• Kafka

  • Topics vs ksqlDB vs Connectors vs Clients

Topics vs ksqlDB vs Connectors vs Clients

🔍 Difference Between Topics vs ksqlDB vs Connectors vs Clients in Confluent Kafka

Confluent Kafka provides various tools for event streaming and real-time data processing. The key components include Topics, ksqlDB, Connectors, and Clients, each serving a distinct role.

1️⃣ Topics (Core Kafka Component)

📌 What Are Kafka Topics?

  • Kafka Topics are logical channels where messages (events) are published by producers and consumed by consumers.
  • They act as distributed logs storing ordered events.
  • Producers write data to a topic, and consumers read from it.

✅ Use Case of Topics

  • Used for event-driven applications.
  • Acts as a buffer between producers and consumers.
  • Example: A payment service publishes transactions to a "payments" topic, and a fraud detection service consumes those events.

🚀 Key Features

  • Supports multiple partitions for parallelism.
  • Retains messages based on retention policies (time-based or size-based).
  • Enables real-time streaming and event sourcing.

2️⃣ ksqlDB (Real-time SQL Processing on Kafka)

📌 What Is ksqlDB?

  • ksqlDB is a SQL engine for Kafka that allows users to query and process real-time streaming data without writing Java/Python code.
  • It enables stateful stream processing on Kafka topics using SQL-like queries.

✅ Use Case of ksqlDB

  • Real-time data transformation, aggregation, filtering, and joins across Kafka topics.
  • Example: If a topic contains raw clickstream data, you can use ksqlDB to filter only clicks from India and write the output to another topic.

🚀 Key Features

  • SQL-based stream processing (easier than Java/Python Kafka Streams API).
  • Real-time analytics on Kafka events.
  • Stateful computations (aggregations, windows, joins).
  • Materialized views for fast querying.

🔹 Example Query in ksqlDB

CREATE STREAM filtered_users AS
SELECT user_id, page_url FROM user_clicks
WHERE country = 'India';

3️⃣ Kafka Connectors (Data Integration Layer)

📌 What Are Kafka Connectors?

  • Kafka Connect provides pre-built connectors to integrate Kafka with external systems (databases, cloud storage, etc.).
  • Types:
    1. Source Connector → Brings data into Kafka from databases, APIs, etc.
    2. Sink Connector → Sends data from Kafka to databases, Elasticsearch, S3, etc..

✅ Use Case of Kafka Connect

  • Moves data from an external database to Kafka (CDC) or from Kafka to a data warehouse.
  • Example:
    • A JDBC Source Connector reads customer data from MySQL and sends it to a Kafka topic.
    • A S3 Sink Connector stores Kafka topic data in Amazon S3.

🚀 Key Features

  • Pre-built connectors for MySQL, PostgreSQL, MongoDB, AWS S3, Elasticsearch, etc.
  • Scalable & distributed data movement.
  • Fault-tolerant & auto-retry mechanisms.

🔹 Example Connector Configuration (JDBC Source)

{
  "name": "jdbc-source",
  "config": {
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "connection.url": "jdbc:mysql://localhost:3306/mydb",
    "topic.prefix": "mysql-data-"
  }
}

4️⃣ Kafka Clients (Producers & Consumers)

📌 What Are Kafka Clients?

  • Kafka Clients are the applications that produce or consume messages from Kafka topics.
  • Clients use Kafka APIs to interact with topics.

✅ Use Case of Kafka Clients

  • Producers send data to Kafka topics.
  • Consumers read data from Kafka topics.
  • Example: A Python application writes stock prices to Kafka, and a Java application reads and processes them.

🚀 Key Features

  • Available in multiple languages: Java, Python, Go, C++, .NET.
  • Supports batch & real-time data processing.
  • Uses consumer groups for parallel processing.

🔹 Example Python Producer (Kafka Client)

from kafka import KafkaProducer

producer = KafkaProducer(bootstrap_servers='localhost:9092')
producer.send('stock-prices', key=b'TSLA', value=b'900')

🔹 Example Java Consumer (Kafka Client)

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("stock-prices"));
while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        System.out.println(record.value());
    }
}

📊 Summary Table: Topics vs ksqlDB vs Connectors vs Clients

| Component | Purpose | Best For | Example |
|--------------|------------|--------------|-------------|
| Kafka Topics | Message bus for streaming data | Event-driven architectures | A "payments" topic storing transactions. |
| ksqlDB | SQL-based stream processing | Real-time transformations & aggregations | Aggregating real-time sales per country. |
| Kafka Connectors | Integration with external systems | Moving data in & out of Kafka | MySQL to Kafka, Kafka to Elasticsearch. |
| Kafka Clients | Producers & Consumers of Kafka messages | Custom applications interacting with Kafka | A Python service sending logs to Kafka. |

🚀 When to Use What?

✅ Use Kafka Topics:

  • If you need a scalable message queue for real-time streaming.

✅ Use ksqlDB:

  • If you want to process, transform, and filter Kafka streams using SQL.

✅ Use Kafka Connectors:

  • If you need to move data between Kafka and databases/cloud storage.

✅ Use Kafka Clients:

  • If you need custom producers and consumers in Java, Python, etc.