Create three servers with the following specifications:

Install the Confluent Community Package on the Broker Nodes

On all three nodes, add the GNU Privacy Guard (GPG) key plus package repository, and then install Confluent Community, plus Java. The format should look like this:

wget -qO - https://packages.confluent.io/deb/5.2/archive.key | sudo apt-key add -

sudo add-apt-repository "deb [arch=amd64] https://packages.confluent.io/deb/5.2 stable main"

sudo apt-get update && sudo apt-get install -y openjdk-8-jre-headless confluent-community-2.12

Add the following entries to the hosts file:

Delete the contents of the config file and replace them with the following:

On each server, set the contents of /var/lib/zookeeper/myid to the server's ID. On Node 1, enter 1, on Node 2, enter 2, and lastly on Node 3, enter 3:

Edit the broker.id and zookeeper.connect in the config file. Set the broker ID to the appropriate ID for each server (1 on Node 1, 2 on Node 2, and so on).

Get the ZooKeeper and Kafka services started, plus enable them:

sudo systemctl enable confluent-zookeeper

Both services should be active (running) on all three servers. Check the services to make sure that they are running:

We can test our cluster by listing the current topics:

kafka-topics --list --bootstrap-server localhost:9092

Since we have not created any topics yet, we will only see a default topic. The output should look like this:

In this lab we will setup new Kafka Servers on CentOS platform

How to Set Up Kafka Servers on CentOS

<h2>How to setup 3-broker Kafka cluster using Confluent Community on Ubuntu Platform</h2><h3>Prerequisite:</h3><ul><li>Deploy or get ready with 3 Ubuntu Servers (Min Req. 1CPU/2GB/30GB HDD)</li><li>Change the hostname and ssh with root credentials to all 3 nodes</li></ul>for eg:IP Address Hostname192.168.0.10 broker1192.168.0.20 broker2192.168.0.30 broker3SSH to all 3 nodes and follow the below steps:On all three nodes, add the GPG key and package repository, then install Confluent and Java.<pre class="code-block"> wget -qO - https://packages.confluent.io/deb/5.2/archive.key | sudo apt-key add -</pre><pre class="code-block"> sudo add-apt-repository &quot;deb [arch=amd64] https://packages.confluent.io/deb/5.2 stable main&quot;</pre><pre class="code-block"> sudo apt-get update &amp;&amp; sudo apt-get install -y openjdk-8-jdk confluent-community-2.12</pre>On all three nodes, edit the hosts file.<pre class="code-block">sudo vi /etc/hosts</pre>Add the following entries to the hosts file on all three servers. Use the private IP addresses of your three servers (you can find them in Cloud Playground).<pre class="code-block">192.168.0.10 zoo1</pre><pre class="code-block">192.168.0.20 zoo2</pre><pre class="code-block">192.168.0.30 zoo3</pre>Edit the ZooKeeper config file.<pre class="code-block"> sudo vi /etc/kafka/zookeeper.properties</pre>Delete the contents of the config file, and replace them with the following:<pre class="code-block"> tickTime=2000</pre><pre class="code-block"> dataDir=/var/lib/zookeeper/</pre><pre class="code-block"> clientPort=2181</pre><pre class="code-block"> initLimit=5</pre><pre class="code-block"> syncLimit=2</pre><pre class="code-block"> server.1=zoo1:2888:3888</pre><pre class="code-block"> server.2=zoo2:2888:3888</pre><pre class="code-block"> server.3=zoo3:2888:3888</pre><pre class="code-block"> autopurge.snapRetainCount=3</pre><pre class="code-block"> autopurge.purgeInterval=24</pre>Set the Zookeeper ID for each server.<pre class="code-block"> sudo vi /var/lib/zookeeper/myid</pre>On each server, set the contents of /var/lib/zookeeper/myid to the server&#x27;s ID. On broker1, enter 1. On broker2, enter 2, and on broker3, enter 3.<pre class="code-block"> &lt;server id 1, 2, or 3&gt;</pre>Edit the Kafka config file.<pre class="code-block"> sudo vi /etc/kafka/server.properties</pre>Edit the broker.id, advertised.listeners, and zookeeper.connect in the config file. Set the broker ID to the appropriate ID for each server (1 on broker1, 2 on broker2, and 3 on broker3 ).For advertised.listeners, provide the hostname for each server: zoo1, zoo2, or zoo3 as appropriate.Set zookeeper.connect to zoo1:2181.<pre class="code-block">broker.id=&lt;server id 1, 2, or 3&gt;</pre><pre class="code-block">advertised.listeners=PLAINTEXT://&lt;hostname zoo1, zoo2, or zoo3&gt;:9092</pre><pre class="code-block">zookeeper.connect=zoo1:2181</pre>Start and enable the Zookeeper service.<pre class="code-block">sudo systemctl start confluent-zookeeper</pre><pre class="code-block">sudo systemctl enable confluent-zookeeper</pre>Wait a few seconds, then do the same for the Kafka service.<pre class="code-block">sudo systemctl start confluent-kafka</pre><pre class="code-block">sudo systemctl enable confluent-kafka</pre>Check the services to make sure they are running. Both services should be active (running) on all three servers.<pre class="code-block">sudo systemctl status confluent*</pre>Test your cluster by listing the current topics.<pre class="code-block">kafka-topics --list --bootstrap-server localhost:9092</pre>Since you have not created any topics yet, you will only see a default topic. The output should look like this:<pre class="code-block">__confluent.support.metrics</pre>

In this blog, I will guide you through the process of setting up your own 3-broker Kafka cluster using the Confluent Community.

Setting Up Kafka cluster on Ubuntu Platform

Suppose there is a requirement to expose your Kafka brokers so that external clients can communicate with them.

So here we will be implementing authentication with client certificates in an existing Kafka cluster, and then authenticate as a client to verify that your implementation works.

Generate a client certificate. Choose a password for the client keystore when prompted:

keytool -keystore client.keystore.jks -alias kafkauser -validity 365 -genkey -keyalg RSA -dname "CN=kafkauser, OU=Unknown, O=Unknown, L=Unknown, ST=Unknown, C=Unknown"

Sign the key, then import the certificate authority and signed key into the keystore. When asked for the password to the ca-key, enter the password AllTheKeys:

keytool -keystore client.keystore.jks -alias kafkauser -certreq -file client-cert-file

openssl x509 -req -CA ca-cert -CAkey ca-key -in client-cert-file -out client-cert-signed -days 365 -CAcreateserial

keytool -keystore client.keystore.jks -alias CARoot -import -file ca-cert

keytool -keystore client.keystore.jks -alias kafkauser -import -file client-cert-signed

Move the client keystore into an appropriate location:

sudo cp client.keystore.jks /var/private/ssl/

sudo chown root:root /var/private/ssl/client.keystore.jks

Enable Client Authentication for the Broker

Set client authentication to required in server.properties:

Locate the line that begins with ssl.client.auth and change it:

Restart Kafka and then verify that everything is working:

Add Client Authentication Settings to Your Client Config File

ssl.keystore.location=/var/private/ssl/client.keystore.jks

ssl.keystore.password=<your client keystore password>

ssl.key.password=<your client key password>

Create a console consumer using client authentication to make verify that everything is working:

kafka-console-consumer --bootstrap-server zoo1:9093 --topic inventory_purchases --from-beginning --consumer.config client-ssl.properties

In this blog, we will learn how to expose our Kafka brokers so that external clients can communicate with them.

How to do client authentication with Kafka

Apache Storm is a distributed real-time big data-processing system. Storm is designed to process vast amount of data in a fault-tolerant and horizontal scalable method. It is a streaming data framework that has the capability of highest ingestion rates. Though Storm is stateless, it manages distributed environment and cluster state via Apache ZooKeeper. It is simple and you can execute all kinds of manipulations on real-time data in parallel.

Apache Storm is continuing to be a leader in real-time data analytics. Storm is easy to setup, operate and it guarantees that every message will be processed through the topology at least once

Apache Kafka is a distributed data store optimized for ingesting and processing streaming data in real-time. Streaming data is data that is continuously generated by thousands of data sources, which typically send the data records in simultaneously. A streaming platform needs to handle this constant influx of data, and process the data sequentially and incrementally.

Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.

Difference Between Apache Storm and Kafka

Apache Kafka use to handle a big amount of data in the fraction of seconds. It is a distributed message broker which relies on topics and partitions. Apache Storm is a fault-tolerant, distributed framework for real-time computation and processing data streams. It takes the data from various data sources such as HBase, Kafka, Cassandra, and many other applications and processes the data in real-time. It has been written in Clojure and Java.

Key Differences Between Apache Storm and Kafka

Following is the key difference between Apache Storm and Kafka:

1) Apache Storm ensure full data security while in Kafka data loss is not guaranteed but it’s very low like Netflix achieved 0.01% of data loss for 7 Million message transactions per day.

2) Kafka can store its data on local filesystem while Apache Storm is just a data processing framework.

3) Storm works on a Real-time messaging system while Kafka used to store incoming message before processing.

4) Apache Kafka is used for processing the real-time data while Storm is being used for transforming the data.

5) Kafka gets its data from the actual source of data while Storm pulls the data from Kafka itself for further processes.

6) Kafka is an application to transfer real-time application data from source application to another while Storm is an aggregation & computation unit.

7) Kafka is a real-time streaming unit while Storm works on the stream pulled from Kafka.

8) It’s mandatory to have Apache Zookeeper while setting up the Kafka other side Storm is not Zookeeper dependent.

9) Kafka works as a water pipeline which stores and forward the data while Storm takes the data from such pipelines and process it further.

10) Kafka is a great source of data for Storm while Storm can be used to process data stored in Kafka.

11) Apache Storm has inbuilt feature to auto-restart its daemons while Kafka is fault-tolerant due to Zookeeper.

Below is the comparison table between Apache Storm and Kafka.

Data Storage: Doesn’t store its data. Data gets transfer from input stream to output stream 

Stream Processing: Micro-Batch Processing	 

Dependency: Not Dependent on any external application

Language Support: It supports all the languages

Data Storage:  File system such as EXT4 or XFS

Stream Processing: Small-Batch Processing

Latency: Depends upon Data Source generally less than 1-2 seconds

 Language Support: Kafka works with all but works best with Java language only

Apache Storm versus Kafka both are autonomous and have an alternate reason in Hadoop group climate.

Apache Storm versus Kafka both are autonomous of one another anyway it is prescribed to utilize Storm with Kafka as Kafka can reproduce the information to storm if there should be an occurrence of bundle drop likewise it verify prior to sending it to Storm.

Kafka's job is to function as middleware it takes information from different sources and afterward Storms processes the messages rapidly. Counting and isolating of online votes is the ongoing model for Apache Storm.

Apache Storm versus Kafka both are having incredible ability in the continuous spilling of information and truly proficient frameworks for performing constant examination.

The differences between Apache Storm and Kafka and how do these two languages connect with Java let see.

Apache Storm vs Kafka

To start with, we should discuss what Apache Kafka is and how it functions.

Kafka is a disseminated distributed buy-in informing framework. It has a vigorous line that can acknowledge a lot of message information. With Kafka, applications can compose and peruse information to points. A point goes about as a classification for naming information. Then, an application can devour messages from one or different classes.

Kafka perseveres every one of the information that it gets to circle stockpiling. Then, at that point, Kafka duplicates information inside a Kafka bunch to forestall information misfortune.

Kafka can be extremely quick for quite some time. In the first place, it doesn't offer many fancy odds and ends. Another explanation is that Kafka doesn't have individual message IDs. It utilizes the offset of when the message was gotten. Additionally, it doesn't follow the shopper for a point or who has burned through a specific message. Customers keen on this information should follow this data.

Upon information recovery, you can just determine an offset. Then, at that point, beginning from that offset, Kafka will return you the information altogether. There are bounty more speed enhancements accessible with Kafka, yet we won't cover them in this post.

These enhancements permit Kafka to deal with huge heaps of information. Yet, to improve comprehension of the abilities of Apache Kafka, we want to initially comprehend the wording.

There are four key benefits of using Kafka:

Reliability: Kafka distributes, replicates, and partitions data. Additionally, Kafka is fault-tolerant.

Scalability: Kafka’s design allows you to handle enormous volumes of data. And it can scale up without any downtime.

Durability: Messages received are persisted as quickly as possible to storage. So, we can say Kafka is durable.

Performance: Finally, Kafka maintains the same level of performance even under extreme loads of data (many terabytes of message data). Kafka can perform up to two million writes per second.

So, you can see that Kafka can large amounts of data with zero downtime and no data loss.

In our last Kafka Tutorial, we discussed Books for Kafka. Today, we will discuss the Advantages and Disadvantages of Kafka. Because, it is very important to know the limitations of any technology before using it, same in case of advantages.

So, let’s discuss Kafka's Advantages and Disadvantages in detail.

Without having not-so-large hardware, Kafka is capable of handling high-velocity and high-volume data. Also, able to support message throughput of thousands of messages per second.

It is capable of handling these messages with the very low latency of the range of milliseconds, demanded by most of the new use cases.

One of the best advantages is Fault Tolerance. There is an inherent capability in Kafka, to be resistant to node/machine failure within a cluster.

Here, durability refers to the persistence of data/messages on a disk. Also, messages replication is one of the reasons behind durability, hence messages are never lost.

Without incurring any downtime on the fly by adding additional nodes, Kafka can be scaled-out. Moreover, inside the Kafka cluster, the message handling is fully transparent and these are seamless.

The distributed architecture of Kafka makes it scalable using capabilities like replication and partitioning.

Kafka tends to work very well as a replacement for a more traditional message broker. Here, a message broker refers to an intermediary program, which translates messages from the formal messaging protocol of the publisher to the formal messaging protocol of the receiver.

Kafka can handle thousands of messages per second and that too in low latency conditions with high throughput. In addition, it permits the reading and writing of messages into it at high concurrency.

As we discussed above that the messages are persistent, which makes them durable and reliable.

It is possible to integrate with the variety of consumers using Kafka. The best part of Kafka is, it can behave or act differently according to the consumer, which it integrates with because each customer has a different ability to handle these messages, coming out of Kafka. Moreover, Kafka can integrate well with a variety of consumers written in a variety of languages.

k. Batch Handling Capable (ETL like functionality)

Kafka could also be employed for batch-like use cases and can also do the work of a traditional ETL, due to its capability of persisting messages.

It can manage the variety of use cases commonly required for a Data Lake. For Example log aggregation, web activity tracking, and so on.

Kafka can handle a real-time data pipeline. Since we need to find a technology piece to handle real-time messages from applications, it is one of the core reasons for Kafka as our choice.

It is seen that it lacks a full set of management and monitoring tools. Hence, enterprise support staff felt anxious or fearful about choosing Kafka and supporting it in the long run.

As we know, the broker uses certain system calls to deliver messages to the consumer. However, Kafka’s performance reduces significantly if the message needs some tweaking. So, it can perform quite well if the message is unchanged because it uses the capabilities of the system.

There is an issue that Kafka only matches the exact topic name, which means it does not support wildcard topic selection. Because that makes it incapable of addressing certain use cases.

There can be a problem because of the lack of pace, while API’s which are needed by other languages are maintained by different individuals and corporates.

In general, there are no issues with the individual message size. However, the brokers and consumers start compressing these messages as the size increases. Due to this, when decompressed, the node memory gets slowly used. Also, compress happens when the data flow in the pipeline. It affects throughput and also performance.

Sometimes, it starts behaving a bit clumsy and slow, when the number of queues in a Kafka cluster increases.

Some of the messaging paradigms are missing in Kafka like request/reply, point-to-point queues, and so on. Not always but for certain use cases, it sounds problematic.

The final result of the performance of this Kafka system depends on how much accuracy occurs in data delivery. The performance depends on the delivery of every message to its consumer correctly, on time without more lag of time. The message delivery has to be done at a perfect time and if not received within that particular time, alerts start to raise.

Read these Top Trending Kafka Interview Q’s now that help you grab high-paying jobs!

In this article, we will know about what is Kafka overview

Kafka: an overview

Apache Kafka is an open-source distributed publish-subscribe messaging platform that has been purpose-built to handle real-time streaming data for distributed streaming, pipelining, and replay of data feeds for fast, scalable operations.

Kafka is a broker-based solution that operates by maintaining streams of data as records within a cluster of servers. Kafka servers can span multiple data centers and provide data persistence by storing streams of records (messages) across multiple server instances in topics. A topic stores records or messages as a series of tuples, a sequence of immutable Python objects, which consist of a key, a value, and a timestamp.

Topics: A topic is a fairly universal concept in publish/subscribe messaging. In Apache Kafka and other messaging solutions, a topic is an addressable abstraction used to show interest in a given data stream (series of records/messages). A topic can be published and subscribed to and is an abstraction layer that is used by the application to show interest in a given stream of data.

Partitions: In Apache Kafka, topics can be subdivided into a series of order queues called partitions. These partitions are continually appended to form a sequential commit log. In the Kafka system, each record/message is assigned a sequential ID called an offset that is used to identify the message or record in the given partition.

Persistence: Apache Kafka operates by maintaining a cluster of servers that durably persist records/messages as they are published. The Kafka cluster uses a configurable retention timeout to determine how long a given record is persisted regardless of consumption. While the record/message is within the retention timeout the record/message is available for consumption. Once the record/message exceeds this retention timeout the record/message is deleted and space is freed.

Topic/Partition Scaling: Because Apache Kafka operates as a cluster of servers, topics/partitions can be scaled by sharing the load to each server on a given topic/partition. This load sharing allows each server in the Kafka cluster the ability to handle the distribution and persistence of records/messages on a given topic/partition. While individual servers handle all distribution and persistence, all servers replicate data providing fault tolerance and high availability in the event a server fails. Partitions are segmented between servers having one server elected to be the partition leader and all other servers acting as followers. The server that is the partition leader handles all distribution and persistence (reads/writes) of data and the followers provide replication services for fault tolerance.

Producers: In Apache Kafka, the concept of a producer is no different than most messaging systems. A Producer of data (records/messages) defines what topic (stream of data) a given record/message should be published on. Since partitions are used to provide additional scalability, a producer also can define what partition a given record/message is published to. Producers do not have to define a given partition and by not defining a partition a round-robin style of load balancing across topic partitions can be achieved.

Consumers: Consumers in Kafka, like in most messaging systems, are the entities that process records/messages. Consumers can be configured to work independently on individual workloads or cooperatively with other consumers on a given workload (load balancing). Consumers manage how they process a workload based on their consumer group name. Using a consumer group name allows consumers to be distributed within a single process, across multiple processes, and even across multiple systems. Using consumer group names, consumers can either load balance (multiple consumers with the same consumer group name) record/message consumption across the consumer set, or process each record/message uniquely (multiple consumers with unique consumer group names) where every consumer subscribed to a topic/partition gets the message for processing.

To stay competitive, businesses today rely increasingly on real-time data analysis allowing them to gain faster insights and quicker response times. Real-time insights allow businesses or organizations to make predictions about what they should stock, promote, or pull from the shelves, based on the most up-to-date information possible.

Traditionally, data has been processed and transmitted across networks in “batches”. This is down to limitations in the pipeline – the speed at which CPUs can handle the calculations involved in reading and transferring information, or at which sensors can detect data. As this interview points out, these “bottlenecks” in our ability to process data have existed since humans first began to record and exchange information in written records.

Due to its distributed nature and the streamlined way it manages incoming data, Kafka is capable of operating very quickly – large clusters can be capable of monitoring and reacting to millions of changes to a dataset every second. This means it becomes possible to start working with – and reacting to – streaming data in real-time.

Kafka was originally designed to track the behavior of visitors to large, busy websites (such as LinkedIn). By analyzing the clickstream data (how the user navigates the site and what functionality they use) of every session, a greater understanding of user behavior is achievable. This makes it possible to predict which news articles, or products for sale, a visitor might be interested in.

Since then, Kafka has become widely used, and it is an integral part of the stack at Spotify, Netflix, Uber, Goldman Sachs, Paypal, and CloudFlare, which all use it to process streaming data and understand customer, or system, behavior. In fact, according to their website, one out of five Fortune 500 businesses uses Kafka to some extent.

One particular niche where Kafka has gained dominance is the travel industry, where its streaming capability makes it ideal for tracking booking details of millions of flights, package holidays, and hotel vacancies worldwide.

Following are a few benefits of Kafka −

Reliability − Kafka is distributed, partitioned, replicated and fault tolerance.

Scalability − Kafka messaging system scales easily without down time..

Durability − Kafka uses Distributed commit log which means messages persists on disk as fast as possible, hence it is durable..

Performance − Kafka has high throughput for both publishing and subscribing messages. It maintains stable performance even many TB of messages are stored.

Kafka is very fast and guarantees zero downtime and zero data loss.

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Trending Blogposts on kafka

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Kafka - An Overview

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