riko vs Apache Spark: What are the differences?
What is 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.
What is Apache Spark? Fast and general engine for large-scale data processing. 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.
riko and Apache Spark are primarily classified as "Stream Processing" and "Big Data" tools respectively.
Some of the features offered by riko are:
- Read csv/xml/json/html files
- Create text and data based flows via modular pipes
- Parse, extract, and process RSS/Atom feeds
On the other hand, Apache Spark provides the following key features:
- Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
- Write applications quickly in Java, Scala or Python
- Combine SQL, streaming, and complex analytics
riko and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than riko with 1.46K GitHub stars and 66 GitHub forks.
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What is riko?
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