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Posted on Mar 18

Study Notes 6.11-12: Kafka ksqlDB, Connect & Schema Registry

#dataengineering #dezoomcamp #kafka #sql

1. Overview of Kafka ksqlDB & Kafka Connect

ksqlDB:

ksqlDB is Kafka’s SQL-based stream processing engine.
It allows you to perform real-time analysis and ad hoc queries on Kafka topics using SQL-like syntax.
Ideal for rapid prototyping, testing, and even certain production analytics—but for complex, long-lived applications, consider using the Java Streams API for enhanced maintainability.

Kafka Connect:

Kafka Connect is a framework to reliably and scalably ingest data into Kafka (source connectors) or push data out of Kafka to external systems (sink connectors).
It offers a rich ecosystem of pre-built connectors (for databases, cloud storage, Elasticsearch, etc.) and is available both in open source and through managed services like Confluent Cloud.

2. ksqlDB: Key Concepts and Usage

a. Interactive Querying and Prototyping

Quick Analysis:
ksqlDB provides a user-friendly interface to run SQL queries on your Kafka streams.

Example:

CREATE STREAM rides_stream AS
  SELECT render_id, trip_distance, payment_type, passenger_count
  FROM rides_topic
  EMIT CHANGES;

You can then run queries like:

SELECT * FROM rides_stream EMIT CHANGES;

Persistent Queries:
- You can create persistent queries (tables) that continuously aggregate or transform data.
- For instance, grouping by payment type to get counts over time:
```
CREATE TABLE payment_counts AS
  SELECT payment_type, COUNT(*) AS cnt
  FROM rides_stream
  GROUP BY payment_type
  EMIT CHANGES;
```
- Persistent queries create internal topics that hold the state, which can be used for dashboards or further processing.

b. Production Considerations

Maintainability: Although ksqlDB is excellent for rapid experimentation, in production you might want to use the Java Streams API with explicit version control over deployed SQL commands.
Deployment: ksqlDB requires a separate cluster (or managed service) distinct from your primary Kafka Streams cluster. This introduces additional maintenance and cost considerations.
Usage Scenarios:
- Quick data exploration and proof-of-concepts.
- Building real-time dashboards.
- Ad hoc stream processing for monitoring or debugging.

3. Kafka Connect: Key Concepts and Usage

a. Purpose and Functionality

Data Integration: Kafka Connect provides an easy way to ingest data from external sources (like databases, cloud storage, or files) into Kafka topics or export Kafka data to external systems.
Connector Ecosystem:
- Numerous connectors are available out of the box (e.g., for Elasticsearch, S3, JDBC, Snowflake).
- Both Confluent Cloud and the open-source Kafka Connect offer various connector plugins.

b. Setting Up and Configuration

Configuration Flow:
- In Connect, you define a connector configuration, specifying the topic, connection parameters (e.g., URI, credentials), and data formats.
- The Connect worker manages the lifecycle of connectors, scaling them based on throughput.
Example Scenario: For instance, to export data to Elasticsearch, you would:
1. Create a connector configuration (typically via REST API or UI).
2. Specify the Kafka topic to read from, along with connection details for your Elasticsearch cluster.
3. Once the connector is deployed, data flows continuously from Kafka to Elasticsearch.

c. Production Considerations

Resource Requirements:
- Running Kafka Connect may require additional infrastructure, particularly if you need custom connectors or need to scale out ingestion/export.
Connector Management:
- Ensure proper monitoring and error handling. Many connectors support automatic retries, but you should be prepared to manage schema evolution and data format changes.
Integration:
- For managed deployments (e.g., Confluent Cloud), many connectors are available as a service, reducing operational overhead.

4. Integrating ksqlDB and Kafka Connect

a. Complementary Roles

ksqlDB for In-Stream Analytics: Use ksqlDB to query, transform, and aggregate stream data on the fly. It offers immediate insights and testing of your Kafka data.
Kafka Connect for Data Movement: Use Kafka Connect to bring data into Kafka (from databases, logs, etc.) or to push data out of Kafka (to dashboards, storage systems, search engines).

b. Example Workflow

Data Ingestion:
- Use a Kafka Connect source connector to ingest data from a SQL database into a Kafka topic.
Real-Time Analysis:
- Use ksqlDB to create streams and tables from the ingested data, applying filtering, aggregation, or joins.
Data Export:
- Use a Kafka Connect sink connector to export processed results (e.g., aggregated metrics) to an external system like Elasticsearch or a data warehouse.

5. Best Practices and Considerations

a. For ksqlDB

Use for Prototyping and Quick Insights: Leverage ksqlDB for interactive data exploration and simple aggregations.
Monitor Persistent Queries: Persistent queries create internal state topics—monitor these for lag and resource usage.
Plan for Production: Consider transitioning complex processing logic to the Java Streams API if your application grows in complexity.

b. For Kafka Connect

Connector Management: Regularly review connector configurations and monitor connector health. Use Kafka Connect REST APIs to manage and scale connectors.
Data Format Consistency: Ensure that your connectors are configured with consistent data formats (e.g., JSON, Avro) to prevent schema mismatches.
Scalability: Plan for scaling Connect workers if your data throughput increases.

c. Integration Strategies

End-to-End Testing: Test your pipelines by simulating both ingestion (via Connect) and transformation/analysis (via ksqlDB). Validate that data flows correctly between systems.
Resource Allocation: Understand the resource implications of running multiple clusters (ksqlDB and Kafka Connect). Consider managed services if operational overhead is a concern.
Cost Management: For proof-of-concept or prototyping, managed services like Confluent Cloud can reduce complexity, but evaluate cost implications for production workloads.

1. Overview Kafka Schema Registry

Purpose:

The Kafka Schema Registry is a centralized service that manages and enforces schemas for Kafka data. It ensures that producers and consumers “speak the same language” by maintaining a contract (schema) for the data format.
Why It’s Important:
- Compatibility: Prevents errors when the data format evolves over time.
- Contract Enforcement: Acts as a dictionary that both producers and consumers use to encode and decode messages.
- Data Integrity: Helps ensure that changes in the data format (e.g., field types or structures) do not break existing applications.

2. The Problem It Solves

Producer–Consumer Mismatch: If a producer changes the message format (for example, changing a field type from string to integer) without coordinating with consumers, it can break data processing downstream.
Schema Evolution Issues: Applications often need to evolve the data schema over time. Without a formal schema, such changes can lead to incompatibilities and runtime failures.

3. How Schema Registry Works

a. Schema Registration and Retrieval

Registration:
- When a producer sends data, it first registers its schema with the Schema Registry.
- The registry assigns a unique identifier (ID) to the schema.
- This schema ID is then embedded in each message, so that consumers can retrieve the schema and decode the message correctly.
Consumption:
- Consumers use the schema ID in the message to fetch the corresponding schema from the registry.
- The retrieved schema acts as a “contract” that tells the consumer how to deserialize the message.

b. Schema Compatibility and Evolution

Compatibility Levels: The Schema Registry supports several compatibility modes:
- Backward Compatibility: New schemas can read data produced with old schemas. (E.g., if a new field is added with a default value, older messages are still readable.)
- Forward Compatibility: Data written with an old schema can be read by consumers using a new schema.
- Full Compatibility: Combines both backward and forward compatibility—any schema version can work with any other.
- None: No compatibility is enforced.
Schema Evolution:
- Producers may need to evolve the schema over time. For instance, a producer might change a field type or add new fields.
- The Schema Registry enforces the defined compatibility rules and rejects any schema changes that would break compatibility.
- This allows you to make enhancements without disrupting existing consumers.

4. Avro and Other Supported Formats

Avro:
- Avro is the most commonly used serialization format with Schema Registry.

- **Features:**
    - Compact binary encoding for efficiency.
    - Schemas defined in JSON format, which makes them human-readable.
    - Supports complex data types (e.g., unions, arrays, maps, enums) and features like default values.
- **Usage:**
Avro’s built-in support for schema evolution (through its compatibility rules) makes it ideal for dynamic, high-volume Kafka environments.

Other Formats:
- JSON Schema and Protobuf are also supported, each with its own advantages depending on the use case.

5. Schema Registry in Practice

a. Integration with Kafka

Producer Side:
- Producers use Avro (or other) serializers that are integrated with the Schema Registry.
- When a producer sends a message, the serializer registers the schema (if not already registered) and writes the schema ID into the message payload.
Consumer Side:
- Consumers use corresponding deserializers that read the schema ID from the message and retrieve the schema to deserialize the data.
Example Workflow:
1. Producer registers schema → gets schema ID.
2. Producer sends message (with embedded schema ID) to a Kafka topic.
3. Consumer reads message → uses schema ID to fetch schema from registry.
4. Consumer deserializes message using the schema.

b. Tools and Plugins

Gradle/Maven Plugins:
- Use plugins to auto-generate Avro classes from .avsc files. This simplifies schema management and code generation.
- When changes are made to the schema file, re-running the build regenerates the classes, ensuring that producers and consumers are using the latest contract.
Schema Registry UI:
- Many schema registry implementations (e.g., Confluent Schema Registry) provide a user interface to view registered schemas, compare versions, and manage compatibility settings.

6. Best Practices

Define Compatibility Settings:
- Set an appropriate compatibility level (backward, forward, full) based on your application’s needs.
- For instance, “full” compatibility is ideal when you need to ensure that all schema versions can interoperate.
Avoid Incompatible Changes:
- Changes such as renaming fields, changing field types without defaults, or removing required fields are typically disallowed.
- Plan schema changes carefully to maintain consumer compatibility.
Version Management:
- Treat the schema as a versioned contract. Use schema evolution strategies to add new fields (with default values) or deprecate old fields gradually.
Testing Schema Evolution:
- Simulate schema evolution in a test environment to ensure that producers and consumers remain compatible.
- Use tools like the TopologyTestDriver (for Kafka Streams) to verify that changes do not break downstream processing.
Documentation and Communication:
- Document your schema evolution strategy and communicate changes with all teams consuming the data.
- Maintain clear version histories to trace changes and ensure proper rollback if needed.

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