Apache Kafka Tutorial – Learn Apache Kafka Online

Apache Kafka Tutorial provide basic concepts of Apache Kafka and this tutorial is designed for beginners and professionals to learn Apache Kafka Tutorial Online. Apache Kafka is well known real time distributed publish-subscribe messaging system designed to replace traditional message.

Understanding the idea of publish/subscribe communications and why it is important is crucial for Apache Kafka. In publish/subscribe communications, the sender (publisher) of a message or piece of data does not immediately direct the recipient to receive it. Instead, the message is classified by the publisher in some way, and the recipient (the subscriber) signs up to receive only that classification of messages. We can see a tutorial for Apache Kafka that includes code samples.

Apache Kafka Tutorial

Kafka consumers, Kafka procedure, Kafka internals, Kafka data pipelines, installing Kafka with other operating system overviews, monitoring Kafka, Kafka Data mirroring, and administering Kafka with simple examples are some of the major topics covered in the Apache Kafka Tutorial.

Apache Kafka Publish/Subscribe Messaging

There is a common starting point for many publish/subscribe use cases: a simple message queue or interprocess communication channel. Consider creating an app that needs to provide monitoring data to a different app. To do this, you include a direct connection between your app and the app that shows your metrics on a dashboard and push metrics through that connection. This is a straightforward fix for a straightforward issue that functions effectively when you first begin monitoring. The dashboard does not perform effectively when you decide you want to analyze your metrics over a longer time period shortly after that.

This is a simple solution to a simple problem that works when you are getting started with monitoring. Before long, you decide you would like to analyze your metrics over a longer term, and that doesn’t work well in the dashboard. You start a new service that can receive metrics, store them, and analyze them.

Individual Queue Systems

One of your coworkers was doing similar work with log messages while you were fighting this war with metrics. Another task has been to monitor website visitors’ behavior on the front end and feed that information to developers using machine learning while also creating some management reports. You’ve all developed systems that distinguish between information publishers and subscribers using a similar methodology.

Enter Kafka

Apache This issue is addressed by the publish/subscribe messaging system known as Kafka. Frequently, it is referred to as a “distributed commit log” or, more recently, as a “distributing streaming platform.” All transactions are recorded permanently in a file system or database commit log, which may then be replayed to consistently reconstruct the state of a system. Similar to that, data within Kafka is persistently stored in order and is readable deterministically.

Kafka Messages and Batches

In Kafka, a message is a data unit. You can think of this as a row or a record if you have database experience. Kafka views a message as nothing more than an array of bytes, with the data it contains having no particular structure or significance. A message may include a key as an optional piece of metadata. The key is a byte array like the message and has no special significance to Kafka. Keys are used when messages need to be written to partitions in a more controlled manner.

Kafka Schemas

Even though Kafka treats messages as opaque byte arrays, it is recommended that the message content be given additional structure, or schema, to make it clearer. Message schema options vary based on the requirements of your application. Simple systems that are legible by humans include Javascript Object Notation (JSON) and Extensible Markup Language (XML). They do not, however, have good type management or compatibility with older versions of the schema.

Kafka Topics and Partitions

In Kafka, communications are categorized using topics. The closest analogs for a topic are a database table or a folder in a file system. The topics are grouped into a number of sections. A partition is a single log, going back to the notion of a “commit log”. It has messages attached to it, which are read in order from start to finish. Message time-ordering is not guaranteed throughout the subject, just inside a single partition because a topic sometimes consists of multiple partitions.

Kafka Producers and Consumers

Users of the system who belong to the producers and consumers categories are referred to as Kafka clients. Additionally, there are sophisticated client APIs like Kafka Connect API and Kafka Streams for data integration and stream processing. The most sophisticated clients offer higher-level functionality by building on producers and consumers. Producers develop fresh messages. In other publish/subscribe systems, these are known as publishers or writers. Typically, a message will be developed for a specific subject. By default, the producer will evenly distribute messages among all partitions of a subject without caring which one a given message is written to. In some cases, the producer may route messages to specific partitions.

Customer messages are read. In other publish/subscribe systems, these clients are referred to as subscribers or readers. The customer subscribes to one or more topics, and they read the communications in the order that they were made. As a member of a consumer group, which consists of one or more consumers, consumers consume a topic. One member of the group consumes each segment solely.

Kafka Brokers and Clusters

The term “broker” refers to a single Kafka server. Assigning offsets to the messages after receiving them from producers, the broker commits the messages to disc storage. By responding to partition retrieve requests and responding with messages that have been committed to disc, it also helps customers. Kafka brokers are made to function in a cluster. One broker will operate as both the cluster controller (chosen automatically from the cluster’s active members) and the primary broker inside a cluster of brokers.

Why Apache Kafka and what makes difference

There are many choices for publish/subscribe messaging systems, so what makes Apache Kafka a good choice to use. We can discuss some of the main features for using Apache Kafka is familiar.

Multiple Producers

Kafka can handle several producers with ease, regardless of whether they are utilizing different topics or the same subject. In turn, this makes the system perfect for combining and standardizing data from diverse front-end platforms. Multiple Clients Kafka was created so that multiple users might read the same stream of messages without interfering with one another. Contrarily, in many queuing systems, a message is no longer accessible to any other clients once it has been digested by one client. In order to ensure that each message is only processed once by the group as a whole, several Kafka consumers can decide to cooperate and share a stream.

Disk-Based Retention

Multiple consumers can be handled by Kafka, and persistent message retention implies that customers don’t always have to respond in real time. The messages are saved using programmable retention rules after being committed to disk. These choices can be made on a topic-by-topic basis, enabling distinct streams of messages to have varying levels of retention based on the demands of the customer.

Scalable

Any amount of data can be handled by Kafka thanks to its scalability. Users can start with a single broker as a proof-of-concept, scale to a modest development cluster of three brokers, and then transition to production with a larger cluster of tens or hundreds of brokers that grows over time as the data scales up.

High Performance

All of these features come together to make Apache Kafka a publish/subscribe messaging system with excellent performance under high load. Producers, consumers, and brokers can all be scaled out to handle very large message streams with ease.

Apache Kafka Tutorial : Kafka Installation

This subject explains how to set up Apache Zookeeper, which Kafka uses to store metadata for the brokers, as well as how to start using the Apache Kafka broker.Before using Apache Kafka, a few things must take place.

To Choose Operating System

Apache Kafka is a Java application, and can run on many operating systems. This includes Windows, MacOS, Linux, and others. The installation steps in this post will be focused on setting up and using Kafka in a Linux environment, as this is the most common OS on which it is installed.

Installing Java

Before setting up and running a Java environment, Zookeeper and Kafka must be installed. This should be Java 8; you can download it directly from java.com or use the version given by your operating system. Although a runtime version of Java is required for Zookeeper and Kafka to function, having the complete Java Development Kit (JDK) may be more practical for creating tools and applications.

Installing Zookeeper

Apache Kafka makes use of Zookeeper to keep track of consumer client information as well as metadata about the Kafka cluster. The Kafka distribution’s scripts can be used to run a Zookeeper server, however installing the complete Zookeeper package directly from the distribution is straightforward.

Standalone Server

The following example installs Zookeeper with a basic configuration in /usr/local/ zookeeper, storing its data in /var/lib/zookeeper:

# tar -zxf zookeeper-3.4.6.tar.gz
# mv zookeeper-3.4.6 /usr/local/zookeeper
# mkdir -p /var/lib/zookeeper
# cat > /usr/local/zookeeper/conf/zoo.cfg << EOF
> tickTime=2000
> dataDir=/var/lib/zookeeper
> clientPort=2181
> EOF
# export JAVA_HOME=/usr/java/jdk1.8.0_51
# /usr/local/zookeeper/bin/zkServer.sh start
JMX enabled by default
Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
#

We can now validate that Zookeeper is running correctly in standalone mode by connecting to the client port and sending the four-letter command srvr:

# telnet localhost 2181
Trying ::1...
Connected to localhost.
Escape character is '^]'.
srvr
Zookeeper version: 3.4.6-1569965, built on 02/20/2014 09:09 GMT
Latency min/avg/max: 0/0/0
Received: 1
Sent: 0
Connections: 1
Outstanding: 0
Zxid: 0x0
Mode: standalone
Node count: 4
Connection closed by foreign host.
#

Installing a Apache Kafka Broker

Once Java and Zookeeper are configured, you are ready to install Apache Kafka. The current release of Kafka can be downloaded at link. At press time, that version is 0.9.0.1 running under Scala version 2.11.0.

Apache Kafka Tutorial for beginners explains what Apache Kafka. It gives a brief understanding of messaging and important Apache Kafka concepts are explained with examples. I will be adding more posts in Apache Kafka Tutorial, so please bookmark and keep visit fro more reference/and information.