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During one of our epic parties, Martin Lorentzon (chairman of Spotify) agreed to help me to arrange a dinner for me and Timbuktu (my favourite Swedish rap and reggae artist), if I prove somehow that I ...
During one of our epic parties, Martin Lorentzon (chairman of Spotify) agreed to help me to arrange a dinner for me and Timbuktu (my favourite Swedish rap and reggae artist), if I prove somehow that I am the biggest fan of Timbuktu in my home country. Because at Spotify we attack all problems using data-driven approaches, I decided to implement a Hive query that processes real datasets to figure out who streams Timbuktu the most frequently in my country. Although this problem seems to be well-defined, one can find many challenges in implementing this query efficiently and they relate to sampling, testing, debugging, troubleshooting, optimizing and executing it over terabytes of data on the Hadoop-YARN cluster that contains hundreds of nodes. During my talk, I will describe all of them, and share how to increase your (and the cluster’s) productivity by following tips and best practices for analyzing large datasets with Hive on YARN. I will also explain how the newly-added features to Hive (e.g. join optimizations, OCR File Format and Tez integration that is coming soon) can be used to make your query extremely fast.