PGConf.Russia 2023

Как поместить весь мир в обычный ноутбук: PostgreSQL и OpenStreetMap

I'll show in PostGIS how everyone can analyze the geodata of the entire Earth and get answers to their global questions in minutes and seconds.

When you use a taxi in small towns, calling a car by phone, then with a high probability your trip is charged by the program based on OSM data. For billing, one of the routing packages is used. Through this use case, the taxi company employees put the house number and street on the buildings and contribute not only to their business, but also to OpenStreetMap.

The data analytics scenario also includes the tasks of where it is better to place an outlet so that buyers come to it. Again, data on walking distance and population of the surroundings can be extracted from geodata. You can calculate the value of real estate based on many factors related to the location of the object and its surroundings.

Scientists can build predictive models to predict epidemics, urban evolution, plan recreational areas and development of existing territories based on open geodata.

Well, you can answer any geography question that comes to your mind: calculate the area of cities and buildings, the length of roads and extract the names of cities, regions and islands. You can, for example, become a champion in the game of "Cities" or establish a new service for renting electric scooters. Everything is limited only by your imagination.

I published https://github.com/igor-suhorukov/openstreetmap_h3 - my project of high performance data loader, which allows you to perform geoanalytics on data from OpenStreetMap in PostGIS. It transform OpenStreetMap World/Region PBF dump into partitioned by H3 regions schema. Columnar storage option activate CitusDB extension in PostgreSQL to speedup aggregation queries.

Восстановление повреждённых данных

How to thoughtfully recover corrupt data...

Борьба с блоатом

Every DBA has in one way or another experienced the situation when PostgreSQL tables and indexes grow significantly in size. While looking for the reason for such behavior, we often conclude that database objects have "bloated". In this talk, we'll discuss the reasons behind bloating, create a testing environment to define the best method to reduce bloating. We'll also compare several anti-bloating utilities and get familiar with one more tool that helps us to efficiently struggle against bloating. We expect this presentation to become helpful for PostgreSQL DBAs of any experience level.

pgCodeKeeper - инструмент для организации современного процесса разработки БД