Filter and map operations in Kafka - Commands & Configuration

Introduction

Kafka streams allow you to process data in real time by filtering unwanted messages and transforming data into a new form. This helps you focus only on the important information and change it as needed while it flows through your system.

When you want to keep only messages with specific values from a stream of data.

When you need to change the format or content of messages before sending them to another system.

When you want to remove noisy or irrelevant data from your processing pipeline.

When you want to enrich or simplify data by applying a function to each message.

When you want to build real-time dashboards that show only filtered and transformed data.

Config File - filter_map_streams.properties

filter_map_streams.properties

application.id=filter-map-app
bootstrap.servers=localhost:9092
key.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
value.serde=org.apache.kafka.common.serialization.Serdes$StringSerde

This configuration file sets up the Kafka Streams application with a unique application ID and connects it to the Kafka server running locally. It also defines how keys and values in messages are converted to and from bytes using string serializers and deserializers.

Commands

Create an input topic where raw messages will be sent for processing.

Terminal

kafka-topics --create --topic input-topic --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1

Expected Output

Created topic input-topic.

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--topic - Name of the Kafka topic to create

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--partitions - Number of partitions for the topic

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--replication-factor - Number of copies of the data for fault tolerance

Start a producer to send messages to the input topic. You can type messages here to test filtering and mapping.

Terminal

kafka-console-producer --topic input-topic --bootstrap-server localhost:9092

Expected Output

No output (command runs silently)

→

--topic - Topic to send messages to

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--bootstrap-server - Kafka server address

Run the Kafka Streams application that filters messages containing the word 'error' and converts messages to uppercase.

Terminal

java -cp kafka-streams-examples.jar org.apache.kafka.streams.examples.filtermap.FilterMapExample filter_map_streams.properties

Expected Output

Starting FilterMapExample

Topology:

... (topology details) ...

Streams started

Consume messages from the output topic to see the filtered and transformed results.

Terminal

kafka-console-consumer --topic output-topic --bootstrap-server localhost:9092 --from-beginning

Expected Output

ERROR: DISK FAILURE

WARNING: CPU HIGH

INFO: SYSTEM OK

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--topic - Topic to read messages from

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--from-beginning - Read all messages from the start

Key Concept

If you remember nothing else from this pattern, remember: filtering removes unwanted messages and mapping changes message content as data flows through Kafka Streams.

Common Mistakes

Not setting the correct serializers and deserializers in the configuration.

Kafka Streams cannot convert messages properly, causing errors or data loss.

Always specify matching key and value serdes for your message types in the properties file.

Trying to filter or map on the wrong topic or without starting the streams application.

No processing happens, so output topic remains empty or unchanged.

Make sure to create and send messages to the input topic and run the streams app before consuming output.

Summary

Create an input Kafka topic to receive raw messages.

Run a Kafka Streams app configured to filter and map messages.

Send messages to the input topic and consume processed messages from the output topic.