Kafka - Streams APIs

Introduction to Kafka Streams APIs

Kafka Streams APIs are a strong part of Apache Kafka. They help us do real-time stream processing of data. With these APIs, we can build applications that process and analyze data while it moves. This is very important for modern applications that need fast insights and actions.

In this chapter, we look at the main features and functions of Kafka Streams APIs. We will cover how to set them up, the key ideas behind them, and some practical examples. By the end, we will understand how to use Kafka Streams APIs for good stream processing.

Introduction to Kafka Streams

Kafka Streams is a strong stream processing library. It is built on Apache Kafka. It helps us to create real-time applications that work with data streams. We can change, combine, and work with data as it moves. Kafka gives us good scalability and fault tolerance.

With Kafka Streams APIs, we can build apps that do complex tasks on data that flows through Kafka topics. We can filter, map, and join streams easily. This library works in a light and distributed way. Each part of our application can process data at the same time.

Here are some key features of Kafka Streams:

Simplicity: It has a simple programming model that works well with Kafka.
Scalability: It grows automatically with the number of partitions in Kafka topics.
Fault Tolerance: It has built-in ways to keep working and recover from problems.
Event Time Processing: It can handle time-based tasks effectively.

By using Kafka Streams APIs, we can create smart data processing applications. These apps can react to real-time events. This makes Kafka Streams an important tool for businesses that rely on data today.

Key Concepts of Kafka Streams

Kafka Streams is a strong library in the Apache Kafka system. It helps us build real-time streaming applications. We need to understand the key ideas of Kafka Streams to use its features well.

Stream and Table:
- A Stream shows a continuous flow of records or events in Kafka topics.
- A Table shows a changelog of records. It is like a snapshot of the latest values of keys.
Topology:
- The way we structure processing logic in Kafka Streams is called a topology. It has nodes or processors and edges or streams. These define how data moves in the application.
KStream and KTable:
- KStream is a way to think about a stream of records. KTable is a way to think about a changelog. We use KStream for event processing and KTable for stateful tasks.
State Stores:
- Kafka Streams uses state stores to keep state. This helps us track intermediate results and do stateful changes.
Serialization:
- In Kafka Streams, we must serialize and deserialize data using the right serializers like JSON or Avro. This helps with good data processing.
Processing Guarantees:
- Kafka Streams gives us “at-least-once” processing guarantees. We can also have “exactly-once” behavior if we set it up right.

These key ideas are the base for making strong Kafka Streams applications. They help us with efficient and scalable data processing. We need to understand these ideas to learn Kafka Streams APIs well.

Setting Up Your Kafka Streams Environment

To use Kafka Streams APIs well, we need to set up our environment correctly. Let’s go through the steps to get started.

Install Apache Kafka: First, we download the latest version from the Apache Kafka website. Next, we extract the files and go to the Kafka directory.

Start Kafka and Zookeeper: We open a terminal and run these commands:

# Start Zookeeper
bin/zookeeper-server-start.sh config/zookeeper.properties

# Start Kafka broker
bin/kafka-server-start.sh config/server.properties

Set Up Maven: If we use Maven for our project, we add this dependency to our pom.xml:

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <version>2.8.0</version> <!-- Use the latest version -->
</dependency>

Configure Kafka Streams Properties: We create a properties file, for example streams.properties. This file should have the important settings:

application.id=my-streams-app
bootstrap.servers=localhost:9092
default.key.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
default.value.serde=org.apache.kafka.common.serialization.Serdes$StringSerde

IDE Setup: We can use an IDE like IntelliJ IDEA or Eclipse. We need to make sure we have the right plugins for Maven and Java.

By doing these steps, we will have a strong environment for making Kafka Streams apps. This will help us use all the features of Kafka Streams APIs.

Creating Your First Kafka Streams Application

To create your first Kafka Streams application, we need to set up a simple project. This project will have the needed dependencies and settings. Kafka Streams APIs give us an easy way to work with data streams in real-time. It uses the power of Apache Kafka.

Step 1: Set Up Your Project

If we are using Maven, we must add this dependency to our pom.xml:

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

Step 2: Configure Kafka Streams

Next, we create a properties file. We can name it streams.properties. This file will have the following settings:

application.id=my-streams-app
bootstrap.servers=localhost:9092
default.key.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
default.value.serde=org.apache.kafka.common.serialization.Serdes$StringSerde

Step 3: Implement the Streams Application

Now, here is a simple example of a Kafka Streams application. It reads from an input topic, processes the data, and then writes to an output topic:

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 MyKafkaStreamsApp {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "my-streams-app");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");

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

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

Step 4: Run Your Application

Now we need to compile and run our application. Make sure we have a Kafka broker running. Also, the input topic must be created. Our Kafka Streams application will now take messages from the input topic. It will change them and send them to the output topic.

By following these steps, we can create our first Kafka Streams application. We will use Kafka Streams APIs for real-time data processing.

Understanding Kafka Streams DSL

We can use Kafka Streams to build real-time applications and microservices. It gives us a special language called Domain-Specific Language (DSL). The Kafka Streams DSL makes it easy to work with data streams. It offers simple tools for changing, combining, and summarizing data.

Here are the main parts of the Kafka Streams DSL:

Streams: These are continuous records that show data moving through the system.
Tables: This shows changes in a stream. It helps us keep track of data.
KStream: This is a way to show a stream of records. It is good for handling endless data.
KTable: This shows a table of records. It is useful for making changes to the state.

We can do many things with the Kafka Streams DSL:

Transformations: We can use map, filter, flatMap, and groupBy.
Aggregations: We can do count, reduce, and aggregate.
Joins: We can combine different streams or tables with join, leftJoin, and outerJoin.

Here is a simple example of using the Kafka Streams DSL:

KStream<String, String> inputStream = builder.stream("input-topic");
KTable<String, Long> aggregatedTable = inputStream
    .groupByKey()
    .count();

This code shows how we can create a KStream from an input topic. Then we can summarize the data into a KTable using the Kafka Streams DSL. Learning about Kafka Streams DSL is very important. It helps us use all the features of Kafka Streams APIs.

Stateful vs Stateless Operations

In Kafka Streams, we have two types of operations: stateful and stateless. It is important for us to understand these ideas. This helps us build better Kafka Streams applications with the Streams APIs.

Stateless Operations
Stateless operations do not keep any state between records. Each record is handled by itself. This makes them easy and quick. Some examples are:

Map: Changes each record.
Filter: Removes records based on certain rules.
FlatMap: Changes each record into zero or more records.

We usually use stateless operations for tasks like changing data and filtering. We do not need to keep context for these tasks.

Stateful Operations
Stateful operations keep state across multiple records. This lets us do more complex tasks like combining data or joining records. Some examples are:

Aggregate: Combines many input records into one output.
Join: Connects records from two streams using a key.
Windowed Aggregations: Groups records into time frames for processing.

Stateful operations need state stores. We can set up and manage these within Kafka Streams. The state is safe even if there is a problem. Kafka’s changelog topics help us recover the state if something goes wrong.

By using both stateful and stateless operations, we can build strong and scalable applications with Kafka Streams APIs.

Windowed Operations in Kafka Streams

Windowed operations in Kafka Streams help us group records by time. This makes it easier to analyze data over certain periods. It is very useful for real-time analytics. For example, we can calculate averages, sums, or counts during specific time frames.

Kafka Streams has different types of time windows:

Tumbling Windows: These are fixed-size windows that do not overlap. Each window has a start and end time.
Hopping Windows: These windows can overlap. They can cover the same time period many times. This helps us do more detailed analysis.
Sliding Windows: These are like hopping windows. But we can set the window size and how often it moves. This creates a way for continuous analysis.

To use windowed operations, we can use the windowedBy() method in a Kafka Streams setup. Here is an example of making a tumbling window:

KStream<String, Long> stream = builder.stream("input-topic");
KTable<Windowed<String>, Long> counts = stream
    .groupByKey()
    .windowedBy(TimeWindows.of(Duration.ofMinutes(5)))
    .count();

In this example, we group records by key. Then we count them in 5-minute tumbling windows. Windowed operations improve what we can do with Kafka Streams APIs. They help us process time-based data and get real-time insights.

Handling Time in Kafka Streams

In Kafka Streams, handling time is very important for processing data correctly and well. Kafka Streams has three types of time: event time, processing time, and ingestion time.

Event Time: This is the time when an event really happened. It is very important for cases where the order of events is important. This is true for windowed aggregations. Event time lets us handle events that arrive late, so it is strong against delays in data coming in.
Processing Time: This is the time when the Kafka Streams application processes the record. It is easier to work with, but it can cause problems if records are late or come in the wrong order.
Ingestion Time: This is the time when the record gets into the Kafka topic. It is a mix between event time and processing time. It helps to make sure that records are processed in the order they were received.

Kafka Streams has built-in support for windowed operations. This helps us set time windows based on event time. When we define windows, we can use methods like TimeWindows.of(Duration.ofMinutes(5)) for event-time-based aggregations.

By using these time types well, we can create strong, time-sensitive applications with Kafka Streams.

Error Handling and Fault Tolerance

In Kafka Streams APIs, good error handling and fault tolerance are very important for making strong streaming applications. Kafka Streams uses the strong durability and reliability of Apache Kafka. This way, messages do not get lost and we can still process them even if there are failures.

Here are some key ways for error handling:

Retries: If a step fails while processing, Kafka Streams can try to do it again. We can set this up using the retries configuration. This tells how many times we want to try again before we log a failure.
Dead Letter Queue (DLQ): Records that we cannot process can go to a DLQ. We can look at them later and do it by hand. We need to make a custom error handler that catches exceptions and sends the records that have problems to a certain Kafka topic.
State Store Backup: Kafka Streams helps to recover state automatically. It uses changelog topics to back up state stores. If something goes wrong with an instance, it can get its state back from these topics when it restarts.

Here is an example setup for retries and DLQ:

# Number of retries for processing failures
retries=5

# Error handling strategy to send failed messages to a DLQ
default.deserialization.exception.handler=org.apache.kafka.streams.errors.DefaultDeserializationExceptionHandler

By using these strategies, Kafka Streams APIs can keep high availability and durability. This helps our applications to recover from errors easily and keep processing data all the time.

Testing Kafka Streams Applications

Testing Kafka Streams applications is very important. It helps us make sure that stream processing is correct and reliable. Kafka gives us different ways to test our applications. These include unit testing, integration testing, and end-to-end testing.

1. Unit Testing:

We can use the TopologyTestDriver to mimic processing records through our streams topology.
We can mock input and output topics to check if the results of transformations are what we expect.

Topology topology = new StreamsBuilder().build();
TopologyTestDriver testDriver = new TopologyTestDriver(topology, new Properties());
KeyValue<String, String> input = new KeyValue<>("key1", "value1");
testDriver.pipeInput(new ConsumerRecord<>("input-topic", 0, 0L, input.key, input.value));
ProducerRecord<String, String> output = testDriver.readOutput("output-topic", new StringDeserializer(), new StringDeserializer());
assertEquals("expected-value", output.value());

2. Integration Testing:

We can start a real Kafka cluster using Docker or local Kafka instances.
We can use embedded Kafka to produce and consume records. This makes sure our application works well with the Kafka ecosystem.

3. End-to-End Testing:

We need to check the whole data flow from the source to the sink.
We must ensure that our Kafka Streams application works well with external systems.

By using these testing strategies, we can make sure our Kafka Streams applications are strong and easy to maintain. Testing helps improve reliability and performance. It is a key part of development.

Kafka - Streams APIs - Full Example

We will show how to use Kafka Streams APIs by making a simple app. This app will process streaming data. We will read text messages from a Kafka topic. Then, we will count the words and send the results to another topic.

Step 1: Setup Dependencies

We need to add some dependencies to our pom.xml file for a Maven project:

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

Step 2: Configure Properties

Next, we create a properties file. We can name it streams.properties:

application.id=word-count-app
bootstrap.servers=localhost:9092
default.key.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
default.value.serde=org.apache.kafka.common.serialization.Serdes$StringSerde

Step 3: Implement the Kafka Streams Application

Now, we write the code for our Kafka Streams app:

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 WordCountApp {
    public static void main(String[] args) {
        Properties properties = new Properties();
        properties.load(WordCountApp.class.getClassLoader().getResourceAsStream("streams.properties"));

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

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

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

In this example, we get messages from input-topic. We count the words and send the results to output-topic. This shows how easy and powerful Kafka Streams APIs are for making real-time data processing apps.

Conclusion

In this article about Kafka - Streams APIs, we look at the basics of Kafka Streams. We cover important ideas, how to set up the environment, and how to make your first app. We also talk about the Kafka Streams DSL, stateful and stateless operations, windowed operations, and how to handle errors.

When we understand Kafka - Streams APIs, we can build strong real-time data processing apps. This helps us use streaming data in a smart way.

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