Sending your first messages You’ll need to run a Kafka broker to run the following examples. In the previous edition of this book, the instructions were to download a binary version of the Kafka tar file and extract it locally. In this edition, I’ve opted to run Kafka via Docker instead. Specifically, we’ll use Docker Compose, making running a multicontainer docker application easy. If you are running macOS or Windows, you can install Docker Desktop, which includes Docker Compose. For more information on installing Docker, see the installation instructions on the Docker site: https://docs.docker.com/get-docker/. Note that you can also use Confluent Cloud (https://www.confluent.io/confluent -cloud/) for a broker for running your Kafka applications. See appendix B for details on resources from Confluent. Let’s start working with a Kafka broker by producing and consuming some records

Q- give the best docker-compose for this 

Here's the formatted version of your Docker Compose setup guide:

Certainly! Below is a comprehensive docker-compose.yml file that sets up both Apache Zookeeper and Apache Kafka using Docker Compose. This setup is ideal for development and testing purposes. It leverages the official Confluent Docker images, which are widely used and well-supported.
docker-compose.yml
yaml
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version: '3.8'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    container_name: zookeeper
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000
      ZOOKEEPER_SYNC_LIMIT: 2
    ports:
      - "2181:2181"

  kafka:
    image: confluentinc/cp-kafka:latest
    container_name: kafka
    depends_on:
      - zookeeper
    ports:
      - "9092:9092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1


Explanation of the Configuration
1. Version
yaml
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version: '3.8'

Specifies the version of the Docker Compose file format. Version 3.8 is compatible with Docker Engine 19.03.0+.
2. Services
a. Zookeeper
yaml
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zookeeper:
  image: confluentinc/cp-zookeeper:latest
  container_name: zookeeper
  environment:
    ZOOKEEPER_CLIENT_PORT: 2181
    ZOOKEEPER_TICK_TIME: 2000
    ZOOKEEPER_SYNC_LIMIT: 2
  ports:
    - "2181:2181"

Image: Uses the latest Confluent Zookeeper image.
Container Name: Names the container zookeeper for easy reference.
Environment Variables:
ZOOKEEPER_CLIENT_PORT: Port on which Zookeeper listens for client connections.
ZOOKEEPER_TICK_TIME: Basic time unit in milliseconds used by Zookeeper.
ZOOKEEPER_SYNC_LIMIT: Number of ticks that can pass between sending a request and getting an acknowledgement.
Ports: Maps container port 2181 to host port 2181.
b. Kafka
yaml
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kafka:
  image: confluentinc/cp-kafka:latest
  container_name: kafka
  depends_on:
    - zookeeper
  ports:
    - "9092:9092"
  environment:
    KAFKA_BROKER_ID: 1
    KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
    KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
    KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
    KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
    KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1

Image: Uses the latest Confluent Kafka image.
Container Name: Names the container kafka.
Depends On: Ensures that Zookeeper starts before Kafka.
Ports: Maps container port 9092 to host port 9092.
Environment Variables:
KAFKA_BROKER_ID: Unique identifier for the Kafka broker.
KAFKA_ZOOKEEPER_CONNECT: Connection string for Zookeeper (zookeeper:2181 refers to the Zookeeper service defined above).
KAFKA_ADVERTISED_LISTENERS: Advertised address for clients to connect (PLAINTEXT://localhost:9092 is suitable for local development).
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: Sets the replication factor for the offset topic. Since we're running a single broker, this is set to 1.
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR and KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: Configure transaction logs, set to 1 for single broker setups.

Steps to Run
1. Ensure Docker and Docker Compose Are Installed
Docker: Install Docker
Docker Compose: Install Docker Compose
2. Save the docker-compose.yml File
Create a new directory for your Kafka project and save the above docker-compose.yml file within it.
bash
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mkdir kafka-docker
cd kafka-docker
vim docker-compose.yml
# Paste the content and save

3. Start the Services
Run the following command to start Zookeeper and Kafka:
bash
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docker-compose up -d

The -d flag runs the containers in detached mode.
4. Verify the Setup
Check Running Containers
bash
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docker-compose ps

You should see both zookeeper and kafka containers up and running.
Q my next question to now the exec the docker container and run this command “Wait a few seconds and then run the following command to open a shell on the docker broker container: docker-compose exec broker bash. Using the Docker broker container shell you just opened up, run this command to create a topic: kafka-topics --create --topic first-topic\ --bootstrap-server localhost:9092\ --replication-factor 1\ --partitions 1 NOTE Although you’re using Kafka in a Docker container, the commands to create topics and run the console producer and consumer are the same. Since you’re running a local broker for testing, you don’t need a replication factor greater than 1. The same thing goes for the number of partitions; at this point, you only need one partition for this local development. Now you have a topic, let’s write some records to it.”

To execute the steps mentioned for creating a topic in your Kafka setup using Docker, here's a step-by-step guide:

### 1. Start your Docker containers
First, make sure your `docker-compose.yml` file is ready and you're in the same directory where it's saved. Then, start the containers:

```bash
docker-compose up -d
```

This command will start the `zookeeper` and `kafka` services in detached mode.

### 2. Verify that the containers are running
Check if both the `zookeeper` and `kafka` containers are running:

```bash
docker-compose ps
```

You should see the status of both services as `Up`.

### 3. Open a shell in the Kafka container
Now that your Kafka container is running, you'll need to open a shell session in the Kafka container to execute the Kafka commands. Run the following command to get inside the Kafka container:

```bash
docker-compose exec kafka bash
```

This will give you access to the shell of the Kafka container, where you can run Kafka commands.

### 4. Create a Kafka topic
Once you're inside the Kafka container, you can create a Kafka topic. Run the following command to create a topic named `first-topic`:

```bash
kafka-topics --create \
  --topic first-topic \
  --bootstrap-server localhost:9092 \
  --replication-factor 1 \
  --partitions 1
```

This will create a Kafka topic named `first-topic` with a replication factor of 1 and 1 partition.

### 5. Verify the topic creation
To confirm that the topic was successfully created, you can list all topics:

```bash
kafka-topics --list --bootstrap-server localhost:9092
```

This should show `first-topic` in the list of topics.

Now you have a Kafka topic ready, and you can proceed to write records to it! Let me know if you need assistance with writing records or consuming them.


Q- next 

Fromat this now “
Producing records on the command line Now, from the same window you ran the --create --topic command, run the following command to start a console producer: kafka-console-producer --topic first-topic\ --bootstrap-server localhost:9092\ --property parse.key=true\ --property key.separator=":" The host:port to connect to the broker Specifies the replication factor The number of partitions The topic you created in the previous step The host:port for the producer client to connect to the broker Specifies that you’ll provide a key Specifies the separator of the key and value 2.5 Sending your first messages 27 When using the console producer, you need to specify whether you will provide keys. Although Kafka works with key-value pairs, the key is optional and can be null. Since the key and value go on the same line, you must specify how Kafka can parse the key and value by providing a delimiter. After you enter the previous command and press Enter, you should see a prompt waiting for your input. Enter some text like the following: key:my first message key:is something key:very simple You type in each line and then press Enter to produce the records. Congratulations, you have sent your first messages to a Kafka topic! Now, let’s consume the records you just wrote to the topic. Keep the console producer running, as you’ll use it again in a few minutes. “