Source: Deep Learning on Medium
Apache Storm architecture: Real-time Big data analysis engine for streaming data
It is wondered when imagine how the system manages the number of calls between the caller and receiver and assemble the calls based on data. We used to see call history in mobile which is displayed in the format of a date, time and call duration.
If the next call happens this will further be added in existing data i.e. data gets updated timely. All this happens in real-time processing, systematized and controlled by Apache Storm.
Introduction of Apache Storm
Apache Storm is a processing engine in big data used for real-time analytics and computation. It is easily available open-source and distributed data framework.
It is hugely scalable and faults tolerance, embedded with assured processing and mechanism of data. For example, due to some fault or circumstances, messages are lost, then they can be synchronized or saved in the network storage, there is no loss of data.
As it is user-friendly, it is much easier to implement and consolidated with any programming language. It generally tasks with the principle of parallelism, i.e. code is executed on various nodes, even if each node has different input data.
Consider the case of Twitter, it is an online social platform to communicate with tweets. Here, user tweets can be sent and received. Subscribed users read and post tweets while unsubscribed users read tweets only. A hashtag is used to classify tweets as a keyword by putting # earlier to an appropriate keyword. So, Apache Storm acts here as a real-time outline of detecting the most used Hashtag per tweet.
It has numerous advantages based on its specific functionality and application. It performs on real-time processing of data frequently used in real-time applications, so it distributes an efficient scheme for capacity designing. Below are its advantages mentioned
- Apache Storm is a highly real-time analysis platform and hence permits real-time processing.
- It is open-source and user-friendly so incorporated with small and high industries.
- It is high-speedy, valid and generates genuine and authentic results.
- It has the operational potential of intelligence and strong capacity for processing.
- It can absorb vast volume and giant velocity of data so much compatible with big datasets.
- It is attainable, flexible and assists any programming language
Apache Storm has the cluster with some specific components, each component works with some functions together assists “The Apache Storm: Architecture”. There are two types of nodes that are present in architecture:
- Master node(Nimbus)
- Worker node(Supervisor)
The Master node comprises nimbus, nimbus acts as a daemon for the master node. The Master node runs the nimbus, nimbus examines and administers the task to cluster or worker node, allots tasks to machines, and supervise on failure. Nimbus permits to accept code (data) in any programming language, in this way anyone can utilize Apache storm without knowing any other language.
The Worker node comprises of a supervisor, the supervisor acts as a daemon for the worker node. The Worker node runs the supervisor, supervisor concentrates on the task given to the machine and monitors worker processes as required based on what Nimbus has assigned to it. Each worker node process operates a part of topology in the form of spouts and bolts. Nimbus daemon communicates with the supervisor daemon via ZooKeeper.
Components of Apache Storm
Topology is the real-time computational and graphical representation data structure. The topology consists of bolt and spouts where spout determines how the output is fixed to the inputs of bolts and output from a single bolt linked to the inputs of other bolts. A storm cluster gets input as topology, the nimbus daemon in the master node seeks information with supervisor daemon in the Worker node and accepts the topology.
Spout acts as an initial point-step in topology, data from unlike sources is acquired by the spout. It ingests the data as a stream of tuples and sends it to bolt for processing of stream as data. A single spout can generate multiple outputs of streams as tuples, these tuples of streams are further consumed by one or many bolts. Spout gets data from various databases, file system distribution or messages like Kafka consistently, converts them in streams of tuples and sends them to bolts for processing.
Bolts are responsible for the processing of data, their work includes filtering, functioning, aggregations, and handing databases, etc. Bolts consume multiple streams as input, process them, and generate new streams for processing of data.
Apache Storm has many industrial applications on a very large scale some of them are following; each of these applications is analyzed with some specific techniques and processes, you may find that here.
- Risk detection: Many companies use Apache Storm in risk detection in audit results, financial statements, correct transactions, etc.
- Fraud detection: Bank system uses Apache storm frequently to detect fraud credit card users, loan defaulters, breakdown of financial investments, etc.
- Real-time analysis: The main application of Apache Storm is real-time analysis like for trade pattern analysis, changes in stokes, weather changes, forecast of rain, etc.
- Retail Stores: Retailers might apply Apache Storm on changes in price-item, demand of products, monitoring payments status.
- Transportation: To divert route from heavy traffic area to low traffic, in the detection of over-speed vehicles and signals management, Apache Storm is particularly preferred.
- Healthcare management: In order to monitor a patient’s health status, instruments and sensors in OT or ICU, operational data support, etc. are controlled by Apache Storm.
- Telecom industry: In processing and switching of data, fraud calls, generating and processing of messages at a single time, Apache storm plays a vital role.
- Music apps: Spotify and Youtube majorly use Apache Storm for music and video recommendations, targeting advertisements, analyzing playlist, etc.
- Travel industry: Travel agencies and websites regularly follow processing data of Apache Storm to save time, fewer payments and convenient traveling. It is used in differentiating and inspecting prices in hotels or flights, etc.
- Social-media platform: On Social media, like Twitter, Facebook, Apache Storm is used for tracing a number of the hashtag, likes, and comments which are given continuously by users. It is a case of real-time analytics.
Comparison of Apache Storm with Hadoop
Basically, Apache Storm and Hadoop can perform analysis for big data, they both are open-source, but have differences in some aspects,
- Apache Storm operates on real-time data processing whereas Hadoop employs batch processing of data.
- Due to real-time processing and actions in the Apache Storm, Latency is quite low in it, in contrast to this, Hadoop has excessive Latency suitable to batch data processing.
- As Apache Storm is accessible to execute so it is stateless, opposite of that, Hadoop is stateful in variety.
- The architecture of Apache Storm incorporated with spouts and bolts whereas Hadoop architecture contains Hadoop Distributed File System( HDFS) and MapReduce for executing and saving of data.
From the above discussion, we can conclude that Apache Storm is a user-friendly and open-source platform. It can be used in small industries as well as in large organizations. It is highly used to process big data as its processing system is high-speed and authentic. It can be used in computation structure for real-time exploration, machine learning problems, unrestricted stream processing, proceeding continuously constructed messages and can yield to numerous systems, etc.