Event streaming concept in Kafka - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Kafka producer code snippet?

Consider this Kafka producer code in Java that sends a message to a topic named test-topic. What will be printed on the console?

Kafka

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

Producer<String, String> producer = new KafkaProducer<>(props);
ProducerRecord<String, String> record = new ProducerRecord<>("test-topic", "key1", "Hello Kafka");
producer.send(record, (metadata, exception) -> {
    if (exception == null) {
        System.out.println("Sent to partition " + metadata.partition());
    } else {
        System.out.println("Error sending message");
    }
});
producer.close();

A Sent to partition 0

B Error sending message

C Sent to partition 1

D No output

💡 Hint

Assume the topic test-topic has only one partition.

The message is sent successfully to the only partition (partition 0) of the topic, so the callback prints "Sent to partition 0".

🧠 Conceptual

intermediate

What does Kafka's consumer group ensure?

In Kafka, what is the main purpose of a consumer group?

A To allow multiple consumers to read the same message independently

B To distribute partitions among consumers for parallel processing

C To store messages permanently in Kafka topics

D To encrypt messages during transmission

💡 Hint

Think about how Kafka balances workload among consumers.

A consumer group allows Kafka to assign partitions to consumers so they can process messages in parallel without duplication.

🔧 Debug

advanced

Why does this Kafka consumer code fail to receive messages?

Look at this Kafka consumer code snippet. It never prints any consumed messages. What is the cause?

Kafka

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("group.id", "group1");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("my-topic"));

while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        System.out.println("Received: " + record.value());
    }
}

A The consumer does not call consumer.close(), causing resource leak

B The consumer group ID is missing, causing subscription failure

C The consumer never commits offsets, so it reads no messages

D The consumer does not call poll() correctly to fetch messages

💡 Hint

Check the poll method usage and its parameters.

The poll method requires a timeout duration. If the argument is incorrect or missing, the consumer will not fetch messages.

📝 Syntax

advanced

Which Kafka Streams code snippet compiles without error?

Which of these Kafka Streams Java code snippets correctly creates a stream from topic input-topic?

A

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

B

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

C

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

D

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> stream = builder.stream('input-topic');

💡 Hint

Check for correct Java syntax and string literals.

Option A uses correct Java syntax with semicolons and double quotes for string literals. Others have syntax errors.

🚀 Application

expert

How many partitions will be assigned to each consumer in this group?

A Kafka topic has 6 partitions. There are 3 consumers in the same consumer group subscribed to this topic. How many partitions will each consumer get assigned?

A Each consumer gets 3 partitions

B Each consumer gets 1 partition

C Each consumer gets 2 partitions

D All consumers get all 6 partitions

💡 Hint

Partitions are divided evenly among consumers in a group.

Kafka assigns partitions evenly among consumers in a group. 6 partitions divided by 3 consumers means each gets 2 partitions.

Practice

1. What is the main purpose of event streaming in Kafka?

easy

A. To batch process data once a day

B. To store data permanently in a database

C. To create static reports from data

D. To send data as it happens for real-time updates

Solution

1. Step 1: Understand event streaming concept

   Event streaming means sending data continuously as events happen, not waiting to batch or store permanently.

2. Step 2: Match purpose with options

   Only To send data as it happens for real-time updates describes sending data as it happens for real-time updates, which is the core of event streaming.

3. Final Answer:

   To send data as it happens for real-time updates -> Option D

4. Quick Check:

   Event streaming = real-time data flow [OK]

Hint: Event streaming means data flows live, not stored or batched [OK]

Common Mistakes:

• Confusing event streaming with batch processing
• Thinking event streaming stores data permanently
• Assuming event streaming creates static reports

2. Which of the following is the correct way to describe a Kafka producer?

easy

A. A database that stores events permanently

B. A component that reads events from topics

C. A component that sends events to topics

D. A tool that visualizes event data

Solution

1. Step 1: Define Kafka producer role

   A Kafka producer is the part that sends or writes events into Kafka topics.

2. Step 2: Compare options with definition

   A component that sends events to topics correctly states the producer sends events to topics; others describe consumers, databases, or tools.

3. Final Answer:

   A component that sends events to topics -> Option C

4. Quick Check:

   Producer = sends events [OK]

Hint: Producer sends events; consumer reads events [OK]

Common Mistakes:

• Mixing up producer and consumer roles
• Thinking producer stores data permanently
• Confusing producer with visualization tools

3. Given this Kafka event flow: Producer sends events to topic 'orders'. Consumer reads from 'orders'. What happens if the consumer is slow?

medium

A. Events are lost immediately

B. Events accumulate in the topic until consumed

C. Producer stops sending events

D. Topic deletes old events instantly

Solution

1. Step 1: Understand Kafka topic behavior

   Kafka stores events in topics until consumers read them; slow consumers cause events to accumulate.

2. Step 2: Analyze options based on behavior

   Events accumulate in the topic until consumed matches this behavior; events wait in topic. Events are not lost immediately, producer keeps sending, and topic deletes old events only after retention period.

3. Final Answer:

   Events accumulate in the topic until consumed -> Option B

4. Quick Check:

   Slow consumer = event backlog in topic [OK]

Hint: Slow consumer causes event backlog, not loss [OK]

Common Mistakes:

• Assuming events are lost if consumer is slow
• Thinking producer stops sending events
• Believing topics delete events instantly

4. You wrote a Kafka consumer but it never receives any events. Which is the most likely mistake?

medium

A. The consumer is subscribed to the wrong topic

B. The producer is sending events correctly

C. The topic has too many partitions

D. The consumer is reading from the correct topic

Solution

1. Step 1: Identify why consumer gets no events

   If a consumer never receives events, a common cause is subscribing to the wrong topic.

2. Step 2: Evaluate options

   The consumer is subscribed to the wrong topic points to wrong topic subscription, which explains no events. Other options do not explain lack of events.

3. Final Answer:

   The consumer is subscribed to the wrong topic -> Option A

4. Quick Check:

   No events = wrong topic subscription [OK]

Hint: Check consumer topic subscription first [OK]

Common Mistakes:

• Ignoring topic subscription errors
• Blaming producer when it sends correctly
• Confusing topic partitions with event delivery

5. You want to build a real-time dashboard that updates instantly when new sales happen. Which Kafka setup best supports this?

hard

A. Producer sends sales events to a topic; consumer reads and updates dashboard immediately

B. Producer batches sales events daily; consumer reads once a day

C. Producer sends events to multiple unrelated topics; consumer reads only one

D. Consumer writes sales events back to producer

Solution

1. Step 1: Identify real-time update needs

   A real-time dashboard requires events sent and consumed instantly to update live.

2. Step 2: Match Kafka setup to real-time flow

   Producer sends sales events to a topic; consumer reads and updates dashboard immediately describes producer sending events to a topic and consumer reading immediately to update dashboard, fitting real-time needs.

3. Step 3: Eliminate other options

   Producer batches sales events daily; consumer reads once a day is batch, not real-time; C is inconsistent topic usage; D is incorrect flow direction.

4. Final Answer:

   Producer sends sales events to a topic; consumer reads and updates dashboard immediately -> Option A

5. Quick Check:

   Real-time dashboard = immediate event flow [OK]

Hint: Real-time needs immediate produce and consume flow [OK]

Common Mistakes:

• Choosing batch processing for real-time needs
• Using multiple unrelated topics incorrectly
• Confusing producer and consumer roles