Last Updated : 10 May, 2024
Apache Kafka is a good choice for distributed messaging systems because of its robust nature. In this article, we will explore advanced strategies to avoid duplicate messages in Apache Kafka consumers.
Challenge of Duplicate Message Consumption
Apache Kafka’s at-least-once delivery system ensures message durability, and it can result in messages being delivered more than once. This becomes particularly challenging in scenarios involving network disruptions, consumer restarts, or Kafka rebalances. It is essential to implement strategies that guarantee to avoid message duplication without compromising the system’s reliability.
Comprehensive Strategies to Avoid Duplicate Messages
Below are some strategies that avoid duplicate messages in Apache Kafka Consumer.
1. Consumer Group IDs and Offset Management
Ensuring unique consumer group IDs is foundational to preventing conflicts between different consumer instances. Additionally, effective offset management is important. Storing offsets in an external and persistent storage system allows consumers to resume processing from the last successfully processed message in the event of failures. This practice enhances the resilience of Kafka consumers against restarts and rebalances.
Java Properties properties = new Properties();properties.put("bootstrap.servers", "your_kafka_bootstrap_servers");properties.put("group.id", "unique_consumer_group_id");KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);// Manually managing offsetsconsumer.subscribe(Collections.singletonList("your_topic"));ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));for (ConsumerRecord<String, String> record : records) { // Process message // Manually commit offset consumer.commitSync(Collections.singletonMap( new TopicPartition(record.topic(), record.partition()), new OffsetAndMetadata(record.offset() + 1)));}
2. Transaction-Aware Consumer
Implementing idempotency on the consumer side is inherently more complex and resource-intensive. Additionally, it is advantageous to allow greater flexibility at the consumer listener level, enabling tailored idempotency handling based on specific requirements and operational contexts. So, we indicate with isolation.level that we should wait to read transactional messages until the associated transaction has been committed:
Java Properties properties = new Properties();properties.put("bootstrap.servers", "your_kafka_bootstrap_servers");properties.put("group.id", "unique_consumer_group_id");properties.put("enable.auto.commit", "false");properties.put("isolation.level", "read_committed");KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);// Consume messages as usual
3. Transaction Support
Kafka’s transactional support is a robust strategy to achieve exactly once semantics. By processing messages within a transaction, consumers can ensure atomicity between message processing and offset commits. In case of processing errors, the transaction is rolled back, preventing offset commits and subsequent message consumption until the issue is resolved.
Java consumer.beginTransaction();try { // Process message consumer.commitTransaction();}catch (Exception e) { // Handle error consumer.rollbackTransaction();}
4. Dead Letter Queues (DLQs)
Implementing Dead Letter Queues for Kafka consumers involves redirecting problematic messages to a separate queue for manual inspection. This approach facilitates isolating and analyzing messages that fail processing, enabling developers to identify and address the root cause before considering reprocessing.
Java // Assuming a DLQ topic named "your_topic_dlq"KafkaProducer<String, String> dlqProducer = new KafkaProducer<>(dlqProperties);try { // Process message dlqProducer.send(new ProducerRecord<>( "your_topic_dlq", record.key(), record.value()));}catch (Exception e) { // Handle error}
5. Message Deduplication Filters
This filter maintains a record of processed message identifiers, allowing the consumer to identify and discard duplicates efficiently. This approach is particularly effective when strict ordering of messages is not a critical requirement.
Java Set<String> processedMessageIds = new HashSet<>();ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));for (ConsumerRecord<String, String> record : records) { // Check if the message ID has been processed if (!processedMessageIds.contains(record.key())) { // Process message // Add the message ID to the set processedMessageIds.add(record.key()); }}
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FAQs
Tracking all successfully consumed messages can help to avoid this scenario. This can be achieved by assigning a unique ID to every message created at the producer side (order service), and tracking them on the consumer side (fulfillment service) by storing each ID in a database table (Message ID Tracking Table).
How to avoid duplicate messages in Kafka consumer? ›
Tracking all successfully consumed messages can help to avoid this scenario. This can be achieved by assigning a unique ID to every message created at the producer side (order service), and tracking them on the consumer side (fulfillment service) by storing each ID in a database table (Message ID Tracking Table).
How do you consume the same messages in Kafka by different consumers? ›
To summarize, you create a new consumer group for each application that needs all the messages from one or more topics. You add consumers to an existing consumer group to scale the reading and processing of messages from the topics, so each additional consumer in a group will only get a subset of the messages.
How can I improve Kafka consumer performance? ›
Optimizing Kafka Consumer Performance involves enhancing the efficiency and throughput of data consumption from Kafka brokers. Key strategies include tuning consumer group settings, adjusting batch sizes, managing offsets, and utilizing parallelism.
Can Kafka consumer the same message multiple times? ›
As soon as Consumer spins up again, Kafka starts to send all 3 messages again. It means that a Consumer may get one message twice and there may be an issue as shown below. Therefore, we should keep in mind during the development that a Consumer may accept multiple times the same message.
How do I stop duplicate messages? ›
Look for this toggle in the settings of the Messages app. On some phones, it's in Message settings > RCS chats; tap Turn on RCS chats to shut it off.
How to avoid duplication of data? ›
CRM Solutions: Advice for Dealing With Duplicate Data
- Search your database before creating new records. ...
- Use deduping tools. ...
- Use dupe prevention tools. ...
- Normalize data before large imports. ...
- Use Salesforce's deduping functionality.
Can two consumers read from the same partition in Kafka? ›
There is the broker, in it you can have topics which can be split into different partition parts (which consist of segments but this is not important) and only one consumer can join a partition at a time.
Can a Kafka consumer consume from multiple topics? ›
Yes, a Kafka consumer can listen to (and subscribe to) more than one topic. This capability is one of Kafka's strengths, making it incredibly versatile in various use cases.
How do you consume messages between two timestamps in Kafka? ›
Use the function offsetsForTimes in KafkaConsumer: Look up the offsets for the given partitions by timestamp. The returned offset for each partition is the earliest offset whose timestamp is greater than or equal to the given timestamp in the corresponding partition.
Reviewing and optimizing load balancing and parallel processing configurations in Kafka is another way to reduce consumer lag. Again, although in general creating multiple consumers is a good thing because it helps to balance load, it's possible you have more consumers than you should based on your topics.
How many messages can Kafka handle? ›
Kafka generally has better performance. If you are looking for more throughput, Kafka can go up to around 1,000,000 messages per second, whereas the throughput for RabbitMQ is around 4K-10K messages per second. This is due to the architecture, as Kafka was designed around throughput.
How do I reduce Kafka consumer latency? ›
A few specific strategies to reduce Kafka latency include: optimizing network settings, increasing hardware resources, and configuring Kafka producers and consumers to operate more efficiently.
How do you avoid duplicates in Kafka consumer? ›
Below are some strategies that avoid duplicate messages in Apache Kafka Consumer.
- Consumer Group IDs and Offset Management. ...
- Transaction-Aware Consumer. ...
- Transaction Support. ...
- Dead Letter Queues (DLQs) ...
- Message Deduplication Filters.
What is the maximum message size in Kafka consumer? ›
The Kafka max message size is 1MB. In this lesson we will look at two approaches for handling larger messages in Kafka. Kafka has a default limit of 1MB per message in the topic.
How to make Kafka consumer idempotent? ›
To implement the Idempotent Consumer pattern the recommended approach is to add a table to the database to track processed messages. Each message needs to have a unique messageId assigned by the producing service, either within the payload, or as a Kafka message header.
How do you prevent duplicate content? ›
In many cases, the best way to fix duplicate content is implementing 301 redirects from the non-preferred versions of URLs to the preferred versions. When URLs need to remain accessible to visitors, you can't use redirect but you can either use a canonical URL or a robots noindex redirective.
How do you prevent duplicates in transactions? ›
How to eliminate duplicate payments
- Reduce manual invoice data entry. ...
- Collect standard vendor documents. ...
- Cleanse your vendor database. ...
- Pay your invoices promptly. ...
- Reduce the number of vendors you work with. ...
- Limit vendor payment methods. ...
- Centralize invoice processing. ...
- Conduct regular AP audits.
Can Kafka do deduplication? ›
If the database transaction commit precedes the Kafka transaction commit, and the service fails before the Kafka transaction is committed, then when the event is redelivered it will be deduplicated by the Idempotent Consumer. This means the resulting outbound event will never be published.
How to prevent message loss in Kafka? ›
To ensure data durability and minimize message loss, it is recommended to:
- Configure a sufficient replication factor (e.g., 3) to maintain multiple copies of the data.
- Set 'min. insync. ...
- Use 'acks=all' or 'acks=-1' to wait for acknowledgment from all in-sync replicas before considering a write successful.