Apache Spark Vs Apache Storm - Head To Head Comparison

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Last updated on
11th Mar, 2021
18th Jul, 2019
Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. 

One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. 

Comparison Table: Apache Spark Vs. Apache Storm

Sr. NoParameterApache SparkApache Storm
1.Processing  ModelBatch ProcessingMicro-batch processing
2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.
3.Stream SourcesHDFSSpout
4.MessagingAkka, NettyZeroMQ, Netty
5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.
6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints
7.Stream PrimitivesDStreamTuple, Partition
8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.
9.State ManagementSupports State ManagementSupports State Management as well
10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.
11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.
12.Throughput100k records per node per second10k records per node per second
13.PersistenceMapStatePer RDD
14.ProvisioningBasic monitoring using GangliaApache Ambari

Apache Spark: 

Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. 

Apache Storm:

Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. 

Apache Spark Vs. Apache Storm

1. Processing Model: 

Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. 

2. Programming Language:

Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.

3. Stream Sources:

For Storm, the source of stream processing is Spout, while that for Spark is HDFS. 

4. Messaging:

Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. 

5. Resource Management:

Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. 

6. Low Latency: 

Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.

7. Stream Primitives:

Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). 

8. Development Cost:

It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  

9. State Management: 

The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. 

10. Message Delivery Guarantees (Handling the message level failures):

Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. 

11. Fault-Tolerant:

Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.

12. Ease of Development: 

In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. 

Apache Spark and Apache Storm Features

Summing Up: Apache Spark Vs Apache Storm

Apache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. 

The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 



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