Over its relatively short history, the discipline of Software Engineering has made rapid advances in the sophistication of its development processes and tools. In the past 15 years alone, the popularization of CI/CD tools has drastically improved software quality and reliability.
However, a large gap remains on the landscape of software tooling. For many engineers, it's a gap they are so accustomed to, they can no longer even see it.
The Elements of Application Behavior
The Database Lab Engine (DLE) is an open-source technology to enable thin cloning for PostgreSQL. Thin clones are exceptionally useful when you need to scale the development process. DLE can manage dozens of an independent clones of your database on a single machine, so each engineer or automation process works with their own database provisioned in seconds without extra costs.
DLE 2.4 brings two major capabilities to those who are interested in working with PostgreSQL thin clones:
Additionally, this release has a lot of improvements and fixes.
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:
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 (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.
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:
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.
During this Summer, we were super-busy achieving two goals that defined version 2.0 of Database Lab Engine:
Both targets happened to be quite challenging, but it is finally done, and now we are happy to see that all the pieces of Database Lab Engine work in containers, the whole workflow is described in a single YAML configuration file, and, last but not least, it works with RDS Postgres databases. Yay!
Check out Database Lab Engine release notes, Tutorial for RDS users, and Database Lab Engine configuration reference.
If you love and use psql (like I do), you're equipped with a lot of power. However, when you want to visualize execution plans — using such services as good old explain.depesz.com or modern explain.dalibo.com — you need to deal with inconvenient copy-pasting.
To solve this problem, my colleague Artyom Kartasov has developed a small utility called plan-exporter. It allows sending EXPLAIN data with minimal efforts:
In addition to Slack integration, Joe Bot can be now integrated with Postgres.ai Platform, providing convenient Web UI for all developers who want to troubleshoot and optimize SQL efficiently. Secure and performant Web UI works in any modern browser (even mobile!) and brings more flexibility, 1:1 communication, and visualization options.
The full list of changes can be found in Changelog. Can't wait to try!
hypo to further boost development processesBuilding indexes for large tables may take a long time. The new release of Joe bot includes the ability to get a sneak peek of the SQL query plan, using hypothetical indexes, before proceeding to actually building large indexes.
A hypothetical index is an index that doesn't exist on disk. Therefore it doesn't cost IO, CPU, or any resource to create. It means that such indexes are created almost instantly.
With the brand new command, hypo, you can create hypothetical indexes with Joe and ensure that PostgreSQL would use them. Once it's done, you can use exec to build the actual indexes (in some cases, you'll need to wait some hours for this) and see the actual plan in action.
Note, since the command works on top of the HypoPG extension, your Database Lab image has to use a Docker image for Postgres that contains HypoPG, because this extension is not a part of the core PostgreSQL distribution. For convenience, we have prepared images with HypoPG (and some other extensions) included, for Postgres versions 9.6, 10, 11, and 12. Of course, you can always use your custom image.
To be able to see the plan without actual execution, we have added one more new command: plan. It is aware of hypothetical indexes, so if one is detected in the plan, it presents two versions of the plan, with and without HypoPG involved.
Update: see the discussion on Hacker News!
Version 0.3 of Database Lab Engine (with a minor update to 0.3.1) adds support of LVM as an alternative to ZFS to enable thin cloning of large databases. This was one of the most requested features after the initial launch of the public Database Lab version a month ago.
Database Lab Engine is an open source technology that helps you clone non-production databases in seconds.
LVM can be chosen as a "thin-clone manager" instead of ZFS for those who do not want to use ZFS and prefer staying on more popular file systems (ext4, xfs) in non-production environments. It is worth noting that ZFS remains the default and recommended option. Postgres.ai team is very satisfied with the experience of using it running Database Labs for multi-terabyte, heavily loaded databases.
Compared to ZFS, the LVM module has a certain restriction: it is not possible to support multiple snapshots and allow choosing the snapshot when requesting a new clone. With LVM, the new clones always are based on the latest state of the database.