Kafka is the game-changer for data-driven organizations. The data streaming platform has surged in popularity due to its speed, scalability, and fault-tolerance. However, it’s a mistake to assume that the clusters of brokers will not be plagued by problems when it’s deployed.
Why You Need To Manage Kafka When Is Up And Running
In an ideal situation, Kafka brokers allow producers and consumers to publish and access messages on the partitions seamlessly. However, brokers can become dysfunctional after Kafka is deployed, as parameters like data load greatly vary from development. Depending on how the brokers are configured, you may suffer data loss when the broker goes down.
Kafka is billed as a low-latency messaging system, but that doesn’t always hold true in post-deployment. In some instances, consumer lag may occur where the consumers fail to read the messages at the same rate that the producers are writing onto the partition.
How To Manage Kafka During Post-Deployment
It’s disastrous to be caught in the dark when data latency or a total breakdown in the brokers occur. Therefore, you’ll need to keep an eye on Kafka even after deployment. Some basic questions that you’ll need to revisit as the load increases are whether the existing CPU, memories, and storage on the servers are sufficient.
Often, the scalability of Kafka hits the ceiling when the disk throughput is pushed to the maximum. As the number of brokers and requests grow, the disks will be burdened by the number of reads/writes committed every split seconds. You’ll want to be aware of the server performance, particularly if it’s hitting the limit.
At all times, you’ll need to be aware of the numbers of clusters, partitions, topics, producers, and consumers that make up the Kafka ecosystem. Getting to know which elements tend to experience a high volume of data flow makes preparation for expansion easier.
If a topic has grown to the extent that it’s slowing down the processing time, you may need to consider splitting it into different partitions or move existing partitions to a new broker. However, the process must be executed with care to prevent disruption to the data flow.
Often, the post-deployment Kafka ecosystem needs a minimum of 3 replicates of the same partition. In the event that one copy becomes inaccessible, the producers and consumers still have access to two copies, which are usually stored on different brokers. As someone overlooking the system health of Kafka, you’ll want to be aware of any of the brokers fails and ensuring that the number of replicas is sufficient.
There are also instances where consumers fail to keep up with the rate of producers’ publishing rate. To address the consumer lag, you’ll need to configure the data retention period to prevent data losses. The right approach is to strike a balance between space and time so that you’ll have sufficient storage and still retain the messages for the consumers.
Managing Kafka is challenging when you’re relying on command lines console. It’ll be much easier with a UI-based interface, where information and configuration of the nodes can be done quickly.
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