As real-time data increases, the requirement for real-time data streaming also increases. Besides that, streaming technologies dominate the world of Big Data. With the latest real-time streaming platforms, it gets complicated for users to pick one. Apache Spark and Storm are among the popular real-time technologies that could be considered. In this blog, let us compare Apache Spark and Storm depending on their features, similarities, and differences and help the users to make a choice.
Apache Spark is an open-source general-purpose lightning-fast cluster computing framework that was designed to carry out fast calculations on large data sets processing. This is a distributed processing engine, but there is no integrated distributed storage system or resource manager. You must connect a storage system and a cluster resource manager of your choice. Apache YARN and Mesos may be used to manage clusters, and Google Cloud Storage and Hadoop Distributed File System, Microsoft Azure, and Amazon S3 may be used to manage resources.
Apache Spark offers High-speed data querying, transformation, and analysis with large data sets.
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Apache Storm is a real-time, open-source, distributed computing system for data streams processing. When it comes to batch processing, Apache storm does it for data streams with no limits in a reliable way, Similar to what Hadoop does. Apache Storm is integrated with Hadoop for increased throughput. It can be easily integrated and can be integrated into any programming language. Storm carries out a fault tolerance mechanism to carry out a calculation or plan several calculations of an event.
It enables us to collaborate with a cluster and consists of retrieval of metric data and configuration information like start and stop topologies.
1) Programming Language Options:
Storm: It is possible to create Storm applications in Java, Scala, and Clojure.
Spark: It is possible to create Spark applications in Java, Python, Scala, or R.
2) Low development Cost:
Storm: We cannot use the same code base in the processing of stream and batch.
Spark: We can use the same code base in the processing of stream and batch.
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Storm: It supports the processing mode "exactly once". It can be used in the processing modes "at least Once" and "at most once".
Spark: It supports the processing mode "exactly once".
Storm: For a specific topology, every employee process carries out executors. Mixing multiple topology tasks is not permitted in the worker process. Despite this, it supports the isolation of run time at the topology level.
Spark: The Spark executor operates in a separate YARN container. Thus, JVM isolation is available at Yarn. As two different topologies cannot be run in one JVM, YARN offers resource-level isolation so that the constraints of the containers can be arranged.
5) Processing Model:
Storm: It supports a true stream processing model with the core storm layer.
Spark: In batch processing, it behaves like a wrapper.
6) State Management:
Storm: By default, it does not provide any framework-level support for storing any intermediate bolt result as a state. As a result, any application must create or update its own state when required.
Spark: It is possible to maintain and change the state through the updateStateByKey API. However, there is no pluggable method of implementing the state in the external system.
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7) Fault Tolerance:
Storm: At its core, it is designed with Fault tolerance. If the process fails, the process will be automatically restarted by the supervisor process as Zookeeper manages the state management.
Spark: Spark is fault-tolerant in nature as well. Through resource managers, like Mesos, Yarn, or its standalone manager, Spark handles restarting workers.
Storm: It provides an extremely rich set of primitives for carrying out an interval tuple level process in a stream. With the group by Semantics aggregation of the messages within a stream are possible. For example, inner join, left join, and right join through the stream are supported by Storm.
Spark: There are two main types of streaming operators, like output operators and stream transformation operators.
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9) Debuggability and Monitoring:
Storm: Its user interface supports every topology's image. But, with the complete break-up of the spouts and bolts inside. Additionally, Storm helps debug issues at a high level and supports metrics-based monitoring. The embedded metric capability supports the framework level for applications that emit metrics. Moreover, it can simply be integrated with external metrics or monitoring systems.
Spark: The additional tab that shows the running statistics of the receivers and finished spark web UI displays. In addition, it is useful to observe the application running. Furthermore, this information in the Spark Web user interface is required for batch size standardization are as follows:
10) Ease of Operability:
Storm: Deploying/installing Storm through a variety of tools and deploying the cluster is not easy. That depends upon the Zookeeper cluster. Additionally, it can respond to coordination on clusters, state of stores, and statistics. On the other hand, in standalone mode, the Storm daemons are forced to operate in supervised mode. During that time, Storm emerged as containers in YARN mode and was driven by the application master.
Spark: It is the essential execution framework for streaming. Therefore, the Spark Cluster of YARN can be easily fed up.
11) Yarn Integration:
Storm: The Apache slider recommends the integration of Storm with YARN. A YARN application "Slider" will deploy non-YARN distributed applications on a YARN cluster. Additionally, with the help of a slider, we can access outstanding app packages for a storm.
Spark: Spark offers native integration with YARN. The entire spark streaming app is reproduced as a single yarn app.
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Storm: It offers improved latency with fewer restrictions.
Spark: The latency is poorer than a storm.
Storm: Zero Netty framework is used for messaging.
Spark: Akka Netty framework is used for messaging.
Storm: The persistence technique that is used is MapState.
Spark: The persistence technique that is used is RDD.
Storm: Apache Ambari is used for monitoring
Spark: Using Ganglia, basic monitoring is supported.
Storm: The source of stream processing is Spout.
Spark: The source of stream processing is HDFS.
Storm: Storm has a lesser throughput than Spark because it only uses 10k records per node per second.
Spark: Spark, in contrast, has a greater throughput and serves 100k records per node per second.
Storm: Many large companies operate Storm, pushing the limits of performance and scale.
Spark: It is a growing community and is therefore limited in expertise with respect to Storm.
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Both Apache Storm and Apache Spark provide excellent solutions for processing and flow ingestion issues. In addition, both can become part of a Hadoop cluster for data processing. Although Storm is used as a solution for real-time processing of streams, developers could find that developing applications is very complex because of its limited resources. The industry always looks for a generalized solution that has the ability to solve all kinds of problems like interactive processing, batch processing, stream processing, and iterative processing.
Considering all these points, Apache Spark steals the spotlight as it is mainly regarded as a general-purpose computing engine, making it an extremely demanding tool for IT professionals. It can deal with different types of issues and offers a flexible environment for it. Also, developers find it simple to integrate Apache Spark with Hadoop.
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