PgConf.Russia 2020

At the master class, we will learn how to execute fast load tests of Postgres databases: optimizing database configurations, data structure, indexes, OS settings, etc. To do this, we will create a code, build the infrastructure for the test from it and will do the test. As a result, we get a flexible tool in the code to which you can attach any monitoring, and for which you don't have to pay a lot of money, because the environment is created in 7 minutes in an empty AWS account and destroyed after test

Greenplum is a horizontal scalable database based on PostgreSQL core. It is used for OLAP workloads and a standard task is to quickly load or unload large amounts of data. The external data resources are usually another distributed systems. In this talk I will show how Greenplum can work with external data. What is the architecture and performance of external and foreign tables and how to handle streaming data. What will be changed in the next version. The examples of Kafka and ClickHouse connectors development.

It is our experience of the medical information system "RT MIS" transfer from MSSQL to PostgreSQL . When the necessity of transfer to PostgreSQL in our "RT MIS", one of the largest medical information systems, became imminent, we felt really terrified having assessed its amount: there was a huge number of stored procedures, functions, SQL-queries in its application code and services. It all requested transcribing, was exacerbated by demands on the system accessibility. So the variant "we awoke in the morning and PostgreSQL was working everywhere" was definitely impossible. That is why we chose another way: began eating "the elephant (PostgreSQL)" in chunks.

In my report, I am going to share our practical experience of the transfer, the instruments we used, the reason for another replication, the problems we met and their solutions. And finally, what turned out to be better: PostgreSQL or MSSQL.

I like stored procedures - it is great technology. But like any other technologies it allows to write not well optimized code. It is not easy to write optimized code, sql statements in complex large applications. On second hand, there are some tools, that can be used very easily, that can help. Postgres has built-in tracking functions possibility. There are PLProfiler and plpgsql_check. With these tools is easy work to detect slow part of applications.With this knowledge, the fix of performance issue is less magic.