PgConf.Russia 2017

Streaming replication has been introduced in 2010 and quickly became one of the most popular features of PostgreSQL. Today, it is hard to imagine PostgreSQL installation without streaming replication. With its stability, high efficiency and ease of configuration one would have thought it is an optimal feature. However, while using it you might sometimes enter murky waters. This often can be resolved by using a combination of built-in and third party troubleshooting tools. In my talk I will provide an overview of these tools and explain how with their help one can diagnose, understand and eliminate problems related to streaming replication. I will also go through the most frequent issues occurring when streaming replication is used and will propose possible solutions. This talk is primarily aimed at DBAs and system administrators who use PostgreSQL in their day-to-day.

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I will tell you about why Postgres is first-choice product as a foundation for your BI system with classical OLAP workload. Briefly it will be said about existing open source BI solutions.

I will also describe specific of our architecture, why we chose snowflake scheme and how we are doing extract, transformation and load procedures. It will be mentioned about special Postgres tuning for OLAP and massive data bulkload workloads. Also I will let you know about Postgres usage as a column database with cstore_fdw by Citus and results achieved. Cons and problems of our approach will be described in the end of the talk.

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Do you test your PostgreSQL releases prior to Production in a dedicated test environment? Are you sure that your test environment (shortly Test) is equal to Production and in an appropriate state?

In Skype we were facing multiple challenges associated with database testing:
- Simplifying complex Production architecture of thousands of PostgreSQL instances, interconnected with RPCs and replications, infrastructure servers and external DB scripts, into their Test counterparts.
- Constantly growing hardware requirements, insufficient cleanup of data generated in Test.
- Differences between Test and Production were appearing and accumulating. Recognizing and fixing them required lots of effort.

Practical experience of carrying out import substitution with using PostgreSQL in government information system including not only the free software, but also the Russian hardware (Elbrus servers and other).

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