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Hadoop is an open-source framework it provides storage and big data processing in a distributed environment in various Groups of computers with simple programming models. It suggests local computation and storage from single servers. This tutorial provides a basic understanding of Big Data, MapReduce algorithm, and Hadoop Distributed File System.
[ Related Article – Hadoop Introduction ]
Kafka is a publish-subscribe messaging application which is programming in “Scala”. It is an open source (open to the public) message broker project which was started by the Apache software. The design pattern of Kafka is mainly based on the design of the transactional log.
[ Related Article – Explain about Kafka? ]
Here is a sequential ID number given to the messages in the partitions what we call, an offset. So, to identify each message in the partition uniquely, we use these offsets.
As the Kafka environment is run on zookeeper, one has to make sure to run zookeeper server first and then ignite Kafka server.
The main concept of Consumer Groups is exclusive to Apache Kafka. Mainly, every Kafka consumer group consists of one or more consumers that jointly consume a set of subscribed topics.
In each every Kafka broker, there are few partitions available. And, here each partition in Kafka can be either a leader or a replica of a topic.
Kafka is nothing but a group which holds multiple brokers as it is called as a distributed system. The topics within the system will hold multiple partitions. Every broker within the system will take on Number of partitions. Based on this the producers and consumers actually exchange the message at the same time and the overall execution happens seamlessly.
Map reduce is an algorithm or concept to process Huge amount of data in a faster way. As per its name you can Split it Map and Reduce.
The major MapReduce job usually splits the input data-set into independent chunks. (Big data sets in the multiple small datasets)
Map Task: It will process these chunks in a completely parallel manner (One node can process one or more chunks).The framework sorts the outputs of the maps.
Reduce Task: And the above output will be the input for the decrease tasks, produces the final result.
[ Related Article – Importance of MapReduce in Hadoop ]
Apache Kafka is a distributed system is built to use Zookeeper. Although, Zookeeper’s main role here is to build coordination between different nodes in a cluster. However, we also use Zookeeper to recover from previously committed offset if any node fails because it works as periodically commit offset.
[ Related Article – Explain about Apache ZooKeeper? ]
The advantages of using Kafka technology:
They are 4 main core API’s:
All the conversation between the clients happens over through high-performance language via TCP protocol.
As the main role of the Leader is to perform the task of all read and write requests for the partition, whereas Followers passively replicate the leader. Hence, at the time of Leader failing, one of the Followers take over the role of the Leader. Basically, this entire process ensures load balancing of the servers.
The Streams API is responsible where it allows the application to act as a processor and within the process, it will be effectively transforming the input streams to output streams.
Within the Kafka cluster, it retains all the published records. It doesn’t check whether they have been consumed or not. Using a configuration setting for the retention period, the records can be discarded. The main reason to discard the records from the Kafka cluster is that it can free up some space.
Within the available producer, the main function of partitioning key is to validate and direct the destination partition of the message. Normally, a hashing based partitioned is used to assess the partition Id if the key is provided.
The MapReduce framework subsist of a single master Job Tracker and Number of slaves, each cluster-node will have one Task Tracker. The master is important for scheduling the jobs’ component tasks on the slaves, monitoring them and re-executing the failed tasks. The victim executes the tasks as directed by the master.
Those Are Kafka and Flume are used for real-time processing where Kafka seems to be more scalable and you can trust on the message durability.
Because of Replication, we can be sure that published messages are not lost and can be consumed in the event of any machine error, program error or frequent software upgrades.
The maximal size of a message that can be received by the Kafka is approx. 1000000 bytes
We can easily deploy Kafka as a multi-tenant solution. However, by configuring which topics can produce or consume data, Multi-tenancy is enabled. Also, it provides operations support for quotas.
For our cluster, Kafka Mirror Maker offers Geo-replication. Basically, messages are replicated across multiple data centers or cloud regions, with Mirror Maker. So, it can be used in active/passive scenarios for backup and recovery; or also to place data closer to our users, or support data locality requirements.
Minimally a Hadoop application would have following components.
ISR refers to In sync replicas. These are generally classified as a set of message replicas which are synced to be leaders.
So, ways to tune Apache Kafka it is to tune its several components:
The Good feature of Kafka is “Variety of Use Cases”.
It means Kafka is able to manage the variety of use cases which are very frequent for a Data Lake. For Example log aggregation, web activity tracking, and so on.
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