Bulk config rollouts, built-in OpenTelemetry, and two-click HA cluster control are all part of one goal: making PostgreSQL admin simpler and safer. PPEM 2.3 is a big step toward that — with user-defined presets, a reworked alerting system, and stronger RBAC — helping you bring order to messy configs and trust the system to warn you before things go sideways.
Some IT companies say they support open source. In practice, that often boils down to using other people’s code and a bit of PR. We believe real contribution means commits to the core. And to do that consistently, we opened an engineering center not in a glossy capital business park, but in a place where fundamental science is part of the cultural DNA. Here’s why we’re building the future of systems programming in Novosibirsk Akademgorodok.
Hitting the 4-billion-row limit in a TOAST table or running into an OidGen lock during a massive document import is a PostgreSQL admin’s nightmare. Sure, architects will tell you to push files to S3 — but real life often means keeping them inside the database. In this post, application optimization lead Alexander Popov breaks down how the standard bytea and pg_largeobject mechanisms work, where their bottlenecks hide, and how Postgres Pro Enterprise helps you get around those limits.
A common task in enterprise systems is to load large volumes of data into PostgreSQL — sometimes tens or even hundreds of millions of rows. At first glance, this seems simple: just write a loop in Java and call save() for every record. But in reality, such an approach can be painfully slow. Even a perfectly tuned PostgreSQL instance won’t help if the application is sending data inefficiently.
This article explains how to significantly accelerate bulk inserts when working with PostgreSQL through Spring and Hibernate. We’ll walk through which Spring and Hibernate settings are worth enabling, why they matter, and how much performance they can actually unlock. We’ll also look at how to build your own data-insertion layer for PostgreSQL — one that lets you switch between different insertion strategies, leverage PostgreSQL’s custom capabilities, and parallelize the process. Finally, we’ll see how to integrate this layer with Spring and what real gains each approach can deliver.
As we wrap up another year, it's time to look back at what our department has accomplished. 2025 brought us 42 published papers spanning fundamental ML theory, applied AI systems, and cutting-edge optimization methods—from transformer Hessians and generative models to hallucination detection and matrix-oriented optimizers.
Beyond publications, our students won competitions and defended their theses: 14 Bachelor's, 9 Master's, 3 PhD, and 1 DSc dissertations. They also launched ambitious group research projects. Three of our faculty and alumni received the prestigious Yandex ML Prize, and our head Konstantin Vorontsov was inducted into the Hall of Fame. If you read our summer overview of thesis defences or last winter's year-in-review for 2024, this post continues that story with the next chapter.
In this year-in-review, we dive into the research highlights, share stories from our educational programs, and celebrate the community that makes it all possible.