This document now lives at:
- https://learn.hashicorp.com/terraform/development/running-terraform-in-automation
...and terraform.io has been redirecting to there for quite a while. This commit
removes the extra copy so that the text of the two versions doesn't drift, and
updates existing links to point to the new location.
When warnings appear in isolation (not accompanied by an error) it's
reasonable to want to defer resolving them for a while because they are
not actually blocking immediate work.
However, our warning messages tend to be long by default in order to
include all of the necessary context to understand the implications of
the warning, and that can make them overwhelming when combined with other
output.
As a compromise, this adds a new CLI option -compact-warnings which is
supported for all the main operation commands and which uses a more
compact format to print out warnings as long as they aren't also
accompanied by errors.
The default remains unchanged except that the threshold for consolidating
warning messages is reduced to one so that we'll now only show one of
each distinct warning summary.
Full warning messages are always shown if there's at least one error
included in the diagnostic set too, because in that case the warning
message could contain additional context to help understand the error.
In the 0.10 release we added an opt-in mode where Terraform would prompt
interactively for confirmation during apply. We made this opt-in to give
those who wrap Terraform in automation some time to update their scripts
to explicitly opt out of this behavior where appropriate.
Here we switch the default so that a "terraform apply" with no arguments
will -- if it computes a non-empty diff -- display the diff and wait for
the user to type "yes" in similar vein to the "terraform destroy" command.
This makes the commonly-used "terraform apply" a safe workflow for
interactive use, so "terraform plan" is now mainly for use in automation
where a separate planning step is used. The apply command remains
non-interactive when given an explicit plan file.
The previous behavior -- though not recommended -- can be obtained by
explicitly setting the -auto-approve option on the apply command line,
and indeed that is how all of the tests are updated here so that they can
continue to run non-interactively.
A common reason to want to use `terraform plan` is to have a chance to
review and confirm a plan before running it. If in fact that is the
only reason you are running plan, this new `terraform apply -auto-approve=false`
flag provides an easier alternative to
P=$(mktemp -t plan)
terraform refresh
terraform plan -refresh=false -out=$P
terraform apply $P
rm $P
The flag defaults to true for now, but in a future version of Terraform it will
default to false.
Previously the behavior for -target when given a module address was to
target only resources directly within that module, ignoring any resources
defined in child modules.
This behavior turned out to be counter-intuitive, since users expected
the -target address to be interpreted hierarchically.
We'll now use the new "Contains" function for addresses, which provides
a hierarchical "containment" concept that is more consistent with user
expectations. In particular, it allows module.foo to match
module.foo.module.bar.aws_instance.baz, where before that would not have
been true.
Since Contains isn't commutative (unlike Equals) this requires some
special handling for targeting specific indices. When given an argument
like -target=aws_instance.foo[0], the initial graph construction (for
both plan and refresh) is for the resource nodes from configuration, which
have not yet been expanded to separate indexed instances. Thus we need
to do the first pass of TargetsTransformer in mode where indices are
ignored, with the work then completed by the DynamicExpand method which
re-applies the TargetsTransformer in index-sensitive mode.
This is a breaking change for anyone depending on the previous behavior
of -target, since it will now select more resources than before. There is
no way provided to obtain the previous behavior. Eventually we may support
negative targeting, which could then combine with positive targets to
regain the previous behavior as an explicit choice.
Nick Fagerlund