[SOLVED] How to commit manually with Kafka Stream? - kafka

[SOLVED] Mastering Manual Commit in Kafka Streams: A Comprehensive Guide

In this chapter, we will look at how to commit manually with Kafka Streams. Kafka Streams is a strong tool for processing data in real-time. It is important to understand how manual commits work. This is key for developers who want to control their data processing better. We will break down the manual commit process into simple steps. We will give you clear instructions and good tips to help you handle data well and avoid mistakes.

In this chapter, we will talk about:

Part 1 - Understanding Kafka Stream Commit Mechanism: We will learn how Kafka Streams manages commits.
Part 2 - Configuring Manual Commit in Kafka Streams: A step-by-step guide on how to set up manual commits in your Kafka Streams app.
Part 3 - Implementing Manual Commit with Stream Processing: We will give clear instructions to make manual commits work well during stream processing.
Part 4 - Error Handling in Manual Commits: We will look at ways to deal with mistakes that can happen during manual commits.
Part 5 - Best Practices for Manual Commits: We will find out the best ways to make your manual commit work better and more reliable.
Part 6 - Testing Manual Commit Implementation: We will learn how to test your manual commit to make sure it works like it should.

By the end of this guide, you will understand how to commit manually with Kafka Streams. This will help you manage your data processing tasks more accurately.

For more information on related Kafka topics, you may find these links useful:

Let’s jump into the first part of our journey: Understanding Kafka Stream Commit Mechanism.

Part 1 - Understanding Kafka Stream Commit Mechanism

Kafka Streams has a commit mechanism. It helps us manage the offsets of messages we have processed. Knowing how this mechanism works is very important for doing manual commits well.

In Kafka Streams, we commit offsets to keep track of which records we have processed. By default, Kafka Streams commits offsets automatically at regular times. We can control this with the auto.commit.interval.ms setting. This setting tells us how often offsets will be committed automatically.

Key Concepts of Kafka Stream Commit Mechanism

Offset Management: When we process a record, we store its offset in Kafka. This lets our application continue from the last committed offset if something goes wrong.
Commit Types:
- Automatic Commit: Offsets get committed automatically based on our settings. This works for most applications that don’t need too much control.
- Manual Commit: This gives us control over when we commit offsets. We can make sure specific processing is done before we say the record is finished.
Processing Guarantees: Kafka Streams gives us different processing guarantees:
- At-most-once: If something goes wrong after processing but before we commit, we might lose the record.
- At-least-once: This means we won’t lose records, but we might process some records more than once if a failure happens after processing.
- Exactly-once: This guarantees that we process records exactly once. This is very important for many applications. We need to set things up correctly and handle state stores properly.
Manual Offset Commit: When we use manual commits, we must call the commit operation after processing records. We usually do this in a Processor or Transformer. Here is an example of how to do manual commits:

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

stream.process(new Processor<String, String>() {
    private ProcessorContext context;

    @Override
    public void init(ProcessorContext context) {
        this.context = context;
    }

    @Override
    public void process(String key, String value) {
        // Processing logic here

        // Manual offset commit
        context.commit();
    }

    @Override
    public void close() {
        // Cleanup logic if needed
    }
});

Configuration Properties: To turn on manual commits, we need to set some properties in our StreamsConfig:

# Disable automatic commits
enable.auto.commit=false

# Set the commit interval for manual commits (if needed)
commit.interval.ms=1000

In summary, understanding the Kafka Stream commit mechanism is important for performing manual commits. When we control when offsets are committed, we can make sure records are processed correctly for our application’s needs. For more details on offset management, we can check the Kafka Offset documentation.

Part 2 - Configuring Manual Commit in Kafka Streams

Configuring manual commit in Kafka Streams is important. It helps us control offsets directly. This gives us better control over how we process records. Normally, Kafka Streams commits offsets automatically after processing a batch of records. But if we want to add custom logic or handle errors, we can change to manual offset management.

Step-by-Step Configuration

Set the enable.auto.commit Property: First, we need to turn off automatic offset commits. We do this by setting the enable.auto.commit property to false. We can do this in the properties file or in the code.
```
# Kafka Streams configuration properties
bootstrap.servers=localhost:9092
application.id=my-stream-app
enable.auto.commit=false
```

Use the ConsumerConfig: When we create the Kafka Streams settings, we have to include the consumer settings that turn off auto-commit.

Properties props = new Properties();
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "my-stream-app");
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false);

Implement Manual Commit Logic: We can manage offsets by ourselves in our processing logic. While we process records, we can call commitSync() to commit offsets after we process the records successfully.

Here is a simple example of using manual commit in a Kafka Streams app:

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

import java.util.Properties;

public class ManualCommitExample {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "my-stream-app");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false);

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

        stream.foreach((key, value) -> {
            // Process the record
            System.out.println("Processing key: " + key + ", value: " + value);
            // Manual commit logic would go here
        });

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        streams.start();

        // Add shutdown hook to respond to SIGTERM and gracefully close the streams
        Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
    }
}

Important Considerations

Offset Management: We must have a good plan for managing offsets. This is really important if there are failures. We need to decide when we will commit offsets based on how our application processes data.
Error Handling: If we face an error while processing, we must handle it correctly. This way, we don’t commit offsets for records that did not process right.
Batch Processing: If we are processing records in batches, we should think about committing offsets after we process the whole batch successfully.

For more details on Kafka Streams and how to manage settings, we can check the Kafka Streams processing documentation for more insights.

Part 3 - Implementing Manual Commit with StreamProcessing

To do manual commit in Kafka Streams, we need to set up our application to manage offset commits ourselves. This gives us control over when offsets are committed. It helps us to ensure message processing without losing data or having duplicates.

Step 1: Configure Kafka Streams for Manual Offset Management

We must set the enable.auto.commit property to false in our Kafka Streams configuration. This turns off automatic offset commits. Now, we can handle commits on our own.

Properties props = new Properties();
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "your-application-id");
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 0); // Optional: Adjust commit interval
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false); // Disable auto commit

Step 2: Process Records from the Stream

To process records and commit offsets ourselves, we can use the Processor API. Here is a simple example. It shows how to read from a stream, process records, and commit offsets manually.

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

stream.process(() -> new Processor<String, String>() {
    private ProcessorContext context;

    @Override
    public void init(ProcessorContext context) {
        this.context = context;
    }

    @Override
    public void process(String key, String value) {
        // Process the record
        System.out.println("Processing record: Key = " + key + ", Value = " + value);

        // Commit the offset manually after processing
        context.commit(); // This commits the offset for the current record
    }

    @Override
    public void close() {
        // Clean up resources if necessary
    }
});

Step 3: Start the Stream Processing Application

After we set up the processor, we need to start our Kafka Streams application:

KafkaStreams streams = new KafkaStreams(builder.build(), props);
streams.start();

// Add a shutdown hook to respond to SIGTERM and exit cleanly
Runtime.getRuntime().addShutdownHook(new Thread(streams::close));

Handling Offset Commit Failures

When we do manual commits, we must be ready for failures. If a commit fails, we can retry or log an error. The next example shows how to handle problems during commit:

@Override
public void process(String key, String value) {
    try {
        // Process the record
        System.out.println("Processing record: Key = " + key + ", Value = " + value);

        // Commit the offset manually after processing
        context.commit();
    } catch (Exception e) {
        // Handle commit failure
        System.err.println("Commit failed for key: " + key + ", Error: " + e.getMessage());
        // Optionally retry or perform additional error handling
    }
}

Conclusion

By doing this, we can manage manual commits in our Kafka Streams application. This way, our data processing is reliable and controlled. For more details on Kafka Streams and its features, check the Kafka Streams APIs and see how we can customize our offset management strategy.

Part 4 - Error Handling in Manual Commits

In Kafka Streams, when we use manual commits, we need to think carefully about error handling. When we choose to manage offsets ourselves, we must make sure our application can handle errors well during processing. Here, we will share some key ways to manage errors during manual commits.

Understanding Error Scenarios

Processing Errors: These are errors that happen when we process records. This can be due to issues like deserialization problems or mistakes in business logic.
Commit Errors: These errors happen during the commit phase. They can be caused by network problems or if the broker is not available.
Duplicate Processing: This happens when we process records again because we failed to commit offsets.

Strategies for Error Handling

Try-Catch Blocks: We can wrap our processing logic in a try-catch block. This helps us catch processing errors. We can log the error and decide if we want to keep processing or stop the stream.

try {
    // Process the record
    processRecord(record);
    // Manual commit after successful processing
    manualCommit(consumer, record);
} catch (ProcessingException e) {
    // Log and handle processing error
    log.error("Processing failed for record: {}", record, e);
}

Retry Mechanism: We can add a retry mechanism for temporary errors. We can use a counter to limit how many times we try and use exponential backoff to not overload the system.

int retryCount = 0;
boolean success = false;
while (retryCount < MAX_RETRIES && !success) {
    try {
        processRecord(record);
        manualCommit(consumer, record);
        success = true;
    } catch (ProcessingException e) {
        retryCount++;
        log.warn("Retrying processing for record: {}. Attempt: {}", record, retryCount);
        Thread.sleep(RETRY_DELAY);
    }
}

Dead Letter Queue (DLQ): If we cannot process records after several tries, we should send them to a Dead Letter Queue. This way, we can keep track of problem records without losing any data.
```
if (!success) {
    // Send to DLQ
    sendToDeadLetterQueue(record);
}
```
Logging and Monitoring: We need to log errors and offsets well. This helps us track and debug issues. We can use tools like Kafka monitoring to see consumer lag and spot issues quickly.
Idempotent Processing: We should make our processing logic idempotent. This means that if we process the same record multiple times, it won’t cause problems. This is important to avoid problems with duplicate records during retries.
Graceful Shutdown: We need a way to handle graceful shutdowns. We should make sure our application can commit offsets for records that it processed before it stops.
```
Runtime.getRuntime().addShutdownHook(new Thread(() -> {
    commitRemainingOffsets(consumer);
}));
```

By using these error handling strategies, we can make our Kafka Streams application more reliable while we commit offsets manually. For more information on Kafka Streams and best practices, we can read about the Kafka Streams APIs or how to read records in JSON.

Part 5 - Best Practices for Manual Commits

When we work with Kafka Streams and use manual commits, it is important to follow some best practices. This helps us keep our data processing reliable and efficient. Here are some best practices we should think about:

Understand Offset Management:
- Kafka Streams manages offsets for us with the default commit strategy. But when we choose manual commits, we must track offsets ourselves. We should learn how offsets work. This includes knowing about offset expiration. For more details, we can check the article on how offsets expire in Kafka.

Commit Offsets After Processing:

We must always commit offsets after we process the records. This makes sure that if something goes wrong after we commit, we won’t lose any data. For example:

try {
    // Process the record
    processRecord(record);
    // Commit the offset
    consumer.commitSync(Collections.singletonMap(new TopicPartition(record.topic(), record.partition()), new OffsetAndMetadata(record.offset() + 1)));
} catch (Exception e) {
    // Handle exceptions
    handleProcessingError(e);
}

Batch Commits:
- Instead of committing offsets for every record, we can batch our commits. This helps improve performance because we make fewer commits. We can set a batch size limit and commit offsets after processing a batch:
```
// Counting processed records
if (processedRecordsCount >= BATCH_SIZE) {
    consumer.commitSync();
    processedRecordsCount = 0; // Reset for next batch
}
```
Error Handling:
- We need to have good error handling for exceptions that may happen during processing. When there is an error, we should decide if we will commit the offsets. If a record does not process, we might skip committing its offset so it can be reprocessed:
```
if (!processRecord(record)) {
    // Log the error and do not commit the offset
} else {
    // Commit offset if processing was successful
}
```
Idempotence:
- We should make sure our processing logic is idempotent. This means reprocessing the same record should not cause problems or inconsistencies. This is very important with manual commits, as we might reprocess if offsets are not committed right.
Monitor Lag:
- We need to check consumer lag. This shows how far behind a consumer is from the latest records in the topic. We can use Kafka’s metrics or other monitoring tools to track and alert us about consumer lag.
Configuration Tuning:
- We should tune our Kafka consumer settings to get the best performance with manual commits. For example, we may want to change the max.poll.records or enable.auto.commit settings to suit our needs. We should turn off enable.auto.commit since we are managing offsets ourselves:
```
enable.auto.commit=false
```
Testing and Validation:
- We must test our manual commit logic carefully in development before we go to production. This means we should have unit tests for processing logic and integration tests to check that offsets are committed correctly.

By following these best practices for manual commits in Kafka Streams, we can make our data processing more reliable and efficient. For more tips about managing Kafka, we can also read about configuring Kafka consumer settings.

Part 6 - Testing Manual Commit Implementation

Testing the manual commit in Kafka Streams is very important. It helps us make sure our application works well in different situations. In this section, we will show you how to test your manually committed offsets and check if your Kafka Streams application is working properly.

1. Setup Test Environment

Before we start testing, we need to make sure we have everything ready:

A running Kafka cluster
A Kafka Streams application set up for manual offset commits
A unit testing framework like JUnit in our project

2. Create a Test Case

We can create a test case using JUnit. This helps us check if our manual commit logic is working. Here is an example of how to write this test:

import org.apache.kafka.clients.consumer.Consumer;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.TopologyTestDriver;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;

import java.time.Duration;
import java.util.Properties;

public class ManualCommitTest {

    private TopologyTestDriver testDriver;
    private Consumer<String, String> consumer;

    @Before
    public void setup() {
        Properties config = new Properties();
        config.put(StreamsConfig.APPLICATION_ID_CONFIG, "test-app");
        config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "dummy:1234");
        config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

        // Build the topology
        StreamsBuilder builder = new StreamsBuilder();
        // Your topology definition here

        testDriver = new TopologyTestDriver(builder.build(), config);
    }

    @Test
    public void testManualCommit() {
        // Produce a test record
        testDriver.pipeInput(new KeyValue<>("key1", "value1"));

        // Consume messages and process them
        ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
        for (ConsumerRecord<String, String> record : records) {
            // Process the record and manually commit the offset
            consumer.commitSync();
            // Add assertions to verify the behavior
        }

        // Validate the outcomes
        // Assertions to verify expected state after commit
    }

    @After
    public void tearDown() {
        testDriver.close();
        consumer.close();
    }
}

3. Validate Offsets

When we test, we must check that the offsets are committed as we expect. We can use the KafkaConsumer to get the committed offsets and see if they match the records we processed.

4. Use Mocking Frameworks

We can use mocking frameworks like Mockito. This helps us create different scenarios, like exceptions during processing or commit failures. This way, we can test how strong our manual commit implementation is.

5. Integration Testing

Besides unit tests, we should do integration tests too. We can run our Kafka Streams application in a staging environment. Let’s send test messages and watch the offsets in the Kafka topic. This helps us be sure they get committed correctly. For more details, we can check the Kafka Streams APIs for help on integrating with our Kafka setup.

6. Logging and Monitoring

We should add logging in our manual commit logic. This helps us see what happens during tests. We can use tools like Kafka Manager or other monitoring solutions. This way, we can check the health of our Kafka Streams application during and after testing.

By following these steps, we can test our manual commit implementation in Kafka Streams. This helps us make sure our streaming application is reliable and correct. In conclusion, we looked at how to commit manually with Kafka Streams. We talked about the commit method, setup, how to use it, handling errors, and some good tips. Knowing how to do manual commits can help us improve our Kafka Stream processing. It gives us more control over how we handle messages and manage offsets.

For more information, check out our guides on how to manually set group ID and offsets and Kafka Stream APIs.

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