PGConf.Russia 2021

Running PostgreSQL in Kubernetes became a common theme with the rise of the operators. In this talk, we will show you how easy it is to deploy a highly available PostgreSQL cluster in Kubernetes with Operator. We will also show how to prepare for the failure of a data center by running a hot-standby cluster on a DR site.

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.

Multiple data leaks can occur during data migration. How to protect your sensitive data and stay compliant with the requirements of regulatory institutions? This is what you will learn from my presentation.

The development of secure system software (like an operating system or a DBMS) and ensuring trust in it are complicated scientific and technical tasks. In our presentation, we will demonstrate how we resolved these tasks for the Astra Linux OS certified to comply with the highest security classes. We will discuss the key directions of our activities, from the formation of national standards to the development and verification of the formal access model, which is the basis for the attack surface protection mechanism on the OS's level. We will also share how we apply the methods and technologies of dynamic and static analysis of the program code and, finally, we will explain how we collect and analyze the data we get from analyzing the OS code. We will also discuss how to fix the code errors in the project with continuous integration.