Apache Flink Stream Processing: Simplified 101

on Apache Flink, Data Storage, Data Streaming, Data Warehouse, Database, ETL, ETL Tutorials • April 22nd, 2022 • Write for Hevo

Flink Stream - Feature Image

Data is generated from disparate sources, including web user behaviors, measurements from the Internet of Things (IoT) devices, financial transactions, and location-tracking feeds. These continuous data streams were previously saved as datasets and then handled by batch processing.

However, with the demand to process data to offer personalized services in real-time, companies are switching to stream processing software. Stream processing software nowadays can drive quantitative analytics applications and evaluate detailed insights on high-throughput streams.

Monitoring user activity, analyzing gameplay records, and identifying fraudulent transactions are all common use cases for stream processing. Apache Flink is popular software that was developed particularly for running stateful streaming applications. 

In this article, we’ll learn about the Apache Flink Stream processing application and its features to perform data transformations on streaming data.

Table of Contents

What is Apache Flink?

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Apache Flink is a big data distributed processing engine that can handle bound and unbound data streams and execute stateful and stateless computations. It’s an open-source platform that lets you handle streams in a scalable, distributed, fault-tolerant, and stateful manner. It’s also used in a variety of cluster setups to do quick computations on data of varied sizes.

Apache Flink allows you to ingest large amounts of streaming data from various sources and process it across many nodes in a distributed fashion before delivering the resulting streams to other services or applications like Apache Kafka, databases, and Elastic search. Its runtime enables fault-tolerant, low-latency processing at exceptionally high throughputs. As a result, Flink’s streaming data and event-based features allow for real-time insights. 

For processing infinite (unbounded) and finite (bounded) data, Flink’s software stack offers the DataStream and DataSet APIs. 

Flink, which means “quick” or “agile” in German, was founded in 2009 by Stratosphere, a group of students from Berlin’s Technical University. It began as a research project with the goal of merging the finest technologies from MapReduce-based systems with parallel database systems, before focusing on data streaming.

In 2014, Stephan Ewen and Kostas Tzoumas launched the commercial firm Data Artisans based on the open-source project. In the same year, in April, the company was renamed Flink and was approved by the Apache Software Foundation as an incubating project. And in January 2015, it was promoted to a top-level project. Since its inception, the Apache Flink Stream processing application has had a very active and expanding community of users and creators.

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Basic Concepts in Apache Flink

Here are some of the basic concepts in Apache Flink:

1) State

It is the information created during computations that play an important part in fault tolerance, failure recovery, and checkpoints. In its most basic form, stream processing refers to the processing of data in a sequential manner. As a result, stream processing with Flink to necessitates continually maintaining and querying a state. To achieve the exactly-once semantics, data must also be stored in the state. When the entire distributed system fails or crashes, persistent state storage ensures that the data is only lost once. 

In other words, Apache Flink Stream processing operations can be stateful, which implies that how one message/event is handled can be influenced by the cumulative effect of all processed events.

2) Time

In Flink, time is divided into three categories: event time, ingestion time, and processing time. The event time is the moment at which an event is created by the data producer. The time it takes for the event to be processed at the node is referred to as processing time. And the moment at which an event is ingested into Flink is known as the ingestion time. In other words, it’s the time when the event enters Apache Flink Stream. As the processing of unbounded data in Apache Flink Stream is a continuous operation, time is a crucial parameter for evaluating if the state suffers delay and whether data is handled on time.

3) Streams

Streams can be divided into two categories:

  • Streams with a definite start and end are known as bounded streams. These streams can be analyzed after all of the data has been ingested before proceeding to the next phase. It means that you already know the boundaries of the data and can view all the data before processing. 
  • Unbounded streams are those that have a beginning but no fixed end. It is unlikely that they will be exhausted since they continually supply data as they are created. Therefore, such data should be handled in real-time, as it is impossible to wait for all of the data to surface.

4) API

This is the most critical and top layer of Apache Flink. It contains two APIs: Dataset API, which handles batch processing, and Datastream API, which does stream processing in Flink.

5) Library

There are two domain-specific libraries in Apache Flink:

  • Gelly: It’s a library that enables users to manipulate data graphs. Gelly library can be used to do operations such as create, transform, and process. It facilitates the creation of graphs.
  • Flink ML: Learning from big data and anticipating future occurrences is just as vital as analyzing it; this is where the Flink ML library comes in handy.

6) Kernel

This is the runtime layer, which includes features such as distributed processing, fault tolerance, dependability, and native iterative processing.

Features of Apache Flink

  • Robust Stream Processing: Apache Flink Stream processing applications provide robust stateful stream processing by allowing users to manage business logic that requires a contextual state while processing data streams utilizing the DataStream API at any scale.
  • Scalability: To scale up or down the number of processing tasks, Flink applications are parallelized into thousands of tasks. As a result, an application can use an almost infinite number of CPUs, main memory, disk, and network IO. Furthermore, its asynchronous and incremental checkpointing approach has a low influence on processor latencies while ensuring state integrity exactly once.
  • Fault Tolerance: Flink is a distributed system, it must be safeguarded against failures in order to prevent data loss in the event of an application or machine failure. Apache Flink Stream ensures this by publishing a consistent checkpoint of the application state to a distant and persistent storage location on a regular basis. It provides a fault-tolerance method based on periodic and asynchronous checkpointing (saving internal state to external persistent storage such as HDFS).
  • Multiple Deployment Mode: Flink has three deployment options: local, cluster, and cloud. Local mode is used for work on a single system, and it’s simple to get started with. If you wish to perform the development yourself, you can utilize the packages in the code, and when you run the program, it will automatically spin up a mini-cluster for your application. On your own PC, you can also use cluster mode on a multi-node cluster with the resource director, and then submit your jobs for deployment. There are two types of cluster deployment modes: Standalone and YARN. 
  • Single Runtime: Apache Flink Stream has a single runtime environment that can handle both stream and batch processing. As a result, the runtime system may support a wide range of applications with the same implementation.
  • Memory management: Apache Flink has its own memory management framework built into the JVM. Therefore, extending the application’s scalability outside the main memory is simple and low-cost.
  • Flexible Deployment: Apache Flink Stream processing works with all popular cluster resource managers, including Hadoop YARN, Apache Mesos, and Kubernetes, and can also be configured to run as a standalone cluster. It can intuitively interface with each resource management thanks to the resource-manager-specific deployment options. Flink automatically detects and requests needed resources from the resource manager when deploying an Apache Flink Stream application depending on the parallelism settings in the application.

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Apache Flink Architecture

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Flink has a master-slave system, where the master is the cluster’s director knot, while slaves are the worker bumps. In the Flink architecture, the master is the hub of the cluster, where customers may submit tasks, jobs, and operations. The master divides a given job into subparts and distributes them to the slaves in the cluster. This architecture enables Apache Flink Stream to benefit from dispersed computing capacity, allowing it to reuse data at incredible speeds.

There are two different kinds of nodes.

  • Master node – On the master node, you configure the Flink master daemon called “Job Manager.”
  • Slave node – On this end, the Flink slave daemon named “Task  Manager” operates on all slave nodes.

The Job Manager and one or more Task Managers are launched when the Apache Flink Stream processing system is started. Job Manager is made up of a JAR file that contains all of the essential classes, libraries, and other resources, as well as a Job Dataflow Graph received from the client. There are one or more data sources, a series of operations, and one or more sinks in a Flink application. The Flink application is represented as a Job graph, with nodes representing operators and connections representing input and output to and fro those operators.

The Task Manager has one or more Task slots that allocate the tasks within an execution environment. It allocates the tasks from the Job graph into one or more Task slots. Multiple tasks can be submitted to a Job Manager, which will establish a Jobmaster for each one. Job Manager also includes Dispatcher, which offers a REST interface for submitting Flink applications for execution and launches a new JobMaster for each job that is submitted.

When a Job Manager requests Task Manager slots, the Resource Manager notifies a TaskManager with available slots to make them available to the Job Manager. During execution, the Job Manager is in charge of any operations that need central coordination and coordinates the recovery of failures.

Installation of Apache Flink

Flink is compatible with Windows, Linux, and Mac OS. For installation, do the following steps:

  1. Check for Requirements: Check to see if you have Java 8 or later installed. To do so, type in your terminal:

$ java -version

  1. Download the Apache Flink binary package. Before downloading, we must first select a Flink binary that meets our needs. If our data is stored in Hadoop, download the binary that corresponds to the version of Hadoop. 
  2. Extract the downloaded bin package.
  3. Configure: Set the path of Apache Flink. Go to This PC>Properties>Advanced system setting>Environmental variable>New, create a new variable with the name of your choice, and copy the path of the bin folder of Apache Flink here.
  4. Create a local instance of the application: The start-local.bat script in the bin folder contains all of the necessary scripts to start the local cluster. Navigate to the Flink bin folder, i.e., /flink-folder.bin/, then open a command prompt from the bin folder to start the local cluster. On the command prompt, type start-local.bat.
  5. Verify Flink is up and running.
  6. Stop the local Flink instance: Enter the command stop-cluster.bat or by pressing the shortcut key Ctrl+C.

Start and Stop a Local Cluster

  1. To start a local cluster, run the bash script that comes with Flink Stream:

$ ./bin/start-cluster.sh

You should see an output like this:

Flink Stream is now running as a background process. You can check its status with the following command:

$ ps aux | grep flink

  1. To quickly stop the cluster and all running components, use this script:

$ ./bin/stop-cluster.sh

Conclusion

Flink is a data processing software that can enable low-latency and high-throughput streaming data transfers, as well as high-throughput batch shuffles, all from a single platform. When compared to previous data processing software like Apache Spark, its low latency consistently beats Spark stream processing, even at larger throughput. Unlike Flink Stream, which can deliver true native streaming, Spark can only achieve near real-time processing due to micro-batching.

Apache Flink Stream processing also makes use of native loop operators, which outperform Spark in terms of machine learning and graph processing methods. Additionally, Flink Stream has an optimizer that optimizes jobs before they are sent to the streaming engine.

This optimizer is unaffected by the programming interface and operates in the same way as relational database optimizers in that it applies optimizations to dataflows transparently.

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Tell us about your experience of learning about Apache Flink Stream Processing in the comment box below.

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