Tag: Dell technologies

  • Using Ansible to run Terraform plans

    Yes, your eyes are not deceiving you 🙂 Usually, when the words Ansible and Terraform appear in the same sentence is for the opposite scenario. Many articles out there provide examples of using Terraform to deploy infrastructure followed by running Ansible to configure it using the local-exec provisioner. This is a good thing because, if you need some post-provisioning configuration, it is better to rely on another declarative and idempotent tool like Ansible, rather than invoking shell scripts.

    But in this post we are not going to do any of that. What we are going to do is the other way around. We are going to have an Ansible playbook that runs your Terraform plan.

    This article is a follow up from a series of posts to introduce infrastructure admins and engineers to Terraform and to explain how to use it to manage on-prem physical infrastructure in a datacenter. You can find access the full series here.

    Reasons to do it

    But why? Well, if you have an physical infrastructure background and are running a traditional datacenter more than likely you have been playing with Ansible. Chances are you started with Ansible running in the command line and hard-coding your infrastructure’s credentials inside the playbook. Then you evolved into using an external credentials file and encrypting it using “ansible-vault”. Eventually you might have deployed Ansible AWX or even RedHat AAP. At this point, you are enjoying RBAC (who can run what playbook and on what infrastructure) and amazing (there is no other way to put it) credential management.

    Then, you talk to your developers over coffee and they tell you about Terraform. But when you get familiar with it, you discover that unless you pay for a SaaS offering you are stuck in the “command-line with hard-coded credentials” scenario. Wouldn’t it be great if you could use AWX to run Terraform? You could enjoy RBAC and credential management and all the other goodness that it provides. This is possible thanks to Ansible’s Terraform module.

    First steps

    Let’s say we have a very basic Terraform project to create a storage group in Dell PowerMax. The “main.tf” shown below starts the PowerMax provider and then creates a storage group “terraform_sg”. I don’t think it is strictly necessary, but if you are interested in learning more about provisioning storage in Dell PowerMax you can check out this previous post. For simplicity we haven’t created or referenced any variables. All the information is contained into a single file, the “main.tf” shown below.

    terraform {
      required_providers {
        powermax = {
          source = "dell/powermax"
        }
      }
    }
    
    provider "powermax" {
      username      = "smc"
      password      = "TFp2ssw0rd"
      endpoint      = "https://10.1.2.3:8443"
      serial_number = "000123456789"
      pmax_version  = 100
      insecure      = true
    }
    
    resource "powermax_storagegroup" "tf_sg" {
      name          = "terraform_sg"
      srp_id         = "SRP_1"
    }

    In the same machine we have both Terraform and Ansible installed. In order to run the above Terraform plan with Ansible, we need at a minimum a playbook that looks like follows. It needs to include two mandatory parameters: “project_path” and “state”. The first parameter specifies the directory that contains the plan, ie the “main.tf” and typically any other files like “variables.tf”. My Ansible playbook is called “runtf.yml”.

    ---
    - hosts: localhost
      gather_facts: no
    
      vars:
        project_dir: "/root/powermax" # folder that contains main.tf, ...
    
      tasks:
        - name: Run terraform
          community.general.terraform:
            project_path: '{{ project_dir }}'
            state: present
          register: output
    
        - debug:
            var: output

    As you can see I have registered the output of the terraform task and then created a second task to print it out on the terminal so that we can get more details. All is left now is to run it using the “ansible-playbook” command

    root@alb-terraform:~# ansible-playbook runtf.yml
    
    PLAY [localhost] ***************************************************************************************
    
    TASK [Run terraform] ***************************************************************************************
    changed: [localhost]
    
    TASK [debug] ***************************************************************************************
    ok: [localhost] => {
        "output": {
            "changed": true,
            "command": "/usr/bin/terraform apply -no-color -input=false -auto-approve -lock=true /tmp/tmpjaojemdl.tfplan",
            "failed": false,
            "outputs": {},
            "state": "present",
            "stderr": "",
            "stderr_lines": [],
            "stdout": "powermax_storagegroup.tf_sg: Creating...\npowermax_storagegroup.tf_sg: Creation complete after 0s [id=terraform_sg]\n\nApply complete! Resources: 1 added, 0 changed, 0 destroyed.\n",
            "stdout_lines": [
                "powermax_storagegroup.tf_sg: Creating...",
                "powermax_storagegroup.tf_sg: Creation complete after 0s [id=terraform_sg]",
                "",
                "Apply complete! Resources: 1 added, 0 changed, 0 destroyed."
            ],
            "workspace": "default"
        }
    }
    
    PLAY RECAP ***************************************************************************************
    localhost  : ok=2    changed=1    unreachable=0    failed=0    skipped=0    ignored=0

    Ansible run Terraform, and Terraform managed to make the changes so as expected, Ansible reports the task as “changed”. In the “debug” task, notice the “command” parameter. This is how Ansible is invoking Terraform. This invocation includes the “-auto-approve” flag so that Terraform doesn’t look for confirmation. The “stdout” and stdout_lines” parameters show the output that has been captured from Terraform about the creation of the different resources .

    A very important feature of both Ansible and Terraform is idempotency, ie the ability to run the same code repeatedly without making any damage. Let’s run it again and observe what happens.

    root@alb-terraform:~# ansible-playbook runtf.yml
    
    PLAY [localhost] ***************************************************************************************
    
    TASK [Run terraform] ***************************************************************************************
    ok: [localhost]
    
    TASK [debug] ***************************************************************************************
    ok: [localhost] => {
        "output": {
            "changed": false,
            "command": "/usr/bin/terraform apply -no-color -input=false -auto-approve -lock=true /tmp/tmp718kkwbo.tfplan",
            "failed": false,
            "outputs": {},
            "state": "present",
            "stderr": "",
            "stderr_lines": [],
            "stdout": "powermax_storagegroup.tf_sg: Refreshing state... [id=terraform_sg]\n\nNo changes. Your infrastructure matches the configuration.\n\nTerraform has compared your real infrastructure against your configuration\nand found no differences, so no changes are needed.\n",
            "stdout_lines": [
                "powermax_storagegroup.tf_sg: Refreshing state... [id=terraform_sg]",
                "",
                "No changes. Your infrastructure matches the configuration.",
                "",
                "Terraform has compared your real infrastructure against your configuration",
                "and found no differences, so no changes are needed."
            ],
            "workspace": "default"
        }
    }
    
    PLAY RECAP ***************************************************************************************
    localhost : ok=2    changed=0    unreachable=0    failed=0    skipped=0     ignored=0
    

    As expected, Ansible is reporting “ok”, which means the current state matched the desired state and therefore no changes were made. It has also captured the relevant from Terraform stating the same thing.

    Using variables

    This is a good start but ideally we would like to have the ability to make the plan more flexible by using variables for information such as the storage group name in this case. We can modify the “main.tf” as follows. Notice how the “default” value of the variable is commented out. It doesn’t matter if it is commented or not because when we invoke Terraform with external variables we overwrite the default value defined in the variable blocks.

    terraform {
      required_providers {
        powermax = {
          source = "dell/powermax"
        }
      }
    }
    
    provider "powermax" {
      username      = "smc"
      password      = var.password
      endpoint      = "https://10.1.2.3:8443"
      serial_number = "000123456789"
      pmax_version  = 100
      insecure      = true
    }
    
    variable "sg_name" {
     description = "Name of volume to create"
     type        = string
     #default     = "terraform_sg"
    }
    
    variable "password" {
      type        = string
      description = "Stores the password of Unisphere."
      #default = ""
    }
    
    resource "powermax_storagegroup" "tf_sg" {
      name          = var.sg_name
      srp_id         = "SRP_1"
    }

    Additionally, notice how in the “provider” block we have also referenced another variable for the “password”. For security reasons we might want to have, sensitive information like that, stored in a separate file and protect it. In this case we haven’t even declared a default value because we can leverage ansible-vault or AWX to encrypt it and to pass it to Terraform at run time.

    Now, the corresponding Ansible playbook needs to use the “variables” parameter. This parameter essentially converts each of the variables into a “-var” flag when invoking Terraform.

    ---
    - hosts: localhost
      gather_facts: no
    
      vars:
        project_dir: "/root/powerstore" # folder that contains main.tf, ...
    
      tasks:
        - name: Run terraform
          community.general.terraform:
            project_path: '{{ project_dir }}'
            state: present
            variables:
              sg_name: "{{ vol_name }}"
              password: "{{ password }}"
          register: output
    
        - debug:
            var: output

    Now, at runtime we are going to select a different storage group name. The storage group is created as expected and the relevant Terraform output is captured.

    root:~# ansible-playbook runtf.yml --extra-vars "sg_name=tf-sg2 password=TFp2ssw0rd"
    
    PLAY [localhost] ***************************************************************************************
    
    TASK [Run terraform] ***************************************************************************************
    changed: [localhost]
    
    TASK [debug] ***************************************************************************************
    ok: [localhost] => {
        "output": {
            "changed": true,
            "command": "/usr/bin/terraform apply -no-color -input=false -auto-approve -lock=true /tmp/tmpimdwzdu1.tfplan",
            "failed": false,
            "outputs": {},
            "state": "present",
            "stderr": "",
            "stderr_lines": [],
            "stdout": "powermax_storagegroup.tf_sg: Creating...\npowermax_storagegroup.tf_sg: Creation complete after 5s [id=vol1]\n\nApply complete! Resources: 1 added, 0 changed, 0 destroyed.\n",
            "stdout_lines": [
                "powermax_storagegroup.tf_sg: Creating...",
                "powermax_storagegroup.tf_sg: Creation complete after 5s [id=vol1]",
                "",
                "Apply complete! Resources: 1 added, 0 changed, 0 destroyed."
            ],
            "workspace": "default"
        }
    }
    
    PLAY RECAP ***************************************************************************************
    localhost  : ok=2    changed=1    unreachable=0    failed=0    skipped=0    ignored=0

    Terraform plan

    We went straight to “terraform apply” but most Terraform workflows also include running the plan command first. This is done with Ansible by running the playbook in “check mode”. Check mode invokes “terraform plan” for us.

    root:~# ansible-playbook runtf.yml --check --extra-vars "sg_name=tf-sg3 password=TFpass"
    
    PLAY [localhost] ***************************************************************************************
    
    TASK [Run terraform] ***************************************************************************************
    ok: [localhost]
    
    TASK [debug] ***************************************************************************************
    ok: [localhost] => {
        "output": {
            "changed": false,
            "command": "/usr/bin/terraform apply -no-color -input=false -auto-approve -lock=true /tmp/tmp6f2o83k5.tfplan",
            "failed": false,
            "outputs": {},
            "state": "present",
            "stderr": "",
            "stderr_lines": [],
            "stdout": "powermax_storagegroup.tf_sg: Refreshing state... [id=tf-sg2]\n\nTerraform used the selected providers to generate the following execution\nplan. Resource actions are indicated with the following symbols:\n  ~ update in-place\n\nTerraform will perform the following actions:\n\n  # powermax_storagegroup.tf_sg will be updated in-place\n  ~ resource \"powermax_storagegroup\" \"tf_sg\" {\n      ~ cap_gb                   = 0 -> (known after apply)\n      + child_storage_group      = (known after apply)\n      ~ compression              = true -> (known after apply)\n      + compression_ratio        = (known after apply)\n      ~ compression_ratio_to_one = 0 -> (known after apply)\n      + device_emulation         = (known after apply)\n      ~ id                       = \"tf-sg2\" -> (known after apply)\n      + maskingview              = (known after apply)\n      ~ name                     = \"tf-sg2\" -> \"tf-sg3\"\n      ~ num_of_child_sgs         = 0 -> (known after apply)\n      ~ num_of_masking_views     = 0 -> (known after apply)\n      ~ num_of_parent_sgs        = 0 -> (known after apply)\n      + num_of_snapshot_policies = (known after apply)\n      ~ num_of_snapshots         = 0 -> (known after apply)\n      ~ num_of_vols              = 0 -> (known after apply)\n      + parent_storage_group     = (known after apply)\n      + service_level            = (known after apply)\n      ~ slo                      = \"NONE\" -> (known after apply)\n      ~ slo_compliance           = \"NONE\" -> (known after apply)\n      + snapshot_policies        = (known after apply)\n      + tags                     = (known after apply)\n      ~ type                     = \"Standalone\" -> (known after apply)\n      ~ unprotected              = true -> (known after apply)\n      ~ unreducible_data_gb      = 0 -> (known after apply)\n      + uuid                     = (known after apply)\n      ~ volume_ids               = [] -> (known after apply)\n      + vp_saved_percent         = (known after apply)\n      + workload                 = (known after apply)\n        # (2 unchanged attributes hidden)\n    }\n\nPlan: 0 to add, 1 to change, 0 to destroy.\n\n─────────────────────────────────────────────────────────────────────────────\n\nSaved the plan to: /tmp/tmp6f2o83k5.tfplan\n\nTo perform exactly these actions, run the following command to apply:\n    terraform apply \"/tmp/tmp6f2o83k5.tfplan\"\n",
            "stdout_lines": [
                "powermax_storagegroup.tf_sg: Refreshing state... [id=tf-sg2]",
                "",
                "Terraform used the selected providers to generate the following execution",
                "plan. Resource actions are indicated with the following symbols:",
                "  ~ update in-place",
                "",
                "Terraform will perform the following actions:",
                "",
                "  # powermax_storagegroup.tf_sg will be updated in-place",
                "  ~ resource \"powermax_storagegroup\" \"tf_sg\" {",
                "      ~ cap_gb                   = 0 -> (known after apply)",
                "      + child_storage_group      = (known after apply)",
                "      ~ compression              = true -> (known after apply)",
                "      + compression_ratio        = (known after apply)",
                "      ~ compression_ratio_to_one = 0 -> (known after apply)",
                "      + device_emulation         = (known after apply)",
                "      ~ id                       = \"tf-sg2\" -> (known after apply)",
                "      + maskingview              = (known after apply)",
                "      ~ name                     = \"tf-sg2\" -> \"tf-sg3\"",
                "      ~ num_of_child_sgs         = 0 -> (known after apply)",
                "      ~ num_of_masking_views     = 0 -> (known after apply)",
                "      ~ num_of_parent_sgs        = 0 -> (known after apply)",
                "      + num_of_snapshot_policies = (known after apply)",
                "      ~ num_of_snapshots         = 0 -> (known after apply)",
                "      ~ num_of_vols              = 0 -> (known after apply)",
                "      + parent_storage_group     = (known after apply)",
                "      + service_level            = (known after apply)",
                "      ~ slo                      = \"NONE\" -> (known after apply)",
                "      ~ slo_compliance           = \"NONE\" -> (known after apply)",
                "      + snapshot_policies        = (known after apply)",
                "      + tags                     = (known after apply)",
                "      ~ type                     = \"Standalone\" -> (known after apply)",
                "      ~ unprotected              = true -> (known after apply)",
                "      ~ unreducible_data_gb      = 0 -> (known after apply)",
                "      + uuid                     = (known after apply)",
                "      ~ volume_ids               = [] -> (known after apply)",
                "      + vp_saved_percent         = (known after apply)",
                "      + workload                 = (known after apply)",
                "        # (2 unchanged attributes hidden)",
                "    }",
                "",
                "Plan: 0 to add, 1 to change, 0 to destroy.",
                "",
                "─────────────────────────────────────────────────────────────────────────────",
                "",
                "Saved the plan to: /tmp/tmp6f2o83k5.tfplan",
                "",
                "To perform exactly these actions, run the following command to apply:",
                "    terraform apply \"/tmp/tmp6f2o83k5.tfplan\""
            ],
            "workspace": "default"
        }
    }
    
    PLAY RECAP ***************************************************************************************
    localhost   : ok=2    changed=0    unreachable=0    failed=0    skipped=0    ignored=0

    No changes are made and the Ansible module has captured the details of the changes that will be made if the plan is applied.

    Terraform destroy

    Finally we need to learn how to use “terraform destroy”. You might have guessed it by now. The native way of destroying something in Ansible is by setting the state to “absent”.

    ---
    - hosts: localhost
      gather_facts: no
    
      vars:
        project_dir: "/root/powermax" # dir that contains main.tf, ...
    
      tasks:
        - name: Run terraform
          community.general.terraform:
            project_path: '{{ project_dir }}'
            state: absent
            variables:
              sg_name: "{{ sg_name }}"
              password: "{{ password }}"
          register: output
    
        - debug:
            var: output

    Let’s run it and see what it does.

    root:~# ansible-playbook runtf.yml --extra-vars "sg_name=tf-sg2 password=TFp2ssw0rd"
    
    PLAY [localhost] ***************************************************************************************
    
    TASK [Run terraform] ***************************************************************************************
    changed: [localhost]
    
    TASK [debug] ***************************************************************************************
    ok: [localhost] => {
        "output": {
            "changed": true,
            "command": "/usr/bin/terraform destroy -no-color -auto-approve -lock=true -var sg_name=tf-sg2 -var password=smc",
            "failed": false,
            "outputs": {},
            "state": "absent",
            "stderr": "",
            "stderr_lines": [],
            "stdout": "powermax_storagegroup.tf_sg: Refreshing state... [id=tf-sg2]\n\nTerraform used the selected providers to generate the following execution\nplan. Resource actions are indicated with the following symbols:\n  - destroy\n\nTerraform will perform the following actions:\n\n  # powermax_storagegroup.tf_sg will be destroyed\n  - resource \"powermax_storagegroup\" \"tf_sg\" {\n      - cap_gb                   = 0 -> null\n      - compression              = true -> null\n      - compression_ratio_to_one = 0 -> null\n      - host_io_limit            = {} -> null\n      - id                       = \"tf-sg2\" -> null\n      - name                     = \"tf-sg2\" -> null\n      - num_of_child_sgs         = 0 -> null\n      - num_of_masking_views     = 0 -> null\n      - num_of_parent_sgs        = 0 -> null\n      - num_of_snapshots         = 0 -> null\n      - num_of_vols              = 0 -> null\n      - slo                      = \"NONE\" -> null\n      - slo_compliance           = \"NONE\" -> null\n      - srp_id                   = \"SRP_1\" -> null\n      - type                     = \"Standalone\" -> null\n      - unprotected              = true -> null\n      - unreducible_data_gb      = 0 -> null\n      - volume_ids               = [] -> null\n    }\n\nPlan: 0 to add, 0 to change, 1 to destroy.\npowermax_storagegroup.tf_sg: Destroying... [id=tf-sg2]\npowermax_storagegroup.tf_sg: Destruction complete after 0s\n\nDestroy complete! Resources: 1 destroyed.\n",
            "stdout_lines": [
                "powermax_storagegroup.tf_sg: Refreshing state... [id=tf-sg2]",
                "",
                "Terraform used the selected providers to generate the following execution",
                "plan. Resource actions are indicated with the following symbols:",
                "  - destroy",
                "",
                "Terraform will perform the following actions:",
                "",
                "  # powermax_storagegroup.tf_sg will be destroyed",
                "  - resource \"powermax_storagegroup\" \"tf_sg\" {",
                "      - cap_gb                   = 0 -> null",
                "      - compression              = true -> null",
                "      - compression_ratio_to_one = 0 -> null",
                "      - host_io_limit            = {} -> null",
                "      - id                       = \"tf-sg2\" -> null",
                "      - name                     = \"tf-sg2\" -> null",
                "      - num_of_child_sgs         = 0 -> null",
                "      - num_of_masking_views     = 0 -> null",
                "      - num_of_parent_sgs        = 0 -> null",
                "      - num_of_snapshots         = 0 -> null",
                "      - num_of_vols              = 0 -> null",
                "      - slo                      = \"NONE\" -> null",
                "      - slo_compliance           = \"NONE\" -> null",
                "      - srp_id                   = \"SRP_1\" -> null",
                "      - type                     = \"Standalone\" -> null",
                "      - unprotected              = true -> null",
                "      - unreducible_data_gb      = 0 -> null",
                "      - volume_ids               = [] -> null",
                "    }",
                "",
                "Plan: 0 to add, 0 to change, 1 to destroy.",
                "powermax_storagegroup.tf_sg: Destroying... [id=tf-sg2]",
                "powermax_storagegroup.tf_sg: Destruction complete after 0s",
                "",
                "Destroy complete! Resources: 1 destroyed."
            ],
            "workspace": "default"
        }
    }
    
    PLAY RECAP ***************************************************************************************
    localhost  : ok=2    changed=1    unreachable=0    failed=0    skipped=0     ignored=0

    The storage group resource has been destroyed and that shows in Ansible as a task with a status of “changed”. The “stdout” and “stdout_lines” parameters provide a record of what was destroyed.

    In summary, in this article we have seen how to use the terraform Ansible module to plan, apply and destroy Terraform projects. In doing that, organizations can leverage a tool like AWX or RedHat AAP to provide RBAC, strong credential management and a powerful upstream REST API and integrate with other tools like ITSM etc. AWX and AAP can also became a polyglot automation platform capable of running the two most popular automation tools out there to satisfy both traditional infrastructure teams and developers.

  • Multi-cloud agility with governance in ServiceNow

    I have been talking a lot lately about using ServiceNow as a tool to deliver unified end-user experience and governance for multi-cloud architectures. Most organizations today have multiple clouds public and private. This hopefully helps provide the best suitable landing zone for every workload.

    However, the mainstream approach to consuming public cloud is to give developers a credit card and say “here you go”, which is not a good governance best practice. So it doesn’t come as a surprise when an outrageous bills come at the end of the month. The other big issue with this approach is that every cloud becomes a silo. On the positive side, developers get more productive and deliver value to the business faster.

    In videos like this one you can see how it is possible to deliver a unified multi-cloud experience across multiple public and private clouds. The example uses ServiceNow but it will work for other tools like it. The principle is to offer the same type of item with comparable T-shirt sizes from a single catalog in ServiceNow. You will notice in the video how the user can see what the same VM costs on each cloud. When you pair this with chargeback, it helps developers to make the right decision as to where to run a workload. You can also throw into the mix approvals based on the characteristics of the item (ex: is the storage capacity larger than a certain amount? does the VM require too much RAM or a GPU? does the VM cost more than $100 a month?) as well as CMDB integration for resources in all clouds, etc.

    The governance benefits are enormous but developers are not equally impressed. They like to use tools like Terraform or Ansible to provision their environments at the speed of light (almost) and they will argue that going to a GUI like a ServiceNow catalog makes them slower. And of course, when developers use “Infrastructure as Code” directly on the public cloud they bypass all the governance.

    By now, probably you can guess where I am heading: is there a way of reconciling both views of the world? Ultimately the key to delivering agility via automation is having an API … and ServiceNow has a great REST API. In particular, you can order items from a service catalog using the REST API, which means that developers can order catalog items using a tool like Terraform or Ansible but still be subjected to the guardrails that the governance policies have put in place

    With this approach:

    • the organization creates a unified governance and end-user experience for all public and private cloud resources
    • developers can use Infrastructure as Code tools and processes that make them agile
    • the same REST API provides programmatic access to resources in all clouds
    • approvals and other control processes remain in place and it is easier to keep costs under control

    If you are interested in the details of how to do this, you can visit my previous post about how to create requests from a ServiceNow catalog using Ansible and Postman (what about Terraform?)

    Let’s see an example of how it works in practice. This is our infrastructure catalog. It allows the user to consume infrastructure from private and public cloud. We are going to use a STaaS (Storage-as-a-Service) as an example because the approvals are very intuitive. This catalog item allows you to provision file storage to an existing virtual machine. This item includes a capacity-based approval. The idea is that any request for more than a certain threshold is going to require an approval. You could have additional thresholds that require 2 or potentially VP approval. You can see how this constraint is going to encourage reasonable consumption by the end users. No one likes to wait for anything these days. In this demo environment the threshold is set very low, to just 50GB.

    The objective is to use code to order storage for a VM and demonstrate that:

    • if the capacity requested is lower than the threshold the request goes straight through
    • if the capacity requested is above the threshold the approval process kicks in

    We are going to use Postman to do this demonstration. You can find the Postman collection I am using in this GitHub repo. The process has essentially 2 steps: add an item to the cart and finally submit the cart. Here you can see we have sent a REST API call to add the File STaaS to the cart

    URL needs the sys_id of the catalog (get it from URL) or programmatically (with this API call). The payload needs to include quantity and all the variables required. You can find out the variables required using this API call. The status code will be 200. At this stage you can see there is 1 item in the cart

    The second step then is to submit the cart. We can do so with this REST API call. Note how it is also a POST call but it doesn’t require a Body parameter. It feels intuitive that you might have to at least specify the cart ID, but this is not necessary because a user only has one cart.

    You can see request “REQ0011028” was created. If I navigate to “Requests” for the user we can see the status of the request. The approval process is triggered because we requested 60GB which is more than the threshold. This is good because we can use Infrastructure-as-Code tools to provision infrastructure while maintaining governance.

    A key ingredient to make this both agile and useful for the business is to be wise with the thresholds. Your organization should consider what threshold provides the 80/20 or even perhaps the 90/10 rule so that the majority of provisioning activity can happen without delays.

    To finish off let’s repeat the request but this time the capacity required will be 40GB which is under the threshold.

    This time, when we go to “Requests” we can see that the request is already complete. It didn’t need an approval.

    If I log in to the Virtual Machine straight away I can see that the storage has been created, mounted and configured. As you can see I can even create a file on it.

    Conclusion

    Many organizations are implementing private clouds to that deliver a self-service experience to end-users by implementing automation and exposing it to users via an ITSM tool like ServiceNow. This architecture can be extended to consume also public cloud resources in such a way that a consistent end-user experience is delivered regardless of where the resources are created. This also brings an opportunity to break down silos and create a unified governance framework for all clouds along with integrated CMDB and incident management. This is very important especially now that cost control has become paramount. On the other hand developers demand accessing resources via API so that they can iterate faster and deliver more value to the business. In this blog post we have demonstrated how developers can leverage the ServiceNow REST API to automate the provisioning of resources in any cloud at the speed they need without jeopardizing governance.

  • ServiceNow catalog request with Postman

    In this blog post we will explore how to order items from a ServiceNow catalog using the ServiceNow REST API. There are a few examples out there that use “REST API Explorer” inside ServiceNow. I will focus here on how to do it from an external tool. In particular I am going to provide instructions for Postman. This post is the stepping stone for this follow up post where I try to solve an interesting use case of providing multi-cloud with governance through ServiceNow. You can get the Postman collection I am using from this GitHub repo.

    This is the latest addition to a series of popular ServiceNow related posts:

    There is also a follow up post that uses what I am covering here to allow developers to provision multi-cloud IT resources while observing any governance that might have been implemented in ServiceNow.

    In the post about creating Incidents we covered the “REST API Explorer” tool in more detail. Please refer to that one if you are new to it. In this post we will use the “Service Catalog API“. In this screenshot you can see it is a very extensive API. It provides 47 API calls as of time of this writing.

    Postman

    Now let’s move to Postman. Feel free to use any other REST API developer tool you are used to. If you want to follow along you can retrieve my Postman collection from this GitHub repo. The Postman collection provides 3 folders. In this post we will use the “Catalog Request” folder. The other 2 folders contain the API calls that were used in 2 previous posts in this series mentioned above.

    Requesting items from catalogs can be done in two ways that mimic the behavior of the user in the GUI:

    • order now
    • add to cart and submit the cart

    Add to Cart and Submit order

    This is a 2-step process but it might be more convenient if you are planning to order multiple items in the same request.

    • add an item to the cart
    • submit the cart

    Adding an item to the cart is done with the following API call. Please replace {instance} with your particular ServiceNow instance.

    There are 3 things you need to include:

    • sys_id“. This is the “sys_id” of the item itself. As you can see it needs to be included in the URL
    • sysparm_quantity“. This is the quantity of this item you want to add to the cart. It is included in the “Body” parameter and it is required
    • variables“. This is also in the “Body” and needs to include all the information that you would normally provide through the GUI when creating the request. In my case this catalog entry allows me to request the installation of an application on a virtual machine. At a minimum you will need to include all variables that are defined as “mandatory” in your catalog. In my example below I am going to request the installation of an application on an existing virtual machine. You can see I am requesting the installation of Apache web server on a VM called “albtest3_01” which lives in my “sin” datacenter

    If all goes well, you should get a status code of 200 and the details of the cart will be provided in the response.

    You can find out the “sys_id” of the catalog item by browsing to the item itself in your catalog. It will be included in the URL itself as seen here

    Alternatively you can use other calls in the “Service Catalog API” to get it programmatically. One example could be executing this call and looking for the “name” of the item you want:

    You can also find out programmatically what variables you need to include with the following API call. It returns information about every parameter the specific item defined by “sys_id”. Use the “name” fields as the keys in your “Body” parameter. Also pay attention to whether variables show as “mandatory: true“.

    Notice how the response is providing even the valid “choices” for a variable that has been configured as a “select box”.

    At this point you have an item in the cart. You could potentially add other items to the cart but if you need things to be ordered in a certain sequence order 1 item at a time and orchestrate them all in your Infrastructure as Code tool of choice. The final step is to “submit the order”. This is done with the following API call

    This API call is also part of the Postman collection you downloaded. As you can see in the screenshot below, even though this is a POST call, it doesn’t require a “Body” payload. If successful you should get a status code of 200 and the “Body” of the response will contain the “requests number”.

    Order now

    This is the most straight forward process as it consists of a single REST API call. It uses the “Buy Item (POST)” call. This is the call as shown in REST API Explorer.

    This call needs the same payload that we used to add an item to the cart, ie the quantity and the specific variables that are typically provided in the form in the GUI.

    As you can see, this API call returned a request number instead of cart details because the request was created in a single step. In the following screenshot we can see the request in the “Requests” application in the GUI.

    And in the details of the “Request Item” we can see that the information we specified in the body of the API call was used to create the request.

    Next steps

    Postman allows you to generate code in your language of choice, ex: Python, Golang, etc. You can use this to include what we have covered into a larger script that runs additional tasks.

    Alternatively, you might want to use configuration management or provisioning tools like Ansible or Terraform to order items from the ServiceNow catalog.

  • Manage Kubernetes with Ansible

    Kubernetes keeps increasing in popularity and not just in public cloud. It keeps making inroads into the on-premises market. This is creating the need for automation. In many Kubernetes environments you tend to find developers using CI/CD pipelines not just for their applications code for the Kubernetes objects that deploy the code in the cluster (ex: deployment, service …). This means that most of the automation needs are covered. However there are several instances where you might want to use automation tools (ex: Ansible) either to replace or to supplement CI/CD tools. By the way, I am not talking about the deployment of the Kubernetes cluster itself, which is a valid use case. I am talking about the things that you would normally do with the “kubectl” tool

    While creating a new video for the IaC Avengers channel in Youtube I came across one such use case and this prompt me to investigate how to manage Kubernetes with Ansible. This article contains my lessons learned.

    My use case is as follows. I wanted to expose the creation of namespaces in any cloud to end-users from ServiceNow. The idea is that rather than giving developers and other personas the right to create their own namespaces an organization would like to keep a central control plane where they can implement the much needed governance and cost transparency. This use case is very important in RedHat OpenShift environments because the general guidance is to share a few clusters as opposed to creating a cluster per tenant as other vendors recommend. Namespaces is the native mechanism to keep tenants separate with this approach

    This “Multi-Cloud Kubernetes as a Service” is the latest in a growing set of demos that we have been creating for a while.

    In this article we are going to cover:

    1. Architecture
    2. Installation in command line Ansible
    3. Installation in AWX/Tower
    4. A practical example

    Architecture

    We will use a single Ansible module for this solution: “kubernetes.core.k8s”, which might surprise many of you. At first when I was thinking about this solution I thought there would be multiple modules to manage all the different objects in the Kubernetes API: pods, deployments, secrets … but no, there is a single one. To put this into perspective let’s bear in mind that there are more than 150 different modules to manage all aspects of vSphere environments.

    So why is there a single module for Kubernetes? At the end of the day Kubernetes and Ansible have much in common. Both frameworks use a declarative syntax where you express your desired state and then the system does whatever is necessary to implement your specified end state. Furthermore, they both use YAML files. So rather than creating multiple modules, you embed your each individual Kubernetes task manifest inside its own Ansible task. You need to watch out for the right indentations but that in essence how it works. We will see some examples in a later section

    Another clever shortcut the creators of the module took is that the module doesn’t include its own Kubernetes client. Instead what the Ansible engine will do is to SSH into a machine that has “kubectl” and the “kubeconfig” installed. You could install “kubectl” in your Ansible system if you wanted (and use “localhost” as the target) but you don’t have to. In my case I have created a separate VM with “kubectl” and all the “kubeconfig” files for all clusters I am managing and the Ansible playbook is targeting that VM which is defined in the inventory. In OpenShift environments your Kubernetes client machine will need to run also the “oc” tool

    In our video we assumed there will be multiple clusters available for different combinations of:

    • Cloud (vSphere based private cloud, AWS, Azure and GCP)
    • Production or development (You might want to have more like UAT …)
    • Different Kubernetes versions (v1.22, v1.23, v1.24)

    The actual selections made by the user determine the target cluster in which to create the “namespace” (a.k.a “project” in RedHat parlance). The playbook takes the 3 parameters selected by the user and builds the name of the “kubeconfig” file to use. The Ansible module allows you to specify a “kubeconfig” file. From that point any tasks are run in the relevant cluster

    The Ansible playbook allows you to specify also a “context”. At the beginning I started using a single “kubeconfig” with multiple contexts but as I kept adding clusters it was getting hard to manage. I think the “kubeconfig” method is easier. Every time you create a new cluster, grab the file, rename it to match the type/location of the cluster (ex: “aws-prod-22.config”) and place it in the directory where the client machine expects to find them and you are done

    Installation in command line Ansible

    The installation requires you to install things in both the Ansible and Kubernetes client system. With other modules you typically install some Python libraries as a prerequisite and then install the Ansible collection. A very important difference with the Kubernetes collection is the libraries are required in the Kubernetes client system, not in the Ansible system. Of course if you have decided to run the Kubernetes client in your Ansible system you will install everything in the same machine.

    Before you start please make sure you are running Python 3.6 or higher in the client. In my case I started installing this in a system with CentOS7 which comes with Python 2.7 by default and I was getting errors until I did

    ln -s /usr/bin/python3 /usr/bin/python

    In terms of libraries you need the following in the Kubernetes client machine:

    • kubernetes >= 12.0.0
    • PyYAML >= 3.11
    • jsonpatch

    In my case I just did “pip install kubernetes” and it installed everything else. OpenShift environments are better managed with the “oc” tool. For that reason you also need an additional library called “openshift”.

    The ‘kubernetes’ library expects the kubeconfig file to be present in .kube/config. However, as we discussed earlier you can specify a different location and kubeconfig file name as part of the task inside the playbook

    Now in the the Ansible machine you need to install the Ansible collection

    ansible-galaxy collection install kubernetes.core

    Finally, you will need to add your Kubernetes client to the inventory in the Ansible machine, This is mine:

    [root@ansible-vm ~] # cat inv.ini
    [kubectl01]
    172.24.167.53
    

    You can test that everything works by running a simple playbook

    [root@ansible-vm ~] # cat create-ns.yaml
    - name: Create namespaces in kubernetes cluster
      hosts: kubectl01
    
      tasks:
      - name: Create namespace in default Kubernetes cluster
        kubernetes.core.k8s:
          name: "ansible-ns"
          api_version: v1
          kind: Namespace
          state: present
    
    [root@ansible-vm ~] # ansible-playbook create-ns.yaml

    The above syntax assumes that the kubeconfig is in the default location, i.e. ~/.kube/config in the home directory of the user running the playbook as in the kubernetes client system. Keep reading to see how to store the config in a different location

    Installation in AWX/Tower

    If we need to run the playbook in AWX or Ansible Tower, nothing of we discussed previously for the Kubernetes clients changes. So you still need the following in the client:

    • the Python libraries
    • a supported version of Python in the client
    • the “kubectl” tool (and “oc” if you are managing OpenShift clusters

    However, on the Ansible system you need to:

    • create the inventory entry that points to the Kubernetes client system
    • install the “kubernetes.core” collection in the “task” container
    • create a job template as usual

    This is how I installed the “kubernetes.core” collection in my AWX system. Notice how I install it in the “awx_task” container

    [root@awx17 ~]# docker exec -it awx_task /bin/bash
    bash-4.4# ansible-galaxy collection install kubernetes.core
    

    However, when I went to trigger the job template I got this error

    TASK [Create namespace in target Kubernetes cluster] ***************************
    fatal: [172.24.167.53]: FAILED! => {"msg": "Could not find imported module support code for ansiblemodule.  Looked for either AnsibleTurboModule.py or module.py"}
    

    I fixed it by installing the “cloud.common” collection also inside the “task” container:

    [root@awx17 ~]# docker exec -it awx_task /bin/bash
    bash-4.4# ansible-galaxy collection install cloud.common
    Process install dependency map
    Starting collection install process
    Installing 'cloud.common:2.1.2' to '/var/lib/awx/.ansible/collections/ansible_collections/cloud/common'
    

    A practical example

    The example we are going to use will do 2 things:

    • create a namespace
    • assign permissions to the namespace to the user that requested the namespace

    In this Kubernetes as a Service design the assumption is that developers and other personas they cannot create or join namespaces by themselves. This is achieved by creating a new namespace or joining an existing one. Hence the need to assign the relevant permissions in the playbook. A future blog post show the “join namespace” scenario which includes including the creator of the namespace in a ServiceNow workflow approval.

    The first thing the playbook does is to figure out what kubeconfig file needs to be use. It does so by combining 3 pieces of information. In the video you can see how these details are provided by the user that is requesting the namespace in ServiceNow. They allow us to uniquely identify the Kubernetes cluster we have to use to apply the changes

      - name: Build the kubeconfig file name out of input parameters
        set_fact:
          configname: "{{ cloud }}-{{ envtype }}-{{ version }}"
    

    So for example if the user selects “aws”, “production” and “1.22” the playbook will look for a file named “aws-prod-22.config” and run the remaining tasks on the cluster that is defined in that kubeconfig file. Note how we decided to drop the “1.” from the Kubernetes version to make the file names more streamlined. With this approach, onboarding a new cluster couldn’t be easier. Let’s say in the future we want to create a new development cluster in GCP that is running v1.25. All we need to do is grab the kubeconfig file and place it in the same directory as the other files in the client and rename it to “gcp-dev-25.config”. No further changes are required

    Let’s take a look at the playbook

    ---
    - name: Create a namespace in a kubernetes cluster
      hosts: kubectl01
      gather_facts: false
    
      vars:
        #nsname: ansible           # needs to be provided by end-user
        #version: 22               # corresponds to k8s version 1.22, 1.23 ...
        #envtype: dev              # type of environment: prod, dev ...
        #cloud: vsphere            # vpshere, gcp, aws ...
        #snow_username: finance1   # this comes also in the API call
        #backup_type: gold         # user needs to choose between gold/silver policies
    
      tasks:
      - name: Build the kubeconfig file name out of input parameters
        set_fact:
          configname: "{{ cloud }}-{{ envtype }}-{{ version }}"
    	  
      - debug:
          msg: "Let's create namespace {{ nsname }} with kubeconfig {{ configname }}.config"
    
      - name: Create namespace in target Kubernetes cluster
        kubernetes.core.k8s:
          state: present
          kubeconfig: "~/.kube/{{ configname }}.config"
          kind: Namespace
          name: "{{ nsname }}"
          definition:
            metadata:
              labels:
                backuptype: "{{ backup_type }}"
                snowowner: "{{ snow_username }}"
    
      - name: Create role binding for user {{ snow_username }}
        kubernetes.core.k8s:
          state: present
          kubeconfig: "~/.kube/{{ configname }}.config"
          definition:
            kind: RoleBinding
            apiVersion: rbac.authorization.k8s.io/v1
            metadata:
              name: "{{ nsname }}-owner"
              namespace: "{{ nsname }}"
            subjects:
            - kind: User
              name: "{{ snow_username }}"
            roleRef:
              kind: ClusterRole
              name: admin
    

    I have commented out all the variables required as they are being passed as parameters but you can remove the comments when you are testing the playbook

    Pay close attention to the “definition” section in the “role binding” task. If you took everything that follows, insert it into a YAML file and use “kubectl apply” it accomplish the same thing. This is what I was referring to about the beauty of how the creators have designed the Ansible module

    Notice how we are adding 2 labels to the namespace. These will be used for the “join namespace” workflow and for automatically adding the namespace to a backup policy in PPDM (PowerProtect Data Manager). We will cover these two features in future posts

    The “snow_username” is the username of the user that places the request in ServiceNow. In our demo we used KeyCloak to create in seamless authentication infrastructure across ServiceNow and the rest of our infrastructure including OpenShift

    Finally, notice how we are binding the default “admin” role to the user, but restricted to the namespace, which is what you would expect from an owner. However, by the rules of least privilege, if you wanted to you could restrict to whatever you need by defining a specific role. You could potentially create this role at the only once at the cluster level. In that case it wouldn’t need to be part of this playbook. We will use this technique for offering various roles in the “join namespace” workflow. The following code is an example for a “deployment manager” role in a specific namespace

      - name: Create a new role for deployment managers
        kubernetes.core.k8s:
          state: present
          kubeconfig: "~/.kube/{{ configname }}.config"
          definition:
            kind: Role
            apiVersion: rbac.authorization.k8s.io/v1beta1  #rbac.authorization.k8s.io/v1
            metadata:
              namespace: office
              name: deployment-manager
            rules:
            - apiGroups: ["", "extensions", "apps"]
              resources: ["deployments", "replicasets", "pods"]
              verbs: ["*"]

    I hope you found this helpful. Keep an eye on the follow up video and the two follow up blog articles