PgConf.Russia 2020

Мы хотим поделиться структурой и результатами теста (производительности и не только), в котором участвовали PostgreSQL/Citus и MongoDB для данных нашей компании. Это был весьма увлекательный процесс с неожиданными поворотами и неоднозначным результатом.

"Заморозка разработки" (feature freeze) PostgreSQL 13 запланирована на апрель 2020. Впереди ещё два commitfest'а, которые принимают новые патчи. Что мы можем сказать про PostgreSQL 13? Возможно сработает закон чередования и в нём окажется не так много прорывных фич, как в PostgreSQL 12. Даже если и так, то это будет хороший эволюционный релиз, куда войдёт много небольших фич, а также инфраструктурных изменений, готовящих постгрес к новому рывку. В докладе я расскажу о всех ожидаемых новинках PostgreSQL 13. О них можно будет судить достаточно точно, потому что останется уже только один последний commitfest, результаты которого можно будет прогнозировать.

PostgreSQL is one of the most readily extensible databases in the world. Custom data types, aggregations, functions, and more can be easily and safely written in C.

This hands-on tutorial covers the basics of writing functions, data types, and aggregates in C. It is recommended (though not strictly required) that bring their own laptops and be prepared to actually try the exercises.

Topics covered include: 1. The PostgreSQL type system 2. Creating a simple custom type in C 3. Common errors for non-C programmers 4. Creating a simple aggregate in C

There will be many opportunities for questions and discussion through the tutorial.

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.