Intel Technologies for PostgreSQL
In this presentation, we'll discuss Intel products and solutions intended for the Data Platform Group segment, such as Xeon 3rd Gen (4S Cooper Lake) server CPUs, PMEm 200 Series RAM and FPGA.
Nikolay Samokhvalov Nombox LLC
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?
Esteban Zimányi ULBMahmoud SAKR université libre de bruxelles
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.
Yana Krasteva Swarm64
PostgreSQL has a long history in DWH. Netezza, Redshift, and Greenplum have turned specific PostgreSQL releases into DWH solutions. Nowadays, with the trends in PostgreSQL towards performance improvements (better partitioning, better statistics, JIT Compilation, etc.) and advanced PostgreSQL extensions, like the Swarm64 Data Accelerator, you can create a forward-looking, no lock-in, versatile, and reliable DWH. This talk will cover the PostgreSQL and DWH trends and touch on key arguments for choosing open source PostgreSQL for DWH.
Daniele Varrazzo Codice Lieve
Python is today one of the most used programming languages in the world: simple to learn and to use and ready to interface to any known service and protocol. psycopg2 is the most used PostgreSQL driver for Python: it offers good performance and makes the communication between the language and the database as smooth as possible.
Python has evolved enormously in the past years and its first-class support for async programming is changing the way new programs are written. PostgreSQL has evolved too: a new generation of the driver is needed to make the most of all the features it has to offer.
psycopg3 is the new generation of the most used Python-PostgreSQL adapter: it offers a familiar interface and smooth upgrade path, but behind the scenes it is engineered to obtain the best performance from the database and the language: async programming, prepared statements, binary parameters.
psycopg3 is also experimenting with innovative JSONB support and query pipelining! Come and discover the forefront of the research between your most loved language and database!