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Apache Kafka & Stream Processing Fundamentals · レッスン

変更データキャプチャ（CDC）

Kafkaを変更データキャプチャに活用し、さまざまなユースケースに向けてデータベースの変更をリアルタイムにストリーミングします。

「変更データキャプチャ（CDC）」はCoddyKit上の無料Apache Kafka & Stream Processing Fundamentalsレッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはApache Kafka & Stream Processing Fundamentals学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Apache Kafka & Stream Processing Fundamentalsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

What is Change Data Capture?

Imagine needing to know every time a record in your database is updated, inserted, or deleted, in real-time. This is where Change Data Capture (CDC) comes in!

CDC is a software design pattern used to track and capture changes made to data in a database. It focuses on identifying and capturing only the data that has changed, rather than performing full scans.

Real-Time Data with CDC & Kafka

Combining CDC with Kafka unlocks powerful capabilities for real-time data processing and integration:

  • Real-time Analytics: Update dashboards and reports instantly.
  • Data Synchronization: Keep multiple databases or data stores consistent.
  • Event Sourcing: Reconstruct the full history of changes for auditing or debugging.
  • Microservices: Enable services to react to changes in other services' data without direct database access.

Log-Based CDC Explained

The most common and efficient CDC method, especially with Kafka, is log-based CDC. Databases like PostgreSQL, MySQL, and SQL Server maintain a transaction log (or write-ahead log - WAL).

This log records every change made to the database. Log-based CDC tools read these logs directly, without impacting the database's performance, to extract changes.

CDC Flow to Kafka

A typical CDC architecture with Kafka involves:

  1. Source Database: The database where changes originate.
  2. CDC Connector/Tool: Reads the database's transaction log.
  3. Kafka Connect: A framework for connecting Kafka with other systems.
  4. Kafka Topic: Where the captured change events are published.
  5. Consumers: Applications that read and process the change events from Kafka.

Debezium: Open-Source CDC

Debezium is a popular open-source distributed platform for Change Data Capture. It provides a set of Kafka Connect connectors that monitor specific database systems.

When changes occur in your database, Debezium streams these changes as events to Kafka topics. It supports various databases like PostgreSQL, MySQL, MongoDB, and SQL Server.

Debezium PostgreSQL Connector

To set up Debezium, you'd typically configure a connector via Kafka Connect's REST API. Here's a simplified example of a Debezium PostgreSQL connector configuration:

{
"name": "pg-connector",
"config": {
"connector.class": "io.debezium.connector.postgresql.PostgresConnector",
"database.hostname": "localhost",
"database.port": "5432",
"database.user": "postgres",
"database.password": "password",
"database.dbname": "mydb",
"topic.prefix": "dbserver1",
"table.include.list": "public.customers"
}
}

This config tells Debezium to monitor the mydb database on localhost:5432 and send changes from the public.customers table to Kafka topics prefixed with dbserver1.

What a CDC Event Looks Like

When Debezium captures a change, it publishes an event to Kafka. This event usually contains structured information:

  • before: The state of the record before the change (for updates/deletes).
  • after: The state of the record after the change (for inserts/updates).
  • op: The operation type (c for create, u for update, d for delete, r for read/snapshot).
  • source: Metadata about the database, table, and transaction.

These events are often serialized as JSON or Avro.

Processing Change Events

A Kafka consumer application can read these CDC events and react to them. For instance, you might update a search index, invalidate a cache, or trigger another microservice.

Here's a basic Java consumer example illustrating how you might parse a Debezium event:

public class CdcConsumer {
  public static void main(String[] args) {
    // This is a simplified example.
    // In reality, use KafkaConsumer and JSON/Avro parsing.

    String jsonEvent = "{ \"payload\": { \"op\": \"c\", \"after\": { \"id\": 1, \"name\": \"Alice\" } } }";
    
    // Imagine parsing jsonEvent here
    String operationType = getOperationFromJson(jsonEvent, "op");
    String newName = getOperationFromJson(jsonEvent, "name");

    if ("c".equals(operationType)) {
      System.out.println("New customer created: " + newName);
    } else if ("u".equals(operationType)) {
      System.out.println("Customer updated: " + newName);
    }
  }

  // Placeholder for JSON parsing logic
  private static String getOperationFromJson(String json, String key) {
    if (key.equals("op")) return "c"; // Simulate 'c' operation
    if (key.equals("name")) return "Alice"; // Simulate 'name'
    return null;
  }
}

Practical CDC Use Cases

CDC with Kafka is incredibly versatile. Some common use cases include:

  • Data Warehousing: Populate data warehouses with only changed data for efficient ETL (Extract, Transform, Load).
  • Cache Invalidation: Automatically clear or update caches when underlying data changes.
  • Audit Logs: Maintain a complete, immutable history of all data changes for compliance.
  • Search Indexing: Keep search indexes (e.g., Elasticsearch) up-to-date with real-time database changes.

CDC Knowledge Check

Which of the following is a primary benefit of using log-based Change Data Capture (CDC) with Kafka?

CDC with Kafka: A Powerful Pattern

We've explored how Change Data Capture (CDC) is a powerful pattern for streaming database changes in real-time to Kafka. Using tools like Debezium, you can efficiently capture inserts, updates, and deletes from your databases.

This enables a wide array of real-time use cases, from data synchronization and analytics to event sourcing and microservice communication. Understanding CDC is key to building responsive, data-driven applications with Kafka.

よくある質問

「変更データキャプチャ（CDC）」レッスンは無料ですか？

はい。「変更データキャプチャ（CDC）」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Apache Kafka & Stream Processing Fundamentalsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Apache Kafka & Stream Processing Fundamentalsコースには全4レッスンが含まれています。

「変更データキャプチャ（CDC）」で何を学びますか？

Kafkaを変更データキャプチャに活用し、さまざまなユースケースに向けてデータベースの変更をリアルタイムにストリーミングします。 ブラウザで直接実行するハンズオンコードでApache Kafka & Stream Processing Fundamentalsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

Apache Kafka & Stream Processing Fundamentalsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのApache Kafka & Stream Processing Fundamentalsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。

「変更データキャプチャ（CDC）」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このApache Kafka & Stream Processing Fundamentalsレッスンでコードを書いて実行できますか？

はい。すべてのApache Kafka & Stream Processing Fundamentalsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. Kafkaによるイベントソーシング
  2. 変更データキャプチャ（CDC）
  3. マイクロサービスの通信パターン
  4. 信頼性の高いイベント発行のためのアウトボックスパターン
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