Skip to main content

Kafka - Kafka Streams

Kafka Streams: An Introduction

Kafka Streams is a strong library for making real-time, event-driven applications. It helps us process data that is stored in Apache Kafka. With Kafka Streams, we can easily create applications that handle data as it moves. This is very important for modern data systems that need to be fast and able to grow.

In this chapter, we will look at the basics of Kafka Streams. We will talk about its structure, main ideas, and some examples. When we understand Kafka Streams, we will learn the skills we need to use stream processing in our applications.

Introduction to Kafka Streams

Kafka Streams is a strong tool for processing streams. It is made on top of Apache Kafka. It helps us process and analyze data streams in real time. We can build strong applications that handle data all the time. It also works well with errors. Kafka Streams fits well with Kafka topics. This makes it easy to send and receive messages.

Here are some key features of Kafka Streams:

  • Simplicity: We can use regular Java APIs to process data streams. No need for a separate cluster.
  • Scalability: We can grow our Kafka Streams applications by adding more instances.
  • Fault Tolerance: There is built-in support for stateful processing. This helps us stay safe from failures.
  • Windowing: It helps us with time-based operations. We can group events into time windows for better analysis.
  • Stateful Processing: We keep state information using state stores. This lets us do complex event processing.

Kafka Streams is great for apps that need real-time analytics, monitoring, and event-driven setups. It is an important tool for making modern data-driven applications. By using Kafka Streams, we can get insights from our data right away. This helps us make better decisions.

Setting Up Kafka and Kafka Streams

To use Kafka Streams well, we need to start with Apache Kafka. Here are the steps to set up our Kafka environment with Kafka Streams:

  1. Download and Install Kafka:

    • First, we get the latest Kafka files from the Apache Kafka website.
    • Then, we unpack the downloaded file and go to the Kafka folder.

  2. Start Zookeeper (needed for Kafka):

    bin/zookeeper-server-start.sh config/zookeeper.properties

  3. Start Kafka Broker:

    bin/kafka-server-start.sh config/server.properties

  4. Add Kafka Streams Dependency: If we use Maven, we need to add this dependency in our pom.xml:

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

  5. Configuration: We configure our Kafka Streams app by setting properties in our app configuration. This usually goes in a Properties object:

    Properties props = new Properties();
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "your-app-id");
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

With these steps, we have set up Kafka and Kafka Streams. Now we are ready to develop stream processing apps.

Understanding Kafka Streams Architecture

Kafka Streams is a strong library for making real-time stream processing apps on Apache Kafka. Its design is made to be very scalable, fault-tolerant, and easy to use. The main parts of Kafka Streams architecture are:

  1. Stream Processing Topology: A Kafka Streams app has a topology. This is a directed graph of processing nodes. Each node does a transformation or operation on the data streams. This can be filtering, mapping, grouping, or aggregating.

  2. Stream and Table Abstractions: Kafka Streams sees data as continuous streams or tables. Streams are a sequence of records. Tables are a snapshot of the latest value for each key. This setup helps us do complex event processing and stateful operations.

  3. Kafka Producers and Consumers: Kafka Streams apps work as both producers and consumers. They read data from input topics. Then they process it and write results to output topics. This smooth interaction with Kafka gives us high throughput and low latency.

  4. Stateful Processing: Kafka Streams allows stateful processing with state stores. These stores let apps keep and query state across records. This helps us do operations like joins and aggregations.

  5. Scalability: Kafka Streams apps can run on many instances. They automatically balance the workload based on the number of partitions in the input topics.

Knowing these parts is very important for using Kafka Streams to build strong stream processing apps.

Key Concepts in Kafka Streams

Kafka Streams is a useful library for making real-time apps and microservices with Apache Kafka. Knowing its main ideas is important for good stream processing.

  1. Streams and Tables:

    • Streams are like a never-ending flow of data records. They show continuous events.
    • Tables give a quick look at the latest state of a group of data records. This helps us with stateful processing.

  2. KStream and KTable:

    • KStream: This is a stream of records. Each record has a key and a value.
    • KTable: This is a changelog stream. It shows the latest value for each key.

  3. Transformations:

    • Kafka Streams lets us use different transformations. We can use map, filter, join, and aggregate to change streams and tables.

  4. Windowing:

    • Windowing helps us group records into time-limited segments. This makes it easier to do things like aggregations over specific time periods.

  5. State Stores:

    • State stores keep track of state information. They help us build more complex apps. We use them to store temporary results and we can query them.

  6. Error Handling:

    • Kafka Streams has ways to manage errors during processing. It includes options for retries and dead-letter topics.

Knowing these key ideas in Kafka Streams is very important for making effective and strong stream processing applications.

Creating a Kafka Streams Application

Creating a Kafka Streams application has some important steps. This helps us to process streams in a good way. Kafka Streams is a strong library. It lets us build apps that can handle data in real-time. Let us see how to create a simple Kafka Streams application.

  1. Add Dependencies: First, we need to include the Kafka Streams library in our project. If we use Maven, we add this dependency in our pom.xml:

    <dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <version>3.2.0</version> <!-- Use the latest version -->
</dependency>

  2. Set Up Configuration: Next, we set up some properties for our application. These include application ID, bootstrap servers, and default serdes:

    Properties props = new Properties();
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "my-streams-app");
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

  3. Create Stream Topology: Now we use the Kafka Streams DSL to create the processing topology. For example, we can read from an input topic, change the data, and write to an output topic:

    StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> input = builder.stream("input-topic");
KStream<String, String> transformed = input.mapValues(value -> value.toUpperCase());
transformed.to("output-topic");

  4. Start the Streams Application: Then we build and start our Kafka Streams application:

    KafkaStreams streams = new KafkaStreams(builder.build(), props);
streams.start();

  5. Graceful Shutdown: Finally, we need to make sure we shut down the application properly when we exit:

    Runtime.getRuntime().addShutdownHook(new Thread(streams::close));

By doing these steps, we can make a strong Kafka Streams application. This application can effectively process and analyze streaming data using Kafka Streams.

Stream Processing with Kafka Streams

We can process data in real-time with Kafka Streams. This lets applications analyze data while it flows. Kafka Streams is a strong library. It helps us make real-time applications easier by giving us a good API for processing and changing data streams.

Kafka Streams works on a publish-subscribe model. This means it takes in data from Kafka topics and processes it all the time. Here are some key features of stream processing with Kafka Streams:

  • Continuous Processing: We process records as they come in. This helps us get quick insights.
  • Stateless and Stateful Operations: We can use operations like map, filter, and aggregation. We can do this in both stateless and stateful ways.
  • Fault Tolerance: It has built-in ways to handle faults. This means we can keep processing even if there are problems.
  • Integration: It works well with Kafka. We can use its scalability and durability.

Let’s look at a simple example of stream processing using Kafka Streams:

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> inputStream = builder.stream("input-topic");
KStream<String, String> transformedStream = inputStream.filter((key, value) -> value.contains("important"));
transformedStream.to("output-topic");

KafkaStreams streams = new KafkaStreams(builder.build(), properties);
streams.start();

In this example, we filter records that have the word “important” from the input topic. Then we send them to an output topic. This shows us how simple and effective stream processing with Kafka Streams can be.

Windowing in Kafka Streams

Windowing in Kafka Streams helps us to group records into specific time ranges. This lets us process data streams during certain time periods. It is very important for things like aggregations. For example, we can calculate results over time slices like every minute or every hour.

Kafka Streams has different types of windowing:

  1. Tumbling Windows: These are fixed-size and do not overlap. For example, a 5-minute tumbling window collects records for 5 minutes. It gives the result only after that window closes.

  2. Hopping Windows: These windows can overlap. Each window can share time with the previous one. For instance, a 5-minute window that hops every 2 minutes will give results for the last 5 minutes every 2 minutes.

  3. Sliding Windows: These are like hopping windows. But we can change the size of the window. This allows us to have more flexible time frames.

  4. Session Windows: These are based on times when there is no activity. A session window groups records until a set delay of inactivity happens. This lets us group data based on user activity.

Example Code for Tumbling Window:

KStream<String, Long> sourceStream = builder.stream("input-topic");

KTable<Windowed<String>, Long> aggregated = sourceStream
    .groupByKey()
    .windowedBy(TimeWindows.of(Duration.ofMinutes(5)))
    .count();

Using windowing well in Kafka Streams helps us get strong real-time analytics and insights from streaming data.

State Stores in Kafka Streams

In Kafka Streams, we use state stores to keep and check stateful information during stream processing. They help us store data that we can access across many records in the same stream. This allows us to do things like aggregations, joins, and time-based calculations.

We can divide state stores into two main types:

  • Key-value Stores: These stores save data as key-value pairs. They are often used for lookups. For example, KeyValueStore.
  • Window Stores: These stores keep data for a certain time period. This helps with time-based aggregations. For example, WindowStore.

Kafka Streams takes care of the state store’s lifecycle. This includes replication and fault tolerance, which helps keep our data safe. We can define state stores using the Materialized class. This lets us customize things like logging and data retention.

Here is an example of how to define a state store:

KTable<String, Long> aggregatedTable = builder.stream("input-topic")
    .groupByKey()
    .aggregate(
        () -> 0L,
        (key, value, aggregate) -> aggregate + value,
        Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("aggregated-store")
            .withValueSerde(Serdes.Long())
    );

In this example, we create a state store called “aggregated-store” to keep the results of an aggregation operation. Using state stores well can make our Kafka Streams applications faster and easier to scale.

Error Handling in Kafka Streams

Error handling in Kafka Streams is very important for making strong streaming applications. Kafka Streams gives us different ways to deal with errors during stream processing.

  1. Deserialization Errors: If there is a problem when we read records, we can set the default.deserialization.exception.handler to choose a handler for these errors. We can use built-in handlers like LogAndContinueExceptionHandler or we can make our own handler.

  2. Processing Errors: When we process records, we can catch errors using the process method in a transformer or processor. We can use a try-catch block to catch exceptions and choose what to do next. This could be logging the error or sending the record to a dead-letter topic.

  3. State Store Errors: For operations that need state, we need to take care of state store errors. We can set default.state.store.exception.handler to deal with errors related to state stores.

  4. Fault Tolerance: Kafka Streams has fault tolerance with changelogs. State stores are saved by Kafka topics, so we do not lose state if the application fails.

  5. Error Reporting: We should use logging and monitoring to notify us about any errors during processing. This helps us find and fix problems quickly.

By managing errors well in Kafka Streams, we can make our stream processing applications more reliable and easier to maintain.

Kafka - Kafka Streams - Full Example

We want to show how Kafka Streams works. We will go through a simple example of a word count app. This app will read text data from a Kafka topic. It will count how many times each word appears. Finally, it will send the results to another Kafka topic.

Prerequisites

  • We need Apache Kafka and Kafka Streams in our project (like Maven or Gradle).
  • We need a Kafka broker running and we can access it.

Maven Dependency

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <version>3.4.0</version>
</dependency>

Code Example

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;

import java.util.Properties;

public class WordCountExample {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-app");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

        StreamsBuilder builder = new StreamsBuilder();
        KStream<String, String> textLines = builder.stream("input-topic");
        textLines
            .flatMapValues(value -> Arrays.asList(value.toLowerCase().split(" ")))
            .groupBy(value -> value)
            .count()
            .toStream()
            .to("output-topic");

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        streams.start();

        Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
    }
}

Explanation

  1. Input and Output Topics: The app gets messages from input-topic. It processes them and sends counts to output-topic.
  2. Stream Processing: The flatMapValues function splits lines into words. The groupBy groups the words. The count adds up how many times each word appears.
  3. Configuration: We need to configure things right to connect to the Kafka broker and control how the stream works.

This full example shows how simple and powerful Kafka - Kafka Streams is for making real-time data processing apps.

Conclusion

In this article on “Kafka - Kafka Streams”, we looked at the basics of Kafka Streams. We talked about its structure, important ideas, and how we can use it in real life.

By knowing how to set up Kafka and Kafka Streams, we can manage state stores and deal with errors. This helps us build good stream processing solutions.

Using Kafka Streams makes our real-time data processing better. It becomes a strong tool for today’s data-focused apps.

Labels:

Comments

Post a Comment