Seamless SQL optimization, v2.0

There are two types of SQL query analysis:

1. "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)

2. "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?

Managing 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.

SQL as data

Almost every business app is essentially just a SQL generator. How to easily build and compose SQL queries? I will explain the "Clojure way" of representing SQL as data (data DSL) and show how it may help you to dynamically build and compose SQL queries up to macros and query analysis.

How-to obfuscate Postgres database for load testing in web apps

Postgres is a well-known database for high load web applications. Such apps require stress/load testing itself to run properly in production. Besides obvious difficulties in preparation a test environment identical to production, generating proper traffic there is another one issue - database preparation for the test environment. And it seems it is not good to use the database from production in the testing environment in the era of personal data protection (GDPR, HIPAA). Data obfuscation is the rescue.

There are few instruments for data obfuscation in Postgres. During this session, I will tell you which of them we've selected and why what type of issues we faced, and if our solution was successful. You will know if it is possible to get an identical response on the test database without real data from production, we will observe some restriction on obfuscation, I'll present our utility which simplify things.