PgConf.Russia 2020

Odyssey is a new PostgreSQL connection pooler designed for the high-load environment. It can significantly scale processing performance by specifying the number of additional worker threads. This allows to increase SSL/TLS performance and provide more balanced pooling control using by sharing global server connection pools. In this talk I will answer 4 questions: 1. When you should use connection pooler? When Odyssey is the most optimal choice? 2. How to set up a cluster with Odyssey? 3. How to sleep well and be sure that everything works? 4. How does the architecture of Odyssey look like? How to implement or propose feature that you want?

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.

200 bases, several clusters, several terabytes of data Share our experience setting up and using patroni cluster DBMS Cluster on Linux, 1C server for windows. We use: PostgreSQL assembly for 1C, Patroni, Consul, Consul dns, Commvault, Ansible Vagrant file and Ansible playbook with roles attached.

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.