Common connectors (JDBC, S3, Elasticsearch) in Kafka - Mini Project: Build & Apply

Using Kafka Connect with Common Connectors (JDBC, S3, Elasticsearch)

📖 Scenario: You work at a company that collects data from various sources and wants to move it efficiently using Kafka. You will learn how to set up Kafka Connect with common connectors to move data from a database, store files in S3, and index data in Elasticsearch.

🎯 Goal: Build a simple Kafka Connect setup that uses the JDBC source connector to read data from a database, the S3 sink connector to save data files, and the Elasticsearch sink connector to index data for search.

📋 What You'll Learn

Create a Kafka Connect configuration for the JDBC source connector

Add a configuration variable for the S3 bucket name

Write the core logic to configure the Elasticsearch sink connector

Print the final combined Kafka Connect configuration

💡 Why This Matters

🌍 Real World

Companies use Kafka Connect to move data between systems like databases, cloud storage, and search engines automatically.

💼 Career

Knowing how to configure Kafka Connect with common connectors is useful for data engineers and backend developers working with streaming data pipelines.

1

Create JDBC Source Connector Configuration

Create a dictionary called jdbc_source_config with these exact entries: "name": "jdbc_source_connector", "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url": "jdbc:postgresql://localhost:5432/mydb", "table.whitelist": "users", "mode": "incrementing", and "incrementing.column.name": "id".

Kafka

# Your code here

Hint

Use a Python dictionary with the exact keys and values given.

2

Add S3 Bucket Configuration Variable

Create a variable called s3_bucket_name and set it to the string "my-kafka-data-bucket".

Kafka

jdbc_source_config = {
    "name": "jdbc_source_connector",
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "connection.url": "jdbc:postgresql://localhost:5432/mydb",
    "table.whitelist": "users",
    "mode": "incrementing",
    "incrementing.column.name": "id"
}
# Your code here

Hint

Assign the exact string to the variable s3_bucket_name.

3

Configure Elasticsearch Sink Connector

Create a dictionary called elasticsearch_sink_config with these exact entries: "name": "elasticsearch_sink_connector", "connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector", "connection.url": "http://localhost:9200", and "topics": "users".

Kafka

jdbc_source_config = {
    "name": "jdbc_source_connector",
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "connection.url": "jdbc:postgresql://localhost:5432/mydb",
    "table.whitelist": "users",
    "mode": "incrementing",
    "incrementing.column.name": "id"
}
s3_bucket_name = "my-kafka-data-bucket"
# Your code here

Hint

Use a dictionary with the exact keys and values given for Elasticsearch sink.

4

Print Final Kafka Connect Configurations

Write a print statement to display the combined dictionary called all_connectors that contains the keys "jdbc_source", "s3_bucket", and "elasticsearch_sink" with values jdbc_source_config, s3_bucket_name, and elasticsearch_sink_config respectively.

Kafka

jdbc_source_config = {
    "name": "jdbc_source_connector",
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "connection.url": "jdbc:postgresql://localhost:5432/mydb",
    "table.whitelist": "users",
    "mode": "incrementing",
    "incrementing.column.name": "id"
}
s3_bucket_name = "my-kafka-data-bucket"
elasticsearch_sink_config = {
    "name": "elasticsearch_sink_connector",
    "connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector",
    "connection.url": "http://localhost:9200",
    "topics": "users"
}
# Your code here

Hint

Create a dictionary with the exact keys and values, then print it.

Practice

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

easy

A. To monitor Kafka cluster health

B. To store Kafka data in cloud object storage

C. To enable full-text search on Kafka data

D. To move data between Kafka and relational databases

Solution

1. Step 1: Understand JDBC connector role

   JDBC connectors are designed to connect Kafka with relational databases using JDBC protocol.

2. Step 2: Match purpose with options

   To move data between Kafka and relational databases correctly states moving data between Kafka and databases, which is the JDBC connector's main use.

3. Final Answer:

   To move data between Kafka and relational databases -> Option D

4. Quick Check:

   JDBC connector = database integration [OK]

Hint: JDBC always relates to databases and Kafka [OK]

Common Mistakes:

• Confusing JDBC with S3 or Elasticsearch connectors
• Thinking JDBC stores data in cloud storage
• Assuming JDBC is for search functionality

2. Which of the following is the correct configuration property to specify the S3 bucket name in a Kafka S3 sink connector?

easy

A. s3.bucket.name

B. bucket.name.s3

C. s3.bucket

D. bucket.s3.name

Solution

1. Step 1: Recall S3 connector config syntax

   The standard property to specify the bucket is s3.bucket.name.

2. Step 2: Compare options with standard property

   Only s3.bucket.name matches the correct property name exactly.

3. Final Answer:

   s3.bucket.name -> Option A

4. Quick Check:

   Bucket name property = s3.bucket.name [OK]

Hint: S3 config keys start with 's3.' prefix [OK]

Common Mistakes:

• Swapping order of words in property name
• Using incorrect prefixes like 'bucket.s3'
• Missing the '.name' suffix

3. Given the following Kafka Elasticsearch sink connector config snippet:

{
  "connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector",
  "topics": "orders",
  "connection.url": "http://localhost:9200",
  "type.name": "_doc",
  "key.ignore": "false"
}

What will happen when a message with key 'order123' and value '{"item":"book"}' is sent to topic 'orders'?

medium

A. The message will be stored in Kafka only, not sent to Elasticsearch

B. The document will be indexed with a random ID ignoring the key

C. The document will be indexed in Elasticsearch with ID 'order123'

D. The connector will fail due to missing authentication

Solution

1. Step 1: Analyze key.ignore setting

   Since "key.ignore" is set to "false", the connector uses the Kafka message key as the Elasticsearch document ID.

2. Step 2: Understand indexing behavior

   The message with key 'order123' will be indexed in Elasticsearch with that key as the document ID.

3. Final Answer:

   The document will be indexed in Elasticsearch with ID 'order123' -> Option C

4. Quick Check:

   key.ignore=false means use key as ID [OK]

Hint: key.ignore=false means use Kafka key as ES document ID [OK]

Common Mistakes:

• Assuming key is ignored by default
• Thinking connector fails without auth (not always true)
• Confusing Kafka storage with Elasticsearch indexing

4. You configured a Kafka JDBC source connector but it is not importing any data. Which of the following is the most likely cause?

medium

A. The Kafka topic name is missing in the connector config

B. The JDBC URL is incorrect or unreachable

C. The connector class is set to ElasticsearchSinkConnector

D. The S3 bucket name is not specified

Solution

1. Step 1: Check connectivity issues

   If the JDBC URL is wrong or unreachable, the connector cannot read data from the database.

2. Step 2: Evaluate other options

   The Kafka topic name is missing in the connector config would cause errors but usually the connector won't start; The connector class is set to ElasticsearchSinkConnector is wrong connector type; The S3 bucket name is not specified is unrelated to JDBC source.

3. Final Answer:

   The JDBC URL is incorrect or unreachable -> Option B

4. Quick Check:

   Bad JDBC URL = no data import [OK]

Hint: Always verify JDBC URL connectivity first [OK]

Common Mistakes:

• Confusing connector class for source vs sink
• Ignoring missing topic config errors
• Checking unrelated S3 settings for JDBC issues

5. You want to archive Kafka topic data to S3 daily and also enable fast search on the same data using Elasticsearch. Which connector setup is the best approach?

hard

A. Use an S3 sink connector for archiving and an Elasticsearch sink connector for search, both consuming from the same Kafka topic

B. Use only an S3 sink connector and query S3 directly for search

C. Use a JDBC sink connector to store data in a database, then export to S3 and Elasticsearch from there

D. Use an Elasticsearch source connector to send data to S3

Solution

1. Step 1: Understand connector roles

   S3 sink connector archives data to cloud storage; Elasticsearch sink connector indexes data for search.

2. Step 2: Choose correct architecture

   Using both sink connectors on the same Kafka topic allows parallel archiving and search capabilities efficiently.

3. Final Answer:

   Use an S3 sink connector for archiving and an Elasticsearch sink connector for search, both consuming from the same Kafka topic -> Option A

4. Quick Check:

   Separate sinks for archive and search = best practice [OK]

Hint: Use separate sink connectors for storage and search [OK]

Common Mistakes:

• Trying to search data directly in S3 without indexing
• Using JDBC sink unnecessarily for this use case
• Confusing source and sink connector roles