»csvdecode Function

csvdecode decodes a string containing CSV-formatted data and produces a list of maps representing that data.

CSV is Comma-separated Values, an encoding format for tabular data. There are many variants of CSV, but this function implements the format defined in RFC 4180.

The first line of the CSV data is interpreted as a "header" row: the values given are used as the keys in the resulting maps. Each subsequent line becomes a single map in the resulting list, matching the keys from the header row with the given values by index. All lines in the file must contain the same number of fields, or this function will produce an error.

»Examples

> csvdecode("a,b,c\n1,2,3\n4,5,6")
[
  {
    "a" = "1"
    "b" = "2"
    "c" = "3"
  },
  {
    "a" = "4"
    "b" = "5"
    "c" = "6"
  }
]

> csvdecode("a,b,c\n1,2,3\n4,5,6")[  {    "a" = "1"    "b" = "2"    "c" = "3"  },  {    "a" = "4"    "b" = "5"    "c" = "6"  }]

»Use with the for_each meta-argument

You can use the result of csvdecode with the for_each meta-argument to describe a collection of similar objects whose differences are described by the rows in the given CSV file.

There must be one column in the CSV file that can serve as a unique id for each row, which we can then use as the tracking key for the individual instances in the for_each expression. For example:

locals {
  # We've included this inline to create a complete example, but in practice
  # this is more likely to be loaded from a file using the "file" function.
  csv_data = <<-CSV
    local_id,instance_type,ami
    foo1,t2.micro,ami-54d2a63b
    foo2,t2.micro,ami-54d2a63b
    foo3,t2.micro,ami-54d2a63b
    bar1,m3.large,ami-54d2a63b
  CSV

  instances = csvdecode(local.csv_data)
}

resource "aws_instance" "example" {
  for_each = { for inst in local.instances : inst.local_id => inst }

  instance_type = each.value.instance_type
  ami           = each.value.ami
}

locals {  # We've included this inline to create a complete example, but in practice  # this is more likely to be loaded from a file using the "file" function.  csv_data = <<-CSV    local_id,instance_type,ami    foo1,t2.micro,ami-54d2a63b    foo2,t2.micro,ami-54d2a63b    foo3,t2.micro,ami-54d2a63b    bar1,m3.large,ami-54d2a63b  CSV
  instances = csvdecode(local.csv_data)}
resource "aws_instance" "example" {  for_each = { for inst in local.instances : inst.local_id => inst }
  instance_type = each.value.instance_type  ami           = each.value.ami}

The for expression in our for_each argument transforms the list produced by csvdecode into a map using the local_id as a key, which tells Terraform to use the local_id value to track each instance it creates. Terraform will create and manage the following instance addresses:

• aws_instance.example["foo1"]
• aws_instance.example["foo2"]
• aws_instance.example["foo3"]
• aws_instance.example["bar1"]

If you modify a row in the CSV on a subsequent plan, Terraform will interpret that as an update to the existing object as long as the local_id value is unchanged. If you add or remove rows from the CSV then Terraform will plan to create or destroy associated instances as appropriate.

If there is no reasonable value you can use as a unique identifier in your CSV then you could instead use the count meta-argument to define an object for each CSV row, with each one identified by its index into the list returned by csvdecode. However, in that case any future updates to the CSV may be disruptive if they change the positions of particular objects in the list. We recommend using for_each with a unique id column to make behavior more predictable on future changes.