PgConf.Russia 2017

Database sharding enables a distribution of the database over a large number of machines, greatly improving performance. With the advent of Foreign Data Wrappers (FDW), it's possible to consider a database sharding in PostgreSQL with acceptable level of code changes using FDW. We've been working on enhancing around FDW infrastructure such as foreign table inheritance and pushing down so that PostgreSQL can execute the distributed query efficiently using FDW. In this talk, I'll cover what FDW-based sharding is and what use-cases it can cover. And then I'll demonstrate how to build sharding and describe our achievement of a FDW-based sharding in PostgreSQL community. Finally, I'll describe further enhancements to FDW such as Async Execution and Distributed Transaction Support.

Alexey will present technical details and share hands-on experience of extreme data normalization application for data infrastructure with exceptional parameters design and development. Extreme normalization-based data infrastructures has the following competitive advantages in comparison with market leaders: - Real-time data processing for 10 PB of data and more - 2-6 times better overall performance - 100% data consistency through total data landscape - Almost linear scalability - 4-10 lower cost of ownership - etc The abovementioned approach has been successfully utilized out of Russian market in telecommunication, retail, fin-tech, manufacturing (Industry 4.0, industrial IoT), and government institutions.

VIDEO

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.

VIDEO

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.