PgConf.Russia 2017

Query optimization is an important problem, which solution has a great influence on DBMS performance, especially for complex queries. In this talk we consider PostgreSQL query optimizer and specifically cardinality estimation problem for correlated clauses, which is one of the most well-known drawbacks of query optimizers in general. In the talk we propose our solution for this problem which involves machine learning methods and is available for PostgreSQL 9.6 as an extension with a patch. We discuss the experimental evaluation, advantages, disadvantages, and fields of application of the proposed approach as well.

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Partitioning is a long-awaited feature in PostgreSQL. Although Postgres supports partitioning via inheritance, this approach has some disadvantages, such as the need to manually create partitions and support triggers, significant planning overhead, and no query execution optimizations. In this talk, we’ll tell you about the pg_pathman extension we are developing. pg_pathman supports HASH and RANGE partitioning, performs planning and execution optimizations, supports fast insert by using Custom Node instead of triggers, provides functions for partition management (add, split, merge, etc.), supports FDW, non-blocking data migration, and more. We'll also speak about pg_pathman integration with Postgres Pro Enterprise Edition and Oracle-like syntax support for partitioning. Finally, we'll discuss new partitioning capabilities in PostgreSQL 10, the already implemented features and further development plans.

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Currently, to execute SQL queries PostgreSQL uses interpreter, which implements Volcano-style iteration model. At the same time it’s possible to get significant speedup by dynamically JIT-compiling query “on-the-fly”. In this case it’s possible to generate code that is specialized for given SQL query, and perform compiler optimizations using the information about table structure and data types that is already known at run time. This approach is especially important for complex queries, which performance is CPU-bound.

MoySklad online service includes important component - semiautomatic database conversion framework. Its history and supported conversions are subject of this speech.

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