compose.specific.yaml

# If all of the environment variables described in the README are set up,
# then this compose file can be used to deploy a service.

services:

  kafka-streams-app:
    build:
      context: .
      dockerfile: Dockerfile
    environment:
      # Connecting to Kafka
      # These will be filled in by Aiven Apps deployment
      KAFKA_BOOTSTRAP_SERVERS: ${KAFKA_BOOTSTRAP_SERVERS}
      KAFKA_CA_CERT: ${KAFKA_CA_CERT}
      KAFKA_ACCESS_CERT: ${KAFKA_ACCESS_CERT}
      KAFKA_ACCESS_KEY: ${KAFKA_ACCESS_KEY}
      # Connecting to the Karapace schema repository
      # You will need to specify these after the Kafka service is running,
      # as Aiven automatically provides a Karapace schema registry for each
      # Aiven for Apache Kafka service
      SCHEMA_REGISTRY_HOST: ${SCHEMA_REGISTRY_HOST}
      SCHEMA_REGISTRY_PORT: ${SCHEMA_REGISTRY_PORT}
      SCHEMA_REGISTRY_USERNAME: ${SCHEMA_REGISTRY_USERNAME}
      SCHEMA_REGISTRY_PASSWORD: ${SCHEMA_REGISTRY_PASSWORD}
      # Which topics are input and output
      # Note: the Kafka Streams app requires the input and output (if used)
      # topics to exist before it runs. The default input topic is named for
      # the topic written to by the produce_avro.py script.
      INPUT_TOPIC: ${INPUT_TOPIC:-metric_data}
      OUTPUT_TOPIC: ${OUTPUT_TOPIC:-anomaly_data}
      # The anomaly specification. The defaults are designed to work with
      # the messages written by the produce_avro.py script.
      FIELD_NAME: ${FIELD_NAME:-temperature}
      MIN_BOUND: ${MIN_BOUND:--50}
      MAX_BOUND: ${MAX_BOUND:--30}