PGConf.Russia 2018

In the pitch I will talk about the most important nuances of deployment and operations of the distributed analytical open-source database based on PostgreSQL - Greenplum. I will analyze the typical mistakes in its use, give the best practices and warn about bottlenecks.

One of advantages of document-oriented databases like MongoDB or Couchbase over RDBMSs is an ability to change the data scheme easily, fast and often. The traditional approach in RDBMS world involves doing an expensive ALTER TABLE operation, slow upgrade of an existing data, and stuff like this. This approach is often slow and inconvenient for application developers.

To solve this issue PostgreSQL provides JSON and JSONB datatypes. Also there are extensions like zson and pg_protobuf. From this talk you will learn how to work with these datatypes and extensions, their pros and cons and also related future work in this area.

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.

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.