"Tracking Methane Sources and Movement Around the Globe" by "NASA/Scientific Visualization Studio" on Nasa.gov

Flexibly loading files in Terraform to license a FortiGate firewall on AWS, Azure and other Cloud platforms

One of the things I’m currently playing with is a project to deploy some FortiGate Firewalls into cloud platforms. I have a couple of Evaluation Licenses I can use (as we’re a partner), but when it comes to automatically scaling, you need to use the PAYG license.

To try to keep my terraform files as reusable as possible, I came up with this work around. It’s likely to be useful in other places too. Enjoy!

This next block is stored in license.tf and basically says “by default, you have no license.”

variable "license_file" {
  default = ""
  description = "Path to the license file to load, or leave blank to use a PAYG license."
}

We can either override this with a command line switch terraform apply -var 'license_file=mylicense.lic', or (more likely) the above override file named license_override.tf (ignored in Git) which has this next block in it:

variable "license_file" {
  default = "mylicense.lic"
}

This next block is also stored in license.tf and says “If var.license is not empty, load that license file [var.license != "" ? var.license] but if it is empty, check whether /dev/null exists (*nix platforms) [fileexists("/dev/null")] in which case, use /dev/null, otherwise use the NUL: device (Windows platforms).”

data "local_file" "license" {
  filename = var.license_file != "" ? var.license_file : fileexists("/dev/null") ? "/dev/null" : "NUL:"
}

πŸ‘‰ Just as an aside, I’ve seen this “ternary” construct in a few languages. It basically looks like this: boolean_operation ? true_value : false_value

That check, logically, could have been written like this instead: "%{if boolean_operation}${true_value}%{else}${false_value}%{endif}"

By combining two of these together, while initially it looks far more messy and hard to parse, I’ve found that, especially in single-line statements, it’s much more compact and eventually easier to read than the alternative if/else/endif structure.

So, this means that we can now refer to data.local_file.license as our data source.

Next, I want to select either the PAYG (Pay As You Go) or BYOL (Bring Your Own License) licensed AMI in AWS (the same principle applies in Azure, GCP, etc), so in this block we provide a different value to the filter in the AMI Data Source, suggesting the string “FortiGate-VM64-AWS *x.y.z*” if we have a value provided license, or “FortiGate-VM64-AWSONDEMAND *x.y.z*” if we don’t.

data "aws_ami" "FortiGate" {
  most_recent = true

  filter {
    name   = "name"
    values = ["FortiGate-VM64-AWS%{if data.local_file.license.content == ""}ONDEMAND%{endif} *${var.release}*"]
  }

  filter {
    name   = "virtualization-type"
    values = ["hvm"]
  }

  owners = ["679593333241"] # AWS
}

And the very last thing is to create the user-data template (known as customdata in Azure), using this block:

data "template_cloudinit_config" "config" {
  gzip          = false
  base64_encode = false

  part {
    filename     = "config"
    content_type = "multipart/mixed"
    content      = templatefile(
      "${path.module}/user_data.txt.tmpl",
      {
        hostname = "firewall"
      }
    )
  }

  part {
    filename     = "license"
    content_type = "text/plain"
    content      = data.local_file.license.content
  }
}

And so that is how I can elect to provide a license, or use a pre-licensed image from AWS, and these lessons can also be applied in an Azure or GCP environment too.

Featured image is β€œTracking Methane Sources and Movement Around the Globe” by β€œNASA/Scientific Visualization Studio”

"Kelvin Test" by "Eelke" on Flickr

In Ansible, determine the type of a value, and casting those values to other types

TL;DR? It’s possible to work out what type of variable you’re working with in Ansible. The in-built filters don’t always do quite what you’re expecting. Jump to the “In Summary” heading for my suggestions.

LATE EDIT: 2021-05-23 After raising a question in #ansible on Freenode, flowerysong noticed that my truth table around mappings, iterables and strings was wrong. I’ve amended the table accordingly, and have added a further note below the table.

One of the things I end up doing quite a bit with Ansible is value manipulation. I know it’s not really normal, but… well, I like rewriting values from one type of a thing to the next type of a thing.

For example, I like taking a value that I don’t know if it’s a list or a string, and passing that to an argument that expects a list.

Doing it wrong, getting it better

Until recently, I’d do that like this:

- debug:
    msg: |-
      {
        {%- if value | type_debug == "string" or value | type_debug == "AnsibleUnicode" -%}
           "string": "{{ value }}"
        {%- elif value | type_debug == "dict" or value | type_debug == "ansible_mapping" -%}
          "dict": {{ value }}
        {%- elif value | type_debug == "list" -%}
          "list": {{ value }}
        {%- else -%}
          "other": "{{ value }}"
        {%- endif -%}
      }

But, following finding this gist, I now know I can do this:

- debug:
    msg: |-
      {
        {%- if value is string -%}
           "string": "{{ value }}"
        {%- elif value is mapping -%}
          "dict": {{ value }}
        {%- elif value is iterable -%}
          "list": {{ value }}
        {%- else -%}
          "other": "{{ value }}"
        {%- endif -%}
      }

So, how would I use this, given the context of what I was saying before?

- assert:
    that:
    - value is string
    - value is not mapping
    - value is iterable
- some_module:
    some_arg: |-
      {%- if value is string -%}
        ["{{ value }}"]
      {%- else -%}
        {{ value }}
      {%- endif -%}

More details on finding a type

Why in this order? Well, because of how values are stored in Ansible, the following states are true:

⬇️Type \ ➑️Checkis iterableis mappingis sequenceis string
a_dict (e.g. {})βœ”οΈβœ”οΈβœ”οΈβŒ
a_list (e.g. [])βœ”οΈβŒβœ”οΈβŒ
a_string (e.g. “”)βœ”οΈβŒβœ”οΈβœ”οΈ
A comparison between value types

So, if you were to check for is iterable first, you might match on a_list or a_dict instead of a_string, but string can only match on a_string. Once you know it can’t be a string, you can check whether something is mapping – again, because a mapping can only match a_dict, but it can’t match a_list or a_string. Once you know it’s not that, you can check for either is iterable or is sequence because both of these match a_string, a_dict and a_list.

LATE EDIT: 2021-05-23 Note that a prior revision of this table and it’s following paragraph showed “is_mapping” as true for a_string. This is not correct, and has been fixed, both in the table and the paragraph.

Likewise, if you wanted to check whether a_float and an_integer is number and not is string, you can check these:

⬇️Type \ ➑️Checkis floatis integeris iterableis mappingis numberis sequenceis string
a_floatβœ”οΈβŒβŒβŒβœ”οΈβŒβŒ
an_integerβŒβœ”οΈβŒβŒβœ”οΈβŒβŒ
A comparison between types of numbers

So again, a_float and an_integer don’t match is string, is mapping or is iterable, but they both match is number and they each match their respective is float and is integer checks.

How about each of those (a_float and an_integer) wrapped in quotes, making them a string? What happens then?

⬇️Type \ ➑️Checkis floatis integeris iterableis mappingis numberis sequenceis string
a_float_as_stringβŒβŒβœ”οΈβŒβŒβœ”οΈβœ”οΈ
an_integer_as_stringβŒβŒβœ”οΈβŒβŒβœ”οΈβœ”οΈ
A comparison between types of numbers when held as a string

This is somewhat interesting, because they look like a number, but they’re actually “just” a string. So, now you need to do some comparisons to make them look like numbers again to check if they’re numbers.

Changing the type of a string

What happens if you cast the values? Casting means to convert from one type of value (e.g. string) into another (e.g. float) and to do that, Ansible has three filters we can use, float, int and string. You can’t cast to a dict or a list, but you can use dict2items and items2dict (more on those later). So let’s start with casting our group of a_ and an_ items from above. Here’s a list of values I want to use:

---
- hosts: localhost
  gather_facts: no
  vars:
    an_int: 1
    a_float: 1.1
    a_string: "string"
    an_int_as_string: "1"
    a_float_as_string: "1.1"
    a_list:
      - item1
    a_dict:
      key1: value1

With each of these values, I returned the value as Ansible knows it, what happens when you do {{ value | float }} to cast it as a float, as an integer by doing {{ value | int }} and as a string {{ value | string }}. Some of these results are interesting. Note that where you see u'some value' means that Python converted that string to a Unicode string.

⬇️Value \ ➑️Castvaluevalue when cast as floatvalue when cast as integervalue when cast as string
a_dict{“key1”: “value1”}0.00“{u’key1′: u’value1′}”
a_float1.11.11“1.1”
a_float_as_string“1.1”1.11“1.1”
a_list[“item1”]0.00“[u’item1′]”
a_string“string”0.00“string”
an_int111“1”
an_int_as_string“1”11“1”
Casting between value types

So, what does this mean for us? Well, not a great deal, aside from to note that you can “force” a number to be a string, or a string which is “just” a number wrapped in quotes can be forced into being a number again.

Oh, and casting dicts to lists and back again? This one is actually pretty clearly documented in the current set of documentation (as at 2.9 at least!)

Checking for miscast values

How about if I want to know whether a value I think might be a float stored as a string, how can I check that?

{{ vars[var] | float | string == vars[var] | string }}

What is this? If I cast a value that I think might be a float, to a float, and then turn both the cast value and the original into a string, do they match? If I’ve got a string or an integer, then I’ll get a false, but if I have actually got a float, then I’ll get true. Likewise for casting an integer. Let’s see what that table looks like:

⬇️Type \ ➑️Checkvalue when cast as floatvalue when cast as integervalue when cast as string
a_floatβœ”οΈβŒβœ”οΈ
a_float_as_stringβœ”οΈβŒβœ”οΈ
an_integerβŒβœ”οΈβœ”οΈ
an_integer_as_stringβŒβœ”οΈβœ”οΈ
A comparison between types of numbers when cast to a string

So this shows us the values we were after – even if you’ve got a float (or an integer) stored as a string, by doing some careful casting, you can confirm they’re of the type you wanted… and then you can pass them through the right filter to use them in your playbooks!

Booleans

Last thing to check – boolean values – “True” or “False“. There’s a bit of confusion here, as a “boolean” can be: true or false, yes or no, 1 or 0, however, is true and True and TRUE the same? How about false, False and FALSE? Let’s take a look!

⬇️Value \ ➑️Checktype_debug is booleanis numberis iterableis mappingis stringvalue when cast as boolvalue when cast as stringvalue when cast as integer
yesboolβœ”οΈβœ”οΈβŒβŒβŒTrueTrue1
YesAnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseYes0
YESAnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseYES0
“yes”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTrueyes0
“Yes”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTrueYes0
“YES”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTrueYES0
trueboolβœ”οΈβœ”οΈβŒβŒβŒTrueTrue1
Trueboolβœ”οΈβœ”οΈβŒβŒβŒTrueTrue1
TRUEboolβœ”οΈβœ”οΈβŒβŒβŒTrueTrue1
“true”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTruetrue0
“True”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTrueTrue0
“TRUE”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTrueTRUE0
1intβŒβœ”οΈβŒβŒβŒTrue11
“1”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈTrue11
noboolβœ”οΈβœ”οΈβŒβŒβŒFalseFalse0
Noboolβœ”οΈβœ”οΈβŒβŒβŒFalseFalse0
NOboolβœ”οΈβœ”οΈβŒβŒβŒFalseFalse0
“no”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseno0
“No”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseNo0
“NO”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseNO0
falseboolβœ”οΈβœ”οΈβŒβŒβŒFalseFalse0
Falseboolβœ”οΈβœ”οΈβŒβŒβŒFalseFalse0
FALSEboolβœ”οΈβœ”οΈβŒβŒβŒFalseFalse0
“false”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalsefalse0
“False”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseFalse0
“FALSE”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalseFALSE0
0intβŒβœ”οΈβŒβŒβŒFalse00
“0”AnsibleUnicodeβŒβŒβœ”οΈβŒβœ”οΈFalse00
Comparisons between various stylings of boolean representations

So, the stand out thing for me here is that while all the permutations of string values of the boolean representations (those wrapped in quotes, like this: "yes") are treated as strings, and shouldn’t be considered as “boolean” (unless you cast for it explicitly!), and all non-string versions of true, false, and no are considered to be boolean, yes, Yes and YES are treated differently, depending on case. So, what would I do?

In summary

  • Consistently use no or yes, true or false in lower case to indicate a boolean value. Don’t use 1 or 0 unless you have to.
  • If you’re checking that you’re working with a string, a list or a dict, check in the order string (using is string), dict (using is mapping) and then list (using is sequence or is iterable)
  • Checking for numbers that are stored as strings? Cast your string through the type check for that number, like this: {% if value | float | string == value | string %}{{ value | float }}{% elif value | int | string == value | string %}{{ value | int }}{% else %}{{ value }}{% endif %}
  • Try not to use type_debug unless you really can’t find any other way. These values will change between versions, and this caused me a lot of issues with a large codebase I was working on a while ago!

Run these tests yourself!

Want to run these tests yourself? Here’s the code I ran (also available in a Gist on GitHub), using Ansible 2.9.10.

---
- hosts: localhost
  gather_facts: no
  vars:
    an_int: 1
    a_float: 1.1
    a_string: "string"
    an_int_as_string: "1"
    a_float_as_string: "1.1"
    a_list:
      - item1
    a_dict:
      key1: value1
  tasks:
    - debug:
        msg: |
          {
          {% for var in ["an_int", "an_int_as_string","a_float", "a_float_as_string","a_string","a_list","a_dict"] %}
            "{{ var }}": {
              "type_debug": "{{ vars[var] | type_debug }}",
              "value": "{{ vars[var] }}",
              "is float": "{{ vars[var] is float }}",
              "is integer": "{{ vars[var] is integer }}",
              "is iterable": "{{ vars[var] is iterable }}",
              "is mapping": "{{ vars[var] is mapping }}",
              "is number": "{{ vars[var] is number }}",
              "is sequence": "{{ vars[var] is sequence }}",
              "is string": "{{ vars[var] is string }}",
              "value cast as float": "{{ vars[var] | float }}",
              "value cast as integer": "{{ vars[var] | int }}",
              "value cast as string": "{{ vars[var] | string }}",
              "is same when cast to float": "{{ vars[var] | float | string == vars[var] | string }}",
              "is same when cast to integer": "{{ vars[var] | int | string == vars[var] | string }}",
              "is same when cast to string": "{{ vars[var] | string == vars[var] | string }}",
            },
          {% endfor %}
          }
---
- hosts: localhost
  gather_facts: false
  vars:
    # true, True, TRUE, "true", "True", "TRUE"
    a_true: true
    a_true_initial_caps: True
    a_true_caps: TRUE
    a_string_true: "true"
    a_string_true_initial_caps: "True"
    a_string_true_caps: "TRUE"
    # yes, Yes, YES, "yes", "Yes", "YES"
    a_yes: yes
    a_yes_initial_caps: Tes
    a_yes_caps: TES
    a_string_yes: "yes"
    a_string_yes_initial_caps: "Yes"
    a_string_yes_caps: "Yes"
    # 1, "1"
    a_1: 1
    a_string_1: "1"
    # false, False, FALSE, "false", "False", "FALSE"
    a_false: false
    a_false_initial_caps: False
    a_false_caps: FALSE
    a_string_false: "false"
    a_string_false_initial_caps: "False"
    a_string_false_caps: "FALSE"
    # no, No, NO, "no", "No", "NO"
    a_no: no
    a_no_initial_caps: No
    a_no_caps: NO
    a_string_no: "no"
    a_string_no_initial_caps: "No"
    a_string_no_caps: "NO"
    # 0, "0"
    a_0: 0
    a_string_0: "0"
  tasks:
    - debug:
        msg: |
          {
          {% for var in ["a_true","a_true_initial_caps","a_true_caps","a_string_true","a_string_true_initial_caps","a_string_true_caps","a_yes","a_yes_initial_caps","a_yes_caps","a_string_yes","a_string_yes_initial_caps","a_string_yes_caps","a_1","a_string_1","a_false","a_false_initial_caps","a_false_caps","a_string_false","a_string_false_initial_caps","a_string_false_caps","a_no","a_no_initial_caps","a_no_caps","a_string_no","a_string_no_initial_caps","a_string_no_caps","a_0","a_string_0"] %}
            "{{ var }}": {
              "type_debug": "{{ vars[var] | type_debug }}",
              "value": "{{ vars[var] }}",
              "is float": "{{ vars[var] is float }}",
              "is integer": "{{ vars[var] is integer }}",
              "is iterable": "{{ vars[var] is iterable }}",
              "is mapping": "{{ vars[var] is mapping }}",
              "is number": "{{ vars[var] is number }}",
              "is sequence": "{{ vars[var] is sequence }}",
              "is string": "{{ vars[var] is string }}",
              "is bool": "{{ vars[var] is boolean }}",
              "value cast as float": "{{ vars[var] | float }}",
              "value cast as integer": "{{ vars[var] | int }}",
              "value cast as string": "{{ vars[var] | string }}",
              "value cast as bool": "{{ vars[var] | bool }}",
              "is same when cast to float": "{{ vars[var] | float | string == vars[var] | string }}",
              "is same when cast to integer": "{{ vars[var] | int | string == vars[var] | string }}",
              "is same when cast to string": "{{ vars[var] | string == vars[var] | string }}",
              "is same when cast to bool": "{{ vars[var] | bool | string == vars[var] | string }}",
            },
          {% endfor %}
          }

Featured image is β€œKelvin Test” by β€œEelke” on Flickr and is released under a CC-BY license.

"Field Notes - Sweet Tooth" by "The Marmot" on Flickr

Multi-OS builds in AWS with Terraform – some notes from the field!

Late edit: 2020-05-22 – Updated with better search criteria from colleague conversations

I’m building a proof of concept for … well, a product that needs testing on several different Linux and Windows variants on AWS and Azure. I’m building this environment with Terraform, and it’s thrown me a few curve balls, so I thought I’d document the issues I’ve had!

The versions of distributions I have tested are the latest releases of each of these images at-or-near the time of writing. The major version listed is the earliest I have tested, so no assumption is made about previous versions, and later versions, after the time of this post should not assume any of this data is also accurate!

(Fujitsu Staff – please contact me on my work email address for details on how to get the internal AMIs of our builds of these images πŸ˜„)

Linux Distributions

On the whole, I tend to be much more confident and knowledgable about Linux distributions. I’ve also done far more installs of each of these!

Almost all of these installs are Free of Charge, with the exception of Red Hat Enterprise Linux, which requires a subscription fee, and this can be “Pay As You Go” or “Bring Your Own License”. These sorts of things are arranged for me, so I don’t know how easy or hard it is to organise these licenses!

These builds all use cloud-init, via either a cloud-init yaml script, or some shell scripting language (usually accepted to be bash). If this script fails to execute, you will find your user-data file in /var/lib/cloud/instance/scripts/part-001. If this is a shell script then you will be able to execute it by running that script as your root user.

Amazon Linux 2 or Amzn2

Amazon Linux2 is the “preferred” distribution for Amazon Web Services (AWS) (surprisingly enough). It is based on Red Hat Enterprise Linux (RHEL), and many of the instructions you’ll want to run to install software will use RHEL based instructions. This platform is not available outside the AWS ecosystem, as far as I can tell, although you might be able to run it on-prem.

Software packages are limited in this distribution, so any “extra” features require the installation of the “EPEL” repository, by executing the command sudo amazon-linux-extras install epel and then using the yum command to install further packages. I needed nginx for part of my build, and this was only in EPEL.

Amzn2 AMI Lookup

data "aws_ami" "amzn2" {
  most_recent = true

  filter {
    name   = "name"
    values = ["amzn2-ami-hvm-2.0.*-gp2"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["amazon"] # Canonical
}

Amzn2 User Account

Amazon Linux 2 images under AWS have a default “ec2-user” user account. sudo will allow escalation to Root with no password prompt.

Amzn2 AWS Interface Configuration

The primary interface is called eth0. Network Manager is not installed. To manage the interface, you need to edit /etc/sysconfig/network-scripts/ifcfg-eth0 and apply changes with ifdown eth0 ; ifup eth0.

Amzn2 user-data / Cloud-Init Troubleshooting

I’ve found the output from user-data scripts appearing in /var/log/cloud-init-output.log.

CentOS 7

For starters, AWS doesn’t have an official CentOS8 image, so I’m a bit stymied there! In fact, as far as I can make out, CentOS is only releasing ISOs for builds now, and not any cloud images. There’s an open issue on their bug tracker which seems to suggest that it’s not going to get any priority any time soon! Blimey.

This image may require you to “subscribe” to the image (particularly if you have a “private marketplace”), but this will be requested of you (via a URL provided on screen) when you provision your first machine with this AMI.

Like with Amzn2, CentOS7 does not have nginx installed, and like Amzn2, installation of the EPEL library is not a difficult task. CentOS7 bundles a file to install the EPEL, installed by running yum install epel-release. After this is installed, you have the “full” range of software in EPEL available to you.

CentOS AMI Lookup

data "aws_ami" "centos7" {
  most_recent = true

  filter {
    name   = "name"
    values = ["CentOS Linux 7*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["aws-marketplace"]
}

CentOS User Account

CentOS7 images under AWS have a default “centos” user account. sudo will allow escalation to Root with no password prompt.

CentOS AWS Interface Configuration

The primary interface is called eth0. Network Manager is not installed. To manage the interface, you need to edit /etc/sysconfig/network-scripts/ifcfg-eth0 and apply changes with ifdown eth0 ; ifup eth0.

CentOS Cloud-Init Troubleshooting

I’ve run several different user-data located bash scripts against this system, and the logs from these scripts are appearing in the default syslog file (/var/log/syslog) or by running journalctl -xefu cloud-init. They do not appear in /var/log/cloud-init-output.log.

Red Hat Enterprise Linux (RHEL) 7 and 8

Red Hat has both RHEL7 and RHEL8 images in the AWS market place. The Proof Of Value (POV) I was building was only looking at RHEL7, so I didn’t extensively test RHEL8.

Like Amzn2 and CentOS7, RHEL7 needs EPEL installing to have additional packages installed. Unlike Amzn2 and CentOS7, you need to obtain the EPEL package from the Fedora Project. Do this by executing these two commands:

wget https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
yum install epel-release-latest-7.noarch.rpm

After this is installed, you’ll have access to the broader range of software that you’re likely to require. Again, I needed nginx, and this was not available to me with the stock install.

RHEL7 AMI Lookup

data "aws_ami" "rhel7" {
  most_recent = true

  filter {
    name   = "name"
    values = ["RHEL-7*GA*Hourly*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["309956199498"] # Red Hat
}

RHEL8 AMI Lookup

data "aws_ami" "rhel8" {
  most_recent = true

  filter {
    name   = "name"
    values = ["RHEL-8*HVM-*Hourly*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["309956199498"] # Red Hat
}

RHEL User Accounts

RHEL7 and RHEL8 images under AWS have a default “ec2-user” user account. sudo will allow escalation to Root with no password prompt.

RHEL AWS Interface Configuration

The primary interface is called eth0. Network Manager is installed, and the eth0 interface has a profile called “System eth0” associated to it.

RHEL Cloud-Init Troubleshooting

In RHEL7, as per CentOS7, logs from user-data scripts are appear in the general syslog file (in this case, /var/log/messages) or by running journalctl -xefu cloud-init. They do not appear in /var/log/cloud-init-output.log.

In RHEL8, logs from user-data scrips now appear in /var/log/cloud-init-output.log.

Ubuntu 18.04

At the time of writing this, the vendor, who’s product I was testing, categorically stated that the newest Ubuntu LTS, Ubuntu 20.04 (Focal Fossa) would not be supported until some time after our testing was complete. As such, I spent no time at all researching or planning to use this image.

Ubuntu is the only non-RPM based distribution in this test, instead being based on the Debian project’s DEB packages. As such, it’s range of packages is much wider. That said, for the project I was working on, I required a later version of nginx than was available in the Ubuntu Repositories, so I had to use the nginx Personal Package Archive (PPA). To do this, I found the official PPA for the nginx project, and followed the instructions there. Generally speaking, this would potentially risk any support from the distribution vendor, as it’s not certified or supported by the project… but I needed that version, so I had to do it!

Ubuntu 18.04 AMI Lookup

data "aws_ami" "ubuntu1804" {
  most_recent = true

  filter {
    name   = "name"
    values = ["*ubuntu*18.04*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["099720109477"] # Canonical
}

Ubuntu 18.04 User Accounts

Ubuntu 18.04 images under AWS have a default “ubuntu” user account. sudo will allow escalation to Root with no password prompt.

Ubuntu 18.04 AWS Interface Configuration

The primary interface is called eth0. Network Manager is not installed, and instead Ubuntu uses Netplan to manage interfaces. The file to manage the interface defaults is /etc/netplan/50-cloud-init.yaml. If you struggle with this method, you may wish to install ifupdown and define your configuration in /etc/network/interfaces.

Ubuntu 18.04 Cloud-Init Troubleshooting

In Ubuntu 18.04, logs from user-data scrips appear in /var/log/cloud-init-output.log.

Windows

This section is far more likely to have it’s data consolidated here!

Windows has a common “standard” username – Administrator, and a common way of creating a password (this is generated on-boot, and the password is transferred to the AWS Metadata Service, which it is retrieved and decrypted with the SSH key you’ve used to build the “authentication” to the box) which Terraform handles quite nicely.

The network device is referred to as “AWS PV Network Device #0”. It can be managed with powershell, netsh (although apparently Microsoft are rumbling about demising this script), or from the GUI.

Windows 2012R2

This version is very old now, and should be compared to Windows 7 in terms of age. It is only supported by Microsoft with an extended maintenance package!

Windows 2012R2 AMI Lookup

data "aws_ami" "w2012r2" {
  most_recent = true

  filter {
    name = "name"
    values = ["Windows_Server-2012-R2_RTM-English-64Bit-Base*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["801119661308"] # AWS
}

Windows 2012R2 Cloud-Init Troubleshooting

Logs from the Metadata Service can be found in C:\Program Files\Amazon\Ec2ConfigService\Logs\Ec2ConfigLog.txt. You can also find the userdata script in C:\Program Files\Amazon\Ec2ConfigService\Scripts\UserScript.ps1. This can be launched and debugged using PowerShell ISE, which is in the “Start” menu.

Windows 2016

This version is reasonably old now, and should be compared to Windows 8 in terms of age. It is supported until 2022 in “mainline” support.

Windows 2016 AMI Lookup

data "aws_ami" "w2016" {
  most_recent = true

  filter {
    name = "name"
    values = ["Windows_Server-2016-English-Full-Base*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["801119661308"] # AWS
}

Windows 2016 Cloud-Init Troubleshooting

The metadata service has moved from Windows 2016 and onwards. Logs are stored in a partially hidden directory tree, so you may need to click in the “Address” bar of the Explorer window and type in part of this path. The path to these files is: C:\ProgramData\Amazon\EC2-Windows\Launch\Log. I say “files” as there are two parts to this file – an “Ec2Launch.log” file which reports on the boot process, and “UserdataExecution.log” which shows the output from the userdata script.

Unlike with the Windows 2012R2 version, you can’t get hold of the actual userdata script on the filesystem, you need to browse to a special path in the metadata service (actually, technically, you can do this with any of the metadata services – OpenStack, Azure, and so on) which is: http://169.254.169.254/latest/user-data/

This will contain userdata between a <powershell> and </powershell> pair of tags. This would need to be copied out of this URL and pasted into a new file on your local machine to determine why issues are occurring. Again, I would recommend using PowerShell ISE from the Start Menu to debug your code.

Windows 2019

This version is the most recent released version of Windows Server, and should be compared to Windows 10 in terms of age.

Windows 2019 AMI Lookup

data "aws_ami" "w2019" {
  most_recent = true

  filter {
    name = "name"
    values = ["Windows_Server-2019-English-Full-Base*"]
  }

  filter {
    name   = "architecture"
    values = ["x86_64"]
  }

  filter {
    name   = "state"
    values = ["available"]
  }

  owners = ["801119661308"] # AWS
}

Windows 2019 Cloud-Init Troubleshooting

Functionally, the same as Windows 2016, but to recap, the metadata service has moved from Windows 2016 and onwards. Logs are stored in a partially hidden directory tree, so you may need to click in the “Address” bar of the Explorer window and type in part of this path. The path to these files is: C:\ProgramData\Amazon\EC2-Windows\Launch\Log. I say “files” as there are two parts to this file – an “Ec2Launch.log” file which reports on the boot process, and “UserdataExecution.log” which shows the output from the userdata script.

Unlike with the Windows 2012R2 version, you can’t get hold of the actual userdata script on the filesystem, you need to browse to a special path in the metadata service (actually, technically, you can do this with any of the metadata services – OpenStack, Azure, and so on) which is: http://169.254.169.254/latest/user-data/

This will contain userdata between a <powershell> and </powershell> pair of tags. This would need to be copied out of this URL and pasted into a new file on your local machine to determine why issues are occurring. Again, I would recommend using PowerShell ISE from the Start Menu to debug your code.

Featured image is β€œField Notes – Sweet Tooth” by β€œThe Marmot” on Flickr and is released under a CC-BY license.

β€œNew shoes” by β€œMorgaine” from Flickr

Making Windows Cloud-Init Scripts run after a reboot (Using Terraform)

I’m currently building a Proof Of Value (POV) environment for a product, and one of the things I needed in my environment was an Active Directory domain.

To do this in AWS, I had to do the following steps:

  1. Build my Domain Controller
    1. Install Windows
    2. Set the hostname (Reboot)
    3. Promote the machine to being a Domain Controller (Reboot)
    4. Create a domain user
  2. Build my Member Server
    1. Install Windows
    2. Set the hostname (Reboot)
    3. Set the DNS client to point to the Domain Controller
    4. Join the server to the domain (Reboot)

To make this work, I had to find a way to trigger build steps after each reboot. I was working with Windows 2012R2, Windows 2016 and Windows 2019, so the solution had to be cross-version. Fortunately I found this script online! That version was great for Windows 2012R2, but didn’t cover Windows 2016 or later… So let’s break down what I’ve done!

In your userdata field, you need to have two sets of XML strings, as follows:

<persist>true</persist>
<powershell>
$some = "powershell code"
</powershell>

The first block says to Windows 2016+ “keep trying to run this script on each boot” (note that you need to stop it from doing non-relevant stuff on each boot – we’ll get to that in a second!), and the second bit is the PowerShell commands you want it to run. The rest of this now will focus just on the PowerShell block.

  $path= 'HKLM:\Software\UserData'
  
  if(!(Get-Item $Path -ErrorAction SilentlyContinue)) {
    New-Item $Path
    New-ItemProperty -Path $Path -Name RunCount -Value 0 -PropertyType dword
  }
  
  $runCount = Get-ItemProperty -Path $path -Name Runcount -ErrorAction SilentlyContinue | Select-Object -ExpandProperty RunCount
  
  if($runCount -ge 0) {
    switch($runCount) {
      0 {
        $runCount = 1 + [int]$runCount
        Set-ItemProperty -Path $Path -Name RunCount -Value $runCount
        if ($ver -match 2012) {
          #Enable user data
          $EC2SettingsFile = "$env:ProgramFiles\Amazon\Ec2ConfigService\Settings\Config.xml"
          $xml = [xml](Get-Content $EC2SettingsFile)
          $xmlElement = $xml.get_DocumentElement()
          $xmlElementToModify = $xmlElement.Plugins
          
          foreach ($element in $xmlElementToModify.Plugin)
          {
            if ($element.name -eq "Ec2HandleUserData") {
              $element.State="Enabled"
            }
          }
          $xml.Save($EC2SettingsFile)
        }
        $some = "PowerShell Script"
      }
    }
  }

Whew, what a block! Well, again, we can split this up into a couple of bits.

In the first few lines, we build a pointer, a note which says “We got up to here on our previous boots”. We then read that into a variable and find that number and execute any steps in the block with that number. That’s this block:

  $path= 'HKLM:\Software\UserData'
  
  if(!(Get-Item $Path -ErrorAction SilentlyContinue)) {
    New-Item $Path
    New-ItemProperty -Path $Path -Name RunCount -Value 0 -PropertyType dword
  }
  
  $runCount = Get-ItemProperty -Path $path -Name Runcount -ErrorAction SilentlyContinue | Select-Object -ExpandProperty RunCount
  
  if($runCount -ge 0) {
    switch($runCount) {

    }
  }

The next part (and you’ll repeat it for each “number” of reboot steps you need to perform) says “increment the number” then “If this is Windows 2012, remind the userdata handler that the script needs to be run again next boot”. That’s this block:

      0 {
        $runCount = 1 + [int]$runCount
        Set-ItemProperty -Path $Path -Name RunCount -Value $runCount
        if ($ver -match 2012) {
          #Enable user data
          $EC2SettingsFile = "$env:ProgramFiles\Amazon\Ec2ConfigService\Settings\Config.xml"
          $xml = [xml](Get-Content $EC2SettingsFile)
          $xmlElement = $xml.get_DocumentElement()
          $xmlElementToModify = $xmlElement.Plugins
          
          foreach ($element in $xmlElementToModify.Plugin)
          {
            if ($element.name -eq "Ec2HandleUserData") {
              $element.State="Enabled"
            }
          }
          $xml.Save($EC2SettingsFile)
        }
        
      }

In fact, it’s fair to say that in my userdata script, this looks like this:

  $path= 'HKLM:\Software\UserData'
  
  if(!(Get-Item $Path -ErrorAction SilentlyContinue)) {
    New-Item $Path
    New-ItemProperty -Path $Path -Name RunCount -Value 0 -PropertyType dword
  }
  
  $runCount = Get-ItemProperty -Path $path -Name Runcount -ErrorAction SilentlyContinue | Select-Object -ExpandProperty RunCount
  
  if($runCount -ge 0) {
    switch($runCount) {
      0 {
        ${file("templates/step.tmpl")}

        ${templatefile(
          "templates/rename_windows.tmpl",
          {
            hostname = "SomeMachine"
          }
        )}
      }
      1 {
        ${file("templates/step.tmpl")}

        ${templatefile(
          "templates/join_ad.tmpl",
          {
            dns_ipv4 = "192.0.2.1",
            domain_suffix = "ad.mycorp",
            join_account = "ad\someuser",
            join_password = "SomePassw0rd!"
          }
        )}
      }
    }
  }

Then, after each reboot, you need a new block. I have a block to change the computer name, a block to join the machine to the domain, and a block to install an software that I need.

Featured image is β€œNew shoes” by β€œMorgaine” on Flickr and is released under a CC-BY-SA license.

"Fishing line and bobbin stuck on tree at Douthat State Park" by "Virginia State Parks" on Flickr

Note to self: Linux shell scripts don’t cope well with combined CRLF + LF files… Especially in User-Data / Custom Data / Cloud-Init scripts

This one is more a nudge to myself. On several occasions when building Infrastructure As Code (IAC), I split out a code sections into one or more files, for readability and reusability purposes. What I tended to do, and this was more apparent with the Linux builds than the Windows builds, was to forget to set the line terminator from CRLF to LF.

While this doesn’t really impact Windows builds too much (they’re kinda designed to support people being idiots with line endings now), Linux still really struggles with CRLF endings, and you’ll only see when you’ve broken this because you’ll completely fail to run any of the user-data script.

How do you determine this is your problem? Well, actually it’s a bit tricky, as neither cat, less, more or nano spot this issue. The only two things I found that identified it were file and vi.

The first part of the combined file with mixed line endings. This part has LF termination.
The second part of the combined file with mixed line endings. This part has CRLF termination.
What happens when we cat these two parts into one file? A file with CRLF, LF line terminators obviously!
What the combined file looks like in Vi. Note the blue ^M at the ends of the lines.

So, how to fix this? Assuming you’re using Visual Studio Code;

A failed line-ending clue in Visual Studio Code

You’ll notice this line showing “CRLF” in the status bar at the bottom of Code. Click on that, which brings up a discrete box near the top, as follows:

Oh no, it’s set to “CRLF”. That’s not what we want!

Selecting LF in that box changes the line feeds into LF for this file, but it’s not saved. Make sure you save this file before you re-run your terraform script!

Notice, we’re now using LF endings, but the file isn’t saved.

Fantastic! It’s all worked!

In Nano, I’ve opened the part with the invalid line endings.

Oh no! We have a “DOS Format” file. Quick, let’s fix it!

To fix this, we need to write the file out. Hit Ctrl+O. This tells us that we’re in DOS Format, and also gives us the keyboard combination to toggle “DOS Format” off – it’s Alt+D (In Unix/Linux world, the Alt key is referred to as the Meta key – hence M not A).

This is how we fix things

So, after hitting Alt+D, the “File Name to write” line changes, see below:

Yey, no pesky “DOS Format” warning here!

Using either editor (or any others, if you know how to solve line ending issues in other editors), you still need to combine your script back together before you can run it, so… do that, and your file will be fine to run! Good luck!

Featured image is β€œFishing line and bobbin stuck on tree at Douthat State Park” by β€œVirginia State Parks” on Flickr and is released under a CC-BY license.

"Root" by "llee_wu" on Flickr

A quick note on using Firefox in Windows in a Corporate or Enterprise environment

I’ve been using Firefox as my “browser of choice” for around 15 years. I tend to prefer to use it for all sorts of reasons, but the main thing I expect is support for extensions. Not many of them, but … well, there’s a few!

There are two stumbling blocks for using Firefox in a corporate or enterprise setting. These are:

  1. NTLM or Kerberos Authentication for resources like Sharepoint and ADFS.
  2. Enterprise TLS certificates (usually deployed via GPO as part of the domain)

These are both trivially fixed in the about:config screen, but first you need to get past a scary looking warning page!

In the address bar, where it probably currently says jon.sprig.gs, click in there and type about:config.

Getting to about:config

This brings you to a scary page!

Proceed with caution! (of course!!)

Click the “Accept the Risk and Continue” (note, this is with Firefox 76. Wording with later or earlier versions may differ).

As if it wasn’t obvious enough from the previous screen, this “may impact performance and security”…

And then you get a search box.

In the “Search preference name” type in ntlm and find the line that says network.automatic-ntlm-auth.trusted-uris.

The “NTLM Options page”

Type in there the suffixes of any TRUSTED domains. For example, if your company uses the domain names of bigcompany.com, bigco.local and big.company then you’d type in:

bigcompany.com,bigco.local,big.company

Any pages that you browse to, where they request NTLM authentication, will receive an NTLM set of credentials if prompted (same as IE, Edge, and Chrome already do!) NTLM is effectively a way to pass a trusted Kerberos ticket (a bit like your domain credentials) into a web page.

Next up, let’s get those pesky certificate errors removed!

This assumes that you have a centrally managed TLS Root Certificate, and the admins in your network haven’t just been dumping self signed certificates everywhere (nothing gets around that… just sayin’).

Still in about:config, clear the search box and type enterprise, like this!

Enterprise Roots are here!

Find the line security.enterprise_roots.enabled and make sure it says true. If it doesn’t double click it, so it does.

Now you can close your preferences page, and you should be fine to visit your internal source code repository, time sheeting system or sharepoint site, with almost no interruptions!

If you’ve been tasked for turning this stuff on in your estate of managed desktop environment machines, then you might find this article (on Autoconfiguration of Firefox) of use (but I’ve not tried it!)

Featured image is “Root” by “llee_wu” on Flickr and is released under a CC-BY-ND license.

Unicorn Rentals and the Red Hat

A No-Spoilers AWS Micro services GameDay review (Go team RedHat)

It’s only been a few months since I last attended an AWS Game Day, but the Microservices Game Day came up in the internal calendar, and I jumped at the chance.

To quote from my last post:

A Game Day (sometimes disambiguated as an β€œAdversarial Game Day”, because of sporting events) is a day where you either have a dummy environment, or, if you have the scale, a portion of your live network is removed from live service and used as a training ground. In this case, AWS provided a specific dummy environment β€œUnicorn.Rentals”, and all the attendees are the new recruits to the DevOps Team… Oh, and all the previous DevOps team members had just left the company… all at once.

My AWS Game Day blog post from 2019-0918

Guess what? We were recruited BACK by Unicorn.Rentals! Again, the Ops Team have all “quit” (someone needs to talk to their HR team, for crying out loud), and we’re left with their migration from a legacy system to a new microservices based system. Teams are groups of 4 people.

Team Red Hats - left to right: Paul Clarke, Ho Kingsley, Jason Daniels and Me (the owner of the mentioned Red Hat)
Team Red Hats – left to right: Paul Clarke, Ho Kingsley, Jason Daniels and Me (the owner of the mentioned Red Hat)

The task was to maintain a “service router”, and three micro services. Like the last session, there were moments where the stability of the network was challenged, with issues in code, environment and even external actors (no spoilers, remember).

The main take-away I had was that even though I’ve been cramming Docker and Kubernetes knowledge like crazy (more blog posts to come, folks), it doesn’t mean anything if you can’t actually put it into practice.

The pressure is on you right from the start – when you’re trying to get your head around the service you’re running, and working out how to make your microservices work right. There’s also an element of negotiation (admirably performed in our team by Jason) to get people to work together, and keep your eye on the “troubles” in your environment.

My role was mostly around getting on top of improving the condition of the Service Router, and about half way through the session, I decided to try and apply my newfound Docker knowledge to the problem. Naturally, as I’ve not done this under live fire before, I completely mangled the attempt, even managing to knock one of the working microservices off in the process. I was working with a great team as there were no recriminations or criticism for doing that, just an understanding that we needed to roll-back and fix things.

Trying to work out what needed to be done with that broken Docker container took a lot of effort and even right to the last minute, I still hadn’t managed to get my head around it enough to trust it at the end. I think it’s fair to say, though, that it gave me a lot of impetus to try to understand how a docker container should work and has made me want to try and build something less purposefully complex to see how it would work “in the real world”…

The AWS Microservices Game Day Scoreboard at the end
The AWS Microservices Game Day Scoreboard at the end

Even without doing something crazy with all the components, Team Red Hats came in second, so I came home with my second LED unicorn, currently sitting on my desk, waiting for a child to be good enough to award them A Unicorn from Unicorn Rentals!

Me and Ho accepting our prize for second place

If you’re offered the opportunity to do one of these, take it!!

"So many coats..." by "Scott Griggs" on Flickr

Migrating from docker-compose to Kubernetes Files

Just so you know. This is a long article to explain my wandering path through understanding Kubernetes (K8S). It’s not an article to explain to you how to use K8S with your project. I hit a lot of blockers, due to the stack I’m using and I document them all. This means there’s a lot of noise and not a whole lot of Signal.

In a previous blog post I created a docker-compose.yml file for a PHP based web application. Now that I have a working Kubernetes environment, I wanted to port the configuration files into Kubernetes.

Initially, I was pointed at Kompose, a tool for converting docker-compose files to Kubernetes YAML formatted files, and, in fact, this gives me 99% of what I need… except, the current version uses beta API version flags for some of it’s outputted files, and this isn’t valid for the version of Kubernetes I’m running. So, let’s wind things back a bit, and find out what you need to do to use kompose first and then we can tweak the output file next.

Note: I’m running all these commands as root. There’s a bit of weirdness going on because I’m using the snap of Docker and I had a few issues with running these commands as a user… While I could have tried to get to the bottom of this with sudo and watching logs, I just wanted to push on… Anyway.

Here’s our “simple” docker-compose file.

version: '3'
services:
  db:
    build:
      context: .
      dockerfile: mariadb/Dockerfile
    image: localhost:32000/db
    restart: always
    environment:
      MYSQL_ROOT_PASSWORD: a_root_pw
      MYSQL_USER: a_user
      MYSQL_PASSWORD: a_password
      MYSQL_DATABASE: a_db
    expose:
      - 3306
  nginx:
    build:
      context: .
      dockerfile: nginx/Dockerfile
    image: localhost:32000/nginx
    ports:
      - 1980:80
  phpfpm:
    build:
      context: .
      dockerfile: phpfpm/Dockerfile
    image: localhost:32000/phpfpm

This has three components – the MariaDB database, the nginx web server and the PHP-FPM CGI service that nginx consumes. The database service exposes a port (3306) to other containers, with a set of hard-coded credentials (yep, that’s not great… working on that one!), while the nginx service opens port 1980 to port 80 in the container. So far, so … well, kinda sensible :)

If we run kompose convert against this docker-compose file, we get five files created; db-deployment.yaml, nginx-deployment.yaml, phpfpm-deployment.yaml, db-service.yaml and nginx-service.yaml. If we were to run kompose up on these, we get an error message…

Well, actually, first, we get a whole load of “INFO” and “WARN” lines up while kompose builds and pushes the containers into the MicroK8S local registry (a registry is a like a package repository, for containers), which is served by localhost:32000 (hence all the image: localhost:3200/someimage lines in the docker-compose.yml file), but at the end, we get (today) this error:

INFO We are going to create Kubernetes Deployments, Services and PersistentVolumeClaims for your Dockerized application. If you need different kind of resources, use the 'kompose convert' and 'kubectl create -f' commands instead.

FATA Error while deploying application: Get http://localhost:8080/api: dial tcp 127.0.0.1:8080: connect: connection refused

Uh oh! Well, this is a known issue at least! Kubernetes used to use, by default, http on port 8080 for it’s service, but now it uses https on port 6443. Well, that’s what I thought! In this issue on the MicroK8S repo, it says that it uses a different port, and you should use microk8s.kubectl cluster-info to find the port… and yep… Kubernetes master is running at https://127.0.0.1:16443. Bah.

root@microk8s-a:~/glowing-adventure# microk8s.kubectl cluster-info
Kubernetes master is running at https://127.0.0.1:16443
Heapster is running at https://127.0.0.1:16443/api/v1/namespaces/kube-system/services/heapster/proxy
CoreDNS is running at https://127.0.0.1:16443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
Grafana is running at https://127.0.0.1:16443/api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
InfluxDB is running at https://127.0.0.1:16443/api/v1/namespaces/kube-system/services/monitoring-influxdb:http/proxy

So, we export the KUBERNETES_MASTER environment variable, which was explained in that known issue I mentioned before, and now we get a credential prompt:

Please enter Username:

Oh no, again! I don’t have credentials!! Fortunately the MicroK8S issue also tells us how to find those! You run microk8s.config and it tells you the username!

roo@microk8s-a:~/glowing-adventure# microk8s.config
apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: <base64-data>
    server: https://10.0.2.15:16443
  name: microk8s-cluster
contexts:
- context:
    cluster: microk8s-cluster
    user: admin
  name: microk8s
current-context: microk8s
kind: Config
preferences: {}
users:
- name: admin
  user:
    username: admin
    password: QXdUVmN3c3AvWlJ3bnRmZVJmdFhpNkJ3cDdkR3dGaVdxREhuWWo0MmUvTT0K

So, our username is “admin” and our password is … well, in this case a string starting QX and ending 0K but yours will be different!

We run kompose up again, and put in the credentials… ARGH!

FATA Error while deploying application: Get https://127.0.0.1:16443/api: x509: certificate signed by unknown authority

Well, now, that’s no good! Fortunately, a quick Google later, and up pops this Stack Overflow suggestion (mildly amended for my circumstances):

openssl s_client -showcerts -connect 127.0.0.1:16443 < /dev/null | sed -ne '/-BEGIN CERTIFICATE-/,/-END CERTIFICATE-/p' | sudo tee /usr/local/share/ca-certificates/k8s.crt
update-ca-certificates
systemctl restart snap.docker.dockerd

Right then. Let’s run that kompose up statement again…

INFO We are going to create Kubernetes Deployments, Services and PersistentVolumeClaims for your Dockerized application. If you need different kind of resources, use the 'kompose convert' and 'kubectl create -f' commands instead.

Please enter Username: 
Please enter Password: 
INFO Deploying application in "default" namespace
INFO Successfully created Service: nginx
FATA Error while deploying application: the server could not find the requested resource

Bah! What resource do I need? Well, actually, there’s a bug in 1.20.0 of Kompose, and it should be fixed in 1.21.0. The “resource” it’s talking about is, I think, that one of the APIs refuses to process the converted YAML files. As a result, the “resource” is the service that won’t start. So, instead, let’s convert the file into the output YAML files, and then take a peak at what’s going wrong.

root@microk8s-a:~/glowing-adventure# kompose convert
INFO Kubernetes file "nginx-service.yaml" created
INFO Kubernetes file "db-deployment.yaml" created
INFO Kubernetes file "nginx-deployment.yaml" created
INFO Kubernetes file "phpfpm-deployment.yaml" created

So far, so good! Now let’s run kubectl apply with each of these files.

root@microk8s-a:~/glowing-adventure# kubectl apply -f nginx-service.yaml
Warning: kubectl apply should be used on resource created by either kubectl create --save-config or kubectl apply
service/nginx configured
root@microk8s-a:~# kubectl apply -f nginx-deployment.yaml
error: unable to recognize "nginx-deployment.yaml": no matches for kind "Deployment" in version "extensions/v1beta1"

Apparently the service files are all OK, the problem is in the deployment files. Hmm OK, let’s have a look at what could be wrong. Here’s the output file:

root@microk8s-a:~/glowing-adventure# cat nginx-deployment.yaml
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  annotations:
    kompose.cmd: kompose convert
    kompose.version: 1.20.0 (f3d54d784)
  creationTimestamp: null
  labels:
    io.kompose.service: nginx
  name: nginx
spec:
  replicas: 1
  strategy: {}
  template:
    metadata:
      annotations:
        kompose.cmd: kompose convert
        kompose.version: 1.20.0 (f3d54d784)
      creationTimestamp: null
      labels:
        io.kompose.service: nginx
    spec:
      containers:
      - image: localhost:32000/nginx
        name: nginx
        ports:
        - containerPort: 80
        resources: {}
      restartPolicy: Always
status: {}

Well, the extensions/v1beta1 API version doesn’t seem to support “Deployment” options any more, so let’s edit it to change that to what the official documentation example shows today. We need to switch to using the apiVersion: apps/v1 value. Let’s see what happens when we make that change!

root@microk8s-a:~/glowing-adventure# kubectl apply -f nginx-deployment.yaml
error: error validating "nginx-deployment.yaml": error validating data: ValidationError(Deployment.spec): missing required field "selector" in io.k8s.api.apps.v1.DeploymentSpec; if you choose to ignore these errors, turn validation off with --validate=false

Hmm this seems to be a fairly critical issue. A selector basically tells the orchestration engine which images we want to be deployed. Let’s go back to the official example. So, we need to add the “selector” value in the “spec” block, at the same level as “template”, and it needs to match the labels we’ve specified. It also looks like we don’t need most of the metadata that kompose has given us. So, let’s adjust the deployment to look a bit more like that example.

root@microk8s-a:~/glowing-adventure# cat nginx-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: nginx
  name: nginx
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - image: localhost:32000/nginx
        name: nginx
        ports:
        - containerPort: 80
        resources: {}
      restartPolicy: Always

Fab. And what happens when we run it?

root@microk8s-a:~/glowing-adventure# kubectl apply -f nginx-deployment.yaml
deployment.apps/nginx created

Woohoo! Let’s apply all of these now.

root@microk8s-a:~/glowing-adventure# for i in db-deployment.yaml nginx-deployment.yaml nginx-service.yaml phpfpm-deployment.yaml; do kubectl apply -f $i ; done
deployment.apps/db created
deployment.apps/nginx unchanged
service/nginx unchanged
deployment.apps/phpfpm created

Oh, hang on a second, that service (service/nginx) is unchanged, but we changed the label from io.kompose.service: nginx to app: nginx, so we need to fix that. Let’s open it up and edit it!

apiVersion: v1
kind: Service
metadata:
  annotations:
    kompose.cmd: kompose convert
    kompose.version: 1.20.0 (f3d54d784)
  creationTimestamp: null
  labels:
    io.kompose.service: nginx
  name: nginx
spec:
  ports:
  - name: "1980"
    port: 1980
    targetPort: 80
  selector:
    io.kompose.service: nginx
status:
  loadBalancer: {}

Ah, so this has the “annotations” field too, in the metadata, and, as suspected, it’s got the io.kompose.service label as the selector. Hmm OK, let’s fix that.

root@microk8s-a:~/glowing-adventure# cat nginx-service.yaml
apiVersion: v1
kind: Service
metadata:
  labels:
    app: nginx
  name: nginx
spec:
  ports:
  - name: "1980"
    port: 1980
    targetPort: 80
  selector:
    app: nginx
status:
  loadBalancer: {}

Much better. And let’s apply it…

root@microk8s-a:~/glowing-adventure# kubectl apply -f nginx-service.yaml
service/nginx configured

Fab! So, let’s review the state of the deployments, the services, the pods and the replication sets.

root@microk8s-a:~/glowing-adventure# kubectl get deploy
NAME     READY   UP-TO-DATE   AVAILABLE   AGE
db       1/1     1            1           8m54s
nginx    0/1     1            0           8m53s
phpfpm   1/1     1            1           8m48s

Hmm. That doesn’t look right.

root@microk8s-a:~/glowing-adventure# kubectl get pod
NAME                      READY   STATUS             RESTARTS   AGE
db-f78f9f69b-grqfz        1/1     Running            0          9m9s
nginx-7774fcb84c-cxk4v    0/1     CrashLoopBackOff   6          9m8s
phpfpm-66945b7767-vb8km   1/1     Running            0          9m3s
root@microk8s-a:~# kubectl get rs
NAME                DESIRED   CURRENT   READY   AGE
db-f78f9f69b        1         1         1       9m18s
nginx-7774fcb84c    1         1         0       9m17s
phpfpm-66945b7767   1         1         1       9m12s

Yep. What does “CrashLoopBackOff” even mean?! Let’s check the logs. We need to ask the pod itself, not the deployment, so let’s use the kubectl logs command to ask.

root@microk8s-a:~/glowing-adventure# kubectl logs nginx-7774fcb84c-cxk4v
2020/01/17 08:08:50 [emerg] 1#1: host not found in upstream "phpfpm" in /etc/nginx/conf.d/default.conf:10
nginx: [emerg] host not found in upstream "phpfpm" in /etc/nginx/conf.d/default.conf:10

Hmm. That’s not good. We were using the fact that Docker just named everything for us in the docker-compose file, but now in Kubernetes, we need to do something different. At this point I ran out of ideas. I asked on the McrTech slack for advice. I was asked to run this command, and would you look at that, there’s nothing for nginx to connect to.

root@microk8s-a:~/glowing-adventure# kubectl get service
NAME         TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
kubernetes   ClusterIP   10.152.183.1    <none>        443/TCP    24h
nginx        ClusterIP   10.152.183.62   <none>        1980/TCP   9m1s

It turns out that I need to create a service for each of the deployments. So, now I have a separate service for each one. I copied the nginx-service.yaml file into db-service.yaml and phpfpm-service.yaml, edited the files and now… tada!

root@microk8s-a:~/glowing-adventure# kubectl get service
NAME         TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
db           ClusterIP   10.152.183.61   <none>        3306/TCP   5m37s
kubernetes   ClusterIP   10.152.183.1    <none>        443/TCP    30h
nginx        ClusterIP   10.152.183.62   <none>        1980/TCP   5h54m
phpfpm       ClusterIP   10.152.183.69   <none>        9000/TCP   5m41s

But wait… How do I actually address nginx now? Huh. No external-ip (not even “pending”, which is what I ended up with), no ports to talk to. Uh oh. Now I need to understand how to hook this service up to the public IP of this node. Ahh, see up there it says “ClusterIP”? That means “this service is only available INSIDE the cluster”. If I change this to “NodePort” or “LoadBalancer”, it’ll attach that port to the external interface.

What’s the difference between “NodePort” and “LoadBalancer”? Well, according to this page, if you are using a managed Public Cloud service that supports an external load balancer, then putting this to “LoadBalancer” should attach your “NodePort” to the provider’s Load Balancer automatically. Otherwise, you need to define the “NodePort” value in your config (which must be a value between 30000 and 32767, although that is configurable for the node). Once you’ve done that, you can hook your load balancer up to that port, for example Client -> Load Balancer IP (TCP/80) -> K8S Cluster IP (e.g. TCP/31234)

So, how does this actually look. I’m going to use the “LoadBalancer” option, because if I ever deploy this to “live”, I want it to integrate with the load balancer, but for now, I can cope with addressing a “high port”. Right, well, let me open back up that nginx-service.yaml, and make the changes.

root@microk8s-a:~/glowing-adventure# cat nginx-service.yaml
apiVersion: v1
kind: Service
metadata:
  labels:
    app: nginx
  name: nginx
spec:
  type: LoadBalancer
  ports:
  - name: nginx
    nodePort: 30000
    port: 1980
    targetPort: 80
  selector:
    app: nginx
status:
  loadBalancer: {}

The key parts here are the lines type: LoadBalancer and nodePort: 30000 under spec: and ports: respectively. Note that I can use, at this point type: LoadBalancer and type: NodePort interchangably, but, as I said, if you were using this in something like AWS or Azure, you might want to do it differently!

So, now I can curl http://192.0.2.100:30000 (where 192.0.2.100 is the address of my “bridged interface” of K8S environment) and get a response from my PHP application, behind nginx, and I know (from poking at it a bit) that it works with my Database.

OK, one last thing. I don’t really want lots of little files which have got config items in. I quite liked the docker-compose file as it was, because it had all the services in as one block, and I could run “docker-compose up”, but the kompose script split it out into lots of pieces. In Kubernetes, if the YAML file it loads has got a divider in it (a line like this: ---) then it stops parsing it at that point, and starts reading the file after that as a new file. Like this I could have the following layout:

apiVersion: apps/v1
kind: Deployment
more: stuff
---
apiVersion: v1
kind: Service
more: stuff
---
apiVersion: apps/v1
kind: Deployment
more: stuff
---
apiVersion: v1
kind: Service
more: stuff

But, thinking about it, I quite like having each piece logically together, so I really want db.yaml, nginx.yaml and phpfpm.yaml, where each of those files contains both the deployment and the service. So, let’s do that. I’ll do one file, so it makes more sense, and then show you the output.

root@microk8s-a:~/glowing-adventure# mkdir -p k8s
root@microk8s-a:~/glowing-adventure# mv db-deployment.yaml k8s/db.yaml
root@microk8s-a:~/glowing-adventure# echo "---" >> k8s/db.yaml
root@microk8s-a:~/glowing-adventure# cat db-service.yaml >> k8s/db.yaml
root@microk8s-a:~/glowing-adventure# rm db-service.yaml
root@microk8s-a:~/glowing-adventure# cat k8s/db.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: db
  name: db
spec:
  replicas: 1
  selector:
    matchLabels:
      app: db
  template:
    metadata:
      labels:
        app: db
    spec:
      containers:
      - env:
        - name: MYSQL_DATABASE
          value: a_db
        - name: MYSQL_PASSWORD
          value: a_password
        - name: MYSQL_ROOT_PASSWORD
          value: a_root_pw
        - name: MYSQL_USER
          value: a_user
        image: localhost:32000/db
        name: db
        resources: {}
      restartPolicy: Always
---
apiVersion: v1
kind: Service
metadata:
  labels:
    app: db
  name: db
spec:
  ports:
  - name: mariadb
    port: 3306
    targetPort: 3306
  selector:
    app: db
status:
  loadBalancer: {}

So, now, if I do kubectl apply -f k8s/db.yaml I’ll get this output:

root@microk8s-a:~/glowing-adventure# kubectl apply -f k8s/db.yaml
deployment.apps/db unchanged
service/db unchanged

You can see the final files in the git repo for this set of tests.

Next episode, I’ll start looking at making my application scale (as that’s the thing that Kubernetes is known for) and having more than one K8S node to house my K8S pods!

Featured image is “So many coats…” by “Scott Griggs” on Flickr and is released under a CC-BY-ND license.

"Captain" by "The Laddie" on Flickr

Trying out Kubernetes (K8S) with MicroK8S in Vagrant

I’m going on a bit of a containers kick at the moment, and just recently I wanted to give Kubernetes (sometimes abbreviated to “K8S”) a try.

Kubernetes is an orchestration engine for Containers, like Docker. It’s designed to take the images that Docker (and other similar tools) produce, and run them across multiple nodes. You need to have a handle on how Docker works before giving K8S a try, but once you do, it’s well worth a shot to understand K8S.

Unlike Docker, K8S is a bit more in-depth on it’s requirements, and often people are pointed at Minikube as their introduction to K8S, however, my colleague and friend Nick suggested I might be better off with MicroK8S.

MicroK8S is an application released by Canonical as a Snap. A Snap is a Linux packaging format, similar to FlatPak and AppImage. It’s mostly used on Ubuntu based operating systems, but can also work on other Linux distributions.

I had an initial, failed, punt with the recommended advice for using MicroK8S on Windows (short story, Hyper-V did not work for me, and the VirtualBox back-end doesn’t expose any network ports, or at least, if it does, I couldn’t see how to make it work), and as I’m reasonably confident in using Vagrant work in Windows, I built a Vagrantfile to deliver MicroK8S.

To use this, you need Vagrant and VirtualBox, and then get the Vagrantfile from repo… then run vagrant up (it will ask you what interface you want to “bridge” to – this will be how you access the Kubernetes pods and Docker containers). Once the machine has finished building, you can run vagrant ssh to connect into it. From here, you can run your kubectl commands, as well as docker commands.

If you want to experiment with a multi-node environment, then I also built a Vagrantfile to deliver two virtual machines, both running MicroK8S, and used the shared storage element of Vagrant to transfer the “join” instruction from the first node to the second.

Of course, now I just need to work out how the hell I do Kubernetes 🀣

Featured image is “Captain” by “The Laddie” on Flickr and is released under a CC-BY-ND license.

nobodys perfect nbc GIF by The Good Place from Giphy

Talk Summary – FDE Conference “Automation in an Infrastructure as Code World”

Format: Theatre Style room. ~70 attendees.

Slides: Available to view (Firefox/Chrome recommended – press “S” to see the required speaker notes), Code referenced in the slides also available to view.

Video: Not on the day, but I recorded a take of it at home after the event. The delivery on the day was better, but the content is there at least! :)

Slot: Slot 2 Wednesday 14:15-15:00

Notes: FDE is the abbreviation of “Fujitsu Distinguished Engineer”, an internal program at Fujitsu. Each year they hold a conference for all the FDEs to attend. This is my second year as an FDE, and the first where I’m presenting.

This slide deck was massively re-worked, following some excellent feedback at BCMcr9. I then, unusually for me, gave the deck two separate run through sessions with colleagues, and tweaked it following each run.

This deck includes Creative Commons licensed images (which is fairly common for my slide decks), but also, in a new and unusual step for me, includes meme gifs from Giphy. I’m not really sure about whether this is step forward or back for me, as I do prefer permissive licenses. That said, the memes seem to be more engaging – particularly as they’re animated. I’ve never had someone comment on the images in my slide deck until I did the first run through with the memes in with a colleague, and then again when I ran it a second time they particularly brought up the animated images… so the memes are staying for now.

I’m also slightly disappointed with myself that I couldn’t stick to the “One Bold Word” style of presentations (the format preferred by Jono Bacon), and found myself littering more and more content into the screen. I was, however, proud of myself for including the “Tweetable content” slide, as recommended, I think, by Lorna Mitchell (@LornaJane). I also included a “Your next steps” slide, as recommended by Andy Bounds (although I suspect he’d be disappointed with the “Questions?” slide at the end!)

This deck required quite a bit of research on my part. I’d never written CloudFormations (CF) before, and I’d only really copied-and-pasted Terraform (I refer to it as TF which probably isn’t right) before. I wrote a full stack of machines in CF, Azure Resource Manager (ARM) for the native technologies, as well as the same stacks in both TF and Ansible for both Azure and AWS. I also looked into how to deploy the CF and ARM templates with both Terraform and Ansible, and finally how to use TF from Ansible. I already knew how to run Ansible from within userdata/customdata arguments in AWS and Azure, but I included it and tested it as part of the deck too.

I had some amazing feedback from the audience and some great questions asked of me. I loved the response from the audience to some of my GIFs (although one comment that was made was that I need to stop the animations after the first run!)

Following the session, as I’d hoped, it brought a few of the fellow attendees to the forefront to ask if we can talk further about the subject and I would encourage you, if you are someone who uses these tools to give me a shout – I want to do more and find out about your projects, processes and tools!

My intention is to start using this slide deck at meet-ups in the Greater Manchester area, hopefully without having to re-write it that much!