Надежная реализация сложной бизнес-логики с помощью pgpro_scheduler

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.

PG_QUERY_STATE: Всё о внутренней жизни ваших запросов

When working with PostgreSQL, we regularly encounter queries that take suspiciously long to be executed. What if it's too late to enable statistics or auto explain, and cancellation of the query doesn't look sensible, but you still need to find out what caused the issue or see at what stage execution the query is? pg_query_state is an open source PostgreSQL extension, which can give you access to the query execution plan in real time. It also makes available statistics for each node, including the number of processed rows.

Внедрение партицирования без простоя

The built-in partitioning mechanism in PostgreSQL has been developing for several years. However, there is no magic button that would transform an ordinary table into a partitioned one. I will explain how to implement partitioning in a production system, how to prepare your table and apps for partitioning, and what pitfalls can make a DBA's life harder. We will also take a deep dive into various techniques of transferring data between partitions, also considering their pros, cons and limitation.

Элегантный поиск ближайших соседей в 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.