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.
|Sr. No||Parameter||Apache Spark||Apache Storm|
|1.||Processing Model||Batch Processing||Micro-batch processing|
|2.||Programming Language||Supports lesser languages like Java, Scala.||Support smultiple languages, such as Scala, Java, Clojure.|
|4.||Messaging||Akka, Netty||ZeroMQ, Netty|
|5.||Resource Management||Yarn and Meson are responsible.||Yarn and Mesos are responsible.|
|6.||Low Latency||Higher latency as compared to Spark||Better latency with lesser constraints|
|7.||Stream Primitives||DStream||Tuple, Partition|
|8.||Development Cost||Same code can be used for batch and stream processing.||Same code cannot be used for batch and stream processing.|
|9.||State Management||Supports State Management||Supports State Management as well|
|10.||Message Delivery Guarantees||Supports one message processing mode: ‘at least once’.||Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.|
|11.||Fault Tolerance||If a process fails, Spark restarts workers via resource managers. (YARN, Mesos)||If a process fails, the supervisor process starts automatically.|
|12.||Throughput||100k records per node per second||10k records per node per second|
|14.||Provisioning||Basic monitoring using Ganglia||Apache Ambari|
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 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 Storm supports micro-batch processing, while Apache Spark supports batch processing.
Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.
For Storm, the source of stream processing is Spout, while that for Spark is HDFS.
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.
Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm.
Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.
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).
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.
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.
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.
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.
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 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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