analysis
In 2018, I started studying Industrial Economics and Technology Management at NTNU Trondheim. At the time, I was interested in both economics and engineering, so I found the degree to be a good balance between the two. But as time went on, my interest in economics decreased, while my interest in programming grew and grew. This came to a head in my fourth year, when I decided to switch my degree to Computer Science. This extended my studies by half a year, but in return I got to take technical courses that I truly loved, and write the master's thesis that I wanted.
The year prior to my thesis, I worked as a software developer for Ignite. After my summer internship there, Ignite's CTO suggested to me a possible thesis one could write about the Ignite platform. You see, Ignite uses Elasticsearch to enable complex data analytics for their customers. This works well, but they've had issues when it comes to the performance of ingesting data, stale reads, correctness of results and more. It would be interesting to study the analytical database landscape, to see if other databases with different tradeoffs might be better suited for the type of platform that Ignite has built. Thus, my thesis was born: "Replacing Elasticsearch in a Data Analytics Platform".
I started out by searching for possible alternatives to Elasticsearch, and found that the world of databases was even more vast than I expected. But eventually, I did find one particularly promising database: ClickHouse, a column-oriented database that promises efficient analytical queries. I then built analysis, a backend service with an analytical query API, where you can toggle between using Elasticsearch or ClickHouse as the backing database. With this, I could measure differences between the two for different types of workloads. In the end, I found ClickHouse to be a viable alternative to Elasticsearch, both in quantitative and qualitative aspects, though some mixed results and limitations of the experiment made it not an obvious choice.