Ускоряясь до миллиона записей в секунду: масштабирование PostgreSQL с помощью Citus MX
Citus allows you to distribute postgres tables across many servers. It extends postgres to transparently delegate or parallelise work across a set of worker nodes, enabling you to scale out the CPU and memory available for queries.
One year ago, we began a long journey to allow Citus to scale out another dimension: write throughput. With writes being routed through a single postgres node, write throughput in Citus was ultimately bottlenecked on the CPUs of a single node. Citus MX is a new edition of Citus which allows distributed tables to be used from from any of the nodes, enabling NoSQL-like write-scalability.
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Другие доклады
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Yury Zhukovets ЗАО Дилжитал-Дизайн
Миграция Системы документационного управления «Приоритет» с MS SQL на Postgres
This talk is about migrating an electronic document management system from MS SQL to PostgreSQL 9.5 or higher as part of the import phaseout initiative. We will touch upon architecture specifics, as well as describe the problems we encountered when migrating T-SQL code to pgsql, and how we resolved them.
Learn more at https://pgconf.ru/news/94168
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Oleg Bartunov Postgres Professional
JSON в PostgreSQL: дорожная карта
The new ISO/IEC 9075-2:2016 standard specifies JSON data and operations syntax and semantics specifics for SQL. This talk overviews the requirements of this standard, and focuses on the differences between them and the actual implementation of JSON/JSONB in PostgreSQL. Special attention will be paid to JSON Path (XPath analogue), SQL/JSON functions and our plans to make Postgres compliant with this standard.
VIDEO
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Markus Nullmeier University of Heidelberg
Оптимизация запросов к данным типа “множество” с помощью индексов GIN, GiST, и пользовательских расширений для индексирования
Sets are apparently a useful data type for many kinds of applications. While PostgreSQL offers no built-in set data type, sets may be emulated to some degree with its built-in array and JSONB data types. Also, acceleration of respective containment (subset) queries is readily available as a built-in feature of the GIN index type.
Starting with the above, we will then explore the performance gains enabled by custom set data types, and especially by customisation code in C ("operator classes") for the GIN and GiST index types.
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Vladimir Borodin Яндекс
Пул соединений в масштабе
It's not a secret that PostgreSQL connections are expensive so you should save them. To solve this problem there are PgPool-II and PgBouncer for quite a long time. At Yandex tens of thousands of connections to a single database is not a surprise so we use pgbouncer since time immemorial. This talk gives an overview of problems we faced and ways to solve them.
VIDEO