PGConf.Russia 2018

For the majority of System Administrators and DBAs performing an upgrade for RDBMS, let alone a major one, is a pain. That’s because one of the key factors that plays a role in a decision if and when to perform an upgrade is the downtime that it might come to during the process. This is true for any databases but especially important for those that are in production or under a high load.

Often, a major upgrade get’s cancelled and a DBA needs to go back to an older version due to the lack of experience or some basic errors that could have been easily avoided at the planning stage.

In our consultancy, we perform upgrades for our clients regularly and it allowed us to streamline the process and take some preventative measures that help us to perform it quickly, efficiently and with minimal or no downtime.

In this talk, I will share some key steps and tools that will help any DBA to become better at major upgrade performance. I will answer the following questions:

How to prepare for an upgrade of PostgreSQL? What one needs to do at the planning stage? How to plan your actions during the actual upgrade process? How to perform an upgrade successfully without going back to the older version? What actions one must perform following an upgrade?

I will also go through the two most popular processes of an upgrade: pg_upgrade и pg_dump/pg_restore, will compare some of the benefits and downfalls using each of these. I will also discuss some of the main issues one might face throughout the process and ways to avoid them.

This talk would be of interest to those who are new to PostgreSQL, as well as experienced DBAs who would like to learn more about upgrades or those who, in general, would like to understand why major upgrades should NOT be avoided like the plague.

In the last six months I have been working with a massive OLAP environment with 20TB shards, spanning around 400TB of data. Come to listen to how we make it all work, the challenges, and the skills involved. This talk has very little in common with the 10TB and Beyond talk because the data environments are very different.

We will cover analytics performance, data alignment, reasons for building extensions in C, and moving data around between servers in multiple data centers.

Bringing the provability and immutability of blockchain to performance and efficiency of traditional DBMS.

Blockchain technology has several unique properties including provability and immutability. Every blockchain transaction is signed by its author, and it could be verified by any blockchain network member. Also, once data is stored in blockchain, it can't be altered in the future. Many databases operating traditional DBMS would also benefit from provability and immutability properties. However, inclusion of all the transaction data in the public blockchain is very expensive.

Credereum is the platform, which allows creation and maintaining of databases, whose contents and history are provable and immutable without sacrifice the performance and efficiency of traditional DBMS. Thanks to Credereum, database owner can prove the validity of query results, while users can verify them. Database owner don't have to reveal the whole database contents or full history of transactions to provide the proof of database query results. Therefore, Credereum database may contain private sensitive information. Credereum utilized bleeding-edge technologies including, but not limited to decentralized cloud, public blockchain with sharding. Credereum is an emerging technology of trusted and private databases.

We will explain why PostgreSQL is suitable database for Credereum and what we need to develop in Postgres to support signed transactions and cryptographic storage.

PostGIS is well known and widely used since two decades, as the best OpenSource database solution for Spatial Analysis. This talk will focus on: spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available); how to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)