Apache Spark is defined as a freeware distributed processing system that we use for big data workloads. Apache Storm is also a distributed real-world big data processing system. Both Apache Spark and Apache Storm are the best and trending streaming technologies, but they have some differences in the aspects like functionality, data streaming, programming language options, etc. This Apache Spark vs Apache storm covers those differences to help you analyze the tool before selecting it.
Due to the rise in real-time data, the requirement for real-time data streaming is increasing aggressively. As streaming technologies are ruling the Big data world, users may find difficulty in choosing the real-time data streaming platform. Two of the most famous real-world technologies we can consider for selecting are Apache Storm and Apache Spark.
Apache Spark is the typical computing engine, while Apache Storm is the stream processing engine to process the real-time streaming data. Spark offers Spark streaming for handling the streaming data. In this Apache Spark vs. Apache Storm article, you will get a complete understanding of the differences between Apache Spark and Apache Storm.
|Table of Contents - Apache Spark vs Apache Storm|
Apache is a freeware distributed processing system that we use for big data workloads. It uses optimized query execution and in-memory caching for rapid analytic queries against any data of any size. It offers development APIs in Python, Java, R, and Scala and endorses code reuse throughout multiple workloads like interactive queries, batch processing, graph processing, machine learning, and real-time analytics.
We use Apache Spark because of the following Advantages:
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Apache Storm is the distributed real-world extensive data-processing system. Apache Storm is designed to process massive horizontally scalable and fault-tolerant data. It is the streaming data framework that possesses the highest ingestion rates. Although Storm is stateless, it can handle cluster state and the distributed environment through Apache Zookeeper.
Professionals in the software sector regard Storm to be Hadoop for real-world processing. Meanwhile, real-world processing is a much-talked topic among Data Analysts and BI professionals. Apache Storm developed with those abilities required for a rapid conventional process. Let us look at the features that made Apache Storm ideal for real-time processing:
1. Storm UI REST API: The Daemon of Storm UI offers the REST API that enables us to communicate with the Storm cluster, which contains fetching metrics data and handling operations like stopping or starting topologies.
2. The Apache Storm is highly scalable, and it has the ability to perform calculations simultaneously at a similar speed in increased load. It is essential to observe that it has generated the benchmark of processing 1 million messages of 100 bytes over a single node that making it one of the rapid technology platforms. Empowered with these features of scalability and speed, this technology surpasses other existing technologies when it comes to the enormous processing of data at an exceptional rate.
Apache Storm: It offers a massive set of primitives for performing tuple-level processes at breaks of the stream(functions and filters). Aggregations on the messages in the stream are performed using groups by semantics. It includes right join, left join, and inner join throughout the stream.
Apache Spark: It offers two types of operators. The first operator is “Stream Transformation Operators” that convert one DStream into another DStream. The second Operator is Output operator that write data to the external systems.
Apache Spark: It has an accurate stream processing model using the core storm layer.
Apache Spark Streaming: It is a wrapper on Spark batch processing.
Apache Storm: By default, Core Storm does not provide framework-level support to save the intermediate bolt as the state. Therefore, any application must update or create its state as and once required.
Apache Spark: By default, the real Spark processes the output of every RDD operation as the intermediate state. It saves it as the RDD. Spark Streaming
Apache Spark: The Apache Spark streaming specifies the guarantees, fault-tolerance semantics offered by the output and recipient operators. According to the Apache Spark Architecture, the incoming data is replicated and read in various Spark executor’s nodes. This creates failure scenarios data received but cannot be reflected. It manages fault tolerance diversely in case of driver failure and worker failure.
Apache Storm: It endorses three message processing guarantees: exactly once, at most once, and at least once; the reliability mechanisms of Storm are fault-tolerant, scalable, and distributed.
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Apache Spark: The web UI of Apache Spark displays the extra streaming tab that shows the statistics of completed batches and running receivers. It helps observe the implementation of the application.
Apache Storm: The UI of Apache Storm supports images of every topology, with a complete break-up of internal bolts and spouts. Moreover, UI provides data containing any errors arriving in fine-grained stats and tasks over the latency and throughput of all the parts of the running topology. It helps us debug the problems at a high level.
Apache Spark: The Driver Node is SPOF. If the driver nodes fail, then all the executors will be gone with their replicated and received in-memory information. Therefore, Spark utilizes data checkpointing for getting from the driver failure.
Apache Storm: Storm is mapped out fault-tolerance at its center. Storm daemons are built to be stateless and fail-fast.
Apache Spark: It offers native integration together with YARN integration. Spark streaming as the layer on the Spark simply utilizes integration. Each Spark streaming application regenerates as the individual Yarn application. ApplicationMaster container implements Spark driver and launches the SparkContext.
ApacheStorm: The Apache Storm, together with YARN, is suggested using Apache Slider. The Slider is the YARN application that executes non-YARN distributed applications on the YARN Cluster. It communicates with YARN RM for spawning the containers for the distributed application and later handles the lifecycle of those containers. The slider offers innovative application packages for Apache Storm.
Apache Spark: Apache Spark is currently developing dynamic scaling for streaming applications. At the moment, the elastic scaling of the Spark streaming applications does not support it. Basically, dynamic allocation does not imply use in Spark Streaming immediately. The cause is that currently receiving topology is static. The number of receivers is determined. One receiver assigns with each DStream instantiated, utilizing one core in the cluster.
Apache Storm: It offers to configure beginning parallelism at several levels per topology - kind of worker executors, processes, and tasks. Moreover, it endorses dynamic rebalancing, which allows us to increase or minimize the number of worker executors and techniques required to restart the topology or the cluster. Yet, several beginning tasks developed remain constant across the topology life.
|Related Article: Spark vs Hadoop|
Apache Spark: It is a different application that runs on the YARN cluster whenever each executor deploys in the other YARN container. Therefore, YARN offers JVM level isolation because two completely different topologies cannot be implemented in the same JVM.
Apache Storm: Every Employee process executes executors for a specific technology. Integrating several topology tasks does not allow a worker process level that supports topology level runtime isolation.
Apache Spark: Spark Streaming utilizes Spark as the basic execution framework. It must be simple to build up the Spark Cluster on the YARN. We have various deployment needs. Generally, we allow checkpointing for the fault tolerance of the application driver.
Apache Storm: It is a bit complicated to install or deploy using various tools and deploy the cluster. It includes a dependency in the Zookeeper cluster. Therefore, it can satisfy the coordination over the clusters, store statistics, and state.
Apache Spark: It provides Java and Scala APIs that include a large amount of hands-on programming. Consequently, the topology is more elliptic. There is an upgrade set of Illustrative samples and API Documentation in the market for the developer.
Apache Storm: It offers highly rich, simple, and instinctive APIs that easily explain the DAG nature of the Process topology(flow). Storm tuples that provide the abstraction of the data flowing among the nodes in the DAG, are dynamically built. The motive here is to modify APIs for easy use.
Apache Spark: The Apache Spark Streaming stays increasing and has limited skill in the production clusters. However, the standard canopy Apache Spark community is good and most enormous; therefore, it is the most active freeware community.
Apache Storm: Apache Storm offered by Page healthy corporations list is working Storm in manufacturing various applications. Almost all of them are full-scale web deployments that are raising the boundaries for scale and performance.
Apache Spark: We can build Spark Applications in Scala, Python, R, and Java.
Apache Storm: We can build Apache Storm Applications in Clojure, Scala, and Java.
Apache Storm is the perfect solution for Stream Processing, but Storm is difficult for developers to build applications. Apache Storm can resolve only one problem, i.e., stream processing. However, the industry requires a standard solution that can solve all problems like Batch Processing, interactive processing, Iterative processing, etc. At this point, Apache Spark comes into the picture, which is a standard computation engine. It handles all kinds of problems. I hope this Apache Spark vs. Apache Storm provides a detailed understanding of Apache Spark and Apache Storm.
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Vinod M is a Big data expert writer at Mindmajix and contributes in-depth articles on various Big Data Technologies. He also has experience in writing for Docker, Hadoop, Microservices, Commvault, and few BI tools. You can be in touch with him via LinkedIn and Twitter.
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