Introduction
Apache Flink is an Open source big data computing engine. In short, it is called Flink. Apache Flink is written in Java and Scala, which supports multiple platforms.
Flink is a powerful framework for both batch processing and stream processing that can be used to create several event-based applications. Flink uses a single architecture to support both stream and batch processing. Flink is a pure stream computing engine with a data stream model.
The stream can be infinite and borderless, which describes stream processing in a general sense, or finite steam with boundaries as batch processing.
![Stream]()
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Flink programs are mapped to streaming dataflow. Every Flink data flow starts with one or more sources (a data input, a message queue, or a file system) and ends with one or more sinks (a data output, a message queue, a file system, or a database). An arbitrary number of transformations can be performed on the stream.
Flink is based on four building block mechanisms
- Checkpoint: Flink implements distributed consistency snapshots based on the Chandy-Lamport algorithm, providing exactly once semantics; by contrast, prior stream computing systems such as Strom and Samza did not effectively solve the exactly-once problem.
- State: Flink introduces a managed state and provides API interfaces that users can use to manage states while programming, making programming as easy as using Java sets.
- Time: To avoid event-time-based processing problems of data out of order or data late arriving, it uses a watermarking mechanism to solve the problem.
- Window: stream computing is generally based on windows, Flink provides a set of out-of-box window operations including tumbling, sliding, and session windows, and supports flexible custom windows to meet special requirements.
Benefits
Synchronized streaming and batch processing
Flink is built around the idea of “streaming first, with batch as a special case of streaming.” Its network stack can support low-latency and high-throughput streaming data transfers and high-throughput batch shuffles — all from a single platform.
This can drastically simplify operations, helping organizations save time and money.
- Process millions of records per minute: Flink consumes an event from the source, processes it, and sends it to a sink. Then, it processes the next event immediately; it doesn’t wait while aggregating a batch of events. Because of this, Flink can process tons of events with ultra-low latency. Using the event at a time processing schematic, it can process millions of events per minute/second.
- Power applications at scale: The main reason for Flink's popularity is that it can run stateful streaming applications that support just about any workload that you feed it. Applications are parallelized into thousands of tasks, distributed, and concurrently executed in a cluster, allowing applications to use virtually any amount of memory, CPU, disk, and network IO. It can also scale effectively by minimizing garbage collection and data-limiting transfers across network nodes.
- Utilize in-memory performance: Flink produces ultra-low processing latencies by utilizing local and in-memory states for all computations. This way, it can process events in real time instead of aggregating them in batches.time instead of aggregating it in batches.
Summary
Flink supports Multiple applications and tools; due to flexibility, it is easy to set up and provides multiple types of connectors and real-time data streaming; multiple domains use it to get real-time data views for decision-making.