Transition tables!

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.

VOPS: vectorized executor for Postgres

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.

Development of delta-backups in WAL-G

WAL-G is simple and effective disaster recovery tool for PostgreSQL using cloud storages. In its core functionality, WAL-G is the successor of WAL-E rewritten in Go. But there is one new neat feature - delate-backups. WAL-G delta-backups, whenever possible, stores only pages, changed since the previous backup. In this talk, I'm going to describe development process of this feature.

Surprisingly, most important and complicated question was the design of the interface: WAL-e is simple and comprehensive, keeping these properties was goal #1. Technical details of implementation were covering some underwater stones too. Besides these, I want to discuss the perspective of technological development and future coordination of recovery tools developers.

Tutorial: Advanced spatial analysis with PostgreSQL, PostGIS and Python

• Spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available)
• How could we mix and tied efficiently PostgreSQL and Python data types (as NumPy ndarray, and Pandas DataFrames)
• Tools to improve our data manipulation environment (Jupyter tricks, easy dataviz...)
• How to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)