title

text

Pavel Molyavin
Pavel Molyavin 2ГИС
10:00 06 February
45 мин

Готовим PostgreSQL в эпоху DevOps. Опыт 2ГИС

The dark age for PostgreSQL started at 2GIS after transitioning to the microservice architecture. Every team tried to cook database on their own — by installing instances, juggling versions, trying to code deployments with numerous tools or using manual operations. It was the right time to develop a “silver bullet” — a common set of tools to solve all the problems at once. We created our own cluster solution based on well-known PostgreSQL, repmgr, pgbouncer and Barman. Despite of the complexity of our final solution, we developed a repeatable flexible deployment to accelerate postgresql cluster deployment and management. Also we deployed the our own cluster to consolidate all databases. It helped to eliminate team efforts for database management and focus on their main goals. Failover works, we tried it :-)

Слайды

Видео

Другие доклады

  • Miroslav Šedivý
    Miroslav Šedivý solute GmbH
    45 мин

    Битемпоральность: отслеживание воспроизводимых изменений в PostgreSQL с помощью типа данных RANGE

    So you finally have your database model for your application and you fill it in with current data. How do you keep it up to date? While INSERT may still be transparent, UPDATE and DELETE will overwrite your previous data, so you won't be able to reproduce them. Cloning the whole huge content for each minor update is not an option. For rich and complex data about hundreds of thousands of power generators in Germany and worldwide, I built a model using range data types in recent PostgreSQL which allows me to insert, update and delete data while granting the full access to the whole state of the database at any historical moment. I'll present a very simplified version of the database so the audience will be immediately able to apply it for their cases. I'll also show a few tricks in Python and Psycopg2 that will allow a whole team to prepare, review, and deploy all revisions to this database without merge conflicts. And I'll give a few ideas on how to retrieve this data efficiently.

  • Joshua Drake
    Joshua Drake Command Prompt, Inc.
    45 мин

    Сила логической репликации

    One of the most soft after features of Postgres v10 is logical replication. In this presentation we will cover what Logical Replication is, how it compares to Binary (Streaming Replication), how Logical Replication works, configuring Logical Replication, Logical Replication limitations, gotchas, security and management. We will also discuss potential deployed architectures with Logical and Binary Replication and some neat features of the underlying technology.

    At the end of this presentation an attendee with a reasonable understanding of how to manage Postgres will be able to configure Logical replication for use.

  • Alexander Kukushkin
    Alexander Kukushkin Zalando SE
    45 мин

    Типичные ошибки при построении высокодоступных кластеров и как их избежать

    You just set up your first PostgreSQL cluster, created a database schema, loaded some data, did some fine tuning of configuration. Now you want to make your cluster highly available. Unfortunately, PostgreSQL doesn't offer built-in automatic failover, but luckily for us, there are plenty of external tools for that. As a next logical step you start choosing a tool, and... you already doing it wrong, because first you have to define SLA, RTO, and RPO. In this talk I am going to cover most of the common mistakes people do when setting up a highly available cluster.

  • Denis Smirnov
    Denis Smirnov КГБУЗ КДЦ Вивея
    45 мин

    Greenplum: внутреннее устройство MPP PostgreSQL для аналитики

    As we all know, PostgreSQL is a classic vertically scalable database for OLTP loads. In parallel with PostgreSQL for many years there is its alternative horizontal-scalable MPP version of PostgreSQL, that is called Greenplum, sharpened for big data and OLAP workload. In my pitch I will show the internal architecture of Greenplum (distributed transactions, data sharding, partitioning with hybrid storage in external systems, column storage engines with compression, and much more), a comparison with the internal structure of PostgreSQL and the application areas of each solution are shown.