PgConf.Russia 2020

Building the Enterprise infrastructure with PostgreSQL as the basis for storing personal data

In my talk, I will tell how we built a geographically distributed system of personal data storage based on Open Source software and PostgreSQL. The concept of the inCountry business is to provide customers with a ready-to-use infrastructure for personal data storage. Our business customers are ensured that their customer’s personal data is securely stored within their country’s borders. We wrote an API and SDK and built a variety of services. Our system complies with generally accepted security standards (SOC Type 1, Type 2, PCI DSS, etc.). We built our infrastructure with Consul, Nomad, and Vault, used PostgreSQL, ElasticSearch as a storage system, Nginx, Jenkins, Artifactory, other tools to automate management and deployment. We have assembled our development and management teams - DevOps, Security, Monitoring, and DBA. We use both cloud providers and bare-metal servers located in different regions of the world. Development of the system architecture and ensuring the stability of the infrastructure, consistent and secure operation of all its components is the main task facing our teams.

The performance problems related migrations from PL/SQL to PL/pgSQL

Porting applications from Oracle to Postgres is common work today. Unfortunately it is not without problems. In presentation I'll try to show the basic performance problems related to differences between Oracle and Postgres and PL/SQL and PL/pgSQL.

PostgreSQL bottlenecks #2

Last year I made a talk about unexpected PostgreSQL bottlenecks, which could make sad surprise to user (or DBA). Feedback to my talk was very positive. Additionally I have new material after year. This is why I'm making a sequel including new unexpected situations when your database hangs. This time focus will be on multicore hardware platforms, but not only them.

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.