PgConf.Russia 2020

You will have a fascinating journey through PostgreSQL settings. We will talk about that with 1C not so or on the contrary so that under it it is necessary to adjust specially DBMS. We will discuss approaches to testing the speed of 1C. Consider the various options for backup schemes and fault tolerance. In the process, we will compare the speed of 1C on PostgreSQL configured by default with the speed of 1C configured for PostgreSQL. Also, we will create a replica of PostgreSQL, and switch to it the 1C Server "live", see what users will see 1C during this operation. And a separate block will be devoted to Postgres Pro Enterprise Edition, how the use of its advantages affects the speed of 1C.

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.

Odyssey is a new PostgreSQL connection pooler designed for the high-load environment. It can significantly scale processing performance by specifying the number of additional worker threads. This allows to increase SSL/TLS performance and provide more balanced pooling control using by sharing global server connection pools. In this talk I will answer 4 questions: 1. When you should use connection pooler? When Odyssey is the most optimal choice? 2. How to set up a cluster with Odyssey? 3. How to sleep well and be sure that everything works? 4. How does the architecture of Odyssey look like? How to implement or propose feature that you want?

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.