array today | switch sketch | |---|---|---|---| -| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | fails: one codec chain per array | ✅ bit for bit, 3 cases | -| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | fails: needs variable-length chunks | ✅ bit for bit, rectilinear grid | -| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | fails: partial chunks break the regular grid | ✅ bit for bit, rectilinear grid + `crop` | -| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | **combines, then 120 of 180 values are wrong, by up to 19.6** | ✅ bit for bit, 3 cases | -| 5. varying dtype | int16, int16, float32 | fails: one array, one dtype | ✅ bit for bit, 2 cases | +| 1. codec eras (GOES-16) | gzip, gzip, shuffle + gzip, uncompressed | not expressible: one codec chain per array | bit for bit, 3 cases | +| 2. rectilinear chunks (monthly files) | 31, 28 and 31 days, one chunk each | not expressible: needs variable-length chunks (ZEP003) | bit for bit, rectilinear grid | +| 3. ragged inner chunks (tiled cubes) | 10 steps each, chunked by 4 (4, 4, 2) | not expressible: partial chunks inside a regular grid | bit for bit, rectilinear grid + `crop` | +| 4. per-file `scale_factor` / `add_offset` (BOM BARPA) | int16, each file with its own CF scaling | one array, one set of attributes (see below) | bit for bit, 3 cases | +| 5. varying dtype | int16, int16, float32 | not expressible: one dtype per array | bit for bit, 2 cases | + +Case 4 is the subtle one, and it isn't specific to any tool. CF scaling is array-level +attributes, and one array has one set, so concatenating the files has to choose: + +- **`combine_attrs="override"`** is the default in both `xr.concat` and + `open_virtual_mfdataset`. It keeps the first file's `scale_factor` and `add_offset`, + so the other files' integers decode with the wrong scaling: 120 of 180 values are + off, by up to 19.6. +- **`"no_conflicts"`** refuses the combination. +- **`"drop_conflicts"`** drops the scaling, leaving the raw integers. + +None of these gives one correctly scaled array, because the scaling varies per file. +That's the issue's point: the per-file context has to live somewhere per chunk. ## How the results are checked @@ -38,9 +55,11 @@ files, `demo.py` and [switch.py](switch.py), run from any directory. applies HDF5's own filters (deflate, shuffle) and xarray's CF decoding (`scale_factor`, `add_offset`, `_FillValue`). The decoded arrays are concatenated along time. -- **The combined arrays.** VirtualiZarr's result is read through Zarr, using refkv's - in-memory store for its manifest. refkv's is saved as a vzip archive, reopened, and - read through zarr-python and xarray. +- **The combined arrays.** + - The concatenated virtual dataset is read through Zarr, using refkv's in-memory + store for its manifest. + - The switch sketch is saved as a vzip archive, reopened, and read through + zarr-python and xarray. - **The comparison.** "Bit for bit" means `np.array_equal(..., equal_nan=True)`, with no tolerance. Otherwise the run reports the largest absolute difference, and how many values are off by more than 0.001. @@ -50,13 +69,10 @@ files, `demo.py` and [switch.py](switch.py), run from any directory. [`figures/`](figures/) has one plot per case: -- **Top panel:** `v` along time at `x = 0`, for the files, refkv, and VirtualiZarr - where it combines. The dotted lines are the file boundaries; each file's encoding is - labeled above its span. -- **Bottom panel:** all of refkv's `v(time, x)`. - -Case 4 is the one where VirtualiZarr produces an array: the second file drifts, and -the third drops by about 20. +- **Top panel:** `v` along time at `x = 0`, for the files and both combined arrays. + The dotted lines are the file boundaries; each file's encoding is labeled above its + span. +- **Bottom panel:** all of the sketch's `v(time, x)`. ![case 4](figures/case4.png) @@ -68,13 +84,13 @@ the third drops by about 20. The archives hold a few kilobytes of metadata; the chunks are read in place from the netCDF files. [output.txt](output.txt) is a full run. -## How: one codec per transformation, and one that chooses +## How the sketch works: one codec per transformation, and one that chooses Each thing that varies between the files is its own, independent codec: | what varies per file | codec | |---|---| -| compression, shuffle | the file's own codecs, as VirtualiZarr reports them (`numcodecs.zlib`, `numcodecs.shuffle`) | +| compression, shuffle | the file's own codecs, as VirtualiZarr's HDF5 parser reports them (`numcodecs.zlib`, `numcodecs.shuffle`) | | stored dtype | [`cast_value`](https://github.com/zarr-developers/zarr-extensions/tree/main/codecs/cast_value) (zarr-extensions, in zarr-python 3.4) | | CF `scale_factor` / `add_offset` | [`scale_offset`](https://github.com/zarr-developers/zarr-extensions/tree/main/codecs/scale_offset) (zarr-extensions, in zarr-python 3.4) | | stored chunk shape (HDF5 stores edge chunks whole) | `crop` (new: decodes by keeping the cell, encodes by padding) | @@ -151,7 +167,7 @@ reason. (zarr-extensions) already puts a per-chunk header in the chunk bytes, to turn codecs of one chain on or off. `switch` chooses among chains, so it also covers what `conditional` can't: a different dtype, scaling, or stored shape per chunk. -- **Concatenating two such arrays** (VirtualiZarr's manifest-level operations) means +- **Concatenating two such arrays** (manifest-level operations, as VirtualiZarr does them) means joining their case lists and renumbering each chunk's index: one literal byte per chunk, rewritten by tooling. No source bytes change. diff --git a/design/experiments/inhomogeneous/demo.py b/design/experiments/inhomogeneous/demo.py index 46aec6ad..d212625e 100644 --- a/design/experiments/inhomogeneous/demo.py +++ b/design/experiments/inhomogeneous/demo.py @@ -12,23 +12,31 @@ # "matplotlib>=3.10", # ] # /// -"""Why many-file datasets fall off the virtualization "complexity cliff", and how -refkv gets them into one array. +"""The many-file cases of zarr-developers/zarr-specs#399 ("Varying codec parameters +per-chunk"), and a sketch of one way to express them as a single Zarr array. -An executable companion to zarr-developers/zarr-specs#399 ("Varying codec parameters -per-chunk"). Each case writes a few small netCDF4 files, each valid and consistent on -its own, holding one smooth field `v(time, x)` that continues across the files. It -virtualizes every file with VirtualiZarr, then combines them into one array along time -twice: with VirtualiZarr (concatenating its ManifestArrays) and with refkv -(switch.py). Each result is read back through Zarr and compared with the files read -one by one: +Each case writes a few small netCDF4 files, each valid and consistent on its own, +holding one smooth field `v(time, x)` that continues across the files. Every file is +virtualized with VirtualiZarr, which parses all of them without trouble. The question +is the next step, one logical array along time: + +- "one Zarr array today": `xr.concat` of the virtual datasets, as + `open_virtual_mfdataset` does. A Zarr array has one codec chain, one dtype, one + regular chunk grid and one set of attributes, so VirtualiZarr refuses combinations + that would need more, and says what is missing. That refusal protects users; it is + the gap the issue describes. +- "switch sketch": the same per-file manifests, combined by switch.py into one array + whose chunks each select their own codec chain (design 2.1 of the issue), saved as a + vzip archive with refkv and read back through Zarr. + +Both are compared with the files read one by one: - the reference is each file decoded by xarray + h5netcdf (HDF5's own filters, CF scaling and _FillValue), concatenated; - "identical" means equal bit for bit (NaN equal to NaN); otherwise the largest absolute difference is reported; -- figures/ gets one plot per case: the files, refkv and VirtualiZarr along time at - x = 0, with the files' boundaries, and refkv minus the files over (time, x). +- figures/ gets one plot per case: the files and the combined arrays along time at + x = 0, with the files' boundaries, and the sketch's whole field over (time, x). uv run demo.py @@ -129,18 +137,20 @@ def compare(got: np.ndarray, want: np.ndarray) -> str: def virtualizarr_result(r: Result, vdss: list[xr.Dataset]) -> None: - """VirtualiZarr: concatenate the ManifestArrays, then read through Zarr.""" + """One Zarr array today: concatenate the virtual datasets (xarray's default + combine_attrs="override", as open_virtual_mfdataset uses), then read through Zarr.""" import refkv import refkv.zarr try: combined = xr.concat(vdss, dim="time", coords="minimal", compat="override", combine_attrs="override") - except Exception as e: # the cliff: no single Zarr array can describe these files - r.vz_note = f"combining fails: {type(e).__name__}: {str(e).splitlines()[0][:160]}" + except Exception as e: # what one Zarr array can't express, as VirtualiZarr reports it + r.vz_note = f"not one array: {type(e).__name__}: {str(e).splitlines()[0][:160]}" return store = refkv.ReferenceStore.from_virtualizarr(combined) r.vz = xr.open_zarr(refkv.zarr.store(store), consolidated=False)["v"].values - r.vz_note = "combined: " + compare(r.vz, r.want) + r.vz_note = ("combined, with the first file's attributes for every file (combine_attrs='override'; " + "'no_conflicts' refuses instead): " + compare(r.vz, r.want)) def refkv_result(r: Result, vdss: list[xr.Dataset], root: Path) -> None: @@ -182,7 +192,8 @@ def run(title: str, make) -> Result: vdss = virtualize(paths, root) # each file alone virtualizes fine virtualizarr_result(r, vdss) refkv_result(r, vdss, root) - print(f" VirtualiZarr: {r.vz_note}\n refkv: {r.rk_note}\n {r.extra['archive']}") + print(f" one Zarr array today: {r.vz_note}\n switch sketch: {r.rk_note}\n" + f" {r.extra['archive']}") return r @@ -199,9 +210,10 @@ def plot(r: Result, path: Path) -> None: t = np.arange(n) ax.plot(t, r.want[:, 0], color="0.75", lw=6, label="the files, each read on its own") if r.vz is not None: - ax.plot(t, r.vz[:, 0], color="tab:red", lw=1.5, marker="x", ms=4, label="VirtualiZarr, combined") + ax.plot(t, r.vz[:, 0], color="tab:orange", lw=1.5, marker="x", ms=4, + label="one Zarr array today (first file's attributes)") if r.rk is not None: - ax.plot(t, r.rk[:, 0], color="k", lw=1.2, ls="--", label="refkv, combined") + ax.plot(t, r.rk[:, 0], color="k", lw=1.2, ls="--", label="switch sketch") ends = r.boundaries[1:] + [n] for b in r.boundaries[1:]: ax.axvline(b - 0.5, color="0.5", lw=0.8, ls=":") @@ -212,7 +224,7 @@ def plot(r: Result, path: Path) -> None: ax.set_xlabel("time index") ax.set_ylabel("v at x = 0") vz = r.vz_note if r.vz is not None else "\n".join(textwrap.wrap(r.vz_note, 120)) - ax.set_title(f"VirtualiZarr: {vz}", fontsize=8, loc="left", color="tab:red") + ax.set_title(f"one Zarr array today: {vz}", fontsize=8, loc="left", color="0.3") ax.legend(fontsize=8, loc="best") im = ax2.imshow(r.rk.T, aspect="auto", cmap="viridis", interpolation="nearest", extent=(-0.5, n - 0.5, NX - 0.5, -0.5)) @@ -220,7 +232,7 @@ def plot(r: Result, path: Path) -> None: ax2.axvline(b - 0.5, color="w", lw=0.8, ls=":") ax2.set_xlabel("time index") ax2.set_ylabel("x") - ax2.set_title(f"refkv, 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