PGConf.Russia 2018

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.

This talk explores the ways attackers with no authorized database access can steal Postgres passwords, see database queries and results, and even intercept database sessions and return false data. Postgres supports features to eliminate all of these threats, but administrators must understand the attack vulnerabilities to protect against them. This talk covers all known Postgres external attack methods.

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.

In the last six months I have been working with a massive OLAP environment with 20TB shards, spanning around 400TB of data. Come to listen to how we make it all work, the challenges, and the skills involved. This talk has very little in common with the 10TB and Beyond talk because the data environments are very different.

We will cover analytics performance, data alignment, reasons for building extensions in C, and moving data around between servers in multiple data centers.