Zookeeper heartbeat timeout synctime free#

Producers publish data to the topics of their choice. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster. If the leader fails, one of the followers will automatically become the new leader. The leader handles all read and write requests for the partition while the followers passively replicate the leader. Each partition is replicated across a configurable number of servers for fault tolerance.Д®ach partition has one server which acts as the 'leader' and zero or more servers which act as 'followers'. The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. Second they act as the unit of parallelism-more on that in a bit. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. First, they allow the log to scale beyond a size that will fit on a single server. The partitions in the log serve several purposes. For example, you can use our command line tools to 'tail' the contents of any topic without changing what is consumed by any existing consumers. This combination of features means that Kafka consumers are very cheap-they can come and go without much impact on the cluster or on other consumers. For example a consumer can reset to an older offset to reprocess. This offset is controlled by the consumer: normally a consumer will advance its offset linearly as it reads messages, but in fact the position is controlled by the consumer and it can consume messages in any order it likes. In fact the only metadata retained on a per-consumer basis is the position of the consumer in the log, called the 'offset'. Kafka's performance is effectively constant with respect to data size so retaining lots of data is not a problem.

Zookeeper heartbeat timeout synctime free#

For example if the log retention is set to two days, then for the two days after a message is published it is available for consumption, after which it will be discarded to free up space. The Kafka cluster retains all published messages-whether or not they have been consumed-for a configurable period of time. The messages in the partitions are each assigned a sequential id number called the offset that uniquely identifies each message within the partition. So, at a high level, producers send messages over the network to the Kafka cluster which in turn serves them up to consumers like this:Д®ach partition is an ordered, immutable sequence of messages that is continually appended to-a commit log.

Kafka is run as a cluster comprised of one or more servers each of which is called a broker.

We'll call processes that subscribe to topics and process the feed of published messages consumers.

We'll call processes that publish messages to a Kafka topic producers.

Kafka maintains feeds of messages in categories called topics.It provides the functionality of a messaging system, but with a unique design.ДЇirst let's review some basic messaging terminology: Kafka is a distributed, partitioned, replicated commit log service.