PGConf.Russia 2018

A database is one of the key components in any information system, requiring the monitoring of multiple metrics. The talk highlights examples and approaches of monitoring and analysis of PostgreSQL performance that allow to minimize the load on the database server from the monitoring and data collection system for the subsequent analysis of problem situations.

• Quantum effects or as an observer affect the observed system
• Features of collecting metrics while monitoring the database with Zabbix
• Data collection for analytics and visualization PostgreSQL queries with rsyslog + kafka + clickhouse + grafana
• Operational Analysis Tools for DB loglile

Many of us know that it is MVCC that provides concurrency access to data in many relational databases that guarantee transactions consistency and isolation. But only deep understanding of the implementation of this mechanism in PostgreSQL allows us to better understand the processes in the database, to design the application logic and table structures to be the most effective in a high-load world. We'll take one of the processes in our product to understand how MVCC is implemented in PostgreSQL and we'll explain one of the peculiarity when seemingly unrelated activities can affect each other.

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.

Developers are often challenged to deliver results that are hard to implement using simple SQL queries. Fortunately, complex SQL capabilities exist in the SQL standards — common table expressions and window functions.

SQL is a declarative language, meaning the user submits an SQL command and the database determines the optimal execution. Common Table Expressions (CTEs) allow queries to be more imperative, allowing looping and processing hierarchical structures that are normally associated only with imperative languages.

Normal SQL queries return rows where each row is independent of the other returned rows. SQL window functions allow queries to return computed columns based on values in other rows in the result set.

This tutorial will help developers use CTE queries in their applications and allow operations that normally could only be done in application code to be done via SQL queries. It also explains the many window function facilities and how they can be used to produce useful SQL query results.

Video

Part I «Programming the SQL Way with CTE»

Part II «Postgres Window Magic»