What Is The Difference Between Apache Storm And Apache Spark?


Let us first discuss the similarities between Apache Storm and Apache Spark: Streaming jobs for both of these run until there is an unrecoverable failure or shutdown by user. Both of these are implemented in JVM based language. Scala and clojure are the two respective JVM languages.

Differences between Apache Spark and Apache Storm are as follows:

On the basis of definition:

S.No
Apache Spark
Apache Storm
1
It is an open source processing engine that provides an interface for programming entire cluster with implicit fault-tolerance and data parallelism.
Storm is simple and easy to reliably process the unbound streams of data.
2
It is a general purpose batch processing engine.
It is a task parallel continuous engine
3
Defines its workflow in DAGs called topologies.
Defines its workflow in style of MapReduce.
4
Executing applications run on own server processes.
It uses Apache ZooKeeper and its own minion worker processes.
5
Spark can run on Mesos clusters
Storm can run on top of Mesos scheduler.

On the basis of development:

S.No
Apache Spark
Apache Storm
1
It is a top level Apache Project
It is currently undergoing incubation
2
Latest stable version is 1.0.2
Latest stable version is 0.9.2
3
Reflect stability
Does not reflect completeness or stability
4
Endorsed by Apache Software Foundation 
Still working on its development process
5
Guarantee stability in API
No guarantee stability in its API

On the basis of velocity:

S.No
Apache Spark
Apache Storm
1
As per the graph the commit velocity over last month has been over 330 commits.
As per the graph the commit velocity over last month has been over 70 commits.
2
As per JIRA chart spark has huge volumes of issues reported.
As per JIRA chart storm has an order of magnitude less of issues reported.

Difference on the basis of Installation:

S.No
Apache Spark
Apache Storm
1
Installation is simple
Installation is though simple but complex in respect of spark
2
Spark application technically just require spark assembly to be present
Storm requires ZooKeeper to be properly installed

Differences on the basis of Fault tolerance:

S.No.
Apache Spark
Apache Storm
1
Resiliency built in Spark’s RDD yields trivial mechanism for fault tolerance.
For fault tolerance Storm keeps the track of each and every record.
2
Micro-batching yields a message delivery guarantees
Data loss is acceptable
3.
Failure scenarios is degrade to at least one delivery
Incur latency costs

Differences on the basis of Applicability:

S.No.
Apache Spark
Apache Storm
1
Proves to be an excellent model for performing interactive analytics, visualized interactive analytics and iterative machine learning.
Excels in ingestion, real time analytics, data normalization, natural language processing and ETL transformations.
2
Excels in ingestion and real time analytics.
Yields fine gained transformation and flexible topologies.


On the basis of Latency:

S.No.
Apache Spark
Apache Storm
1
Latency rate is high
Give sub second latency
2
More restrictions
Less restrictions

      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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