|Functionality||Publish-subscribe model messaging system||System for data collection, aggregation & movement|
The most important elements of Kafka:
Kafka is an open source system and also a distributed system is built to use Zookeeper. The basic responsibility of Zookeeper is to build coordination between different nodes in a cluster. Since Zookeeper works as periodically commit offset so that if any node fails, it will be used to recover from previously committed to offset.
The zoo keeper is also responsible for configuration management, leader detection, detecting if any node leaves or joins the cluster, synchronization, etc.
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Kafka is a message divider project coded in Scala. Kafka is originally developed by LinkedIn and developed as an open sourced in early 2011. The purpose of the project is to achieve the best stand for conducting the real-time statistics nourishment.
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Duplication assures that issued messages which are available are absorbed in the case of any appliance mistake, plan fault or recurrent software promotions.
It is responsible for covering the two producers- kafka.producer.SyncProducer and the kafka.producer.async.AsyncProducer. The main aim is to disclose all the producer performance through a single API to the clients.
Flume’s major use-case is to gulp down the data into Hadoop. The flume is incorporated with the Hadoop’s monitoring system, file formats, file system and utilities such as Morphlines. Flume’s design of sinks, sources and channels mean that with the aid of Flume one can shift data among other systems lithely, but the main feature is its Hadoop integration.
The flume is the best option used when you have non-relational data sources if you have a long file to stream into the Hadoop.Kafka’s major use-case is a distributed publish- subscribe messaging system. Kafka is not developed specifically for Hadoop and using Kafka to read and write data to Hadoop is considerably trickier than it is in Flume.
Kafka can be used when you particularly need a highly reliable and scalable enterprise messaging system to connect many multiple systems like Hadoop.
For Additional Reading :- Processing JSON Data in Real Time Streaming using Storm & Kafka
Its role is to specify the target divider of the memo, within the producer. Usually, a hash-oriented divider concludes the divider ID according to the given factors. Consumers also use the tailored Partitions.
QueueFullException naturally happens when the manufacturer tries to propel communications at a speed which Broker can’t grip. Consumers need to insert sufficient brokers to collectively grip the amplified load since the Producer doesn’t block.
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It is impossible to use Kafka without Zookeeper because it is not feasible to go around Zookeeper and attach in a straight line to the server. If the Zookeeper is down for a number of causes, then we will not be able to serve any customer demand.
A cluster contains multiple brokers since it is a distributed system. Topic in the system will get divided into multiple partitions and each broker store one or more of those partitions so that multiple producers and consumers can publish and retrieve messages at the same time.
Given that Kafka exercises Zookeeper, we have to start the Zookeeper’s server.
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One can use the convince script packaged with Kafka to get a crude but effective single node Zookeeper instance> bin/zookeeper-server-start.shconfig/zookeeper.propertiesNow the Kafka server can start> bin/Kafka-server-start.shconfig/server.properties
Kafka provides single consumer abstractions that discover both queuing and publish-subscribe Consumer Group. They tag themselves with a user group and every communication available on a topic is distributed to one user case within every promising user group. User instances are in disconnected process. We can determine the messaging model of the consumer based on the consumer groups.
The messages in the partitions will be given a sequential ID number known as an offset, the offset will be used to identify each message in the partition uniquely. With the aid of Zookeeper Kafka stores the offsets of messages consumed for a specific topic and partition by this consumer group.
A partition key can be precise to point to the aimed division of a communication, in Kafka producer. Usually, a hash-oriented divider concludes the division id with the input and people uses modified divisions also.
Kafka being distributed publish-subscribe system has the advantages as below.Fast: Kafka comprises of a broker and a single broker can serve thousands of clients by handling megabytes of reads and writes per second.Scalable: facts are partitioned and streamlined over a cluster of machines to enable large informationDurable: Messages are persistent and is replicated in the cluster to prevent record loss Distributed by Design: It provides fault tolerance guarantees and robust.
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