Everything about Apache Flink: You need to know
Apache Flink is a real-time processing framework capable of handling streaming data. It is an open source stream processing framework for real-time applications that require high performance, scalability, and accuracy. It operates on a true streaming model and does not accept input data in batches or micro-batches.
Data Artisans created Apache Flink, which is now developed under the Apache License by the Apache Flink Community. So far, this community has 479 contributors and 15500+ commits.
Ecosystem of Apache Flink
Apache Flink has multiple options from where it can Read/Write data. Below is a basic storage list −
HDFS (Hadoop Distributed File System)
Local File System
RDBMS (MySQL, Oracle, MS SQL etc.)
We can deploy apache flink in local mode, In cluster mode or on the cloud Cluster modes include standalone, YARN, and MESOS.
Flink can be deployed in the cloud on AWS or GCP.
The runtime layer provides distributed processing, fault tolerance, reliability, native iterative processing, and other features.
4. APIs and Libraries
This is Apache Flink's top and most important layer. It has a Dataset API that handles batch processing and a Datastream API that handles stream processing. Other libraries include Flink ML (for machine learning), Gelly (for graph processing), and SQL Tables. This layer provides Apache Flink with a variety of capabilities.
Features of Apache Flink
The features of Apache Flink are as follows −
It has a streaming processor, which can run both batch and stream programs.
It can process data at lightning fast speed.
APIs available in Java, Scala and Python.
Provides APIs for all the common operations, which is very easy for programmers to use.
Processes data in low latency (nanoseconds) and high throughput.
Its fault tolerant. If a node, application or a hardware fails, it does not affect the cluster.
Can easily integrate with Apache Hadoop, Apache MapReduce, Apache Spark, HBase and other big data tools.
In-memory management can be customized for better computation.
It is highly scalable and can scale upto thousands of node in a cluster.
Windowing is very flexible in Apache Flink.
Provides Graph Processing, Machine Learning, Complex Event Processing libraries.
Flink provides a robust set of APIs that allow developers to perform transformations on both batch and real-time data. Mapping, filtering, sorting, joining, grouping, and aggregation are examples of transformations. Apache Flink performs these transformations on distributed data. Let's go over the various APIs that Apache Flink provides.
The Apache Flink Dataset API is used to perform batch operations on data over time. This API is available in Java, Scala, and Python. It can perform various transformations on datasets such as filtering, mapping, aggregating, joining, and grouping.
Datasets are generated from sources such as local files or by reading a file from a specific source, and the resulting data can be written to various sinks such as distributed files or command line terminals. Both the Java and Scala programming languages support this API.
This API handles data in a continuous stream. On the stream data, you can perform operations such as filtering, mapping, windowing, and aggregation. This data stream contains various sources such as message queues, files, and socket streams, and the output data can be written to various sinks such as command line terminals. This API is supported by the Java and Scala programming languages.
Here is a DataStream API streaming Wordcount programme with a continuous stream of word counts and data grouped in the second window.
Year of Origin
Place of Origin
University of California, Berkeley
Technical University of Berlin
Data Processing Engine
Slower than Spark and Flink
100x faster than Hadoop
Faster than Spark
Java, C, C++, Spark, Ruby, Groovy, Python
Java, Scala, Python and R
Java and Scala
Pipelined and Batch
Table API and SQL
Machine Learning Support
The comparison table from the previous chapter pretty much sums up the pointers. For real-time processing and use cases, Apache Flink is the best framework. Its single engine system is unique in that it can process batch and streaming data using various APIs such as Dataset and DataStream.
This is not to say that Hadoop and Spark are no longer relevant; the choice of the best big data framework is always dependent on and varies depending on the use case. A combination of Hadoop and Flink or Spark and Flink may be appropriate for a variety of use cases.
Nonetheless, Flink is currently the best framework for real-time processing. Apache Flink has grown tremendously, and the number of contributors to its community is growing by the day.
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