What is Apache Storm?
Apache Storm is a distributed, real-time computing system. The storm is designed to process a large amount of data in a fault-tolerant and horizontally scalable way. It is managed by Apache Software Foundation. It is developed to full fill the requirement of real-time data processing. It is an open-source platform in which application developers can easily run dynamic data processing applications without having to worry about configuration or system management. Apache Storm has large libraries and scripts for different analytical and statistical applications and also provides a dynamic programming tool to facilitate data processing.
Apache Storm Advantage
Apache Storm offers several advantages such as.
1. Higher Throughput
Apache Storm offers an incredible throughput rate that can boost data processing thousands of times and hence provide better response time to customers. It can support a high volume of data processing over a short period. It is capable of managing data across multiple servers and can be hosted on the most affordable infrastructure. With this in mind, it can accommodate large-scale processing over a server cluster, and hence it can scale up quickly when required.
2. High Availability
Since it is an open-source project, it can easily handle large-scale data processing with ease. When it is used properly, it can manage and maintain the scalability of a system in terms of the number of nodes in which the application runs, the number of servers, the capacity, and the bandwidth. The application can also be easily deployed and managed over the internet or other remote devices.
3. Open Source
Since it is an open-source project, it provides developers with a platform that they can leverage to develop and deploy applications. Its community and its support system help in bringing better functionality and better performance to an application.
4. Flexibility
It has a great degree of flexibility and therefore it can be used to run applications that are not necessarily related to each other. In addition to this, it can be used to build custom server-side code or to build application server-side code as well. Furthermore, since it is an open-source project, it is always evolving and thus it allows users to contribute to its development. In addition to this, it has a lot of community support that helps to improve its performance and features.
Features of Apache Storm
The following are a few of the important features of Apache Strom.
1. Fast
Apache Storm can process up to 1 million tuples per second per node.
2. Horizontally Scalable
Apache Storm is an open-source and distributed system that allows adding more nodes to the Storm cluster and increases the processing capacity of the application. It is linearly scalable, which means we can double the processing capacity by doubling the nodes.
3. Fault-Tolerant
In the Storm cluster, work is executed by worker processes and if a worker process dies, the Storm will start that worker process and if the node on which worker is running is down then Storm will restart that worker process on some other node in the cluster.
4. Guaranteed Data Processing
Storm provides guarantees that every message should be processed at least once. If there is a failure then Storm will reprocess lost tuples.
5. Support any Programming Language
Storm runs on Java Virtual Machine and it can be used with any programming language.
6. Easy to Deploy and Operate
The storm is an easy tool to deploy which requires very little effort to set up. Once the Storm cluster is started it will keep running month on month.
Apache Storm History
Let us see a year-by-year evaluation of Apache Storm.
2011: The storm was created by Nathan Marz when he was working at Backtype. It was open-sourced to GitHub on 19th September 2011.
2013: Nathan moved storm to Apache Incubator on 18th September 2013.
2014: On 17th September 2014, Storm became a top-level project in the Apache foundation.
Apache Storm Use Cases
The following are some of the use cases of Apache Strom.
1. Data Stream Processing
The storm is used to process a stream of data in real-time. After processing, it updates records to a variety of databases.
2. Uninterrupted Computation
The storm can perform continuous computation on streams of data and send the result to the client in real-time.
3. Distributed RPC
Storm provides parallelism for a powerful query so that it can be computed in real-time.
4. Real-Time Data Analytics
The storm can analyze and process data in real-time which are generated from different sources.
Difference Between Apache Storm and Hadoop
The following are the difference between Apache Storm and Hadoop.
| Storm | Hadoop |
|---|---|
| Apache Storm is a real-time processing framework. | Hadoop is the batch processing framework. |
| Apache Storm is a Master and Slave architecture in which the Master node is represented as nimbus and slave nodes are represented as supervisors. | Hadoop is a Master and Slave architecture in which the Master node is represented as a job tracker and slave nodes are represented as task trackers. |
| The storm process is capable to access tens of thousands of messages per second on the cluster. | Hadoop uses an HDFS filesystem to store huge data and MapReduce framework to process those data. Processing in Hadoop can take a few minutes or it can take hours. |
| The storm will operate till it is canceled by the user or an unexpected failure has occurred. | Hadoop MapReduce job will run sequentially. |
Enterprises are using Apache Storm
Apache Storm is being used by many companies.
The following is the list of some use cases.
Yahoo!
Yahoo uses Hadoop to perform batch processing which is the primary technology and Storm to perform streaming, micro-batch processing for user events, feeding of contents, and generating application logs.
Twitter uses Storm in multiple systems such as in real-time analytics, personalization, search, and so on. The storm is integrated with Twitter’s databases such as (Cassandra, Memcached), messaging systems, and monitoring / alarming systems. Twitter uses Storm’s scheduler to connect the same cluster for production applications as well as non-production applications.
Spotify
Spotify uses Storm to perform real-time music recommendations, monitoring, analytics for its 40 million active customers.
Cerner
Cerner uses Storm to process massive amounts of clinical data in real-time which helps to provide data quickly to clinicians to make a medical decision.