Pavel on AsyncAPI

Appearance

Apache Kafka Protocol Bindings ​

Apache Kafka is a distributed streaming platform that enables you to build real-time data pipelines and streaming applications. AsyncAPI provides detailed bindings for Kafka, allowing you to specify how your event-driven APIs interact with Kafka clusters.

What is Apache Kafka? ​

Kafka is designed for high-throughput, fault-tolerant, and scalable event streaming. Its key features include:

  • Publish-subscribe messaging: Producers publish messages to topics, and consumers subscribe to those topics.
  • Distributed and fault-tolerant: Data is replicated across multiple brokers for high availability.
  • Scalability: Kafka can handle trillions of events per day.
  • Data persistence: Messages are stored on disk for a configurable period.
  • Real-time processing: Kafka Streams and ksqlDB enable real-time data processing.

AsyncAPI Kafka Bindings Overview ​

AsyncAPI Kafka bindings define how your API specification maps to Kafka concepts:

Binding Types ​

Binding TypePurposeDescription
Channel BindingDefines Kafka-specific channel configurations.Specifies how channels map to Kafka topics, including partitions, replicas, and configuration settings.
Operation BindingConfigures message publishing and consumption.Defines consumer group IDs, client IDs, and other operational parameters.
Message BindingSpecifies message-level configurations.Defines the message key and schema ID lookups in a Schema Registry.
Server BindingDefines Kafka-specific server configurations.Specifies the Schema Registry URL and other cluster-level properties.

Supported Versions ​

VersionStatusKey Features
0.5.0LatestFull feature set, including topic configurations.
0.4.0StableIntroduces topic configuration properties.
0.3.0StableAdds schema look up from schema registry.
0.1.0LegacyBasic support for Kafka bindings.

Key Kafka Concepts ​

Topics and Partitions ​

  • Topics: Categories or feeds to which messages are published.
  • Partitions: Topics are divided into partitions, allowing for parallelism and scalability. Each message in a partition has a unique offset.

Producers and Consumers ​

  • Producers: Publish messages to Kafka topics.
  • Consumers: Subscribe to topics and process the messages. Consumers are organized into consumer groups.

Brokers and Clusters ​

  • Brokers: Kafka runs as a cluster of one or more brokers.
  • Clusters: A Kafka cluster manages the persistence and replication of message data.

Schema Registry ​

  • Schema Registry: Manages schemas for message data, ensuring data compatibility between producers and consumers.

Use Cases ​

Kafka bindings are ideal for:

Event-Driven Architectures ​

  • Microservices Communication: Asynchronous communication between services.
  • Event Sourcing: Storing and replaying domain events.
  • CQRS: Separating read and write models.

Real-Time Data Pipelines ​

  • Log Aggregation: Collecting and processing logs from multiple sources.
  • IoT Data Processing: Ingesting and analyzing data from IoT devices.
  • ETL and Data Integration: Building real-time data pipelines.

Streaming Analytics ​

  • Fraud Detection: Real-time analysis of transactions to detect fraud.
  • Real-Time Monitoring: Monitoring application and system metrics.
  • Personalization: Providing real-time recommendations to users.

Getting Started ​

Basic Channel Configuration ​

yaml
channels:
  user-events:
    bindings:
      kafka:
        topic: user-events-topic
        partitions: 10
        replicas: 3
        topicConfiguration:
          cleanup.policy: "delete"
          retention.ms: 604800000
        bindingVersion: '0.5.0'

Basic Operation Configuration ​

yaml
operations:
  onUserEvent:
    bindings:
      kafka:
        groupId: 
          type: string
          enum: ["user-events-group"]
        clientId:
          type: string
          enum: ["user-events-client"]
        bindingVersion: '0.5.0'

Basic Message Configuration ​

yaml
messages:
  UserEvent:
    bindings:
      kafka:
        key:
          type: string
          description: "The user ID."
        schemaIdLocation: "payload"
        schemaIdPayloadEncoding: "apicurio-registry-binary"
        schemaLookupStrategy: "TopicIdStrategy"
        bindingVersion: '0.5.0'

Basic Server Configuration ​

yaml
servers:
  production:
    bindings:
      kafka:
        schemaRegistryUrl: "https://my-schema-registry.com"
        schemaRegistryVendor: "confluent"
        bindingVersion: '0.5.0'

Version Migration Guide ​

From 0.1.0 to 0.3.0 ​

  • Added schemaIdLocation, schemaIdPayloadEncoding and schemaLookupStrategy to message bindings.
  • Added schemaRegistryUrl and schemaRegistryVendor to server bindings.

From 0.3.0 to 0.4.0 ​

  • Added topicConfiguration to channel bindings.

From 0.4.0 to 0.5.0 ​

  • No breaking changes.
  • Improved documentation and examples.

Best Practices ​

Topic Management ​

  • Use a consistent naming convention for topics.
  • Choose an appropriate number of partitions to balance load and throughput.
  • Configure retention policies based on your data retention requirements.

Message Design ​

  • Use a message key to ensure messages with the same key are sent to the same partition.
  • Use a Schema Registry to manage schemas and ensure data compatibility.

Consumer Group Management ​

  • Use consumer groups to enable parallel processing of messages.
  • Monitor consumer lag to ensure consumers are keeping up with producers.

Binding Documentation ​

Channel Bindings ​

Operation Bindings ​

Message Bindings ​

Server Bindings ​