72 lines
4.1 KiB
Markdown
72 lines
4.1 KiB
Markdown
---
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title: Flink
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---
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## Flink
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Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.
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The core of Apache Flink is a distributed streaming dataflow engine written in Java and Scala. Flink executes arbitrary dataflow programs in a data-parallel and pipelined manner.
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Flink's pipelined runtime system enables the execution of bulk/batch and stream processing programs. Furthermore, Flink's runtime supports the execution of iterative algorithms natively.
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Flink provides a high-throughput, low-latency streaming engine as well as support for event-time processing and state management. Flink applications are fault-tolerant in the event of machine failure and support exactly-once semantics.
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Programs can be written in Java, Scala, Python, and SQL and are automatically compiled and optimized into dataflow programs that are executed in a cluster or cloud environment.
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Flink does not provide its own data storage system and provides data source and sink connectors to systems such as Amazon Kinesis, Apache Kafka, HDFS, Apache Cassandra, and ElasticSearch.
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**What Is New in Apache Flink?**
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* Flink implements actual streaming processing and not imitates it with micro-batch processing. In Spark streaming is a special case of batching, while in Flink batching is a special case of streaming (stream of a finite size)
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* Flink has better support for cyclical and iterative processing
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* Flink has lower latency and higher throughput
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* Flink has more powerful windows operators
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* Flink implements lightweight distributed snapshots that has low overhead and only-once processing guarantees in stream processing, without using micro batching as Spark does
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* Flink supports mutable state in stream processing
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### Features
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* A streaming-first runtime that supports both batch processing and data streaming programs
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* Elegant and fluent APIs in Java and Scala
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* A runtime that supports very high throughput and low event latency at the same time
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* Support for *event time* and *out-of-order* processing in the DataStream API, based on the *Dataflow Model*
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* Flexible windowing (time, count, sessions, custom triggers) accross different time semantics (event time, processing time)
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* Fault-tolerance with *exactly-once* processing guarantees
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* Natural back-pressure in streaming programs
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* Libraries for Graph processing (batch), Machine Learning (batch), and Complex Event Processing (streaming)
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* Built-in support for iterative programs (BSP) in the DataSet (batch) API
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* Custom memory management for efficient and robust switching between in-memory and out-of-core data processing algorithms
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* Compatibility layers for Apache Hadoop MapReduce and Apache Storm
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* Integration with YARN, HDFS, HBase, and other components of the Apache Hadoop ecosystem
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### Flink Usage
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Prerequisites for building Flink:
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* Unix-like environment (We use Linux, Mac OS X, Cygwin)
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* git
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* Maven (we recommend version 3.0.4)
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* Java 7 or 8
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```
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git clone https://github.com/apache/flink.git
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cd flink
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mvn clean package -DskipTests # this will take up to 10 minutes
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```
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## Developing Flink
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The Flink committers use IntelliJ IDEA to develop the Flink codebase.
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We recommend IntelliJ IDEA for developing projects that involve Scala code.
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Minimal requirements for an IDE are:
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* Support for Java and Scala (also mixed projects)
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* Support for Maven with Java and Scala
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#### More Information:
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* Flink website: <a href='https://flink.apache.org/' target='_blank' rel='nofollow'>Apache Flink</a>
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* Flink documentation: <a href='https://ci.apache.org/projects/flink/flink-docs-release-1.3/' target='_blank' rel='nofollow'>flinkdocs</a>
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* Quick flink tutorial: <a href='https://www.linkedin.com/pulse/introduction-apache-flink-quickstart-tutorial-malini-shukla/' target='_blank' rel='nofollow'>quick start</a>
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* How to guide: <a href='https://data-artisans.com/blog/kafka-flink-a-practical-how-to' target='_blank' rel='nofollow'>howto</a>
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* Flink vs Spark: <a href='http://www.developintelligence.com/blog/2017/02/comparing-contrasting-apache-flink-vs-spark/' target='_blank' rel='nofollow'>comparison</a>
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