PgConf.Russia 2017

1. 1C:Enterprise 8 and PostgreSQL 9 interoperability 1.1 Changes in new 1C platform versions 1.2 v81c_data and v81c_index schemas 1.3 Sending 1C queries to SQL 1.4 Using 1C technological log events for PostgreSQL diagnostics
2. Analyzing queries that affect PostgreSQL performance 2.1 A free tool for automating log parsing 2.2 Pareto principle in action 2.3 Installation and configuration of the tool 2.4 A case study of query optimization 2.4.1 An issue in a PostgreSQL query 2.4.2 Finding non-optimal operations in a query 2.4.3 Resolving inefficiencies
3. PostgreSQL statistics for performance diagnostics 3.1 Comparing Postgres with MS SQL Server 3.2 Troubleshooting locks 3.3 Operating load diagnostics 4 Case studies by the gilev.ru team

With big data threads, even the upload of data to a database can often be problematic – apart from the data upload itself, you need to create indexes, perform VACUUM after the upload for correct Index-only scans, etc. From this talk, you will learn how to avoid most of such problems (if not all of them).

VIDEO

Most of the main Skype services use a database platform based on PostgreSQL and other open-source technologies, such as Skytools, plProxy, pgBouncer, etc. This platform consists of several hundreds of servers with thousands of databases, which process hundreds of thousands of transactions per second. At the same time, the platform architecture allows its users (applications and their developers) to work with "logical" databases, without any worries about their real “physical” structure.

Our Skype Database Platform team is responsible for the database platform infrastructure. We develop automation systems for various processes that help us ensure service reliability and facilitate development, testing, and deployment of code. In this presentation, I will outline the database platform architecture, review its main components, and tell you about the methods we use in our every-day work to ensure high availability, scalability, replication, fault tolerance, and more.

Nowadays one can make a decent urban research based simply on public datasets, making interesting and unexpected insights. In the presentation, I'll show examples of these calculations in PostGIS, the industry standard de-facto.

But just PostGIS is not enough. You need tools to import, verify and visualize the data. It's critically important to visualize the data live, to debug your calculations and shorten iterations. I'll describe all these steps:

1. Collecting the data: public API, OpenStreetMap; direct user input.
2. 3rd party APIs for calculations.
3. Visualization of GIS and other sorts of data: QGIS, Matplotlib, Zeppelin integrated with PostGIS.
4. Debugging the calculations: live visualization (Arc, QGIS, NextGIS Web)
5. Scripting and minimizing the chores: Makefile, Gulp