Apache Storm and Spark are both used to process data and are a great aid in this day and age. However, there are significant differences between Apache Storm and Spark. Let’s get to learn about them.
Apache Storm vs. Apache Spark: An Overview
Storm is a framework that has real-time stream processing capabilities. It supports parallel computation and can do multiple tasks at once.
Spark, on the other hand, focuses on high-speed computation and processing large sets of data. It can perform distributed processing but lacks a resource manager.
The processing model of Storm goes through one record at a time, whereas Spark divides data into batches before processing. Storm is based on tuple streams, while Spark makes use of operators. Two types of operators are available for use in Spark.
What Is Apache Storm?
Apache Storm is mainly written in the Clojure programming language and is used for real-time stream processing.
Spout and bolts are used to mark information sources which allow for efficient streaming of data. It handles data in real-time and does not make use of batches unless specified.
It is compatible with a large number of programming languages making it popular and easy to use.
The multi-language support allows for easier development using Storm. When faults or failures are encountered, the supervisor will restart and restore the state.
For quick problem-solving in data streaming, Apache Storm is quite useful. However, it is much more challenging to use and install.
What Is Apache Spark?
Spark is made using Big Data technology distribution systems and large data sets. However, it lacks a cluster resource manager, so an external one is required for use.
Data is divided into batches and then grouped, allowing large amounts of data to be appropriately processed with ease. A discretized stream system is used in conjunction with stream operators to achieve this.
Developers can use Spark with Python, Java, and Scala but not any other programming languages.
It is easier to understand and work with Spark due to a large amount of API documentation. Since you can use the same base code, development costs can be cut down.
Although Spark does not give a quick solution, it is more versatile and can be used to solve more problems. It can handle both batch and iterative processing.
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Main Differences Between Apache Storm and Apache Spark
- Apache Storm supports real-time data streaming capabilities and processing. Apache Spark makes use of batch processing to handle large data sets.
- Partition and Tuples are the building blocks of Storm, while Spark makes use of DStream.
- Storm can work with many different programming languages due to the built-in multi-language feature. Spark can only work with Java, Python, and Scala.
- Development cost using Storm is higher as stream processing cannot make use of the same base code. Spark can, and as a result, prices are lower.
- The throughput for Storm is 10k records per node per second, while for Spark, it is 100k records per node per second.
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A Comparison Table to Summarize
|Parameters Of Comparison
|A multi-language feature exists, so it is usable with almost all programming languages
|Spark only supports Java, Scala, and Python
|Can process real-time streams and processes one record at a time
|Processes data in batches and can also split data into micro-batches
|More flexible due to having three options of data delivery
|Less flexible as it has only one option of data delivery
|The resource manager restarts the process in case of failure
|Automatically restarts when it encounters a failure and a checkpoint is made
|Ease Of Use
|Difficult to use
|Easy to use
|Has lower latency
|Has higher latency
|Ease of Development
|Easier to develop since almost all programming languages are supported
|More complicated to develop since not all programming languages are supported
|Fewer resources and samples are available for use
|API documentation and samples are more available
Developers are constantly debating on Apache Storm vs. Spark. Although Storm allows for faster solutions, it is much harder to use and can incur higher costs.
Spark is easier to understand due to the many resources available, so developers using Spark have a smoother transition.