StreamXL

Problem

openpyxl and similar pure-Python Excel readers load the whole workbook into memory before you can touch a single row — fine for small files, a real ceiling for ETL pipelines and data engineering workloads working against large .xlsx exports.

Solution

Stream large .xlsx files row-by-row in constant memory, powered by a Rust core with no unsafe code.

pip installs as streamxl, import streamxl. Read multi-sheet Excel workbooks without loading them fully into memory, extract formulas and comments, write new .xlsx files, and append to existing ones — all through a small, plain Python API backed by a Rust engine.

Use cases

• ETL against large Excel exports that don't fit comfortably in memory with openpyxl — read() keeps memory flat regardless of file size.
• Extracting formulas/comments for audit or migration tooling, not just cell values.
• Appending to a growing log-style .xlsx file without rewriting the whole workbook or losing other sheets.
• Not yet a good fit for: SQL-style querying across sheets, formula evaluation (only extraction/classification), or pandas/Parquet/Arrow export built in — see Honest feature list for the full "what's not here" list.

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Install

pip install streamxl

A prebuilt wheel is currently published only for macOS (arm64); other platforms install from the source distribution, which requires a Rust toolchain (see rust-toolchain.toml) and maturin to build. Every PyPI release to date (1.2.0 through 5.2.0) has shipped exactly one platform wheel plus an sdist — no Linux or Windows wheels have been published yet.

Quick start

import streamxl

for row in streamxl.read("data.xlsx"):
    print(row)  # ['Name', 'Age', 'Score']

read() streams rows one at a time — memory use stays flat regardless of file size.

Real, working examples

Read as dictionaries, keyed by header row:

import streamxl

for row in streamxl.read("sales.xlsx", as_dict=True):
    print(row["Customer"], row["Amount"])

Read only specific columns:

for row in streamxl.read("sales.xlsx", as_dict=True, columns=["Customer", "Amount"]):
    ...

Read every sheet in a workbook:

sheet_names = streamxl.sheets("workbook.xlsx")
all_data = streamxl.read_all("workbook.xlsx")  # {sheet_name: [rows...]}

Write a new .xlsx file:

import datetime
import streamxl

streamxl.write("report.xlsx", [
    ["Name", "Joined", "Score"],
    ["Alice", datetime.date(2024, 1, 15), 95.5],
    ["Bob", datetime.date(2024, 3, 2), 88.0],
])

Stream-write multiple sheets without holding the whole file in memory:

with streamxl.writer("report.xlsx") as w:
    w.write_row(["Name", "Age"])
    w.write_row(["Alice", 30])
    w.add_sheet("Summary")
    w.write_row(["Total", 1])

Append rows to an existing file (other sheets are preserved):

streamxl.write("log.xlsx", [["Date", "Event"]])
streamxl.append("log.xlsx", [[datetime.date.today(), "started"]])
streamxl.append("log.xlsx", [[datetime.date.today(), "finished"]])

Extract formulas and comments:

rows = list(streamxl.read("model.xlsx", with_formulas=True))
# each cell is a dict: {"value": ..., "formula": ..., "formula_type": ...,
#                        "comment": ..., "comment_author": ...}

from streamxl import FormulaSerializer
export = FormulaSerializer.export_formulas(rows)
FormulaSerializer.export_to_json(rows, "formulas.json")
FormulaSerializer.export_to_csv(rows, "formulas.csv")  # sanitized against CSV/formula injection

Export to CSV safely — untrusted cell content is never written to CSV verbatim (see Security below):

import csv
import streamxl
from streamxl.security import sanitize_csv_cell

with open("output.csv", "w", newline="") as f:
    writer = csv.writer(f)
    for row in streamxl.read("large.xlsx"):
        writer.writerow([sanitize_csv_cell(cell) for cell in row])

Validate a file and recover from bad cells instead of crashing:

from streamxl import validate_excel_file

report = validate_excel_file("questionable.xlsx")
if report.has_fatal_errors():
    print(report.format_summary())

More runnable examples live in examples/.

Honest feature list

What's here and real, backed by the Rust core and covered by the test suite:

• Streaming reads — read() / stream(): real, pull-based streaming backed by a Rust __iter__/__next__ iterator over the sheet, not a full-sheet materialization dressed up as a generator — O(1) memory per row, regardless of file size. read_rows_all_at_once()/read_rows_with_metadata_all_at_once() remain available as an explicit escape hatch for callers that need random access or to iterate the result more than once.
• Multi-sheet support — sheets(), read_all(), and writer().add_sheet().
• Streaming writes — write(), writer(), append(), all producing real .xlsx files.
• Formula extraction — read formula text and a best-effort formula-type classification (with_formulas=True), plus FormulaReferenceMapper for shifting/rewriting cell references and FormulaSerializer for exporting/importing formulas as JSON or CSV.
• Comment extraction — cell comments and authors, via with_formulas=True.
• Conditional formatting rules — conditional_formats() reads every <conditionalFormatting>/<cfRule> in a sheet (type, operator, formulas, priority, stopIfTrue) and resolves each rule's dxfId against xl/styles.xml's <dxfs> into concrete font color/bold/italic and fill colors. colorScale/dataBar/iconSet rules are captured (type, sqref, priority) but their inline color-stop/threshold definitions aren't modeled — those rule types don't use dxfId in the first place.
• Type-aware cells — strings, numbers, booleans, dates, datetimes, and empty cells round-trip correctly.
• Error recovery & validation — validate_excel_file() and ErrorRecoveryHandler classify and (optionally) recover from malformed cells instead of hard-failing on the whole file.
• Security hardening — path validation, file-size limits, and ZIP-bomb defenses (entry-size, compression-ratio, and total-decompressed-size limits) enforced before/while a file is opened. CSV export is sanitized against formula-injection (see below).
• REST API (optional) — streamxl.server.StreamXLServer / create_flask_app() wrap the real streaming engine behind HTTP endpoints (/sources, /sources/<id>/query, /sources/<id>/export, ...). Requires pip install "streamxl[server]".

What's not here, so you don't have to find out the hard way:

• No SQL-style query language — execute_query() in the REST API streams rows from a named sheet, it does not parse arbitrary queries.
• No pandas/Parquet/Arrow export built in. Convert read()'s output yourself, or open an issue if this matters to you.
• No formula evaluation — formula text is extracted and classified, not recalculated.
• The pystreamxl dashboard CLI command currently renders sample data, not live telemetry. Only pystreamxl dashboard --static (and --alerts/--recommendations/--export) labels this clearly, with an explicit "SAMPLE DATA — not live" warning; the bare pystreamxl dashboard (default interactive mode) currently prints an unlabeled placeholder (Status: Active) with no such disclaimer.

Security

• Path & size validation — validate_read_path() / validate_write_path() reject non-.xlsx paths, path traversal, and oversized files before any parsing happens.
• ZIP-bomb defenses — the Rust core enforces a per-entry size limit, a compression-ratio limit, and a total-decompressed-size limit while unpacking a workbook (see core/src/zip_reader.rs), tested against real crafted archives in core/tests/zip_bomb_defense.rs.
• CSV/formula-injection protection — streamxl.security.sanitize_csv_cell() neutralizes any string cell that starts with =, +, -, @, TAB, or CR (the standard CSV-injection trigger set) by prefixing it with ', so a malicious workbook can't turn a CSV export into an executable formula when reopened in Excel/LibreOffice/Google Sheets. FormulaSerializer.export_to_csv() applies this automatically; apply it yourself when writing CSV from read() output (see the example above).

Limits, enforced by default (no configuration needed):

Limit	Value
Max file size	512 MB
Max size per ZIP entry	512 MB
Max total decompressed size	1 GB
Max compression ratio	30:1

Handle malformed or malicious files by catching SecurityError:

from streamxl import SecurityError, read

try:
    for row in read("data.xlsx"):
        process(row)
except SecurityError as e:
    print(f"Security violation: {e}")

Found a security issue? See SECURITY.md.

Performance

Streaming keeps memory flat regardless of file size, since rows are parsed and yielded one at a time instead of materializing the whole workbook. See benchmarks/ for the scripts used to compare against openpyxl, and examples/memory_benchmark.py to measure it yourself against your own files:

python examples/memory_benchmark.py your_file.xlsx

Actual numbers depend heavily on your file's structure (shared strings, formulas, formatting) — measure on your own workloads rather than trusting a generic table.

CLI

pystreamxl dashboard          # sample extraction dashboard (unlabeled placeholder, see note above)
pystreamxl dashboard --static # same sample data, clearly labeled "SAMPLE DATA — not live"
pystreamxl --version

Development

git clone https://github.com/Mullassery/StreamXL.git
cd StreamXL
pip install -e ".[dev]"       # builds the Rust extension via maturin and installs test deps
pytest tests/ -v
cargo test --all-features     # Rust unit + integration tests

License

This project is licensed under the Apache License 2.0.

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StreamXL | Constant-memory Excel streaming | Rust core, Python API