feat : upgrade to spring boot 3.3.0-RC1 and polish #480
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Warning Rate Limit Exceeded@rajadilipkolli has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 32 minutes and 31 seconds before requesting another review. How to resolve this issue?After the wait time has elapsed, a review can be triggered using the We recommend that you space out your commits to avoid hitting the rate limit. How do rate limits work?CodeRabbit enforces hourly rate limits for each developer per organization. WalkthroughThis update brings significant changes across various modules, focusing on upgrading Java and Spring Boot versions, refining Docker configurations, and enhancing the development environment setup. Key highlights include transitioning to Spring Boot Changes
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README.md
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| - **Low latency**: Kafka is designed for low-latency processing, making it suitable for real-time applications. | ||
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| - **High-throughput**: Kafka is able to handle high-throughput data streams, allowing it to process large amounts of data in real-time. | ||
| - **High-throughput**: Kafka can handle high-throughput data streams, allowing it to process large amounts of data in real-time. | ||
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| - **Flexibility**: Kafka is highly flexible and can be used for a wide range of data processing use cases, including real-time analytics, data pipelines, and event-driven architectures. | ||
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| This repository contains sample projects integrating with kafka using different mechanisms available | ||
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| - [avro](./kafka-avro/README.md) | ||
| - [Kafka avro type](./kafka-avro/README.md) | ||
| - [DSL integration](./kafka-dsl-integration/ReadMe.md) | ||
| - [reactor](./kafka-reactor/README.md) | ||
| - [sample](./kafka-sample) | ||
| - [sample](./kafka-sample/README.md) | ||
| - [Examples using spring boot](./kafka-spring-boot/README.md) | ||
| - [Kafka implementation using cloud bindings](./kafka-spring-cloud-sample/README.md) | ||
| - [kafka streams implementation](./kafka-streams/README.md) | ||
| - [Kafka implementation using cloud bindings](./spring-cloud/README.md) | ||
| - [Outbox Pattern Implementation using Modulith](./spring-modulith-outbox-pattern/README.md) | ||
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| ### Reference | ||
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📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [11-30]
Fix the indentation of list items to align with Markdown best practices.
- - **Scalability**: Kafka is designed to handle a large volume of data and can scale to handle millions of messages per second.
- - **Durability**: Kafka stores messages on disk, making it possible to recover from failures and maintain data integrity.
- - **Low latency**: Kafka is designed for low-latency processing, making it suitable for real-time applications.
- - **High-throughput**: Kafka can handle high-throughput data streams, allowing it to process large amounts of data in real-time.
- - **Flexibility**: Kafka is highly flexible and can be used for a wide range of data processing use cases, including real-time analytics, data pipelines, and event-driven architectures.
+ - **Scalability**: Kafka is designed to handle a large volume of data and can scale to handle millions of messages per second.
+ - **Durability**: Kafka stores messages on disk, making it possible to recover from failures and maintain data integrity.
+ - **Low latency**: Kafka is designed for low-latency processing, making it suitable for real-time applications.
+ - **High-throughput**: Kafka can handle high-throughput data streams, allowing it to process large amounts of data in real-time.
+ - **Flexibility**: Kafka is highly flexible and can be used for a wide range of data processing use cases, including real-time analytics, data pipelines, and event-driven architectures.
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Summary by CodeRabbit
New Features
Pivotal.vscode-boot-dev-packextension for enhanced development support.Updates
3.3.0-RC1.7.6.1for improved stability and features.Documentation