PGConf.Russia 2018

Many of us know that it is MVCC that provides concurrency access to data in many relational databases that guarantee transactions consistency and isolation. But only deep understanding of the implementation of this mechanism in PostgreSQL allows us to better understand the processes in the database, to design the application logic and table structures to be the most effective in a high-load world. We'll take one of the processes in our product to understand how MVCC is implemented in PostgreSQL and we'll explain one of the peculiarity when seemingly unrelated activities can affect each other.

In the pitch I will talk about the most important nuances of deployment and operations of the distributed analytical open-source database based on PostgreSQL - Greenplum. I will analyze the typical mistakes in its use, give the best practices and warn about bottlenecks.

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.

Storing binary data in database tables is sometimes a good solution for a particular project. But sometimes, due to changes in conditions or insufficient consideration of decisions, such storage is becoming a real nightmare. If there is an understanding of how and where to place these data, the transition to the new solutions are often very hard, often require modification in the application code and downtime the system for migration. The presentation is a particular solution of such problems. Our extension allows to move binary data from database to the storage Ceph and not only. And does it seamless for the applications.