With the increasing popularity of big data, real-time data streaming platforms are also seeing increased traction. The two most popular real-time streaming platforms, Apache Storm and Apache Spark, however, can get confusing for new users.
In this blog, we have provided a comparison of Apache Storm vs. Spark considering various parameters, to help users understand the similarities and differences between both that can help them in making better-informed decisions. Before starting with the Apache Storm vs. Spark comparison, let’s get a basic understanding of each technology.
Understanding Apache Storm vs. Spark
Let’s begin with the fundamentals of Apache Storm vs. Spark.
Apache Storm is an open-source, fault-tolerable stream processing system used for real-time data processing.
Apache Spark is an open-source lightning-fast general-purpose cluster computing framework.
Feature comparison of Apache Storm vs. Spark
Now let’s have a feature-by-feature comparison of Apache Storm vs. Spark to get a better understanding of both the technologies.
1. Processing Model
Storm: Apache Storm supports true stream micro-batch processing through the core Storm layer.
Spark: Spark supports batch processing, and Spark streaming is a wrapper over Spark batch processing.
Storm: It uses ZeroMQ, Netty framework for messaging.
Spark: It uses Akka, Netty framework for messaging.
3. Programming language
Storm: Apache Storm supports Java, Scala, and Clojure.
Spark: Apache Spark supports lesser languages than Storm. It has support for only Java and Scala.
Storm: Apache Storm provides a wide range of primitives. These primitives perform tuple level processing at intervals. With semantics, aggregation over messages is possible. For Example, Left join, right join, and inner join.
Spark: Apache Spark provides two varieties of operators. The first is the stream transformation operators. These operators transform one DStream into another. The second operator type is the output operator. The output operators write information on the external system.
Also Read: Apache Spark Tutorial For Beginners
5. Fault tolerance
Both frameworks are similar in fault-tolerance.
Storm: The supervisor process restarts automatically when a process fails. The state management is managed by Zookeeper in Apache Storm.
Spark: In Spark, if a process fails, the work is restarted through its standalone process manager or Mesos and Yarn.
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Storm: Storm provides low latency with fewer constraints.
Spark: Spark has higher latency as compared to Storm.
Storm: Storm has a lower throughput as compared to Spark as it serves only 10k records per node per sec.
Spark: Spark, on the other hand, has a high throughput and serves 100k records per node per sec.
Storm: The source of stream processing in Storm is Spout.
Spark: Spark uses HDFS as the source for stream processing.
Storm: Apache Storm uses Apache Ambari for monitoring.
Spark: It supports basic monitoring using Ganglia.
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10. Ease of operability
Storm: Storm can get tricky with installation and deployment. It is dependent on the Zookeeper cluster to coordinate with other clusters, store states, and statistics.
Spark: It, itself, is the basic framework for Spark streaming. Spark clusters can be easily maintained on YARN.
11. Message level failure handling
Storm: Apache Storm supports three message processing guarantees.
- At least once
- At most once
- Exactly once
Spark: Apache Spark streaming supports only one message processing handle – at least once.
Storm: Apache Storm allows the configuration of initial parallelism at various topology levels. It also supports dynamic rebalancing.
Spark: Apache Spark community is currently developing dynamic scaling.
Storm: Storm uses the MapState persistence technique.
Spark: Spark uses per RDD persistence technique.
Storm: A large number of big corporations are running Storm, pushing the boundaries for performance and scale.
Spark: Apache Spark streaming is a developing community and is thus limited in expertise when compared to Storm.
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Must Read: Apache Storm Overview
After comparing Apache Storm vs. Spark, we can conclude that both have their own sets of pros and cons. Apache Storm is an excellent solution for real-time stream processing but can prove to be complex for developers. Similarly, Apache Spark can help with multiple processing problems, such as batch processing, stream processing, and iterative processing, but there are issues with high latency. However, both of these prove to be excellent big data streaming solutions.
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What is the difference between Apache Storm and Kafka?
Apache Storm is a resilient, distributed framework that is used to carry out real-time computational tasks and processing of data streams. Apache Kafka is an application that is used to handle a huge amount of data within a fraction of a second. It acts as a distributed messaging platform that mainly relies on partitions and topics. Storm receives the data from different sources such as Kafka, HBase, Cassandra, and other applications and processes this fetched data in real-time. So while Kafka fetches the data from the actual data sources, Storm fetches its data from Kafka. Kafka works well with all programming languages but works best with Java only. Apache Storm all programming languages.
Does Apache Storm offer any benefits?
Apache Storm is an open-source, distributed platform that helps process Big Data in real-time. Apache Storm offers several benefits. It is not just fault-tolerant and scalable but is highly reliable and supports all programming languages. It processes real-time data streams at remarkable lightning-fast speeds, indicating its tremendous data processing power. And it continues to offer the same quality of performance even under increasing data load. Apache Storm provides operational intelligence, is user-friendly and robust and is excellent for use by all kinds of organizations, both big and small.