This is the terraform code used by the Daml repository to deploy supporting infrastructure such as the Bazel caches, Nix caches and Azure Pipeline (VSTS) Agents.

To deploy the infrastructure changes, you will to get access to the da-dev-gcp-daml-language Google project from DA IT. Then run gcloud auth login to configure the local credentials.

All the infrastructure is currently deployed using Terraform. For convenience we have a little wrapper script that you can run to apply the latest changes:

$ ./apply

Changing the template of an instance manager tends to kill all the machines in the current group. Therefore, we're experimenting with keeping two groups per logical group. (This is currently done only for the Hoogle servers.)

You can follow along with an example of such a deployment in the commit-by-commit view of #9362.

The default state should be to split machines between the two groups evenly, and have the exact same configuration in each group. When a change needs to be made, one can set the size of one of the groups to 0, make the appropriate changes, then ramp up the size of the "changed" group. Once enough nodes in the new group have been started and are confirmed to work, the old group can be resized to 0 (then updated to match so we don't accidentally roll back later).

For example, if we were to change the way in which the hoogle servers initiailize, we could:

1. Change the size of the green group to 0 and bump the blue group to 3.
2. Add a new field to both groups containing the lines we want to change, and splice those lines in the init script. At this point a terraform plan should report no change.
3. Change the value for the green group and bump its size to 1. Deploy, wait for the green machine to start.
4. If everything went well, bump the size of the green group to 2 and zero the size of the blue group.
5. Update the variables in the blue group to match the green group, and bump the size of the blue group to 1.

This way, we can do a rolling upgrade while controlling how many working machines we have at all times, and thus perform zero-downtime upgrades with hopefully no impact on users. (Assuming the only failure mode of the new machines is to fail at startup.)

For CI nodes, it's better to do a whole group at once, with overlap: first spin up the second group with the new settings, wait for the nodes to be available, deactivate the old nodes in the Azure UI, and when all of the old nodes are idle, set the old group size to 0. This is because when changing a group's size we do not control which node gets killed, and we'd rather not kill a node that is currently running a job.

Alternatively, killing a node in an instance group through the GCP Console UI will both kill that node and reduce the instance group count by one, so that can be used to manually reduce a group's size one machine at a time if we don't want to wait for all the nodes in a group to be idle.

At the Terraform level, this is achieved by using the special count field, which allows one to duplicate (multiplicate) a Terraform resource description. Within a resource that has a count entry, the special variable count.index will be the index of the "current" iteration of a resource. When other resources reference one that has a count property, they have to address is as an array. For example, if we have a resouce defined by:

resource text_field my_counter {
  count = 2
  value = count.index
}

we would create two text_field resources both called my_counter. They could be accessed with text_field.my_counter[0] and text_field.my_counter[1]. If we wanted to create two corresponding resources we could write something like:

resource derived_text_field my_derivation {
  count = length(text_field.my_counter)
  value = "the count is ${text_field.my_counter[count.index]}"
}

A word of warning: because of the way in which Terraform keeps track of state, if you swapped two such resources, Terraform will destroy and recreate both. In other words, it keeps track of each indivividual element in the list, not of the list itself. Similarly, if you deleted an element in the list, all elements after it would be destroyed and recreated.

To avoid holding the secret key into the store, creating the key has to be done through the UI.

This can be done here: https://console.cloud.google.com/iam-admin/serviceaccounts/details/104272946446260011088?project=da-dev-gcp-daml-language

In order to interact with these Terraform files, you will need security to give you access to the relevant GCP project (da-dev-gcp-daml-language), and login via gcloud by running:

gcloud auth application-default login --account your.name@gcloud-domain.com

Permissions to reset build nodes are defined in periodic-killer.tf using the killCiNodes role. CI nodes are managed so killed nodes will be immediately replaced by a new one with the exact same configuration (but starting its initialization from scratch); we can therefore see killing a node and resetting a node as the same operation.

Nodes can be listed with

gcloud compute instances list --project=da-dev-gcp-daml-language

and individual nodes can be killed with

gcloud compute instances --project=da-dev-gcp-daml-language delete --zone=us-east4-a vsts-agent-linux-dhw4

where zone and name have to match.

As a reference, here are a couple zsh functions I have added to my shell to make my life easier:

refresh_machines() {
    machines=$(gcloud compute instances list --format=json --project=da-dev-gcp-daml-language | jq -c '[.[] | select (.name | startswith("vsts-")) | {key: .name, value: .zone | sub (".*/"; "")}] | from_entries')
}

kill_machine() {
    if [ -z "$machines" ]; then
        refresh_machines
    fi
    for machine in $@; do
        gcloud -q compute instances --project=da-dev-gcp-daml-language delete --zone=$(echo $machines | jq -r ".[\"$machine\"]") $machine
    done
}
_kill_machine() {
    local machine_names
    if [ -z "$machines" ]; then
        refresh_machines
    fi
    machine_names=$(echo $machines | jq -r "keys - $(echo -n $words | jq -sRc 'split(" ")') | .[]")
    _arguments "*: :($machine_names)"
}
compdef _kill_machine kill_machine

Nodes should be deactivated and idle before they are killed, to avoid killing a running job.

Deactivating a node can be done from the Azure Pipelines UI in Organization Settings / Agent Pools, for people with the appropriate access rights. That page also allows one to see how many pending jobs there are and, for each machine, which build they are currently running.

On the Agents tab of a pool's page, there is an Enabled column with a toggle. Clicking that toggle deactivates a node: it stays in the pool and is still running, but will not get assigned new jobs. This is a good step towards killing the node while still allowing it to finish the job it is currently processing (or making sur it doesn't get assigned another job in-between you looking at the node state and killing it).

As there is no way to tell Azure to run a job on a specific node, deactiating a node is the only way to ensure a job gets assigned to "another one" if that node is suspected of being corrupted in some way.

The Windows Bazel cache tends to get corrupted. Over time, we have tried to address various ways in which it gets corrupted through patches to Bazel (that have since been upstreamed) and a periodic clean-up job (fix_bazel_cache) as part of the CI build.

Though rare, there are still cases where the cache gets corrupted, either locally on a machine or globally in the GCS bucket.

When a machine's cache is suspected corrupt, it is best to just kill the node.

If killing a node doesn't solve the issue (i.e. the cache appears corrupt even on "new" nodes running their first job), it means the network cache is corrupt. Fortunately, the Windows network cache is segmented across builds: there is a separate cache prefix for the PRs build, the main branch build, the compatibility run, etc.

This means that corruption is limited to one build, limiting the impact to some extent, and that we can wipe only the relevant cache, leaving the rest intact.

Wiping the cache amounts to running a command along the lines of:

gsutil -m rm -r gs://daml-bazel-cache/3ecf0dac8af5-v13/

You need delete access on the daml-bazel-cache GCS bucket for this to work.

The name of the cache (in this example 3ecf0dac8af5-v13) can be derived from the version number in ci/configure-bazel.sh when setting the suffix; the hash is printed by the Configure Bazel step, and needs to be taken from the output of an affected failed build.

Note that, due to the sheer number of files, wiping the cache for the PRs build can take upwards of 15 hours, during which the machine on which the command is run must be turned on and connected to the internet.