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Apache Beam Format

Description​

Apache Beam is a unified programming model for batch and streaming data processing. This implementation handles Beam format for reading/writing Beam data records. Beam records contain window, timestamp, key, and value.

File Extensions​

  • No specific extension (Beam data files)

Implementation Details​

Reading​

The Beam implementation:

  • Parses Beam record format
  • Extracts window, timestamp, key, and value
  • Handles binary record data
  • Converts records to dictionaries
  • Supports metadata inclusion/exclusion

Writing​

Writing is not currently supported for Beam format.

Key Features​

  • Record format: Handles Beam record structure
  • Metadata support: Includes window and timestamp
  • Key-value: Supports record keys and values
  • Nested data: Supports complex record structures

Usage​

from iterable import open_iterable

# Basic reading
source = open_iterable('beam.data', iterableargs={
    'key_name': 'key',
    'value_name': 'value',
    'include_metadata': True
})
for record in source:
    print(record)  # Contains key, value, window, timestamp
source.close()

Parameters​

  • key_name (str): Key name for record key (default: key)
  • value_name (str): Key name for record value (default: value)
  • include_metadata (bool): Include window, timestamp (default: True)

Limitations​

  1. Read-only: Beam format does not support writing
  2. Binary format: Not human-readable
  3. Beam-specific: Designed for Beam record format
  4. Format complexity: Beam format can be complex
  5. Simplified implementation: May not support all Beam features

Compression Support​

Beam files can be compressed with all supported codecs:

  • GZip (.beam.gz)
  • BZip2 (.beam.bz2)
  • LZMA (.beam.xz)
  • LZ4 (.beam.lz4)
  • ZIP (.beam.zip)
  • Brotli (.beam.br)
  • ZStandard (.beam.zst)

Use Cases​

  • Stream processing: Processing Beam data streams
  • Batch processing: Processing Beam batch data
  • Data pipelines: Beam data processing pipelines
  • ETL operations: ETL with Beam
  • Kafka - Apache Kafka format
  • Flink - Apache Flink format