Apache Kafka is a distributed streaming platform. It can publish records or message streams, store records in a fault-tolerant way, and it has the ability to process streams of records as they occur. Most IT companies have a large load of messages, and Kafa is a big data solution for handling these billions of messages.
10 Best Practices for Working with Apache Kafka
Kafka best practices
An Apache Kafka course will help developers understand what Kafka is about and how best to apply it. But, along with this basic training, having some idea about the best practices for using the application can help you navigate the learning curve easily. Here are some Apache Kafka best practices every Kafka developer needs to know.
Leverage Apache Kafka ZooKeeper
Apache ZooKeeper cluster is a necessity for running Kafka, but for leveraging ZooKeeper to its fullest, the maximum ZooKeeper nodes used should be five. If you have a development environment, one node is enough, while three nodes are sufficient for production Kafka clusters. Having too many nodes increases the load, which slows down production.
Make topic configurations carefully
When settings like replication factors are altered, it impacts the Kafka clusters’ performance, so you want to get the configuration right the first time. If any further changes are necessary, it is easier to create a new topic. Break large messages into pieces to avoid disrupting the use case.
Opt for parallel processing
Kafka is built to facilitate parallel processing, but utilizing it right means a balancing act. When you have more partitions, the parallelization is better. Calculate the partition you need by estimating your throughput for the hardware. Generally, one partition can deliver 1/10 MB/s, so you can determine your throughput based on this estimate.
Raise the Ulimit
This is a common scenario, where outages occur when there are too many files open, which could result in outages. To avoid such outages, you can configure the Ulimit to allow more than 1,20,000 open pages at a time.
Configure the retention space right
Understand the data rate of your partition so that you can configure the retention space correctly. The data rate is the rate at which messages are sent per second. This rate will give you an estimate of how much retention space you need, in bytes, to get retention for a certain period.
Use random partitioning
It can be difficult to manage uneven data rates. This happens if consumers with higher throughput have to process more messages than those in consumer groups. Such a partition could have 10 times the weight of another partition on a similar topic.
Upgrade older versions of Kafka
If any of your consumers are using a version of Kafka older than 0.10, upgrade it. The bugs in older versions can cause failure in the to rebalance algorithm.
Select FileSystem carefully
Kafka doesn’t depend on any file system since it uses regular files on disk. But if using a file system, XPS or the EXT4 are the best options. XPS has undergone many performance improvements recently, which enables it to handle Kafka workload better.
Keep security in mind
When securing Kafka, two things need to be considered: the infrastructure on which Kafka runs and Kafka’s internal configuration. Also, isolating Kafka and ZooKeeper ensures better security.
Monitor brokers for replications
If you experience frequent ISR shrinks in a single partition, it could mean that the data rate exceeds the leader’s ability to service the consumer and replication threads. To avoid this, monitor your brokers for ISR (in-sync replicas) and unpreferred leaders.
How will a Kafka training help you?
By taking Apache Kafka training, you will learn the following Kafka essentials.
- An introduction to Kafka messaging system, its architecture, and its configuration.
- Understanding how Kafka processes real-time data, and what kind of technology it uses to do so.
- Effectively construct and process messages in Kafka APIs, like producer or consumer.
- Understand how the Kafka cluster can integrate with other Big Data frameworks.
- Hands-on understanding of developing messages in Kafka and subscribing topics.
- Learn the various ways to integrate Kafka with other Apache frameworks, like Storm or Spark.
The learning doesn’t end there
Apache Kafka is an easy-to-understand application once the initial learning curve is passed. And, learners don’t require extensive knowledge of Big Data to take the Kafka training. Having a basic idea of Big Data and data science will help you get through the training. But, you need to know and understand the core concepts of Java and Python before applying for this training.