PGConf.Russia 2018

This talk will compare architectural decisions that are made in PostgreSQL vs. ORACLE and will provide a closer look at the following components of both DBMSs:

1. The ins and outs of the working DBMS, its processes and their function
2. Structures that DBMS manages
3. Durability mechanics of each respective DBMS
4. MVCC design and database restoration options
5. Storage of data on the physical media

Each architectural decision will be evaluated based on the experience with DBMS of choice, ease of administration and future improvement possibilities.

This review will demonstrate the notable strengths of PostgreSQL as an open-source DBMS compared to the commercial solution in many cases.

This talk will be interesting for:

• PostgreSQL users, as it will allow to take a closer look into an alternative DBMS;
• PostgreSQL administrators, that will be able to see huge administration possibilities that ORACLE offers and that could be adopted in PostgreSQL;
• PostgreSQL hackers, as Postgres is being actively developed and this talk will review new development segments;
• Those who are willing to migrate from ORACLE (or any other commercial DBMS) into an open-source project, as this talk will show the features of PostgreSQL compared to the commercial product.

Tutorial on Full Text Seach in PostgreSQL, containing all recent improvemets. All recipies necessary for building an application will be given: dictionary and parser configuration, faceted search, fuzzy search, multilanguage search, ranking etc. Participants will be provided with a test database for exercises.

Postgres has a nice feature called Point-in-time Recovery (PITR) that would allow you to go back in time. In this talk, we will discuss what are the use-cases of PITR, how to prepare your database for PITR by setting good base backup and WAL shipping setups, with some examples. We will expand the discussion with how to achieve PITR if you have a distributed and sharded Postgres setup by mentioning challenges such as clock differences and ways to overcome them, such as two-phase commit and pg_create_restore_point.

• Spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available)
• How could we mix and tied efficiently PostgreSQL and Python data types (as NumPy ndarray, and Pandas DataFrames)
• Tools to improve our data manipulation environment (Jupyter tricks, easy dataviz...)
• How to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)