kafka streams partitioning

Kafka Streams partitions data for processing it. In both cases, this partitioning is what enables data locality, elasticity, scalability, high performance, and fault tolerance. Kafka Streams uses the concepts of partitions and tasks as logical units of its parallelism model based on Kafka topic partitions.

1. What is Kafka partitioning?
2. How does Kafka partition data?
3. How many partitions should a Kafka topic have?
4. Is Kafka streams distributed?
5. How many Kafka partitions is too many?
6. Is Kafka pull or push?
7. Can we increase Kafka partitions?
8. Why Apache Kafka is used?
9. How do I increase the size of a Kafka partition?
10. Can we use Kafka without zookeeper?
11. How do I choose a Kafka partition?
12. Can Kafka have multiple consumers?

What is Kafka partitioning?

Partitions are the main concurrency mechanism in Kafka. A topic is divided into 1 or more partitions, enabling producer and consumer loads to be scaled. Specifically, a consumer group supports as many consumers as partitions for a topic.

How does Kafka partition data?

Kafka topics are divided into a number of partitions. Partitions allow you to parallelize a topic by splitting the data in a particular topic across multiple brokers — each partition can be placed on a separate machine to allow for multiple consumers to read from a topic in parallel.

How many partitions should a Kafka topic have?

For most implementations you want to follow the rule of thumb of 10 partitions per topic, and 10,000 partitions per Kafka cluster. Going beyond that amount can require additional monitoring and optimization.

Is Kafka streams distributed?

The Apache Kafka Streams library is used by enterprises around the world to perform distributed stream processing on top of Apache Kafka. One aspect of this framework that is less talked about is its ability to store local state, derived from stream processing.

How many Kafka partitions is too many?

As guideline for optimal performance, you should not have more than 4000 partitions per broker and not more than 200,000 partitions in a cluster.

Is Kafka pull or push?

With Kafka consumers pull data from brokers. Other systems brokers push data or stream data to consumers. ... Since Kafka is pull-based, it implements aggressive batching of data. Kafka like many pull based systems implements a long poll (SQS, Kafka both do).

Can we increase Kafka partitions?

Apache Kafka provides us with alter command to change Topic behaviour and add/modify configurations. We will be using alter command to add more partitions to an existing Topic. Note: While Kafka allows us to add more partitions, it is NOT possible to decrease number of partitions of a Topic.

Why Apache Kafka is used?

Apache Kafka can be used for logging or monitoring. It is possible to publish logs into Kafka topics. The logs can be stored in a Kafka cluster for some time. There, they can be aggregated or processed.

How do I increase the size of a Kafka partition?

Example use case:

If you have a Kafka topic but want to change the number of partitions or replicas, you can use a streaming transformation to automatically stream all the messages from the original topic into a new Kafka topic which has the desired number of partitions or replicas.

Can we use Kafka without zookeeper?

You can not use kafka without zookeeper. ... So zookeeper is used to elect one controller from the brokers. Zookeeper also manages the status of the brokers, which broker is alive or dead. Zookeeper also manages all the topics configuration, which topic contains which partitions etc.

How do I choose a Kafka partition?

How to choose the number of topics/partitions in a Kafka cluster?

1. More Partitions Lead to Higher Throughput. ...
2. More Partitions Requires More Open File Handles. ...
3. More Partitions May Increase Unavailability. ...
4. More Partitions May Increase End-to-end Latency. ...
5. More Partitions May Require More Memory In the Client. ...
6. Summary. ...
7. Interested in More?

Can Kafka have multiple consumers?

While Kafka allows only one consumer per topic partition, there may be multiple consumer groups reading from the same partition. Multiple consumers may subscribe to a Topic under a common Consumer Group ID, although in this case, Kafka switches from sub/pub mode to a queue messaging approach.