PGConf.Russia 2018

Liquibase is a very convenient tool for sequential database migration, used both on our projects and in a large number of other projects and frameworks. It allows you to keep the code of the database together with the application code in VSC, track the attempts of repeated migrations and much, much more. But sooner or later the project grows, the data occupy terabytes, and liquibase still rolls the migration sequentially.

We could not afford to deploy migrations for 100 hours and came up with a framework for liquibase that expanded its capabilities and allowed to execute a whole series of scripts in parallel or to split one large migration into small partitions and migrate them in parallel.

Avito is the biggest classified site of Russia, and the third largest classified site in the world (after Craigslist of USA and 58.com of China). In Avito, ads are stored in PostgreSQL databases. At the same time, for many years already the logical replication is actively used. With its help, the following issues are successfully solved: the growth of data volume and growth of number of requests to it, the scaling and distribution of the load, the delivery of data to the DWH and the search subsystems, inter-base and internetwork data synchronization etc. But nothing happens "for free" - at the output we have a complex distributed system. Hardware failures can happen - it is natural - you need to be always ready for it. There is plenty of samples of logical replication configuration and lots of success stories about using it. But with all this documentation there is nothing about samples of the recovery after crashes and data corruptions, moreover there are no ready-made tools for it. Over the years of constantly using PgQ replication, we have gained extensive experience, rethought a lot, implemented our own add-ins and extensions to restore and synchronize data after crashes in distributed data processing systems. In this report, we would like to show how our experience can be shifted to a new logical replication subsystem in 10th version of PostgreSQL. In the current implementation, these are only non-trivial solutions - there is a number of issues for the community, that come down to implementing simple recovery mechanisms - as simple as configuring the replication in 10th version.

• 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)

PostGIS is well known and widely used since two decades, as the best OpenSource database solution for Spatial Analysis. This talk will focus on: spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available); how to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)