Skip to content

Latest commit

 

History

History
executable file
·
761 lines (610 loc) · 32.1 KB

api_changes.md

File metadata and controls

executable file
·
761 lines (610 loc) · 32.1 KB
Raw

WARNING WARNING WARNING WARNING WARNING

PLEASE NOTE: This document applies to the HEAD of the source tree

If you are using a released version of Kubernetes, you should refer to the docs that go with that version.

The latest release of this document can be found [here](http://releases.k8s.io/release-1.4/docs/devel/api_changes.md).

Documentation for other releases can be found at releases.k8s.io.

*This document is oriented at developers who want to change existing APIs. A set of API conventions, which applies to new APIs and to changes, can be found at API Conventions.

Table of Contents

So you want to change the API?

Before attempting a change to the API, you should familiarize yourself with a number of existing API types and with the API conventions. If creating a new API type/resource, we also recommend that you first send a PR containing just a proposal for the new API types, and that you initially target the extensions API (pkg/apis/extensions).

The Kubernetes API has two major components - the internal structures and the versioned APIs. The versioned APIs are intended to be stable, while the internal structures are implemented to best reflect the needs of the Kubernetes code itself.

What this means for API changes is that you have to be somewhat thoughtful in how you approach changes, and that you have to touch a number of pieces to make a complete change. This document aims to guide you through the process, though not all API changes will need all of these steps.

Operational overview

It is important to have a high level understanding of the API system used in Kubernetes in order to navigate the rest of this document.

As mentioned above, the internal representation of an API object is decoupled from any one API version. This provides a lot of freedom to evolve the code, but it requires robust infrastructure to convert between representations. There are multiple steps in processing an API operation - even something as simple as a GET involves a great deal of machinery.

The conversion process is logically a "star" with the internal form at the center. Every versioned API can be converted to the internal form (and vice-versa), but versioned APIs do not convert to other versioned APIs directly. This sounds like a heavy process, but in reality we do not intend to keep more than a small number of versions alive at once. While all of the Kubernetes code operates on the internal structures, they are always converted to a versioned form before being written to storage (disk or etcd) or being sent over a wire. Clients should consume and operate on the versioned APIs exclusively.

To demonstrate the general process, here is a (hypothetical) example:

  1. A user POSTs a Pod object to /api/v7beta1/...
  2. The JSON is unmarshalled into a v7beta1.Pod structure
  3. Default values are applied to the v7beta1.Pod
  4. The v7beta1.Pod is converted to an api.Pod structure
  5. The api.Pod is validated, and any errors are returned to the user
  6. The api.Pod is converted to a v6.Pod (because v6 is the latest stable version)
  7. The v6.Pod is marshalled into JSON and written to etcd

Now that we have the Pod object stored, a user can GET that object in any supported api version. For example:

  1. A user GETs the Pod from /api/v5/...
  2. The JSON is read from etcd and unmarshalled into a v6.Pod structure
  3. Default values are applied to the v6.Pod
  4. The v6.Pod is converted to an api.Pod structure
  5. The api.Pod is converted to a v5.Pod structure
  6. The v5.Pod is marshalled into JSON and sent to the user

The implication of this process is that API changes must be done carefully and backward-compatibly.

On compatibility

Before talking about how to make API changes, it is worthwhile to clarify what we mean by API compatibility. An API change is considered backward-compatible if it:

  • adds new functionality that is not required for correct behavior (e.g., does not add a new required field)
  • does not change existing semantics, including:
    • default values and behavior
    • interpretation of existing API types, fields, and values
    • which fields are required and which are not

Put another way:

  1. Any API call (e.g. a structure POSTed to a REST endpoint) that worked before your change must work the same after your change.
  2. Any API call that uses your change must not cause problems (e.g. crash or degrade behavior) when issued against servers that do not include your change.
  3. It must be possible to round-trip your change (convert to different API versions and back) with no loss of information.
  4. Existing clients need not be aware of your change in order for them to continue to function as they did previously, even when your change is utilized.

If your change does not meet these criteria, it is not considered strictly compatible.

Let's consider some examples. In a hypothetical API (assume we're at version v6), the Frobber struct looks something like this:

// API v6.
type Frobber struct {
  Height int    `json:"height"`
  Param  string `json:"param"`
}

You want to add a new Width field. It is generally safe to add new fields without changing the API version, so you can simply change it to:

// Still API v6.
type Frobber struct {
  Height int    `json:"height"`
  Width  int    `json:"width"`
  Param  string `json:"param"`
}

The onus is on you to define a sane default value for Width such that rule #1 above is true - API calls and stored objects that used to work must continue to work.

For your next change you want to allow multiple Param values. You can not simply change Param string to Params []string (without creating a whole new API version) - that fails rules #1 and #2. You can instead do something like:

// Still API v6, but kind of clumsy.
type Frobber struct {
  Height int           `json:"height"`
  Width  int           `json:"width"`
  Param  string        `json:"param"`  // the first param
  ExtraParams []string `json:"extraParams"` // additional params
}

Now you can satisfy the rules: API calls that provide the old style Param will still work, while servers that don't understand ExtraParams can ignore it. This is somewhat unsatisfying as an API, but it is strictly compatible.

Part of the reason for versioning APIs and for using internal structs that are distinct from any one version is to handle growth like this. The internal representation can be implemented as:

// Internal, soon to be v7beta1.
type Frobber struct {
  Height int
  Width  int
  Params []string
}

The code that converts to/from versioned APIs can decode this into the somewhat uglier (but compatible!) structures. Eventually, a new API version, let's call it v7beta1, will be forked and it can use the clean internal structure.

We've seen how to satisfy rules #1 and #2. Rule #3 means that you can not extend one versioned API without also extending the others. For example, an API call might POST an object in API v7beta1 format, which uses the cleaner Params field, but the API server might store that object in trusty old v6 form (since v7beta1 is "beta"). When the user reads the object back in the v7beta1 API it would be unacceptable to have lost all but Params[0]. This means that, even though it is ugly, a compatible change must be made to the v6 API.

However, this is very challenging to do correctly. It often requires multiple representations of the same information in the same API resource, which need to be kept in sync in the event that either is changed. For example, let's say you decide to rename a field within the same API version. In this case, you add units to height and width. You implement this by adding duplicate fields:

type Frobber struct {
  Height         *int          `json:"height"`
  Width          *int          `json:"width"`
  HeightInInches *int          `json:"heightInInches"`
  WidthInInches  *int          `json:"widthInInches"`
}

You convert all of the fields to pointers in order to distinguish between unset and set to 0, and then set each corresponding field from the other in the defaulting pass (e.g., heightInInches from height, and vice versa), which runs just prior to conversion. That works fine when the user creates a resource from a hand-written configuration -- clients can write either field and read either field, but what about creation or update from the output of GET, or update via PATCH (see In-place updates)? In this case, the two fields will conflict, because only one field would be updated in the case of an old client that was only aware of the old field (e.g., height).

Say the client creates:

{
  "height": 10,
  "width": 5
}

and GETs:

{
  "height": 10,
  "heightInInches": 10,
  "width": 5,
  "widthInInches": 5
}

then PUTs back:

{
  "height": 13,
  "heightInInches": 10,
  "width": 5,
  "widthInInches": 5
}

The update should not fail, because it would have worked before heightInInches was added.

Therefore, when there are duplicate fields, the old field MUST take precedence over the new, and the new field should be set to match by the server upon write. A new client would be aware of the old field as well as the new, and so can ensure that the old field is either unset or is set consistently with the new field. However, older clients would be unaware of the new field. Please avoid introducing duplicate fields due to the complexity they incur in the API.

A new representation, even in a new API version, that is more expressive than an old one breaks backward compatibility, since clients that only understood the old representation would not be aware of the new representation nor its semantics. Examples of proposals that have run into this challenge include generalized label selectors and pod-level security context.

As another interesting example, enumerated values cause similar challenges. Adding a new value to an enumerated set is not a compatible change. Clients which assume they know how to handle all possible values of a given field will not be able to handle the new values. However, removing value from an enumerated set can be a compatible change, if handled properly (treat the removed value as deprecated but allowed). This is actually a special case of a new representation, discussed above.

For Unions, sets of fields where at most one should be set, it is acceptable to add a new option to the union if the appropriate conventions were followed in the original object. Removing an option requires following the deprecation process.

Incompatible API changes

There are times when this might be OK, but mostly we want changes that meet this definition. If you think you need to break compatibility, you should talk to the Kubernetes team first.

Breaking compatibility of a beta or stable API version, such as v1, is unacceptable. Compatibility for experimental or alpha APIs is not strictly required, but breaking compatibility should not be done lightly, as it disrupts all users of the feature. Experimental APIs may be removed. Alpha and beta API versions may be deprecated and eventually removed wholesale, as described in the versioning document. Document incompatible changes across API versions under the appropriate {v? conversion tips tag in the api.md doc.

If your change is going to be backward incompatible or might be a breaking change for API consumers, please send an announcement to kubernetes-dev@googlegroups.com before the change gets in. If you are unsure, ask. Also make sure that the change gets documented in the release notes for the next release by labeling the PR with the "release-note" github label.

If you found that your change accidentally broke clients, it should be reverted.

In short, the expected API evolution is as follows:

  • extensions/v1alpha1 ->
  • newapigroup/v1alpha1 -> ... -> newapigroup/v1alphaN ->
  • newapigroup/v1beta1 -> ... -> newapigroup/v1betaN ->
  • newapigroup/v1 ->
  • newapigroup/v2alpha1 -> ...

While in extensions we have no obligation to move forward with the API at all and may delete or break it at any time.

While in alpha we expect to move forward with it, but may break it.

Once in beta we will preserve forward compatibility, but may introduce new versions and delete old ones.

v1 must be backward-compatible for an extended length of time.

Changing versioned APIs

For most changes, you will probably find it easiest to change the versioned APIs first. This forces you to think about how to make your change in a compatible way. Rather than doing each step in every version, it's usually easier to do each versioned API one at a time, or to do all of one version before starting "all the rest".

Edit types.go

The struct definitions for each API are in pkg/api/<version>/types.go. Edit those files to reflect the change you want to make. Note that all types and non-inline fields in versioned APIs must be preceded by descriptive comments - these are used to generate documentation. Comments for types should not contain the type name; API documentation is generated from these comments and end-users should not be exposed to golang type names.

Optional fields should have the ,omitempty json tag; fields are interpreted as being required otherwise.

Edit defaults.go

If your change includes new fields for which you will need default values, you need to add cases to pkg/api/<version>/defaults.go. Of course, since you have added code, you have to add a test: pkg/api/<version>/defaults_test.go.

Do use pointers to scalars when you need to distinguish between an unset value and an automatic zero value. For example, PodSpec.TerminationGracePeriodSeconds is defined as *int64 the go type definition. A zero value means 0 seconds, and a nil value asks the system to pick a default.

Don't forget to run the tests!

Edit conversion.go

Given that you have not yet changed the internal structs, this might feel premature, and that's because it is. You don't yet have anything to convert to or from. We will revisit this in the "internal" section. If you're doing this all in a different order (i.e. you started with the internal structs), then you should jump to that topic below. In the very rare case that you are making an incompatible change you might or might not want to do this now, but you will have to do more later. The files you want are pkg/api/<version>/conversion.go and pkg/api/<version>/conversion_test.go.

Note that the conversion machinery doesn't generically handle conversion of values, such as various kinds of field references and API constants. The client library has custom conversion code for field references. You also need to add a call to api.Scheme.AddFieldLabelConversionFunc with a mapping function that understands supported translations.

Changing the internal structures

Now it is time to change the internal structs so your versioned changes can be used.

Edit types.go

Similar to the versioned APIs, the definitions for the internal structs are in pkg/api/types.go. Edit those files to reflect the change you want to make. Keep in mind that the internal structs must be able to express all of the versioned APIs.

Edit validation.go

Most changes made to the internal structs need some form of input validation. Validation is currently done on internal objects in pkg/api/validation/validation.go. This validation is the one of the first opportunities we have to make a great user experience - good error messages and thorough validation help ensure that users are giving you what you expect and, when they don't, that they know why and how to fix it. Think hard about the contents of string fields, the bounds of int fields and the requiredness/optionalness of fields.

Of course, code needs tests - pkg/api/validation/validation_test.go.

Edit version conversions

At this point you have both the versioned API changes and the internal structure changes done. If there are any notable differences - field names, types, structural change in particular - you must add some logic to convert versioned APIs to and from the internal representation. If you see errors from the serialization_test, it may indicate the need for explicit conversions.

Performance of conversions very heavily influence performance of apiserver. Thus, we are auto-generating conversion functions that are much more efficient than the generic ones (which are based on reflections and thus are highly inefficient).

The conversion code resides with each versioned API. There are two files:

  • pkg/api/<version>/conversion.go containing manually written conversion functions
  • pkg/api/<version>/conversion_generated.go containing auto-generated conversion functions
  • pkg/apis/extensions/<version>/conversion.go containing manually written conversion functions
  • pkg/apis/extensions/<version>/conversion_generated.go containing auto-generated conversion functions

Since auto-generated conversion functions are using manually written ones, those manually written should be named with a defined convention, i.e. a function converting type X in pkg a to type Y in pkg b, should be named: convert_a_X_To_b_Y.

Also note that you can (and for efficiency reasons should) use auto-generated conversion functions when writing your conversion functions.

Once all the necessary manually written conversions are added, you need to regenerate auto-generated ones. To regenerate them run:

hack/update-codegen.sh

As part of the build, kubernetes will also generate code to handle deep copy of your versioned api objects. The deep copy code resides with each versioned API:

  • <path_to_versioned_api>/zz_generated.deepcopy.go containing auto-generated copy functions

If regeneration is somehow not possible due to compile errors, the easiest workaround is to comment out the code causing errors and let the script to regenerate it. If the auto-generated conversion methods are not used by the manually-written ones, it's fine to just remove the whole file and let the generator to create it from scratch.

Unsurprisingly, adding manually written conversion also requires you to add tests to pkg/api/<version>/conversion_test.go.

Generate protobuf objects

For any core API object, we also need to generate the Protobuf IDL and marshallers. That generation is done with

hack/update-generated-protobuf.sh

The vast majority of objects will not need any consideration when converting to protobuf, but be aware that if you depend on a Golang type in the standard library there may be additional work required, although in practice we typically use our own equivalents for JSON serialization. The pkg/api/serialization_test.go will verify that your protobuf serialization preserves all fields - be sure to run it several times to ensure there are no incompletely calculated fields.

Edit json (un)marshaling code

We are auto-generating code for marshaling and unmarshaling json representation of api objects - this is to improve the overall system performance.

The auto-generated code resides with each versioned API:

  • pkg/api/<version>/types.generated.go
  • pkg/apis/extensions/<version>/types.generated.go

To regenerate them run:

hack/update-codecgen.sh

Making a new API Group

This section is under construction, as we make the tooling completely generic.

At the moment, you'll have to make a new directory under pkg/apis/; copy the directory structure from pkg/apis/authentication. Add the new group/version to all of the hack/{verify,update}-generated-{deep-copy,conversions,swagger}.sh files in the appropriate places--it should just require adding your new group/version to a bash array. See docs on adding an API group for more.

Adding API groups outside of the pkg/apis/ directory is not currently supported, but is clearly desirable. The deep copy & conversion generators need to work by parsing go files instead of by reflection; then they will be easy to point at arbitrary directories: see issue #13775.

Update the fuzzer

Part of our testing regimen for APIs is to "fuzz" (fill with random values) API objects and then convert them to and from the different API versions. This is a great way of exposing places where you lost information or made bad assumptions. If you have added any fields which need very careful formatting (the test does not run validation) or if you have made assumptions such as "this slice will always have at least 1 element", you may get an error or even a panic from the serialization_test. If so, look at the diff it produces (or the backtrace in case of a panic) and figure out what you forgot. Encode that into the fuzzer's custom fuzz functions. Hint: if you added defaults for a field, that field will need to have a custom fuzz function that ensures that the field is fuzzed to a non-empty value.

The fuzzer can be found in pkg/api/testing/fuzzer.go.

Update the semantic comparisons

VERY VERY rarely is this needed, but when it hits, it hurts. In some rare cases we end up with objects (e.g. resource quantities) that have morally equivalent values with different bitwise representations (e.g. value 10 with a base-2 formatter is the same as value 0 with a base-10 formatter). The only way Go knows how to do deep-equality is through field-by-field bitwise comparisons. This is a problem for us.

The first thing you should do is try not to do that. If you really can't avoid this, I'd like to introduce you to our semantic DeepEqual routine. It supports custom overrides for specific types - you can find that in pkg/api/helpers.go.

There's one other time when you might have to touch this: unexported fields. You see, while Go's reflect package is allowed to touch unexported fields, us mere mortals are not - this includes semantic DeepEqual. Fortunately, most of our API objects are "dumb structs" all the way down - all fields are exported (start with a capital letter) and there are no unexported fields. But sometimes you want to include an object in our API that does have unexported fields somewhere in it (for example, time.Time has unexported fields). If this hits you, you may have to touch the semantic DeepEqual customization functions.

Implement your change

Now you have the API all changed - go implement whatever it is that you're doing!

Write end-to-end tests

Check out the E2E docs for detailed information about how to write end-to-end tests for your feature.

Examples and docs

At last, your change is done, all unit tests pass, e2e passes, you're done, right? Actually, no. You just changed the API. If you are touching an existing facet of the API, you have to try really hard to make sure that all the examples and docs are updated. There's no easy way to do this, due in part to JSON and YAML silently dropping unknown fields. You're clever - you'll figure it out. Put grep or ack to good use.

If you added functionality, you should consider documenting it and/or writing an example to illustrate your change.

Make sure you update the swagger API spec by running:

hack/update-swagger-spec.sh

The API spec changes should be in a commit separate from your other changes.

Alpha, Beta, and Stable Versions

New feature development proceeds through a series of stages of increasing maturity:

  • Development level
    • Object Versioning: no convention
    • Availability: not committed to main kubernetes repo, and thus not available in official releases
    • Audience: other developers closely collaborating on a feature or proof-of-concept
    • Upgradeability, Reliability, Completeness, and Support: no requirements or guarantees
  • Alpha level
    • Object Versioning: API version name contains alpha (e.g. v1alpha1)
    • Availability: committed to main kubernetes repo; appears in an official release; feature is disabled by default, but may be enabled by flag
    • Audience: developers and expert users interested in giving early feedback on features
    • Completeness: some API operations, CLI commands, or UI support may not be implemented; the API need not have had an API review (an intensive and targeted review of the API, on top of a normal code review)
    • Upgradeability: the object schema and semantics may change in a later software release, without any provision for preserving objects in an existing cluster; removing the upgradability concern allows developers to make rapid progress; in particular, API versions can increment faster than the minor release cadence and the developer need not maintain multiple versions; developers should still increment the API version when object schema or semantics change in an incompatible way
    • Cluster Reliability: because the feature is relatively new, and may lack complete end-to-end tests, enabling the feature via a flag might expose bugs with destabilize the cluster (e.g. a bug in a control loop might rapidly create excessive numbers of object, exhausting API storage).
    • Support: there is no commitment from the project to complete the feature; the feature may be dropped entirely in a later software release
    • Recommended Use Cases: only in short-lived testing clusters, due to complexity of upgradeability and lack of long-term support and lack of upgradability.
  • Beta level:
    • Object Versioning: API version name contains beta (e.g. v2beta3)
    • Availability: in official Kubernetes releases, and enabled by default
    • Audience: users interested in providing feedback on features
    • Completeness: all API operations, CLI commands, and UI support should be implemented; end-to-end tests complete; the API has had a thorough API review and is thought to be complete, though use during beta may frequently turn up API issues not thought of during review
    • Upgradeability: the object schema and semantics may change in a later software release; when this happens, an upgrade path will be documented; in some cases, objects will be automatically converted to the new version; in other cases, a manual upgrade may be necessary; a manual upgrade may require downtime for anything relying on the new feature, and may require manual conversion of objects to the new version; when manual conversion is necessary, the project will provide documentation on the process (for an example, see v1 conversion tips)
    • Cluster Reliability: since the feature has e2e tests, enabling the feature via a flag should not create new bugs in unrelated features; because the feature is new, it may have minor bugs
    • Support: the project commits to complete the feature, in some form, in a subsequent Stable version; typically this will happen within 3 months, but sometimes longer; releases should simultaneously support two consecutive versions (e.g. v1beta1 and v1beta2; or v1beta2 and v1) for at least one minor release cycle (typically 3 months) so that users have enough time to upgrade and migrate objects
    • Recommended Use Cases: in short-lived testing clusters; in production clusters as part of a short-lived evaluation of the feature in order to provide feedback
  • Stable level:
    • Object Versioning: API version vX where X is an integer (e.g. v1)
    • Availability: in official Kubernetes releases, and enabled by default
    • Audience: all users
    • Completeness: same as beta
    • Upgradeability: only strictly compatible changes allowed in subsequent software releases
    • Cluster Reliability: high
    • Support: API version will continue to be present for many subsequent software releases;
    • Recommended Use Cases: any

Adding Unstable Features to Stable Versions

When adding a feature to an object which is already Stable, the new fields and new behaviors need to meet the Stable level requirements. If these cannot be met, then the new field cannot be added to the object.

For example, consider the following object:

// API v6.
type Frobber struct {
  Height int    `json:"height"`
  Param  string `json:"param"`
}

A developer is considering adding a new Width parameter, like this:

// API v6.
type Frobber struct {
  Height int    `json:"height"`
  Width  int    `json:"height"`
  Param  string `json:"param"`
}

However, the new feature is not stable enough to be used in a stable version (v6). Some reasons for this might include:

  • the final representation is undecided (e.g. should it be called Width or Breadth?)
  • the implementation is not stable enough for general use (e.g. the Area() routine sometimes overflows.)

The developer cannot add the new field until stability is met. However, sometimes stability cannot be met until some users try the new feature, and some users are only able or willing to accept a released version of Kubernetes. In that case, the developer has a few options, both of which require staging work over several releases.

A preferred option is to first make a release where the new value (Width in this example) is specified via an annotation, like this:

kind: frobber
version: v6
metadata:
  name: myfrobber
  annotations:
    frobbing.alpha.kubernetes.io/width: 2
height: 4
param: "green and blue"

This format allows users to specify the new field, but makes it clear that they are using a Alpha feature when they do, since the word alpha is in the annotation key.

Another option is to introduce a new type with an new alpha or beta version designator, like this:

// API v6alpha2
type Frobber struct {
  Height int    `json:"height"`
  Width  int    `json:"height"`
  Param  string `json:"param"`
}

The latter requires that all objects in the same API group as Frobber to be replicated in the new version, v6alpha2. This also requires user to use a new client which uses the other version. Therefore, this is not a preferred option.

A related issue is how a cluster manager can roll back from a new version with a new feature, that is already being used by users. See kubernetes#4855.

Analytics