terraform on Digi Hunch

Debating between count and for_each in Terraform

Tue, 27 Aug 2024 22:36:39 -0400

In Terraform, we often have to create an array of resources of the same type but similar attribute values. For code reusability, manageability and for DRY principle, it’s better to use loop. Terraform HCL supports loop via the use of meta-argument. Currently, there are two options to drive a loop: count and for_each .

Problem with `count` loop

The book Terraform Up and Running (Chapter 5 Terraform Tips and Tricks) regards count as Terraform’s oldest, simplest and most limited iteration construct. One of the big limitations is the shifting of index if the length of resource array changes. The point comes with a good example:

variable "user_names" {
 description = "Create IAM users with these names" 
 type = list(string)
 default = ["neo", "trinity", "morpheus"]
}
# The Example from the book Terraform Up and Running
resource "aws_iam_user" "example" { 
 count = length(var.user_names)
 name = var.user_names[count.index]
}

As you execute Terraform apply, three IAM users will be created, with the plan looking like:

# aws_iam_user.example[0] will be created
+ resource "aws_iam_user" "example" {
 + name = "neo"
 (...) 
 }
# aws_iam_user.example[1] will be created
+ resource "aws_iam_user" "example" {
 + name = "trinity"
 (...) 
}
# aws_iam_user.example[2] will be created
+ resource "aws_iam_user" "example" {
 + name = "morpheus"
 (...) 
}

Then if you remove “trinity” from the variable user_names, and run terraform plan, the plan would look like:

Terraform will perform the following actions:
 # aws_iam_user.example[1] will be updated in-place
 ~ resource "aws_iam_user" "example" {
 id = "trinity"
 ~ name = "trinity" -> "morpheus"
 }
 # aws_iam_user.example[2] will be destroyed
 - resource "aws_iam_user" "example" {
 - id = "morpheus" -> null
 - name = "morpheus" -> null
 }
Plan: 0 to add, 1 to change, 1 to destroy.

In this plan, instead of deleting the second user, it renames the second user and deletes the third user. While the plan matches the code logic, it is often an unwanted result, considering the resource could be one that many other resources depends on, such as a subnet.

This is a good example of the problem with count. Terraform identifies each resource in the generated list of resource by position(index) . When the length changes, the index shifts. If you remove an item from the middle of the list, Terraform will delete every resource after the deleted item, then re-create all the resources that come after the deleted one. As a consequence, you may loose availability or even worse, lose data.

Embrace `for_each` loop

If we modify the example above to use for_each, the code looks like:

variable "user_names" {
 description = "Create IAM users with these names" 
 type = list(string)
 default = ["neo", "trinity", "morpheus"]
}
# The Example from the book Terraform Up and Running
resource "aws_iam_user" "example" 
{ 
 for_each = toset(var.user_names) 
 name = each.value
}

This results in the creation of three IAM users. If you remove the “trinity” user from the middle of the input collection and apply, the plan looks like this:

Terraform will perform the following actions:
 # aws_iam_user.example["trinity"] will be destroyed
 - resource "aws_iam_user" "example" {
 - arn = "arn:aws:iam::123456789012:user/trinity" -> null
 - name = "trinity" -> null
 }
Plan: 0 to add, 0 to change, 1 to destroy.

The plan suggests that Terraform will delete the very resource that was taken out from the middle of the input collection and no existing resources in the array are impacted.

Note that in the code snippet above, we use function toset() to convert the input list to a set (ordered and de-duped list of string). This is because we can only loop over a set or map when creating an array of resource. If the array of resource being created have another attribute whose value needs to be individualized, we can loop over a map and store the individualized attribute values as key-value pairs.

A few pages down, the book discusses an important limitation for both count and for_each. The length of the resource array that you are creating with count or for_each meta-argument must not be computed from other resources. Terraform must be able to compute count and for_each during the plan phase, before any resources are created or modified. The length of the resource array can be from hardcoded values, data sources, or even a list of other resources to create in the same file, so long as the length can be determined during the plan, instead of not being computed from other resource outputs.

A real-life example with classic pattern

The book then touches on another advantage of for_each: the ability to create multiple inline blocks within a resource. The guide from Hashicorp documentation also has a section on when to use for_each Instead of count, with a similar example. The section merely mentions when to use count in the opening sentence: If your instances are almost identical, count is appropriate.

That makes for_each sound like a no-brainer, after reading all the literatures about this topic. In my experience with a specific use case at the beginning, count feels more efficient. The example from the book is too simplistic. To better compare the two options, I need a realistic example. Let’s consider this use case where, after creating a VPC, I need to create the followings:

one NAT gateway for each availability zone (each NAT Gateway maps to one subnet and one allocation ID)
one public subnet for each availability zone
one public IP allocation in each availability zone

We can summarize the relationships between resources in the following diagram:

I deliberately pick this example because they are self-contained. So are all code snippets in this post. The example also demonstrate a classic relation between resources that we can find everywhere in infrastructure automation. Here’s another example off the bat:

create an array of aws_subnet, each has a subnet_id attribute;
create an array of aws_route_table, each has a reout_table_id attribute;
now, create an array of aws_route_table_association, each referencing one aws_subnet (by subnet_id) and one aws_route (by route_table_id);

If we address the NAT gateway example, we’re good with many other resources that shares the same relation pattern. In the next section, we’ll first implement the NAT gateway example, using count loop.

Implementation using `count`

The use case exemplifies the pattern where we have multiple types of resources related to each other. We need a loop in each type of resources, resulting in multiple arrays of different resource types. Moreover, the elements in the array for aws_nat_gateway has 1-to-1 mappings with both the array for aws_subnet, and the array for aws_eip.

With count, I created Terraform code with everything in a single main.tf file for the convenience of illustration, like this:

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provider "aws" {}

variable "vpc_cidr_block" {
 type = string
 default = "147.206.0.0/16"
}
variable "public_subnets_cidr_list" {
 type = list(any)
 default = ["147.206.0.0/22", "147.206.4.0/22"]
 #default = ["147.206.0.0/22", "147.206.4.0/22", "147.206.8.0/22"]
}

data "aws_availability_zones" "this" {}

resource "aws_vpc" "base_vpc" {
 cidr_block = var.vpc_cidr_block
}

resource "aws_internet_gateway" "internet_gw" {
 vpc_id = aws_vpc.base_vpc.id
}

resource "aws_subnet" "public_subnets" {
 count = length(var.public_subnets_cidr_list)
 vpc_id = aws_vpc.base_vpc.id
 cidr_block = var.public_subnets_cidr_list[count.index]
 map_public_ip_on_launch = true
 availability_zone = data.aws_availability_zones.this.names[count.index]
}

resource "aws_eip" "nat_eips" {
 count = length(var.public_subnets_cidr_list)
}

resource "aws_nat_gateway" "nat_gws" {
 count = length(var.public_subnets_cidr_list)
 subnet_id = aws_subnet.public_subnets[count.index].id
 allocation_id = aws_eip.nat_eips[count.index].id
 depends_on = [aws_internet_gateway.internet_gw]
}

The intent is to create subnet, public IP and NAT gateway for two availability zones. I also want to add one more AZ in the future and have the code to handle the addition gracefully. To add the new AZ, I uncomment line 10 and comment out line 9. The plan after this code change looks like this:

Terraform will perform the following actions:

 # aws_eip.nat_eips[2] will be created
 + resource "aws_eip" "nat_eips" {
 (...) 
 }

 # aws_subnet.public_subnets[2] will be created
 + resource "aws_subnet" "public_subnets" {
 + availability_zone = "us-east-1c"
 + cidr_block = "147.206.8.0/22"
 + id = (known after apply)
 (...) 
 }

 # aws_nat_gateway.nat_gws[2] will be created
 + resource "aws_nat_gateway" "nat_gws" {
 + allocation_id = (known after apply)
 + subnet_id = (known after apply)
 (...) 
 }

Plan: 3 to add, 0 to change, 0 to destroy.

The plan creates a set of resources required for the new availability zone without touching any existing resource, which is expected.

Why do I only focus on the use case of adding a new subnet in new AZ, and not deleting or modifying CIDR on an existing subnet? That’s because we rarely do that with production. We rarely remove the use of an availability zone. Nor do we modify the CIDRs on an existing subnet. In fact, AWS SDK does not even have an API to change CIDRs on a subnet or a VPC. In our infrastructure operation, we make such decisions upfront so they are immutable once provisioned. We simply don’t need to consider all the possible CRUD actions on a resource.

So, the count loop does just the job. Now, what about for_each?

Implementation with `for_each`: first attempt

Since for_each takes a set or map, I have to make some adjustment. My first attempt looks like this:

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provider "aws" {}

variable "vpc_cidr_block" {
 type = string
 default = "147.206.0.0/16"
}

variable "public_subnets_cidr_list" {
 type = list(any)
 default = ["147.206.0.0/22", "147.206.4.0/22"] # 2 AZ
 #default = ["147.206.0.0/22", "147.206.4.0/22", "147.206.8.0/22"] # 3 AZ
}

data "aws_availability_zones" "this" {}

resource "aws_vpc" "base_vpc" {
 cidr_block = var.vpc_cidr_block
}

resource "aws_internet_gateway" "internet_gw" {
 vpc_id = aws_vpc.base_vpc.id
}

locals {
 subnet_config = [
 for i in range(length(var.public_subnets_cidr_list)) : {
 cidr = var.public_subnets_cidr_list[i]
 az = data.aws_availability_zones.this.names[i]
 }
 ]
}

resource "aws_subnet" "public_subnets" {
 for_each = { for idx, rec in local.subnet_config : idx => rec }
 vpc_id = aws_vpc.base_vpc.id
 cidr_block = each.value.cidr
 map_public_ip_on_launch = true
 availability_zone = each.value.az
 tags = { Name = "PUBLIC-SUBNET" }
}

resource "aws_eip" "nat_eips" {
 for_each = toset(var.public_subnets_cidr_list)
 tags = { Name = "NATEIP" }
}

data "aws_subnets" "public_subnets" {
 filter {
 name = "tag:Name"
 values = ["PUBLIC-SUBNET"]
 }
 depends_on = [aws_subnet.public_subnets]
}

data "aws_eips" "nat_eips" {
 filter {
 name = "tag:Name"
 values = ["NATEIP"]
 }
 depends_on = [aws_eip.nat_eips]
}

locals {
 nat_gw_config = [
 for i in range(length(var.public_subnets_cidr_list)) : {
 subnet_id = data.aws_subnets.public_subnets.ids[i]
 alloc_id = data.aws_eips.nat_eips.allocation_ids[i]
 }
 ]
}

resource "aws_nat_gateway" "nat_gws" {
 for_each = { for idx, rec in local.nat_gw_config : idx => rec }
 subnet_id = each.value.subnet_id
 allocation_id = each.value.alloc_id
 depends_on = [aws_internet_gateway.internet_gw]
}

Note that I have to create a couple of data resources (nat_eips and public_subnets) and local variables (subnet_config and nat_gw_config) in order build the required map data structures and feed them to the for_each parameters.

After Terraform apply, let’s edit public_subnets_cidr_list with the additional subnet CIDR for the 3rd AZ. The plan looks like this:

Terraform will perform the following actions:

 # data.aws_eips.nat_eips will be read during apply
 # (depends on a resource or a module with changes pending)
 <= data "aws_eips" "nat_eips" {
 + allocation_ids = (known after apply)
 (...) 
 }

 # data.aws_subnets.public_subnets will be read during apply
 # (depends on a resource or a module with changes pending)
 <= data "aws_subnets" "public_subnets" {
 + id = (known after apply)
 + ids = (known after apply)
 (...) 
 }

 # aws_eip.nat_eips["147.206.8.0/22"] will be created
 + resource "aws_eip" "nat_eips" {
 + allocation_id = (known after apply)
 (...) 
 }

 # aws_nat_gateway.nat_gws["0"] must be replaced
-/+ resource "aws_nat_gateway" "nat_gws" {
 ~ allocation_id = "eipalloc-0ffe32519d20b9b7f" # forces replacement -> (known after apply) # forces replacement
 ~ subnet_id = "subnet-0b4b202056c75bb0a" # forces replacement -> (known after apply) # forces replacement
 (...) 
 # (1 unchanged attribute hidden)
 }

 # aws_nat_gateway.nat_gws["1"] must be replaced
-/+ resource "aws_nat_gateway" "nat_gws" {
 ~ allocation_id = "eipalloc-0ff1d32fb271b5cf4" # forces replacement -> (known after apply) # forces replacement
 ~ subnet_id = "subnet-09b6486fbe44e9795" # forces replacement -> (known after apply) # forces replacement
 (...) 
 # (1 unchanged attribute hidden)
 }

 # aws_nat_gateway.nat_gws["2"] will be created
 + resource "aws_nat_gateway" "nat_gws" {
 + allocation_id = (known after apply)
 + subnet_id = (known after apply)
 (...) 
 }

 # aws_subnet.public_subnets["2"] will be created
 + resource "aws_subnet" "public_subnets" {
 + availability_zone = "us-east-1c"
 + cidr_block = "147.206.8.0/22"
 (...) 
 }

Plan: 5 to add, 0 to change, 2 to destroy.

Wait a second, I expect the template to create a new subnet, a new elastic IP and a new NAT gateway in that new AZ. But why does it plan to delete the two existing NAT gateways and recreate two? This doesn’t make for_each an appealing option at all.

Apart from the interruptive plan, there are also other problems. First, since the aws_subnet resources requires cidr_block and availability_zone values, I have to build a map (subnet_config) for its resource array to consume. Similarly, I have to build a second map (nat_gw_config) to create resource array for aws_nat_gateway, which requires subnet_id and allocation_id. This map takes more work to build. Because of the 1-to-1 relationship between subnet_id and alloc_id, I have to fetch the values from two data sources (line 47-61), use a common index (line 63-70). Can I neat it up and combine two maps into one? Not really. Because the second map (nat_gw_config) uses a data source depending on the subnets, which depends on the first map (subnet_config). Trying to combine the maps causes circular dependency!

Also, the additions of data sources makes the code less readable. As Marcel L pointed out in his post, two cons with for_each are: complexity and requiring a map (to store multiple attribute values). Now we seem to have one more: it may cause unintended deletions

Is `for_each` a bad idea?

Let’s find out why for_each could destroy two existing NAT gateways.

Notice that I built the map nat_gw_config by looping through the list of variable public_subnets_cidr_list. After apply, we appended it one more string at the end, without changing the existing order. However, the devil lies in the order of the string lists returned from the data sources. By printing this map, we found that the originally value before the AZ addition is:

index	alloc_id	subnet_id
0	eipalloc-0ffe32519d20b9b7f	subnet-0b4b202056c75bb0a
1	eipalloc-0ff1d32fb271b5cf4	subnet-09b6486fbe44e9795

Based on this, NAT Gateway with index 0 is created with eipalloc-***b7f and subnet-***b0a. NAT Gateway with index 1 is created with eipalloc-***cf4 and subnet-***795. After we add the third AZ, and apply the run, the new map, with a new alloc_id and a new subnet_id looks like this:

index	alloc_id	subnet_id
0	eipalloc-0b70460721596e33f (new)	subnet-0b4b202056c75bb0a
1	eipalloc-0ffe32519d20b9b7f	subnet-0335071ced2dc9922 (new)
2	eipalloc-0ff1d32fb271b5cf4	subnet-09b6486fbe44e9795

There are two factors at play. When the data sources return the ids (data.aws_subnets.public_subnets.ids and data.aws_eips.nat_eips.allocation_ids), the return is sorted. It doesn’t matter whether the order alphabetical or the opposite. Because in any given order, the randomly generated new ID, can fall anywhere in the list. In this particular result, the new alloc_id falls at the beginning, and the new subnet_id falls in the middle. As a result, NAT Gateway with index 0 and 1 are both changed. Therefore they have to be destroyed and replaced.

All these come from having to build a map. The values of each object in the map come from two different data sources. The values are not predetermined and contain a random part. When we add more AZ, the entire map get shuffled, leading to deletion of existing resources. Yikes.

Implementation with `for_each`: second attempt

The first draft of this post drew some ideas on Reddit. One redditor pointed out that the snippet above with for_each isn’t the optimal way. With some tricks to we can manage the map so that it maintain relative order if we have to add new AZ. The strategy is:

Avoid using data sources to retrieve attribute values
Use a unique key to identify objects in the map;
Directly look up from the resource by the unique key

We’re able to do #2 and #3 because when a resource has the for_each argument set, the resource itself becomes a map of objects. We can then locate that resource by the key. We can determine what that key is so long as it uniquely identifies the resource. Below is the revised code snippet with for_each:

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provider "aws" {}

variable "vpc_cidr_block" {
 type = string
 default = "147.206.0.0/16"
}

variable "public_subnets_cidr_list" {
 type = list(any)
 default = ["147.206.0.0/22", "147.206.4.0/22"] # 2 AZ
 #default = ["147.206.0.0/22", "147.206.4.0/22", "147.206.8.0/22"] # 3 AZ
}

data "aws_availability_zones" "this" {}

resource "aws_vpc" "base_vpc" {
 cidr_block = var.vpc_cidr_block
}

resource "aws_internet_gateway" "internet_gw" {
 vpc_id = aws_vpc.base_vpc.id
}

locals {
 subnet_config = {
 for cidr in var.public_subnets_cidr_list : md5(cidr) => {
 cidr = cidr
 az = data.aws_availability_zones.this.names[index(var.public_subnets_cidr_list, cidr)]
 }
 }
}

resource "aws_subnet" "public_subnets" {
 for_each = local.subnet_config
 vpc_id = aws_vpc.base_vpc.id
 cidr_block = each.value.cidr
 map_public_ip_on_launch = true
 availability_zone = each.value.az
}

resource "aws_eip" "nat_eips" {
 for_each = { for cidr in var.public_subnets_cidr_list : md5(cidr) => cidr }
}

locals {
 nat_gw_config = {
 for cidr in var.public_subnets_cidr_list : md5(cidr) => {
 subnet_id = aws_subnet.public_subnets[md5(cidr)].id
 alloc_id = aws_eip.nat_eips[md5(cidr)].allocation_id
 }
 }
}

resource "aws_nat_gateway" "nat_gws" {
 for_each = local.nat_gw_config
 subnet_id = each.value.subnet_id
 allocation_id = each.value.alloc_id
 depends_on = [aws_internet_gateway.internet_gw]
}

In this example, I use the MD5 hash of CIDR as the unique identifier key to ensure we have a consistent mapping between allocation id and subnet id. When a new AZ is created, the new allocation-subnet id pair will have its own new key. The unique key can be any identifier (even the CIDR itself) as long as it is unique and we do not change the selection of unique key after the first apply.

One more shot with `for_each`

The code snippet above got rid of data sources, but still have to leverage two local values (subnet_config and nat_gw_config) as helpers. Are they absolutely necessary?

Not really. The Terraform documentation has a page about References to Values, where it states:

If the resource has the count argument set, the reference’s value is a list of objects representing its instances.
If the resource has the for_each argument set, the reference’s value is a map of objects representing its instances.

In other words, using for_each with a map as input, we’re also creating a map as output, which is the resource array itself. The key is the same as the input map. Therefore, we can reuse the key. I know that sounds too abstract. Here’s the code refined:

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provider "aws" {}

variable "vpc_cidr_block" {
 type = string
 default = "147.206.0.0/16"
}

variable "public_subnets_cidr_list" {
 type = list(any)
 #default = ["147.206.0.0/22", "147.206.4.0/22"] # 2 AZ
 default = ["147.206.0.0/22", "147.206.4.0/22", "147.206.8.0/22"] # 3 AZ
}

data "aws_availability_zones" "this" {}

resource "aws_vpc" "base_vpc" {
 cidr_block = var.vpc_cidr_block
}

resource "aws_internet_gateway" "internet_gw" {
 vpc_id = aws_vpc.base_vpc.id
}

resource "aws_subnet" "public_subnets" {
 for_each = { for cidr in var.public_subnets_cidr_list : md5(cidr) => {
 cidr = cidr
 az = data.aws_availability_zones.this.names[index(var.public_subnets_cidr_list, cidr)]
 }
 }
 vpc_id = aws_vpc.base_vpc.id
 cidr_block = each.value.cidr
 map_public_ip_on_launch = true
 availability_zone = each.value.az
}

resource "aws_eip" "nat_eips" {
 for_each = { for cidr in var.public_subnets_cidr_list : md5(cidr) => cidr }
}

resource "aws_nat_gateway" "nat_gws" {
 for_each = aws_subnet.public_subnets
 subnet_id = aws_subnet.public_subnets[each.key].id
 allocation_id = aws_eip.nat_eips[each.key].allocation_id
 depends_on = [aws_internet_gateway.internet_gw]
}

Voila. I use md5 of the CIDR as the key again, first to create both aws_subnet and aws_eip. I also followed the example of chaining for_each between resource types. This way, when creating aws_nat_gateway, I can reference an instance in each resource array by the same key. Chaining for_each is very handy. But admittedly, it takes several iterations for me to get there. The code is neater, but not as straightforward to read due to the list/map comprehension.

Conclusion

I came across a team where the code review guideline favours for_each strongly. I see where that comes from after reading the book. But I don’t find count to be evil. That triggered my initiative to dive deep into this topic.

In this post we brought up a classic pattern of relationship between resources, and examined several ways to implement them using count and for_each. Using count can be straightforward but carries the risk of index shifting if additional element is added in the middle of the resource array. On the other hand, for_each is more powerful, but it requires some crafting with the Python-style list/map comprehension.

My recommendation is, start with a holistic look at the types of resources to create with loop, and how they are related with each other. Go with count if index shifting isn’t a risk. For example, when you need to create one instance of a resource conditionally. Otherwise, use for_each loop if the team is comfortable with the list/map comprehension. In some cases where we need to conditionally create several instances of the same resource, we can use a technique such as:

for_each = variable.disabled ? {} : data.any_resource.map
for_each = variable.disabled ? toset([]) : data.any_resource.list

In fact, the recommendation from AWS Terraform best practice is highly in favour of for_each.

Orchestrate Landing Zone with AWS Control Tower

Sat, 19 Aug 2023 17:25:00 -0400

Following an introduction to AWS Landing Zone, I’ll dive deeper into Control Tower as an orchestration tool in this post.

More on Landing Zone

In data center operation, there are numerous tasks that other teams have to complete before the deployment of an application. For example, the 42U server cabinet must be in place with dual powers. The cabinet comes with a network switch in the middle and each ethernet port must be provisioned. Once the server is connected to the central network, the NOC team assigns IP address, configure dynamic VLAN on the switches, and configures firewalls etc according to the connectivity requirement. The new server also needs to report to centralized monitoring solutions such as SolarWinds. In cloud operations, the scope and target of a landing zone is similar to those data center operations work, with networking being the core. The idea is that the landing zone ensures security, compliance and governance, so that applications (analogous to paratroopers) can focus on their primary responsibility.

Although Landing Zone is a general concept for any cloud service provider (CSP), each CSP has some prescriptive guidances on setting up landing zone in their particular environment. For example, an AWS prescriptive landing zone typically covers the following apsects:

Multi-account structure
Identity and Access Management
Governance (controls and guardrails)
Networking
Additional Security Services

The multi-account best practice is an important aspect in AWS as the OU structure dictates how effective Service Control Policies (SCPs) can govern the entire footprint. A landing zone orchestration solution should also apply guardrails and controls based on the organization’s security and compliance requirement. In addition, it is common expectation that landing zone orchestration solutions create required networking resources such that applications are ready to deploy securely.

This post is about Control Tower.

Intro to Control Tower

Control Tower is a landing zone orchestration solution available as an AWS service. I have three impressions over Control Tower. First, it is good with managing multi-account structure. It integrates closely with AWS Organizations and requires client to have a dedicated log archive account and a dedicated security tooling account, which also serves as audit account.

The second impression is Control Tower makes governance more straightforward. AWS seems to use the term control and guardrail interchangeably. Below is an illustration:

The detective controls only captures and reports violations. The preventive controls stops the violating API request. The proactive controls remediates the detected violations. Another way to look at the controls is by considering their priority:

Mandatory controls: there are about 23 mandatory controls that are enforced on each account. As soon as a new workload account becomes part of Control Tower, these controls will come enforced.
Optional controls including strongly recommended controls and Elective controls.

There are many existing controls and AWS is still releasing new one (example). AWS Config service uses conformance pack to organize relevant controls. For example, there are sample conformance packs such as “Operational Best Practices for “NIST 800 53 rev4” and “Security Best Practices for EKS”. In addition, you can even build your own conformance pack. Once you deploy a conformance pack, AWS config deploys additional rules against the current environment.

The third impression is that Control Tower falls short with infrastructure automation. It provides a number of customization mechanism to leave infrastructure (mostly networking) automation with users. I will explain later.

Mandatory Accounts

The function of Control Tower does not depend on a specific account structure that aligns with the multi-account best practice. However it does require three mandatory accounts to set up automatically when creating control tower. The account where Control Tower is configured is the management account. The log archive account is the owner of S3 buckets that hosts buckets for loggings. The audit account is a restricted account that’s designed to give your security and compliance teams read and write access to all accounts in your landing zone. We also use this account as delegated administrator account for several security services at organization level. I’ll elaborate in the next section.

The management account is directly under root OU and there is no SCPs applied to the management account. Also Control Tower does not turn on AWS Config recorder and configure delivery channel on the management account itself.

The log archive account and audit account are under the Security OU (or otherwise named during the setup). At the end of Control Tower setup a set of mandatory controls will be applied at the OU level and thus effective to each account.

Apart from the mandatory accounts, oftentimes there is an Infrastructure OU. Typically we place shared services account under this OU. The account is for common services such as central networking, managed Active Directory, DevOps pipeline etc. Another optional OU is workload OU, where we can create new workload accounts or enrol existing workload accounts.

Each enrolled workload account have its CloudTrail configured with an organization-level trail, which sends log to the central bucket that log archive account owns. Each enrolled account also has AWS Config recorder configured with a delivery channel pointing to the configuration log bucket also owned by log archive account. Note that Control Tower does not turn on recorder on the management account itself.

Account Enrolment

Most likely a customer is already in use of AWS Organization. They can delegate one account as management account, and use Control Tower console to create a landing zone for the intended regions. During the creation, they will have to create two additional account, one as log archive account, and the other as security tooling or audit account. This is in alignment with multi-account best practices. After the creation of landing zone, the two new account are automatically enrolled in Control Tower.

Going forward, the client should create new accounts using Control Tower’s Account Factory to save a separate enrolment step. For existing accounts and OUs we’ll need to enroll them into Control Tower. At the beginning, the Control Tower Landing Zone (CTLZ) only has the mandatory controls, which are the bare minimum governance. When we enroll existing OUs and accounts, these controls (mostly detective and preventive) will extend to the newly enrolled OUs or accounts. Therefore, it is important to not introduce numerous controls prior to having all accounts and OUs enrolled. It is important to have a proper OU hierarchy upfront because both detective and preventive controls (Config Rules and SCPs) are typically applied at OU level and effective to all the children of the OU.

To enroll an existing account, the account must meet some prerequisites. An IAM role with cross-account trust must be manually created. Since each account can have only one AWS Config configuration recorder and delivery channel, if an existing account already has one of them, they must be manually deleted using CLI.

Account Factory defines what happens when we create a new account and it is where we can bake in the customizations. When it comes to infrastructure automation, Control Tower leaves pretty much everything up to the account factory customization.

Delegated Administrator

Many AWS services are available as organization wide service. For example, CloudTrail, GuardDuty and even AWS Organization. Most of these organization level services allow you to specify one account as delegated administrator for the entire organization. For example, for System Manager and Service Catalog, the best practice is to designate an operation account or shared services account as the delegated administrator.

There are several security-related, organization-wide services, such as GuardDuty, Detective, Macie, Security Hub, Inspector, Audit Manager, and Firewall Manager. The best practices for these services are to designate the audit account as the delegated administrator. We think of the audit account as an aggregation point (or points for organizations that split the functionality across multiple accounts) for these AWS services. You can find this recommendation from the documentation of each service. However, currently Control Tower does not enforce this best practice. Even though Control Tower dashboard gives you visibility to security services such as GuardDuty and SecurityHub, you are still on your own to configure these services outside of Control Tower.

Whether you had never used these services, or you had previously designated another account as administrator for these services, it is good time to align with the best practice for delegated administrator right after Control Tower setup.

If you had previously designated other accounts, you need to take additional steps to revoke the old delegation and designate new delegated administrator account. These general steps can be performed from console or CLI and apply to all these services:

From the old administrator account, remove all the members
From the management account, revoke delegation
From the management account, delegate new administrator account (audit account)
From the new administrator account, invite all the members, and make sure to tell the service to automatically include new accounts going forward

Note that when you invite an account, that account must not have created, invited or associated with other accounts (as a member). In other words, we should never let a non-delegated administrator account create, invite or associate other account as member.

Control Tower Customization

Control Tower by itself does not play a big role in networking automation. You can create a VPC when provisioning a new account in Control Tower. That’s about what you can do. It does not address how the new VPC connects to existing networking space. It does not distinguish the existing VPC topology. To address this, Control Tower has a few customization options:

	AFC Account Factory Customization	AFT Account Factory Terraform	CfCT Customizations for Control Tower
Supported IaC language	Anything that Service Catalog Support (e.g. CloudFormation and Terraform HCL)	Terraform HCL	CloudFormation
Readiness	AFC is a native mechanism. You can specify the Service Catalog product during creation of an Account in Control Tower console.	Users need to first bootstrap the solution, which creates Terraform pipelines and other components.	Users need to first bootstrap the solution which creates a pipeline and other components.
How it works	Control Tower launches the specified Service Catalog product (aka blueprint) during account creation.	Quite complex. Explained in the section below	Control Tower posts a lifecycle events to Amazon EventBridge. A lambda function will process the event using pre-baked CloudFormation templates and Step Functions.
Prebuilt customizations	No. However, for certain products, there are some partner-maintained blueprints	Yes	No

AFC is fairly straightforward to use based on this post, as long as you know Service Catalog. It is a native capability of Control Tower. The other two ways for CT landing zone customization are not native capability and each require a separate bootstrapping process to deploy the pipeline-based solution.

Customizations for Control Tower

In CfCT, we first bootstrap the solution from management account, using this CloudFormation template on GitHub. We usually need to version control the custom configuration so we should select “AWS CodeCommit” as the value for the CodePipelineSource parameter. The bootstrapping (execution of this CloudFormation template) takes about 5 minutes.

The solution is based on Control Tower’s lifecycle event to trigger the pipeline with a source code repository (or S3 bucket). The source code repository (AWS CodeCommit) stores the custom configuration. A custom configuration consists of a manifest file in YAML format, made up with a number of resource sections. Each resource section references an artifact, either a CloudFormation template or a policy file to apply. The artifact can either be stored in the same repository, or in a remote S3 bucket.

Account Factory Terraform

Introduced in late 2021, the AFT solution is based on the terraform-aws-control_tower_account_factory repository. It is too complex as a solution in my opinion. At the beginning you need to have a designated OU and account for AFT and install the solution itself with Terraform. Note that, the solution does not address the state storage of the installation of the solution itself (chicken-or-egg). The installation also installed a few IAM roles required on the AFT management account. Having a landing zone management account and a AFT management account is not a neat setup.

Once installed, the solution consists of four repositories. Their purposes are as follows (copied from workshop instruction):

Account requests – handles placing or updating account requests. See example here.
AFT account provisioning customizations – manages non-Terraform customizations that are applied to all accounts. This stage runs before the global customizations stage. Examples available here
Global customizations – Global customizations – manages Terraform-based customizations that are applied to all accounts created by and managed with AFT. Examples available
Account customizations – Account customizations – manages Terraform-based customizations that are applied only to specific accounts created by and managed with AFT. Examples available

With this solution in place, when we create a new account via account request repo, quite a number of steps will happen after that, as the workshop instruction illustrated:

When I first looked into how this work I’m very concerned about the maintainability of this “solution”. The amount of services and pipelines involved in this solution makes it difficult to troubleshoot end to end. To improve that AWS even added an enhancement for request tracing. Nonetheless, I would not go with AFT just with how complex it looks like. For more details, check out the Control Tower workshop.

Summary

Control Tower helps you set up a Landing Zone without network infrastructure automation. It provides a couple of customization mechanisms, allowing you to bake in your own infrastructure automation. To build a landing zone, you can go with Control Tower in the following situations:

you have general regulatory requirement and need to enable governance capabilities
your networking stack isn’t centrally managed or isn’t large enough to warrant infrastructure automation
If you do need networking automation, then you will need the customization capability of Control Tower. In this case, your team should have good handle on infrastructure as code (CloudFormation or Terraform)

Out of the customization options, AFC seems the least complex to me. The other two, especially AFT, is too complex. We use pipelines to trail and error with other stacks, and the pipeline solution itself must be simple.

On the other hand, if you environment has strong regulatory requirement, or you are seeking a prescriptive network architecture, or your team does not have the capacity with CloudFormation or Terraform Templates, consider Landing Zone Accelerator on AWS.

EKS impression

Fri, 23 Dec 2022 18:18:19 -0400

I’ve worked on a few AKS projects previously. Since I joined AWS I wanted to put aside some time to check out EKS (Elastic Kubernetes Service). Here in this post, I put down my first impression on EKS, and also share my Terraform template in cloudkube project to create an EKS cluster.

Similar to AKS, EKS exposes API endpoint and the control plane components are hidden from AWS users. When creating EKS cluster it does not create the underlying VPC and subnets. Therefore, you have create an existing VPC and at least two subnets ahead of time, and specify them during EKS creation. Bear in mind that there is a list of requirement for the VPC and subnets.

In the cluster, the CNI that EKS officially supports is Amazon VPC CNI plugin. It is available as an add-on. Similar to Azure CNI, each Pod gets its own IP address. In addition, EKS supports other compatible CNI plugins such as Calico, Cilium, Weave Net and Antrea.

Computing Nodes in EKS

There are three modes to address computing capacity: self-managed nodes, EKS managed node groups and AWS Fargate. The documentation has a comparison table.

With self-managed nodes, users create EC2 instances separately and then register them to the control plane. The instances must use the same IAM role and AMI. You can use Auto Scaling groups of Bottlerocket (AWS-sponsored purpose-built Linux distro for container host) nodes. The self-managed node option is mostly for AWS outpost customers who bring in their own computing capacity from data centre.

If you provision computing capacity from AWS, it makes sense to assign EKS managed node groups when creating EKS cluster. We can turn on Cluster Autoscaler, a Kubernetes construct to manage the auto scaling of node groups. Sometimes we want to have more than one node groups. For example, to build a multi-architecture cluster, we need one node group with amd64 nodes and the other with arm64 nodes (e.g. instances with Graviton processor). In general, arm-based CPU delivers better performance with less power consumption and the industry is slowly moving towards more arm-based CPU architecture.

Fargate is what I call managed computing service for EKS. With Fargate you do not need to tweak Cluster Autoscaler to self-manage computing capacity. The Fargate documentation has a long list of considerations. For example, Pods must match a Fargate profile (here‘s an example) at the time that they’re scheduled to run on Fargate. So we need to build Fargate profile and Pod labelling properly. Also, Fargate does not support DaemonSet. Another big consideration is that Fargate does not support non-VPC CNI. In my opinion these are pretty significant limitations. Many workloads (system-level or application-level) would need Daemonset (e.g. kube-proxy, some CNI or CSI drivers, Dynatrace monitoring).

The pro of Fargate is the serverless computing model. The construct of a Fargate profile isn’t complicated. You just specify subnets, namespace and labels. However, the downside is the long list of considerations. Some teams may consider these restrictions too much. The other overhead is the need to manage Fargate profile to ensure all Pods are scheduled somewhere.

To me, using Fargate alone impairs portability of workload. The good thing is that Fargate and Managed Node Group are not mutually exclusive on a cluster. In most cases, we can go partially serverless, and reap the benefits of both of them.

Node AutoScaling

For workloads that don’t have a matching Fargate profile, we have to figure out node autoscaling ourselves. I touched on Cluster Autoscaler in “Autoscaling on Kubernetes Platform“. CA works on AWS as well and is triggered upon a Pod coming to unschedulable status in Scheduler. There is some limitations though. For example, CA interacts with Autoscaling Group (instead of EC2 instances directly). When it determines it’s time to scale up, it bumps up the desired capacity by one at a time in the Autoscaling group. The configurations in Autoscaling group may also be at play and CA do not have direct control. For example, the “scaling cooldown“. The pool of nodes is homogenous as per the pre-configured launch template and CA has no control. If a Pod requires a different type of node (e.g. ARM64 CPU, spot instance, etc), then we’d first have to create a node group with the desired node type. Moreover, in the worst cases, one-at-a-time scale-up does not meet the increase of demand driven by Pod increases, causing nuances such as racing conditions.

Because the Cluster Autoscaler doesn’t really deal with the nodes themselves, this kind of integration is clunky and slow. Nearly half of Kubernetes customers on AWS report that configuring cluster auto scaling using the Kubernetes Cluster Autoscaler is challenging and restrictive, according to this blog post. As a result, AWS launched an open-source cluster autoscaler project, Karpenter. Karpenter first only supported EKS but now the support includes other CSPs. For EKS, Karpenter directly interact with different types of EC2 instances.

Karpenter makes node scaling work in a more cloud-native manner. In the presence of unschedulable Pods, Karpenter bypasses the Kubernetes scheduler and works directly with the Cloud provider, to launch the minimal compute resources needed to fit those Pods and immediately binds the Pods to the newly provisioned Nodes without waiting for scheduler. As Pods are removed or rescheduled to other nodes, Karpenter looks for opportunities to terminate under-utilized nodes. Karpender defines a CR called Provisioner to specify node provisioning configuration, such as instance size, zone, CPU architecture, etc. It is a manifest that describes a node group so the node scaler is aware of all the available node types. You can have multiple Provisioners for different needs, just like node groups. The Provisioner CR can also set TTL for empty Nodes, such that once a Node has no pods other than DaemonSet, Karpenter will terminate the Node on TTL expiry.

Karpenter’s idea is similar to the idea of AutoPilot cluster in GKE. The new EKS workshop has an section on how to set up CA and Karpenter in practice.

Identity Management for EKS

For IAM, we need to be concerned with three aspects. The management traffic to the cloud service, the management traffic for Kubernetes cluster and business traffic.

Traffic type	AWS	Azure
I. Cloud Service Endpoint (Management Traffic for Cloud Service)	AWS IAM identity	Azure RBAC
II. Kubernetes API (Management Traffic for K8s Cluster)	IAM mapping or OIDC	Azure RBAC (implementation of OIDC)
III. Business traffic	Up to Kubernetes Ingress	Up to Kubernetes Ingress

For business traffic (type III), it is all up to the Ingress. I’ve written another article on managing ingress traffic on Kubernetes platforms. We interact with cloud service endpoint (type II) with either AWS CLI or Terraform, to create any object, including resources needed for a cluster. This is generally how we work with cloud service, not specific to Kubernetes. Usually the IAM identity assumes another IAM role, which empowers it with a lot of permissions.

For access to Kubernetes API (type III), EKS supports OIDC and IAM mapping. AWS documentation refers to this as “Cluster Authentication“. There is one special scenario where your identity for type II access inherits your identity for type I access. As the document puts:

When you create an Amazon EKS cluster, the AWS Identity and Access Management (IAM) entity user or role, such as a federated user that creates the cluster, is automatically granted system:masters permissions in the cluster’s role-based access control (RBAC) configuration in the Amazon EKS control plane. This IAM entity doesn’t appear in any visible configuration, so make sure to keep track of which IAM entity originally created the cluster.

This special scenario (I call it the “implicit master user“) allows us to perform critical activities on the cluster, such as creating IAM mapping, or OIDC configuration.

The above addressed how AWS resource access Kubernetes resource. On the other hand, to address how a Kubernetes resource access AWS resources, we need IRSA (IAM Roles for Service Account). We have a service account in Kubernetes and map it to an IAM role.

AppMesh

AppMesh is AWS’ Envoy based service-mesh offering supporting Kubernetes cluster, ECS service and even EC2 instance. AppMesh’s control plane is a managed AWS service, with a controller running on the Kubernetes cluster. To install AppMesh on the cluster:

On the EKS cluster, install AppMesh Controller using Helm
Associate the cluster with IAM OIDC provider
Create an IAM role for the appmesh-controller service account

After these steps, you can create a mesh using CloudFormation, Terraform, etc. The data plane (Envoy proxy) can run on Kubernetes (as sidecar). Traffic between control plane and data plane can go through private link (Interface VPC endpoint) for added security. Like Istio, AppMesh enables mTLS. For observability, you can export Envoy metrics with Prometheus. Coupled with XRay, AppMesh also supports distributed tracing.

AppMesh uses a different set of CRDs than Istio. Key CRDs are:

Mesh: represents an entire service mesh. At mesh level you can configure Egress filter (to allow or deny external traffic) and set IP version (v4 vs v6)
VirtualGateway: a CRD that represents an Ingress in to the Mesh. A virtual gateway allows resources that are outside of your mesh to communicate to resources that are inside of your mesh. A virtual gateway references Envoy proxy deployment by podSelector. It references GatewayRoutes by namespaceSelector, and optionally gatewayRouteSelector. You also specify listeners in the manifest to reference Envoy proxy Service (LoadBalancer Type).
GatewayRoute: A gateway route is attached to a virtual gateway and routes traffic to an existing virtual service. If a route matches a request, it can distribute traffic to a target virtual service. In the manifest, you specify a list of httpRoute, each with matching condition and action. In the action section you can specify virtualService as target.
VirtualService: an abstraction of a real service provided by a virtual node directly or indirectly by means of a virtual router. Dependent services call your virtual service by its virtualServiceName, and those requests are routed to the VirtualNode or VirtualRouter that is specified as the provider for the VirtualService.
VirtualRouter: Virtual routers handle traffic for virtual services. In a virtual router manifest, you can define Route to direct incoming requests to virtual nodes as target.
VirtualNode: A virtual node acts as a logical pointer to a particular task group (i.e. ECS service, Kubernetes deployment). It represents a Service in the AppMesh. In the manifest, you reference Pods by podSelector, specify listeners for any inbound traffic that your virtual node expects, and specify serviceDiscovery for your task group.

You can configure those Custom Resources using YAML manifests (and check the API reference a lot). Alternatively, you can configure them from AWS CLI or AWS console. The console will help you visualize what can be configured. For further details on how these CRs play together, there is a workshop for AppMesh.

EKS cluster using Terraform

Officially, there is an EKS blueprint project for provisioning EKS cluster in Terraform.

I also keep my own Terraform code in the AWS directory of cloudkube project. It works out to be a little more complex than my Terraform template to create Azure Kubernetes Cluster (Azure directory). Because I had to create Cognito resources with initial credential to allow users to connect to cluster without using the implicit master account.

Below is the diagram of the processes.

Create EKS cluster with Terraform module

The template configures kubectl access on a Bastion host, which assumed the same role that our IAM user uses to create the Kubernetes cluster. Therefore, the IAM role is the master identity.

Note that the IAM user (power-user) has very powerful permissions. Usually it is ideal to assign lots of permission to IAM Roles (temporary credential) instead of IAM user (long-term credential). So the role chaining would look like:

The IAM user that Terraform uses has no permission other than assuming a “PowerUser” role
The PowerUser role trusts the IAM user. It also has the permission to assume the “EKS-Manager” role
The EKS-Manager role trusts PowerUser’s role session.

However, the role chaining scenario above is not currently supported in Terraform.

I use a Bastion host because the cluster endpoint is on private subnet. The bastion host is on a public subnet. However, if we do not like public subnet and public IP, we can place the bastion host on a private subnet, and use SSM system manager agent with SSH tunnel plugin to have SSH access to private bastion host.

Summary

I first came across this article about EKS and its awfulness and then decided to check out EKS. I’m not sure all points are still valid but it’s generally real-life experiences. There are also many peripheral services, such as AMP (AWS Managed Prometheus), AMG (AWS Managed Grafana), ADOT (AWS Distro for Open Telemetry), AppMesh (Another Envoy-based Service Mesh, easier to manage than Istio but less Powerful), with a lot to explore.

Kubernetes Storage on Azure 1 of 3 – built-in storage and NFS

Sun, 31 Jul 2022 15:22:00 -0400

In the previous post, we understand that to host stateful workload, we need to manage persistent storage to the Kubernetes platform. In this post, I will explore the different storage options. These options are specific to Azure Kubernetes service. However, the principals apply to any Kubernetes platform regardless of cloud vendor.

In another old post, I discussed in-tree and CSI storage classes, and from a developer’s perspective, how to mount volumes statically and dynamically once the storage class is available. Here in this post we are concerned with how to make storage classes available, from a platform specialist perspective.

Storage in Azure Kubernetes

As soon as the Azure Kubernetes cluster is launched, a number of built-in storage classes are available. Unlike third-party storage classes, they do not require kubelet identity to be contributor for node resource group. As discussed we shall use CSI based storage classes.

managed-csi
managed-csi-premium
azurefile-csi
azurefile-csi-premium

The main difference between them is the backing technology. However, form Kubernetes workload’s perspective, the Pods as storage consumers are concerned with the access mode instead of backing technology. Here are supported access modes:

ReadWriteOnce: read-write by a single node
ReadOnlyMany: read only by many nodes
ReadWriteMany: read-write by many nodes
ReadWriteOncePod: new in Kubernetes 1.22 to restrict volume access to a single Pod

The storage classes managed-csi and managed-csi-premium support ReadWriteOnce. The storage classes azurefile-csi and azurefile-csi-premium support ReadWriteMany.

Apart from these built-in options, Azure also suggests a few more options based on other types of Azure resources. For example, AKS can integrate with HPC cache and it for HPC. We can also self-manage a virtual machine configured as NFS server, and use the NFS subdir external provisioner to configure storage class. Despite of the overhead with managing a VM, you have more configurability. My previous client reports that they gain better performance than the built-in options.

Another alternative is Azure NetApp Files. However, being a full enterprise grade solution (similar to FSx ONTAP), Azure NetApp Files costs an arm and a leg. In this comparison, it cost 60 to 100 times as the cost by built-in options.

Another option is Azure Ultra Disk, which needs to be enabled at cluster level. You can provision performance target (DiskIOPSReadWrite and DiskMBpsReadWrite) in the storage class. Ultra Disk is a good middle ground between the pricey NetApp files and the less performant built-in options.

Terraform Template for AKS

To explore the storage options, I use my own terraform template to create an AKS cluster. The template is in the azure directory of the cloudkube repo. The template consists a few configurations with Azure Kubernetes to facilitate storage configuration. First, it configures an SSH key pair to use between the bastion host and the node. Users can SSH to Kubernetes nodes from bastion host as soon as terraform apply is completed. Second, the third party storage options installed after the cluster creation need their Pod to instruct Azure to create Azure disks. This requires that a Kubernetes node agent have the permission to provision resources in the node resource group. This is important to understand because there are a couple of managed identities at play (refer to this post) when building an AKS cluster.

In Azure Kubernetes, it is the managed identity of kubelet, that needs to have contributor permission over the resource group for the nodes (not the one for the AKS cluster itself). A managed identity is expressed by a client ID, an object ID (aka principal ID), and the identity ID. We can find them out with an AZ CLI command as below:

We can also tell that the kubelet managed identity represents node agent, by connecting to a node and looking at the argument (kubernetes.azure.com/kubelet-identity-client-id) of kubelet process:

In the template, I also chose to designate the same BYO identity for both the cluster and for kubelet (node agent), in order to minimize my requirement on permission. If I had left it with a system assigned identity for node agent, I would have to assign that identity as a contributor for the node resource group, either as a user, or via Terraform’s identity. Either way, it is beyond what a Contributor is allowed to do.

Benchmarking with kubestr

I used fio utility for storage benchmarking from virtual machines. However, fio utility is not for container. For fio testing on Kubernetes, I’d have to use a Docker image, and test with target volume attached. Fortunately, the Kasten team shared their initiative in the open source project Kubestr. The kubestr release is available as an executable on common platforms. It connects to the cluster the same way as kubectl and here is a demo. To begin with, download the utility to bastion host, and run it without any argument, which prints the storage classes and volume snapshot classes:

curl -L -o kubestr.tar.gz https://github.com/kastenhq/kubestr/releases/download/v0.4.31/kubestr_0.4.31_Linux_amd64.tar.gz
tar -xvf kubestr.tar.gz && rm kubestr.tar.gz && chmod +x kubestr

./kubestr # if kubectl is configured, this command will print out the details of storage classes and volume snapshot classes

In addition to outputting details, it is also very simple to perform storage benchmarking with kubestr. All we need to do is giving it the storage class name and it will run four tests by default with common global options (ioengine=libaio verify=0 direct=1 gtod_reduce=1). The four tests are:

JobName	block_size	filesize	iodepth	rw
read_iops	4k	2G	64	randread
write_iops	4k	2G	64	randwrite
read_bw	128k	2G	64	randread
write_bw	128k	2G	64	randwrite

During each test, it measures and reports IOPS and bandwidth (throughput). If your I/O profile falls out of the four jobs, you can even customize your test by supplying a fio config file. For example, you need a longer test duration, or you need a larger total size for the test. Before the test, kubestr automatically mount their test volumes using the storage class being tested.

./kubestr fio -s my-storage-class # benchmarking a storage class

For read_iops and write_iops, we mainly look at the IOPS. For read_bw and write_bw, we mainly look at the bandwidth. The iops and bw based on samples are reported as first line of result, followed by min, max and average.

Metrics

With kubestr I ran a performance test amongst the native storage classes with Azure Kubernetes Service, with results as below:

	read_iops	write_iops	read_bw	write_bw
managed-csi	IOPS=314.729797 BW(KiB/s)=1275	IOPS=297.071136 BW(KiB/s)=1204	IOPS=315.311188 BW(KiB/s)=40887	IOPS=261.048645 BW(KiB/s)=33941
managed-csi-premium	IOPS=493.395844 BW(KiB/s)=1990	IOPS=426.731812 BW(KiB/s)=1723	IOPS=455.950348 BW(KiB/s)=58894	IOPS=422.888855 BW(KiB/s)=54662
azurefile-csi	IOPS=259.333282 BW(KiB/s)=1053	IOPS=283.985779 BW(KiB/s)=1152	IOPS=240.447403 BW(KiB/s)=31298	IOPS=230.689804 BW(KiB/s)=30048
azurefile-csi-premium	IOPS=394.044739 BW(KiB/s)=1586	IOPS=371.181793 BW(KiB/s)=1494	IOPS=380.360535 BW(KiB/s)=49018	IOPS=491.313446 BW(KiB/s)=63310

These numbers are based on default test parameters (e.g. 100Gi PVC size). The numbers indicate that block storage generally performs better in default setting. So we should use managed disk instead of azure file unless multiple pods needs to access the same volume.

Validate Persistent Storage

We can use mysql as a quick and dirty test of storage classes. We can deploy the following manifest:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
 name: mysql-pvc
spec:
 storageClassName: managed-csi-premium # the storage class being tested
 accessModes:
 - ReadWriteOnce
 resources:
 requests:
 storage: 5Gi
---
apiVersion: v1
kind: Secret
metadata:
 name: mysql
data:
 password: eHl6  # base64 code of xyz
---
apiVersion: apps/v1
kind: Deployment
metadata:
 name: mysql
 labels:
 app: mysql
spec:
 replicas: 1
 selector:
 matchLabels:
 app: mysql
 template:
 metadata:
 labels:
 app: mysql
 spec:
 containers:
 - image: mysql:5.6
 name: mysql
 env:
 - name: MYSQL_ROOT_PASSWORD
 valueFrom:
 secretKeyRef:
 name: mysql
 key: password
 ports:
 - containerPort: 3306
 name: mysql
 volumeMounts:
 - name: mysql-persistent-storage
 mountPath: /var/lib/mysql
 volumes:
 - name: mysql-persistent-storage
 persistentVolumeClaim:
 claimName: mysql-pvc
---
apiVersion: v1
kind: Service
metadata:
 name: mysql-service
 labels:
 service: mysql
spec:
 selector:
 app: mysql
 ports:
 - name: tcp-mysql
 protocol: TCP
 port: 3306
 targetPort: 3306

Once the Pod has been created, then we use a throw-away Pod to connect to mysql service and build some data:

$ kubectl run mysql-cli --rm -i --tty --image imega/mysql-client -- /bin/sh
If you don't see a command prompt, try pressing enter.
/ # mysql --host=mysql-service.default.svc.cluster.local --user=root --password=xyz
Welcome to the MariaDB monitor. Commands end with ; or \g. Your MySQL connection id is 9 Server version: 5.6.51 MySQL Community Server (GPL) Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. MySQL [(none)]>

From the mysql shell, we create a test database with dummy data populated:

CREATE DATABASE `testdb`; 
USE testdb; 
CREATE TABLE IF NOT EXISTS tasks ( 
 task_id INT AUTO_INCREMENT PRIMARY KEY, 
 title VARCHAR(255) NOT NULL, 
 description TEXT, 
 created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP 
) ENGINE=INNODB; 
INSERT INTO tasks (title, description) VALUES ('Job A','Morning Standup');
INSERT INTO tasks (title, description) VALUES ('Job B','Latte with two shots of espresso');
INSERT INTO tasks (title, description) VALUES ('Job C','Coding coding and coding');
INSERT INTO tasks (title, description) VALUES ('Job D','git commit');

Each SQL command should return with number of rows affected and then we can exit the MySQL shell and Pod shell. Once we exit out of the Pod shell, the Pod is deleted. We can re-connect to validate the data are still present:

$ kubectl run mysql-tester --rm -i --tty --image imega/mysql-client -- mysql --host=mysql-service.default.svc.cluster.local --user=root --password=xyz --database testdb --execute='SELECT * FROM tasks;'
If you don't see a command prompt, try pressing enter.
Got error: Access denied for user 'root'@'147.206.3.15' (using password: NO)
+---------+-------+----------------------------------+---------------------+
| task_id | title | description | created_at |
+---------+-------+----------------------------------+---------------------+
| 1 | Job A | Morning Standup | 2022-06-22 20:29:12 |
| 2 | Job B | Latte with two shots of espresso | 2022-06-22 20:29:12 |
| 3 | Job C | Coding coding and coding | 2022-06-22 20:29:12 |
| 4 | Job D | git commit | 2022-06-22 20:29:13 |
+---------+-------+----------------------------------+---------------------+
Session ended, resume using 'kubectl attach mysql-tester -c mysql-tester -i -t' command when the pod is running
pod "mysql-tester" deleted

The output validates the persistent storage of the data. When testing a different storage class, simply start over with a different storage class specified in the PVC part of the manifest.

Summary

This post focuses on storage options for persistent volumes on Azure Kubernetes service. I use my own Terraform template with some custom configuration. I also covered kubestr as benchmarking tool and ran it against the built-in storage classes.

In some situations, the built-in options do not suit your needs. For example, you might want your application to use persistent volume in a consistent way across multiple cloud vendors. We will have to resort to third-party software defined storage layer. In the next post, I will explore a couple of SDS-based options, namely Portworx and Ceph. The custom configurations in the Terraform template will be helpful when we configure Portworx.

AKS Lessons Learned 2 of 2

Sat, 18 Dec 2021 01:18:00 -0400

Even though Azure Kubernetes Service (AKS) is a managed service, building a cluster is not trivial. For help resources, I would start with the webinar “Configure Your AKS cluster with Confidence” from April 2021, which focuses on a set of working best practices (convention over configuration) but obviously not every recommendation suits every use case. For a deeper technical tour, the John Savill’s Technical Training channel has good videos (from 2020) on AKS overview, high availability and networking. Lastly, there is also an AKS checklist to remind you of the implementation details to consider.

All the references aside, I need to write down some gotchas from my implementation experience in the last two month.

Identity and Access Management

AKS is a special type of Azure resource in the sense that it manages other Azure services on user’s behalf. Therefore the access management needs to consider several aspects:

Access Type	Mechanisms involved	Example
User access Kubernetes API	Azure AD, Azure RBAC and Kubernetes RBAC. – Azure AD is for authentication – Azure RBAC for Kubernetes – Kubernetes RBAC	A user connects to Kube-API server using kubectl
AKS access other Azure resource	There are several identities that represents different components of AKS. For example, the AKS cluster, the node agent pool, and each add-on. The AKS cluster can be represented as a service principal, or managed identity (system assigned or user assigned). The node agent pool can be represented as a managed identity	AKS cluster connects to a VNet in a different resource group. (requiring cluster’s identity to have network contributor role on the target network resource group) AKS node agent pulls images from ACR (requiring the node agent pool’s identity to have ArcPull role on the target ACR)
Pod access other Azure resource	AAD-Pod Managed Identity	Business workload connects to managed database service such as PostgreSQL on Azure.
Pod access Kubernetes API	Access Kubernetes API using Service Account. This issue is solved completely by Kubernetes native mechanisms. Roles and ClusterRoles define permissions. RoleBindings and ClusterRoleBindings associates Service Accounts with permissions	Workload access ConfigMap, Secret etc.

In the first access type, for RBAC with user to access Kubernetes API, there is an overlap between Azure RBAC and Kubnernetes RBAC. Azure RBAC has four built-in roles and three of them (reader, writer, admin) are namespaced. When you use Azure CLI to manage to assign one of those roles, the rolebinding and cluster rolebinding record stored in etcd will be updated accordingly.

RBAC mechansim	Use case
Azure RBAC for Kubernetes	Manage RBAC programmatically using Azure CLI, or infrastructure as code
Kubernetes RBAC	Manage RBAC declaratively with more granularity for all types of Kubernetes resources including CRD

For ease of operation it is advised to use Kubernetes RBAC whenever possible. Azure RBAC is still used for RBAC at the level of Azure resource but not at the level of Kubernetes resource.

In the second access type, AKS cluster may use managed identity or service principal. Azure’s recommendation is managed identity over service principal. Managed Identity is a wrapper around Service Principal with less overhead. Managed Identity can be system assigned (created at the time of cluster creation), or user assigned (can be created ahead of time by Azure administrator and imported to the cluster’s context).

The second access type can be further broken down because there are several components in AKS that uses their own identities. I list the related managed identities as below:

Name	Purpose	BYO identity with Terraform
cluster identity	This identity represents the cluster	Specify in identity block in kubernetes_cluster resource
agent pool identity	This identity represents kubelet running in the agent pool	Specify in kubelet_identity block.
addon: azurepolicy	This identity represents azure policy addon to access the policy	N/A
addon: omsagent	This identity represents OMS agent to access monitoring etc	Specify in oms_agent_identity block
addon: secret	This identity represents to the secret addon, to access AKV	Specify in secret_identity block
addon: ingress gateway	This identity represents the ingress application gateway	ingress_application_gateway_identity block

By default, the system creates a new managed identity for each of the required identity above. For simplicity with identity management, we may create a managed identity and use it for all the occasions where an identity is needed and user assigned (BYO) identity is supported.

In the “az aks show” command return (a JSON document), the identity section (root level) reports the cluster identity, the identityProfile section (root level) reports the agent pool (kubelet) identity. Other identities such as omsagent, are reported in their own child document.

Node Networking

In Azure, a subnet can span across multiple availability zones. Therefore an AKS cluster can put its nodes on a single subnet with nodes evenly distributed across three AZs for high availability. The AZ of each node is indicated in the node label, and can be displayed with kubectl command.

Within a single AZ, a good practice to minimize latency between nodes is to place the nodes in a proximity placement group (PPG). However, only a single PPG can be associated with a node group. You can’t have three PPGs, one in each AZ, for a single subnet.

Pod Networking

The default Pod networking model is kubenet, which involves overlay network. Pod-to-Pod traffic across nodes requires Network Address Translation (NAT). To overcome this performance tax, Azure introduces Azure CNI which gives each Pod an routable IP address from the VNet’s CIDR. This requires advanced IP planning to prevent IP exhaustion. A risk introduced in Azure CNI is that all Pods are exposed on the V-net, which needs to be protected by Network Security Group and/or outbound firewall.

DNS

On the DNS side, when AKS cluster integrate with an external node network, it may create weird issues that are hard to troubleshoot. Another example is with DNS. If the V-Net uses an external DNS server (which is common for enterprises with hybrid network to use an on-premise DNS server), then the cluster creation failed with time-out with misleading error messages (for example, this comment). This is because the DNS name of the newly created cluster is not resolvable within the V-NET, which points to the on-prem DNS server. The fix to that is:

Use a BYO DNS zone (in Azure) for AKS cluster creation;
The AKS cluster will publish the A-record to the zone. To allow this to happen, the AKS cluster’s managed identity needs to have DNS contributor permission for the zone;
Configure the on-prem DNS for conditional forwarding to the DNS zone

This fix will allow AKS to resolve its name and therefore confirm its own creation. Here is a good blog about the DNS zone options.

Another potential issue introduced with the use of on-prem DNS server, is the resolution of single-label hostname of the nodes. This is not just an issue in the context of AKS. It is a generic issue with VMs running on a V-Net pointing to on-prem DNS, as explained in detail here.

In this situation, we should use the fully qualified hostname instead of single-label hostname. The fully qualified hostname with DNS suffix can help the on-prem server to configure conditional forwarding. For example, when the DNS suffix is *.internal.cloudapp.net, then forward it to Azure’s virtual internal DNS server 168.63.129.16 which can resolve the hostname.

If only the Pods need to resolve those FQDNs, then we can configure Core-DNS with conditional forwarding, which will take effect only at the cluster level without the need for changing the on-prem DNS. The Core-DNS configuration looks like this:

apiVersion: v1
kind: ConfigMap
metadata:
 name: coredns-custom # this is the name of the configmap you can overwrite with your changes
 namespace: kube-system
data:
 cloudapp.override: | # you may select any name here, but it must end with the .override file extension
 log
 rewrite continue {
 name regex ^(.*[0-9]{7}-vmss[0-9]{6})$ {1}.internal.cloudapp.net
 answer name ^(.*)\.internal\.cloudapp\.net$ {1}
 }
 forward internal.cloudapp.net 168.63.129.16
 cloudapp.server: |
 internal.cloudapp.net:53 {
 errors
 log
 cache 10
 forward . 168.63.129.16
 }

Alternatively, use Pod DNS policy so that the Pod can use an external DNS server.

Initial Service Account

When a cluster is created, an Azure AD user or group can be assigned as cluster administrator. For a CI/CD pipeline to interact with the newly created cluster, a service account in Kubernetes is needed. Suppose we use Terraform to create the AKS cluster, we can create such service account automatically with the Kubernetes provider. This requires that the Terraform execution environment to have network access to the cluster. If the AKS cluster is located in a private network, then the agent where Terraform CLI runs should also be on the network. Alternatively, use Terraform Enterprise hosted in an environment with access to the cluster’s network.

Integration with Azure KeyVault

Azure Key Vault can store several types of secrets, key value pair, X509 keys and certificate. When AKV is integrated with an AKS cluster, the Kubernetes workload can access the secrets as mounted volumes, using CRD named SecretProviderClass. Further, they can be presented as Kubernetes Secret, using a Pod to sync between mounted content and Secret. AKV has three types of entries: key, certificate and secret (key-value). The certificate entry requires both key and certificate are stored, with optional certificate chain. In my opinion this is an over design. Unless we need Azure to manage the certificate (e.g. rotation) I would simply use secret to store my own X509 key and certificate.

Infrastructure deployment in Terraform 2/2

Fri, 05 Nov 2021 01:05:00 -0400

In a previous post, I introduced Terraform Cloud and covered how to use AWS profiles with Terraform. This time I explored some alternatives to Terraform Cloud, in the context of Azure. I use Scalr as an example of multi-cloud management platform. I will also discuss some issues I’ve came across while managing permissions and variables for Terraform.

Scalr

Scalr is a multi-cloud management platform. I first used it in January but since then it seemed to focus on being a collaboration platform for Terraform. It organizes deployment by environments and workspaces. Accounts in the free tiers is allowed to have one Environment. You will also need to configure (cloud) providers and VCS providers. Once configured, it is important to link a cloud provider with an Environment. Each workspace inside of an Environment can be associated with a VCS provider. In the case of Terraform, this limits a workspace with a single cloud provider.

Permission with Azure

I have a resource group (e.g. named AutomationTest) under a subscription. My account has Contributor role of this resource group. To run Terraform, I could login to Azure as my own account on my environment using AWS CLI. Terraform will pick up the session from Azure CLI and execute as my user. However, it is recommended to run Terraform as a separate own entity. This would allow me to run Terraform template from Scalr, or Terraform Cloud. It is also a good practice for Terraform to use a separate account than a regular user account. There are a number of ways to do this as suggested on the guides for Terraform azurerm provider, including:

Authenticating via a Service Principal and a Client Secret
Authenticating via a Service Principal and a Client Certificate
Authenticating via Managed Identity
Authenticating via the Azure CLI, only recommended when running Terraform locally.

I chose the first option and followed the instruction, using the following CLI command to create the service principal:

az ad sp create-for-rbac -n tf-sp --role="Contributor" --scopes="/subscriptions/9dd2c898-8111-4322-91d6-a039a00bd513/resourceGroups/AutomationTest"

The command returns a few attributes (client ID, tenant ID, secret) that I needed to configure cloud providers in Scalr. The service principal will also be visible under App Registrations in Azure. Once configured I needed to link the provider to an Environment, for Scalr to make an connection to Azure. Otherwise, the Scalr run will return the following Error:

Terraform error

Once provider linking is completed, Scalr automatically populate required environment variables in the workspace. They show up as “Shell” variables under VARIABLES tab.

Under the Terraform tab are input variables that you wish to put in for Terraform template to pick up. Then you can run the template. This works well until I came across a permission issue when I added azurerm_role_assignment resource in Terraform template. What I was trying to do is something like this:

resource "azurerm_role_assignment" "admin_assignment" {
 scope = var.rbac_aks_id
 role_definition_name = "Azure Kubernetes Service RBAC Admin"
 principal_id = var.rbac_principal_object_id
}

And whenever at this line, the following error returned:

Apparently the code 403 indicates Azure doesn’t think the Terraform Service Principal has the privilege to perform Microsoft.Authorization/roleAssignments action. The reason dates back to the way I created service principle above, where I specified contributor role for resource group. However, contributor as a built-in role does not include the permission to assign roles in Azure RBAC. To address this issue, I needed a custom role, named TerraformContributor, with the following definition:

 {
 "assignableScopes": [
 "/subscriptions/9dd2c898-8111-4322-91d6-a039a00bd513/resourceGroups/AutomationTest"
 ],
 "description": "Grants full access to manage all resources, but does not allow you to manage assignments in Azure Blueprints, or share image galleries.",
 "id": "/subscriptions/9dd2c898-8111-4322-91d6-a039a00bd513/providers/Microsoft.Authorization/roleDefinitions/637824aa-52ae-42f6-a24e-26b2a443afdf",
 "name": "637824aa-52ae-42f6-a24e-26b2a443afdf",
 "permissions": [
 {
 "actions": [
 "*"
 ],
 "dataActions": [],
 "notActions": [
 "Microsoft.Blueprint/blueprintAssignments/delete",
 "Microsoft.Compute/galleries/share/action",
 "Microsoft.Blueprint/blueprintAssignments/write"
 ],
 "notDataActions": []
 }
 ],
 "roleName": "TerraformContributor",
 "roleType": "CustomRole",
 "type": "Microsoft.Authorization/roleDefinitions"
 }

Compare this with the JSON statement of built-in contributor role, the exclusion of actions for Microsoft.Authorization are removed. The creation can be completed with CLI command “az role definition create” in the subscription, or use Azure portal. Once the role is created, create a new service principal using the

az ad sp create-for-rbac -n tf-sp --role="TerraformContributor" --scopes="/subscriptions/9dd2c898-8111-4322-91d6-a039a00bd513/resourceGroups/AutomationTest"

This solution is suggested on this thread.

Composite type for Input variable

Sometimes we want to define an input variable that describes a hierarchy of attributes on a resource. A good example would be Azure Kubernetes service. We can use either object or map as the variable type. The example below has a variable of each type.

variable "cluster_detail" {
 description = "AKS cluster"
 type = object({
 resource_group = string,
 cluster_name = string,
 kubernetes_version = string,
 node_subnet = object({
 subnet_name = string
 vnet_name = string
 resource_group = string
 }),
 pod_subnet = object({
 subnet_name = string
 vnet_name = string
 resource_group = string
 }),
 lb_subnet = object({
 subnet_name = string
 vnet_name = string
 resource_group = string
 }),
 ad_admin_group_object_ids = list(string)
 })
}

variable "common_tags" {
 description = "common tags"
 type = map(any)
 default = {
 tagA = "valueA"
 tagB = "valueB"
 }
}

When using object as the type, the default value needs to define all fields.

In this example where node pool configuration is exposed, we can see how using variables with hierarchy helps template user customize infrastructure specification.

Parsing Map as Input variable

To find out the best multi-cloud management platform, I tested a few of them. I find it inconsistent when I have an input variable of the map type. With Terraform Cloud, I have to specify the variable to be parsed as HCL, and the value has to be:

{"Environment" = "Dev", "Owner" = "info@digihunchtest.com"}

With Scalr, I also have to specify the variable to be parsed as HCL, and the value can be either the one above, or the one below:

{"Environment":"Dev","Owner":"info@digihunchtest.com"}

So Scalr is more flexible in parsing maps. Apart from Scalr and Terraform, I also tested env0 but I gave up after an hour. I could not specify to parse a variable as HCL. They need to work harder on this. Neither was I able to figure out the right syntax as plain variable. I did not test Atlantis or SpaceLift.

Enterprise Deployment

When deploying code to enterprises with their own network environment, Scalr supports running a self-hosted agent inside of the Enterprise network. This is also supported by Terraform Cloud (manage in cloud, execution in enterprise network). This is very useful when the execution machine needs to access the resource created in the enterprise environment.

A good example, is using Terraform’s Azure provider to provision an AKS cluster on the corporate network. Then use Terraform’s kubernetes provider to connect to the newly created cluster and create some Kubernetes object such as service account, as illustrated in this blog post.

Azure Deets

Mon, 25 Oct 2021 23:13:52 -0400

Both Azure and AWS are leading players in public cloud. AWS developed a lot of SMB customer in technology. Azure attracted many enterprises from their on-prem customers. In addition to clienteles, their models to manage resources in the cloud are also different in several aspects. While this post is by no means a comprehensive comparison, it serves as a refresher on how Azure is different from AWS in cloud engineering.

Subscription and Resource Group

Both subscription and resource group are means to manage resources in group. Subscription is associated with a credit card and groups resource financially. Resource group groups resources logically. A subscription can have multiple resource groups, as illustrated here in the hierarchy map.

Azure CLI

There are two CLI tools: Azure PowerShell for PowerShell users, and Azure CLI for Linux users. The distinction is not clear, because PowerShell can also run on multiple platforms such as MacOS and Linux. On the other hand, Linux Bash can run on Windows (e.g. using WSL2). The different lies more in the command nomenclature. For example, to list subscriptions, the PowerShell Cmdlets reads:

Get-AzSubscription

The Azure CLI comes much cleaner:

az account list

The command and argument naming in Azure CLI align with Linux commands. I prefer Azure CLI for its succinctness. When looking up VM skus, we can use this command:

az vm list-skus -l eastus2

The return is a JSON document. In advanced use cases, we can filter the result by using jq utility. Alternatively, we can filter the result and groom the output with jmespath query, just like AWS CLI. For example, in eastus2 region, we look for instances that:

Has virtualMachines as resource Type
Has AcceleratedNetworking enabled
Has EncryptionAtHostSupported enabled
Has PremiumIO enabled

For each of the result, we print out:

number of vCPUs
Memory size

For this use case we will have to provide a JMESPath query as below, then output the result as a table:

az vm list-skus -l eastus2 --query "[?resourceType=='virtualMachines' && capabilities[?name=='AcceleratedNetworkingEnabled' && value=='True'] && capabilities[?name=='EncryptionAtHostSupported' && value=='True'] && capabilities[?name=='PremiumIO' && value=='True']].{Name:name,vCPUs:capabilities[?name=='vCPUs'].value|[0],MemoryGB:capabilities[?name=='MemoryGB'].value|[0]}" --output table

The result looks like this:

I use variations of the command above very often to find out the best instance for AKS nodes.

Infrastructure as Code

The native infrastructure as code option is ARM (Azure Resource Manager) template in JSON format. It is extremely wordy and perhaps why Azure later developed Bicep as the second generation of IaC tool. Terraform has a provider for Azure as well. For comparison among ARM, Terraform and Bicep, I have written a blog post for Slalom build covering more details.

Networking

Here is a great post comparing Azure Network with AWS.

At a high level, Azure Virtual Network (or VNet) is the equivalent of VPC in Amazon. Likewise, peering can be configured between VNets. As to subnet, Azure is different because there is no conceptual distinction between public subnet and private subnet. In AWS, public subnet is subnet attached with an Internet Gateway via a network route. So “private” or “public” are in terms of outbound traffic. On the other side, Azure does not distinguish between private or public subnet. Resources connected to a VNet have access out to the Internet by default. As to inbound traffic, you can make a VM available on Internet by giving it a public IP (same as AWS). You can make it available to other VNet, by configuring a service endpoint. Customers typically need custom routes to redirect outbound traffic (e.g. through firewall). In VPC, subnets are mapped to availability zones one-to-one, whereas in Azure, a subnet may traverse multiple availability zones.

With Azure, it is also important to understand difference between Azure service endpoint and Azure private endpoint:

Azure service endpoint: provides connectivity to Azure services over n optimized route over the Azure backbone network. Traffic will leave your VNet.
Azure private endpoint: a NIC that uses private IP from your VNet. This NIC connects you privately and securely to a service powered by Azure Private Link. By enabling a private endpoint, you’re bringing the service into your VNet.

On security group, we can associated a network security group with a network interface, or with a subnet. In contrast, in AWS, a security group can only be associated with an instance’s network interface.

Azure Bastion and Jump Box

There is a managed service called Azure Bastion. It is a SSH/RDP proxy fully managed as PaaS. However, its use case is virtual machines. It cannot be used to access other services. For example, if you create a private AKS cluster, then you need a command terminal to access the API server. This is not what Azure Bastion can do. Instead, you either need a virtual machine in the AKS network as jump box.

The alternative is an Azure Cloud Shell, which will require storage but can be configured to be placed inside of a V-Net. However, Azure Cloud Shell is not running inside of a full-fledged Linux operating system. You cannot install commands.

To create a bastion host, e.g. without public IP address, use the following CLI command:

az vm create -n MyBastion -g AutomationTest --image UbuntuLTS --subnet suitable-porpoise-node-subnet --vnet-name suitable-porpoise-vnet --ssh-key-values ~/.ssh/id_rsa.pub --authentication-type ssh

If the bastion host is needed with a public Ip, configure the network security group accordingly. The AZ CLI command above will create a VM, with an OS user named after the command line terminal user.

IAM

Azure AD is a managed identity service. Here is the difference between Active Directory and Azure AD.

Azure RBAC is a mechanism for authorization. Just like IAM policies, Azure RBAC enforces permissions using role assignment, which consists of:

security principal ( user, group, service principal, or managed idenity)
role definition: defines what actions is allowed and what is not allowed
scope: the object of the action

To enforce RBAC, one needs to create role assignment objects, each specifying principal, role, and scope.

Service Principle and Managed Identity

The two concepts may appear confusing. I find this article a great reference to demystify them. The takeaway is: service principle is the equivalent of service account in old Active Directory. Managed identity is a service principle automatically managed by a resource. Managed identity can be user assigned or system assigned.

Managed Identity is a “wrapper” around a service principal. It is automatically created and automatically rotated.

Azure DevOps

I think of Azure DevOps (ADO) of a managed pipeline implementation, with a repository (just like BitBucket), a board to manage tickets (similar to JIRA), Wiki (just like Confluence), Artifactory. The Pipelines is the part that’s similar to Jenkins. ADO calls a build pipeline a Pipeline, and a release/deployment pipeline a Release. A pipeline and a release are fundamentally the same but they are used in different ways. A pipeline’s input is usually the code repository, and the output is artifact. A release’s input is usually an artifact, and it connects to infrastructure in different environments.

ADO has its own ecosystem for plugins, managed under Visual Studio marketplace. Many extensions are open-source. If you are not happy with an extension, you can publish your own extension to market place.

Logging and Monitoring

Azure Monitor manages metrics, logs and alerts. To further analyze logs, create a log analytics workspaces, where you can run Kusto queries. You can create a workbook and embed Kusto queries into visual objects on the workbook.

Storage

Azure manages storage resources under storage account. The resource classes include Blob (object), File, Queue, Table and Disk (block). They have a few acronyms on redundancy levels.

Redundancy Option	Data Copy	Access level
LRS – Locally redundant	synchronously copy your data three times within the AZ in the primary region.	Write is acknowledged after three synchronous writes.
ZRS – Zone-redundant	synchronously copy your data across three AZs in the primary region. your data is still accessible for both read and write even if one AZ becomes unavailable.	Write is acknowledged after three synchronous writes. If an AZ becomes unavailable, Azure undertakes networking updates (e.g. DNS re-pointing). Application may perceive a blip where re-try policies may help.
GRS – Geo-redundant	LRS in primary region + asynchronously copy your data to a single AZ in the secondary region + LRS in secondary region	your data in the secondary region isn’t available for read or write access unless there is a failover to the secondary region. for read access to the secondary region, configure your storage account to use RA-GRS (read-access geo-redundant storage) If the primary region becomes unavailable, you can choose to fail over to the secondary region. After the failover has completed, the secondary region becomes the primary region, and you can again read and write data.
GZRS – Geo-zone-redundant	ZRS in primary region + asynchronously copy your data to a single AZ in the secondary region + LRS in secondary region	your data in the secondary region isn’t available for read or write access unless there is a failover to the secondary region. for read access to the secondary region, configure your storage account to use RA-GZRS (read-access geo-zone-redundant storage) If the primary region becomes unavailable, you can choose to fail over to the secondary region. After the failover has completed, the secondary region becomes the primary region, and you can again read and write data.

The disaster recovery and failover happens at storage account level.

Infrastructure deployment in Terraform 1/2

Wed, 11 Aug 2021 21:44:00 -0400

Terraform is an excellent Infrastructure-as-Code (IaC) tool based on Hashicorp Configuration Language (HCL). Compared to JSON or YAML based declarative templates (e.g. CloudFormation and ARM), HCL is more concise, thanks to the flexibility of HCL. On the other hand, HCL is not as flexible as general purpose languages. For that sake, I see HCL as semi-declarative IaC. This post is my notes about best practices with Terraform development, from the context of AWS, but also applies to other cloud platforms.

Complex Types

There are three primitive types (string, number and bool) that forms collection types and structural types. Here are some common ones:

list: element may repeat, and order is maintained:

["orange", "banana", "orange", "apple"]

set: elements are unique and unordered

["apple", "banana", "orange"]

tuple: each element has its own type

["a", 15, true]

object: defined by a schema with named attributes each with its own type

{
 name = "John"
 age = 52
}

list of object

[
 {
 alloc_id = "0b7271a3219bc1fc2"
 subnet_id = "0c02af76c2c3e46fa"
 },
 {
 alloc_id = "0440c334c48d4247f"
 subnet_id = "02652e69fa2a71de8"
 }
]

map of string

{
 property = "foo"
 attribute = "bar"
}

map of object

{
 objkey1 = {
 alloc_id = "0b7271a3219bc1fc2"
 subnet_id = "0c02af76c2c3e46fa"
 }
 objkey2 = {
 alloc_id = "0440c334c48d4247f"
 subnet_id = "02652e69fa2a71de8"
 }
}

Whenever applicable, Terraform converts types implicitly or explicitly. For example, when a list or tuple is converted to set, all elements are converted to string and duplicates are removed. Object and map are very similar. Map of string can be converted to object if the attributes comply with the schema. Additional attributes not in the schema are discarded.

HCL Types is similar to Python

Although being totally different beasts, the complex types between HCL and Python are similar, to the point I suspect the HCL design is influenced by Python. I summarize the similarities as such:

Python Types	list []	tuple ()	set {}	dict {}
Terraform Types	list []	tuple []	set []	map {}
ordered	Y You can access item by index	Y	N you cannot access an item by index or key; however you can loop over all itmes	N key-value pair that allows you to access item by key
changeable (mutable)	Y	N You cannot update, add or remove items	Y Add or remove only. no change to existing elements	Keys must remain unique or the values get overwritten; Values are mutable
allow duplicate elements	Y	Y	N	Keys must be unique; values don’t have to

In Python, list and tuple allow elements of mixed types but in IaC like Terraform we mostly don’t need mixed types. In Terraform, an object is a map without a defined type. In most situations, lists and tuples behave identically, as do maps and objects.

Another area of similarity is with the comprehension of list and dict/maps. In Python for example,

## Supposed you need to create a list:
lst=[]
for x in range(10):
 lst.append(x**2)

print(lst)

### that can be simplified as the following to create the list:
lst = [x**2 for x in range(10)]

## You can even add contidion
even_numbers=[num for num in range(10) if num%2==0]

## You can introduce function calls:
words = ["hello", "world", "python", "list", "comprehension"]
lengths = [len(word) for word in words]

## You can even combine two lists
lst1=[1,2,3,4]
lst2=['a','b','c','d']

pair=[[i,j] for i in lst1 for j in lst2]

print(pair)

## With dict, it's similar
evens={x:x**2 for x in range(10) if x%2==0}
print(evens)

In Terraform, we use similar techniques:

[for s in var.list : upper(s)] # build a tuple/list from a list

[for k, v in var.map : length(k) + length(v)] # build a list from a map

{for s in var.list : s => upper(s)} # build a map from a list

[for s in var.list : upper(s) if s != ""] # build a tuple/list from a list with condition

Note that the documentation of Terraform doesn’t explicitly call them out as comprehensions. However, it’s exactly the same idea as comprehensions in Python. Even the range() function exists both in Python and Terraform.

Modularization

Modules allows you to group related resources together. They can also be re-used and called by other modules. It is fairly straightforward to create a module:

put the resource declarations into a sub-directory
define input and output in the directory

However, the introduction of module complicates the directory structure and variable referencing, which is important to take into account before starting creating modules. This guideline has further discussion about when to create a module. I re-wrote the terraform templates in Orthweb project to leverage modularization wherever possible, but there is still some stand-alone resource (e.g. random_id) not belonging to any module.

To reference resources across modules, you need to import those resources (using data source) from within the module. There are a couple of ways. You may pass the argument of data source as input variable, or you can leverage the filter capability of data source. Let’s look at one example of each mechanism.

In the example below, we import a subnet by subnet id:

data "aws_subnet" "private_subnet" {
 id = var.private_subnet_id
}

In the example below, we import a subnet by filtering from all subnets in the VPC by tag:

data "aws_subnet" "private_subnet" {
 vpc_id = var.vpc_id
 filter {
 name = "tag:Name"
 values = ["Private"]
 }
}

There are pros and cons of each approach. A module with mechanism 1 is more transferrable across different environment, because the ID of subnet is explicitly provided. However, authors needs to manage those explicit variables with code. Mechanism 2 fetches target resources with filter. It depends on a well-implemented tagging policy in the resource farm.

The Terraform Registry (since 2017) contains a lot of pre-built modules for each backend platform (e.g. AWS). If you find any module that can be used in your project, the module repo can be referenced directly by Git repository URL. You should be aware of the risk of this practice though. Many platforms are keen to publish modules for their platform. Anyone can publish their own modules to the community as well.

You can quickly generate module documentation with terraform-docs.

Local Execution

Local execution is the basic workflow mode which is mostly seen with very small collaboration team. In this mode, the developer executes terraform binary (Terraform CLI) from their workstation (e.g. Laptop). The Terraform CLI converts code into API calls to interface cloud provider. The most frequently used commands (from terraform directory) are:

terraform init
terraform plan
terraform apply

The init command initializes the working directory. The plan command figures out the delta between code and infrastructure. It outlines the changes it is about to make. The apply command commits the change. The documentation of Terraform CLI commands is here.

Terraform keeps track of the infrastructure it manages in state file. This article explains the purpose of state. State management collaboration difficult with local execution because the state file by default is created in the working directory on user’s workstation. Although the state file can be configured to be stored in a shared location such as S3, it still requires a mechanism to lock the state in a multi-developer collaboration.

In large operations, the same code base in Terraform, is usually used to created several different sets of infrastructures, for example, in different geographic regions. So it is a 1-to-many relationship between the code repo and the infrastructure state. To further complicate things, each state might have been deployed using different revisions of the code. To overcome that challenge, Terraform introduced the concept of workspace, which is essentially an instance of state describing a particular group of infrastructure being managed by the same source code. When there are many workspaces, it becomes tricky to manage them with CLI commands.

State management is a major challenge that needs to be solved for team collaboration in local execution workflow. Each state must use the same revision of Terraform code. You can use Git in combination as a workaround to that limitation but the point is you cannot tie a workspace to a commit with the workspace commands.

In some enterprise environment, the execution is from a VM (e.g. ADO agent on-premise) without Internet access, which poses another challenge. First, we need to pre-load required providers manually. The enterprise needs a proxy solution to safely download packages from Hashicorp website. One good option is Nexus Repository, with both open-source and pro supports. It is a full-function artifactory repo that can host helm repo, apt repo, yum repo, etc. Second, we also need to configure Terraform so it picks up providers locally. Managing plugins without Internet access requires understanding of the order in which Terraform tries to load plugins during initialization.

Remote Execution

In remote execution, the code is executed in Terraform Enterprise or Terraform Cloud. Both are remote web servers. The difference is that Terraform Enterprise is self-hosted service, requiring IT specialist to install and maintain Terraform Enterprise. Terraform Cloud on the other hand, is a managed SaaS service. The pricing model includes a free plan for small number of users.

Terraform workspace configuration

In Terraform Enterprise or Cloud, the remote execution is organized in workspaces. You need to create an organization, and then create workspace under the organization in order to execute code. With each workflow, you can specify version control system (VCS) and subdirectory, to tell the workspace where to fetch Terraform code from. The workspace also allows you to define secrets and variables specific to the workspace. When you execute a workspace plan, the secrets and variables are passed from workspace to the execution logic.

Terraform Workspace Variable configuration

You will also need to design the Terraform code in a way to work seamlessly with the secrets and variables loaded from the workspace. The variable declaration in code should match the definition in workspace. There are already a number of variables that came in handy. Check out this guide.

Each execution is referred to as a “run”, with its own run id. A workspace involves may runs, which may succeed or fail. Each run pulls a specific commit of the source repository, and goes through stages such as plan, and apply. The UI from each run result list out the status of each result, in a very easy to read format.

The state data is persisted in the web server as they were generated. Therefore the collaborator does not need to worry about managing state with CLI tools. If there are files that you do not want picked up by the execution engine, their locations can be added to a file .terraformignore. Refer to this guide.

AWS profile

Local execution still has a lot of use cases in enterprises such as testing with temporary resources. A common challenge is authentication. As discussed, Terraform CLI picks up identity information from AWS CLI and authenticates its way into the backend to run API calls against. So AWS CLI must be configured correctly with the sufficient permission to provision resources. On the other hand, enterprises usually offload IAM to an identity store, such as AzureAD, Okta, etc. Putting those together, the pattern of authentication and authorization usually looks like this:

User logs on via SSO (e.g. SAML). The validation response gives a name of an IAM role.
Upon successful authentication, user takes the IAM role. The role does not have any capability, except for assuming a second IAM role.
The second IAM role (the functional role) grants user the permission to do its business.

The steps above, can be carried out in AWS console, or with AWS cli using assume-role command. However, when we put Terraform in the picture, it becomes a little involving because the credential information is updated whenever the functional role is assumed, and the assume-role command takes a pretty long argument.

To skip typing the long command every time, there are some handy tools, such as aws-azure-login. An even better tool that works with a variety of identity stores is saml2aws. The tool allows you to configure identity backend, assume the functional role, and update credential information in aws credential file, all with a single command. The AWS CLI configuration reads:

[default]
region = us-east-1
output = json
cli_history = enabled
cli_pager =
role_session_name = functional_operation

[profile function_user]
source_profile = default
role_session_name = functional_operation
role_arn = arn:aws:iam::9998887766:role/admin-access
region = us-east-1

In Terraform provider, we need to tell it to assume that role as well:

provider "aws" {
 region = "us-east-1"
 assume_role {
 role_arn = "arn:aws:iam::9998887766:role/admin-access"
 session_name = "terraform"
 }
}

This will ensure Terraform assumes appropriate role before doing its job.

AWS EC2 SSH Key Pair

RSA key authentication for SSH should be used for Linux Instances. When creating an EC2 instance, we give it our public key so we can then later authenticate through SSH. If the key is already stored in AWS, we just need to tell EC2 the name of the key, in the key_name property. If the code is likely to be executed from several different places by different users, then we can write the code so it picks up public key from user’s workstation (~/.ssh/id_rsa.pub). Here is an example:

variable "local_pubkey_file" {
 type = string
 default = "~/.ssh/id_rsa.pub"
}
data "local_file" "pubkey" {
 filename = pathexpand(var.local_pubkey_file)
}
resource "aws_key_pair" "user-pubkey" {
 key_name = "runner-pubkey"
 public_key = data.local_file.pubkey.content
}
resource "aws_instance" "bastion" {
 instance_type = "t2.micro"
 key_name = aws_key_pair.user-pubkey.key_name
 ......
}

For remote execution, we can even add an option to pass public key in as variable, to override the key file variable. For an example, check out my orthweb project.

To upload files to EC2 instance from Terraform execution environment, we can use the file provisioner with ssh as connection type.

Automatic deployment of Orthanc on AWS

Sun, 08 Nov 2020 00:54:06 -0400

[Update] I changed reverse proxy from Nginx to Envoy. Here‘s the detail.

[Update] Some security improvement was introduced in may 2021. Here‘s detail.

[Update] Here’s the link to the orthweb repository.

In this project we introduce a medical imaging web service based on Orthanc, an open-source project of DICOM server, and a pipeline to deploy such server automatically and consistently. We deploy Orthanc on AWS automatically. This little project involves a number of technical deets in DevOps, to deliver a web application prototype with an automated deployment pipeline.

A brief on imaging

In medical imaging, scanning devices are the data collectors. It consists of various categories of scanners, such as Computed Tomography (CT), and Ultrasound (US). They are collectively referred to as modality, but vary significantly in terms of image generation and hardware manufacturing. The challenges to exchange data between these heterogeneous scanning devices and centralized computers came around as early as the 1980s, which brought about ACR-NEMA standard in 1985, under the initiative between American College Radiology (ACR) and National Electrical Manufacturers Association (NEMA). The standard lately evolved into DICOM (Digital Imaging Communication in Medicine), a comprehensive set of standard in the ISO framework that governs modern imaging data storage and exchange across several disciplines (radiology, cardiology, pathology, etc) that operate around images in medicine.

In addition to defining a file format to store imaging data, DICOM also includes an upper layer protocol that dictates how two compliant devices (referred as application entity, each identified by AE title) can negotiate a common syntax to transfer objects (e.g. an image, a report or a discovery). Upper layer refers to layer 5-7 in OSI model, or application layer in TCP/IP model.

Imaging server

Once scanner acquires images from patient, they stores the exams to imaging server for persistent storage. The functionalities of such server expands overtime since 1990s and hence go by different names in different eras, such as PACS (Picture Archive and Communication Systems), VNA (Vendor Neutral Archive) and EI (enterprise imaging) archive. Regardless of naming, they can be generally seen as a highly specialized variation of enterprise content management system. They are usually hosted with a centralized database to index clinical information at patient, exam and image levels. The other key component is the persistent storage devices, usually in the form of a NAS.

Orthanc is an open-source initiative for such imaging servers. It provides a DICOM endpoint, allowing scanning devices to store medical images. It also provides a web viewer allowing users to see the images stored. It is released for many platforms, including Docker images.

Infrastructure as code

We use Amazon Web Service (AWS) for infrastructure as service, and Terraform as the tool to provision resources off AWS, in a reliable and consistent mechanism, known as Infrastructure-as-Code. Terraform is an alternative to CloudFormation, AWS’s proprietary infrastructure-as-code technology. Terraform is developed by Hashicorp as an open-source project, and therefore is vendor neutral. It supports multiple public cloud vendor through different providers. Each provider accesses the vendor specific SDK. For example, the AWS provider integrates with AWS SDK. As a result, the code used in one vendor cannot just be applied to a different vendor without a major overhaul. Terraform’s current version is 0.13 as of Oct 2020, and has gone through some syntax changes since version 0.11. Terraform also produces files for state management locally in the working directory.

When executing, Terraform combines all files in the working directory to assess variables, and create required resources. It is compatible with the most of AWS resources. For example, you can specify user data with templates when creating EC2 instances. You can also create managed service instance as long as it is supported by the provider.

Architecture

Orthanc web server stores data in sqlite by default, but also has a plugin to support PostgreSQL, an open-source relational database. AWS has managed service (RDS) based on PostgreSQL. In this project, we create an RDS instance that span across two availability zones for minimum high availability. Orthanc also supports storing imaging data including pixels in PostgreSQL, which obviates the need for a dedicated file storage system.

We deploy the application in Docker’s containers for compatibility and portability. The Orthanc server is shipped in Docker images, available in Docker hub registry. The docker environment is configured as part of EC2 instance bootstrapping, including installing packages with YUM, initializing and customizing environment variables. The docker-compose file, and the auxiliary configuration files are provided in the repo. The bootstrapping script installs git and pulls required files from this GitHub repo.

This demo project does not include load balancing, DNS management, or container orchestration.

Security

Orthanc’s web browser natively supports HTTPS. However, the DICOM port does not support TLS natively, as their development has made clear in the FAQ. This leaves a severe security vulnerability because all patient data (protected health information in HIPPA context) would be sent across the Internet in the clear, visible to every network interface along the route. To address this issue we brought in Nginx as a reverse proxy to work at TCP layer to terminate encrypted traffic for Orthanc’s DICOM end point. DICOM upper layer works on top of TCP layer.

In Nginx literature, this use case is referred to as SSL Termination for TCP Upstream Servers. Note that Nginx is providing layer 4 capability in this use case so the certificate and key configuration should not be placed under http section of the configuration file. This layer 4 capability in fact enables security configurations of all protocol that operates in upper layers and can be used in a broad range of situations. It is also noteworthy that Nginx can re-encrypt the traffic on the way out to upstream, for even tighter security control measure as outlined in this use case.

user nginx;
worker_processes 1;

error_log /var/log/nginx/error.log warn;
pid /var/run/nginx.pid;

events {
 worker_connections 1024;
}

stream {
 upstream dicom_backend {
 server orthanc-backend:4242;
 }

 server {
 listen 11112 ssl;
 proxy_pass dicom_backend;

 ssl_certificate conf.d/site.pem;
 ssl_certificate_key conf.d/site.pem;
 ssl_protocols SSLv3 TLSv1 TLSv1.2 TLSv1.3;
 ssl_ciphers HIGH:!aNULL:!MD5:ECDH+AESGCM;
 ssl_session_cache shared:SSL:20m;
 ssl_session_timeout 4h;
 ssl_handshake_timeout 30s;
 }
}

It is also helpful to use Nginx to terminate HTTPS traffic, using a pair of certificate and key. When testing with self-signed certificate I realized that Chrome browser has specific requirement on self-signed certificate, or it won’t load the page. So the certificate has to be created as instructed here.

For better security, it is advisable that the RDS instance is provisioned in private subnet, with its data encrypted both in-transit and at-rest. Docker service should also manage sensitive information as secrets.

Summary

The deliverable is stored in this Github repo. The docker part of it can be executed on MacBook with PostgreSQL. The entire hardware stack represented by terraform code, can be executed against AWS to create required resources. Checkout README for further instruction. To emulate a modality, one will need a TLS supported DICOM application entity, Horos is a great project on MacOS to serve this purpose, both as DICOM-compliant sender and a viewer. Alternatively, consider some command-line based DICOM toolkit such as dcmtk, or grassroot dicom.

terraform on Digi Hunch

Debating between count and for_each in Terraform

Problem with count loop

Embrace for_each loop

A real-life example with classic pattern

Implementation using count

Implementation with for_each: first attempt

Is for_each a bad idea?

Implementation with for_each: second attempt

One more shot with for_each

Conclusion

Orchestrate Landing Zone with AWS Control Tower

More on Landing Zone

Intro to Control Tower

Mandatory Accounts

Account Enrolment

Delegated Administrator

Control Tower Customization

Customizations for Control Tower

Account Factory Terraform

Summary

EKS impression

Computing Nodes in EKS

Node AutoScaling

Identity Management for EKS

AppMesh

EKS cluster using Terraform

Summary

Kubernetes Storage on Azure 1 of 3 – built-in storage and NFS

Storage in Azure Kubernetes

Terraform Template for AKS

Benchmarking with kubestr

Metrics

Validate Persistent Storage

Summary

AKS Lessons Learned 2 of 2

Identity and Access Management

Node Networking

Pod Networking

DNS

Initial Service Account

Integration with Azure KeyVault

Infrastructure deployment in Terraform 2/2

Scalr

Permission with Azure

Composite type for Input variable

Parsing Map as Input variable

Enterprise Deployment

Azure Deets

Subscription and Resource Group

Azure CLI

Infrastructure as Code

Networking

Azure Bastion and Jump Box

IAM

Service Principle and Managed Identity

Azure DevOps

Logging and Monitoring

Storage

Infrastructure deployment in Terraform 1/2

Complex Types

HCL Types is similar to Python

Modularization

Local Execution

Remote Execution

AWS profile

AWS EC2 SSH Key Pair

Automatic deployment of Orthanc on AWS

A brief on imaging

Imaging server

Infrastructure as code

Architecture

Security

Summary

Problem with `count` loop

Embrace `for_each` loop

Implementation using `count`

Implementation with `for_each`: first attempt

Is `for_each` a bad idea?

Implementation with `for_each`: second attempt

One more shot with `for_each`