3 posts tagged with "thin PostgreSQL clones"

About Database Lab Engine​

The Database Lab Engine (DLE) is an open-source experimentation platform for PostgreSQL databases. The DLE instantly creates full-size thin clones of your production database which you can use to:

1. Test database migrations
2. Optimize SQL queries
3. Deploy full-size staging applications

The Database Lab Engine can generate thin clones for any size database, eliminating the hours (or days!) required to create “thick” database copies using conventional methods. Thin clones are independent, fully writable, and will behave identically to production: they will have the same data and will generate the same query plans.

Learn more about the Database Lab Engine and sign up for an account at https://postgres.ai/.

Database Lab Engine 2.2.0​

Database Lab Engine (DLE) 2.2.0 further improves support for both types of PostgreSQL data directory initialization and synchronization: “physical” and “logical”. Particularly, for the “logical” type (which is useful for managed cloud PostgreSQL such as Amazon RDS users), it is now possible to setup multiple disks or disk arrays and automate data retrieval on a schedule. This gracefully cleans up the oldest versions of data, without downtime or interruptions in the lifecycle of clones.

Database Lab Engine 2.1 for PostgreSQL released​

We are happy to announce version 2.1.0 of Database Lab Engine (DLE), an open-source tool for building powerful development and testing environments based on thin cloning of PostgreSQL databases. Using Database Lab API or CLI (and if you are using Database Lab SaaS, GUI), on a single machine with, say, a 1 TiB disk, you can easily create and destroy dozens of database copies of size 1 TiB each. All these copies are independently modifiable and created/destroyed in just a few seconds. This can become a game-changer in your development and testing workflow, improving time-to-market, and reducing costs of your non-production infrastructure.

In 2.1, the main new features are:

• Better data protection and security:
  • robust configuration defining how data is patched when snapshots are automatically created (both shell and SQL scripts are now supported),
  • an option specifying whether or not passwords for the existing DB users need to be preserved.
• [experimental] DLE API and the CLI tool are extended to have a new feature: "CI Observer" helping control DB schema changes (DB migrations) — here is the reference on how to use it https://postgres.ai/docs/reference-guides/dblab-client-cli-reference#subcommand-start-observation. This is a small step towards the big goal: have 100% coverage for testing DB migrations in CI using full-sized thin clones. Watch the demo (turn captions on):

Database Lab Engine 2.0 for PostgreSQL released​

The Postgres.ai team is proud to announce version 2.0 of Database Lab Engine (DLE) for PostgreSQL, a modern database tool for building powerful development and testing environments based on thin cloning. Using Database Lab API or CLI (and if you are using Database Lab SaaS, GUI), on a single machine with, say, a 1 TiB disk, you can easily create and destroy dozens of database copies of size 1 TiB each. All these copies are independently modifiable and created/destroyed in just a few seconds. This can become a game-changer in your development and testing workflow, improving time-to-market, and reducing costs of your non-production infrastructure.

This release continues our strategy to automate all routine tasks such as initialization of the PostgreSQL data directory, data transformation, and snapshot management. In DLE 2.0, all these tasks can be flexibly configured in a single configuration file. As a result, building dev&test environments for projects with many databases (such as those that adopted microservice architecture) becomes much easier.

The previous versions of the Database Lab introduced the core technology: thin clone provisioning, based on either ZFS (default) or LVM. It was already possible to provision full-sized multi-terabyte database clones in just a few seconds and use them for a broad spectrum of tasks such as database schema changes verification, SQL query analysis, or general application testing.

Version 2.0 speeds up and empowers the initialization of DLE itself. Instead of using custom scripts for initial and continuous data retrieval, it is now possible to configure everything in a declarative manner to get the data and be up and running.