Apache Storm vs. Spark Side-by-Side Comparison

In world of big data processing, Apache Storm and Apache Spark are two popular distributed computing systems that have gained traction in recent years. Both of these systems are designed to process massive amounts of data, but they have different strengths and weaknesses. In this article, we will do a side-by-side comparison of Apache Storm and Apache Spark and explore their similarities, differences, and use cases.

What is Apache Storm?

Apache Storm is an open-source distributed computing system that is used for real-time stream processing. It was developed by Nathan Marz and his team at BackType, which was later acquired by Twitter. Storm is designed to process large streams of data in real-time, which makes it ideal for use cases like fraud detection, stock trading, and social media analytics.

What is Apache Spark?

Apache Spark, on other hand, is an open-source distributed computing system that is used for batch processing and real-time stream processing. It was developed by Matei Zaharia at University of California, Berkeley, and later donated to Apache Software Foundation. Spark is designed to process massive amounts of data in a distributed and parallel manner, which makes it ideal for use cases like machine learning, graph processing, and data analytics.

Architecture

Apache Storm is based on a master-slave architecture, where a master node is responsible for distributing tasks to worker nodes. worker nodes then process data in real-time and send results back to master node. Storm is designed to be fault-tolerant, which means that it can automatically recover from failures and continue processing data without any interruptions.

Apache Spark, on other hand, is based on a cluster manager architecture, where a cluster manager is responsible for managing resources of cluster and distributing tasks to worker nodes. Spark uses a data processing engine called Resilient Distributed Datasets (RDDs) to process data in a distributed and parallel manner. Spark is also designed to be fault-tolerant, which means that it can recover from failures and continue processing data.

Processing Model

Apache Storm uses a data processing model called a topology, which is a directed acyclic graph of spouts and bolts. Spouts are responsible for reading data from a source and emitting tuples to bolts, which then process data and emit tuples to other bolts or sinks. A sink is responsible for writing processed data to a destination.

Apache Spark, on other hand, uses a data processing model called a pipeline, which is a series of transformations that are applied to RDDs. Spark provides a rich set of transformations and actions that can be used to manipulate data in a distributed and parallel manner. Transformations are operations that produce a new RDD, while actions are operations that return a value to driver program or write data to a storage system.

Programming Languages

Both Apache Storm and Apache Spark support multiple programming languages. Apache Storm supports Java, Python, and Clojure, while Apache Spark supports Java, Python, Scala, and R. This makes it easy for developers to choose language that they are most comfortable with and use it to develop their applications.

Ease of Use

Apache Storm and Apache Spark have different levels of ease of use. Apache Storm is a low-level system that requires developers to write code for each part of processing pipeline. This can be time-consuming and difficult for developers who are not familiar with distributed systems. However, Storm provides a high degree of flexibility and control, which makes it ideal for complex use cases.

Apache Spark, on other hand, provides a higher level of abstraction that makes it easier for developers to write applications. Spark provides a rich set of libraries and APIs that can be used to manipulate data in a distributed and parallel manner. This makes it easier for developers to write applications without having to worry about low-level details of system.

Use Cases

Both Apache Storm and Apache Spark are used for different use cases. Apache Storm is commonly used for real-time stream processing use cases such as fraud detection, social media analytics, and real-time analytics. Since Apache Storm can process data in real-time, it is well-suited for use cases that require immediate analysis of data.

On other hand, Apache Spark is commonly used for batch processing use cases such as machine learning, data analytics, and graph processing. Since Apache Spark can process data in a distributed and parallel manner, it is well-suited for use cases that require processing large amounts of data.

Performance

In terms of performance, Apache Spark is generally faster than Apache Storm. Apache Spark can process data in a distributed and parallel manner, which allows it to process large amounts of data quickly. However, Apache Storm can process data in real-time, which makes it well-suited for use cases that require immediate analysis of data.

Cost

Both Apache Storm and Apache Spark are open-source systems, which means that they are free to use. However, there may be additional costs associated with running these systems, such as hardware costs and cloud service costs. cost of running these systems will depend on size of data being processed, complexity of processing pipeline, and number of nodes in cluster.

Scalability

Both Apache Storm and Apache Spark are designed to be highly scalable. Apache Storm allows for easy scaling by adding or removing worker nodes to cluster. This allows system to handle increasing amounts of data. Apache Spark, on other hand, provides horizontal scaling by adding more nodes to cluster. This allows system to handle larger datasets and more complex processing pipelines.

Real-time vs. Batch Processing

One of biggest differences between Apache Storm and Apache Spark is type of processing they are designed for. Apache Storm is designed for real-time stream processing, which means it processes data as it arrives. Apache Spark is designed for both batch processing and real-time stream processing. Batch processing means data is processed in batches, after it has been collected.

Complexity

Apache Storm is more complex than Apache Spark, as it requires developers to write code for each part of processing pipeline. Apache Spark provides a higher level of abstraction, which makes it easier to write applications without worrying about underlying system. However, this can also make it more difficult to customize and fine-tune system for specific use cases.

Community and Support

Both Apache Storm and Apache Spark have strong communities and support from their respective organizations. However, Apache Spark has a larger community and is more widely adopted, which means there are more resources available for developers. Apache Spark also has a larger number of contributors, which leads to more frequent updates and improvements.

Apache Storm vs. Spark Tabular Representation

Apache Spark	Apache Storm
Support Batch Processing Model	Support Micro-batch processing Model
It supports lesser languages like Java, Scala.	It supports smultiple languags such as Scala, Java, Clojure.
It support Stream Sources HDFS	It support Stream Sources Spout
It support Messaging with Akka, Netty	It support Messaging with ZeroMQ, Netty
It has higher latency as compared to Spark	It has better latency with lesser constraints
Spark's same code can be used for batch and stream processing.	Spark's same code cannot be used for batch and stream processing.
It supports one message processing mode: ‘at least once’.	It supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.
If a process fails, Spark restarts workers via resource managers. (YARN, Mesos)	If a process fails, the supervisor process starts automatically.
It has 100k records per node per second throughput	It has 10k records per node per second throughput

Conclusion

Apache Storm and Apache Spark are two powerful distributed computing systems that have different strengths and weaknesses. Apache Storm is designed for real-time stream processing use cases, while Apache Spark is designed for batch processing use cases. Both systems are fault-tolerant and support multiple programming languages. Apache Spark provides a higher level of abstraction, while Apache Storm provides a higher degree of flexibility and control. When choosing between Apache Storm and Apache Spark, it is important to consider specific use case, performance requirements, and ease of use.