PGConf.Russia 2018

Patroni is a Python application to create high-availability PostgreSQL clusters based on the streaming replication. It is used by Red Hat, IBM Compose, Zalando and many other companies. This tutorial will highlight Patroni architecture, provide attendees with hands-on experience of configuring high-availability PostgreSQL clusters with Patroni, describe how to take advantage of numerous additional features and give an opportunity to learn more about common mistakes related to running Patroni and its troubleshooting.

In order to take most out of the Patroni tutorial one needs a laptop with git, vagrant and virtual box installed.

Vagrant can be obtained from https://www.vagrantup.com Virtualbox is at https://www.vagrantup.com

Alternatively, one can install your Linux distribution packages (or use homebrew on Mac).

Once Vagrant and Virtualbox are installed one can run the Patroni VM by issuing the following commands:

$ git clone https://github.com/alexeyklyukin/patroni-training
$ cd patroni-training
$ vagrant up

When the setup concludes Patroni box can be accessed via ssh using vagrant ssh command.

This report is about my PostgreSQL extension pg_profile. This extension all you need to create periodic statistics snapshots and to keep them. You can build a report on one or many serial snapshots. This report will contain statistics information about database workload in specified time period. It is very useful to start investigation on performance degragation or excessive resoure consumption in the past.

Tutorial will show specifics of server programming in these languages. I will present practical examples and compare the features of these languages in PostgreSQL environment from the viewpoint of solving practical tasks.

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.