Адаптивная оптимизация запросов в PostgreSQL
Query optimization is an important problem, which solution has a great influence on DBMS performance, especially for complex queries. In this talk we consider PostgreSQL query optimizer and specifically cardinality estimation problem for correlated clauses, which is one of the most well-known drawbacks of query optimizers in general. In the talk we propose our solution for this problem which involves machine learning methods and is available for PostgreSQL 9.6 as an extension with a patch. We discuss the experimental evaluation, advantages, disadvantages, and fields of application of the proposed approach as well.
VIDEO
Слайды
Другие доклады
-
Markus Nullmeier University of Heidelberg
Оптимизация запросов к данным типа “множество” с помощью индексов GIN, GiST, и пользовательских расширений для индексирования
Sets are apparently a useful data type for many kinds of applications. While PostgreSQL offers no built-in set data type, sets may be emulated to some degree with its built-in array and JSONB data types. Also, acceleration of respective containment (subset) queries is readily available as a built-in feature of the GIN index type.
Starting with the above, we will then explore the performance gains enabled by custom set data types, and especially by customisation code in C ("operator classes") for the GIN and GiST index types.
-
Roland Sonnenschein Hesotech GmbH
Оптимизация PostgreSQL для реальных промышленных систем
Often it is enforced by the customer or even by law, to document the circumstances of the production of parts or lots. This talk is about the automated generation and storage of the corresponding administrative data and their correlation to measurements. Administrative data are entities like suppliers batch, article, serial number or production date. They are often to be exchanged with ERP systems.
-
Dmitry Lebedev BestPlace
Исследования геоданных при помощи PostGIS и смежных инструментов
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:
- Collecting the data: public API, OpenStreetMap; direct user input.
- 3rd party APIs for calculations.
- Visualization of GIS and other sorts of data: QGIS, Matplotlib, Zeppelin integrated with PostGIS.
- Debugging the calculations: live visualization (Arc, QGIS, NextGIS Web)
- Scripting and minimizing the chores: Makefile, Gulp
-
Michael Shurutov СтандартПроект
Автономные транзакции в Postgres
- What is an autonomous transaction?
- An overview of autonomous transactions in "big" DBMS: Oracle.
- Autonomous transaction logic in Postgres Pro.
- An overview of emulation methods for autonomous transactions in PostgreSQL.
- Comparing performance of the built-in Postgres Pro autonomous transaction mechanism and PostgreSQL emulation methods.
VIDEO