The demand for real-time streaming data is growing with the increasing availability of real-time information. Streaming technologies are now leading the Big Data revolution. It is difficult for users to choose between the many new real-time streaming platforms. Two of the most popular real-time streaming technologies are Apache Storm and Spark.
Let’s compare Apache Storm vs Apache Spark based on their features. This will help users make a decision. This article Apache Storm vs Apache Spark does not make a judgement about either one, but rather to examine the similarities and differences.
This blog will discuss the Apache Storm Vs Apache Spark comparison. Let’s begin with an introduction to each. Next, we’ll compare Apache Storm Vs Apache Spark based on the features of both.
What is Apache Storm Vs Apache Spark, and how do they compare?
Let’s start by understanding Spark Vs Storm.
Apache Storm
Apache Storm is an open-source, fault-tolerant, scalable and real-time stream processing computing system. It is the framework that allows real-time distributed data processing. It is focused on stream processing and event processing. Storm provides a fault-tolerant mechanism for scheduling multiple computations or performing a computation. Apache storm is based upon streams and tuples.
Apache Spark
Apache Spark is a fast Big Data technology framework that allows cluster computing to be done with lightning speed. It is designed to speed up large data processing. Although it is an engine for distributed processing, it does not include a distributed storage system or resource manager. You will need to connect to a storage system or cluster resource manager of your choice.
Apache YARN and Mesos can be used as a cluster manager, and Google Cloud Storage and Microsoft Azure, Google Cloud Storage, Microsoft Azure and HDFS (Hadoop Distributed File System), and Amazon S3 can all be used as resource managers.
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Comparison between Apache Storm Vs Apache Spark
We will be comparing feature-wise real-time processing tools such as Apache Spark and Apache Storm. Let’s take a closer look at each feature to see how Apache Storm compares to Apache Spark. It will help us learn and decide which one is better to adopt based on that particular feature.
1. Processing Model
Storm: Apache Storm is a streaming model that allows stream processing via the core storm layer.
Spark: Apache Spark Streaming acts over batch processing.
2. Primitives
Storm: Apache Storm offers a wide range of primitives that perform tuple-level processing at stream intervals (functions and filters). Semantic groups, such as e.g., allow for aggregations of information messages in a stream. Apache Storm supports left join, inner join (by default), and right join across the stream.
Spark: There are two types of streaming operators in Apache Spark: Output operators and stream transforming. Output operators are used to write information about external systems, while stream transformation operators are used in order to transform DStream into another.
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3. State Management
Storm: Apache Storm doesn’t provide any framework to store any interfering bolt output as an entity. Each application must create its own state whenever it is needed.
Spark: UpdateStateByKey allows you to change and maintain state in Apache Spark. However, there is no pluggable strategy that can be used to implement state in an external system.
4. Language Options
Storm: Storm applications are possible in Java, Scala and Clojure.
Spark: Spark applications are available in Java, Python Scala, R, and Scala.
5. Auto Scaling
Storm: Apache Storm allows you to create primary parallelism at different topologies – a variety of tasks, executors, and worker processes. Storm also provides dynamic rebalancing, which can reduce or increase the number of executors or worker processes without having to restart the topology or cluster. Some primary tasks are not affected by changes to the topology.
Spark: The Spark community works to develop dynamic scaling for streaming apps. Spark streaming applications do not support elastic scaling. Spark’s receiving topology is static, so dynamic allocation cannot be used. Once the StreamingContext has been started, it is impossible to change the topology. Aborting receivers will also result in the termination of the topology.
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6. Fault-Tolerant
Both Apache Spark Framework and Apache Storm Frameworks are fault-tolerant to the same degree.
Storm: When a process fails in Apache Storm, the