Seamless SQL optimization, v2.0
There are two types of SQL query analysis:
"Macro": analyzing the workload as a whole (three major approaches: using metrics provided by pg_stat_statements or similar, log analysis with pgBadger or similar, and sampling of pg_stat_activity)
"Micro": diving into details of single query execution (EXPLAIN command being the central tool here)
And there are huge gaps between them that become noticeable at scale. The main challenges:
- Switching between "macro" and "micro" without a huge overhead
- Verifying optimization ideas reliably
- Deploying changes risk-free
Solving these tasks at a scale requires advanced DBA experience and–sometimes–intuition. Or better tools that (fortunately!) very recently started to appear.
In this tutorial, we will learn how to establish a smooth and seamless SQL optimization process in your organization: * what tools should you choose in your particular case? * how to close the gaps mentioned above?
Видео
Другие доклады
-
45 мин
Bruce Momjian EnterpriseDBPostgres and the Artificial Intelligence Landscape
Artificial intelligence, machine learning, and deep learning are intertwined capabilities that attempt to solve problems that defy traditional computational solutions — problems include fraud detection, voice recognition, and search result recommendations. While they defy simple computation, they are computationally expensive, involving computation of perhaps millions of probabilities and weights. While these computations can be done outside of the database, there are specific advantages of doing machine learning inside the database, close to where the data is stored. This presentation explains how to do machine learning inside the Postgres database.
-
45 мин
Vasiliy Puchkov ОООDevelopment of an integration database for production data of oil depots based on PostgreSQL
An architectural approach as the basis for a sustainable solution. Old and new technologies - unity and struggle of opposites. Information security and business requirements - is there a compromise?
-
90 мин
.jpg.180x180.jpg "Mahmoud SAKR")
Mahmoud SAKR université libre de bruxelles
Esteban Zimányi ULBManaging moving objects data with MobilityDB
MobilityDB is a moving object database extension to PostgreSQL and PostGIS. It has types and functions for storing an querying geospatial trajectories, as first class citizens. The main type is called tgeompoint (temporal geometry point). It represents a complete movement track of a geometry point, such as a car, a bird, or a person. The function speed(tgeompoint) computes the time varying speed of the object, as a tfloat (temporal float). Similar to these examples, MobilityDB has 6 temporal types, and over 300 functions. As such, it is a function-rich platform for Mobility Data Management.
In this tutorial you will:
- learn about moving object databases
- write MobilityDB SQL queries and explore a database of geospatial trajectories
- walk through the different type, indexes, and functions of MobilityDB.
-
45 мин
Anton Doroshkevich ИнфоСофтPostgres Pro Data Compression (CFS) for 1C ERP platform data
Postgres Pro Enterprise has a great compression engine. The year 2020 was devoted to the study of this mechanism in the real work of 1C. We have accumulated some statistical data and of course the subtleties of the use and behavior of 1C compared to other popular DBMS, which I want to share.