Apache Spark vs Storm

Overview

Apache Storm

Stacks207

Followers282

Votes25

GitHub Stars6.7K

Forks4.1K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Storm: What are the differences?

Developers describe Apache Spark as "Fast and general engine for large-scale data processing". Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. On the other hand, Storm is detailed as "Distributed and fault-tolerant realtime computation". Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Apache Spark can be classified as a tool in the "Big Data Tools" category, while Storm is grouped under "Stream Processing".

Some of the features offered by Apache Spark are:

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
Write applications quickly in Java, Scala or Python
Combine SQL, streaming, and complex analytics

On the other hand, Storm provides the following key features:

Storm integrates with the queueing and database technologies you already use
Simple API
Scalable

"Open-source" is the primary reason why developers consider Apache Spark over the competitors, whereas "Flexible" was stated as the key factor in picking Storm.

Apache Spark and Storm are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Storm with 5.75K GitHub stars and 3.91K GitHub forks.

Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas Storm is used by Spotify, Twitter, and Yelp. Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to Storm, which is listed in 37 company stacks and 8 developer stacks.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Apache Storm, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k

Comments

Detailed Comparison

Apache Storm	Apache Spark
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars 6.7K	GitHub Stars 42.2K
GitHub Forks 4.1K	GitHub Forks 28.9K
Stacks 207	Stacks 3.1K
Followers 282	Followers 3.5K
Votes 25	Votes 140
Pros & Cons
Pros 10 Flexible 6 Easy setup 4 Event Processing 3 Clojure 2 Real Time	Pros 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Apache Storm, Apache Spark?

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Some of the features offered by Apache Spark are:

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
Write applications quickly in Java, Scala or Python
Combine SQL, streaming, and complex analytics

On the other hand, Storm provides the following key features:

Storm integrates with the queueing and database technologies you already use
Simple API
Scalable

"Open-source" is the primary reason why developers consider Apache Spark over the competitors, whereas "Flexible" was stated as the key factor in picking Storm.

Apache Spark vs Storm