Apache Kafka vs Flink
Apache Kafka and Apache Flink are two powerful tools in big data and stream processing. While Kafka is known for its robust messaging system, Flink is good in real-time stream processing and analytics. Understanding the differences between these two tools is important for choosing the right one for our use case.
In this article, we’ll explore the key features, advantages, and disadvantages of Apache Kafka and Apache Flink and compare them in a tabular format to highlight their differences.
What is Apache Kafka?
- Apache Kafka a lightweight library is specifically designed for stream processing activities. From message passing to stream processing applications, Kafka serves multiple functions.
- It finds applications in stream processing, website activity tracking, metrics collecting, log aggregation, real–time analytics and microservices.
- Developers can focus on their applications without worrying about deployment.
- Kafka uses a binary TCP-based protocol to optimize for efficiency and relies on a “message set” abstraction that naturally groups messages to reduce the overhead of the network.
Advantages of Apache Kafka
- Fully integrated with the rest of the Kafka ecosystem and resulting in simplified operations and reduced latency.
- It enables the development of typical Java applications without the need for a separate processing cluster.
- Provides an exact-once processing guarantee to assure data integrity.
- It is lightweight and no additional cluster setup is required.
Disadvantages of Apache Kafka
- The stream processing capabilities are less feature-rich than those of competing systems, such as Apache Flink.
- Kafka Streams only supports Java, which limits its use to developers who are experienced with other languages.
- It does not have a web-based UI for visualization or an SQL interface.
- Out-of-order event handling is more complex than in systems like Flink.
What is Apache Flink?
- Apache Flink, developed at Berlin TU University, Flink allow the lambda architecture and functions as a genuine streaming engine.
- It handles batch processing as a subset of streaming, especially for constrained data. Auto-adjustment is a key feature of Flink minimizing the need for extensive parameter tuning and establishing it as the first true streaming framework.
- Flink offers a streaming engine with high throughput and low latency, as well as event-time processing and state management capabilities.
- Flink applications are fault-tolerant in the case of a machine failure and use exactly-once semantics.
Advantages of Apache Kafka
- Apache Flink has a distributed architecture which makes it scalable.
- Apache Flink can handle real-time data pipelines. Processors, analytics, storage and other components are included to build a real-time data pipeline.
- Flink can manage a larger number of messages with high volume and velocity.
- It provides a SQL interface and a web-based UI for visualization.
Disadvantages of Apache Kafka
- Apache Flink does not have a complete set of monitoring and management capabilities. Thus, new startups and enterprises fear using Flink.
- Brokers and consumers decrease Flink’s performance by compressing and decompressing the data flow.
- Compared to Kafka Streams, setting up Flink may be operationally complex to run in a separate processing cluster.
- While it offers many features, its API is more complex than Kafka Streams.
Difference between Apache Kafka and Flink
| Feature | Apache Kafka | Apache Flink |
|---|---|---|
| Type | Distributed streaming platform | Distributed stream processing framework |
| Use Case | Messaging system for real-time data streams | Stream processing and analytics |
| Processing Model | Publish-subscribe messaging system | Event-driven, real-time stream processing |
| Core Concept | Topic, Producer, Consumer | DataStream, Stream Processing, Windowing |
| Scalability | Highly scalable, horizontally distributed | Highly scalable, fault-tolerant |
| Durability | Persistent message storage | Checkpointing, fault tolerance |
| Processing Time | Latency in milliseconds | Milliseconds to seconds |
| State Management | Limited support for stateful processing | Built-in support for stateful stream processing |
| Windowing | Limited support for windowing operations | Rich support for windowing operations |
| Ecosystem | Well-established with large community | Growing ecosystem, closely integrated with Hadoop |
| Language | Written in Scala and Java | Supports multiple languages including Java, Scala |
| Maintenance | Mature project with stable releases | Active development, frequent releases |
Conclusion
In this article, we have learned about Apache Kafka and Flink. Apache Kafka is a stream-processing client library that is mostly used in combination with the latter to serve as the data source and destination. Apache Flink has a stream processing framework, it can handle large volumes of data and go through over multiple servers in parallel.
Apache Kafka and Flink – FAQs
How does Kafka and Flink work together?
Flink’s Kafka connectors provide some metrics through Flink’s metrics system to check the connector behavior.
When to use Kafka and when to use Flink?
Kafka Streams follows a messaging approach and Flink uses a dataflow model. Kafka Streams is generally considered easier to learn and use than Flink. However, Flink has advanced features and is suitable for a wider range of applications.
Does Flink commit offsets to Kafka?
If checkpointing is enabled, the Flink Kafka Consumer will commit the offsets saved in checkpointed states after the checkpoints are completed.
Is Flink a message broker?
Flink is the most used computational framework for performing complex event processing and analytics on streaming data.
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