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Big Data Engineering

Distributed Data Processing with Apache Flink

Chandan Gaur | 27 December 2022

Distributed Data Processing with Apache Flink

What is Apache Flink?

Apache Flink is an open-source, distributed processing engine and framework of stateful computations written in JAVA and Scala. Stateful computations are performed over bounded (predictable, finite data) and unbounded (variable, infinite data) streams of data.

The first phase of Flink development was based on a complex continuous data stream. The runtime environment is designed in such a manner that it supports all common cluster environment (YARN, MapR, Mesos, etc.), can perform computations at in-memory speed and any scale.With time it becomes more stable and gets some new advancements for historical data or batch processing. Both processing systems i.e., Stream processing system and Batch processing systems, can work under the same environment to deliver expected processing data as output, which in this case is known as a sink. It has Exactly-once Semantics for Stateful Computations and Fault-tolerance through Lightweight Distributed Snapshots.

A stream processing framework which is developed by Apache Software Foundation. Click to explore about, Stream Processing with Apache Flink

Batch Processing Problem Statement

The batch processing system is the approach in which a real-time data query system is hard or never be achieved. High throughput with low latency is also the case that needs to be work on for real-time data. To solve this problem, Apache flink was introduced with Stream processing. Some several issues or problems lead to the development of Flink. Few of them were -

  • For many projects, there is an issue of the too slow pipeline from data ingestion to analytics.
  • Generally, database backend was the only thing that acts as a “single source of information access” for data needs.
  • Both the above problems lead to complex failure modes that make it hard to obtain expected performance.

Flink Solution offerings

Flink had a solution to all these problems, which is, Continuous stream processing. This processes data in the form of Windows (keyed or non-keyed windows), and then many transformations can be applied to sort or manage that data. Operations such as cluster and deployment can be done on any of the following -

Flink is easy to install the binary and can start working in just one command through CLI. Apache flink works in fields, including Data Analytics and Machine learning using Gelly. The dataflow programming model of Flink had the choice to select from the stream, graph & batch Processing. The core is Streaming the data flow engine.

Big Data Architecture helps design the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. Click to explore about, Big Data Architecture

What is Delay in Real-Time Data or Stream Processing?

Real-time data is that in which information needs to be processed immediately when data arrives, or data is generated. But how data can be processed when data is not available within no time of its generation? So, in this case, it is always stated that data is going to be processed within 1ms of time when it gets generated.

Distributed Data Processing with Apache Flink Architecture

Flink had a Dataflow programming model containing levels of abstraction that help to choose from DataSet API or DataStream API.

DataSet API had Table API for structured sets, flink ML for machine learning and Gelly for Graph processing.DataStream API contains Table API for structured stream and Complex Event Processing (FlinkCEP) API for Event Processing. It supports all standard file systems such as FTP, HDFS, S3, and so on. There are three use cases of Apache Flink -

  • Event Driven Processing
  • Data analytics processing
  • Data Pipeline Applications

Flink allows using of 30+ built-in transformation functions based on both Functional and Algebraic approach that makes it easy to manage through Apache Maven. Moreover, Python integration can be used to execute code of flink as a python script. Operators transform one or more input DataStreams (Distributed File Systems, Databases, etc.) into one or more output DataStreams.

Programs can combine multiple transformations into complex dataflow topologies. Microservices implementing on Flink would manage the state. For real-time applications, it reduces latency and overhead. Thus, provide high throughput and low latency This is not a new idea; with n-tier applications, the state was often managed by the Java middleware layer. And if you go back even in past time, transaction monitors abstracted that capability from the database. Java and Scala are the language choices one can opt for the developing system in Apache Flink.

Consequences of not streaming well

Suppose a world without sensors these days. There would be no fun of resources if sensors were not present. It will make everyone feel like a smartphone without Internet access. The same goes for batch and stream processing. If batch processing systems and Stream Processing systems were not troubleshot and managed adequately, Analytics might differ from time to time. This may lead to cause bad management decisions that are the backbone of every company. Retail and marketing systems may fail. IoT systems that are always following Streaming of data also got into the trash. Telecom and financial sectors may also suffer from the loss.

The process used for analyzing the huge amount of data at the moment it is used or produced. Click to explore about, Real Time Data Streaming

Challenges in Stream Processing with Batch Engine?

It has to be admitted that Stream processing provides some relaxation to the developers with inbuilt architecture APIs. Still, some problems also occur related to Stream processing with the batch engine. These are -

  • High latency
  • Periodic Job Scheduler (e.g., Oozie)
  • Data loading to HDFS file system (e.g., Flume)
  • Batch processor
  • Sometimes for “Lambda architecture” stream processor is also needed

For proper work throughput, batching in stream processing systems must satisfy the following requirements -

  • Batching should be used in such a way to improve performance. The larger the batches, the better the throughput a system can scale to.
  • Batching for performance must be independent of buffering for defining windows, or commits for fault tolerance. API must not contain these commits and buffer.
  • The pairing of Buffering windows and commits may deliver the systems that are limited, complex to use, and manage.

How does Backpressure Occur in Case of Flink?

Yes, it does. There were some situations when backpressure could occur and slow down the approach to the targeted instance. Some of the cases were -

  • When the operator creates checkpoints
  • When windows are evaluated
  • When the operator depends on external resource
  • When JVMs do garbage collection
  • Checkpoint, garbage collection occurs when certain savepoint needs to be processed.
  • It increases the overhead as the past job is still pending, and new delays are being arrived.

How can backpressure be handled?

Backpressure can be handled by Network Transfer (Netty) or by local buffer transfer (in case of client and server on the same system). High throughput, in any case, is the combination of continuous streaming with latency bound buffering and Distributed snapshots i.e. Continuous Streaming + Latency Bound Buffering + Distributed Snapshots = High throughput.

Apache Flink Distributed Processing Capabilities

Some questions may arise for Flink working and adaptation, some of them, with answers, are -

  • Is it possible to Rollback a job?
  • Yes, Flink supports Timestamping that makes it feasible to roll back a job or replay it.
  • How to manage data?
  • Windowing helps to manage data.
  • How are large-sized windows handled?
  • By managed state.
  • Is it possible to operate such a system for 24*7? Stopping may Cause replay of past events. What to do then?
  • Savepoints are used in Flink that helps to operate such a system. To ensure correct results across failures, checkpoints are also provided to manage.
  • Does Parallelism support by Flink?
  • Yes, Scaling out implements parallelism in Flink
IoT-based smart city systems provide real-time surveillance, analytics, and decision-making tools to improve public safety. Click to explore about, Data Processing in IoT

What are the benefits of Apache Flink Distributed Processing?

  • Flink provides Performance evaluation based on functions as compared with Other Apache Big data tools, i.e. HADOOP, Hbase, Spark, Oozie, etc.
  • Alibaba is using Flink to optimize and track the real-time rankings of website pages and named it as Blink.
  • Uber is using streaming processing platform on flink.
  • Apache Flink processes both the stream or real-time data as well as the Records or batches data by using stream replay.
  • Continual machine learning is the key also.
  • Some companies are managing the Directed Acyclic Graph (DAG) processing systems over Apache Flink.
  • Risk calculation systems that are traditionally using batch processing are applying ETL on Stream processing systems.

What are the features of Apache Flink?

  • Gesture recognition systems are kind of real-time event-driven applications & can be extended to implement for live video or media streaming applications for fraud detection, anomaly detection, etc. (if not fall under user privacy terms)
  • Alert driven systems for Desktop, mobile notifications can be developed for offline systems by using both DataSet and DataStream API one over others.

A Holistic Approach for Stream Processing

Apache Flink requires Java to be installed on the system as JVM is used to deploy as Cluster. Downloading the bin file from the Official Apache flink website and extracting it does the installation work, and then one command leads to start Flink on the local machine. Jobs can be managed and administered from the Web Dashboard Interface of Flink.