PGConf.Russia 2018

It is often required to asynchronously perform several transactions in a strictly defined sequence, not just a single transaction. There are several ways to achieve this, and one of the solutions available is the pgpro_scheduler module.

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.

It's not a secret for anyone that statistics can not be transferred with a major upgrade. For small and not heavily loaded databases this is not a problem, you can quickly collect new statistics. But we have databases with a volume of about 5TB and a load of about 100k rps, for which it became a big problem: taking off without statistics, the replicas could not even replay WAL. In my report I'll tell you what tricks we went to upgrade these databases with requirements of 100% read only availability, about what mistakes were made, and about how these errors were painfully corrected. The result of these errors was the extension called "pg_dirty_hands", in which we will collect various hacks, which can be last resort to repair data corruption.

"And telling GIN from SP-GIST was quite beyond his wit, we found", said the classic. Can you? This masterclass is about not-so-often used index types (compared to conventional B-tree) which however can do a great job for you. We will look into internal mechanics of these indexes and discuss cases where they can be successfully applied. Also we will talk about some peculiarities of PostgreSQL index access. To spend time efficiently, listeners are required to have basic knowledge of PostgreSQL and should be used to read plans of simple queries.

Materials of the master class

• Slides
• Demo 1
• Demo 2
• Demo 3

Backup copy of the database with demo data can be downloaded here:

• Recovery with pg_restore (338 MB)