Kafka Partitions: 3 Easy Steps to Create and Use
Apache Kafka is an Event-streaming Platform that streams and handles billions and trillions of real-time data per day. Various Dedicated and Distributed Servers are present across the Apache Kafka Cluster and Kafka Partitions to collect, store, and organize real-time data. Because of the continuous streaming of real-time data into Kafka Clusters, it is complex for Kafka Servers to sort and organize the incoming data.
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As a result, Kafka allows Producers to sort and organize messages by writing them inside the specific Topics. Later, Kafka Consumers can fetch required data from a Particular Topic from the Kafka Cluster.
However, there is a chance of Kafka Servers getting shut down or failing in some cases. Since users can push hundreds of thousands of messages or data into Kafka Servers, issues such as Data Overloading and Data Duplication may arise.
In such unexpected situations, the messages present in that respective Kafka Server will be entirely erased and deleted, leading to permanent data loss. To eliminate this complication and loss of customers’ data, you can split a single Topic into separate divisions called Apache Kafka Partitions.
With Kafka Partitions, you can effectively divide Kafka Topic to distribute them across different Kafka Servers in the Kafka cluster. Even if one of the Servers fail in the future, the messages will be present across other Kafka Servers, eliminating the permanent loss of data.
In this article, you will learn about Apache Kafka, Apache Kafka Partitions, and how to create Topic Partitions in Apache Kafka.
Table of Contents
- What is Apache Kafka?
- How does Apache Kafka Topic Partitions Work?
- How to Create Kafka Topic Partitions?
- Effective Strategies to Customize Kafka Partitions for Topics
- Fundamental knowledge of Streaming Data.
What is Apache Kafka?
Apache Kafka is an Open-source Data Streaming platform or service that allows you to store and organize real-time continuously streaming data into Kafka Servers or Brokers. Using such instantaneous data, you can develop real-time and event-driven applications.
With Kafka, you can also use real-time streaming data to make Event-driven Decisions or build Recommendation Systems for your applications. In other words, Apache Kafka provides a Distributed Framework that comprises a vast collection of Servers or Brokers for collecting, storing, organizing, and managing real-time data.
Apache Kafka is otherwise called a Publish-Subscribe Messaging Service since it allows Producers (publishes data) and Consumers (subscribes data) to read and write Messages (data) to and fro the Kafka Servers, according to their use cases or requirements.
Because of its Distributive nature and efficient Throughput, Apache Kafka is being used by the world’s most prominent companies, including 80% of Fortune 500 companies like Netflix, Spotify, and Uber.
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How does Apache Kafka Topic Partitions Work?
In the Apache Kafka Ecosystem, messages or data received from Producers are stored and organized inside the entity called Topics. Inside Kafka Brokers, Topics are further subdivided into multiple parts called Kafka Partitions. The representation of Topic Partitions is similar to linear data structures like arrays, which store and linearly append whenever new data arrives in the Kafka Brokers.
You already know that Producers store messages inside topics for Consumers to access and fetch from respective Kafka Servers. Apache Kafka originally stores Producers’ Messages inside Different Partitions of a specific Topic, present across various Apache Kafka Brokers in a Kafka Cluster. In other words, the topic is only a logical entity, but the actual place where the messages get stored in Kafka is under Topic Partitions.
For example, consider the above representation of topic partitions in Kafka servers or brokers. You have created a new topic in the name of “Topic-A” with 3 Partitions across 3 Brokers, namely “Broker 101,” “Broker 102,” and “Broker 103.” Similarly, you have created “Topic-B” with 2 Partitions across “Broker 101” and “Broker 102.”
According to this criteria, topics are internally partitioned inside Kafka Brokers or Servers in the Kafka Cluster. Furthermore, you can decide the number of partitions while creating topics in Kafka Partition by executing commands in the command prompt.
The above image represents how a Topic Partition internally Stores Messages or Records. A Topic Partition of Kafka is identified in the form of a log file that writes and appends messages or records to its tail.
Furthermore, new messages from Producers are always appended at the rear end of the Partition. Since a Partition or Log File appends or adds records to its tail, the data can easily be sorted according to the arrival period. i.e., older to newer, as shown in the above image.
In Kafka topics, every partition has a Partition Number that uniquely identifies and represents the partition of a specific topic. In the above image, you can see the partition numbers named Partition 0, Partition 1, and Partition 2, which uniquely identify the Partitions of a single Kafka Topic.
In addition, every topic partition has an increasing sequence of numbers or indexes called Offset. For example, as shown in the above image, M1, M2, M3, etc., are the messages received from the Producers, which are stored in the ordered sequence inside a Topic Partition.
Such ordered sequences of numbers are called Offset. When a Producer writes a Message to a Topic Partition, the Log File gets appended by assigning the following sequential offset number to the Partition.
Such offsets are particularly used by Kafka consumers while reading or fetching messages from a specific topic partition. Furthermore, Offsets are Immutable in nature because you cannot change or replace the order of messages once you have published messages inside a Topic Partition.
High Availability and Fault Tolerance are effectively achieved in Kafka Servers by providing a Replication Factor parameter while creating Kafka topics.
The Replication Factor is nothing but the number of copies or replicas of a Single Topic Partition. You can decide the Replication Factor of the partition while creating Topics in Apache Kafka.
When providing a Replication Factor in your Topic Creation command, you can make different copies of topic partitions and store them in different Kafka servers. For example, consider the above image. You have created a Kafka Partition topic with two partitions with a Replication Factor of 2.
Consequently, the Kafka Server has distributed “Topic 1 & Partition 0” in Broker 1 and Broker 2 while distributing “Topic 1 & Partition 1” in Broker 2 and Broker 3.
With this method, producer messages are distributed into Partitions, and Partitions are replicated among different Kafka Servers in the Kafka Cluster.
In some rare cases, when one Kafka Server shuts down or fails, a message will be safely present in other Servers instead of getting completely erased from the Kafka System.
Such capabilities make Apache Kafka a highly fault-tolerant and more scalable platform, thereby assuring the safety and security of user data.
How to Create Kafka Topic Partitions?
In Kafka, you can create Topic Partitions and set configurations only while creating Topics. To create Topic Partitions, you have to create Topics in Kafka as a prerequisite. Below are the steps to create Kafka Partitions.
- Kafka Partitions Step 1: Check for Key Prerequisites
- Kafka Partitions Step 2: Start Apache Kafka & Zookeeper Severs
- Kafka Partitions Step 3: Creating Topics & Topic Partitions
Kafka Partitions Step 1: Check for Key Prerequisites
Before proceeding into the steps for creating a Kafka Topic Partition, ensure that Kafka and Zookeeper are pre-installed, configured, and running on your local machine. You have to also make sure that the Java 8+ Version is installed and running on your computer.
Further, set up the File Path and Java_Home environment variables for enabling your operating system to point or head towards the Java utilities.
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Kafka Partitions Step 2: Start Apache Kafka & Zookeeper Servers
After all the above-mentioned prerequisites are satisfied, you are now all set to start and set up the Kafka and Zookeeper Servers. Initially, you have to start the Kafka Server. For that, open the Command Prompt or Windows PowerShell to execute the Kafka Commands.
You can use Batch scripts (.bat) to work with Kafka Configuration while working with the Windows Operating System. If you are using the Linux Operating System, you can use Shell Scripts (.sh) to proceed further with Kafka configurations.
This article concentrates on creating Kafka Topics and Partition configurations using a command-line tool in Windows OS. For that, you can use the (.bat) scripts in Kafka.
For starting the Kafka server, execute the following command.
Then, open a new command terminal for starting the Zookeeper Server and execute the following command:
After executing the above commands, Kafka and Zookeeper Servers are started and running successfully. Ensure you do not close both the command windows that run Zookeeper and Kafka Instances.
Kafka Partitions Step 3: Creating Topics & Topic Partitions
Now, you are ready to create Topics in Kafka. Open another command prompt and execute the following command.
kafka-topics.bat --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
The above command will successfully create a new Kafka Topic in the name of “Topic Test.” with One Partition and One Replication Factor. The Replication Factor is the number of copies or replicas of a topic partition across the Kafka Cluster.
After the execution of the command, you will get a success message saying “Created Topic Test.” in your command terminal. From this message, you can ensure that Kafka’s Topics are successfully created for sending and receiving events or data.
Since you provide the Partition Parameter as 1, Kafka will create a single partition under the topic named “Topic Test.” Similarly, according to the command mentioned above, Apache Kafka will create a single replication factor for the Respective Topic Partition.
However, if the size of your message is large, Kafka will allow you to create many partitions or divisions under a Single Topic.
For example, when you want to create a new topic with 2 Partitions and 3 Replication Factors, you can execute the command, as given below.
kafka-topics.bat --create --zookeeper localhost:2181 --replication-factor 3 --partitions 2 --topic test
You can use the same command, as shown above, for creating different topics with specific Topic Configuration parameters by just customizing the Topic Name, Number of Partitions, and Replication Factors.
With the above steps, you have successfully created Topic and Topic Partitions in Kafka.
Effective Strategies to Customize Kafka Partitions for Topics
You can perform some Configurations and Customizations while creating Kafka Partitions by choosing the Proper Number of Partitions for a specific Topic. For example, if you are setting up and running a basic Kafka Cluster with very few brokers, you can choose the approximate partitions for performing Stream Processing operations.
However, if you are about to run a high-end Kafka Cluster with a huge number of Brokers, you have to implement some effective strategies to properly Partition Messages to achieve maximum throughput.
The first prerequisite to achieving a high degree of Throughput and Parallelism in Kafka is choosing the appropriate number for Kafka partitions across Kafka servers.
By splitting Producer Messages into Partitions over multiple Kafka Servers, end-consumers can effectively read a message with respect to the specific topic instead of searching between the messy and unorganized data.
Furthermore, in a Kafka Cluster, the larger the Partitions, the greater the message parallelization and consequently greater the throughput you can achieve by effectively splitting messages.
The simple formula to determine the number of partitions for each Kafka topic is given below.
Partitions = Desired Throughput / Partition Speed
The default Partition speed of a single Kafka Topic is 10MB/s. For example, consider that the desired message throughput is 5 TB per day, about 58 MB/s. By keeping the default partition speed as 10MB/s, calculate the number of Partitions.
When you apply the parameters in the formula, you will get 5.8, which can be approximated as 6. Now, you can confirm that your Apache Kafka Topics need six partitions to achieve the maximum throughput.
This article informed you about Apache Kafka, Apache Kafka Topics, and Apache Kafka Partitions. By diving into the internal architecture of Kafka Partitions, you have learned about the working of Kafka Topics and partitions. There are various techniques and strategies for implementing Kafka Topic Partitions in the Kafka Cluster. However, you can also select the specific partition in Kafka to which you want to send messages.
For implementing Customized Partitions in Kafka Servers, you can follow different Kafka Partition Strategies or methods like Round Robin Assignor and Range Assignor to customize the Partition Distribution across the Kafka Cluster. Once you are well acquainted with basic Partition Creation in Kafka, you can explore such methods later.
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