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Add graph pattern for common query functions #512

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# Exposing functions for common queries

Microsoft Graph API Design Pattern

*Exposing functions for common queries is providing functions with friendly names as an additional mechanism for clients to make certain kinds of requests that involve the use of OData query options.*
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Wording feels a bit awkward.

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*Exposing functions for common queries is providing functions with friendly names as an additional mechanism for clients to make certain kinds of requests that involve the use of OData query options.*
*Exposing functions for common queries provides friendly names as an additional mechanism for clients to make certain kinds of requests that involve the use of OData query options.*


## Problem

Some APIs are very data-focused.
These APIs are generally capable of providing robust `$fitler`ing and `$expand`ing functionality.
However, the OData query options that need to be used by clients can become complicated and confusing.
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However, the OData query options that need to be used by clients can become complicated and confusing.
However, the OData query options used by clients to express certain more complex queries can become complicated and confusing.

Because of this, API producers have a tendency to avoid supporting query options that require client developers to have OData experience; avoiding these query options results in APIs that are not fully featured and are not externally extensible, limiting what clients can actually accomplish via the APIs.
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in this context, I would think of it not as experience with OData (most graph folks probably don't think of themselves as OData) but as experience with OData query options (subtle distinction, I know)

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Because of this, API producers have a tendency to avoid supporting query options that require client developers to have OData experience; avoiding these query options results in APIs that are not fully featured and are not externally extensible, limiting what clients can actually accomplish via the APIs.
Because of this, API producers have a tendency to avoid supporting common query options to express such requests in favor of special purpose functions. However, avoiding these common query options results in APIs that are not fully featured and are not externally extensible, limiting what clients can actually accomplish via the APIs. This can also lead to a proliferation of special purpose functions to express each unique request.


## Solution

The solution to this problem is to provide support for the query options and to additionally define OData functions that act as aliases for a specific set of query options.
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Although you do allude to it in the Issues and Considerations, I would mention here that the solution is applied when the request being aliased is more complex/less intuitive to construct.

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The solution to this problem is to provide support for the query options and to additionally define OData functions that act as aliases for a specific set of query options.
The solution to this problem is to provide support for the query options and to additionally define OData functions that act as aliases for a specific set of query options that represent a common request that may be more complex/less intuitive to construct.

Doing this gives client developers a low barrier-of-entry to using the API (familiarizing themselves with the shape of the data), and it also gives those developers extensiblity in the future to grow beyond the basic cases to write clients that are specific to their own business needs.
The functions can further provide discoverability by using names for concepts that customers are familiar with, letting them know that those concepts are supported.

## When to use this pattern

This pattern can be employed in almost any circumstance.
It is also able to be used once an API as shipped and in any order.
It is ok to ship a data-focused API with robust `$filter`ing support, and *later* ship functions that act in the same way as certain specific filters.
It is likewise fine to ship a handful of functions and then later ship an API that is a collection of data with a more fully-featured set of filters.
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This makes it sound like it's fine to ship a special purpose function because, if in the future they decide they want something more flexible, they could add it. I'm afraid that would lead to workloads using this guidance to say "I have a bunch of functions today, and might add more in the future, cause the guidance says that I can always add $filter later."

I would think that we would want to encourage workloads to move to the more general solution and not rely on exposing everything through custom functions.

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It is likewise fine to ship a handful of functions and then later ship an API that is a collection of data with a more fully-featured set of filters.
While it is possible to start with one or two custom functions, you should have a plan to support a more fully-featured set of query options as the utility of your API will be significantly limited in the interim.


## Issues and considerations

The tradeoff with this pattern is ensuring that the functions don't become so numerous that they remove the aliasing benefit.
It is important to remember that the functions exist to increase discoverability, decrease onboarding costs, and prevent client mistakes writing complicated OData queries.
If a function is being considered that does not directly address one of these issues, it likely shouldn't be introduced.

## Example

Suppose that there is a collection of attempted sign-ins for an organization. The model might look like this:

```xml
<EntityContainer Name="container">
<EntitySet Name="signInAttempts" EntityType="self.signInAttempt" />
</EntityContainer>

<EntityType Name="signInAttempt">
<Key>
<PropertyRef Name="id" />
</Key>
<Property Name="id" Type="Edm.String" Nullable="false" />

<Property Name="firstFactorUsed" Type="self.authenticationFactor" Nullable="false" />
<Property Name="otherFactorsUsed" Type="Collection(self.authenticationFactor)" Nullable="false" />
<Property Name="isSuccessful" Type="Edm.Boolean" Nullable="false" />
</EntityType>

<ComplexType Name="authenticationFactor">
<!--other properties here-->
</ComplexType>
```

Clients could then request all of the successful, multifactor sign-in attempts by calling:

```http
GET /signInAttempts?$filter=isSuccessful eq true and otherFactorsUsed/$count ne 0

HTTP/1.1 200 OK
{
"value": [
{
"id": "{id1}",
"firstFactorUsed": {
// other properties here
},
"otherFactorsUsed": [
{
// other properties here
},
...
],
"isSuccessful": true
},
...
]
}
```

An API producer could alias this common filtering use case by adding a function to the CSDL:

```xml
<Function Name="successfulMultifactorSignIns" IsBound="true">
<Parameter Name="bindingParameter" Type="Collection(self.signInAttempt)" Nullable="false" />
<ReturnType Type="self.signInAttempt" />
</Function>
```

Clients would then be able to call:

```http
GET /signInAttempts/successfulMultifactorSignIns

HTTP/1.1 200 OK
{
"value": [
{
"id": "{id1}",
"firstFactorUsed": {
// other properties here
},
"otherFactorsUsed": [
{
// other properties here
},
...
],
"isSuccessful": true
},
...
]
}
```

to retreive the same data.