Stream topology in Kafka - Mini Project: Build & Apply

Stream Topology with Kafka Streams

📖 Scenario: You are working on a simple Kafka Streams application that processes user activity data. The data comes as key-value pairs where the key is a user ID and the value is the activity type. You want to build a stream topology that reads from an input topic, processes the data, and writes to an output topic.

🎯 Goal: Build a Kafka Streams topology that reads from the user-activity-input topic, processes the stream by filtering only login activities, and writes the filtered stream to the user-login-output topic.

📋 What You'll Learn

Create a Kafka Streams builder

Define a stream from the user-activity-input topic

Filter the stream to keep only records where the value is login

Write the filtered stream to the user-login-output topic

💡 Why This Matters

🌍 Real World

Kafka Streams is widely used for real-time data processing in applications like monitoring user activity, fraud detection, and event-driven microservices.

💼 Career

Understanding stream topology is essential for roles involving real-time data engineering, backend development, and building scalable event-driven systems.

1

Create the Kafka Streams builder and input stream

Create a StreamsBuilder object called builder. Then create a stream called userActivityStream from the topic "user-activity-input" using builder.stream("user-activity-input").

Kafka

import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

// Your code here

Hint

Use StreamsBuilder to create the builder. Then use builder.stream("user-activity-input") to get the stream.

2

Add a filter to keep only login activities

Create a new stream called loginStream by filtering userActivityStream to keep only records where the value equals "login". Use filter((key, value) -> value.equals("login")).

Kafka

import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

StreamsBuilder builder = new StreamsBuilder();
KStream userActivityStream = builder.stream("user-activity-input");

// Your code here

Hint

Use the filter method on userActivityStream with a lambda that checks if value.equals("login").

3

Write the filtered stream to the output topic

Write the loginStream to the topic "user-login-output" using loginStream.to("user-login-output").

Kafka

import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

StreamsBuilder builder = new StreamsBuilder();
KStream userActivityStream = builder.stream("user-activity-input");
KStream loginStream = userActivityStream.filter((key, value) -> value.equals("login"));

// Your code here

Hint

Use the to method on loginStream to send data to the "user-login-output" topic.

4

Print the topology description

Create a Topology object by calling builder.build(). Then print the topology description using System.out.println(topology.describe()).

Kafka

import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.Topology;

StreamsBuilder builder = new StreamsBuilder();
KStream userActivityStream = builder.stream("user-activity-input");
KStream loginStream = userActivityStream.filter((key, value) -> value.equals("login"));
loginStream.to("user-login-output");

// Your code here

Hint

Call builder.build() to get the topology, then print its description with System.out.println(topology.describe()).

Practice

1. What does a Kafka Streams topology represent?

easy

A. The configuration settings for Kafka brokers

B. The flow and processing steps of data in a Kafka Streams application

C. The list of Kafka topics available in the cluster

D. The schema of messages in a Kafka topic

Solution

1. Step 1: Understand the role of topology in Kafka Streams

   A topology defines how data flows and is processed in a Kafka Streams app.

2. Step 2: Differentiate topology from other Kafka concepts

   It is not about broker configs, topic lists, or message schemas, but about processing steps.

3. Final Answer:

   The flow and processing steps of data in a Kafka Streams application -> Option B

4. Quick Check:

   Topology = Data flow and processing [OK]

Hint: Topology means data flow and processing steps [OK]

Common Mistakes:

• Confusing topology with Kafka broker settings
• Thinking topology lists topics
• Assuming topology defines message schema

2. Which code snippet correctly builds a Kafka Streams topology?

easy

A. StreamsBuilder builder = new StreamsBuilder(); builder.run();

B. StreamsBuilder builder = new StreamsBuilder(); builder.start();

C. StreamsBuilder builder = new StreamsBuilder(); builder.create();

D. StreamsBuilder builder = new StreamsBuilder(); builder.build();

Solution

1. Step 1: Recall the correct method to build topology

   The method to finalize and get the topology is build().

2. Step 2: Check each option's method

   Only StreamsBuilder builder = new StreamsBuilder(); builder.build(); uses build(), others use invalid methods.

3. Final Answer:

   StreamsBuilder builder = new StreamsBuilder(); builder.build(); -> Option D

4. Quick Check:

   Use build() to create topology [OK]

Hint: Use build() method on StreamsBuilder to create topology [OK]

Common Mistakes:

• Using start(), create(), or run() instead of build()
• Confusing topology building with starting the stream

3. Given the code snippet:

StreamsBuilder builder = new StreamsBuilder();
builder.stream("input-topic").mapValues(value -> value.toUpperCase()).to("output-topic");
Topology topology = builder.build();
System.out.println(topology.describe());

What will the printed topology description include?

medium

A. A sink node for 'input-topic' and a source node for 'output-topic'

B. Only a source node for 'input-topic' without any processing

C. A source node for 'input-topic', a processor node for mapValues, and a sink node for 'output-topic'

D. An error because mapValues is not allowed in topology

Solution

1. Step 1: Analyze the stream processing steps

   The code reads from 'input-topic', applies mapValues to uppercase, then writes to 'output-topic'.

2. Step 2: Understand topology description output

   The description shows source nodes (input), processor nodes (mapValues), and sink nodes (output).

3. Final Answer:

   A source node for 'input-topic', a processor node for mapValues, and a sink node for 'output-topic' -> Option C

4. Quick Check:

   Topology describe shows source, processor, sink [OK]

Hint: Topology describe shows source, processor, and sink nodes [OK]

Common Mistakes:

• Thinking mapValues is not part of topology
• Confusing source and sink nodes
• Expecting no processor nodes in description

4. Identify the error in this Kafka Streams topology code:

StreamsBuilder builder = new StreamsBuilder();
builder.stream("input-topic").filter(value -> value.length() > 5);
Topology topology = builder.build();
System.out.println(topology.describe());

medium

A. The filtered stream is not sent to any sink topic, so no output node is created

B. The filter method is invalid and causes a syntax error

C. StreamsBuilder cannot build topology without a source topic

D. Calling build() before defining streams causes runtime error

Solution

1. Step 1: Check the stream processing chain

   The code filters the stream but does not write the result to any output topic.

2. Step 2: Understand topology implications

   Without a sink node, the topology has no output, which is usually an error or incomplete.

3. Final Answer:

   The filtered stream is not sent to any sink topic, so no output node is created -> Option A

4. Quick Check:

   Missing sink node causes incomplete topology [OK]

Hint: Always send processed stream to sink topic [OK]

Common Mistakes:

• Assuming filter causes syntax error
• Thinking build() requires no streams
• Ignoring missing sink topic after processing

5. You want to create a Kafka Streams topology that reads from two topics, merges their streams, filters messages containing 'error', and writes to an output topic. Which code snippet correctly builds this topology?

hard

A. StreamsBuilder builder = new StreamsBuilder();
KStream stream1 = builder.stream("topic1");
KStream stream2 = builder.stream("topic2");
KStream merged = stream1.merge(stream2);
merged.filter((k, v) -> v.contains("error")).to("output-topic");
Topology topology = builder.build();

B. StreamsBuilder builder = new StreamsBuilder();
KStream stream = builder.stream("topic1", "topic2");
stream.filter(v -> v.contains("error")).to("output-topic");
Topology topology = builder.build();

C. StreamsBuilder builder = new StreamsBuilder();
KStream stream1 = builder.stream("topic1");
KStream stream2 = builder.stream("topic2");
KStream merged = stream1.join(stream2, (v1, v2) -> v1 + v2);
merged.filter((k, v) -> v.contains("error")).to("output-topic");
Topology topology = builder.build();

D. StreamsBuilder builder = new StreamsBuilder();
builder.stream("topic1").filter(v -> v.contains("error")).to("output-topic");
builder.stream("topic2").filter(v -> v.contains("error")).to("output-topic");
Topology topology = builder.build();

Solution

1. Step 1: Understand the requirement

   Read from two topics, merge streams, filter messages with 'error', write to output.

2. Step 2: Evaluate each option

   StreamsBuilder builder = new StreamsBuilder(); KStream stream1 = builder.stream("topic1"); KStream stream2 = builder.stream("topic2"); KStream merged = stream1.merge(stream2); merged.filter((k, v) -> v.contains("error")).to("output-topic"); Topology topology = builder.build(); merges streams correctly, filters, then writes once. StreamsBuilder builder = new StreamsBuilder(); KStream stream = builder.stream("topic1", "topic2"); stream.filter(v -> v.contains("error")).to("output-topic"); Topology topology = builder.build(); incorrectly tries to stream two topics in one call. StreamsBuilder builder = new StreamsBuilder(); KStream stream1 = builder.stream("topic1"); KStream stream2 = builder.stream("topic2"); KStream merged = stream1.join(stream2, (v1, v2) -> v1 + v2); merged.filter((k, v) -> v.contains("error")).to("output-topic"); Topology topology = builder.build(); joins streams (not merge). StreamsBuilder builder = new StreamsBuilder(); builder.stream("topic1").filter(v -> v.contains("error")).to("output-topic"); builder.stream("topic2").filter(v -> v.contains("error")).to("output-topic"); Topology topology = builder.build(); filters separately and writes twice, not merging.

3. Final Answer:

   Option A correctly merges, filters, and writes to output topic -> Option A

4. Quick Check:

   Merge streams, filter, then write [OK]

Hint: Merge streams before filtering and writing output [OK]

Common Mistakes:

• Using join instead of merge for combining streams
• Trying to stream multiple topics in one call incorrectly
• Writing filtered streams separately without merging