Sunday, 4 August 2024

redpanda s3

 To configure the S3 Sink Connector for storing Redpanda topic messages in S3 while using Protobuf, follow these steps:

1. Prerequisites

  • Redpanda is configured and running with SASL authentication enabled.
  • Your topic uses Protobuf serialization.
  • An AWS S3 bucket is set up, with proper permissions to write data.
  • Kafka Connect is installed (standalone or distributed mode).

2. Install and Enable the S3 Sink Connector

If you're using Confluent's Kafka Connect or a similar Kafka-compatible service:

  1. Download the S3 Sink Connector Plugin:

    • For Confluent users:
      bash
      confluent-hub install confluentinc/kafka-connect-s3:latest
    • For open-source setups, download and place the connector .jar file in the plugin.path directory.

  2. Restart Kafka Connect:

    • Ensure the connector plugin is loaded by restarting your Kafka Connect service.

3. Define the Protobuf Schema

  • Protobuf Deserializer: The connector requires a Protobuf deserializer to interpret the data correctly. You can use Confluent’s Protobuf deserializer or implement your own.

  • Schema Registry: If you’re using a schema registry, configure it with your Protobuf schema.

4. Configure the S3 Sink Connector

Create a JSON configuration file, for example, s3-sink-connector-config.json:

json
{
  "name": "s3-sink-connector",
  "config": {
    "connector.class": "io.confluent.connect.s3.S3SinkConnector",
    "tasks.max": "1",
    "topics": "your_topic",
    "s3.bucket.name": "your-s3-bucket",
    "s3.region": "your-region",
    "s3.part.size": "5242880",
    "flush.size": "100",
    "storage.class": "io.confluent.connect.s3.storage.S3Storage",
    "format.class": "io.confluent.connect.s3.format.json.JsonFormat",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "io.confluent.connect.protobuf.ProtobufConverter",
    "value.converter.schema.registry.url": "http://your-schema-registry:8081",
    "schema.compatibility": "FULL",
    "aws.access.key.id": "your-access-key",
    "aws.secret.access.key": "your-secret-key",
    "s3.credentials.provider.class": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain"
  }
}

Key Parameters:

  • connector.class: Specifies the S3 Sink Connector class.
  • topics: The topic(s) you want to sink to S3.
  • s3.bucket.name: The name of your S3 bucket.
  • value.converter: Set to io.confluent.connect.protobuf.ProtobufConverter for Protobuf.
  • value.converter.schema.registry.url: URL for your schema registry.
  • format.class: Choose the file format for S3 storage (JsonFormat, AvroFormat, etc.).

5. Start the Connector

Run the connector in standalone or distributed mode:

  • Standalone Mode:
    bash
    connect-standalone connect-standalone.properties s3-sink-connector-config.json
  • Distributed Mode: Post the configuration to the Connect REST API:
    bash
    curl -X POST -H "Content-Type: application/json" --data @s3-sink-connector-config.json http://localhost:8083/connectors

6. Verify Data in S3

Once the connector is running:

  • Messages from your topics will be written to S3 in the specified format.
  • The default structure is:
    php
    s3://your-bucket-name/topics/<topic-name>/partition=<partition-id>/<timestamp>.json

7. Troubleshooting

  • Authentication Issues: Ensure your AWS credentials (Access Key and Secret Key) are correctly configured or use IAM roles for authentication.
  • Schema Registry Compatibility: Verify the Protobuf schema is registered and accessible.

Would you like me to help tailor this setup for your specific Redpanda environment?

You said:
i want to use rpk connect for connector. I dont have internet where redpanda is installed
ChatGPT said:
ChatGPT

To configure the S3 Sink Connector using Redpanda's rpk connect without internet access, you'll need to handle the following:

  1. Download Connector Files and Dependencies:

    • Download the S3 Sink Connector files (JARs and related dependencies) on a machine with internet access.
    • Transfer these files to the Redpanda host.

  2. Set Up the rpk Connect Plugin Path:

    • Place the downloaded connector files in a local directory on the Redpanda host.
    • Update the plugin.path in the redpanda.yaml file to include this directory.

  3. Prepare the S3 Sink Connector Configuration:

    • Use rpk to create and manage the S3 Sink Connector.

Step-by-Step Guide

Step 1: Download the S3 Sink Connector JARs

  1. Download the S3 Sink Connector:

    • Download the latest Confluent S3 Sink Connector JAR file and dependencies:
    • Ensure you also download the Protobuf and Schema Registry Converter JARs if needed.

  2. Transfer to Redpanda Host:

    • Use scp or a USB drive to move the downloaded files to the Redpanda host.

Step 2: Configure plugin.path

  1. Place JARs in a Local Directory:

    • Create a directory for Kafka Connect plugins on the Redpanda host:
      bash
      mkdir -p /opt/redpanda/plugins
    • Move all the JARs into this directory.

  2. Update redpanda.yaml:

    • Open the Redpanda configuration file:
      bash
      nano /etc/redpanda/redpanda.yaml
    • Add or update the plugin.path entry to include the connector path:
      yaml
      rpk:
  connect:
    plugin.path: "/opt/redpanda/plugins"

  3. Restart Redpanda:

    • Apply the changes:
      bash
      systemctl restart redpanda

Step 3: Create S3 Sink Connector Configuration

  1. Prepare the Connector Config File:
    • Create a file, e.g., s3-sink-config.json:
      json
      {
  "name": "s3-sink-connector",
  "config": {
    "connector.class": "io.confluent.connect.s3.S3SinkConnector",
    "tasks.max": "1",
    "topics": "your_topic",
    "s3.bucket.name": "your-s3-bucket",
    "s3.region": "your-region",
    "s3.part.size": "5242880",
    "flush.size": "100",
    "storage.class": "io.confluent.connect.s3.storage.S3Storage",
    "format.class": "io.confluent.connect.s3.format.json.JsonFormat",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "io.confluent.connect.protobuf.ProtobufConverter",
    "value.converter.schema.registry.url": "http://your-schema-registry:8081",
    "schema.compatibility": "FULL",
    "aws.access.key.id": "your-access-key",
    "aws.secret.access.key": "your-secret-key",
    "s3.credentials.provider.class": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain"
  }
}

Step 4: Deploy the Connector with rpk

Use rpk to deploy the connector:

  1. Start the Connect Framework:

    bash
    rpk connect enable

  2. Deploy the Connector:

    bash
    rpk connect create --config s3-sink-config.json

  3. Check the Status:

    bash
    rpk connect status

Step 5: Verify Data in S3

  • Check your S3 bucket to confirm the messages are being written.
  • The default directory structure is:
    php
    s3://your-bucket-name/topics/<topic-name>/partition=<partition-id>/<timestamp>.json

Let me know if you need help preparing the JAR files, configuring the schema registry, or setting up the connector!

You said:
ass more details about s3 Sink Connector with protobuf registory
ChatGPT said:
ChatGPT

Here’s a detailed guide to configuring and deploying the S3 Sink Connector with Protobuf and Schema Registry integration, specifically for your setup using rpk connect and Redpanda.

Understanding the Workflow

  1. Messages are produced to Redpanda topics serialized with Protobuf.
  2. The S3 Sink Connector consumes these messages, deserializes them using the Protobuf schema from the Schema Registry, and writes the data to an S3 bucket.
  3. The messages are stored in JSON, Avro, or Parquet format, based on your configuration.

Detailed Steps

Step 1: Download and Transfer Dependencies

  1. Download the Connector:

    • Download the S3 Sink Connector from Confluent Hub.
    • Download the Protobuf converter JARs:
      • kafka-connect-protobuf-converter.jar
      • protobuf-java.jar

  2. Transfer Files:

    • Move the files to your Redpanda host (e.g., /opt/redpanda/plugins).

Step 2: Configure Redpanda

  1. Update the Plugin Path:

    • Open /etc/redpanda/redpanda.yaml and add:
      yaml
      rpk:
  connect:
    plugin.path: "/opt/redpanda/plugins"

  2. Restart Redpanda:

    bash
    systemctl restart redpanda

Step 3: Create Schema Registry Setup

  1. Start a Schema Registry:

    • Use a Confluent-compatible Schema Registry or deploy your own. Ensure it's accessible from your Redpanda environment.

  2. Register Protobuf Schemas:

    • Use the Schema Registry REST API to register your Protobuf schemas. Example using curl:
    bash
    curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
  --data '{"schema": "<protobuf_schema_string>", "schemaType": "PROTOBUF"}' \
  http://your-schema-registry:8081/subjects/your-topic-value/versions

Step 4: Create the S3 Sink Connector Configuration

Create a file named s3-sink-config.json with the following content:

json
{
  "name": "s3-sink-connector",
  "config": {
    "connector.class": "io.confluent.connect.s3.S3SinkConnector",
    "tasks.max": "1",
    "topics": "your_topic",
    "s3.bucket.name": "your-s3-bucket",
    "s3.region": "your-region",
    "s3.part.size": "5242880",
    "flush.size": "100",
    "storage.class": "io.confluent.connect.s3.storage.S3Storage",
    "format.class": "io.confluent.connect.s3.format.json.JsonFormat",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "io.confluent.connect.protobuf.ProtobufConverter",
    "value.converter.schema.registry.url": "http://your-schema-registry:8081",
    "schema.compatibility": "FULL",
    "aws.access.key.id": "your-access-key",
    "aws.secret.access.key": "your-secret-key",
    "s3.credentials.provider.class": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain",
    "partitioner.class": "io.confluent.connect.storage.partitioner.DefaultPartitioner",
    "locale": "en",
    "timezone": "UTC"
  }
}

Key Configuration Options:

  • Schema Registry URL:
    • value.converter.schema.registry.url: Points to your Schema Registry.
  • Partitioning:
    • By default, messages are stored with the structure:
      php
      s3://your-bucket-name/topics/<topic-name>/partition=<partition-id>/<timestamp>.json
    • Customize partitioning using partitioner.class.

Step 5: Deploy the Connector with rpk

  1. Enable rpk connect:

    bash
    rpk connect enable

  2. Create the Connector:

    bash
    rpk connect create --config s3-sink-config.json

  3. Check Connector Status:

    bash
    rpk connect status

Step 6: Test and Verify

  1. Produce Messages:
    • Use your producer application to send Protobuf-serialized messages to the topic.
  2. Verify in S3:
    • Check the S3 bucket for uploaded files. Ensure they match the configured format (e.g., JSON).

Troubleshooting

  1. Authentication Issues:
    • If AWS credentials aren’t working, use an IAM role or ensure the aws.access.key.id and aws.secret.access.key are correctly set.
  2. Schema Errors:
    • Ensure your Protobuf schema is registered and matches the topic data structure.

Would you like a step-by-step guide to registering schemas or testing Protobuf deserialization?

You said:
can i use copy .proto file locally and setup schema registory?
ChatGPT said:
ChatGPT

Yes, you can use local .proto files to set up your own Schema Registry without internet access. This approach allows you to register Protobuf schemas manually from .proto files instead of relying on a remote source. Here's how you can achieve this:

Steps to Set Up Schema Registry Locally Using .proto Files

1. Deploy a Schema Registry

  1. Download a Schema Registry Compatible with Redpanda:

    • Use Confluent Schema Registry or open-source alternatives (e.g., Karapace or Apicurio).
    • Transfer the Schema Registry binaries to your offline environment.

  2. Start the Schema Registry:

    • Configure and start the Schema Registry on your Redpanda host or a separate server.
    • Example command for Confluent Schema Registry:
      bash
      ./bin/schema-registry-start ./etc/schema-registry/schema-registry.properties
    • Update the listeners property in the configuration file to match your network setup.

2. Create Local .proto Files

  1. Prepare Your Protobuf Files:

    • Place all .proto files in a local directory on the machine hosting the Schema Registry.

    Example Protobuf file (trade.proto):

    proto
    syntax = "proto3";

package trade;

message Trade {
    string tradeId = 1;
    string symbol = 2;
    int64 timestamp = 3;
    double price = 4;
    int32 quantity = 5;
}

  2. Compile the .proto Files (Optional):

    • If needed, compile the .proto files for your application:
      bash
      protoc --java_out=./generated trade.proto

3. Register Schemas Locally

You can use the Schema Registry API to manually register the schema for your topics using the .proto file.

  1. Use the Schema Registry REST API:

    • Register the schema by sending the .proto definition in a request:
      bash
      curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
  --data '{"schema": "syntax = \"proto3\"; package trade; message Trade { string tradeId = 1; string symbol = 2; int64 timestamp = 3; double price = 4; int32 quantity = 5; }", "schemaType": "PROTOBUF"}' \
  http://localhost:8081/subjects/your-topic-value/versions

    Replace:

    • http://localhost:8081 with your Schema Registry URL.
    • your-topic-value with the topic name for which this schema applies.

  2. Register Using a Local Script:

    • Write a script to read .proto files and register them programmatically:
      bash
      #!/bin/bash
for proto_file in /path/to/protos/*.proto; do
  schema=$(cat "$proto_file")
  topic_name=$(basename "$proto_file" .proto)
  curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data "{\"schema\": \"$schema\", \"schemaType\": \"PROTOBUF\"}" \
    http://localhost:8081/subjects/$topic_name-value/versions
done
    • Adjust the path and endpoint as needed.

4. Configure the S3 Sink Connector

Once schemas are registered, update the S3 Sink Connector configuration to use the local Schema Registry.

  • Example configuration snippet:
    json
    {
  "value.converter": "io.confluent.connect.protobuf.ProtobufConverter",
  "value.converter.schema.registry.url": "http://localhost:8081",
  "schema.compatibility": "FULL"
}

5. Verify the Setup

  1. List Registered Schemas:
    • Use the Schema Registry API:
      bash
      curl http://localhost:8081/subjects
  2. Produce Messages:
    • Send Protobuf-encoded messages to your Redpanda topic.
  3. Verify S3 Output:
    • Check that messages are written to S3 in the specified format.

Advantages of Using Local .proto Files

  • No internet dependency.
  • Full control over schema management.
  • Easy to version and maintain schemas locally.

Would you like an example of a full .proto file or a script for automating the schema registration process?

You said:
no can I user local file system as a registory?
ChatGPT said:
ChatGPT

Yes, you can use the local filesystem as a schema registry by storing your Protobuf schemas in files rather than using a dedicated schema registry service. This approach allows you to manage your schemas directly from your file system without needing a network-based registry. Here’s how you can set this up:

Steps to Use Local Filesystem for Schema Management

1. Create a Directory for Protobuf Schemas

  • Choose a directory on your local filesystem where you will store all your .proto files. For example:
    bash
    mkdir /path/to/protos

2. Create Protobuf Files

  • Add your Protobuf definitions as .proto files in the directory you created. For example, create a file called trade.proto:
    proto
    syntax = "proto3";

package trade;

message Trade {
    string tradeId = 1;
    string symbol = 2;
    int64 timestamp = 3;
    double price = 4;
    int32 quantity = 5;
}

3. Modify Your Application to Load Schemas

  • When you produce or consume messages in your application, you can load the corresponding .proto file from the filesystem to dynamically compile the schema and serialize/deserialize messages.
Example in Python Using protobuf Library

If you are using Python, you can use the protobuf library to load the schema from a file:

python
import os
from google.protobuf import json_format
from trade_pb2 import Trade  # Generated from trade.proto

def load_proto_schema(proto_file_path):
    with open(proto_file_path, 'r') as f:
        return f.read()

# Example usage
schema_file = '/path/to/protos/trade.proto'
proto_schema = load_proto_schema(schema_file)

# Create a Trade message
trade = Trade(tradeId="T123", symbol="AAPL", timestamp=1635705600, price=150.0, quantity=100)

# Serialize the message to bytes
serialized_trade = trade.SerializeToString()

# Deserialize the message from bytes
received_trade = Trade()
received_trade.ParseFromString(serialized_trade)

4. Configure the S3 Sink Connector

  • If you are using the S3 Sink Connector with Protobuf messages, you can configure it to work with the local schema files. However, the S3 Sink Connector typically requires a schema registry endpoint for Protobuf deserialization.

  • Instead, you could use a custom connector or script that reads the .proto files from the filesystem, compiles them dynamically, and handles the serialization/deserialization.

5. Additional Considerations

  • Versioning: You will need to manage schema versions manually by maintaining different .proto files for each version.
  • Error Handling: Implement error handling in your application to manage cases where a schema might not be found.
  • Schema Compatibility: Without a dedicated schema registry, you will need to ensure that the producer and consumer applications are compatible with the schema changes.

Conclusion

Using a local filesystem for Protobuf schemas allows for flexibility and simplicity, especially in offline environments. However, it does require that you manage schema versions and compatibility manually. If your applications can load and compile the schemas dynamically, this approach can work well.

If you need further assistance with code examples or setup, feel free to ask!

You said:
i have asked questiion about ass more details about s2 sink connectore with protobuf as a local or file registory, create detail document about how to install and configure
ChatGPT said:
ChatGPT

Here’s a detailed document on how to install and configure the S3 Sink Connector with Protobuf using a local file system as a schema registry. This guide covers everything from setup to configuration and testing.

Installing and Configuring S3 Sink Connector with Protobuf Using Local File Registry

Overview

This document provides step-by-step instructions to set up the S3 Sink Connector for Redpanda, using Protobuf for serialization and a local file system to manage Protobuf schemas. The connector will read messages from a specified Redpanda topic and write them to an S3 bucket.

Prerequisites

  1. Redpanda Cluster: Ensure you have a running instance of Redpanda.
  2. S3 Bucket: Create an S3 bucket where messages will be stored.
  3. Local File System: Set up a directory to hold your .proto files for schema management.
  4. JDK: Ensure Java is installed on your system (Java 8 or higher).
  5. Access to AWS Credentials: Ensure you have AWS credentials to write to the S3 bucket.

Step 1: Download and Install Redpanda and the S3 Sink Connector

  1. Download Redpanda:

  2. Install the S3 Sink Connector:

    • Download the S3 Sink Connector from the Confluent Hub or another source and transfer it to your Redpanda host.
    • The necessary connector files should be placed in the appropriate plugin directory (e.g., /opt/redpanda/plugins).

Step 2: Configure Redpanda for Connectors

  1. Edit the Redpanda Configuration:

    • Open the Redpanda configuration file (redpanda.yaml) and ensure it includes the connector plugin path:
      yaml
      rpk:
  connect:
    plugin.path: "/opt/redpanda/plugins"

  2. Restart Redpanda:

    bash
    systemctl restart redpanda

Step 3: Set Up Local Protobuf Schema Files

  1. Create a Directory for Protobuf Schemas:

    bash
    mkdir /path/to/protos

  2. Create Your Protobuf Schema Files:

    • For example, create a file named trade.proto with the following content:
    proto
    syntax = "proto3";

package trade;

message Trade {
    string tradeId = 1;
    string symbol = 2;
    int64 timestamp = 3;
    double price = 4;
    int32 quantity = 5;
}

Step 4: Create the S3 Sink Connector Configuration

  1. Create the Connector Configuration File:

    • Create a file named s3-sink-config.json with the following content:
    json
    {
  "name": "s3-sink-connector",
  "config": {
    "connector.class": "io.confluent.connect.s3.S3SinkConnector",
    "tasks.max": "1",
    "topics": "your_topic",
    "s3.bucket.name": "your-s3-bucket",
    "s3.region": "your-region",
    "s3.part.size": "5242880",
    "flush.size": "100",
    "storage.class": "io.confluent.connect.s3.storage.S3Storage",
    "format.class": "io.confluent.connect.s3.format.json.JsonFormat",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "io.confluent.connect.protobuf.ProtobufConverter",
    "value.converter.schema.registry.url": "file:///path/to/protos",
    "schema.compatibility": "FULL",
    "aws.access.key.id": "your-access-key",
    "aws.secret.access.key": "your-secret-key",
    "s3.credentials.provider.class": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain",
    "partitioner.class": "io.confluent.connect.storage.partitioner.DefaultPartitioner"
  }
}

    Replace:

    • your_topic with the topic you will produce messages to.
    • your-s3-bucket with the name of your S3 bucket.
    • your-region with your AWS region.
    • Provide your AWS access key and secret key.

Step 5: Deploy the Connector

  1. Enable rpk connect:

    bash
    rpk connect enable

  2. Create the Connector:

    bash
    rpk connect create --config s3-sink-config.json

  3. Check the Connector Status:

    bash
    rpk connect status

Step 6: Testing the Setup

  1. Produce Protobuf Messages:

    • Use your producer application to send Protobuf-serialized messages to the specified Redpanda topic.

  2. Verify the Output in S3:

    • Check your S3 bucket to ensure that messages are written in the expected format.

Troubleshooting

  • Connector Issues: Check logs for errors related to the S3 Sink Connector using:
    bash
    rpk connect logs
  • Schema Errors: Ensure the Protobuf schema is correctly defined and matches the messages being produced.

Conclusion

By following this guide, you have set up the S3 Sink Connector for Redpanda to store messages in S3, using local Protobuf schema files for serialization. This setup allows you to manage your schemas efficiently without an internet connection.

If you need further customization or assistance, feel free to ask!

You said:
add detailsd about insatll the s3 sink connector
ChatGPT said:
ChatGPT

Here's a detailed section on installing the S3 Sink Connector as part of the document for setting up the S3 Sink Connector with Protobuf using a local file registry.

Step 1: Download and Install the S3 Sink Connector

1.1 Prerequisites

Before installing the S3 Sink Connector, ensure that you have the following:

  • Java Development Kit (JDK): The S3 Sink Connector requires Java 8 or higher. You can check if Java is installed with the following command:
    bash
    java -version
    If it is not installed, you can install it using:
    bash
    sudo apt-get update
sudo apt-get install openjdk-11-jdk

1.2 Downloading the Connector

You can download the S3 Sink Connector from the Confluent Hub or build it from source. Below are instructions for both methods:

Method A: Download from Confluent Hub

  1. Go to the Confluent Hub and download the latest version of the S3 Sink Connector.
  2. Alternatively, you can use the following command to download the connector directly to your server (replace x.y.z with the desired version):
    bash
    curl -O https://packages.confluent.io/archive/7.0/confluent-hub-client-latest.zip

Method B: Build from Source

  1. Clone the Kafka Connect S3 repository:

    bash
    git clone https://github.com/confluentinc/kafka-connect-storage-plugins.git
cd kafka-connect-storage-plugins

  2. Navigate to the S3 directory:

    bash
    cd kafka-connect-s3

  3. Build the connector using Maven:

    bash
    mvn clean package

  4. The built connector will be available in the target directory.

1.3 Install the Connector

  1. Create a Directory for Connectors: Create a directory where you will store the connector files:

    bash
    mkdir -p /opt/redpanda/plugins/s3

  2. Copy the Connector Files: If you downloaded the connector from Confluent Hub, unzip it and copy the contents to the connector directory:

    bash
    unzip confluent-hub-client-latest.zip -d /opt/redpanda/plugins/s3

    If you built the connector from source, copy the JAR files from the target directory to the same location:

    bash
    cp target/* /opt/redpanda/plugins/s3/

  3. Verify Installation: Ensure that the connector files are correctly placed in the /opt/redpanda/plugins/s3 directory:

    bash
    ls /opt/redpanda/plugins/s3

1.4 Configure Redpanda to Use the Connector

  1. Edit the Redpanda Configuration: Open the Redpanda configuration file (redpanda.yaml) and add the connector plugin path:

    yaml
    rpk:
  connect:
    plugin.path: "/opt/redpanda/plugins"

  2. Restart Redpanda: After editing the configuration, restart Redpanda to load the new connector:

    bash
    systemctl restart redpanda

Conclusion of Installation

You have successfully installed the S3 Sink Connector. This connector can now be used to transfer messages from Redpanda topics to an S3 bucket, leveraging Protobuf for serialization. The next steps involve configuring the connector and setting up your Protobuf schemas.

If you have any questions or need further assistance, feel free to ask!

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