This change introduces decorator support for CocoJS and the corresponding
IL/AST changes to store them on definition nodes. Nothing consumes these
at the moment, however, I am looking at leveraging this to indicate that
certain program fragments are "code" and should be serialized specially
(in support of Functions-as-lambdas).
This change includes a first basic whack at implementing S3 bucket
objects. It leverages the assets infrastructure put in place in the
last commit, supporting uploads from text, files, or arbitrary URIs.
Most of the interesting object properties remain unsupported for now,
but with this we can upload and delete basic S3 objects, sufficient
for a lot of the lambda functions management we need to implement.
This change introduces the basic concept of assets. It is far from
fully featured, however, it is enough to start adding support for various
storage kinds that require access to I/O-backed data (files, etc).
The challenge is that Coconut is deterministic by design, and so you
cannot simply read a file in an ad-hoc manner and present the bytes to
a resource provider. Instead, we will model "assets" as first class
entities whose data source is described to the system in a more declarative
manner, so that the system and resource providers can manage them.
There are three ways to create an asset at the moment:
1. A constant, in-memory string.
2. A path to a file on the local filesystem.
3. A URI, whose scheme is extensible.
Eventually, we want to support byte blobs, but due to our use of a
"JSON-like" type system, this isn't easily expressible just yet.
The URI scheme is extensible in that file://, http://, and https://
are supported "out of the box", but individual providers are free to
recognize their own schemes and support them. For instance, copying
one S3 object to another will be supported simply by passing a URI
with the s3:// protocol in the usual way.
Many utility functions are yet to be written, but this is a start.
This change implements simple index-based CocoPy subscripts (and
not the more fully featured slicing ones).
Alongside this, we relax a binder-time check that all dynamic
access types must be strings. The eval code already handles
numeric (array) accesses, so we will permit these to flow through.
This permits non-nil `this` objects for dynamically loaded properties
of classes and modules, provided they are the prototype or module
object for the target function, respectively.
This change emits all CocoJS interfaces as records. This allows us to
safely construct instances of them from Python using anonymous properties,
essentially emulating object literals.
For example, given a CocoJs interface defined as such:
interface Foo {
x;
y?;
z;
}
we can easily construct fresh instances using normal JS literals:
let f = { x: 42, z: "bar" };
But, Python doesn't have the equivalent literal syntax, wedging us.
It's now possible to initialize an instance from CocoPy as follows:
let f = Foo(x=42, z="bar")
This leverages the notion of records and primary properties, as
described in our CocoPack/CocoIL design documents.
* Pushing a class scope should permit subclasses (assertion).
* Returning nothing is legal for voids *and* dynamic (the latter was missing).
* The dynamic load readonly lval check is redundant; we check at the site of
assignment and doing it here as well led to two errors per usage.
Due to Python's ... interesting ... scoping rules, we want to avoid
forcing block scopes in certain places. Instead, we will let arbitrary
statements take their place. Of course, this could be a block, but it
very well could be a multi-statement (essentially a block that doesn't
imply a new lexical scope), or anything, really.
* Move checking the validity of a derived class with no constructor
from evlauation to binding (verification). Furthermore, make it
a verification error instead of an assert, and make the checking
complete (i.e., don't just check for the existence of a base class,
also check for the existence of a ctor, recursively).
* Refactor the constructor accessor out of the evaluator and make it
a method, Ctor, on the Function abstraction.
* Add a recursive Module population case to property initialization.
* Only treat the top-most frame in a module's initializer as belonging
to the module scope. This avoids interpreting block scopes within
that initializer as belonging to the module scope. Ultimately, it's
up to the language compiler to decide where to place scopes, but this
gives it more precise control over module scoping.
* Assert against unsupported named/star/keyword args in CocoPy.
If we encounter a dynamic invocation of a prototype object, we will
interpret it as an object allocation. This corresponds to code like
import ec2 from aws
instance = ec2.Instance(...)
where the second line dynamically loads the prototype object for the
Instance class from the module object for the aws/ec2 module, and
invokes it.
This change adds support for naming imports. At the moment, this simply
makes the names dynamically accessible for languages that do dynamic loads
against module objects, versus strongly typed tokens. The basic scheme
is to keep two objects per module: one that contains the globals and its
prototype parent that contains just the exports. This ensures we can
share the same slots while attaining the desired information hiding
(e.g., when handing out an object for dynamic access, we give out this
parent objects, while when loading globals from within a module, we use
the childmost one that contains all private and exported variables).
The old model for imports was to use top-level declarations on the
enclosing module itself. This was a laudible attempt to simplify
matters, but just doesn't work.
For one, the order of initialization doesn't precisely correspond
to the imports as they appear in the source code. This could incur
some weird module initialization problems that lead to differing
behavior between a language and its Coconut variant.
But more pressing as we work on CocoPy support, it doesn't give
us an opportunity to dynamically bind names in a correct way. For
example, "import aws" now needs to actually translate into a variable
declaration and assignment of sorts. Furthermore, that variable name
should be visible in the environment block in which it occurs.
This change switches imports to act like statements. For the most
part this doesn't change much compared to the old model. The common
pattern of declaring imports at the top of a file will translate to
the imports happening at the top of the module's initializer. This
has the effect of initializing the transitive closure just as it
happened previously. But it enables alternative models, like imports
inside of functions, and -- per the above -- dynamic name binding.
Previously, it was an error to look up a local that didn't exist.
Now it is common, thanks to dynamic lookups. A few code-paths didn't
previously handle this adequately; now they do.
This rearranges the way dynamic loads work a bit. Previously, they¬
required an object, and did a dynamic lookup in the object's property¬
map. For real dynamic loads -- of the kind Python uses, obviously,¬
but also ECMAScript -- we need to search the "environment".
This change searches the environment by looking first in the lexical¬
scope in the current function. If a variable exists, we will use it.¬
If that misses, we then look in the module scope. If a variable exists¬
there, we will use it. Otherwise, if the variable is used in a non-lval
position, an dynamic error will be raised ("name not declared"). If
an lval, however, we will lazily allocate a slot for it.
Note that Python doesn't use block scoping in the same way that most
languages do. This behavior is simply achieved by Python not emitting
any lexically scoped blocks other than at the function level.
This doesn't perfectly achieve the scoping behavior, because we don't
yet bind every name in a way that they can be dynamically discovered.
The two obvious cases are class names and import names. Those will be
covered in a subsequent commit.
Also note that we are getting lucky here that class static/instance
variables aren't accessible in Python or ECMAScript "ambiently" like
they are in some languages (e.g., C#, Java); as a result, we don't need
to introduce a class scope in the dynamic lookup. Some day, when we
want to support such languages, we'll need to think about how to let
languages control the environment probe order; for instance, perhaps
the LoadDynamicExpression node can have an "environment" property.
This changes the CocoPy default load type from static to dynamic,
since we don't have enough information at compile-time to emit
fully qualified tokens. Previously, Coconut only supported dynamic
loads with object targets, however we will need to support the full
scope search (class, module, global, etc).
This change makes node optional in the lookupBasicType function, which is
necessary in cases where diagnostics information isn't available (such as
with configuration application). This eliminates an assert when you fat-
finger a configuration key. The associated message was missing apostrophes.
This change eliminates the need to constantly type in the environment
name when performing major commands like configuration, planning, and
deployment. It's probably due to my age, however, I keep fat-fingering
simple commands in front of investors and I am embarrassed!
In the new model, there is a notion of a "current environment", and
I have modeled it kinda sorta just like Git's notion of "current branch."
By default, the current environment is set when you `init` something.
Otherwise, there is the `coco env select <env>` command to change it.
(Running this command w/out a new <env> will show you the current one.)
The major commands `config`, `plan`, `deploy`, and `destroy` will prefer
to use the current environment, unless it is overridden by using the
--env flag. All of the `coco env <cmd> <env>` commands still require the
explicit passing of an environment which seems reasonable since they are,
after all, about manipulating environments.
As part of this, I've overhauled the aging workspace settings cruft,
which had fallen into disrepair since the initial prototype.
This changes the object mapper infrastructure to offer more fine-grained
reporting of errors, and control over verification, during the mapping from
an untyped payload to a typed one. As a result, we can eliminate a bit of
the explicit unmarshaling goo in the AWS providers (but not all of it; I'm
sure there is more we can, and should, be doing here...)
This change adds the ability to specify analyzers in two ways:
1) By listing them in the project file, for example:
analyzers:
- acmecorp/security
- acmecorp/gitflow
2) By explicitly listing them on the CLI, as a "one off":
$ coco deploy <env> \
--analyzer=acmecorp/security \
--analyzer=acmecorp/gitflow
This closes out pulumi/coconut#119.
This change introduces the basic requirements for analyzers, as per
pulumi/coconut#119. In particular, an analyzer can implement either,
or both, of the RPC methods, Analyze and AnalyzeResource. The former
is meant to check an overall deployment (e.g., to ensure it has been
signed off on) and the latter is to check individual resources (e.g.,
to ensure properties of them are correct, such as checking style,
security, etc. rules). These run simultaneous to overall checking.
Analyzers are loaded as plugins just like providers are. The difference
is mainly in their naming ("analyzer-" prefix, rather than "resource-"),
and the RPC methods that they support.
This isn't 100% functional since we need a way to specify at the CLI
that a particular analyzer should be run, in addition to a way of
recording which analyzers certain projects should use in their manifests.
This changes a few naming things:
* Rename "husk" to "environment" (`coco env` for short).
* Rename NutPack/NutIL to CocoPack/CocoIL.
* Rename the primary Nut.yaml/json project file to Coconut.yaml/json.
* Rename the compiled Nutpack.yaml/json file to Cocopack.yaml/json.
* Rename the package asset directory from nutpack/ to .coconut/.
This change adds a new Check RPC method on the provider interface,
permitting resource providers to perform arbitrary verification on
the values of properties. This is useful for validating things
that might be difficult to express in the type system, and it runs
before *any* modifications are run (so failures can be caight early
before it's too late). My favorite motivating example is verifying
that an AWS EC2 instance's AMI is available within the target region.
This resolvespulumi/coconut#107, although we aren't using this
in any resource providers just yet. I'll add a work item now for that...
This change is mostly just a rename of Moniker to URN. It does also
prefix resource URNs to have a standard URN namespace; in other words,
"urn🥥<name>", where <name> is the same as the prior Moniker.
This is a minor step that helps to prepare us for pulumi/coconut#109.
This change adds a new resource.NewUniqueHex API, that simply generates
a unique hex string with the given prefix, with a specific count of
random bytes, and optionally capped to a maximum length.
This is used in the AWS SecurityGroup resource provider to avoid name
collisions, which is especially important during replacements (otherwise
we cannot possibly create a new instance before deleting the old one).
This resolvespulumi/coconut#108.
This change tracks which updates triggered a replacement. This enables
better output and diagnostics. For example, we now colorize those
properties differently in the output. This makes it easier to diagnose
why an unexpected resource might be getting deleted and recreated.
This change, part of pulumi/coconut#105, rearranges support for
resource replacement. The old model didn't properly account for
the cascading updates and possible replacement of dependencies.
Namely, we need to model a replacement as a creation followed by
a deletion, inserted into the overall DAG correctly so that any
resources that must be updated are updated after the creation but
prior to the deletion. This is done by inserting *three* nodes
into the graph per replacement: a physical creation step, a
physical deletion step, and a logical replacement step. The logical
step simply makes it nicer in the output (the plan output shows
a single "replacement" rather than the fine-grained outputs, unless
they are requested with --show-replace-steps). It also makes it
easier to fold all of the edges into a single linchpin node.
As part of this, the update step no longer gets to choose whether
to recreate the resource. Instead, the engine takes care of
orchestrating the replacement through actual create and delete calls.
This change introduces a new RPC function to the provider interface;
in pseudo-code:
UpdateImpact(id ID, t Type, olds PropertyMap, news PropertyMap)
(bool, PropertyMap, error)
Essentially, during the planning phase, we will consult each provider
about the nature of a proposed update. This update includes a set of
old properties and the new ones and, if the resource provider will need
to replace the property as a result of the update, it will return true;
in general, the PropertyMap will eventually contain a list of all
properties that will be modified as a result of the operation (see below).
The planning phase reacts to this by propagating the change to dependent
resources, so that they know that the ID will change (and so that they
can recalculate their own state accordingly, possibly leading to a ripple
effect). This ensures the overall DAG / schedule is ordered correctly.
This change is most of pulumi/coconut#105. The only missing piece
is to generalize replacing the "ID" property with replacing arbitrary
properties; there are hooks in here for this, but until pulumi/coconut#90
is addressed, it doesn't make sense to make much progress on this.
For cerain update shapes, we will need to recover an ID of an already-deleted,
or soon-to-be-deleted resource; in those cases, we have a moniker but want to
serialize an ID. This change implements support for remembering/recovering them.
This change fixes a couple issues that prevented restarting a
deployment after partial failure; this was due to the fact that
unchanged resources didn't propagate IDs from old to new. This
is remedied by making unchanged a map from new to old, and making
ID propagation the first thing plan application does.
This change does a few things:
* First and foremost, it tracks configuration variables that are
initialized, and optionally prints them out as part of the
prelude/header (based on --show-config), both in a dry-run (plan)
and in an actual deployment (apply).
* It tidies up some of the colorization and messages, and includes
nice banners like "Deploying changes:", etc.
* Fix an assertion.
* Issue a new error
"One or more errors occurred while applying X's configuration"
just to make it easier to distinguish configuration-specific
failures from ordinary ones.
* Change config keys to tokens.Token, not tokens.ModuleMember,
since it is legal for keys to represent class members (statics).
This change adds support for configuration maps.
This is a new feature that permits initialization code to come from markup,
after compilation, but before evaluation. There is nothing special with this
code as it could have been authored by a user. But it offers a convenient
way to specialize configuration settings per target husk, without needing
to write code to specialize each of those husks (which is needlessly complex).
For example, let's say we want to have two husks, one in AWS's us-west-1
region, and the other in us-east-2. From the same source package, we can
just create two husks, let's say "prod-west" and "prod-east":
prod-west.json:
{
"husk": "prod-west",
"config": {
"aws:config:region": "us-west-1"
}
}
prod-east.json:
{
"husk": "prod-east",
"config": {
"aws:config:region": "us-east-2"
}
}
Now when we evaluate these packages, they will automatically poke the
right configuration variables in the AWS package *before* actually
evaluating the CocoJS package contents. As a result, the static variable
"region" in the "aws:config" package will have the desired value.
This is obviously fairly general purpose, but will allow us to experiment
with different schemes and patterns. Also, I need to whip up support
for secrets, but that is a task for another day (perhaps tomorrow).
* Delete husks if err == nil, not err != nil.
* Swizzle the formatting padding on array elements so that the
diff modifier + or - binds more tightly to the [N] part.
* Print the un-doubly-indented padding for array element headers.
* Add some additional logging to step application (it helped).
* Remember unchanged resources even when glogging is off.
This change adds a --show-sames flag to `coco husk deploy`. This is
useful as I'm working on updates, to show what resources haven't changed
during a deployment.
This change checkpoints deployments properly. That is, even in the
face of partial failure, we should keep the huskfile up to date. This
accomplishes that by tracking the state during plan application.
There are still ways in which this can go wrong, however. Please see
pulumi/coconut#101 for additional thoughts on what we might do here
in the future to make checkpointing more robust in the face of failure.
This command is handy for development, so I whipped up a quick implementation.
All it does is print all known husks with their associated deployment time
and resource count (if any, or "n/a" for initialized husks with no deployments).
This change recognizes TextUnmarshaler during object mapping, and
will defer to it when we have a string but are assigning to a
non-string target that implements the interface.
As part of pulumi/coconut#94 -- adding targeting capabilities -- I've
decided to (yet again) reorganize the deployment commands a bit. This
makes targets ("husks") more of a first class thing.
Namely, you must first initialize a husk before using it:
$ coco husk init staging
Coconut husk 'staging' initialized; ready for deployments
Eventually, this is when you will be given a choice to configure it.
Afterwards, you can perform deployments. The first one is like a create,
but subsequent ones just figure out the right thing to do and do it:
$ ... make some changes ...
$ coco husk deploy staging
... standard deployment progress spew ...
Finally, should you want to teardown an entire environment:
$ coco husk destroy staging
... standard deletion progress spew for all resources ...
Coconut husk 'staging' has been destroyed!
This change partially implements pulumi/coconut#94, by adding the
ability to name targets during creation and reuse those names during
deletion and update. This simplifies the management of deployment
records, checkpoints, and snapshots.
I've opted to call these things "husks" (perhaps going overboard with
joy after our recent renaming). The basic idea is that for any
executable Nut that will be deployed, you have a nutpack/ directory
whose layout looks roughly as follows:
nutpack/
bin/
Nutpack.json
... any other compiled artifacts ...
husks/
... one snapshot per husk ...
For example, if we had a stage and prod husk, we would have:
nutpack/
bin/...
husks/
prod.json
stage.json
In the prod.json and stage.json files, we'd have the most recent
deployment record for that environment. These would presumably get
checked in and versioned along with the overall Nut, so that we
can use Git history for rollbacks, etc.
The create, update, and delete commands look in the right place for
these files automatically, so you don't need to manually supply them.
* Eliminate some superfluous "\n"s.
* Remove the redundant properties stored on AWS resources.
* Compute array diff lengths properly (+1).
* Display object property changes from null to non-null as
adds; and from non-null to null as deletes.
* Fix a boolean expression from ||s to &&s. (Bone-headed).
* Print a pretty message if the plan has nothing to do:
"info: nothing to do -- resources are up to date"
* Add an extra validation step after reading in a snapshot,
so that we detect more errors sooner. For example, I've
fed in the wrong file several times, and it just chugs
along as though it were actually a snapshot.
* Skip printing nulls in most plan outputs. These just
clutter up the output.
This change detects duplicate object names (monikers) and issues a nice
error message with source context include. For example:
index.ts(260,22): error MU2006: Duplicate objects with the same name:
prod::ec2instance:index::aws:ec2/securityGroup:SecurityGroup::group
The prior code asserted and failed abruptly, whereas this actually points
us to the offending line of code:
let group1 = new aws.ec2.SecurityGroup("group", { ... });
let group2 = new aws.ec2.SecurityGroup("group", { ... });
^^^^^^^^^^^^^^^^^^^^^^^^^
This change overhauls the way we do object monikers. The old mechanism,
generating monikers using graph paths, was far too brittle and prone to
collisions. The new approach mixes some amount of "automatic scoping"
plus some "explicit naming." Although there is some explicitness, this
is arguably a good thing, as the monikers will be relatable back to the
source more readily by developers inspecting the graph and resource state.
Each moniker has four parts:
<Namespace>::<AllocModule>::<Type>::<Name>
wherein each element is the following:
<Namespace> The namespace being deployed into
<AllocModule> The module in which the object was allocated
<Type> The type of the resource
<Name> The assigned name of the resource
The <Namespace> is essentially the deployment target -- so "prod",
"stage", etc -- although it is more general purpose to allow for future
namespacing within a target (e.g., "prod/customer1", etc); for now
this is rudimentary, however, see marapongo/mu#94.
The <AllocModule> is the token for the code that contained the 'new'
that led to this object being created. In the future, we may wish to
extend this to also track the module under evaluation. (This is a nice
aspect of monikers; they can become arbitrarily complex, so long as
they are precise, and not prone to false positives/negatives.)
The <Name> warrants more discussion. The resource provider is consulted
via a new gRPC method, Name, that fetches the name. How the provider
does this is entirely up to it. For some resource types, the resource
may have properties that developers must set (e.g., `new Bucket("foo")`);
for other providers, perhaps the resource intrinsically has a property
that explicitly and uniquely qualifies the object (e.g., AWS SecurityGroups,
via `new SecurityGroup({groupName: "my-sg"}`); and finally, it's conceivable
that a provider might auto-generate the name (e.g., such as an AWS Lambda
whose name could simply be a hash of the source code contents).
This should overall produce better results with respect to moniker
collisions, ability to match resources, and the usability of the system.
This change is a first whack at implementing updates.
Creation and deletion plans are pretty straightforward; we just take
a single graph, topologically sort it, and perform the operations in
the right order. For creation, this is in dependency order (things
that are depended upon must be created before dependents); for deletion,
this is in reverse-dependency order (things that depend on others must
be deleted before dependencies). These are just special cases of the more
general idea of performing DAG operations in dependency order.
Updates must work in terms of this more general notion. For example:
* It is an error to delete a resource while another refers to it; thus,
resources are deleted after deleting dependents, or after updating
dependent properties that reference the resource to new values.
* It is an error to depend on a create a resource before it is created;
thus, resources must be created before dependents are created, and/or
before updates to existing resource properties that would cause them
to refer to the new resource.
Of course, all of this is tangled up in a graph of dependencies. As a
result, we must create a DAG of the dependencies between creates, updates,
and deletes, and then topologically sort this DAG, in order to determine
the proper order of update operations.
To do this, we slightly generalize the existing graph infrastructure,
while also specializing two kinds of graphs; the existing one becomes a
heapstate.ObjectGraph, while this new one is resource.planGraph (internal).
This change introduces a simple retry package which will be used in
resource providers in order to perform waits, backoffs, etc. It's
pretty basic right now but leverages the fancy new Golang context
support for deadlines and timeouts. Soon we will layer AWS-specific
acceptors, etc. atop this more general purpose thing.
This change introduces a new informational message category to the
overall diagnostics infrastructure, and then wires up the resource
provider plugins stdout/stderr streams to it. In particular, a
write to stdout implies an informational message, whereas a write to
stderr implies an error. This is just a very simple and convenient
way for plugins to provide progress reporting; eventually we may
need something more complex, due to parallel evaluation of resource
graphs, however I hope we don't have to deviate too much from this.
This change adds custom serialization and deserialization to preserve
the ordering of resources in snapshot files. Unfortunately, because
Go maps are unordered, the results are scrambled (actually, it turns out,
Go will sort by the key). An alternative would be to resort the results
after reading in the file, or storing as an array, but this would be a
change to the MuGL file format and is less clear than simply keying each
resource by its moniker as we are doing today.
This change repivots the plan/apply commands slightly. This is largely
in preparation for performing deletes and updates of existing environments.
The old way was slightly confusing and made things appear more "magical"
than they actually are. Namely, different things are needed for different
kinds of deployment operations, and trying to present them each underneath
a single pair of CLI commands just leads to weird modality and options.
The new way is to offer three commands: create, update, and delete. Each
does what it says on the tin: create provisions a new environment, update
makes resource updates to an existing one, and delete tears down an existing
one entirely. The arguments are what make this interesting: create demands
a MuPackage to evaluate (producing the new desired state snapshot), update
takes *both* an existing snapshot file plus a MuPackage to evaluate (producing
the new desired state snapshot to diff against the existing one), and delete
merely takes an existing snapshot file and no MuPackage, since all it must
do is tear down an existing known environment.
Replacing the plan functionality is the --dry-run (-n) flag that may be
passed to any of the above commands. This will print out the plan without
actually performing any opterations.
All commands produce serializable resource files in the MuGL file format,
and attempt to do smart things with respect to backups, etc., to support the
intended "Git-oriented" workflow of the pure CLI dev experience.
Per Eric's suggestion, this moves the colors we use into a central
location so that it'll be easier someday down the road to reconfigure
and/or disable them, etc. This does not include a --no-colors option
although we should really include this soon before it gets too hairy.
This change adds support for serializing snapshots in MuGL, per the
design document in docs/design/mugl.md. At the moment, it is only
exposed from the `mu plan` command, which allows you to specify an
output location using `mu plan --output=file.json` (or `-o=file.json`
for short). This serializes the snapshot with monikers, resources,
and so on. Deserialization is not yet supported; that comes next.
* Specify MinCount/MaxCount when creating an EC2 instance. These
are required properties on the RunInstances API.
* Only attempt to unmarshal egressArray/ingressArray when non-nil.
* Remember the context object on the instanceProvider.
* Move the moniker and object maps into the shared context object.
* Marshal object monikers as the resource IDs to which they refer,
since monikers are useless on "the other side" of the RPC boundary.
This ensures that, for example, the AWS provider gets IDs it can use.
* Add some paranoia assertions.
This change does two things:
First, and foremost, it adds interface members to prototypes. This
ensures that interface members are available statically on all objects
of types that implement those interfaces.
Second, we permit dynamic loads through LoadLocationExpression when
the `this` object is `dynamic`. This is a convenient shortcut compared
to creating different LoadDynamicExpressions when the `this` happens to
be of a dynamic type.
This commit includes a basic AWS resource provider. Mostly it is just
scaffolding, however, it also includes prototype implementations for EC2
instance and security group resource creation operations.