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Prior to 3.1, the Ansible Tower HA solution was not a true high-availability system. This system has been entirely rewritten in 3.1 with a focus towards a proper highly-available clustered system. This has been extended further in 3.2 to allow grouping of clustered instances into different pools/queues.
* PostgreSQL is still a standalone instance and is not clustered. Replica configuration will not be managed. If the user configures standby replicas, database failover will also not be managed.
* All instances should be reachable from all other instances and they should be able to reach the database. It's also important for the hosts to have a stable address and/or hostname (depending on how you configure the Tower host).
* RabbitMQ is the cornerstone of Tower's Clustering system. A lot of AWX's configuration requirements and behavior are dictated by its needs. For this reason, it is generally inflexible to customize beyond what the setup playbook allows. Each AWX/Tower instance has a deployment of RabbitMQ which will cluster with the other instances' RabbitMQ instances.
* Set up playbook changes to configure RabbitMQ and give hints to the type of network the hosts are on.
* The `inventory` file for Tower deployments should be saved/persisted. If new instances are to be provisioned, the passwords and configuration options as well as host names will need to be available to the installer.
The current standalone instance configuration doesn't change for a 3.1+ deployment. The inventory file does change in some important ways:
* Since there is no primary/secondary configuration, those inventory groups go away and are replaced with a single inventory group `tower`. The customer may *optionally* define other groups and group instances in those groups. These groups should be prefixed with `instance_group_`. Instances are not required to be in the `tower` group alongside other `instance_group_` groups, but one instance *must* be present in the `tower` group. Technically `tower` is a group like any other `instance_group_` group, but it must always be present and if a specific group is not associated with a specific resource, then job execution will always fall back to the `tower` group:
```
[tower]
hostA
hostB
hostC
[instance_group_east]
hostB
hostC
[instance_group_west]
hostC
hostD
```
The `database` group remains in order to specify an external Postgres. If the database host is provisioned separately, this group should be empty.
```
[tower]
hostA
hostB
hostC
[database]
hostDB
```
* It's common for customers to provision Tower instances externally but prefer to reference them by internal addressing. This is most significant for RabbitMQ clustering, where the service isn't available at all on an external interface. Because of this, it is necessary to assign the internal address for RabbitMQ links as such:
```
[tower]
hostA rabbitmq_host=10.1.0.2
hostB rabbitmq_host=10.1.0.3
hostC rabbitmq_host=10.1.0.3
```
* The `redis_password` field is removed from `[all:vars]`.
-`rabbitmq_port=5672` - RabbitMQ is installed on each instance and is not optional, it's also not possible to externalize. It is possible to configure what port it listens on and this setting controls that.
-`rabbitmq_username=tower` and `rabbitmq_password=tower` - Each instance will be configured with these values and each instance's Tower instance will be configured with it also. This is similar to our other uses of usernames/passwords.
-`rabbitmq_cookie=<somevalue>` - This value is unused in a standalone deployment but is critical for clustered deployments. This acts as the secret key that allows RabbitMQ cluster members to identify each other.
-`rabbitmq_use_long_names` - RabbitMQ is pretty sensitive to what each instance is named. We are flexible enough to allow FQDNs (_host01.example.com_), short names (`host01`), or IP addresses (192.168.5.73). Depending on what is used to identify each host in the `inventory` file, this value may need to be changed. For FQDNs and IP addresses, this value needs to be `true`. For short names it should be `false`
The most important field to point out for variability is `rabbitmq_use_long_name`. This cannot be detected and no reasonable default is provided for it, so it's important to point out when it needs to be changed. If instances are provisioned to where they reference other instances internally and not on external addresses, then `rabbitmq_use_long_name` semantics should follow the internal addressing (*i.e.*, `rabbitmq_host`).
In Tower versions 3.2+, customers may optionally define isolated groups inside of security-restricted networking zones from which to run jobs and ad hoc commands. Instances in these groups will _not_ have a full install of Tower, but will have a minimal set of utilities used to run jobs. Isolated groups must be specified in the inventory file prefixed with `isolated_group_`. An example inventory file is shown below:
In the isolated rampart model, "controller" instances interact with "isolated" instances via a series of Ansible playbooks over SSH. At installation time, a randomized RSA key is generated and distributed as an authorized key to all "isolated" instances. The private half of the key is encrypted and stored within Tower, and is used to authenticate from "controller" instances to "isolated" instances when jobs are run.
* The "controller" instance compiles metadata required to run the job and copies it to the "isolated" instance via `rsync` (any related project or inventory updates are run on the controller instance). This metadata includes:
* Once the metadata has been `rsync`ed to the isolated host, the "controller instance" starts a process on the "isolated" instance which consumes the metadata and starts running `ansible`/`ansible-playbook`. As the playbook runs, job artifacts (such as `stdout` and job events) are written to disk on the "isolated" instance.
* While the job runs on the "isolated" instance, the "controller" instance periodically copies job artifacts (`stdout` and job events) from the "isolated" instance using `rsync`. It consumes these until the job finishes running on the "isolated" instance.
Isolated groups are architected such that they may exist inside of a VPC with security rules that _only_ permit the instances in its `controller` group to access them; only ingress SSH traffic from "controller" instances to "isolated" instances is required.
- Do not create a group named `isolated_group_tower`.
- Do not put any isolated instances inside the `tower` group or other ordinary instance groups.
- Define the `controller` variable as either a group var or as a hostvar on all the instances in the isolated group. Please _do not_ allow isolated instances in the same group have a different value for this variable - the behavior in this case can not be predicted.
- Do not put an isolated instance in more than one isolated group.
At installation time, by default, a randomized RSA key is generated and distributed as an authorized key to all "isolated" instances. The private half of the key is encrypted and stored within Tower, and is used to authenticate from "controller" instances to "isolated" instances when jobs are run.
For users who wish to manage SSH authentication from controlling instances to isolated instances via some system _outside_ of Tower (such as externally-managed, password-less SSH keys), this behavior can be disabled by unsetting two Tower API settings values:
* **Provisioning** - Provisioning Instances after installation is supported by updating the `inventory` file and re-running the setup playbook. It's important that this file contain all passwords and information used when installing the cluster, or other instances may be reconfigured (this can be done intentionally).
* **Deprovisioning** - Tower does not automatically de-provision instances since it cannot distinguish between an instance that was taken offline intentionally or due to failure. Instead, the procedure for de-provisioning an instance is to shut it down (or stop the `ansible-tower-service`) and run the Tower de-provision command:
* **Removing/Deprovisioning Instance Groups** - Tower does not automatically de-provision or remove instance groups, even though re-provisioning will often cause these to be unused. They may still show up in API endpoints and stats monitoring. These groups can be removed with the following command:
An `Instance` that is added to an `InstanceGroup` will automatically reconfigure itself to listen on the group's work queue. See the following section `Instance Group Policies` for more details.
Tower `Instances` can be configured to automatically join `Instance Groups` when they come online by defining a policy. These policies are evaluated for
*`policy_instance_percentage`: This is a number between 0 - 100. It guarantees that this percentage of active Tower instances will be added to this `Instance Group`. As new instances come online, if the number of Instances in this group relative to the total number of instances is fewer than the given percentage, then new ones will be added until the percentage condition is satisfied.
*`policy_instance_minimum`: This policy attempts to keep at least this many `Instances` in the `Instance Group`. If the number of available instances is lower than this minimum, then all `Instances` will be placed in this `Instance Group`.
*`Instances` that are assigned directly to `Instance Groups` by posting to `/api/v2/instance_groups/x/instances` or `/api/v2/instances/x/instance_groups` are automatically added to the `policy_instance_list`. This means they are subject to the normal caveats for `policy_instance_list` and must be manually managed.
*`policy_instance_percentage` and `policy_instance_minimum` work together. For example, if you have a `policy_instance_percentage` of 50% and a `policy_instance_minimum` of 2 and you start 6 `Instances`, 3 of them would be assigned to the `Instance Group`. If you reduce the number of `Instances` to 2, then both of them would be assigned to the `Instance Group` to satisfy `policy_instance_minimum`. In this way, you can set a lower bound on the amount of available resources.
* Policies don't actively prevent `Instances` from being associated with multiple `Instance Groups` but this can effectively be achieved by making the percentages sum to 100. If you have 4 `Instance Groups`, assign each a percentage value of 25 and the `Instances` will be distributed among them with no overlap.
If you have a special `Instance` which needs to be _exclusively_ assigned to a specific `Instance Group` but don't want it to automatically join _other_ groups via "percentage" or "minimum" policies:
2. Update the `Instance`'s `managed_by_policy` property to be `False`.
This will prevent the `Instance` from being automatically added to other groups based on percentage and minimum policy; it will **only** belong to the groups you've manually assigned it to:
Tower itself reports as much status as it can via the API at `/api/v2/ping` in order to provide validation of the health of the Cluster. This includes:
Tower is configured in such a way that if any of these services or their components fail, then all services are restarted. If these fail sufficiently (often in a short span of time), then the entire instance will be placed offline in an automated fashion in order to allow remediation without causing unexpected behavior.
Ideally a regular user of Tower should not notice any semantic difference to the way jobs are run and reported. Behind the scenes it is worth pointing out the differences in how the system behaves.
When a job is submitted from the API interface, it gets pushed into the Dispatcher queue on RabbitMQ. A single RabbitMQ instance is the responsible master for individual queues, but each Tower instance will connect to and receive jobs from that queue using a fair-share scheduling algorithm. Any instance on the cluster is just as likely to receive the work and execute the task. If an instance fails while executing jobs, then the work is marked as permanently failed.
If a cluster is divided into separate Instance Groups, then the behavior is similar to the cluster as a whole. If two instances are assigned to a group then either one is just as likely to receive a job as any other in the same group.
As Tower instances are brought online, it effectively expands the work capacity of the Tower system. If those instances are also placed into Instance Groups, then they also expand that group's capacity. If an instance is performing work and it is a member of multiple groups, then capacity will be reduced from all groups for which it is a member. De-provisioning an instance will remove capacity from the cluster wherever that instance was assigned.
If an Instance Group is configured but all instances in that group are offline or unavailable, any jobs that are launched targeting only that group will be stuck in a waiting state until instances become available. Fallback or backup resources should be provisioned to handle any work that might encounter this scenario.
Project updates behave differently than they did before. Previously they were ordinary jobs that ran on a single instance. It's now important that they run successfully on any instance that could potentially run a job. Projects will sync themselves to the correct version on the instance immediately prior to running the job. If the needed revision is already locally checked out and Galaxy or Collections updates are not needed, then a sync may not be performed.
When the sync happens, it is recorded in the database as a project update with a `launch_type` of "sync" and a `job_type` of "run". Project syncs will not change the status or version of the project; instead, they will update the source tree _only_ on the instance where they run. The only exception to this behavior is when the project is in the "never updated" state (meaning that no project updates of any type have been run), in which case a sync should fill in the project's initial revision and status, and subsequent syncs should not make such changes.
If the Job Template, Inventory, or Organization have instance groups associated with them, a job run from that Job Template will not be eligible for the default behavior. This means that if all of the instance associated with these three resources are out of capacity, the job will remain in the `pending` state until capacity frees up.
To expand further: If instance groups are associated with the Job Template and all of them are at capacity, then the job will be submitted to instance groups specified on Inventory, and then Organization.
The global `tower` group can still be associated with a resource, just like any of the custom instance groups defined in the playbook. This can be used to specify a preferred instance group on the job template or inventory, but still allow the job to be submitted to any instance if those are out of capacity.
* Instances should, optionally, be able to be grouped arbitrarily into different Instance Groups
* Capacity should be tracked at the group level and capacity impact should make sense relative to what instance a job is running on and what groups that instance is a member of
* Jobs should be able to run on hosts for which they are targeted; if assigned implicitly or directly to groups, then they should only run on instances in those Instance Groups
* We should test behavior of large and small clusters; small clusters usually consist of 2 - 3 instances and large clusters have 10 - 15 instances.
* Failure testing should involve killing single instances and killing multiple instances while the cluster is performing work. Job failures during the time period should be predictable and not catastrophic.
* Instance downtime testing should also include recoverability testing (killing single services and ensuring the system can return itself to a working state).
* Persistent failure should be tested by killing single services in such a way that the cluster instance cannot be recovered and ensuring that the instance is properly taken offline.
* Network partitioning failures will also be important. In order to test this:
- Break the link between instances such that it forms two or more groups where Group A and Group B can't communicate but all instances can communicate with the database.
* Crucially, when network partitioning is resolved, all instances should recover into a consistent state.
* Upgrade Testing - verify behavior before and after are the same for the end user.
* Project Updates should be thoroughly tested for all SCM types (`git`, `svn`, `hg`) and for manual projects.
Organizations, Inventories, and Job Templates should be variously assigned to one or many groups and jobs should execute in those groups in preferential order as resources are available.
These should also be benchmarked against the same playbooks using the 3.0.X Tower release and a stable Ansible version. For a large volume playbook (*e.g.*, against 100+ hosts), something like the following is recommended:
