Kafka - Topics

Understanding Kafka Topics

Kafka topics are the basic parts of Apache Kafka. They act like channels for data to travel. Each Kafka topic is a name for a category or feed. Records get sent to these topics. This helps us organize and manage the flow of data in Kafka. It is very important to understand Kafka topics. They help us stream and manage data well. This also makes sure that producers and consumers can talk to each other reliably.

In this chapter, we will look at different parts of Kafka topics. We will learn about how to create them, how to set them up, and how to manage them. We will talk about things like partitioning, replication factors, and retention policies. By the end, we want to give you a clear guide to mastering Kafka topics. This will help us improve our data-driven applications.

Understanding Kafka Topics

Kafka topics are important parts of the Apache Kafka messaging system. They act as categories or feeds where we publish records. Each topic has a special name. This way, producers can send messages and consumers can subscribe to them.

Some key features of Kafka topics are:

Separation of Producers and Consumers: Producers send messages to topics. They do not need to know who will read them. This makes the system flexible.
Storing Messages: Messages in a Kafka topic are saved on disk. This means we can deliver messages reliably, even if there are problems.
Growing Ability: Topics can be split into parts. This helps with processing many messages at the same time. Each part is a fixed order of records.

We can set different options for topics to make them work better. These options include how long to keep messages and how to manage storage. Knowing about Kafka topics is very important for making good data streaming solutions. They control how data moves in the Kafka system. By using Kafka topics well, we can create strong applications that handle real-time data easily.

Creating a Kafka Topic

Creating a Kafka topic is a basic step when we use Apache Kafka. A Kafka topic is like a category or a name for a feed where we send records. We can create a Kafka topic using command-line tools or by using Kafka’s AdminClient API.

Using Command-Line Tools:

To make a Kafka topic from the command line, we use this command:

kafka-topics.sh --create --topic <topic_name> --bootstrap-server <broker_address> --partitions <num_partitions> --replication-factor <replication_factor>

Parameters:

<topic_name>: This is the name of the topic we want to create.
<broker_address>: This is the address of our Kafka broker (for example, localhost:9092).
<num_partitions>: This is the number of partitions for the topic. This helps with parallel tasks.
<replication_factor>: This tells us how many copies we want for safety.

Example:

kafka-topics.sh --create --topic my_topic --bootstrap-server localhost:9092 --partitions 3 --replication-factor 2

This command makes a topic called my_topic with 3 partitions and 2 copies.

Programmatic Creation:

If we want to create a topic using Kafka’s AdminClient API, we can do it like this:

import org.apache.kafka.clients.admin.AdminClient;
import org.apache.kafka.clients.admin.NewTopic;

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
try (AdminClient adminClient = AdminClient.create(props)) {
    NewTopic newTopic = new NewTopic("my_topic", 3, (short) 2);
    adminClient.createTopics(Collections.singletonList(newTopic));
}

This Java code makes the same my_topic with the same partitions and copies. Creating a Kafka topic helps us to organize messages and makes our Kafka system work better.

Topic Configuration Parameters

In Apache Kafka, we use topic configuration parameters to decide how a Kafka topic works and performs. When we create or change a Kafka topic, we can set many configuration properties. This helps us adjust the topic for different needs. Here are some important configuration parameters:

num.partitions: This tells us how many partitions the topic will have. More partitions can help us process messages faster and better.
replication.factor: This shows how many copies of each partition we want. It helps keep our data safe and available.
retention.ms: This sets how long we keep messages in the topic, measured in milliseconds. After this time, messages can be deleted.
cleanup.policy: This decides how we keep messages. We can choose delete, which is the default, or compact for log compaction.
min.insync.replicas: This tells us the least number of replicas that must confirm a write for it to be successful. This helps make sure our data is strong.
compression.type: This sets the way we compress messages, like using gzip, snappy, or lz4. It helps us save space and use less network.

These topic configuration parameters are very important for making Kafka topics work better. They help us meet our needs for performance, reliability, and storage. If we configure them correctly, we can really improve how messages are processed in our Kafka system.

Partitions and Replication Factors

In Kafka, we divide topics into partitions. These are basic units that help with parallel work and growth. Each partition is a list of messages that we cannot change. They help Kafka handle a lot of data quickly. Let’s see how partitions and replication factors work together to keep data safe and available.

Partitions: Each topic can have many partitions. Messages in a partition have a strict order. This lets Kafka share the work across different brokers. So, if a topic has 3 partitions, we can have 3 different consumers reading messages at the same time. This makes everything faster.
Replication Factors: To keep data safe and to handle problems, Kafka can copy each partition to several brokers. The replication factor tells us how many copies of each partition we have. For example, if the replication factor is 3, that means each partition has 3 copies on different brokers. This way, if one broker goes down, we can still get the data from other copies.

When we create a topic, we can choose how many partitions and the replication factor we want. For example:

kafka-topics --create --topic my-topic --partitions 3 --replication-factor 2 --bootstrap-server localhost:9092

In this example, we create my-topic with 3 partitions and a replication factor of 2. This helps with both growth and trustworthiness. We need to understand partitions and replication factors to make Kafka topics work best for our application. To list Kafka topics, we can use the command-line tool kafka-topics.sh. This tool comes with the Apache Kafka package. It helps us work with Kafka topics easily.

Command to List Kafka Topics

To see all the topics in our Kafka cluster, we need to run this command in the terminal:

bin/kafka-topics.sh --list --bootstrap-server localhost:9092

Explanation of Parameters

--list: This option tells the tool to show all the topics we have.
--bootstrap-server: This option gives the address of the Kafka broker. For example, we can use localhost:9092.

Example Output

When we run the command, we will see something like this:

topic1
topic2
topic3

Using Kafka Admin Client

We can also list Kafka topics using the Kafka Admin Client in Java. Here is how we can do it:

import org.apache.kafka.clients.admin.AdminClient;
import org.apache.kafka.clients.admin.ListTopicsOptions;
import org.apache.kafka.clients.admin.ListTopicsResult;

import java.util.Properties;

public class ListKafkaTopics {
    public static void main(String[] args) {
        Properties properties = new Properties();
        properties.put("bootstrap.servers", "localhost:9092");
        try (AdminClient adminClient = AdminClient.create(properties)) {
            ListTopicsResult topics = adminClient.listTopics(new ListTopicsOptions());
            topics.names().get().forEach(System.out::println);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

This way is good to list Kafka topics in our program. By using these commands and code, we can manage Kafka topics well in our Kafka system.

Publishing Messages to a Topic

Publishing messages to a Kafka topic is an important task in Apache Kafka. It lets producers send data to topics for processing.

To publish messages, we usually use a Kafka producer client. Here is a simple example in Java using the Kafka Producer API:

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;

import java.util.Properties;

public class ProducerExample {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:9092");
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        KafkaProducer<String, String> producer = new KafkaProducer<>(props);

        for (int i = 0; i < 10; i++) {
            ProducerRecord<String, String> record = new ProducerRecord<>("my-topic", "key" + i, "value" + i);
            producer.send(record, (RecordMetadata metadata, Exception e) -> {
                if (e != null) {
                    e.printStackTrace();
                } else {
                    System.out.println("Sent: " + record.key() + " to partition: " + metadata.partition());
                }
            });
        }

        producer.close();
    }
}

In this code, we send ten messages to the Kafka topic called “my-topic”. The properties show the location of the Kafka broker and the serializers for the key and value.

When we publish messages to a Kafka topic, we should think about these points:

Asynchronous sending: The send() method works asynchronously by default. This means we can send many messages at the same time.
Message keys: Using keys can help keep the order of messages and decide how to divide them.
Error handling: We need to add callbacks to catch any errors while sending messages.

Publishing messages to a Kafka topic is very important for building strong streaming applications in Kafka.

Consuming Messages from a Topic

Consuming messages from a Kafka topic is very important when we work with Kafka topics. Consumers read messages that we publish to a topic. They can also join a consumer group. This helps with load balancing and scaling.

To consume messages from a Kafka topic, we usually use the Kafka Consumer API. Here is a simple example in Java:

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.ConsumerRecord;

import java.time.Duration;
import java.util.Collections;
import java.util.Properties;

public class TopicConsumer {
    public static void main(String[] args) {
        Properties properties = new Properties();
        properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        properties.put(ConsumerConfig.GROUP_ID_CONFIG, "example-group");
        properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
        properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");

        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);
        consumer.subscribe(Collections.singletonList("your-topic"));

        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
            for (ConsumerRecord<String, String> record : records) {
                System.out.printf("Consumed message: key = %s, value = %s, offset = %d%n", record.key(), record.value(), record.offset());
            }
        }
    }
}

In this example, the Kafka consumer connects to the Kafka broker. It subscribes to one topic and keeps checking for new messages. We need to set the consumer properties correctly for good consumption. Knowing how to consume messages from a Kafka topic is very important for us to build reliable and efficient data pipelines.

Topic Retention Policies

Kafka topics have rules for how long we keep messages before we delete them. These rules help us manage disk space and handle data better. Kafka has two main types of retention policies.

Time-Based Retention: This rule lets us set a time limit for how long we keep messages. After this time is up, messages can be deleted. We use the retention.ms setting for this. It is in milliseconds. For example, if we want to keep messages for 7 days, we set:
```
retention.ms=604800000
```
Size-Based Retention: This rule controls the total size of the log for a topic. When the log gets too big, we delete the oldest messages. We use the retention.bytes setting for this. For example, if we want to limit the log size to 100 MB, we set:
```
retention.bytes=104857600
```

We also have log compaction in Kafka. This keeps the latest message for each key in the topic. It does this even if the time limit is over. We set this up with the cleanup.policy setting:

cleanup.policy=compact

It is important for us to understand and set up topic retention policies in Kafka. This helps us manage data well and use our resources smartly.

Using Topic Compaction

Kafka topics can gather a lot of data over time. Some of this data may not be needed for all uses. Topic compaction is a useful tool in Apache Kafka. It helps us keep only the latest value for each key in a topic. This way, we clean up old data but keep the important information.

When we turn on log compaction for a Kafka topic, Kafka keeps only the last message for each unique key. It deletes the older messages. This is very helpful for applications that need the current state, not the whole history of changes.

To turn on topic compaction, we can set this configuration:

cleanup.policy=compact

We can also mix log compaction with log retention by using:

cleanup.policy=compact,delete

In this case, Kafka will compact the log and also delete messages after some time.

To check if log compaction is working, we can use Kafka’s command-line tools to describe the topic:

kafka-topics.sh --describe --topic <your-topic-name> --bootstrap-server <broker-address>

This command will show us the current settings. It will tell us if log compaction is on. Using topic compaction can help us save space and make our Kafka system work better. It is an important tool for managing Kafka topics.

Monitoring Kafka Topics

We need to monitor Kafka topics. This is very important for keeping our messaging system working well. Good monitoring helps us see the status of Kafka topics. It also lets us check their performance and fix problems before they become big issues.

Here are some key things we should watch:

Message Throughput: We can measure how many messages we produce and use each second. This tells us how busy the topic is.
Consumer Lag: We should watch the gap between the last message produced and the last message consumed. If consumer lag is high, it can mean there are problems.
Partition Distribution: We need to make sure partitions are spread out evenly across brokers. This helps us avoid overloading one broker.
Replication Status: We have to check how replicas are doing. This is important for keeping our data safe and available.

We can use different tools to help us monitor Kafka topics:

Kafka Manager: This is a well-known web tool. It helps us manage and monitor our Kafka clusters.
Prometheus and Grafana: We can use these tools to gather metrics from Kafka. They help us see data in real-time dashboards.
Confluent Control Center: This tool gives us a complete solution for monitoring Kafka topics.

When we monitor Kafka topics well, we can make performance better. We also keep our data safe and improve the reliability of the system. It is good to check these metrics often to keep our Kafka operations running smoothly.

Kafka - Topics - Full Example

We will show how to use Kafka topics in a simple way. Let’s go through an example. We will create a Kafka topic, send messages, and read them. This will help us see how Kafka topics work.

Setting up Kafka: First, we need to have Kafka running. After we download and extract Kafka, we start Zookeeper and the Kafka broker. We can do it like this:
```
bin/zookeeper-server-start.sh config/zookeeper.properties
bin/kafka-server-start.sh config/server.properties
```
Creating a topic: Next, we create a topic called “test-topic”. This topic will have 1 replication factor and 3 partitions. We can create it with this command:
```
bin/kafka-topics.sh --create --topic test-topic --bootstrap-server localhost:9092 --partitions 3 --replication-factor 1
```
Producing messages: Now, we will send messages to “test-topic” using the Kafka console producer. We can do this with the command below:
```
bin/kafka-console-producer.sh --topic test-topic --bootstrap-server localhost:9092
```
We just type our messages and press Enter.
Consuming messages: We open another terminal to read messages from “test-topic”. We can use this command:
```
bin/kafka-console-consumer.sh --topic test-topic --bootstrap-server localhost:9092 --from-beginning
```
Monitoring: We can use Kafka tools or JMX to check how our topic is doing.

This example shows the steps to work with Kafka topics. From creating topics to sending and reading messages. It shows us how easy and powerful Kafka can be for streaming data in real time. In conclusion, this article on ‘Kafka - Topics’ gives us a clear look at important ideas. We learned about understanding Kafka topics. We also saw how to create and set them up. Managing the lifecycle of topics is important too. This includes using retention policies and compaction.

When we understand Kafka topics well, we can publish and consume messages better. This helps us make sure data streaming is smooth. By using these ideas about Kafka topics, we can use Kafka for strong data processing solutions.

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Kafka - Topics

Understanding Kafka Topics

Understanding Kafka Topics

Creating a Kafka Topic

Topic Configuration Parameters

Partitions and Replication Factors

Command to List Kafka Topics

Explanation of Parameters

Example Output

Using Kafka Admin Client

Publishing Messages to a Topic

Consuming Messages from a Topic

Topic Retention Policies

Using Topic Compaction

Monitoring Kafka Topics

Kafka - Topics - Full Example

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