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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22 мин
Dmitry Vagin AvitoМониторинг PostgreSQL в Авито, с примерами
A short talk about collecting data and monitoring database workload in Avito. Exporting metrics from stored procedures to Graphite. Collecting and visualizing pg_stat* metrics in Grafana. Case studies.
VIDEO
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45 мин
Yury Zhukovets ЗАО Дилжитал-ДизайнМиграция 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
VIDEO
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45 мин
.JPG.180x180.jpg "Radoslav Glinsky")
Radoslav Glinsky Skype (Microsoft)Тестовая среда по требованию
Do you test your PostgreSQL releases prior to Production in a dedicated test environment? Are you sure that your test environment (shortly Test) is equal to Production and in an appropriate state?
In Skype we were facing multiple challenges associated with database testing:
- Simplifying complex Production architecture of thousands of PostgreSQL instances, interconnected with RPCs and replications, infrastructure servers and external DB scripts, into their Test counterparts.
- Constantly growing hardware requirements, insufficient cleanup of data generated in Test.
- Differences between Test and Production were appearing and accumulating. Recognizing and fixing them required lots of effort.
