PGConf.Russia 2021

Working with queries - DBA perspective

Optimisation of queries can have a significant impact on database performance. In my talk, I will review the tools that we use in our practice and discuss some use-cases. This talk is of interest to database administrators or developers who would like to optimise their database performance and learn new query optimisation techniques.

Elegant K-nearest neighbor (KNN) searching in PostgreSQL

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.

Current status of the PostgreSQL protocol compression

PostgreSQL protocol compression has been discussed in the community for a long time. Many different hypotheses have been expressed and tested, and the compression patch has received a large number of changes and improvements. I will review the various approaches tested during the implementation of protocol compression, and will highlight the current status.

PostgreSQL for Python Software Developers

I will not teach you database design or query optimization. This talk will cover Python drivers, PostgreSQL usage in Python, and various tools from sync and async world enabling such usage, and how this ecosystem evolved. We'll discuss Python libraries including aiopg, psycopg2, asyncpg, pgbouncer and how they interact with Postgres.