PGConf.Russia 2018

DBeaver is the Universal Databases Manager. We creating DBeaver using Open Source Software model. We pay special attention to the PotgreSQL because of its capabilities, popularity and OSS nature. We will talk about DBeaver evolution and structure, will demonstrate the basic functionality. We are going to cover challenges of PostgreSQL client creation. Also we will discuss requirements management model and interaction with the PostgreSQL community. We will show in details how to debug the PL/pgSQL stored procedures interactively. We are going to introduce some new features of the nearest DBeaver release and to share our plans.

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.

Postgres has a nice feature called Point-in-time Recovery (PITR) that would allow you to go back in time. In this talk, we will discuss what are the use-cases of PITR, how to prepare your database for PITR by setting good base backup and WAL shipping setups, with some examples. We will expand the discussion with how to achieve PITR if you have a distributed and sharded Postgres setup by mentioning challenges such as clock differences and ways to overcome them, such as two-phase commit and pg_create_restore_point.

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.