PGConf.Russia 2018

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.

PostGIS is a spatial extension to PostgreSQL that enables spatial datatypes, access methods and a set of functions to perform geometric operations on them.

Typically PostGIS is used to select a small subset of a big static dataset. In this talk I'll cover issues that arise when working with big dynamic data flows, and ways to resolve them, on examples that we've met developing Juno ride sharing service backend.

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.

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.