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How to install DBLab manually


You can use Console or AWS Marketplace for fast and automated installation of DBLab Standard Edition. This document describes step-by-step manual installation of DBLab Community Edition.

Here describes how to manually install the DBLab Engine Community Edition (DBLab CE).


  1. Prepare a virtual machine with an additional disk to store data, install Docker to run containers, and ZFS to enable copy-on-write for thin cloning
  2. Configure and launch your DBLab CE instance

Step 1. Prepare a machine with disk, Docker, and ZFS​

Prepare a machine​

Create a virtual machine with Ubuntu 22.04, and add a disk to store the data. You can use any cloud provider (e.q, AWS, Google Cloud, etc) or run your Database Lab on a hypervisor (e.q, VMware), or on bare metal.

(optional) Ports need to be open​

You will need to open the following ports:

  • 22: to connect to the instance using SSH
  • 2346: to work with Database Lab Engine UI and API (can be changed in the Database Lab Engine configuration file)
  • 6000-6100: to connect to PostgreSQL clones (this is the default port range used in the Database Lab Engine configuration file, and can be changed if needed)

For real-life use, it is not a good idea to open ports to the public. Instead, it is recommended to use VPN or SSH port forwarding to access both Database Lab API and PostgreSQL clones, or to enforce encryption for all connections using NGINX with SSL and configuring SSL in PostgreSQL configuration.

Install Docker​

If needed, you can find the detailed installation guides for Docker here.

Install dependencies:

sudo apt-get update && sudo apt-get install -y \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \

Install Docker:

curl -fsSL | sudo apt-key add -

sudo add-apt-repository -y \
"deb [arch=amd64] \
$(lsb_release -cs) \

sudo apt-get update && sudo apt-get install -y \
docker-ce \
docker-ce-cli \


Further, we will need environment variable $DBLAB_DISK. It must contain the device name that corresponds to the disk where all the Database Lab Engine data will be stored.

To understand what needs to be specified in $DBLAB_DISK in your case, check the output of lsblk:

sudo lsblk

Some examples:

  • AWS local ephemeral NVMe disks; EBS volumes for instances built on the Nitro system:

    $ sudo lsblk
    nvme0n1 259:0 0 8G 0 disk
    └─nvme0n1p1 259:1 0 8G 0 part /
    nvme1n1 259:2 0 777G 0 disk

    $ export DBLAB_DISK="/dev/nvme1n1"
  • AWS EBS volumes for older (pre-Nitro) EC2 instances:

    $ sudo lsblk
    xvda 202:0 0 8G 0 disk
    └─xvda1 202:1 0 8G 0 part /
    xvdb 202:16 0 777G 0 disk

    $ export DBLAB_DISK="/dev/xvdb"

Set up either ZFS or LVM to enable thin cloning​

ZFS is a recommended way to enable thin cloning in Database Lab. LVM is also available, but has certain limitations:

  • much less flexible disk space consumption and risks for a clone to be destroyed during massive operations in it
  • inability to work with multiple snapshots ("time travel"), cloning always happens based on the most recent version of data

Install ZFS:

sudo apt-get install -y zfsutils-linux

Create a new ZFS storage pool (make sure $DBLAB_DISK has the correct value, see the previous step!):

sudo zpool create -f \
-O compression=on \
-O atime=off \
-O recordsize=128k \
-O logbias=throughput \
-m /var/lib/dblab/dblab_pool \
dblab_pool \

If you're going to keep the state of DBLab up-to-date with the source (physicalRestore.sync.enabled: true in the DBLab config), then consider lower values for recordsize. Using recordsize=128k might give you a better compression ratio and performance of massive IO-bound operations like the creation of an index, but worse performance of WAL replay, so the lag can be higher. And vice versa, with recordsize=8k, the performance of WAL replay will be better, but the trade-off is a lower compression ratio and longer duration of index creation.

And check the result using zfs list and lsblk, it has to be like this:

$ sudo zfs list
dblab_pool 106K 777G 24K /var/lib/dblab/dblab_pool

$ sudo lsblk
nvme0n1 259:0 0 8G 0 disk
└─nvme0n1p1 259:1 0 8G 0 part /
nvme1n1 259:0 0 777G 0 disk
β”œβ”€nvme1n1p1 259:3 0 777G 0 part
└─nvme1n1p9 259:4 0 8M 0 part

Step 2. Configure and launch the Database Lab Engine​


To make your work with Database Lab API secure, do not open Database Lab API and Postgres clone ports to the public and instead use VPN or SSH port forwarding. It is also a good idea to encrypt all the traffic: for Postgres clones, set up SSL in the configuration files; and for Database Lab API, install, and configure NGINX with a self-signed SSL certificate. See the How to Secure Database Lab Engine.

Prepare database data directory​

Next, we need to get the data to the Database Lab Engine server. For our testing needs, we have 3 options:

  1. "Generated database": generate a synthetic database for testing purposes
  2. "Physical copy" (pg_basebackup): copy an existing database (perform "think cloning" once) using a "physical" method such as pg_basebackup
  3. "Logical copy" (dump/restore): copy an existing database using the "logical" method (dump/restore)

If you don't have an existing database for testing, then let's just generate some synthetic database in the data directory ("PGDATA") located at /var/lib/dblab/dblab_pool/data. A simple way of doing this is to use PostgreSQL standard benchmarking tool, pgbench. With scale factor -s 100, the database size will be ~1.4 GiB; feel free to adjust the scale factor value according to your needs.

To generate PGDATA with pgbench, we are going to run a regular Docker container with Postgres temporarily. We will use POSTGRES_HOST_AUTH_METHOD=trust to allow a connection without authentication (not suitable for real-life use).

sudo docker run \
--name dblab_pg_initdb \
--label dblab_sync \
--env PGDATA=/var/lib/postgresql/pgdata \
--volume /var/lib/dblab/dblab_pool/data:/var/lib/postgresql/pgdata \
--detach \

Create the test database:

sudo docker exec -it dblab_pg_initdb psql -U postgres -c 'create database test'

Generate data in the test database using pgbench:

# 10,000,000 accounts, ~1.4 GiB of data.
sudo docker exec -it dblab_pg_initdb pgbench -U postgres -i -s 100 test

PostgreSQL data directory is ready. Now let's stop and remove the container:

sudo docker stop dblab_pg_initdb
sudo docker rm dblab_pg_initdb

Now, we need to take care of Database Lab Engine configuration. Copy the contents of configuration example config.example.logical_generic.yml from the Database Lab repository to ~/.dblab/engine/configs/server.yml:

mkdir -p ~/.dblab/engine/configs

curl -fsSL \
--output ~/.dblab/engine/configs/server.yml

Open ~/.dblab/engine/configs/server.yml and edit the following options:

  • Set secure server:verificationToken, it will be used to authorize API requests to the Database Lab Engine
  • Remove logicalDump section completely
  • Remove logicalRestore section completely
  • Leave logicalSnapshot as is
  • If your Postgres major version is not 14 (default), set the proper version in Postgres Docker image tag:
    • databaseContainer:dockerImage

Launch Database Lab server​

sudo docker run \
--name dblab_server \
--label dblab_control \
--privileged \
--publish \
--volume /var/run/docker.sock:/var/run/docker.sock \
--volume /var/lib/dblab:/var/lib/dblab/:rshared \
--volume ~/.dblab/engine/configs:/home/dblab/configs \
--volume ~/.dblab/engine/meta:/home/dblab/meta \
--volume ~/.dblab/engine/logs:/home/dblab/logs \
--volume /sys/kernel/debug:/sys/kernel/debug:rw \
--volume /lib/modules:/lib/modules:ro \
--volume /proc:/host_proc:ro \
--detach \
--restart on-failure \

Parameter --publish means that only local connections will be allowed.

To allow external connections, consider either using additional software such as NGINX or Envoy or changing this parameter. Removing the host/IP part (--publish 2345:2345) allows listening to all available network interfaces. See more details in the official Docker command-line reference.

Check the Database Lab Engine logs​

sudo docker logs dblab_server -f

Need to start over? Here is how to clean up​

If something went south and you need to make another attempt at the steps in this tutorial, use the following steps to clean up:

# Stop and remove all Docker containers
sudo docker ps -aq | xargs --no-run-if-empty sudo docker rm -f

# Remove all Docker images
sudo docker images -q | xargs --no-run-if-empty sudo docker rmi

# Clean up the data directory
sudo rm -rf /var/lib/dblab/dblab_pool/data/*

# Remove dump directory
sudo umount /var/lib/dblab/dblab_pool/dump
sudo rm -rf /var/lib/dblab/dblab_pool/dump

# To start from the very beginning: destroy ZFS storage pool
sudo zpool destroy dblab_pool