JGit blame for fun and profit(?)(2nd April 2018) |
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Software projects are typically "tracked" on a version control system (VCS). Each "version" of the code is called a "commit", which does not only store file contents, but also plenty of metadata. This creates a very rich set of data, and in the age of open source there are thousands of projects to study. A few examples are Git of Theseus and Gitential, but by focusing on "git blame" (see who has committed each line on each file) I hope to bring something new to the table. In short I have analyzed how source code gets replaced by newer code, tracking the topics of who, when and why, and how old the code was.
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Benchmarking Elasticsearch and MS SQL on NYC Taxis(7th May 2017) |
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The NYC Taxi dataset has been used on quite many benchmarks (for example by Mark Litwintschik), perhaps because it has a quite rich set of columns but their meaning is mostly trivial to understand. I developed a Clojure project which generates Elasticsearch and SQL queries with three different templates for filters and four different templates of aggregations. This should give a decent indication of these databases performance under a typical workload, although this test did not run queries concurrently and it does not mix different query types when the benchmark is running. However benchmarks are always tricky to design and execute properly so I'm sure there is room for improvements. In this project the tested database engines were Elasticsearch 5.2.2 (with Oracle JVM 1.8.0_121) and MS SQL Server 2014.
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Analyzing NYC Taxi dataset with Elasticsearch and Kibana(19th March 2017) |
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The NYC taxicab dataset has seen lots of love from many data scientists such as Todd W. Scheider and Mark Litwintschik. I decided to give it a go while learning Clojure, as I suspected that it might be a good language for ETL jobs. This article describes how I loaded the dataset, normalized its conventions and columns, converted from CSV to JSON and stored them to Elasticsearch.
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