random_shuffle takes a list of strings and returns a new list with the
same items in a random permutation.
Optionally allows the result list to be a different length than the
input list. A shorter result than input results in some items being
excluded. A longer result than input results in some items being
repeated, but never more often than the number of input items.
This resource generates a cryptographically-strong set of bytes and
provides them as base64, hexadecimal and decimal string representations.
It is intended to be used for generating unique ids for resources
elsewhere in the configuration, and thus the "keepers" would be set to
any ForceNew attributes of the target resources, so that a new id is
generated each time a new resource is generated.
This provider will have logical resources that allow Terraform to "manage"
randomness as a resource, producing random numbers on create and then
retaining the outcome in the state so that it will remain consistent
until something explicitly triggers generating new values.
Managing randomness in this way allows configurations to do things like
random distributions and ids without causing "perma-diffs".
Provider nodes interpolate their config during the input walk, but this
is very early and so it's pretty likely that any resources referenced are
entirely absent from the state.
As a special case then, we tolerate the normally-fatal case of having
an entirely missing resource variable so that the input walk can complete,
albeit skipping the providers that have such interpolations.
If these interpolations end up still being unresolved during refresh
(e.g. because the config references a resource that hasn't been created
yet) then we will catch that error on the refresh pass, or indeed on the
plan pass if -refresh=false is used.
A companion to the null_resource resource, this is here primarily to
enable manual quick testing of data sources workflows without depending
on any external services.
The "inputs" map gets copied to the computed "outputs" map on read,
"rand" gives a random number to exercise cases with constantly-changing
values (an anti-pattern!), and "has_computed_default" is settable in
config but computed if not set.
Internally a data source read is represented as a creation diff for the
resource, but in the UI we'll show it as a distinct icon and color so that
the user can more easily understand that these operations won't affect
any real infrastructure.
Unfortunately by the time we get to formatting the plan in the UI we
only have the resource names to work with, and can't get at the original
resource mode. Thus we're forced to infer the resource mode by exploiting
knowledge of the naming scheme.
New resources logically don't have "old values" for their attributes, so
showing them as updates from the empty string is misleading and confusing.
Instead, we'll skip showing the old value in a creation diff.
Data resources don't have ids when they refresh, so we'll skip showing the
"(ID: ...)" indicator for these. Showing it with no id makes it look
like something is broken.
Since the data resource lifecycle contains no steps to deal with tainted
instances, we must make sure that they never get created.
Doing this out in the command layer is not the best, but this is currently
the only layer that has enough information to make this decision and so
this simple solution was preferred over a more disruptive refactoring,
under the assumption that this taint functionality eventually gets
reworked in terms of StateFilter anyway.
The ResourceAddress struct grows a new "Mode" field to match with
Resource, and its parser learns to recognize the "data." prefix so it
can set that field.
Allows -target to be applied to data sources, although that is arguably
not a very useful thing to do. Other future uses of resource addressing,
like the state plumbing commands, may be better uses of this.
Previously they would get left behind in the state because we had no
support for planning their destruction. Now we'll create a "destroy" plan
and act on it by just producing an empty state on apply, thus ensuring
that the data resources don't get left behind in the state after
everything else is gone.
The handling of data "orphans" is simpler than for managed resources
because the only thing we need to deal with is our own state, and the
validation pass guarantees that by the time we get to refresh or apply
the instance state is no longer needed by any other resources and so
we can safely drop it with no fanfare.
This implements the main behavior of data resources, including both the
early read in cases where the configuration is non-computed and the split
plan/apply read for cases where full configuration can't be known until
apply time.
The key difference between data and managed resources is in their
respective lifecycles. Now the expanded resource EvalTree switches on
the resource mode, generating a different lifecycle for each mode.
For this initial change only managed resources are implemented, using the
same implementation as before; data resources are no-ops. The data
resource implementation will follow in a subsequent change.
data resources are a separate namespace of resources than managed
resources, so we need to call a different provider method depending on
what mode of resource we're visiting.
Managed resources use ValidateResource, while data resources use
ValidateDataSource, since at the provider level of abstraction each
provider has separate sets of resources and data sources respectively.
Once a data resource gets into the state, the state system needs to be
able to parse its id to match it with resources in the configuration.
Since data resources live in a separate namespace than managed resources,
the extra "mode" discriminator is required to specify which namespace
we're talking about, just like we do in the resource configuration.
Joseph Anthony Pasquale Holsten