DestroyValueReferenceTransformer is used during destroy to reverse the
edges for output and local values. Because destruction is going to
remove these from the state, nodes that depend on their value need to be
visited first.
When working on an existing plan, the context always used walkApply,
even if the plan was for a full destroy. Mark in the plan if it was
icreated for a destroy, and transfer that to the context when reading
the plan.
A Targeted graph may include outputs that were transitively included,
but if they are missing any dependencies they will fail to interpolate
later on.
Prune any outputs in the TargetsTransformer that have missing
dependencies, and are not depended on by any resource. This will
maintain the existing behavior of outputs failing silently ni most
cases, but allow errors to be surfaced where the output value is
required.
Module outputs may not have complete information during Input, because
it happens before refresh. Continue process on output interpolation
errors during the Input walk.
Remove the Input flag threaded through the input graph creation process
to prevent interpolation failures on module variables.
Use an EvalOpFilter instead to inset the correct EvalNode during
walkInput. Remove the EvalTryInterpolate type, and use the same
ContinueOnErr flag as the output node for consistency and to try and
keep the number possible eval node types down.
Locals don't need to be evaluated during destroy. Rather than simply
skipping them, remove them from the state as they are encountered. Even
though they are not persisted in the state, it keeps the state up to
date as the destroy happens, and we reduce the chance of other
inconstancies later on.
The fact that we clean up data source state by applying a "destroy" action
for them is an implementation detail, and so should not be visible to
outside callers or to the user.
Signalling these as real destroys creates confusion for users because
they see Terraform say things like:
data.template_file.foo: Refreshing state..."
...which, to an understandably-nervous sysadmin, might make them suspect
that the underlying object was deleted, rather than just Terraform's
record of it.
Previously the rendered plan output was constructed directly from the
core plan and then annotated with counts derived from the count hook.
At various places we applied little adjustments to deal with the fact that
the user-facing diff model is not identical to the internal diff model,
including the special handling of data source reads and destroys. Since
this logic was just muddled into the rendering code, it behaved
inconsistently with the tally of adds, updates and deletes.
This change reworks the plan formatter so that it happens in two stages:
- First, we produce a specialized Plan object that is tailored for use
in the UI. This applies all the relevant logic to transform the
physical model into the user model.
- Second, we do a straightforward visual rendering of the display-oriented
plan object.
For the moment this is slightly overkill since there's only one rendering
path, but it does give us the benefit of letting the counts be derived
from the same data as the full detailed diff, ensuring that they'll stay
consistent.
Later we may choose to have other UIs for plans, such as a
machine-readable output intended to drive a web UI. In that case, we'd
want the web UI to consume a serialization of the _display-oriented_ plan
so that it doesn't need to re-implement all of these UI special cases.
This introduces to core a new diff action type for "refresh". Currently
this is used _only_ in the UI layer, to represent data source reads.
Later it would be good to use this type for the core diff as well, to
improve consistency, but that is left for another day to keep this change
focused on the UI.
The implementation of ResourceAddress.Less was flawed because it was only
testing each field in the "less than" direction, and falling through in
cases where an earlier field compared greater than a later one.
Now we test for inequality first as the selector, and only fall through
if the two values for a given field are equal.
There is some additional, early validation on the "count" meta-argument
that verifies that only suitable variable types are used, and adding local
values to this whitelist was missed in the initial implementation.
It seems that this somehow got lost in the commit/rebase shuffle and
wasn't caught by the tests that _did_ make it because they were all using
just one file.
As a result of this bug, locals would fail to work correctly in any
configuration with more than one .tf file.
Along with restoring the append/merge behavior, this also reworks some of
the tests to exercise the multi-file case as better insurance against
regressions of this sort in future.
This fixes#15969.
Previously we were checking required_version only during "real" operations, and not during initialization. Catching it during init is better because that's the first command users run on a new working directory.
Go 1.9 adds this new function which, when called, marks the caller as
being a "helper function". Helper function stack frames are then skipped
when trying to find a line of test code to blame for a test failure, so
that the code in the main test function appears in the test failure output
rather than a line within the helper function itself.
This covers many -- but probaly not all -- of our test helpers across
various packages.
The shadow graph was incredibly useful during the 0.7 cycle but these days
it is idle, since we're not planning any significant graph-related changes
for the forseeable future.
The shadow graph infrastructure is somewhat burdensome since any change
to the ResourceProvider interface must have shims written. Since we _are_
expecting changes to the ResourceProvider interface in the next few
releases, I'm calling "YAGNI" on the shadow graph support to reduce our
maintenence burden.
If we do end up wanting to use shadow graph again in future, we'll always
be able to pull it out of version control and then make whatever changes
we skipped making in the mean time, but we can avoid that cost in the
mean time while we don't have any evidence that we'll need to pay it.
We stash the locals in the module state in a map that is ignored for JSON
serialization. We don't include locals in the persisted state because they
can be trivially recomputed and this allows us to assume that they will
pass through verbatim, without any normalization or other transforms
caused by the JSON serialization.
From a user standpoint a local is just a named alias for an expression,
so it's desirable that the result passes through here in as raw a form
as possible, so it behaves as closely as possible to simply using the
given expression directly.
A local value is similar to an output in that it exists only within state
and just always evaluates its value as best it can with the current state.
Therefore it has a single graph node type for all walks, which will
deal with that evaluation operation.
