Effective Strategy to Avoid Duplicate Messages in Apache Kafka Consumer (2024)

FAQs

Effective Strategy to Avoid Duplicate Messages in Apache 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).

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).

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.

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.

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.

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.

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.

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.

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.

How to fix consumer lag in Kafka? ›

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.

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.

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.

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.

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.

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.

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.