Apache Flink Use Cases and Architecture for Streaming Data Platform

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Apache Flink - Streaming Processing Framework?

Before having a look at Apache Flink lets have a look at some basic concepts in stream processing Keys to an efficient Big Data Stream Processing engines -

Keep the data moving (Streaming architecture, How to treat stream event)
Declarative access eg. Stream SQL, CQL
Handle imperfections eg. late event, missing events, unordered events.
Integrate batch and streaming data.
Data safety and availability. (Fault tolerance, Durable state)
Automatic partitioning and scaling.

The Apache Flink Framework is written in Java which provides -

Several APIs in Java/Scala/Python
DataSet API - Batch processing
DataStream API - Real-Time streaming analytics
Table API - Relational Queries
DSL ( Domain-Specific Libraries)
CEP - Complex Event Processing
FlinkML - Machine Learning Library for Flink
Gelly - Graph Library for Flink
Shell for interactive data analysis

Some unique features in Apache Flink -

True Streaming Capabilities - Execute everything as streams
Native iterative execution - Allow some cyclic dataflows
Handling of mutable state
Custom memory manager - Operate on managed memory
Cost-Based Optimizer - For both stream and batch processing

Streaming Data Visualization gives users Real-Time Data Analytics to see the trends and patterns in the data to take action rapidly. Click to explore about, Real Time Streaming Data Visualizations

How does Apache Flink support Streaming Analytics System?

The requirements of a Streaming Analytics System are listed below:

Keep the data moving

Flink treats data streams in the form of a data stream. Flink has a data stream using which we can manipulate the streaming data. Flink can handle -

Bounded data
Unbounded data
Real-time streams
Recorded streams

Declarative access

Apache Flink has Table API and SQL API, which is unified for both streaming and batch data, which implies same semantics can be used on all types of data.SQL and Table api are built upon Apache Calcite and leverage the features such as parsing, validations, and query optimizations.

Handle imperfections

Using the process functions in Apache Flink, we can handle imperfections in data and manipulate event, time and state of streaming data. Time-related features in Flink -

Event time mode
Watermark support
Late data handling
Process-Time mode

Integrate Batch and Streaming data

Apache Flink has Dataset API available for batch processing, and the SQL and Table API would work on batch data as well.

Data Safety and Availability

Fault tolerance Apache Flink is made to handle failures. Durable state Flink maintains strong state via checkpoint the state from time to time. Checkpoints allow Flink to recover state and positions in streams to recover from a failed state. Flink interacts with the persistent storages to store checkpoints. We can configure the various back-end such as Message Queue's - Kafka, Google Pub/Sub, AWS Kinesis, RabbitMQ Filesystems - HDFS, GFS, S3, NFS, Ceph

Automatic partitioning and scaling

Flink has excellent support for partitioning and scaling. What makes flink great is the support for both stateless as well as stateful streaming.

Apache Flink provides first-class support for authentication of Kerberos only while providing effortless requirement to all connectors related to security. Click to explore about, Apache Flink Security and its Deployment

Apache Flink Use Cases

Here are some concise use cases for Apache Flink:

Real-Time Analytics: Detect fraud, monitor systems, and trigger alerts.
Event-Driven Applications: Deliver personalized recommendations and content.
Data Pipelines and ETL: Process and transform data streams, integrate batch and stream data.
IoT Data Processing: Aggregate sensor data and create real-time dashboards.
Financial Services: Implement algorithmic trading and real-time risk management.
Telecommunications: Monitor network performance and manage billing systems.
Gaming: Analyze game data and update leaderboards in real-time.
Log and Event Processing: Aggregate and analyze logs, and perform clickstream analysis.
Geospatial Data Processing: Provide location-based services and geo-fencing.
Machine Learning and AI: Serve real-time model predictions and update models with streaming data.

What are the benefits of Apache Flink?

The benefits of of Apache Flink are listed below:

True Low latency Streaming engine

Flink is a low latency streaming engine that unifies batch and streaming in a single Big Data processing framework.

Custom Memory Manager

Flink contains its memory management stack. Flink includes its serialization and type extraction components. Flink uses C++ style memory management and User data stored in serialized byte arrays in JVM. Memory is allocated, de-allocated, and used strictly using an internal buffer pool implementation.

Apache Flink Advantages

Flink will not throw an OOM(out of memory )exception
Reduction of Garbage Collection
Very efficient disk spilling and network transfers
No Need for runtime tuning
More reliable and stable performance
Built-in Cost-Based Optimizer
Custom state maintenance

Native closed-loop iteration operators Flink support iterative computation. Flink iterates data by using streaming architecture. It's pipelined architecture allows processing the streaming data faster with lower latency. Flink used an iterative algorithm which is tightly bounded into flink query optimizer. Unified Framework Flink is a unified framework which allows building a single data workflow that holds streaming, batch, SQL and Machine learning. Analyze real-time streaming data Process graphs Machine Learning algorithms

An open source dynamic data management framework which is licensed by Apache software foundation and is written in Java programming language. Click to explore about, Apache Calcite Architecture and Streaming SQL

Why Apache Flink matters in Big data Ecosystem?

Apache Flink	Apache Spark	Samza	Apache Storm
Native streaming means Processing every record as it arrives	Fast Batching, means it Processing records in batches of some seconds. Supports native streaming using spark structured streaming API.	Native streaming means Processing every record as it arrives	Native streaming means Processing every record as it arrives
Exactly once guarantee	Exactly once	At least once guarantee	At least once guarantee Exactly once guarantee using Trident as an abstraction
Supports advanced streaming features like Watermarks, triggers, Sessions, etc.	Supports advanced streaming features like Watermarks, Sessions, triggers, etc.	Lacks advanced streaming features like Watermarks, Sessions, triggers, etc.	Supports advanced streaming features like Watermarks, Sessions, triggers, etc.
Scala, Java, Python	Scala, Java, Python	Java	Scala, Java, Python
Hybrid framework( batch + stream processing )	Hybrid framework( batch + stream processing )	Stream only framework	Stream only framework

Apache Flink in Production

apache-flink-best-practices In production Apache Flink can be integrated with familiar cluster managers

Haddop Yarn
Apache Mesos
Kubernetes
Stand Alone

We can deploy flink in the resource-manager specific deployment mode, and Flink interacts with the resource manager in their specific appropriate way. Flink communicates with the resource managers to ask for the resources required by the application from its parallelism configuration. In the case of a failover situation where a job fails, flink automatically requests a new resource accordingly. It has been reported that flink can support -

Multiple trillions of events per day.
Multiple terabytes of state.
Running on thousands of cores.

Manage the large distributed environments which form a complex cluster an is difficult to manage properly. Click to explore about, How to Secure Apache Zookeeper with Kerberos?

What are the best practices of Apache Flink?

Parsing command line arguments and passing them around in Flink application Getting configuration values into the ParameterTool Using the parameters in Flink program

Naming large TupleX types - Used POJO (Plain old java object ) instead of TupleX for data types with many fields. Used POJOs to give large Tuple-types a name.

Instead of using -

 
Tuplell<String,String,String> var = new ...;

Use -


CustomType var=new . . . ;
public static class CustomType extends Tuplell<String, String, String>{

}
Using Logback instead of Log4j
Use Logback when running Flink out of the IDE/java application
Use Logback when running Flink on a cluster

What are the best tools for Apache Flink?

Flink has the following useful tools -

Command Line Interface (CLI)- It is used for operating Flink's utilities directly from a command prompt.
Job Manager - It is a management interface which is used to track jobs, status, failure, etc.
Job Client - It is a client interface which is used to submit, execute, debug and inspect jobs.
Zeppelin - Zeppelin is an interactive web-based computational platform along with visualization tools and analytics.
Interactive Scala Shell/REPL - It is used for interactive queries.

Conclusion

Apache Flink is a community-driven open source framework for shared Big Data Analytics. Apache Flink engine exploits in-memory processing and data streaming and iteration operators to improve performance. XenonStack offers Real-Time Data Analytics and Big Data Engineering Services for Enterprises and Startups.