Codenvy workspaces can run on different physical nodes managed by Docker Swarm. This is an essential part of managing large development teams, as workspaces are both RAM and CPU intensive operations, and developers do not like to share their computing power. You will want to allocate enough nodes and resources to handle the number of concurrently running workspaces, each of which will have its own RAM and CPU requirements.

Codenvy requires a Docker overlay network to exist for our workspace nodes. An overlay network is a network that spans across the various nodes that allows Docker containers to simplify how they communicate with one another. This is mandatory for Codenvy since your workspaces can themselves be composed of multiple containers (such as defined by Docker Compose). If a single workspace has multiple runtimes, we can deploy those runtimes on different physical nodes. An overlay network allows those containers to have a common nework so that they can communicate with each other using container names, without each container having to understand the location of the other.

Overlay networks require a distributed key-value store. We embed ZooKeeper, a key-value store in the Codenvy master node. We currently only support adding Linux nodes into an overlay network.

The default network in Docker is a “bridge” network. If your system will only require a single physical node, you can scale it with larger amounts of RAM using bridge networking (its default configuration).

Scaling With Overlay Network (Linux Only)

Each workspace node that you add will need to have Docker installed. It should be the same version of Docker that is installed on the master node running Codenvy. Additionally, the IP address of each workspace node must be publicly reachable by browsers. While each workspace is going to be clustered internally with Swarm, the nature of Swarm requires that each IP address is externally accessible.

1: Collect the IP address of Codenvy CODENVY-IP from your master node.

2: Collect the network interface of the new workspace node WS-IF:

# Get the network interface from your ws node, typically 'eth1' or 'eth0':

3: On each workspace node, configure and restart Docker with additional options:

  • --cluster-store=zk://<CODENVY-IP>:2181
  • --cluster-advertise=<WS-IF>:2375
  • --host=tcp://
  • --insecure-registry=<CODENVY-IP>:5000

The first parameter tells Docker where the key-value store is located. The second parameter tells Docker how to link its workspace node to the key-value storage broadcast. The third parameter opens Docker to communicate on Codenvy’s swarm cluster (this parameter is not needed if your workspace node is in a VM). And the fourth parameter allows the Docker daemon to push snapshots to Codenvy’s internal registry (this parameter is not needed if you are using an external registry). If you are running Codenvy behind a proxy, each workspace node Docker daemon should get the same proxy configuration that you placed on the master node. If you would like your Codenvy master node to also host workspaces, you can add these parameters to your master Docker daemon as well.

4: Verify that Docker is running properly. Docker will not start if it is not able to connect to the key-value storage. Run a simple docker run hello-world to verify Docker is happy. Each workspace node that successfully runs this command is part of the overlay network.

5: On the Codenvy master node, modify codenvy.env to uncomment or add:

# Uncomment this property to switch Codenvy from bridge to overlay mode:

# Comma-separated list of IP addresses for each workspace node
# The ports must match the `--cluster-advertise` port added to Docker daemons
  1. Restart Codenvy with codenvy/cli restart.

Simulated Scaling

You can simulate what it is like to scale Codenvy with different nodes by launching Codenvy and its various cluster nodes within VMs using docker-machine, a utility that ships with Docker. Docker machine is a way to launch VMs that have Docker pre-installed in the VM using boot2docker. Docker machine uses different “drivers”, such as HyperV or VirtualBox as the underlying hypervisor engine to launch the VMs. By lauching a set of VMs with different IP addresses, you can then simulate using Codenvy’s Docker commands to start a main system and then having the other nodes add themselves to the cluster.

This simulated scaling can be used for production, but it is generally discouraged because you would be running Docker in VMs that are on a host, and you are just taking on some extra I/O overhead that may not generally be necessary. However, this simulated-based approach gives good pointers on configuration of a distributed, cluster-based system if you were to use VMs-only.

As an example, the following sequence launches a 3-node cluster of Codenvy using Docker machine with a VirtualBox hypervisor. In this example, we launch 3 VMs: a Codenvy node and 2 additional workspace nodes.

Start 3 VMs named codenvy, ws1, ws2:

# Codenvy
# Grab the IP address of this VM and use it in other commands where we have <CODENVY-IP>
docker-machine create -d virtualbox --engine-env DOCKER_TLS=no --virtualbox-memory "2048" codenvy

# Workspace Node 1
# 3GB RAM - enough to run a couple workspaces at the same time
# Also binds the Docker daemon to listen on port 2375, which is the port Codenvy expects
docker-machine create -d virtualbox --engine-env DOCKER_TLS=no --virtualbox-memory "3000" \
                      --engine-opt="cluster-store=zk://<KV-IP>:2181" \
                      --engine-opt="cluster-advertise=eth1:2375" \
                      --engine-insecure-registry="<CODENVY-IP>:5000" ws1

# Workspace Node 2
docker-machine create -d virtualbox --engine-env DOCKER_TLS=no --virtualbox-memory "3000" \
                      --engine-opt="cluster-store=zk://<KV-IP>:2181" \
                      --engine-opt="cluster-advertise=eth1:2375" \
                      --engine-insecure-registry="<CODENVY-IP>:5000" ws2

Connect to the Codenvy VM and start Codenvy:

# SSH into the VM
docker-machine ssh codenvy

# Initialize a Codenvy installation
docker run -it --rm -v /var/run/docker.sock:/var/run/docker.sock \
           -v /home/docker/.codenvy:/data codenvy/cli:nightly init

# Setup Codenvy's configuration file to use overlay networking and to add the ws nodes
sudo sed -i "s/^CODENVY_SWARM_NODES=.*/CODENVY_SWARM_NODES=<WS1-IP>:2375,<WS2-IP>:2375/g" \

# Start Codenvy with this configuration
docker run -it --rm -v /var/run/docker.sock:/var/run/docker.sock \
           -v /home/docker/.codenvy:/data codenvy/cli:nightly start

# You can then access Codenvy at http://<CODENVY-IP>


Upgrading Codenvy is done by downloading a codenvy/cli:<version> that is newer than the version you currently have installed. You can run codenvy version to see the list of available versions that you can upgrade to.

For example, if you have 5.0.0-M2 installed and want to upgrade to 5.0.0-M8, then:

# Get the new version of Codenvy
docker pull codenvy/cli:5.0.0-M8

# You now have two codenvy/cli images (one for each version)
# Perform an upgrade - use the new image to upgrade old installation
docker run <volume-mounts> codenvy/cli:5.0.0-M8 upgrade

The upgrade command has numerous checks to prevent you from upgrading Codenvy if the new image and the old version are not compatible. In order for the upgrade procedure to advance, the CLI image must be newer that the version in /instance/codenvy.ver.

The upgrade process:

  1. Performs a version compatibility check
  2. Downloads new Docker images that are needed to run the new version of Codenvy
  3. Stops Codenvy if it is currently running
  4. Triggers a maintenance window
  5. Backs up your installation
  6. Initializes the new version
  7. Starts Codenvy


You can run codenvy backup to create a copy of the relevant configuration information, user data, projects, and workspaces. We do not save workspace snapshots as part of a routine backup exercise. You can run codenvy restore to recover Codenvy from a particular backup snapshot. The backup is saved as a TAR file that you can keep in your records.

Microsoft Windows and NTFS

Due to differences in file system types between NTFS and what is commonly used in the Linux world, there is no convenient way to directly host mount Postgres database data from within the container onto your host. We store your database data in a Docker named volume inside your boot2docker or Docker for Windows VM. Your data is persisted but if the underlying VM is destroyed, then the data will be lost.

However, when you do a codenvy backup, we do copy the Postgres data from the container’s volume to your host drive, and make it available as part of a codenvy restore function. The difference is that if you are browsing your /instance folder, you will not see the database data on Windows.


It is possible to migrate your configuration and user data from a puppet-based installation of Codenvy (5.0.0-M8 and earlier) to the Dockerized version of Codenvy. Please contact our support team for instructions.


The Codenvy audit service provides information on workspace ownership across all accounts.

A system administrator can generate an audit report at any time:

  1. Log in with administrator credentials.
  2. Navigate in your browser to: <Codenvy host url>/api/audit.
  3. A file named report_<datetime>.txt will be downloaded.

Sample audit log:

2017 Jan 09 - 17:40:39: *** added paid license 1460727747815.
2017 Jan 31 - 17:41:40: Paid license 1460727747815 expired.
2017 Jan 31 - 17:41:40: System returned to previously accepted Fair Source license.

Number of users: 1
Number of licensed seats: 50
License expiration: 15 January 2017
***@codenvy.onprem is owner of 1 workspace and has permissions in 1 workspace
└ wksp-pp0m, is owner: true, permissions: [read, use, run, configure, setPermissions, delete]
*** is owner of 0 workspaces and has permissions in 0 workspaces
*** is owner of 0 workspaces and has permissions in 0 workspaces
*** is owner of 1 workspace and has permissions in 1 workspace
└ wksp-0aku, is owner: true, permissions: [read, use, run, configure, setPermissions, delete]

Disaster Recovery

You can run Codenvy with hot-standy nodes, so that in the event of a failure of one Codenvy, you can perform a switchover to another standby system.

A secondary Codenvy system should be installed and kept at the same version level as the primary system. On a nightly or more frequent basis the Codenvy data store, Docker images and configuration can be transferred to the secondary system.

In the event of a failure of the primary, the secondary system can be powered on and traffic re-routed to it. Users accounts and workspaces will appear in the state they were in as of the last data transfer.

Initial Setup

### Create the Secondary System Install Codenvy on a secondary system taking care to ensure:

  • Version matches the primary system
  • Number of nodes and their resource capacity is the same as the primary system.
  • Source code repositories, artifact repositories and Docker registries are accessible from the secondary system.

Transfer Data

  1. Execute codenvy-backup on the primary system to get a copy of the /instance folder and the codevy.env.
  2. Execute codenvy-restore on the secondary system.

Setup Integrations

Any integrations that are used with Codenvy must be setup on both primary and secondary systems (e.g. LDAP, JIRA and others).

Setup Network Routing

Codenvy requires a DNS entry. In the event of a failure traffic will need to be re-routed from the primary to secondary systems. There are a number of ways to accomplish this - consult with your networking team to determine which is most appropriate for your environment.

Test the Secondary System

Log into the newly created secondary system and ensure that it works as expected, including any integrations. The tests should include logging in, instantiating a workspace using your custom images and snapshotting workspaces (if appropriate). Exercise any integrations at this time to ensure they function as expected.

Once everything checks out you can leave the system idle (hot standby) or power it down (cold standby).

Encourage Developers to Commit

Source code should be committed to the code repository frequently - this will facilitate the smooth transition from primary to secondary systems.

On-Going Maintenance

### System Updates and Changes Each time the primary system is updated to a new Codenvy version the secondary system should be updated as well. Test both systems after update to confirm that they are functioning correctly.

If new nodes are added or removed, or if existing nodes are resized, a matching change should be made to the secondary system.

Triggering Failover

If there is a failure with the primary system, log into the secondary system to ensure that everything is working as expected. Then re-route DNS to the secondary nodes. The secondary must have the same DNS name as the primary after switchover to ensure all functions operate correctly.

User Administration

As Codenvy system administrator, you can manage the users from the Dashboard. You can also configure certain set of limits and permissions by using the API.

List of users

As Codenvy system administrator, use the menu in the left sidebar to open the list of users:


A new page is displayed, which list all the users registered into your system:


Add a User

Click on the top-left button to add a new user:


A new popup is displayed where login, e-mail and password have to be defined in order to create a new user into the system:


Remove a User

You can remove a user by clicking on the “delete” icon. A new popup will be displayed and ask for confirmation:


User Details

Click on a user in the list to modify the user’s details.



Configure Account Limits

By default each user is able to use resources that are set in the system configuration. These limits affect all users but if needed the Codenvy admin can override the default values for a particular user via the Resource API.

Set Limits for a Single User

To override the default limits for a single user use POST /resource/free : [{host}/swagger/#!/resource-free/storeFreeResourcesLimit](). The body must have the following format:

  "userId": "{userId}",
  "resources": [
      "type": "{resourceType}",
      "amount": 1234,
      "unit": "{unit}"

Where: - userId parameter corresponds to the ID of the user to grant resources; - resources parameter contains list of resources to assign to the specified user; - unit parameter is optional, if it’s absent default unit for corresponding resources type will be used.

The following resource types with their resources units are applicable:

Resource Supported Units Default Unit
RAM mb mb
workspace item item
runtime item item
timeout minute minute

An admin can override all default limits, or only a subset. Limits that are not overridden will remain at the settings inherited from the default configuration.

An example body to grant user with ID userId the ability to use 2GB of RAM for running workspaces and create only 10 workspaces. Total number of created workspaces and idle timeout will remain at their defaults.

  "userId": "userId",
  "resources": [
      "type": "RAM",
      "amount": 2048,
      "unit": "mb"
      "type": "workspace",
      "amount": 10

List All Limit Overrides

To see the list of resource limit overrides for all users use the following API: GET /resource : [{host}/swagger/#!/resource-free/getFreeResourcesLimits]().

List Limit Overrides for a User

To see the list of resource limit overrides for a specific users use the following API: GET /resource/{userId} : [{host}/swagger/#!/resource-free/getFreeResourcesLimit]().

Reset Limits for a User to Defaults

To replace previously overriden limits for a specific user with default limits from the system use the following API: DELETE /resource/free : [{host}/swagger/#!/resource-free/removeFreeResourcesLimit]().