Storm is an open source, big-data processing system that is different from other systems. Storm is designed for distributed real-time processing and is language independent. This free software makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. It is a complex-event processing system.
It uses custom created “spouts” and “bolts” to define information sources and manipulations to allow batch, distributed processing of streaming data. A Storm application is designed as a topology of interfaces which create a “stream” of transformations. It provides similar functionality as a MapReduce job with the exception that it will theoretically run indefinitely until it is manually terminated.
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.
Storm integrates with any queueing system and any database system. Storm’s spout abstraction makes it easy to integrate a new queuing system. Storm has a large and growing ecosystem of libraries and tools to use in conjunction with Storm.
- Easy integration with any queueing and database system.
- Simple and easy to use API.
- Highly scalable – processes very high throughputs of messages with very low latency.
- Fault-tolerant – runs the topology until it is killed or the cluster is shut down.
- Uses tuples as its data model. A tuple is a named list of values, and a field in a tuple can be an object of any type.
- Guarantees every tuple will be fully processed.
- Easy to deploy, requiring a minimum of setup and configuration to get up and running.
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