Error handling in streams in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Kafka Streams builder.

Kafka

StreamsBuilder [1] = new StreamsBuilder();

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

AkafkaBuilder

BstreamBuilder

Cstream

Dbuilder

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name that is not descriptive or confusing.

Forgetting to create the StreamsBuilder instance.

The variable name for the StreamsBuilder instance is commonly 'builder'.

2fill in blank

medium

Complete the code to add a global error handler to the Kafka Streams configuration.

Kafka

props.put(StreamsConfig.DEFAULT_DESERIALIZATION_EXCEPTION_HANDLER_CLASS_CONFIG, [1].class);

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

ALogAndContinueExceptionHandler

BFailOnInvalidTimestampExceptionHandler

CLogAndFailExceptionHandler

DIgnoreExceptionHandler

Attempts:

3 left

💡 Hint

Common Mistakes

Using a handler that stops the stream on error.

Choosing a handler that ignores errors silently.

LogAndContinueExceptionHandler logs errors and continues processing, useful for error handling.

3fill in blank

hard

Fix the error in the code to handle deserialization exceptions by skipping bad records.

Kafka

builder.stream("input-topic", Consumed.with(Serdes.String(), Serdes.String())
       .with[1](new LogAndContinueExceptionHandler()));

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AerrorHandler

BdeserializationExceptionHandler

CdeserializationExceptionHandlerClass

DexceptionHandler

Attempts:

3 left

💡 Hint

Common Mistakes

Using a method name that does not exist.

Passing an instance instead of a class.

The correct method is 'withDeserializationExceptionHandler' to set the handler instance.

4fill in blank

hard

Fill both blanks to create a try-catch block that logs and skips processing on exceptions.

Kafka

try {
    processRecord(record);
} catch ([1] e) {
    logger.[2]("Error processing record, skipping", e);
}

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AException

Berror

Cwarn

DThrowable

Attempts:

3 left

💡 Hint

Common Mistakes

Catching Throwable which is too broad.

Using logger.error which may stop processing.

Catch general Exception and log a warning to skip bad records.

5fill in blank

hard

Fill all three blanks to define a custom deserialization exception handler class.

Kafka

public class [1] implements DeserializationExceptionHandler {

    @Override
    public DeserializationHandlerResponse handle(Exception exception, ConsumerRecord<byte[], byte[]> record) {
        // Log the error
        logger.[2]("Deserialization error for record: " + record, exception);
        return DeserializationHandlerResponse.[3];
    }
}

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

ACustomDeserializationExceptionHandler

Bwarn

CCONTINUE

DhandleException

Attempts:

3 left

💡 Hint

Common Mistakes

Returning FAIL which stops the stream.

Using logger.error which is too severe here.

The class name is custom, logs a warning, and returns CONTINUE to skip bad records.

Practice

1. What is the main purpose of error handling in Kafka streams?

easy

A. To delete all bad data permanently

B. To keep the stream running smoothly even if some data is bad

C. To stop the stream immediately when an error occurs

D. To ignore errors and continue processing without any checks

Solution

1. Step 1: Understand error handling goal

   Error handling aims to manage problems without stopping the whole process.

2. Step 2: Identify correct purpose in Kafka streams

   Kafka streams use error handling to keep processing even if some data is bad.

3. Final Answer:

   To keep the stream running smoothly even if some data is bad -> Option B

4. Quick Check:

   Error handling = keep stream running [OK]

Hint: Error handling means keep running despite bad data [OK]

Common Mistakes:

• Thinking error handling stops the stream
• Assuming bad data is deleted automatically
• Ignoring the need to handle errors

2. Which syntax correctly shows how to catch exceptions in a Kafka Streams processor in Java?

easy

A. catch (Exception e) { processRecord(); } try { handleError(e); }

B. processRecord() catch Exception e handleError(e)

C. try { processRecord(); } catch (Exception e) { handleError(e); }

D. try processRecord() catch Exception handleError()

Solution

1. Step 1: Recall Java try-catch syntax

   Java uses try { } catch (Exception e) { } to handle exceptions.

2. Step 2: Match correct syntax for Kafka Streams processor

   try { processRecord(); } catch (Exception e) { handleError(e); } correctly wraps processRecord() in try and catches Exception e to handle errors.

3. Final Answer:

   try { processRecord(); } catch (Exception e) { handleError(e); } -> Option C

4. Quick Check:

   Java try-catch syntax = try { processRecord(); } catch (Exception e) { handleError(e); } [OK]

Hint: Remember Java try-catch blocks use braces and catch after try [OK]

Common Mistakes:

• Placing catch before try
• Missing braces {} around try or catch blocks
• Using incorrect keywords or order

3. Given this Kafka Streams code snippet, what happens when a bad record causes an exception?

streams.foreach((key, value) -> {
  try {
    process(value);
  } catch (Exception e) {
    deadLetterTopic.send(key, value);
  }
});

medium

A. The exception crashes the whole application

B. The stream stops processing immediately

C. The bad record is ignored and lost

D. The bad record is sent to a dead-letter topic for later review

Solution

1. Step 1: Analyze try-catch block in code

   The code tries to process each record; if an exception occurs, it catches it.

2. Step 2: Check catch block action

   In catch, the bad record is sent to deadLetterTopic for later review.

3. Final Answer:

   The bad record is sent to a dead-letter topic for later review -> Option D

4. Quick Check:

   Exception caught = send to dead-letter topic [OK]

Hint: Catch exceptions and send bad data to dead-letter topic [OK]

Common Mistakes:

• Assuming stream stops on exception
• Thinking bad data is lost without handling
• Believing exception crashes app without catch

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

streams.foreach((key, value) -> {
  try {
    process(value);
  } catch Exception e {
    log.error(e.getMessage());
  }
});

medium

A. Missing parentheses around Exception e in catch block

B. Using foreach instead of map for error handling

C. Logging error message is incorrect

D. Try block should be outside the foreach

Solution

1. Step 1: Check catch block syntax

   In Java, catch must have parentheses around the exception type and variable.

2. Step 2: Identify missing parentheses

   The code has 'catch Exception e' missing parentheses; correct is 'catch (Exception e)'.

3. Final Answer:

   Missing parentheses around Exception e in catch block -> Option A

4. Quick Check:

   Catch syntax requires parentheses [OK]

Hint: Catch blocks always need parentheses around exception [OK]

Common Mistakes:

• Omitting parentheses in catch
• Confusing foreach and map usage
• Thinking logging method is wrong

5. You want to build a Kafka Streams application that processes user events but sends any malformed events to a separate topic without stopping the main stream. Which approach best achieves this?

hard

A. Use a try-catch inside the stream processor to send bad events to a dead-letter topic

B. Stop the stream when a malformed event is detected to fix data immediately

C. Ignore malformed events and continue processing without logging

D. Filter out malformed events before processing without saving them

Solution

1. Step 1: Understand requirement to keep stream running

   The stream must continue processing even if some events are bad.

2. Step 2: Choose method to handle bad events

   Using try-catch to catch errors and send bad events to a dead-letter topic allows this.

3. Final Answer:

   Use a try-catch inside the stream processor to send bad events to a dead-letter topic -> Option A

4. Quick Check:

   Try-catch + dead-letter topic = best error handling [OK]

Hint: Try-catch plus dead-letter topic keeps stream healthy [OK]

Common Mistakes:

• Stopping stream on error instead of handling
• Ignoring bad data without saving for review
• Filtering bad data without storing it