PGConf.Russia 2018

When one faces the issues with PostgreSQL, the main suspicion falls on vacuum. Experience of Data Egret team proves how many DBAs are attacking this rake. While there are tons of information, documentation and discussions on vacuum itself, the topic is still associated with a lot of myths, tales, horror stories and misconceptions. In my talk I will try to reveal the key points concerning the inner structure of vacuum, basic approaches to its adjustment and tuning, performance monitoring, and so on.

It’s 3am. Your phone rings. PostgreSQL is down, you need to promote a replica to master. Why the h**l isn’t this automatic?

If you thought of this before, you want automatic High Availability (HA). Don’t miss this talk! We will enter the world of Modern PostgreSQL HA.

Good news, there are several new, “modern” solutions for PostgreSQL HA. However, there are several solutions and it's not easy to pick one. Most require non-trivial setups, and there are many small caveats about HA like how to provide entry points to the application, HA correctness, HA vs. read scaling, external dependencies, interaction with cloud environments, and so forth.

Join this talk to master PostgreSQL HA and how to deploy it on current times.

PostgreSQL looks very competitive with other mainstream databases on OLTP workload (execution of large number of simple queries). But on OLAP queries, requiring processing of larger volumes of data, DBMS-es oriented on analytic queries processing can provide an order of magnitude better speed. The following factors limit Postgres OLAP performance:

• Unpacking tuple overhead (tuple_deform)
• Interpretation overhead (Postgres executor has to interpret query execution plan)
• Abstraction penalty (support of abstract data types)
• Pull model overhead (operators are pulling tuples from heap page one-by-one, resulting numerous repeated accesses to the page)
• MVCC overhead (extra per-tuple storage + visibility check cost)

All this issues can be solved using vectorized executor, which proceed bulk of values at once. In this presentation I will show how vector operations can be implemented in Postgres as standard Postgres extension, not affecting Postgres core. The approach is based on introducing special types: tile types, which can be used instead of normal (scalar) types and implement vector operations. Postgres extension mechanism, such as UDT (user-defined type), FDW (foreign data wrappers), executor hooks are used to let users work with vectorized tables almost in the same way as with normal tables. But more than 10 times faster because of vector operations.

This talk explores the ways attackers with no authorized database access can steal Postgres passwords, see database queries and results, and even intercept database sessions and return false data. Postgres supports features to eliminate all of these threats, but administrators must understand the attack vulnerabilities to protect against them. This talk covers all known Postgres external attack methods.