Apache Flink vs Storm

Overview

Apache Storm

Stacks208

Followers282

Votes25

GitHub Stars6.7K

Forks4.1K

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Storm: What are the differences?

Apache Flink: Fast and reliable large-scale data processing engine. 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; Storm: 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 Flink and Storm are primarily classified as "Big Data" and "Stream Processing" tools respectively.

Some of the features offered by Apache Flink are:

Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
Flexible and expressive windowing semantics for data stream programs

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

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

"Unified batch and stream processing" is the primary reason why developers consider Apache Flink over the competitors, whereas "Flexible" was stated as the key factor in picking Storm.

Apache Flink and Storm are both open source tools. Apache Flink with 9.36K GitHub stars and 5.01K forks on GitHub appears to be more popular than Storm with 5.75K GitHub stars and 3.91K GitHub forks.

According to the StackShare community, Storm has a broader approval, being mentioned in 37 company stacks & 8 developers stacks; compared to Apache Flink, which is listed in 20 company stacks and 22 developer stacks.

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Advice on Apache Storm, Apache Flink

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.

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Detailed Comparison

Apache Storm	Apache Flink
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 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.
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	Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars 6.7K	GitHub Stars 25.4K
GitHub Forks 4.1K	GitHub Forks 13.7K
Stacks 208	Stacks 534
Followers 282	Followers 879
Votes 25	Votes 38
Pros & Cons
Pros 10 Flexible 6 Easy setup 4 Event Processing 3 Clojure 2 Real Time	Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency
Integrations
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Apache Storm, Apache Flink?

Apache Spark

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.

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.

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 Flink are:

Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
Flexible and expressive windowing semantics for data stream programs

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

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

"Unified batch and stream processing" is the primary reason why developers consider Apache Flink over the competitors, whereas "Flexible" was stated as the key factor in picking Storm.

Apache Flink vs Storm