Kestrel vs Apache Spark: What are the differences?
Developers describe Kestrel as "Simple, distributed message queue system". Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM. On the other hand, Apache Spark is detailed as "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.
Kestrel and Apache Spark are primarily classified as "Message Queue" and "Big Data" tools respectively.
Some of the features offered by Kestrel are:
- Written by Robey Pointer
- Starling clone written in Scala (a port of Starling from Ruby to Scala)
- Queues are stored in memory, but logged on disk
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
Kestrel and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than Kestrel with 2.8K GitHub stars and 326 GitHub forks.
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