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chore(dblab-engine): expand notes
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Michele Cereda
@@ -30,8 +30,10 @@
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1. [Resource tagging](#resource-tagging)
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1. [API](#api)
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1. [Python](#python)
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1. [Container images](#container-images)
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1. [Amazon Linux](#amazon-linux)
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1. [Further readings](#further-readings)
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1. [Sources](#sources)
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1. [Sources](#sources)
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## TL;DR
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@@ -868,6 +870,21 @@ machine if not.
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</details>
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## Container images
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### Amazon Linux
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Refer [Pulling the Amazon Linux container image].
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Amazon Linux container images are **infamous** for having issues when connecting to their package repositories from
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**outside** of AWS' network.<br/>
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While it can connect to them _sometimes™_ when running locally, one can get much easier and more consistent results by
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just running it from **inside** AWS.
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Disconnect from the VPN, start the container, and reconnect to the VPN before installing packages when running the
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container locally.<br/>
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If one can, prefer just build the image from an EC2 instance.
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## Further readings
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- [Learn AWS]
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@@ -1001,11 +1018,13 @@ machine if not.
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[what is amazon vpc?]: https://docs.aws.amazon.com/vpc/latest/userguide/what-is-amazon-vpc.html
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[what is aws config?]: https://docs.aws.amazon.com/config/latest/developerguide/WhatIsConfig.html
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[what is aws global accelerator?]: https://docs.aws.amazon.com/global-accelerator/latest/dg/what-is-global-accelerator.html
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[Pulling the Amazon Linux container image]: https://docs.aws.amazon.com/AmazonECR/latest/userguide/amazon_linux_container_image.html
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<!-- Others -->
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[a guide to tagging resources in aws]: https://medium.com/@staxmarketing/a-guide-to-tagging-resources-in-aws-8f4311afeb46
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[automating dns-challenge based letsencrypt certificates with aws route 53]: https://johnrix.medium.com/automating-dns-challenge-based-letsencrypt-certificates-with-aws-route-53-8ba799dd207b
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[aws config tutorial by stephane maarek]: https://www.youtube.com/watch?v=qHdFoYSrUvk
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[AWS Fundamentals Blog]: https://awsfundamentals.com/blog
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[aws savings plans vs. reserved instances: when to use each]: https://www.cloudzero.com/blog/savings-plans-vs-reserved-instances/
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[date & time policy conditions at aws - 1-minute iam lesson]: https://www.youtube.com/watch?v=4wpKP1HLEXg
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[difference in boto3 between resource, client, and session?]: https://stackoverflow.com/questions/42809096/difference-in-boto3-between-resource-client-and-session
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@@ -1017,4 +1036,3 @@ machine if not.
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[using aws kms via the cli with a symmetric key]: https://nsmith.net/aws-kms-cli
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[VPC Endpoints: Secure and Direct Access to AWS Services]: https://awsfundamentals.com/blog/vpc-endpoints
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[What Is OIDC and Why Do We Need It?]: https://awsfundamentals.com/blog/oidc-introduction
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[AWS Fundamentals Blog]: https://awsfundamentals.com/blog
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@@ -1,93 +0,0 @@
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# Database Lab
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Database Lab Engine is an open-source platform developed by Postgres.ai to create instant, full-size clones of
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production databases.<br/>
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Use cases of the clones are to test database migrations, optimize SQL, or deploy full-size staging apps.
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The website <https://Postgres.ai/> hosts the SaaS version of the Database Lab Engine.
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Configuration file examples are available at <https://gitlab.com/postgres-ai/database-lab/-/tree/v3.0.0/configs>.
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1. [Engine](#engine)
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1. [Clones](#clones)
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1. [Further readings](#further-readings)
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1. [Sources](#sources)
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## Engine
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Config file in YAML format, at `~/.dblab/engine/configs/server.yml` by default.
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Metadata files at `~/.dblab/engine/meta` by default. The metadata folder **must be writable**.
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```sh
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# Reload the configuration without downtime.
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docker exec -it 'dblab_server' kill -SIGHUP 1
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# Follow logs.
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docker logs --since '1m' -f 'dblab_server'
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docker logs --since '2024-05-01' -f 'dblab_server'
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docker logs --since '2024-08-01T23:11:35' -f 'dblab_server'
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```
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Images for the _Standard_ and _Enterprise_ editions are available at
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<https://gitlab.com/postgres-ai/se-images/container_registry/>.<br/>
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Images for the _Community_ edition are available at <https://gitlab.com/postgres-ai/custom-images>.
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## Clones
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Database clones comes in two flavours:
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- _Thick_ cloning: the regular way to copy data.<br/>
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It is also how data is copied to Database Lab the first time a source is added.
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Thick clones can be:
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- _Logical_: do a regular dump and restore using `pg_dump` and `pg_restore`.
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- _Physical_: done using `pg_basebackup` or restoring data from physical archives created by backup tools such as
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WAL-E/WAL-G, Barman, pgBackRest, or pg_probackup.
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> Managed PostgreSQL databases in cloud environments (e.g.: AWS RDS) support only the logical clone type.
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The Engine supports continuous synchronization with the source databases.<br/>
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Achieved by repeating the thick cloning method one initially used for the source.
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- _Thin_ cloning: local containerized database clones based on CoW (Copy-on-Write) spin up in few seconds.<br/>
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They share most of the data blocks, but logically they look fully independent.<br/>
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The speed of thin cloning does **not** depend on the database size.
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As of 2024-06, Database Lab Engine supports ZFS and LVM for thin cloning.<br/>
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With ZFS, the Engine periodically creates a new snapshot of the data directory and maintains a set of snapshots. When
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requesting a new clone, users choose which snapshot to use as base.
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Clone DBs configuration starting point is at `~/.dblab/postgres_conf/postgresql.conf`.
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## Further readings
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- [Website]
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- [Main repository]
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- [Documentation]
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- [`dblab`][dblab]
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- [Installation guide for DBLab Community Edition][how to install dblab manually]
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### Sources
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- [Database Lab Engine configuration reference]
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<!--
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Reference
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═╬═Time══
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-->
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<!-- In-article sections -->
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<!-- Knowledge base -->
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[dblab]: dblab.md
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<!-- Files -->
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<!-- Upstream -->
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[database lab engine configuration reference]: https://postgres.ai/docs/reference-guides/database-lab-engine-configuration-reference
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[documentation]: https://postgres.ai/docs/
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[how to install dblab manually]: https://postgres.ai/docs/how-to-guides/administration/install-dle-manually
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[main repository]: https://gitlab.com/postgres-ai/database-lab
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[website]: https://postgres.ai/
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<!-- Others -->
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123
knowledge base/dblab engine.md
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123
knowledge base/dblab engine.md
Normal file
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# DBLab engine
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Creates **instant**, **full-size** clones of PostgreSQL databases.<br/>
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Mainly used to test database migrations, optimize SQL, or deploy full-size staging apps.
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Can be self-hosted.<br/>
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The [website] hosts the SaaS version.
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1. [TL;DR](#tldr)
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1. [Further readings](#further-readings)
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1. [Sources](#sources)
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## TL;DR
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It leverages thin clones to provide full-sized database environments in seconds, regardless of the source database's
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size.<br/>
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It relies on copy-on-write (CoW) filesystem technologies (currently ZFS or LVM) to provide efficient storage and
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provisioning for database clones.
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Relies on Docker containers to isolate and run PostgreSQL instances for each clone.<br/>
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Each clone gets its own network port.
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The _Retrieval Service_ acquires data from source PostgreSQL databases and prepares it for cloning.<br/>
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It supports:
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- **Physical** retrieval, by using physical backup methods like `pg_basebackup`, WAL-G, or `pgBackRest` to copy the
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entire `PGDATA` directory.
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- **Logical** retrieval, by using logical dump and restore tools like `pg_dump` and `pg_restore` to copy database
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objects and data.
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> [!important]
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> Managed PostgreSQL databases in cloud environments (e.g.: AWS RDS) support only logical synchronization.
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The _Pool Manager_ manages storage pools and filesystem operations.<br/>
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It abstracts the underlying filesystem (ZFS or LVM) and provides a consistent interface for snapshot and clone
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operations.<br/>
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It supports different pools, each with its own **independent** configuration and filesystem manager.
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The _Provisioner_ manages the resources it needs to run and handle the lifecycle of database clones.<br/>
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It creates and manages PostgreSQL instances by allocating network ports to them from a pool, creating and managing the
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containers they run on, mounting filesystem clones for them to use, and configuring them.
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The _Cloning Service_ orchestrates the overall process of creating and managing database clones by coordinating the
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Provisioner and Pool Manager to fulfill cloning requests from clients.
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The _API Server_ exposes HTTP endpoints for interactions by providing RESTful APIs that allow creating and managing
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clones, viewing snapshots, and monitoring systems' status.
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Database Lab Engine uses a YAML-based configuration file, which is loaded at startup and **can be reloaded at
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runtime**.<br/>
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It is located at `~/.dblab/engine/configs/server.yml` by default.
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Metadata files are located at `~/.dblab/engine/meta` by default.<br/>
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The metadata's folder **must be writable**.
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```sh
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# Reload the configuration without downtime.
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docker exec -it 'dblab_server' kill -SIGHUP 1
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# Follow logs.
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docker logs --since '1m' -f 'dblab_server'
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docker logs --since '2024-05-01' -f 'dblab_server'
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docker logs --since '2024-08-01T23:11:35' -f 'dblab_server'
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```
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Before DLE can create thin clones, it must first obtain a **full** copy of the source database.<br/>
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The initial data retrieval process is also referred to as _thick cloning_, and is typically a one-time or a scheduled
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operation.
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Each clone runs in its own PostgreSQL container, and its configuration can be customized.<br/>
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Clone DBs configuration starting point is at `~/.dblab/postgres_conf/postgresql.conf`.
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Database clones come as _thick_ or _thin_ clones.
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Thick clones work as normal replica would, **continuously** synchronizing with their source database.
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Thin clones:
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1. Prompt the creation of a dedicated filesystem snapshot.
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1. Spin up a local database container that mounts that snapshot as volume.
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The creation speed of thin clones does **not** depend on the database's size.
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When thin clones are involved, DLE **periodically** creates a new snapshot from the source database, and maintains a
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set of them.<br/>
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When requesting a new clone, users choose which snapshot to use as its base.
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Container images for the _Community_ edition are available at <https://gitlab.com/postgres-ai/custom-images>.<br/>
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Specialized images for only the _Standard_ and _Enterprise_ editions are available at
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<https://gitlab.com/postgres-ai/se-images/container_registry/>.
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## Further readings
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- [Website]
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- [Codebase]
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- [Documentation]
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- [`dblab`][dblab]
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### Sources
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- [DeepWiki][deepwiki postgres-ai/database-lab-engine]
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- [Database Lab Engine configuration reference]
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- [Installation guide for DBLab Community Edition][how to install dblab manually]
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<!--
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Reference
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═╬═Time══
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-->
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<!-- In-article sections -->
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<!-- Knowledge base -->
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[dblab]: dblab.md
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<!-- Files -->
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<!-- Upstream -->
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[database lab engine configuration reference]: https://postgres.ai/docs/reference-guides/database-lab-engine-configuration-reference
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[Documentation]: https://postgres.ai/docs/
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[how to install dblab manually]: https://postgres.ai/docs/how-to-guides/administration/install-dle-manually
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[Codebase]: https://gitlab.com/postgres-ai/database-lab
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[Website]: https://postgres.ai/
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<!-- Others -->
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[DeepWiki postgres-ai/database-lab-engine]: https://deepwiki.com/postgres-ai/database-lab-engine
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@@ -1,6 +1,6 @@
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# `dblab`
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Database Lab Engine client CLI.
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DBLab Engine's CLI client.
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1. [TL;DR](#tldr)
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1. [Further readings](#further-readings)
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@@ -91,7 +91,7 @@ curl -X 'DELETE' 'https://dblab.company.com:1234/api/clone/smth' \
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## Further readings
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- [Database Lab]
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- [DBLab engine]
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- [Database Lab Client CLI reference (dblab)]
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- [API reference]
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@@ -107,11 +107,11 @@ curl -X 'DELETE' 'https://dblab.company.com:1234/api/clone/smth' \
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<!-- In-article sections -->
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<!-- Knowledge base -->
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[database lab]: database%20lab.md
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[DBLab engine]: dblab%20engine.md
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<!-- Files -->
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<!-- Upstream -->
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[api reference]: https://dblab.readme.io/reference/
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[API reference]: https://dblab.readme.io/reference/
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[database lab client cli reference (dblab)]: https://postgres.ai/docs/reference-guides/dblab-client-cli-reference
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[how to install and initialize database lab cli]: https://postgres.ai/docs/how-to-guides/cli/cli-install-init
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[How to refresh data when working in the "logical" mode]: https://postgres.ai/docs/how-to-guides/administration/logical-full-refresh
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