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12 Kafka Best Practices: Run Kafka Like the Pros

Apache Kafka is a distributed message streaming platform designed to build real-time data pipelines and streaming apps.

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What is Apache Kafka?

Apache Kafka is a distributed message streaming platform designed to build real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, and fast, which makes it a popular choice for handling real-time data feeds. Kafka’s architecture enables massive streams of records to be sorted and processed across clusters of servers.

Apache Kafka was originally developed by LinkedIn, and later open-sourced in 2011. Since then, it has been adopted by leading technology companies for various data streaming requirements. It is designed to handle data streams from websites, applications, sensors, and other sources, enabling users to process and analyze data in real-time.

To operate Kafka, producers send records to topics, and consumers read records from topics. Topics are split into partitions, and each partition is an ordered and immutable sequence of records, enabling data to be distributed across multiple servers for parallel processing.

Best practices for Apache Kafka deployment and configuration

Deploying Apache Kafka in a production environment requires careful planning and adherence to best practices. These practices are designed to maximize the performance, reliability, and durability of your Kafka deployment.

1. Use a single topic per application

While Kafka allows you to use multiple topics, this is not always the best approach. Using multiple topics can increase the complexity of your application and make it harder to manage and monitor.

This is because every topic in Kafka is divided into partitions. Every partition is an independent unit of storage and processing. The more topics you have, the more partitions you’ll need to manage. This can lead to increased resource usage and potential performance issues.

Therefore, it’s recommended to use a single topic for each application. This approach can significantly reduce the complexity of your application and make it easier to manage. It can also improve the performance of your application, as fewer partitions mean less resource usage and better throughput.

2. Set appropriate retention for your topics

Retention is the period for which Kafka retains messages in a topic before they are deleted. By default, Kafka retains all messages for seven days, but this can be adjusted based on your requirements.

Setting the correct retention period is crucial for ensuring the availability of data and the performance of your application. If the retention period is too short, you may lose important data. On the other hand, if it’s too long, it can lead to increased storage usage and potential performance issues.

Therefore, it’s recommended to carefully consider your data consumption patterns and set the retention period accordingly. For example, if your consumers consume data in real-time, a shorter retention period may be appropriate. But if your consumers need to access historical data, a longer retention period may be required.

3. Use parallel processing

Parallel processing is a key feature of Kafka that allows you to process data simultaneously across multiple threads or processes. This can significantly improve the performance of your application, especially when dealing with large volumes of data.

To leverage parallel processing in Kafka, you can use multiple consumer groups. A consumer group can have multiple consumers, each consuming data from a different partition. This allows you to process data in parallel across multiple consumers, thereby improving throughput and reducing processing time.

However, it’s important to ensure that the number of consumers in a group does not exceed the number of partitions in a topic. If there are more consumers than partitions, some consumers will be idle and won’t receive any data. Therefore, it’s recommended to carefully plan your consumer groups and partitions to maximize parallel processing.

4. Set log configuration parameters to keep logs manageable

Logs record all data changes in a topic. Managing these logs effectively is essential for maintaining the performance and reliability of your Kafka deployment.

Kafka provides several configuration parameters that you can adjust to manage your logs. For example, you can set the log retention period, the maximum size of a log segment, and the frequency of log cleanup. These settings can help you control the size of your logs and prevent them from becoming too large.

5. Run Kafka in KRaft mode

KRaft stands for Kafka Raft. Running Kafka in KRaft mode means running Kafka without ZooKeeperTM, which was a dependency in earlier Kafka versions but has since been phased out (ZooKeeper is deprecated since version 3.5.1, and is planned to be completely removed from version 4).

The advantage of KRaft mode is that it simplifies the architecture, reduces the number of moving parts and removes the potential of a single point of failure. This mode also allows Kafka to leverage the Raft consensus algorithm for leader election and replication, which is more efficient and robust than Zookeeper’s Zab consensus algorithm.

Setting up Kafka in KRaft mode is easy (these instructions assume you have Kafka installed):

  1. Generate a cluster UUID by running this command:
  2. Format log directories:
  3. Start the Kafka server:

Once your Kafka cluster is running in KRaft, you should notice improved stability and performance.

6. Configure and isolate Kafka with security in mind

Kafka offers several features to help secure your deployment, such as SSL/TLS for encrypted communication, simple authentication and security layer (SASL) for authentication, and access control lists (ACLs) for authorization. You should use these features to secure your Kafka cluster from both external and internal threats.

For example, you can use SSL/TLS to encrypt the traffic between your Kafka brokers and clients to prevent eavesdropping, and you can use ACLs to control who can produce and consume messages from your Kafka topics.

Isolating your Kafka cluster is also important for security. You should run your Kafka brokers in a separate network segment, and limit access to this segment to only the necessary clients and administrative tools. This will help mitigate the risk of a security breach and protect your Kafka data.

7. Avoid outages by raising the Ulimit

A common issue that can lead to Kafka outages is running out of file descriptors. Each Kafka broker maintains a file descriptor for each log segment, and if a broker runs out of file descriptors, it can crash or become unresponsive.

To avoid this issue, you can raise the Ulimit (the limit on the number of file descriptors a process can open) on your Kafka brokers. The exact Ulimit value you should set depends on the size of your Kafka cluster and the number of topics and partitions you have, but a good rule of thumb is to set the Ulimit to a value much higher than the maximum number of log segments you expect to have.

8. Monitor your cluster

Monitoring your Kafka cluster is crucial for maintaining performance and reliability, and alerts can help you quickly identify and resolve issues before they impact your users.

You should monitor key Kafka metrics such as broker uptime, consumer lag, and message throughput. These metrics can give you insights into the health and performance of your Kafka cluster, and help you identify potential issues early.

Best practices for managing Kafka consumers

Once Kafka is deployed, it’s important to set the right configurations for the Kafka consumers and consumer groups.

9. Choose the right number of partitions

When setting up your Kafka consumers, one of the Kafka best practices you should follow is choosing the right number of partitions for your topics. The number of partitions determines the maximum parallelism of your Kafka consumers, as each partition can be consumed by a separate consumer thread.

If you have too few partitions, you won’t be able to fully utilize your consumer resources, and your message processing may be slower than necessary. On the other hand, if you have too many partitions, you can end up with too much overhead due to the increased coordination between consumers, and your Kafka cluster may become less stable.

A good rule of thumb is to start with a moderate number of partitions, monitor your consumer performance, and adjust the number of partitions as necessary based on your observations.

10. Maintain consumer consistency

Consumer consistency means that the same consumer always consumes the same partition. Maintaining consistency can help improve the performance and reliability of your Kafka consumers. It allows consumers to keep their local caches warm, which can reduce the impact of network latency and improve message processing speed. It also avoids the need for consumers to re-fetch data they have already consumed, which can reduce network traffic and improve overall Kafka cluster performance.

To maintain consumer consistency, you should use Kafka’s consumer groups feature. When you assign a consumer to a consumer group, Kafka will ensure that the same consumer always consumes the same partition, as long as the consumer is part of the same consumer group.

11. Ensure a replication factor greater than 2

When setting up your Kafka topics, you should use a replication factor greater than 2. The replication factor determines the number of copies of each message that Kafka stores.

Using a replication factor greater than 2 can significantly improve the reliability and fault tolerance of your Kafka cluster. If one of your Kafka brokers fails, Kafka can transparently switch to a replica, ensuring that your consumers can continue to consume messages without interruption.

However, a higher replication factor also means more network traffic and storage requirements, so you should find a balance that works for your specific needs. A good rule of thumb is to start with a replication factor of 3, and adjust as necessary based on your requirements and observations.

12. Enable idempotence

Idempotence means that an operation can be applied multiple times and always has the same result. In the context of Kafka, enabling idempotence means ensuring that a message is only processed once, even if it is delivered multiple times. This is particularly important in scenarios where messages are replicated across multiple brokers, as it can prevent duplicate processing and ensure data consistency.

To enable idempotence in Kafka, you can set the enable.idempotence configuration parameter to true in your consumer configuration. This will ensure that your consumers only process each message once, regardless of how many times it is delivered.

Fully managed Apache Kafka in the cloud with Instaclustr

Instaclustr offers a fully managed Apache Kafka service, providing a reliable, scalable, and SOC 2 certified solution either in the cloud or on-premises. This service allows you to concentrate on your application development by handling the configuration and optimization of your Kafka cluster.

Instaclustr Managed Kafka is the optimal choice for running Kafka in the cloud, delivering a production-ready and fully supported Apache Kafka cluster swiftly. This fully hosted and managed solution relieves you from the complexities of data infrastructure management, enabling you to focus on innovating your application stack. With Instaclustr, you receive around-the-clock support and a service level agreement (SLA) guaranteeing 99.999% uptime. The platform is SOC 2 certified and complies with PCI-DSS and HIPAA, ensuring top-tier security and reliability.

The service includes built-in monitoring, managed mirroring, and the option to easily integrate Kafka Connect. Instaclustr’s offering is 100% open source, allowing customization and flexibility. You have the choice to run it in your own cloud provider account or use Instaclustr’s, further enhancing its adaptability to your specific needs.

Learn more about Instaclustr for Apache Kafka

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