PGConf.Russia 2021

Finding the nearest neighbor can be required for various tasks. For example, when you need to find the closest object to a given point on the map. This task looks trivial to non-programmer (a person can easily cope with it if they have a map). In a software developer's reality, this task doesn't have a common solution available to everyone. To get rid of this headache, programmers often create ad hoc solutions also known as "crutches". These workarounds don't look nice and often ruin the mood of a creative programmer who needs to go to a beer pub to cope with the cognitive dissonance :)

Indeed, while a person has a typical field of view and a map with a certain scale, the programmer has only one given point and a huge number of other points (i.e. billions of stars). This multitude of points gets a lot of incoming requests, including the write requests, not just read ones. You can write a perfect query in SQL, however, the real-world query execution plan will be depressingly long. To find the closest neighbor, you will have to read the entire table, compute all the distances from the given point and return the given number of good enough results. Indexing doesn't help in this case, as you will have to fully scan the search tree and read the entire table in random order. This will take much longer than simple table reading. In reality, tasks, where you need to efficiently find nearest neighbors, aren't limited to spatial search. It can also be used for classification tasks, finding typos, data clustering, and deduplication. All such tasks will benefit from efficient nearest neighbor search in DBMSs that are now a de facto standard for storing the data. What do we mean by "efficient search"? It means that our search is fast, concurrent, scalable, and supports various data types (most likely, non-standard ones). We implemented such KNN search in PostgreSQL 11 years ago. I will cover its implementation, today's state and share some use cases for KNN.

The pgpro_scheduler extension has an interesting but little-known feature - one-time jobs. Despite its simplicity, this feature can be used for complex transaction processing. On the one hand, it helps to reliably execute tasks taking a very long time, on the other hand, it ensures app scaling if certain conditions are met.

This talk is about postgres extension pg_profile - simple historic database workload profiler. I'll talk about its new features and new statistics available in Postgres 14. There is a branch of pg_profile named pgpro_pwr, designed to run in PostgresPro Enterprise Edition and PostgresPro Standard Edition databases. It is using extended performance statistics of those databases providing some valuable benefits. We'll see what's new in pgpro_pwr extension. Also I'll talk about some problems in postgres performance monitoring and their possible solutions in the future.