Make Your PostgreSQL 10x Faster on Cloud in Minutes

Cloud storage has some unique characteristics compared to traditional storage mainly because it is virtualized and controlled by software. One example is that AWS EBS shows higher throughput with larger I/O size up to 256 KiB without hurting latency. Hence, a user can get only about 4 MiB/sec with 1,000 IOPS EBS volume if the I/O request size is 4 KiB, whereas a user can get about 250 MiB/sec if the I/O request size is 256 KiB. This is because EBS consumes one I/O in a given IOPS budget for every I/O request regardless of the I/O size (up to 256 KiB). Unfortunately, PostgreSQL cannot exploit the full potential of cloud storage because PostgreSQL has designed without considering the unique characteristics of cloud storage.

In this talk, I will introduce the AppOS extension that improves the throughput of a write-intensive workload by 10x by transparently making PostgreSQL cloud storage-native. AppOS works like a storage driver that efficiently exploits the characteristics of cloud storage, such as I/O size dependency to storage throughput and latency, atomic write support in cloud block storage, and fast, but non-durable local SSDs. To do this, AppOS comprises a Linux-compatible file I/O stack including virtual file system, page cache, block I/O layer, cloud storage driver. On top of the file I/O stack, syscall module supports registering pre- and post-handler for file I/O-related system calls in order to transparently work without modifying PostgreSQL codes.

I will focus on presenting key use cases and performance results of the AppOS extension after explaining the internals. Specifically, I will show the performance results of OLTP and some batch workloads using standard benchmarking tools like pgbench and sysbench. I will also present performance results and implications on multiple clouds including AWS, GCP, and Azure.

PostgreSQL cluster высокой доступности под управлением Patroni для 1С. Единая точка входа организована Consul DNS на Windows.

200 баз, несколько кластеров, несколько терабайт данных Поделимся своим опытом настройки и использования patroni cluster Кластер СУБД под Linux, сервер 1С под windows. Используем: Сборка PostgreSQL для 1С, Patroni, Consul, Consul dns, Commvault, Ansible Vagrant файл и Ansible playbook c ролями прилагается.

Опыт плавной миграции высоконагруженного проекта (20+ млн. пользователей) c MySQL на PostgreSQL

Небольшая история о том, как переход на PostgreSQL увеличил на порядок эффективность работы компании:

1. Программный код сократился в 50 раз, в команде разработки бэкенда вместо 15 человек осталось трое
2. Сроки разработки нового функционала стали измеряться днями, а не месяцами
3. Стоимость владения в расчете на 1 млн. пользователей снизилась в 20 раз
4. Структура данных и документация существенно упростилась: вместо 100 тыс. таблиц со сложными связями стало 20 простых таблиц
5. Повышение безопасности системы за счет запрета внешних SQL команд к базе данных
6. Быстрый сбор аналитики по ключевым параметрам
7. Без остановки работы бизнеса

Writing a User-defined datatype

Walk-through of extending PostgreSQL with a user-defined type. The journey begins from the basics, from creating simple domain types over existing types, and continues to implementing a full-blown datatype from scratch in C.

PostgreSQL's advanced index types, GiST, GIN, and SP-GiST, are covered in enough detail to give an understanding of what each of them is good for. Support functions for each of them are shown for the example 'color' datatype.