PGConf.Russia 2025

Many developers often face performance issues in the systems they develop. One common solution for optimizing slow business processes is parallelization. But what do you do if the bottleneck is the data insertion into the database, which needs to maintain atomicity?

In this talk, I’ll explain how to speed up data insertion by parallelizing the process in Spring, while ensuring the atomicity of the entire operation. We'll cover batch updates in Spring and PostgreSQL, discuss why updates are heavy operations, and explore ways to speed up the process in the current tech stack. Additionally, I will present other approaches to maintaining atomicity and demonstrate their differences in benchmarks.

This will be useful for practicing engineers.

Not a PgBouncer, But a Connection Pooler. Not Odyssey, But with Coroutines

If you’ve ever set up a high-availability PostgreSQL cluster, you’ve likely faced the challenge of redirecting traffic to the primary node after a failover.

Typically, this requires additional software to monitor cluster status and reroute traffic accordingly. To avoid a single point of failure, you may have had to implement failover handling within that layer. Additionally, you might have encountered PostgreSQL’s limitations on handling large numbers of concurrent client sessions, necessitating query pooling.

To solve these issues, we developed Proxima — a scalable, fault-tolerant proxy and connection pooler. Proxima automatically redirects traffic to the primary node and, in BiHA setups, seamlessly switches to a new primary in case of failure.

In this talk, I’ll cover why and how we built Proxima, the key architectural decisions behind the extension, and the

implementation details that enable it to handle 10,000+ concurrent client sessions.

We’ll also explore use cases and answer your questions.

Currently, WAL files can be compressed with a ratio of up to 6x, but the existing compression system is inefficient.

In this talk, I’ll share my work on improving WAL compression.

The visibility map in PostgreSQL is an important mechanism for optimizing database performance, accelerating read operations, data cleanup, and indexing. Despite some limitations, such as memory consumption and fragmentation, its use can significantly enhance performance in high-load systems. Effective utilization of the visibility map requires proper configuration and monitoring, which is especially crucial in systems with large data volumes and high transaction concurrency.