Performance Tuning, Cost Optimization / Internals, Research by Dmitry Tolpeko
Global sorting is one the most important operations on data, and it is not only used to define how you can see the query result in UI but more importantly it is widely used to solve various performance issues in data pipelines i.e. to provide a better data compression, clustering, pruning and so on.
Let’s see how ORDER BY is implemented in Spark.
One of our event tables is very large, it contains billions of rows per day. Since the analytics team is often interested in specific events only it makes sense to process the raw events and generate a partition for every event type. Then when a data analyst runs an ad-hoc query, it reads data for the required event type only increasing the query performance.
The problem is that there are 3,000 map tasks are launched to read the daily data and there are 250 distinct event types, so the mappers will produce 3,000 * 250 = 750,000 files per day. That’s too much.