PgConf.Russia 2019

Reliable backup and recovery, at enterprise level for the PostgreSQL environment. No more traditional backups. CBT (Change Block Tracking) technology is the next generation incremental backup. Faster than snapshots, CBT back up blocks that change, not all of your data, reducing server and network traffic and eliminating the need for traditional backups. Benefits: • Data protection mode close to Real-Time • Update with ease

IIn the world of programming, the creation of source code for databases "procedural extensions" is something solitary. Most DBMS offers procedural languages and "stored procedures" to create procedural extensions. In Postgres the number of supported procedural languages has already exceeded a dozen.

Traditionally, stored procedures have many applications: it is difficult to resist the attraction to perform a data operation directly in the storage, especially in the Enterprise development. This approach quickly leads to the spreading of the business logic and dramatically increases the cost of support and development of the system as a whole.

The life cycle of stored procedures makes it difficult to use standard Change Management tools and practices. It is necessary to adapt the operations upon the stored procedures to the standards of Change Management, yet staying within the comfortable development practices.

We'll look at the typical tasks of the procedural extensions development and discuss the solutions we are implementing in the Xobot IDE.

At the end of 2018, I've got a request to extend the syntax of the ltree contrib. I'm finalizing the patch and going to speak about:

• the current state of the extension,
• the extended syntax, and
• the process of development and testing the extension.

As we all know, PostgreSQL is a classic vertically scalable database for OLTP loads. In parallel with PostgreSQL for many years there is its alternative horizontal-scalable MPP version of PostgreSQL, that is called Greenplum, sharpened for big data and OLAP workload. In my pitch I will show the internal architecture of Greenplum (distributed transactions, data sharding, partitioning with hybrid storage in external systems, column storage engines with compression, and much more), a comparison with the internal structure of PostgreSQL and the application areas of each solution are shown.