7 Best Practices to Optimize Your Apache Kafka Data Deployment & Scale Performance Image credit: Unsplash If you are eager to get a good performance, understanding, rewards, in-depth ideas etc you can trust Apache Kafka, as it is always number one. We are making a transition through the old systems to the new generation. An access towards some strong data streaming tool which you will find in Apache Kafka, makes your work much easier and productive. In this article we have discussed about 8 best practices to enhance the performance of Apache Kafka. Log Set Up Framework To set up any log behavior you need to maintain the load time. For the maintenance of time your setting must be in an appropriate format. Changing the log behavior becomes easy by this like the log hold, setting of cache, shortening and curtailment. While you are changing any log behavior, deletion of some unused files with log.cleanup.policy is usual. There are others like log.segments.ms or log.segments.bytes. They take care of the logs. Like maintenance of time is required, frequency of shortening of the log is also required. It is done to achieve better performance. That is why it is said that to keep a few files compressed for later use. It is done so that the messages can be found later in Kafka. Kafka is a much needed thing when you are up with the horizontal scaling process. The scaling process involves hardwares like: CPU: Strong Central Processing Unit is the least required hardware. It becomes important while using SSL or log compression. Also, if the parallelization is greater then the employment of cores will be higher too. Compression of the log is important, but in case if it is not then you must add LZ4 codec. RAM: Run your RAM or Random Access Memory with efficiency in any use cases and that becomes possible if you have 6GB RAM. If you want efficiency then you must do it with a device of 32 GB memory or more. Heavy memories are used for heavy production of loads. The extra loads are also taken care of by utilizing the cache of the OS page and the delivery to the clients. But Kafka's system enables it for less usage of RAM. Kafka's load taking is restricted somehow. You will be able to grow more with Kafka if the sequence of the drive is in RAID format. You must avoid NAS and utilize SSDs for the benefit. Network and filesystem: Networking and file systems are important that is why it is very necessary to keep the clusters under a single data place. Make the most of Apache ZooKeeper. While we are talking about Zoopkeeper cluster, it is one important part of Kafka when it is in use. Also, when you are using Kafka you must know about the key practices when you are using Zookeeper. The use of Kafka docker can be done in many places. While using Kafka, upto five Zookeeper nodes can be used, but, if it is Kafka cluster, one is enough. When it is about latency in Kafka deployment, its improvement can be made through usage of at least five Zookeeper. But all these processes give load on the nodes which must be looked after. If the load is not considered at time then if the process needs seven nodes and if they are synced together, the load increases and so the performance. If we talk about network bandwidth and latency, Zookeeper gives the best available bandwidth. With the help of discs you will be able to compress the latency. It gives your log a storage, Sides the Zookeeper process and stops the swaps. Use caution when configuring topics To have any kind of modifications few things that you must see are the dividing factor and partition count as their changes might be little different and difficult. To make it easy you can check all these specifications properly in the first place before you are going to create a new type of topic. That is why configuration of the topic is very important in performance of Kafka cluster. There is a property of server default which helps in configuration of the topics which can be used while topic creation or any other configuration. While you are dealing with topics you will come across large messages. That time your application factor that is three folded, can also apply caution and can break the messages evenly. Sometimes it can happen that all these things may not work at that time you can use the compression method to the producer's side. Your use case will be active once your messages are very big and this is possible if your default log segment is 1 GB in size. Partition count is also a key factor. Image credit Unsplash Parallel processing Implementation of parallel processing is itself a balancing act and kafka is built for that. The performance of the processing will be more effective if the partitions are of topic level parameter. If the partitions are more, the performance also enhances. It is because partitions give more replications of good latency; it creates a balance and opens server files. The delivery and content that you want you can get by estimating the number of partitions you want and the delivery you want from your system. You can also choose other ways like choosing any particular partition per subject and then you can estimate by doubling the participation in case you want more content. Securely configure and isolate Kafka Every data after deployment also needs security. But the issue comes with the internal function of the kafka and the structure in which Kafka runs. . While that was about breaching kafka's security after kafka's 9 release there were many types of security added to it. It includes Authentication for Kafka or client and Kafka or Zookeeper TLS support system These two are the important protection of kafka fr. The unnecessary involvement. If it talks about the DLS support system then it has a feature of separating and securing communication to the brokers. It is very difficult to secure cough ka and zookeeper that is why they are mostly secured by the fire walls and fuse security groups and the brokers being in a single network that is private which does not allow any public communication. Kafka can also be secured through middleware or load balancing layers. Increase the Ulimit to avoid outages We do this a lot of times, open multiple files at the same time. This causes breakdown of the brokers due to the load which forces it to happen like this. But, you may not suffer from the same problem if you change your Ulimit. That will avoid any type of outrage. This is because the breakage of brokers happens very often. Low latency network Low latency network is perfect for Kafka implementation. While positioning the brokers you must be sure that they should be near to the regions of customers. You will find the network performance when you are selecting the instances. Those instances are provided by the cloud providers. But, it’s preferable to have updated bandwidth to get the best effects.