How Long Does Kafka Keep Data?

Can Kafka lost messages?

Kafka is speedy and fault-tolerant distributed streaming platform.

However, there are some situations when messages can disappear.

It can happen due to misconfiguration or misunderstanding Kafka’s internals..

Does Kafka persist data?

As we described, Kafka stores a persistent log which can be re-read and kept indefinitely. Kafka is built as a modern distributed system: it’s runs as a cluster, can expand or contract elastically, and replicates data internally for fault-tolerance and high-availability.

Why Kafka is so fast?

Kafka relies on the filesystem for the storage and caching. The problem is disks are slower than RAM. This is because the seek-time through a disk is large compared to the time required for actually reading the data. But if you can avoid seeking, then you can achieve latencies as low as RAM in some cases.

How do you check Kafka retention period?

If you want to view the configurations for all topic Either you can view these properties log. retention. hours or log.retention.ms in server. properties in kafka config directory.

Where Kafka offset is stored?

The offsets for your groups are stored in zookeeper. For brokers 0.9 and higher you should use the new ConsumerGroup . The offsets are stored with kafka brokers.

Where are Kafka partitions stored?

dirs is defining where your logs/partitions will be stored on disk. By default on Linux it is stored in /tmp/kafka-logs .

How long does Kafka take to rebalance?

5 minutesDuring the entire rebalancing process, i.e. as long as the partitions are not reassigned, consumers no longer process any data. By default, the rebalance timeout is fixed to 5 minutes which can be a very long period during which the increasing consumer-lag can become an issue.

How do I clean up my Kafka topic?

To delete manually:Shutdown the cluster.Clean kafka log dir (specified by the log. dir attribute in kafka config file ) as well the zookeeper data.Restart the cluster.

Can Kafka pull data?

With Kafka consumers pull data from brokers. Other systems brokers push data or stream data to consumers. Messaging is usually a pull-based system (SQS, most MOM use pull). With the pull-based system, if a consumer falls behind, it catches up later when it can.

Is Kafka a Nosql database?

Developers describe Kafka as a “Distributed, fault-tolerant, high throughput, pub-sub, messaging system.” Kafka is well-known as a partitioned, distributed, and replicated commit log service. It also provides the functionality of a messaging system, but with a unique design.

How data is stored in Kafka?

Partitions are Kafka’s storage unit. Partitions are split into segments. Segments are two files: its log and index. … The data stored on disk is the same as what the broker receives from the producer over the network and sends to its consumers.

Can I use Kafka as database?

The main idea behind Kafka is to continuously process streaming data; with additional options to query stored data. Kafka is good enough as database for some use cases. However, the query capabilities of Kafka are not good enough for some other use cases.

What is retention MS in Kafka?

retention. hours is a property of a broker which is used as a default value when a topic is created. When you change configurations of currently running topic using kafka-topics.sh , you should specify a topic-level property. A topic-level property for log retention time is retention.ms .

How does Kafka retention work?

A message sent to a Kafka cluster is appended to the end of one of the logs. The message remains in the topic for a configurable period of time or until a configurable size is reached until the specified retention for the topic is exceeded. The message stays in the log even if the message has been consumed.

Can Kafka be used for ETL?

Companies use Kafka for many applications (real time stream processing, data synchronization, messaging, and more), but one of the most popular applications is ETL pipelines. … You can use Kafka connectors to read from or write to external systems, manage data flow, and scale the system—all without writing new code.