1.
Presented By:
Kundan Kumar
Software Consultant
An introduction to
Apache Flink: 4G of
Big Data

2.
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3.
Agenda
01 Big Data evolution
02
Introduction to Flink
03
Features of Flink
Architecture of Flink
Anatomy of a Flink program
Demo
04
05
06

4.
Big Data Evolution
Problems with Big Data:
● Storing huge and exponentially growing datasets.
● Processing of huge data datasets having complex structure.
● 3v’s of Big Data - Volume, Variety, Velocity

5.
Continue..
● At early 2000, Big Data era started with multiple frameworks focusing on
specifying Big Data problem.

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Continue..
● A unified platform that alone can handle various Big Data problem:
➢ Batch processing
➢ Stream processing
➢ Graph processing
➢ Iterative processing
● A unified platform must have following characteristics to solve Big
Data Problem:
➢ Distributed/ parallel computation
➢ Fault tolerance
➢ Ease of use (developer friendly API’s)
➢ Powerful predefined operators/functions(Like Join, filter)
➢ Fast

7.
Apache Spark (3G Big Data Framework)
● Spark is a lightning-fast cluster computing engine that is 100 times faster than
Hadoop in running applications in memory
● Apache Spark is best known for its in-memory computing capabilities that
deliver high-speed processing.
➢ Problem
● Process data streams in micro batches and not in real time.
● High throughput but medium latency in some use cases.

8.
Introduction to Flink
● Apache Flink is a Big Data framework and distributed processing engine for
stateful computations over unbounded and bounded data streams.
● Flink is based on the streaming first principle which means it is real streaming
processing engine Flink considers batch processing as a special case of
streaming
● Flink has been designed to run in all common cluster environments, perform
computations at in-memory speed and at any scale.

9.
Source
Transformations
Sink

10.
➢ A Flink application may consume real-time data from streaming sources such as
message queues or distributed logs, like Apache Kafka or Kinesis.
➢ Flink can also consume bounded, historic data from a variety of data sources.
➢ The streams of results being produced by a Flink application can be sent to a wide
variety of systems that can be connected as sinks

11.
➢ Programs in Flink are inherently parallel and distributed.
➢ During execution, a stream has one or more stream partitions, and each
operator has one or more operator subtasks.

12.
➢ Flink facilitate stateful operations.
➢ Current handling event can depend on the accumulated effect of all the events
that came before it.
➢ The set of parallel instances of a stateful operator is effectively a sharded
key-value store. Each parallel instance is responsible for handling events for a
specific group of keys, and the state for those keys is kept locally.

13.
Flink Architecture
➢ Flink 1.X's architecture consists of various components such as deploy,
core processing, and APIs.
➢ Flink has a layered architecture and each component is a part of a
specific layer.
➢ Each layer is built on top of the others for clear abstraction.

14.
Flinks Distributed Execution
➢ Flink is based on master slave architecture.
➢ Various processes take part in the Flink’s program execution, namely
Job Manager, Task Manager, and Job Client.

15.
Flink Task Manager

16.
Flink Features
➢ High performance
➢ Exactly-once stateful computation
➢ Fault tolerance
➢ Memory management
➢ Optimizer
➢ Unified platform for stream and batch
➢ Rich Libraries

17.
Basic Anatomy of a Flink Program

18.
DEMO

19.
Q/A

20.
References
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3.

21.
Thank You !