Why Source connectors in Kafka? - Purpose & Use Cases

The Big Idea

What if you could stop writing endless code just to get your data flowing into Kafka?

The Scenario

Imagine you have data scattered across many places like databases, files, or apps, and you want to bring all that data into Kafka to process it. Doing this by hand means writing lots of custom code to connect, read, and send data continuously.

The Problem

Manually coding each connection is slow and tricky. You might miss updates, lose data, or create bugs. Every time the source changes, you must fix your code. It's like trying to carry water with a leaky bucket--inefficient and frustrating.

The Solution

Source connectors automate this work. They are ready-made tools that connect to your data sources and stream data into Kafka reliably and continuously. You just configure them once, and they handle the rest, saving time and avoiding errors.

Before vs After

✗ Before

while True:
    data = read_from_database()
    send_to_kafka(data)
    sleep(5)

✓ After

configure_source_connector({"name": "db-connector", "tasks.max": 1, "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url": "jdbc:mysql://..."})

What It Enables

Source connectors let you easily and reliably stream data from many systems into Kafka, unlocking real-time data processing and integration.

Real Life Example

A company wants to stream new sales records from their SQL database into Kafka to update dashboards instantly. Using a source connector, they set it up once and get live sales data flowing without writing extra code.

Key Takeaways

Manually connecting data sources to Kafka is slow and error-prone.

Source connectors automate and simplify streaming data into Kafka.

This enables real-time data integration and faster insights.

Practice

1. What is the main purpose of a Kafka source connector?

easy

A. To automatically import data from external systems into Kafka topics

B. To export data from Kafka topics to external systems

C. To monitor Kafka cluster health

D. To manage Kafka topic partitions

Solution

1. Step 1: Understand the role of source connectors

   Source connectors are designed to bring data from outside systems into Kafka topics automatically.

2. Step 2: Differentiate from sink connectors

   Sink connectors do the opposite by exporting data from Kafka to external systems, so they are not the correct answer here.

3. Final Answer:

   To automatically import data from external systems into Kafka topics -> Option A

4. Quick Check:

   Source connector = import data [OK]

Hint: Source connectors bring data into Kafka topics [OK]

Common Mistakes:

• Confusing source connectors with sink connectors
• Thinking connectors manage Kafka internals like partitions
• Assuming connectors monitor Kafka health

2. Which of the following is the correct JSON field to specify the connector class in a Kafka source connector configuration?

easy

A. "sourceConnector"

B. "source.class"

C. "connectorType"

D. "connector.class"

Solution

1. Step 1: Recall Kafka Connect configuration syntax

   The key to specify the connector implementation class is always "connector.class" in the JSON config.

2. Step 2: Eliminate incorrect options

   Other options like "source.class", "connectorType", or "sourceConnector" are not valid Kafka Connect configuration keys.

3. Final Answer:

   "connector.class" -> Option D

4. Quick Check:

   Connector class key = "connector.class" [OK]

Hint: Use "connector.class" to specify connector type [OK]

Common Mistakes:

• Using incorrect or made-up JSON keys
• Confusing source connector config keys with sink connector keys
• Omitting quotes around JSON keys

3. Given this Kafka Connect source connector configuration snippet:

{
  "name": "my-source-connector",
  "connector.class": "FileStreamSource",
  "tasks.max": "1",
  "file": "/tmp/input.txt",
  "topic": "test-topic"
}

What will happen when this connector runs?

medium

A. It fails because 'tasks.max' must be an integer, not a string

B. It reads data from /tmp/input.txt and writes it to the Kafka topic 'test-topic'

C. It creates a new Kafka topic named '/tmp/input.txt'

D. It writes data from Kafka topic 'test-topic' to /tmp/input.txt

Solution

1. Step 1: Identify connector type and purpose

   The "FileStreamSource" connector reads data from a file and sends it to a Kafka topic.

2. Step 2: Understand configuration keys

   "file" specifies the input file, "topic" specifies the Kafka topic to write to, and "tasks.max" as a string is acceptable in JSON configs.

3. Final Answer:

   It reads data from /tmp/input.txt and writes it to the Kafka topic 'test-topic' -> Option B

4. Quick Check:

   FileStreamSource reads file to topic [OK]

Hint: FileStreamSource reads file into Kafka topic [OK]

Common Mistakes:

• Confusing source and sink connectors
• Thinking 'tasks.max' must be integer type (string is valid in JSON)
• Assuming connector creates topics automatically

4. You configured a source connector with this JSON:

{
  "name": "my-connector",
  "connector.class": "JdbcSourceConnector",
  "tasks.max": 2,
  "connection.url": "jdbc:mysql://localhost:3306/mydb",
  "topic.prefix": "db-"
}

But the connector fails to start. What is the most likely cause?

medium

A. Missing required property 'mode' for JdbcSourceConnector

B. Incorrect 'tasks.max' value; must be a string

C. The 'topic.prefix' should be 'topic.name' instead

D. The 'connection.url' must use PostgreSQL syntax

Solution

1. Step 1: Review JdbcSourceConnector required properties

   JdbcSourceConnector requires a 'mode' property to specify how to query the database (e.g., 'incrementing', 'timestamp+incrementing').

2. Step 2: Check other properties

   'tasks.max' as integer is valid, 'topic.prefix' is correct, and 'connection.url' can be MySQL syntax if using MySQL.

3. Final Answer:

   Missing required property 'mode' for JdbcSourceConnector -> Option A

4. Quick Check:

   JdbcSourceConnector needs 'mode' property [OK]

Hint: JdbcSourceConnector requires 'mode' property [OK]

Common Mistakes:

• Assuming 'tasks.max' must be string
• Confusing 'topic.prefix' with 'topic.name'
• Thinking connection URL must be PostgreSQL always

5. You want to ingest data from multiple CSV files located in a directory into Kafka using a source connector. Which approach best applies?

hard

A. Use JdbcSourceConnector with CSV files as database tables

B. Use the FileStreamSource connector with the directory path as 'file' property

C. Use a FilePulse source connector configured with the directory path to read all CSV files

D. Manually write a Kafka producer to read files and send data

Solution

1. Step 1: Identify connector capabilities

   FilePulse source connector supports reading multiple files from a directory and processing CSV files.

2. Step 2: Evaluate other options

   FileStreamSource reads only a single file, JdbcSourceConnector is for databases, and manual coding is not using source connectors.

3. Final Answer:

   Use a FilePulse source connector configured with the directory path to read all CSV files -> Option C

4. Quick Check:

   FilePulse reads multiple files from directory [OK]

Hint: FilePulse connector handles multiple files ingestion [OK]

Common Mistakes:

• Using FileStreamSource for multiple files
• Trying to use JdbcSourceConnector for CSV files
• Ignoring built-in connectors and coding manually