aid: avro
name: Apache Avro
description: >-
  Apache Avro is a data serialization system that provides rich data structures,
  a compact binary format, and container files for storing persistent data. Avro
  uses JSON for defining data types and protocols, and serializes data in a
  compact binary format.
type: Index
image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg
tags:
- Apache
- Big Data
- Binary Format
- Data Serialization
- Schema Evolution
url: >-
  https://raw.githubusercontent.com/api-evangelist/avro/refs/heads/main/apis.yml
created: '2025-01-01'
modified: '2026-04-19'
specificationVersion: '0.19'
apis:
- aid: avro:avro-schema
  name: Apache Avro Schema Format
  description: >-
    JSON Schema for validating Apache Avro schema definitions. Covers all
    Avro types including primitive types (null, boolean, int, long, float,
    double, bytes, string), complex types (records, enums, arrays, maps,
    unions, fixed), logical types, and schema evolution features like
    aliases and default values.
  humanURL: https://avro.apache.org/docs/current/specification/
  tags:
  - Data Serialization
  - JSON
  - Schema
  - Schema Evolution
  properties:
  - type: Documentation
    url: https://avro.apache.org/docs/current/specification/
  - type: JSONSchema
    url: json-schema/avro-schema.yml
common:
- type: Website
  url: https://avro.apache.org/
- type: Documentation
  url: https://avro.apache.org/docs/
- type: GitHubOrganization
  url: https://github.com/apache/avro
- type: SpectralRules
  url: https://raw.githubusercontent.com/api-evangelist/avro/refs/heads/main/rules/avro-spectral-rules.yml
- type: Vocabulary
  url: https://raw.githubusercontent.com/api-evangelist/avro/refs/heads/main/vocabulary/avro-vocabulary.yaml
- type: Features
  data:
  - name: Schema-First Design
    description: Avro requires schemas to be defined in JSON before serialization, enabling strong typing and schema validation.
  - name: Schema Evolution
    description: Avro supports backward, forward, and full schema compatibility through aliases, defaults, and type promotions.
  - name: Compact Binary Format
    description: Avro serializes data in a compact binary format without field names, reducing payload size significantly.
  - name: Rich Type System
    description: Supports primitive types, complex types (records, enums, arrays, maps, unions, fixed), and logical types (date, time, decimal, UUID).
  - name: Language Agnostic
    description: Official implementations in Java, Python, C, C++, C#, PHP, Ruby, and Rust with broad ecosystem support.
  - name: Container Files
    description: Avro Object Container Files (OCF) embed the schema with the data for self-describing data files.
  - name: RPC Support
    description: Avro defines an RPC protocol mechanism using schemas for both request and response messages.
  - name: Kafka Native Format
    description: Apache Kafka ecosystem uses Avro as a primary serialization format with the Confluent Schema Registry.
- type: UseCases
  data:
  - name: Event Streaming
    description: Serialize Kafka events with Avro schemas stored in a Schema Registry for high-throughput data pipelines.
  - name: Data Lake Storage
    description: Store large datasets in Avro container files in Hadoop-compatible storage with embedded schema metadata.
  - name: Schema Registry Integration
    description: Use Confluent Schema Registry to manage schema versions and enforce compatibility across producers and consumers.
  - name: Inter-Service Messaging
    description: Define message contracts between microservices using Avro schemas for type-safe data exchange.
  - name: Batch Data Processing
    description: Process large volumes of structured data with Apache Spark, Hive, or Flink using Avro as the interchange format.
- type: Integrations
  data:
  - name: Apache Kafka
    description: Native serialization format for Kafka messages via the Confluent Schema Registry and Kafka clients.
  - name: Apache Spark
    description: Spark SQL and DataFrames support reading and writing Avro files natively.
  - name: Apache Hive
    description: Hive tables can be backed by Avro container files with schema stored in the Hive Metastore.
  - name: Confluent Schema Registry
    description: Centralized schema management service for validating and evolving Avro schemas in Kafka ecosystems.
  - name: Apache Flink
    description: Flink supports Avro for serialization and deserialization of streaming data.
  - name: Apache Hadoop
    description: Avro is a native storage format supported by the Hadoop ecosystem for distributed processing.
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com