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Apache
FLINK
Agenda
Introduction
Processing
Types
Batch Processing
Stream Processing
Live Stock Feed
(Stream processing example)
Differences between Batch and Real-Time Processing
Batch Processing Real-Time Processing
Data Static Files Event Streams
Speed
Processed Periodically in minute,
hour, day etc.
Processed immediately
nanoseconds
Storage Past data on disk storage In Memory Storage
Example Bill Generation ATM Transaction Alert
Deeper into
FLINK
Eco-system
Apache
FLINK
FLINK program
Data source
Source is responsible for reading data from data
sources such as HDFS, KAFKA …
Transformation
Responsible for data transformation operations
Reduce(), sum(), max(), min() …
Data Sink
Responsible for final data outputs ()
Architecture
Job Running
Process
FLINK time & window
EVENT TIME CLASSIFICATION TYPES
Event Time:
Time when an
event occurs
Ingestion time:
Time when an
event arrives at the
stream processing
system
Processing Time:
Time when an
event is processed
by the stream.
Different Between Three Time
FLINK time & window
DEFINITION
Window is a
method for splitting
infinite data sets
into finites blocks
for processing.
Windows split the
stream into buckets
of infinite size,
which we can apply
computation.
TYPES
Time Windows based on Processing
Time
TUMBLING WINDOWS SLIDING WINDOWS
FLINK Watermark
OUT-OF-ORDER PROBLEM WATERMARK SOLUTION
Tips and
useful
resources
Flink vs Spark vs Hadoop
Apache Hadoop Apache Spark Apache Flink
Data Processing Engine Batch Batch Stream
Processing Speed
Slower than Spark and
Flink
100x Faster than
Hadoop
Faster than spark
Throughput Medium High High
Optimization Manual Manual Automatic
Streaming Support NA Spark Streaming Flink Streaming
Graph Support NA GraphX Gelly
Machine Learning
Support
NA SparkML FlinkML
SQL Support Hive, Impala SparkSQL Table API and SQL
Data Transfer Batch Batch Pipelined and Batch
Features of Apache Flink
1) Has a streaming processor, which can run both batch and stream programs.
2) Can process data at lightning-fast speed.
3) APIs available in Java, Scala and Python.
4) Processes data in low latency (nanoseconds) and high throughput.
5) Its fault tolerant. If a node, application or a hardware fails, it does not affect the
cluster.
6) In-memory management can be customized for better computation.
7) Windowing is very flexible in Apache Flink.
Thank You

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Apache FLINK.pptx

  • 7. Live Stock Feed (Stream processing example)
  • 8. Differences between Batch and Real-Time Processing Batch Processing Real-Time Processing Data Static Files Event Streams Speed Processed Periodically in minute, hour, day etc. Processed immediately nanoseconds Storage Past data on disk storage In Memory Storage Example Bill Generation ATM Transaction Alert
  • 11. FLINK program Data source Source is responsible for reading data from data sources such as HDFS, KAFKA … Transformation Responsible for data transformation operations Reduce(), sum(), max(), min() … Data Sink Responsible for final data outputs ()
  • 14. FLINK time & window EVENT TIME CLASSIFICATION TYPES Event Time: Time when an event occurs Ingestion time: Time when an event arrives at the stream processing system Processing Time: Time when an event is processed by the stream.
  • 16. FLINK time & window DEFINITION Window is a method for splitting infinite data sets into finites blocks for processing. Windows split the stream into buckets of infinite size, which we can apply computation. TYPES
  • 17. Time Windows based on Processing Time TUMBLING WINDOWS SLIDING WINDOWS
  • 20. Flink vs Spark vs Hadoop Apache Hadoop Apache Spark Apache Flink Data Processing Engine Batch Batch Stream Processing Speed Slower than Spark and Flink 100x Faster than Hadoop Faster than spark Throughput Medium High High Optimization Manual Manual Automatic Streaming Support NA Spark Streaming Flink Streaming Graph Support NA GraphX Gelly Machine Learning Support NA SparkML FlinkML SQL Support Hive, Impala SparkSQL Table API and SQL Data Transfer Batch Batch Pipelined and Batch
  • 21. Features of Apache Flink 1) Has a streaming processor, which can run both batch and stream programs. 2) Can process data at lightning-fast speed. 3) APIs available in Java, Scala and Python. 4) Processes data in low latency (nanoseconds) and high throughput. 5) Its fault tolerant. If a node, application or a hardware fails, it does not affect the cluster. 6) In-memory management can be customized for better computation. 7) Windowing is very flexible in Apache Flink.