This commit adds an `alltrue` function to Terraform configuration. A
reason we might want this function is because it will enable more
powerful custom variable validations. For example:
```hcl
variable "amis" {
type = list(object({
id = string
}))
validation {
condition = (alltrue([
for a in var.amis : length(a.id) > 4 && substr(a.id, 0, 4) == "ami-"
]))
error_message = "The ID of at least one AMI was invalid."
}
}
```
* add setdifference and setsubtract functions and docs
* remove setdifference as it is not implemented correct in underlying lib
* Update setintersection.html.md
* Update setproduct.html.md
* Update setunion.html.md
These are intended to make it easier to work with arbitrary data
structures whose shape might not be known statically, such as the result
of jsondecode(...) or yamldecode(...) of data from a separate system.
For example, in an object value which has attributes that may or may not
be set we can concisely provide a fallback value to use when the attribute
isn't set:
try(local.example.foo, "fallback-foo")
Using a "try to evaluate" model rather than explicit testing fits better
with the usual programming model of the Terraform language where values
are normally automatically converted to the necessary type where possible:
the given expression is subject to all of the same normal type conversions,
which avoids inadvertently creating a more restrictive evaluation model
as might happen if this were handled using checks like a hypothetical
isobject(...) function, etc.
Reference: https://github.com/hashicorp/terraform/issues/16697
Enumerates a set of regular file names from a given glob pattern. Implemented via the Go stdlib `path/filepath.Glob()` functionality. Notably, stdlib does not support `**` or `{}` extended patterns. See also: https://github.com/golang/go/issues/11862
To support the extended glob patterns, it will require adding a dependency on a third party library or adding our own matching code.
These existing upstream cty functions allow matching strings against
regular expression patterns, which can be useful if you need to consume
a non-standard string format that Terraform doesn't (and can't) have a
built-in function for.
These follow the same principle as jsondecode and jsonencode, but use
YAML instead of JSON.
YAML has a much more complex information model than JSON, so we can only
support a subset of it during decoding, but hopefully the subset supported
here is a useful one.
Because there are many different ways to _generate_ YAML, the yamlencode
function is forced to make some decisions, and those decisions are likely
to affect compatibility with other real-world YAML parsers. Although the
format here is intended to be generic and compatible, we may find that
there are problems with it that'll we'll want to adjust for in a future
release, so yamlencode is therefore marked as experimental for now until
the underlying library is ready to commit to ongoing byte-for-byte
compatibility in serialization.
The main use-case here is met by yamldecode, which will allow reading in
files written in YAML format by humans for use in Terraform modules, in
situations where a higher-level input format than direct Terraform
language declarations is helpful.
This is similar to the function of the same name in Python, generating a
sequence of numbers as a list that can then be used in other
sequence-oriented operations.
The primary use-case for it is to turn a count expressed as a number into
a list of that length, which can then be iterated over or passed to a
collection function to produce that number of something else, as shown
in the example at the end of its documentation page.
* lang/funcs: testing of functions through the lang package API
The function-specific unit tests do not cover the HCL conversion that happens when the functions are called in a terraform configuration. For e.g., HCL converts sets to lists before passing it to the function. This means that we could not test passing a set in the function _unit_ tests.
This adds a higher-level acceptance test, plus a check that every (pure) function has a test.
* website/docs: update function documentation
Terraform is way bigger than the core CLI tools and the language
now, and the docs have grown accordingly. So we're adding a
global index page to help users get around the many sections of the
docs site, and bumping the CLI/core docs down so they're no longer at the
top of the hierarchy.
The "right" (as in, conceptually pure) way to do this would be to actually
create a new level of directory hierarchy in between. But that would be real
expensive and annoying — the amount of 301s and links to edit would be
monumental, and it wouldn't gain us much beyond a certain picture-straightening
satisfaction, so I'm resisting the temptation.
In prior versions, we recommended using hash functions in conjunction with
the file function as an idiom for detecting changes to upstream blobs
without fetching and comparing the whole blob.
That approach relied on us being able to return raw binary data from
file(...). Since Terraform strings pass through intermediate
representations that are not binary-safe (e.g. the JSON state), there was
a risk of string corruption in prior versions which we have avoided for
0.12 by requiring that file(...) be used only with UTF-8 text files.
The specific case of returning a string and immediately passing it into
another function was not actually subject to that corruption risk, since
the HIL interpreter would just pass the string through verbatim, but this
is still now forbidden as a result of the stricter handling of file(...).
To avoid breaking these use-cases, here we introduce variants of the hash
functions a with "file" prefix that take a filename for a disk file to
hash rather than hashing the given string directly. The configuration
upgrade tool also now includes a rule to detect the documented idiom and
rewrite it into a single function call for one of these new functions.
This does cause a bit of function sprawl, but that seems preferable to
introducing more complex rules for when file(...) can and cannot read
binary files, making the behavior of these various functions easier to
understand in isolation.
It's not normally necessary to make explicit type conversions in Terraform
because the language implicitly converts as necessary, but explicit
conversions are useful in a few specialized cases:
- When defining output values for a reusable module, it may be desirable
to force a "cleaner" output type than would naturally arise from a
computation, such as forcing a string containing digits into a number.
- Our 0.12upgrade mechanism will use some of these to replace use of the
undocumented, hidden type conversion functions in HIL, and force
particular type interpretations in some tricky cases.
- We've found that type conversion functions can be useful as _temporary_
workarounds for bugs in Terraform and in providers where implicit type
conversion isn't working correctly or a type constraint isn't specified
precisely enough for the automatic conversion behavior.
These all follow the same convention of being named "to" followed by a
short type name. Since we've had a long-standing convention of running all
the words together in lowercase in function names, we stick to that here
even though some of these names are quite strange, because these should
be rarely-used functions anyway.
The sethaselement, setintersection, and setunion functions are defined in
the cty stdlib. Making them available in Terraform will make it easier to
work with sets, and complement the currently-Terraform-specific setproduct
function.
In the long run setproduct should probably move into the cty stdlib too,
but since it was submitted as a Terraform function originally we'll leave
it here now for simplicity's sake and reorganize later.
In our new world it produces either a set of a tuple type or a list of a
tuple type, depending on the given argument types.
The resulting collection's element tuple type is decided by the element
types of the given collections, allowing type information to propagate
even if unknown values are present.
This function is similar to the template_file data source offered by the
template provider, but having it built in to the language makes it more
convenient to use, allowing templates to be rendered from files anywhere
an inline template would normally be allowed:
user_data = templatefile("${path.module}/userdata.tmpl", {
hostname = format("petserver%02d", count.index)
})
Unlike the template_file data source, this function allows values of any
type in its variables map, passing them through verbatim to the template.
Its tighter integration with Terraform also allows it to return better
error messages with source location information from the template itself.
The template_file data source was originally created to work around the
fact that HIL didn't have any support for map values at the time, and
even once map support was added it wasn't very usable. With HCL2
expressions, there's little reason left to use a data source to render
a template; the only remaining reason left to use template_file is to
render a template that is constructed dynamically during the Terraform
run, which is a very rare need.
This commit is a wide-ranging set of edits to the pages under
/docs/configuration. Among other things, it
- Separates style conventions out into their own page.
- Separates type constraints and conversion info into their own page.
- Conflates similar complex types a little more freely, since the distinction is
only relevant when restricting inputs for a reusable module or resource.
- Clarifies several concepts that confused me during edits.
In the heirarchy, both "Terraform Language" and "Functions" are "up" from
the individual function reference pages, so we'll class them as such to
use the back-facing arrow instead of the forward-facing arrow.
Previously we just listed out all of the functions in alphabetical order
inside the "Interpolation Syntax" page, but that format doesn't leave much
room for details and usage examples.
Now we give each function its own page, and categorize them for easier
navigation. While many functions are very simple and don't really warrant
a full page, certain functions do have additional details that are worth
mentioning and this structure scales better for those more complicated
functions.
So far this includes only the numeric and string functions. Other
categories will follow in subsequent commits.
Arthur Burkart