PGConf.Russia 2018

For over 15 years JetBrains have strived to make effective developer tools. IntelliJ IDEA for java is the most popular of them. IntelliJ supports databases as well, and finally we in JetBrains decided to bring this functionality and our experience in programming languages to the world of SQL. That’s how DataGrip appeared. In my talk I will tell how DataGrip helps developers by automating routine checks and corrections and speeds up developer’s production. I will also show how to extend IDE’s functionality and try to understand which actual problems we can solve in future.

Storing binary data in database tables is sometimes a good solution for a particular project. But sometimes, due to changes in conditions or insufficient consideration of decisions, such storage is becoming a real nightmare. If there is an understanding of how and where to place these data, the transition to the new solutions are often very hard, often require modification in the application code and downtime the system for migration. The presentation is a particular solution of such problems. Our extension allows to move binary data from database to the storage Ceph and not only. And does it seamless for the applications.

Apart from its main purpose of scheduling tasks, pgpro_scheduler can also deal with chained transactions. It can be used in various scenarios of asynchronous data processing.

This tutorial demonstrates pgpro_scheduler features that ensure secure processing of chained transactions. We'll be using cryptocurrency transactions as an example.

pgpro_scheduler is included into Postgres Pro Enterprise as an extension.

Jsonb is a popular data type in PostgreSQL, it provides the web developers an ability to work with ubiquitous json inside the database and use all the power of proven relational database. Fast querying of jsonb data is a challenge for database and PostgreSQL provides several options for indexing jsonb. We present the new way of efficient indexing of jsonb, based on improvement of indexing infrastructure.

It's known, that json is a greedy data type, it may contains many auxiliary data not interesting for searching and that affects the size of index. Partial index will not helps, since it filters the rows before indexing, while we are interested in extracting of parts of jsonb. Functional indexes on specific keys could introduce too big overhead. We present an improvement of indexing infrastructure, which allows to control the index behaviour by passing parameters to operator class at index creation. For example, to index a user-defined subset of jsonb it is possible to pass to operator class the powerful path expression (either jsonpath of upcoming sql/json or jspath from jsquery extension), which can be used to extract the parts of jsonb tree. That makes index more effective and reduces the overhead of its maintaining.

Another use of parameterized operator classes is to allow a user to specify parameters instead of hard coding them, for example, the GiST signature size is currently hard coded inside the implementations of several opclasses (tsvector, hstore, intarray, pg_trgm, ltree), while it is natural to use different signature length for different data to have optimal size of index and its performance.

Full text search on parts of document can be improved by passing labels to the operator class and letting him index only specified parts of document, that allow to avoid currently used recheck of the rows returned by the index.