Introduction to Apache Flink
Apache Flink is a powerful open-source framework designed for real-time stream processing applications. Initially released in 2011, Flink has continuously evolved, with a stable version launched in July 2020.
It is a distributed processing engine optimized for handling stateful computations over both bounded and unbounded data streams. In the digital landscape, data is generated as a continuous flow of events, which can be categorized into these two types:
- Bounded Streams: These have a defined start and end point. The entire dataset is ingested into the system before computation begins.
- Unbounded Streams: These have a start but no defined end, meaning data is continuously received and processed in real-time as it is generated.
Common examples of streaming data include credit card transactions, server logs, website user interactions, IoT sensor data, and weather station observations.
Flink is highly versatile, supporting both bounded and unbounded streams. It is designed to run on various cluster management systems such as Hadoop YARN, Apache Mesos, and Kubernetes. The framework is known for its in-memory computations and high scalability.
Key Features of Apache Flink for Real-Time Data Processing
- Supports both real-time stream processing and batch data processing.
- Enables stateful event-driven applications with advanced state management.
- Deployable on cluster managers like Hadoop YARN, Mesos, Kubernetes, or standalone setups.
- Capable of scaling to thousands of nodes and managing terabytes of application state.
- Offers low-latency (fast event processing) and high-throughput (efficient data handling).
Apache Flink Deployment Modes: Session, Per-Job, and Application Mode Explained
The execution of Flink applications is determined by its deployment modes. These modes define resource allocation strategies and specify where the application's main() method is executed.
- Session Mode: Uses an existing cluster to run applications. The main() method executes on the client side.
- Per-Job Mode: Creates a new cluster for each job based on the available cluster manager, ensuring better resource allocation. The main() method runs on the client side.
- Application Mode: Launches a dedicated cluster for each application. The main() method executes on the master node.
In the next post, we will explore the architecture of an Apache Flink cluster in detail.
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