What are the differences between Apache Spark and Apache Flink?


Firstly let us understand what Apache Spark is and what is Apache Flink?

Apache Spark: It is an open source processing engine that provides an interface for programming entire cluster with implicit fault-tolerance and data parallelism. It was originally development at Berkeley's AMP Lab, which was later donated to Apache Software Foundation.

Apache Flink: Flink means agile. It is a data processing tool and process big data efficiently with high fault tolerance and low data latency on a large scale. It process streaming data in real time. Later it became the part of Apache Software Foundation.

Similarities between Apache Spark and Apache Flink:

Both projects are of Apache Software Foundation.

  • Both are general purpose data processing platform.
  • .Both come with in-built memory
  • Both are used for big data scenarios.
  • Both share a good performance.
  • Both can run in standalone mode.
  • Both have a wide field of applications.
Although, they have many similarities but these similarities are not considered when it comes to data processing. 

Differences between Apache Spark and Apache Flink:

1. Based on Data processing:

S.No.
Apache Spark
Apache Flink
1.
Processes data in batch mode
Processes streaming data in real time
2.
Processes chunks of data (FDD)
Processes rows after rows in real time
3.
Has minimum data latency
High data latency as compared to Spark



2. Based on Iterations:

S.No.
Apache Spark
Apache Flink
1.
Supports data iterations in batches
Iterate its data by streaming architecture

3. Based on memory management:

S.No.
Apache Spark
Apache Flink
1.
Optimize and adjust its individual datasets manually.
Automatically adapt to varied datasets
2.
Do manual partitioning and caching
Automatic portioning and caching
3.
Slow and delay processing time
Fast processing time


4. Based on Data Flow:

S.No.
Apache Spark
Apache Flink
1.
Follows a procedural programming system
Follows a distributed data flow system

Flink provides intermediate results and whenever these results are required, broadcast variables are required for distributing pre-calculated results to all worker nodes.


5. Based on data Visualization:

S.No.
Apache Spark
Apache Flink
1.
Do not require a web interface to submit its jobs
Provides a web interface to execute all operations

Spark and Flink both are integrated with Apache Zeppelin and provide data visualization, data ingestion, discovery, data analytics and data collaboration. It also provides multi language backend, which allows you to execute Flink programs.

6. Based on Processing time: To calculate the processing time, an experiment was conducted in which, Both Spark and Flink are given same resources in form of node configuration and machine specifications.

The differences are as follows:

S.No.
Apache Spark
Apache Flink
1.
Spark takes more time o process data
Flink followed pipeline execution and process faster
2.
Data process time was 2171 seconds
Data process time was 1490 seconds
3.
For 10Gb data it took 387 seconds
For 10Gb data it took 157 seconds
4.
For 160Gb data it took 4927 seconds
For 160Gb data it took 3127 seconds


Although, Apache Spark has lots of advantages but in case of batch data processing, Flink is gaining more commercial support.

With the arrival of concept of Big Data and the increasing need of better handling of Big Data, it has become really important to acquire expertise in technologies like Apache Spark. To take a technological lead, enroll yourself right away to a Spark training in Bangalore and become a professional.




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