PGConf.Russia 2021

TimescaleDB extension allows to turn good old Postgres into a real distributed cluster for storing time series data while maintaining the relational model, convenient SQL and a time-tested ecosystem. And additional features such as continuous materialized views and data compression allow to build truly powerful telematic hubs.

This talk is a brief overview of Intel technologies: CPU development, including features for AI algorithms. The memory speed/volume pyramid > PMem space, encryption development, software tools to improve parallel computing performance. I will also share a couple of cases where PG and Intel were implemented.

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.

In this presentation, we'll reveal the process and peculiarities of our Oracle to PostgreSQL migration project. We have moved our ELDOKA corporate ECM platform to PostgreSQL, and during this talk we will explain how we ensured object-based and role-based access to data, which features were missing in the community-developed PostgreSQL, how we organized our work with spatial data and changed approach to storing our file content. We will also share our experience in time and resource savings, as well as data replication between our nodes. Performance test results will also be shared with the audience.