If you are using Apache Kafka for real-time data processing, you have to make sure that the interaction with the Kafka Ecosystem remains seamless. Since Apache Kafka supports various programming languages, it becomes vital to understand how we can interact with Apache Kafka while working with different frameworks and programming languages.
Kafka Libraries allow us to interact with Apache Kafka so that we can easily build real-time data processing systems. Therefore, it is necessary to understand what Kafka Library to choose and when to use it. This article examines what Kafka Libraries are and why they are important. So, let us begin.
Overview of Apache Kafka
Before we learn about the Libraries of Apache Kafka, let us first understand Apache Kafka. In simple words, Apache Kafka is a distributed system that is based on the Publish-Subscribe Model to process the data in real-time.
This means that users can easily send and receive messages over different processes, applications, and servers. The organizations mainly use it for real-time data processing operations like Event Streaming. But how does it communicate with the applications? That is where Kafka Libraries come into the picture.
What are Kafka Libraries?
It often happens that you want your applications to interact with the Apache Kafka system. For this, you would need various components and functions. Kafka Libraries works exceptionally well in this case. It provides you the built-in functions for tasks like sending and receiving messages from Kafka topics, managing data partitions, and handling errors.
These features of Kafka Libraries help us to ensure overall data integrity and consistency in distributed environments. Kafka Training is very effective for aspirants to gain knowledge in this competitive field.
Let us see some features of these Libraries.
Features of Kafka Libraries
We Can Use Kafka Client Libraries Through Various Languages:
These libraries are available in different programming languages, like Java, Scala, Python, Golang, etc., so you can easily merge the application with the Kafka System while working in different programming environments.
They Support Kafka APIs:
Apache Kafka Libraries support the Producer, Consumer, Admin, Streams, and Connector APIs of Kafka to enable real-time data processing.
Kafka Libraries Support Schema Registry Integration:
Kafka libraries can easily integrate with Schema Registry which also acts as a centralized repository for managing Avro schemas. Here, Avro is the data-serialization format.
They Provide Various Security Mechanisms:
To make sure that there is secure communication between clients and brokers, they provide various security mechanisms like SSL/TLS encryption, SASL authentication, and authorization.
Let us see the various libraries for each of the programming languages supported by Kafka.
Kafka Libraries List
Java
- The Official Client Library: It supports direct interaction with Kafka to perform tasks like message production and consumption at a low level.
- Official Kafka Streams Client Library: This library can be used fot high level abstractions and for DSL stream processing tasks like stateful operations and fault tolerance
- Kafka for Spring Boot: Kafka for Spring Boot provides auto-configuration and dependency injection of Spring Boot. You can rapidly develop scalable Kafka-based microservices.
- Spring Cloud Stream: This library enables declarative programming, using which, you can develop event-driven microservices. Kafka topics can be integrated with the Spring components for message-driven communication and event processing.
- Akka Streams and Alpakka Kafka: Akka Streams and Alpakka Kafka implement the actor-based concurrency model and backpressure handling. This helps with easy integration with Kafka for message ingestion and processing.
C++
- CPP Kafka and Modern CPP Kafka: They both have been built on top of librdkafka and provide the interface for interacting with Kafka. CPP Kafka provides integration with Kafka clusters for tasks like message production, consumption, and administrative operations in C++ applications. Modern CPP Kafka can be used to build scalable and reliable Kafka applications using modern C++ features.
- librdkafka: It is the low-level implementation of the Kafka client. You can use it as the foundation for Kafka client libraries in higher-level programming languages.
Scala
- FS2 Kafka: It acts like the functional Kafka Producer and Consumer and enables the developers to implement the functional programming approach when working with the System.
- Kafka Streams Scala: Scala support for Kafka came in the Kafka 2.0 release. So, you can now use Scala to create complex stream processing topologies within the Kafka ecosystem.
- Alpakka Kafka: As part of the Alpakka project, this library provides integration with Kafka with Akka Streams and other Akka-based applications in Scala.
- ZIO Kafka: It provides Kafka client support for ZIO, which is the functional programming library for Scala. Thus, we can use the ZIO’s concurrency and composability to create Kafka applications.
Golang
- Confluent Kafka Go: It is the wrapper of the librdkafka library, which we just discussed above. Users can merge Go applications with Kafka without directly interacting with librdkafka’s low-level APIs.
- Schema Registry Client for Go: This library is used to write Golang programs that can write and read schema-compatible records across Kafka using the Avro, Protobuf, and JSON Schemas.
- Segment’s Kafka Go: As the pure Go-based implementation of the Kafka Client, this library provides both high-level and low-level APIs for interacting with Kafka.
- Franz Go: This is also the pure Go-based implementation. Using this, you can utilize features like transactions, regex topic consumption, the latest partitioning strategies, data loss detection, closest replica fetching, etc.
Python
- Confluent Kafka Python: This is the Python Library for Kafka that provides you with the Admin client and Avro support with the Confluent Schema Registry. Not only that, you can also perform administrative operations and support Avro serialization.
Kafka Python offers Kafka integration for Python applications. But this Kafka Library is no longer active and does not support the ongoing updates.
Rust
- Rust rdkafka: This is the Rust-based library that has been developed as the wrapper around the librdkafka library. You can perform integration for Rust applications with good performance and reliability with this library.
- Rust Schema Registry Converter: This library can be used to perform integration with the Confluent Schema Registry for Avro support.
- Kafka Rust: You can integrate Rust based applications with Kafka libraries. This is pure Rust-based implementation and due to its low maintenance activity, it is not very reliable for newer Kafka features.
REST API
- Confluent REST Proxy: Use the RESTful interface to interact with the Kafka Cluster. It gives us the features to easily produce and consume data, view the cluster state, and perform administrative tasks without using the native Kafka protocol or clients.
Kotlin
For Kafka integration in Kotlin, you can simply use the standard Java library. With Kotlin, developers can use the features and functionalities of the Java Kafka client as they work within the Kotlin ecosystem.
Haskell
- Haskell HW Kafka Client: This Kafka client library for Haskell is based on librdkafka, which allows efficient Kafka integration for Haskell applications.
- Haskell HW Kafka Avro: This library enhances the functionality of the HW Kafka Client for Haskell applications. It does this by enabling integration with the Confluent Schema Registry for Avro support.
Ruby
- Ruby rdkafka-ruby: It provides Ruby developers with efficient Kafka integration by utilizing the capabilities of librdkafka.
- Ruby Kafka: It has good logging and metrics support to make debugging easier. However, it provides limited support for the new Kafka API.
Javascript / Node.js
- KafkaJS: This JavaScript library supports good performance and requires no external dependencies. In addition to this, it also has support for the Schema Registry
- Blizzard Node rdkafka: It is the Node.js wrapper for librdkafka and provides Kafka integration for Node.js applications. However, it has low maintenance activity.
.NET / C#
- Confluent Kafka DotNet: It provides Kafka integration for .NET / C# applications. Also, it has full Schema Registry support for Avro, JSON, and Protobuf serialization formats.
How Do You Choose the Right Kafka Library?
After learning about so many Kafka libraries, you need to look into factors that should be considered to pick out the most suitable library. Almost all the libraries support the Kafka APIs. Thus, the main points to consider while picking the Kafka Library depend on:
If the Library is Supported or Not:
Many libraries are no longer active or maintained. This is why you need to make sure that picking the Kafka library is supported or not.
Does the Organization Need Pure Implementation or librdkafka-based Library:
Pure implementation means the library is written only using the specific programming language on which it is based. But librdkafka is a C/C++-based library and many other libraries are built around it as wrappers. Thus, if the organization needs a Kafka Library that is purely language-based, the choice should be made accordingly.
How Well the Library Supports the Security Mechanisms of the Organization:
The choice of libraries also depends on what security mechanism the company wants to use. The library should support security mechanisms like SSL and SASL which are important for Kafka deployment.
If the Library Supports the Confluent Schema Registry:
If your organization uses Confluent Schema Registry for Avro serialization and schema management, you should choose the library that supports seamless integration with the Schema Registry.
Can the Library Show the Performance as Per the Organization’s Standards:
Choosing the Kafka Library also depends on the performance standards defined as per the organization’s policy. The Kafka Library should be chosen if it offers the appropriate performance.
Conclusion
Kafka Libraries are useful tools that help us interact with Apache Kafka. They are supported by multiple programming languages, which enables easy interaction with Kafka with different programming ecosystems.
They also support multiple features like serialization and deserialization, Partitioning Control, Error handling, etc. Thus, You have gained a sufficient amount of insights about the Apache Kafka Libraries and how to choose the right Kafka Library for your ecosystem.
