Avro is a row-oriented remote procedure call and data serialization framework developed within the Apache Software Foundation. It uses JSON for defining data types and protocols, and serializes data in a compact binary format. Here's a detailed look at Avro:
History
- Avro was created by Doug Cutting, the founder of Hadoop, to address some of the limitations found in existing serialization formats when used in big data environments.
- It was developed as part of the Apache Hadoop ecosystem to support the efficient storage and retrieval of data for MapReduce jobs.
- The project was announced in 2009 and became an official Apache Top-Level Project in 2011.
Key Features
- Rich Data Structures: Avro supports complex data types including maps, arrays, enums, unions, and records.
- Schema Evolution: It provides a mechanism for schema evolution, allowing the schema to change over time while still being able to read old data.
- Dynamic Typing: Data can be processed without needing to compile or link against a schema.
- Compact Binary Format: The binary format used by Avro is designed to be compact, reducing the size of the data, which is beneficial for storage and transmission.
- Integration with Hadoop: Avro integrates seamlessly with Apache Hadoop, providing an efficient way to handle data in Hadoop Ecosystem.
- Code Generation: Avro can generate code for reading and writing data in various languages, which helps in reducing the amount of boilerplate code needed.
- Interoperability: It supports multiple programming languages, ensuring data can be shared across different systems.
Use Cases
Components
- Schemas: Written in JSON which define the structure of data.
- Data File: Contains serialized data with an embedded schema.
- Protocol: Defines the messages exchanged between client and server in RPC scenarios.
- Code Generation Tools: Tools that generate classes from schemas to handle data.
External Links
Related Topics