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.

High availability cluster using crmsh

Some OS distributions do not have a pcs configuration utility to create a high availability cluster PostgreSQL. In this case, the crm utility from the crmsh package will help us. It is more difficult to use, but powerful and effective.

In my master class, I will show how to use this utility, as well as configure a failover cluster in different configurations.

Postgres 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.

How query execution history can help with re-execution

Postgres is able to build optimal query plans for most practical cases. However, sometimes, for objective reasons, for complex queries or because of open issues in the planner itself, it can make mistakes and produce a suboptimal plan. Because of this, the execution time of such a request can increase tenfold. If the query is executed frequently, then from time to time this query takes longer than it could, and the DBMS as a whole produces a lower TPS. If the planner is able to record his mistakes and take them into account in the subsequent planning of the same query, then this will improve the characteristics of the DBMS during its operation. We present the results of the development of a PostgreSQL DBMS extension that stores the query execution history and implements the planner recommendation mechanism. We show how knowledge about previously executed queries can improve the performance of subsequent ones.