* lang/funcs: lookup() can work with maps of lists, maps and objects
lookup() can already handle aribtrary objects of (whatever) and should
handle maps of (whatever) similarly.
The function would previously panic when one or more null values were among the arguments.
The new behavior treats nulls as empty strings, therefore, it removes them.
Added higher-level test for matchkeys to exercise mixing
types in searchset. This had to be in the functions tests so the HCL
auto conversion from tuple to list would occur.
`matchkeys` was returning a (false) error if the searchset was a
variable, since then the type of the keylist and searchset parameters
would not match.
This does slightly change the behavior: previously matchkeys would
produce an error if the parameters were not of the same type, for e.g.
if searchset was a list of strings and keylist was a list of integers.
This no longer produces an error.
Previously the type-selection codepath for an input tuple referred
unconditionally to the start and end index values. In the Type callback,
only the types of the arguments are guaranteed to be known, so any access
to the values must be guarded with an .IsKnown check, or else the function
will not short-circuit properly when an unknown value is passed.
Now we will check the start and end indices are in range when we have
enough information to do so, but we'll return an approximate result if
either is unknown.
FlattenFunc can return lists and tuples when individual elements are
unknown. Only return an unknown tuple if the number of elements cannot
be determined because it contains an unknown series.
Make sure flatten can handle non-series elements, which were previously
lost due to passing a slice value as the argument.
When slicing a list containing unknown values, the list itself is known,
and slice should return the proper slice of that list.
Make SliceFunc return the correct type for slices of tuples, and
disallow slicing sets.
cty now guarantees that sets of primitive values will iterate in a
reasonable order. Previously it was the caller's responsibility to deal
with that, but we invariably neglected to do so, causing inconsistent
ordering. Since cty prioritizes consistent behavior over performance, it
now imposes its own sort on set elements as part of iterating over them so
that calling applications don't have to worry so much about it.
This change also causes cty to consistently push unknown and null values
in sets to the end of iteration, where before that was undefined. This
means that our diff output will now consistently list additions before
removals when showing sets, rather than the ordering being undefined as
before.
The ordering of known, non-null, non-primitive values is still not
contractually fixed but remains consistent for a particular version of
cty.
* 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
* funcs/coalesce: return the first non-null, non-empty element from a
sequence.
The go-cty coalesce function, which was originally used here, returns the
first non-null element from a sequence. Terraform 0.11's coalesce,
however, returns the first non-empty string from a list of strings.
This new coalesce function aims to preserve terraform's documented
functionality while adding support for additional argument types. The
tests include those in go-cty and adapted tests from the 0.11 version of
coalesce.
* website/docs: update coalesce function document
The templatefile function only has two arguments, so ArgErrorf can be
called with only zero or one as the argument index. If we are out of
bounds then HCL itself will panic trying to build the error message for
this call when called as an HCL function.
Unfortunately there isn't really a great layer in Terraform to test for
this class of bug systematically, because we are currently testing these
functions directly rather than going through HCL to do it. For the moment
we'll just live with that, but if we see this class of error arise again
we might consider either reworking the tests in this package to work with
HCL expression source code instead of direct calls or adding some
additional tests elsewhere that do so.
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.
These all follow the pattern of creating a hash and converting it to a
string using some encoding function, so we can write this implementation
only once and parameterize it with a hash factory function and an encoding
function.
This also includes a new test for the sha512 function, which was
previously missing a test and, it turns out, actually computing sha256
instead.
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.
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.
Now that our language supports tuple/object types in addition to list/map
types, it's convenient for zipmap to be able to produce an object type
given a tuple, since this makes it symmetrical with "keys" and "values"
such the the following identity holds for any map or object value "a"
a == zipmap(keys(a), values(a))
When the values sequence is a tuple, the result has an object type whose
attribute types correspond to the given tuple types.
Since an object type has attribute names as part of its definition, there
is the additional constraint here that the result has an unknown type
(represented by the dynamic pseudo-type) if the given values is a tuple
and the given keys contains any unknown values. This isn't true for values
as a list because we can predict the resulting map element type using
just the list element type.
The "values" function wasn't producing consistently-ordered keys in its
result, leading to crashes. This fixes#19204.
While working on these functions anyway, this also improves slightly their
precision when working with object types, where we can produce a more
complete result for unknown values because the attribute names are part
of the type. We can also produce results for known maps that have unknown
elements; these unknowns will also appear in the values(...) result,
allowing them to propagate through expressions.
Finally, this adds a few more test cases to try different permutations
of empty and unknown values.
When the value we're looking in has an object type, we need to know the
key in order to decide the result type. Therefore an object lookup with
an unknown key must produce cty.DynamicVal, not an unknown value with a
known type.
Since we need to know the index to know the result type for a tuple, we
need a special case here to deal with that situation and return
cty.DynamicVal; we can't predict the result type exactly until we know the
element type.
Alex Pilon