Apache Storm vs riko: What are the differences?

Apache 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; riko: A Python stream processing engine modeled after Yahoo! Pipes. riko is a pure Python library for analyzing and processing streams of structured data. riko has synchronous and asynchronous APIs, supports parallel execution, and is well suited for processing RSS feeds. riko also supplies a command-line interface for executing flows, i.e., stream processors aka workflows.

Apache Storm and riko can be primarily classified as "Stream Processing" tools.

Some of the features offered by Apache Storm are:

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

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

• Read csv/xml/json/html files
• Create text and data based flows via modular pipes
• Parse, extract, and process RSS/Atom feeds

Apache Storm and riko are both open source tools. Apache Storm with 5.81K GitHub stars and 3.94K forks on GitHub appears to be more popular than riko with 1.47K GitHub stars and 67 GitHub forks.