What is Kafka Streams API ?

Kafka Streams API is a powerful, lightweight library provided by Apache Kafka for building real-time, scalable, and fault-tolerant stream processing applications. It allows developers to process and analyze data stored in Kafka topics using simple, high-level operations such as filtering, transforming, and aggregating data. In this article, we are going discuss deeply what Kafka, Kafka stream API, Use Cases, and advantages and disadvantages of Kafka stream API.

A distributed event streaming framework called Apache Kafka is made to manage fault-tolerant, high-throughput data streams. It offers a centralized platform for developing real-time data pipelines and applications, enabling smooth data producer and consumer connection.

Kafka Streams API can be used to simplify the Stream Processing procedure from various disparate topics. It can provide distributed coordination, data parallelism, scalability, and fault tolerance.

This API makes use of the ideas of tasks and partitions as logical units that communicate with the cluster and are closely related to the subject partitions.

The fact that the apps you create with Kafka Streams API are regular Java apps that can be packaged, deployed, and monitored like any other Java application is one of its unique features

• Tasks: Within the Kafka Streams API, tasks are logical processing units that take in input data, process it, and then output the results.
• Partitions: Segments of Kafka topics that allow applications using Kafka Streams to scale and process data in parallel.
• Stateful Processing: This refers to the Kafka Streams API’s capacity to save and update state data across stream processing operations, enabling intricate analytics and transformations.
• Windowing is a method for processing and aggregating data streams in predetermined time frames, making windowed joins and aggregation possible.

How Kafka Streams API Works?

1. Initialization: Include the kafka-streams dependency in your project in order to start using the Kafka Streams API.
2. Order of magnitude Construction: Use the Processor API or Streams API DSL to specify the application’s processing logic. This entails defining the data transformations, output topics, and input subjects.
3. Implementation: Create an instance of the Kafka Streams Topology object and set up characteristics like state storage, input/output serializers, and processing semantics.
4. Installation: Install your Kafka Streams application in a runtime environment, like a containerised environment or a standalone Java process.
5. Scaling: To provide higher throughput and fault tolerance, Kafka Streams applications automatically scale horizontally by dividing work across several instances.

Kafka Stream API Workflow With a Diagram

The following diagram illustrates the workflow of Kafka Stream APIs in between producers and consumers:

Here are a few handy Kafka Streams examples that leverage Kafka Streams API to simplify operations:

• Finance Industry can build applications to accumulate data sources for real-time views of potential exposures. It can also be leveraged for minimizing and detecting fraudulent transactions.
• It can also be used by logistics companies to build applications to track their shipments reliably, quickly, and in real-time.
• Travel companies can build applications with the API to help them make real-time decisions to find the best suitable pricing for individual customers. This allows them to cross-sell additional services and process reservations and bookings.
• Retailers can leverage this API to decide in real-time on the next best offers, pricing, personalized promotions, and inventory management.

• To start working with Kafka Streams API you first need to add Kafka_2.12 package to your application. You can avail of this package in maven:

<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka-streams</artifactId>
  <version>1.1.0</version>
</dependency>

• A unique feature of the Kafka Streams API is that the applications you build with it are normal Java applications. These applications can be packaged, deployed, and monitored like any other Java application – there is no need to install separate processing clusters or similar special-purpose and expensive infrastructure.

The following are the advantages of Kafka Stream APIs:

• Simplified Stream Processing: The Kafka Streams API allows developers to concentrate on application logic by abstracting away the intricacies of stream processing.
• Seamless Integration: Its smooth integration with the current Kafka infrastructure is due to its membership in the Kafka ecosystem.
• Scalability: Because of the horizontal scalability provided by the Kafka Streams API, applications can manage growing data loads.
• Fault Tolerance: Fault tolerance is ensured by built-in processes, which provide dependable stream processing even in the event of malfunctions.

The following are the disadvantages of Kafka Stream APIs:

• Java-Centric: Mostly concentrated on Java, which could be difficult for developers familiar to other languages.
• Learning Curve: While streamlining many parts of stream processing, there is some learning involved in understanding the ideas and APIs of Kafka Streams.
• Complexity: Especially for inexperienced users, managing stateful processing and windowed processes might be complicated.
• Resource Consumption: Kafka Streams applications have the potential to use a large amount of memory and compute power, depending on their size.

The adaptability of the Kafka Streams API makes it possible to use it in a wide range of sectors, such as retail, banking, logistics, and travel. The possibilities are infinite, ranging from dynamic pricing optimisation to real-time fraud detection.

• Organisations may analyse streaming data in real-time for insights and decision-making thanks to real-time analytics.
• Fraud Detection: Offers a platform for identifying and addressing fraudulent activity in online and financial transactions.
• Supply chain management makes it easier to track and keep an eye on shipments, inventories, and logistics processes in real time.
• Personalised marketing: Enables real-time analysis of consumer behaviour and preferences to power customised marketing initiatives.

In conclusion, With the help of the Apache Kafka Streams API, developers may easily create complex real-time streaming applications. Through comprehension of its fundamental concepts, jargon, and operational procedures, entities can effectively utilise Kafka Streams API to unleash the complete possibilities of their streaming data pipelines and stimulate creativity in a range of sectors.

What is API for Kafka Streams?

Using the Apache Kafka library, real-time streaming applications may be constructed, thanks to the Kafka Streams API.

Can I use languages other than Java with the Kafka Streams API?

No, although there are bindings for other JVM languages like Scala, the main focus of the Kafka Streams API is Java.

How is fault tolerance guaranteed by the Kafka Streams API?

The Kafka Streams API makes use of the replication and partitioning built-in fault tolerance features of Kafka.

For small-scale applications, is the Kafka Streams API appropriate?

Indeed, applications of various sizes, from small-scale prototypes to large-scale production deployments, can utilise the Kafka Streams API.

Can I combine my current Kafka clusters with the Kafka Streams API?

Yes, by utilising the same stream processing architecture, the Kafka Streams API easily interacts with already-existing Kafka clusters.