Common connectors (JDBC, S3, Elasticsearch) in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the connector class for JDBC source.

Kafka

connector.class=[1]

Drag options to blanks, or click blank then click option'

Aio.confluent.connect.jdbc.JdbcSourceConnector

Borg.apache.kafka.connect.file.FileStreamSourceConnector

Cio.confluent.connect.s3.S3SinkConnector

Dorg.apache.kafka.connect.mirror.MirrorSourceConnector

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a sink connector instead of a source connector.

Selecting a connector for S3 or file instead of JDBC.

The JDBC source connector class is io.confluent.connect.jdbc.JdbcSourceConnector. It allows Kafka Connect to read data from a relational database.

2fill in blank

medium

Complete the code to specify the S3 sink connector class.

Kafka

connector.class=[1]

Drag options to blanks, or click blank then click option'

Aio.confluent.connect.elasticsearch.ElasticsearchSinkConnector

Bio.confluent.connect.jdbc.JdbcSinkConnector

Corg.apache.kafka.connect.file.FileStreamSinkConnector

Dio.confluent.connect.s3.S3SinkConnector

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing S3 sink with JDBC sink.

Choosing a source connector instead of a sink.

The S3 sink connector class is io.confluent.connect.s3.S3SinkConnector. It writes Kafka data to Amazon S3 storage.

3fill in blank

hard

Fix the error in the connector class for Elasticsearch sink.

Kafka

connector.class=[1]

Drag options to blanks, or click blank then click option'

Aio.confluent.connect.s3.S3SinkConnector

Bio.confluent.connect.elasticsearch.ElasticsearchSinkConnector

Corg.apache.kafka.connect.elasticsearch.ElasticsearchSinkConnector

Dio.confluent.connect.elasticsearch.ElasticsearchSourceConnector

Attempts:

3 left

💡 Hint

Common Mistakes

Using a source connector class which does not exist.

Using a connector class from the wrong package.

The correct class for Elasticsearch sink connector is io.confluent.connect.elasticsearch.ElasticsearchSinkConnector. There is no official Elasticsearch source connector in this package.

4fill in blank

hard

Fill both blanks to configure the JDBC connector to connect to a MySQL database.

Kafka

connection.url=[1]
connection.user=[2]

Drag options to blanks, or click blank then click option'

Ajdbc:mysql://localhost:3306/mydb

Bjdbc:postgresql://localhost:5432/mydb

Croot

Dadmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using PostgreSQL URL for MySQL connection.

Using wrong username for MySQL.

The JDBC URL for MySQL looks like jdbc:mysql://localhost:3306/mydb. The username is often root for local MySQL setups.

5fill in blank

hard

Fill all three blanks to configure the Elasticsearch sink connector with topic and connection details.

Kafka

topics=[1]
connection.url=[2]
index.name.format=[3]

Drag options to blanks, or click blank then click option'

Amy_topic

Bhttp://localhost:9200

Cmy_index

Dkafka_topic

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing topic name with index name.

Using incorrect Elasticsearch URL format.

The topics property specifies the Kafka topic to read from. The connection.url is the Elasticsearch server address. The index.name.format configures the target index name in Elasticsearch.

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