Please see the following blog posts for the latests updates:
- ETL Language Showdown - Sept. 2014
- ETL Language Showdown Part 2 - Now with Python - May. 2015
- ETL Language Showdown Part 3 - 10 Languages and growing - Nov. 2015
Analyses and discussions done here have led to the following language pull requests:
ETL Language Showdown
This repo implements the same map reduce ETL (Extract-Transform-Load) task in multiple languages in an effort to compare language productivity, terseness and readability. The performance comparisons should not be taken seriously. If anything, it is a bigger indication of my skillset in that language rather than their performance capabilities.
Count the number of tweets that mention 'knicks' in their message and bucket based on the neighborhood of origin. The ~1GB dataset for this task, sampled below, contains a tweet's message and its NYC neighborhood.
fetch_tweets in the repo directory or downloaded here.
91 west-brighton Brooklyn Uhhh 121 turtle-bay-east-midtown Manhattan Say anything 175 morningside-heights Manhattan It feels half-cheating half-fulfilling to cite myself.
- These tasks are not run on Hadoop but do run concurrently. Performance numbers are moot since the CPU mostly sits idle waiting on Disk IO.
- **UPDATE: Boy was the IO bound assumption wrong.
Below you will find the languages run. Note that frameworks also play a big role, for example the Scala implementation compares the parallel collection to futures and the Akka framework. Click through on each language to read more.