PGConf.Russia 2018

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.

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.

The presentation describes crypto-related parameters of PostgreSQL configuration (both authentication and TLS-protection of the connection to DB) and what do they mean

Transition tables, a new feature in PostgreSQL 10, offer broad new capabilities including new ways to maintain materialized views. At the end of this talk, you will have seen new ways to use this feature and have it in your tool chest for the future.