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Apache Avro Data Format Overview
sqream.com/blog/a-detailed-introduction-to-the-avro-data-format/Yapay zekadan makale özeti
- Introduction and Purpose
- Avro is a row-oriented data serialization framework developed within Apache Hadoop
- Converts objects in memory to byte streams for storage and network transmission
- Alternative to CSV, XML, and JSON formats with different limitations
- Schema Structure
- Uses JSON-formatted schema to define data types and structures
- Supports both human-editable (Avro IDL) and machine-readable (JSON) schemas
- Includes primitive types like boolean, int, float, and complex types like record
- Data Formats
- Offers JSON format for debugging but less performant
- Provides binary format as default, optimized for performance
- Supports single object encoding with schema fingerprint
- Implementation
- Writes data using DatumWriter for individual records
- Reads data using DatumReader with writer and reader schemas
- Supports schema evolution through versioning and registry management
- Advantages
- Compact and fast binary data format
- Rich data types including arrays, maps, and enumerations
- Supports distributed processing through container file format
- Compatible with popular programming languages