Stream topology in Kafka - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple Kafka Streams topology

What is the output printed by this Kafka Streams application when it processes the input record with key "key1" and value "hello"?

Kafka

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> source = builder.stream("input-topic");
KStream<String, String> uppercased = source.mapValues(value -> value.toUpperCase());
uppercased.to("output-topic");

// Assume the input record is ("key1", "hello")
// The application prints the output record key and value after processing.

A Output record: key=key1, value=hello

B Output record: key=key1, value=HELLO

C Output record: key=HELLO, value=key1

D No output record is produced

💡 Hint

Remember that mapValues changes only the value, not the key.

The mapValues function transforms the value to uppercase but keeps the key unchanged. So the output record has the same key "key1" and the value "HELLO".

🧠 Conceptual

intermediate

Understanding Kafka Streams topology nodes

In a Kafka Streams topology, which node type is responsible for consuming records from Kafka topics?

A State store node

B Processor node

C Source node

D Sink node

💡 Hint

Think about where the data enters the topology.

Source nodes read data from Kafka topics and feed it into the topology for processing.

❓ Predict Output

advanced

Output of a Kafka Streams topology with branching

Given this Kafka Streams topology code, what output records are sent to the "even-topic" and "odd-topic" when the input records have values 2 and 3?

Kafka

StreamsBuilder builder = new StreamsBuilder();
KStream<String, Integer> source = builder.stream("numbers-topic", Consumed.with(Serdes.String(), Serdes.Integer()));

Map<String, KStream<String, Integer>> branches = source.split(Named.as("branch-"))
    .branch((key, value) -> value % 2 == 0, Branched.withConsumer(ks -> ks.to("even-topic")))
    .branch((key, value) -> value % 2 != 0, Branched.withConsumer(ks -> ks.to("odd-topic")))
    .noDefaultBranch();

A even-topic receives (key, 2), odd-topic receives (key, 3)

B No records are sent to either topic

C even-topic receives both (key, 2) and (key, 3), odd-topic receives nothing

D even-topic receives (key, 3), odd-topic receives (key, 2)

💡 Hint

Check the condition for each branch carefully.

The first branch filters even numbers and sends them to "even-topic"; the second branch filters odd numbers and sends them to "odd-topic".

🔧 Debug

advanced

Identify the error in Kafka Streams topology definition

What error will this Kafka Streams topology code produce when building the topology?

Kafka

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> source = builder.stream("input-topic");
source.filter((key, value) -> value.length() > 3);
source.to("output-topic");

A Compilation error due to missing semicolon

B Runtime NullPointerException

C No records will be sent to output-topic

D The output topic will contain all records, including those with value length <= 3

💡 Hint

Consider what happens to the filtered stream.

The filter operation returns a new stream, but the original stream is sent to the output topic. So all records are sent, ignoring the filter.

❓ Predict Output

expert

Final output of a Kafka Streams topology with stateful aggregation

What is the output printed by this Kafka Streams topology after processing the input records ("key1", "apple"), ("key1", "banana"), and ("key1", "apple") on the "fruits-topic"?

Kafka

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> source = builder.stream("fruits-topic");

KTable<String, Long> counts = source.groupByKey().count();

counts.toStream().foreach((key, count) -> System.out.println(key + ": " + count));

A key1: 3

B key1: 2

C key1: 1

D No output is printed

💡 Hint

Count counts the number of records per key, regardless of value.

The count aggregates the number of records with the same key. Three records with key "key1" are processed, so the count is 3.

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