Apache Kafka is a software bus that implements a stream-processing framework. The Apache Software Foundation has created a Scala and Java-based open-source development environment. Kafka was created at LinkedIn and then released as an open source project in early 2011.
Based on the commit log, Apache Kafka allows users to subscribe to it and publish data to a variety of systems and real-time applications. Managing passenger and driver matching at Uber, providing real-time analytics and predictive maintenance for British Gas smart homes, and executing a variety of real-time functions across all of LinkedIn are examples of uses. By now it must be a little clear to you What is Apache Kafka?
Why does Kafka need Optimization?
Apache Kafka is an extremely useful technology. It enables the production of scalable real-time, high-throughput data streams with low latency. When Kafka is optimized, it provides additional benefits like resilience to machine/node failure within the cluster and data and message permanence on the cluster. This is why optimizing Kafka is so crucial. Continuous monitoring is required to achieve and sustain a Kafka deployment. Once you learn Apache Kafka, this will be easy for you to learn. That’s why we’ve put up a list of Kafka best features and practices to help you get the most out of the framework.
1. Recognize data throughput rates.
The layers of the platform stack must be optimized in order to optimize your Apache Kafka implementation. Throughput performance is based on partitions, which are the storage layer.
The average message size multiplied by the number of messages-per-second equals the data-rate-per-partition. Simply said, it’s the speed at which data passes across a partition. The target architecture of the partitions is determined by the desired throughput rates. You can increase the minimum quantity of data fetched in a request to improve throughput. As a result, there are fewer requests. After that, the messages are distributed in greater bunches. This is especially important when there is a small amount of data being produced. In this scenario, users with a thorough understanding of Kafka throughput metrics will be able to properly optimize their Kafka systems.
2. Update Kafka to the most recent version.
Although this may seem self-evident, you’d be amazed how many people still use outdated versions of Kafka. Upgrading to the current version of the platform is a very straightforward Kafka optimization step. You must determine whether your clients are utilizing previous Kafka versions (ver. 0.10 or older). If this is the case, they should upgrade right away with the help of Kafka tools.
3. Make good use of monitoring and notifications.
While being cautious when setting up your Kafka cluster can help you avoid a slew of difficulties down the line, you’ll still want to be on the lookout for and appropriately address any glitches before they become problems.
Monitoring system metrics like network performance, open file handles, memory, load, disc utilization, and other parameters, as well as JVM analytics like GC pauses and heap usage, is critical. Dashboards and history tools that can speed up debugging operations can be quite useful.
4. Raise the Ulimit to avoid outages.
It’s a scenario that happens far too often: brokers crash due to what appears to be excessive load, but is actually a harmless “too many open files’ ‘ error. You can bypass this problem by modifying /etc/sysctl.conf and adjusting Ulimit to allow 128,000 or more open files.
5. Keep network latency to a minimum.
Make sure that brokers are physically positioned in the regions closest to customers while pursuing low latency for your Kafka implementation, and take network performance into account when picking cloud provider instance types. If you’re having trouble with bandwidth, upgrading to a larger and more powerful server can be a good idea.
6. To accomplish high-speed ingest, adjust the consumer socket buffers.
The setting receive.buffer.bytes is set to 64kB by default in older Kafka versions. The argument is socket.receive.buffer.bytes in newer Kafka versions, with 100kB as the default. What does this imply for Kafka performance? These default settings are far too tiny for high-throughput environments, and thus insufficient. When the network’s bandwidth-delay product between the broker and the client is greater than the LAN’s, this is very much the case (local area network).
When there aren’t enough discs, threads slow down and become constrained. Increasing the size of the buffers for network requests is one of the most essential Apache Kafka best practices. This will assist you in increasing throughput.
7. When it comes to topic configurations, be cautious.
Topic configurations have a significant impact on Kafka cluster performance. Because changing settings like replication factor or partition count might be difficult, you’ll want to set them up correctly the first time, and then create a new topic if you need to make changes. Use a three-fold replication factor and be cautious while handling huge messages. Break lengthy messages into orderly chunks if possible, or simply use references to the data. The replication factor, as previously mentioned, is one of the most significant configurations.
8. Take use of parallel processing.
Kafka is built for parallel processing, and fully leveraging it, like parallelization itself, necessitates a delicate balancing act. The number of partitions is a topic-level parameter, and the higher the number, the better parallelization and throughput. Partitions, on the other hand, result in increased replication delay, rebalances, and open server files.