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OASIS Open Data Protocol (OData) TC
Ralf Handl (ralf.handl@sap.com), SAP SE
Michael Pizzo (mikep@microsoft.com), Microsoft
Ralf Handl (ralf.handl@sap.com), SAP SE
Hubert Heijkers (hubert.heijkers@nl.ibm.com), IBM
Gerald Krause (gerald.krause@sap.com), SAP SE
Michael Pizzo (mikep@microsoft.com), Microsoft
Heiko Theißen (heiko.theissen@sap.com), SAP SE
Martin Zurmuehl (martin.zurmuehl@sap.com), SAP SE
This document is one component of a Work Product that also includes:
This specification is related to:
This specification adds basic grouping and aggregation functionality (e.g. sum, min, and max) to the Open Data Protocol (OData) without changing any of the base principles of OData.
This document was last revised or approved by the OASIS Open Data Protocol (OData) TC on the above date. The level of approval is also listed above. Check the “Latest stage” location noted above for possible later revisions of this document. Any other numbered Versions and other technical work produced by the Technical Committee (TC) are listed at https://groups.oasis-open.org/communities/tc-community-home2?CommunityKey=e7cac2a9-2d18-4640-b94d-018dc7d3f0e2#technical.
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When referencing this specification the following citation format should be used:
[OData-Data-Agg-v4.0]
OData Extension for Data Aggregation Version 4.0. Edited by Ralf Handl, Hubert Heijkers, Gerald Krause, Michael Pizzo, Heiko Theißen, and Martin Zurmuehl. 19 September 2023. OASIS Committee Specification 03. https://docs.oasis-open.org/odata/odata-data-aggregation-ext/v4.0/cs03/odata-data-aggregation-ext-v4.0-cs03.html. Latest stage: https://docs.oasis-open.org/odata/odata-data-aggregation-ext/v4.0/odata-data-aggregation-ext-v4.0.html.
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$apply
isdefined
$apply
as an Expand and Select OptionThis specification adds aggregation functionality to the Open Data Protocol (OData) without changing any of the base principles of OData. It defines semantics and a representation for aggregation of data, especially:
This specification defines the following terms:
aggregate
transformation or function defined in section 3.2.1.1Edm.Stream
or subtypes of Edm.Geography
or Edm.Geometry
/
). Segments are names of declared or dynamic structural or navigation properties, or type-cast segments consisting of the (optionally qualified) name of a structured type that is derived from the type identified by the preceding path segment to reach properties declared by the derived type.commonExpr
rule (see OData-ABNF)The following non-exhaustive list contains variable names that are used throughout this document:
Keywords defined by this specification use this monospaced font
.
Some sections of this specification are illustrated with non-normative examples.
Example 1: text describing an example uses this paragraph style
Non-normative examples use this paragraph style.
All examples in this document are non-normative and informative only. Examples labeled with ⚠ contain advanced concepts or make use of keywords that are defined only later in the text, they can be skipped at first reading.
All other text is normative unless otherwise labeled.
Open Data Protocol (OData) services expose a data model that describes the schema of the service in terms of the Entity Data Model (EDM, see OData-CSDL) and then allows for querying data in terms of this model. The responses returned by an OData service are based on that data model and retain the relationships between the entities in the model.
Extending the OData query features with simple aggregation capabilities avoids cluttering OData services with an exponential number of explicitly modeled “aggregation level entities” or else restricting the consumer to a small subset of predefined aggregations.
Adding the notion of aggregation to OData without changing any of the base principles in OData has two aspects:
Implementing any of these two aspects is valuable in itself independent of the other, and implementing both provides additional value for consumers. The provided aggregation annotations help a consumer understand more of the data structure looking at the service’s exposed data model. The query extensions allow the consumers to explicitly express the desired aggregation behavior for a particular query. They also allow consumers to formulate queries that utilize the aggregation annotations.
Example 2: The following diagram depicts a simple model that is used throughout this document.
The Amount
property in the Sale
entity type is an aggregatable property, and the properties of the related entity types are groupable. These can be arranged in hierarchies, for example:
Category
and Product
entity typesCountry
and Customer
Year
, Month
, and Date
In the context of Online Analytical Processing (OLAP), this model might be described in terms of a Sales “cube” with an Amount “measure” and three “dimensions”. This document will avoid such terms, as they are heavily overloaded.
Query extensions and descriptive annotations can be applied to normalized schemas as well as partly or fully denormalized schemas.
Example 3: The following diagram depicts a denormalized schema for the simple model.
Sale | |
---|---|
Sales | ID: Edm.String {id} |
Amount: Edm.Decimal | |
Category | CategoryID: Edm.String |
CategoryName: Edm.String | |
Product | ProductID: Edm.String |
ProductName: Edm.String | |
ProductColor: Edm.String | |
ProductTaxRate: Edm.Decimal | |
Food | FoodProductRating: Edm.Byte |
Non-Food | NonFoodProductRatingClass: Edm.String |
Sales Organization | SalesOrganizationID: Edm.String |
SalesOrganizationName: Edm.String | |
SalesOrganizationSuperordinateID: Edm.String | |
Time | TimeDate: Edm.Date |
TimeMonth: Edm.String | |
TimeQuarter: Edm.String | |
TimeYear: Edm.Int16 | |
Customer | CustomerID: Edm.String |
CustomerName: Edm.String | |
CustomerCountry: Edm.String |
Example 4: The following entity sets and sample data will be used to further illustrate the capabilities introduced by this extension.
Food
Rating |
---|
5 |
n/a |
n/a |
Non-Food
RatingClass |
---|
n/a |
n/a |
average |
Time
Date | Month | Quarter | Year |
---|---|---|---|
2022-01-01 | 2022-01 | 2022-1 | 2022 |
2022-04-01 | 2022-04 | 2022-2 | 2022 |
2022-04-10 | 2022-04 | 2022-2 | 2022 |
… |
Categories
ID | Name |
---|---|
PG1 | Food |
PG2 | Non-Food |
Customers
ID | Name | Country |
---|---|---|
C1 | Joe | USA |
C2 | Sue | USA |
C3 | Sue | Netherlands |
C4 | Luc | France |
Legend
Property |
---|
Key |
Navigation Property |
Example 5: In the example model, one prominent use case is the relation of customers to products. The first question that is likely to be asked is: “Which customers bought which products?”
This leads to the second more quantitative question: “Who bought how much of what?”
The answer to the second question typically is visualized as a cross-table:
Food | Non-Food | |||||
Sugar | Coffee | Paper | ||||
USA | 14 | 2 | 12 | 5 | 5 | |
Joe | 6 | 2 | 4 | 1 | 1 | |
Sue | 8 | 8 | 4 | 4 | ||
Netherlands | 2 | 2 | 3 | 3 | ||
Sue | 2 | 2 | 3 | 3 |
The data in this cross-table can be written down in a shape that more closely resembles the structure of the data model, leaving cells empty that have been aggregated away:
Customer/Country | Customer/Name | Product/Category/Name | Product/Name | Amount |
---|---|---|---|---|
USA | Joe | Non-Food | Paper | 1 |
USA | Joe | Food | Sugar | 2 |
USA | Joe | Food | Coffee | 4 |
USA | Sue | Food | Coffee | 8 |
USA | Sue | Non-Food | Paper | 4 |
Netherlands | Sue | Food | Sugar | 2 |
Netherlands | Sue | Non-Food | Paper | 3 |
USA | Food | Sugar | 2 | |
USA | Food | Coffee | 12 | |
USA | Non-Food | Paper | 5 | |
Netherlands | Food | Sugar | 2 | |
Netherlands | Non-Food | Paper | 3 | |
USA | Joe | Food | 6 | |
USA | Joe | Non-Food | 1 | |
USA | Sue | Food | 8 | |
USA | Sue | Non-Food | 4 | |
Netherlands | Sue | Food | 2 | |
Netherlands | Sue | Non-Food | 3 | |
USA | Food | 14 | ||
USA | Non-Food | 5 | ||
Netherlands | Food | 2 | ||
Netherlands | Non-Food | 3 |
Note that this result contains seven fully qualified aggregate values, followed by fifteen rollup rows with subtotal values.
$apply
A set transformation (transformation for short) is an operation on an input set that produces an output set. A transformation sequence is a sequence of set transformations, separated by forward slashes to express that they are consecutively applied. A transformation sequence may be invoked using the system query option $apply
. The input set of the first set transformation is the collection addressed by the resource path. The output set of each set transformation is the input set for the next set transformation. The output set of the last set transformation in the transformation sequence invoked by the system query option $apply
is the result of $apply
. This is consistent with the use of service-defined bound and composable functions in path segments. Set transformations may also appear as a parameter of certain other set transformations defined below.
The system query option $apply
MUST NOT be used if the resource path addresses a single instance.
The system query option $apply
is evaluated first, then the other system query options are evaluated, if applicable, on the result of $apply
, see OData-Protocol, section 11.2.1. Stability across requests for system query options $top
and $skip
OData-Protocol, section 11.2.6.3 is defined in section 3.3.7.
Each set transformation:
$apply
in the absence of an $expand
query option.Instances of an output set can contain structural and navigation properties, which can be declared or dynamic, as well as instance annotations.
The allowed set transformations are defined in this section as well as in the section on Hierarchical Transformations.
Service-defined bound functions that take a collection of instances of a structured type as their binding parameter and return a collection of instances of a structured type MAY be used as set transformations within $apply
. Further transformations can follow the bound function. The parameter syntax for bound function segments is identical to the parameter syntax for bound functions in resource path segments or $filter
expressions. See section 7.7 for an example.
If a data service that supports $apply
does not support it on the collection identified by the request resource path, it MUST fail with 501 Not Implemented
and a meaningful human-readable error message.
On resource paths ending in /$count
the system query option $apply
is evaluated on the set identified by the resource path without the /$count
segment, the result is the plain-text number of items in the result of $apply
. This is similar to the combination of /$count
and $filter
.
During serialization of the result of $apply
declared properties and dynamic properties are represented as defined by the response format. Other properties have been aggregated away and are not represented in the response. The entities returned in the request examples in the following sections that involve aggregation are therefore transient.
The definitions of italicized terms made in this section are used throughout this text, always with a hyperlink to this section.
All input sets and output sets in one transformation sequence are collections of the input type, that is the entity type or complex type of the first input set, or in other words, of the resource to which the transformation sequence is applied. The input type is determined by the entity model element identified within the metadata document by the context URL of that resource OData-Protocol, section 10. Individual instances in an input or output set can have a subtype of the input type. (See example 74.) The transformation sequence given as the $apply
system query option is applied to the resource addressed by the resource path. The transformations defined below can have nested transformation sequences as parameters, these are then applied to resources that can differ from the current input set.
The structure of an instance that occurs in an input or output set is defined by the names of the structural and navigation properties that the instance contains. Instances of an input type can have different structures, subject to the following rules:
Here is an overview of the structural changes made by different transformations:
An output set thus consists of instances with different structures. This is the same situation as with a collection of an open type (OData-CSDL, section 6.3 and OData-CSDL, section 9.3) and it is handled in the same way.
If the first input set is a collection of entities from a given entity set, then so are all input sets and output sets in the transformation sequence. The {select-list}
in the context URL OData-Protocol, section 10 MUST describe only properties that are present or annotated as absent (for example, if Core.Permissions
is None
OData-Protocol, section 11.2.2) in all instances of the collection, after applying any $select
and $expand
system query options. The {select-list}
SHOULD describe as many such properties as possible, even if the request involves a concatenation that leads to a non-homogeneous structure. If the server cannot determine any such properties, the {select-list}
MUST consist of just the instance annotation AnyStructure
defined in the Core
vocabulary. (See example 75.)
Input sets and output sets are not sets of instances in the mathematical sense but collections, because the same instance can occur multiple times in them. In other words: A collection contains values (which can be instances of structured types or primitive values), possibly with repetitions. The occurrences of the values in the collection form a set in the mathematical sense. The cardinality of a collection is the total number of occurrences in it. When this text describes a transformation algorithmically and stipulates that certain steps are carried out for each occurrence in a collection, this means that the steps are carried out multiple times for the same value if it occurs multiple times in the collection.
A collection addressed by the resource path is returned by the service either as an ordered collection OData-Protocol, section 11.4.10 or as an unordered collection. The same applies to collections that are nested in or related to the addressed resource as well as to collections that are the result of evaluating an expression starting with $root
, which occur, for example, as the first parameter of a hierarchical transformation.
But when such a collection is transformed by the $apply
system query option, additional cases can arise that are neither ordered nor totally unordered. For example, the groupby
transformation retains any order within a group but not between groups.
⚠ Example 6: Request the top 10 sales per customer. The processing of the request can be parallelized per customer and the responses per customer can be interleaved in the overall response. This means that for any given customer, their top 10 sales appear in the desired order, though not consecutively.
GET /service/Sales?$apply=groupby((Customer),orderby(Amount desc)/top(10))
For every transformation defined in the following sections, it will be specified how it orders its output set, based on the order of its input set. The order of the last output set can be further influenced by a $orderby
system query option before it is observed in the response payload.
An order of a collection is more precisely defined as follows: Given two different occurrences \(u_1\) and \(u_2\) in a collection, which may be of the same value or of different values, \(u_1\) precedes \(u_2\) or \(u_2\) precedes \(u_1\), but not both. It can be neither, in which case the relative order of \(u_1\) and \(u_2\) does not matter. If \(u_1\) precedes \(u_2\) and \(u_2\) precedes \(u_3\), then \(u_1\) also precedes \(u_3\), and \(u_1\) never precedes \(u_1\). (This is a partial order in the mathematical sense defined on the set of occurrences.)
When transformations are defined in the following sections, the algorithmic description sometimes contains an order-preserving loop over a collection. Such a loop processes the occurrences in an order chosen by the service in such a way that \(u_1\) is processed before \(u_2\) whenever \(u_1\) precedes \(u_2\). Likewise, in an order-preserving sequence \(u_1,…,u_n\) we have \(i<j\) whenever \(u_i\) precedes \(u_j\).
A collection can be stable-sorted by a list of expressions. In the stable-sorted collection an occurrence \(u_1\) precedes \(u_2\) if and only if either
Stable-sorting of an ordered collection produces another ordered collection. A stable-sort does not necessarily produce a total order, the sorted collection may still contain two occurrences whose relative order does not matter. The transformation orderby
performs a stable-sort.
The output set of a basic aggregation transformation can contain instances of an entity type without entity-id. After a concat
transformation, different occurrences of the same entity can differ in individual non-declared properties. To account for such cases, the definition of sameness given in OData-URL, section 5.1.1.1.1 is refined here. Instances of structured types are the same if
Collections are the same if there is a one-to-one correspondence \(f\) between them such that
This document specifies how a data aggregation path that occurs in a request is evaluated by the service. If such an evaluation fails, the service MUST reject the request.
For a data aggregation path to be a common expression according to OData-URL, section 5.1.1, its segments must be single-valued with the possible exception of the last segment, and it can then be evaluated relative to an instance of a structured type. For the transformations defined in this document, a data aggregation path can also be evaluated relative to a collection \(A\), even if it has arbitrary collection-valued segments itself.
To this end, the following notation is used in the subsequent sections: If \(A\) is a collection and \(p\) a data aggregation path, optionally followed by a type-cast segment, the result of such a path evaluation is denoted by \(\Gamma(A,p)\) and defined as the unordered concatenation, possibly containing repetitions, of the collections \(γ(u,p)\) for each \(u\) in \(A\) that is not null. The function \(γ(u,p)\) takes a non-null value and a path as arguments and returns a collection of instances of structured types or primitive values, depending on the type of the final segment of \(p\). It is recursively defined as follows:
This notation is extended to the case of an empty path \(e\) by setting \(\Gamma(A,e)=A\) with null values removed. Note the collections returned by \(\Gamma\) and \(γ\) never contain the null value. Also, every instance \(u\) in \(\Gamma(A,p)\) occurs also in \(A\) or nested into \(A\), therefore an algorithmic step like “Add a dynamic property to each \(u\) in \(\Gamma(A,p)\)” effectively changes \(A\).
aggregate
The aggregate
transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns an output set with a single instance of the input type without entity-id containing one property per aggregate expression, representing the aggregated value of the input set.
An aggregate expression MUST have one of the types listed below or be constructed with the from
keyword. To compute the value of the property for a given aggregate expression, the aggregate
transformation first determines a collection \(A\) of instances of structured types or primitive values, based on the input set of the aggregate
transformation, and a path \(p\) that occurs in the aggregate expression. Let \(p_1\) denote a data aggregation path with single- or collection-valued segments and \(p_2\) a type-cast segment. Depending on its type, the aggregate expression contains a path \(p=p_1\) or \(p=p_2\) or \(p=p_1/p_2\). Each type of aggregate expression defines a function \(f(A)\) which the aggregate transformation evaluates to obtain the property value.
The property is a dynamic property, except for a special case in type 4. In types 1 and 2, the aggregate expression MUST end with the keyword with
and an aggregation method \(g\). The aggregation method also determines the type of the dynamic property. In types 1, 2, and 3 the aggregate expression MUST, and in type 4 it MAY, be followed by the keyword as
and an alias, which is then the name of the dynamic property.
Types of aggregate expressions:
CustomAggregate
annotation.Determination of \(A\):
Let \(I\) be the input set. If \(p\) is absent, let \(A=I\) with null values removed.
Otherwise, let \(q\) be the portion of \(p\) up to and including the last navigation property, if any, and any type-cast segment that immediately follows, and let \(r\) be the remainder, if any, of \(p\) that contains no navigation properties, such that \(p\) equals the concatenated path \(q⁄r\). The aggregate transformation considers each entity reached via the path \(q\) exactly once. To this end, using the \(\Gamma\) notation:
Then, if \(r\) is empty, let \(A=E\), otherwise let \(A=\Gamma(E,r)\), this consists of instances of structured types or primitive values, possibly with repetitions.
as
Aggregate expressions can be followed by the as
keyword followed by an alias.
Example 7:
GET /service/Sales?$apply=aggregate(Amount with sum as Total,
Amount with max as MxA)
results in
{
"@context": "$metadata#Sales(Total, MxA)",
"value": [
{ "Total@type": "Decimal", "Total": 24,
"MxA@type": "Decimal", "MxA": 8 }
]
}
Example 8:
GET /service/Sales?$apply=aggregate(Amount mul Product/TaxRate
with sum as Tax)
results in
{
"@context": "$metadata#Sales(Tax)",
"value": [
{ "Tax@type": "Decimal", "Tax": 2.08 }
]
}
An alias affects the structure of the output set: each alias corresponds to a dynamic property in a $select
option.
Values can be aggregated using the standard aggregation methods sum
, min
, max
, average
, and countdistinct
, or with custom aggregation methods defined by the service. Only types 1 and 2 of the aggregation algorithm involve aggregation methods, and the algorithm ensures that no null values occur among the values to be aggregated.
sum
The standard aggregation method sum
can be applied to numeric values to return the sum of the values, or null if there are no values to be aggregated. The provider MUST choose a single type for the property across all instances of that type in the result that is capable of representing the aggregated values. This may require a larger integer type, Edm.Decimal
with sufficient Precision
and Scale
, or Edm.Double
.
Example 9:
GET /service/Sales?$apply=aggregate(Amount with sum as Total)
results in
{
"@context": "$metadata#Sales(Total)",
"value": [
{ "Total@type": "Decimal", "Total": 24 }
]
}
min
The standard aggregation method min
can be applied to values with a totally ordered domain to return the smallest of the values, or null if there are no values to be aggregated.
The result property will have the same type as the input property.
Example 10:
GET /service/Sales?$apply=aggregate(Amount with min as MinAmount)
results in
{
"@context": "$metadata#Sales(MinAmount)",
"value": [
{ "MinAmount@type": "Decimal", "MinAmount": 1 }
]
}
max
The standard aggregation method max
can be applied to values with a totally ordered domain to return the largest of the values, or null if there are no values to be aggregated.
The result property will have the same type as the input property.
Example 11:
GET /service/Sales?$apply=aggregate(Amount with max as MaxAmount)
results in
{
"@context": "$metadata#Sales(MaxAmount)",
"value": [
{ "MaxAmount@type": "Decimal", "MaxAmount": 8 }
]
}
average
The standard aggregation method average
can be applied to numeric values to return the sum of the values divided by the count of the values, or null if there are no values to be aggregated.
The provider MUST choose a single type for the property across all instances of that type in the result that is capable of representing the aggregated values; either Edm.Double
or Edm.Decimal
with sufficient Precision
and Scale
.
Example 12:
GET /service/Sales?$apply=aggregate(Amount with average as AverageAmount)
results in
{
"@context": "$metadata#Sales(AverageAmount)",
"value": [
{ "AverageAmount@type": "Decimal", "AverageAmount": 3.0 }
]
}
countdistinct
The aggregation method countdistinct
can be applied to arbitrary collections to count the distinct values. Instance comparison uses the definition of equality in OData-URL, section 5.1.1.1.1.
The result property MUST have type Edm.Decimal
with Scale
0 and sufficient Precision
.
Example 13:
GET /service/Sales?$apply=aggregate(Product with countdistinct
as DistinctProducts)
results in
{
"@context": "$metadata#Sales(DistinctProducts)",
"value": [
{ "DistinctProducts@type": "Decimal", "DistinctProducts": 3 }
]
}
The number of instances in the input set can be counted with the aggregate expression $count
.
Services can define custom aggregation methods if the functionality offered by the standard aggregation methods is not sufficient for the intended consumers.
Custom aggregation methods MUST use a namespace-qualified name (see OData-ABNF), i.e. contain at least one dot. Dot-less names are reserved for future versions of this specification.
⚠ Example 14: custom aggregation method that concatenates distinct string values separated by commas
GET /service/Sales?$apply=groupby((Customer/Country),
aggregate(Amount with sum as Total,
Product/Name with Custom.concat as ProductNames))
results in
{
"@context": "$metadata#Sales(Customer(Country),Total,ProductNames)",
"value": [
{ "Customer": { "Country": "Netherlands" },
"Total@type": "Decimal", "Total": 5,
"ProductNames": "Paper,Sugar" },
{ "Customer": { "Country": "USA" },
"Total@type": "Decimal", "Total": 19,
"ProductNames": "Coffee,Paper,Sugar" }
]
}
$count
The aggregate expression $count
is defined as type 3 in the aggregation algorithm. It MUST always specify an alias and MUST NOT specify an aggregation method.
The result property MUST have type Edm.Decimal
with Scale
0 and sufficient Precision
.
Example 15:
GET /service/Sales?$apply=aggregate($count as SalesCount)
results in
{
"@context": "$metadata#Sales(SalesCount)",
"value": [
{ "SalesCount@type": "Decimal", "SalesCount": 8 }
]
}
from
The from
keyword offers a shortcut for a sequence of groupby
and aggregate
transformations with the pattern \({\tt groupby}(…,{\tt aggregate}(…{\tt\ as\ }D_1))/{\tt aggregate}(D_1{\tt\ with\ }…)\).
In the following \(p_1,…,p_n\) are data aggregation paths that are allowed in groupby
for simple grouping.
from
and with
clauses that were introduced through application of these rules, then \[α{\tt\ from\ }p_1,…,p_n\] is an aggregate expression which evaluates to the value of property \(c\) in the single instance in the output set of the following transformation sequence: \[{\tt groupby}((p_1,…,p_n),{\tt aggregate}(α{\tt\ as\ }D_1))/{\tt aggregate}(p/c).\]Aggregate expressions constructed by these rules MUST be followed in the aggregate
transformation by the keyword as
and an alias. These rules can be applied repeatedly and lead to multiple from
and with
clauses in an aggregate expression.
⚠ Example 16: illustrates rule 1 where \(α={\tt Amount\ with\ sum}\), \(p_1={\tt Time}\), \(g={\tt average}\)
GET /service/Sales?$apply=aggregate(Amount with sum from Time with average
as DailyAverage)
is equivalent to (but avoids the intermediate dynamic property Total
)
GET /service/Sales?$apply=groupby((Time),aggregate(Amount with sum as Total))
/aggregate(Total with average as DailyAverage)
and results in the average sales volume per day
{
"@context": "$metadata#Sales(DailyAverage)",
"value": [
{ "DailyAverage@type": "Decimal", "DailyAverage": 3.428571428571429 }
]
}
⚠ Example 17: illustrates rule 1 where \(α={\tt Forecast}\), \(p_1={\tt Time}\), \(g={\tt average}\)
GET /service/Sales?$apply=aggregate(Forecast from Time with average
as DailyAverage)
is equivalent to
GET /service/Sales?$apply=groupby((Time),aggregate(Forecast))
/aggregate(Forecast with average as DailyAverage)
⚠ Example 18: the maximal daily average for sales of any product
GET /service/Sales?$apply=aggregate(Amount with average from Time,Product/Name
with max as MaxDailyAverage)
results in
{
"@context": "$metadata#Sales(MaxDailyAverage)",
"value": [
{ "MaxDailyAverage@type": "Decimal", "MaxDailyAverage": 8 }
]
}
concat
The concat
transformation takes two or more parameters, each of which is a sequence of set transformations.
It applies each transformation sequence to the input set and concatenates the intermediate output sets in the order of the parameters into the output set, preserving the ordering of the individual output sets as well as the structure of each instance in these sets, potentially leading to a non-homogeneously structured output set. If different intermediate output sets contain dynamic properties with the same alias, clients SHOULD ensure they have the same type and meaning in each intermediate output set.
⚠ Example 19:
GET /service/Sales?$apply=concat(topcount(2,Amount),
aggregate(Amount))
results in
{
"@context": "$metadata#Sales(Amount)",
"value": [
{ "ID": 4, "Amount": 8 },
{ "ID": 3, "Amount": 4 },
{ "Amount": 24 }
]
}
Note that two Sales entities with the second highest amount 4 exist in the input set; the entity with ID
3 is included in the result, because the service chose to use the ID
property for imposing a stable ordering.
groupby
The groupby
transformation takes one or two parameters where the second is a list of set transformations, separated by forward slashes to express that they are consecutively applied. If the second parameter is not specified, it defaults to a single transformation whose output set consists of a single instance of the input type without properties and without entity-id.
In its simplest form the first parameter of groupby
specifies the grouping properties, a comma-separated parenthesized list \(G\) of one or more data aggregation paths with single-valued segments. The same path SHOULD NOT appear more than once; redundant property paths MAY be considered valid, but MUST NOT alter the meaning of the request. Navigation properties and stream properties specified in grouping properties are expanded by default (see example 72).
The algorithmic description of this transformation makes use of the following definitions: Let \(u[q]\) denote the value of a structural or navigation property \(q\) in an instance \(u\). A path \(p_1\) is called a prefix of a path \(p\) if there is a non-empty path \(p_2\) such that \(p\) equals the concatenated path \(p_1/p_2\). Let \(e\) denote the empty path.
The output set of the groupby
transformation is constructed in five steps.
Definition of \(\Pi_G(s)\):
Prerequisites: \(G\) is a list of data aggregation paths and \(s\) is an instance of the input type.
The output set of the transformation \(\Pi_G(s)\) is in one-to-one correspondence with its input set via the order-preserving mapping \(u↦a_G(u,s,e)\). The function \(a_G(u,s,p)\) takes two instances and a path relative to the input set as arguments and is computed recursively as follows:
Example 20:
GET /service/Sales?$apply=groupby((Customer/Country,Product/Name),
aggregate(Amount with sum as Total))
results in
{
"@context": "$metadata#Sales(Customer(Country),Product(Name),Total)",
"value": [
{ "Customer": { "Country": "Netherlands" },
"Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 3 },
{ "Customer": { "Country": "Netherlands" },
"Product": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 2 },
{ "Customer": { "Country": "USA" },
"Product": { "Name": "Coffee" },
"Total@type": "Decimal", "Total": 12 },
{ "Customer": { "Country": "USA" },
"Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 5 },
{ "Customer": { "Country": "USA" },
"Product": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 2 }
]
}
If the second parameter is omitted, steps 2 and 3 above produce one instance containing only the grouping properties per distinct value combination.
⚠ Example 21:
GET /service/Sales?$apply=groupby((Product/Name,Amount))
results in
{
"@context": "$metadata#Sales(Product(Name),Amount)",
"value": [
{ "Product": { "Name": "Coffee" }, "Amount": 4 },
{ "Product": { "Name": "Coffee" }, "Amount": 8 },
{ "Product": { "Name": "Paper" }, "Amount": 1 },
{ "Product": { "Name": "Paper" }, "Amount": 2 },
{ "Product": { "Name": "Paper" }, "Amount": 4 },
{ "Product": { "Name": "Sugar" }, "Amount": 2 }
]
}
Note that the result has the same structure, but not the same content as
GET /service/Sales?$expand=Product($select=Name)&$select=Amount
A groupby
transformation affects the structure of the output set similar to $select
where each grouping property corresponds to an item in a $select
clause.
rollup
The rollup
grouping operator allows applying set transformations to instances of an input set organized in a leveled hierarchy. It can be used instead of a grouping property in the first parameter of groupby
. It has two overloads, depending on the number of parameters.
If used with two or more parameters, it defines an unnamed leveled hierarchy of grouping properties as a list of data aggregation paths with single-valued segments. The first path in the list is the root level of the hierarchy defining the coarsest granularity, and the other paths define consecutively finer-grained levels of the hierarchy. This unnamed hierarchy is used for grouping instances.
A groupby with rollup
applied to a leveled hierarchy allows requesting aggregation for all levels of that hierarchy. It splits the input set into groups using all grouping properties (see (1) below), then removes the last property from the hierarchy (see (2)) and repeats this process using the remaining grouping properties until all of the levels have been used up (see terminating rule (3)).
Such a grouping with rollup
for a leveled hierarchy is processed using the following equivalence relationships, in which \(p_1,…,p_k\) are groupable property paths representing a level, \(T\) is a transformation sequence, the ellipsis (\(…\)) stands in for zero or more property paths, \(P_1\) stands in for zero or more property paths and \(P_2\) for zero or more rollup
or rolluprecursive
operators or property paths:
Example 22: rolling up two hierarchies, the first with two levels, the second with three levels: \[({\tt rollup}(p_{1,1},p_{1,2}),{\tt rollup}(p_{2,1},p_{2,2},p_{2,3}))\] will result in the six groupings \[\matrix{ (p_{1,1},p_{1,2},\hfill&p_{2,1},p_{2,2},p_{2,3})\hfill\\ (p_{1,1},p_{1,2},\hfill&p_{2,1},p_{2,2})\hfill\\ (p_{1,1},p_{1,2},\hfill&p_{2,1})\hfill\\ (p_{1,1},\hfill&p_{2,1},p_{2,2},p_{2,3})\hfill\\ (p_{1,1},\hfill&p_{2,1},p_{2,2})\hfill\\ (p_{1,1},\hfill&p_{2,1})\hfill }\] The leveled hierarchy of the first rollup has 2 levels, the one of the second has 3 levels, and the groupings represent all possible \(6=2⋅3\) combinations of levels from both hierarchies.
Example 23: answering the second question in section 2.3
GET /service/Sales?$apply=groupby((rollup(Customer/Country,Customer/Name),
rollup(Product/Category/Name,Product/Name)),
aggregate(Amount with sum as Total))
results in seven entities for the finest grouping level
{
"@context": "$metadata#Sales(Customer(Country),
Product(Category(Name)),Total)",
"value": [
{ "Customer": { "Country": "USA", "Name": "Joe" },
"Product": { "Category": { "Name": "Non-Food" }, "Name": "Paper" },
"Total@type": "Decimal", "Total": 1 },
…
plus additional fifteen rollup entities for subtotals: five without customer name
{ "Customer": { "Country": "USA" },
"Product": { "Category": { "Name": "Food" }, "Name": "Sugar" },
"Total@type": "Decimal", "Total": 2 },
…
six without product name
{ "Customer": { "Country": "USA", "Name": "Joe" },
"Product": { "Category": { "Name": "Food" } },
"Total@type": "Decimal", "Total": 6 },
…
and four with neither customer nor product name
{ "Customer": { "Country": "USA" },
"Product": { "Category": { "Name": "Food" } },
"Total@type": "Decimal", "Total": 14 },
…
]
}
Note that the absence of one or more properties of the output structure declared by the surrounding OData context allows distinguishing rollup entities from other entities.
If rollup
is used with one parameter, the parameter references a named leveled hierarchy to be used for grouping instances, and therefore MUST be the value of the Qualifier
attribute of an annotation with term LeveledHierarchy
. If the annotation has qualifier \(Q\) and as value a collection consisting of \(p_1,…,p_n\) with \(n≥2\), then \({\tt rollup}(Q)\) is equivalent to \({\tt rollup}(p_1,…,p_n)\).
Another grouping operator rolluprecursive
which similarly works with a recursive hierarchy is defined later.
These transformations produce an output set that is a subset of their input set, possibly in a different order. Some of the algorithmic descriptions below make use of the following definition: A total order of a collection is called stable across requests if it is the same for all requests that construct the collection by executing the same resource path and transformations, possibly nested, on the same underlying data.
⚠ Example 24: A stable total order is required for the input set of a skip
transformation. The following request constructs that input set by executing the groupby
transformation on the Sales
entity collection, computing the total sales per customer. Because of the subsequent skip
transformation, the service must endow this with a stable total order. Then the request divides the total sales per customer into pages of \(N\) customers and returns page number \(i\) in a reproducible manner (as long as the underlying data do not change).
GET /service/Sales?$apply=
groupby((Customer),aggregate(Amount with sum as Total))
/skip(M)/top(N)
where the number in skip
is \(M=(i-1)⋅N\). Other values of \(M\) can be used to skip, for example, half a page.
These transformations take two parameters. The first parameter MUST be an expression that is evaluable on the input set as a collection, without reference to an individual instance (and which therefore cannot be a property path). The second parameter MUST be an expression that is evaluated on each instance of the input set in turn.
The output set is constructed as follows:
bottom
) or descending (for transformations starting with top
) order of the value specified in the second parameter. (This is the order in which contributions to the output set are considered.)For example, if the input set consists of non-transient entities and the datastore contains an index ordered by the second parameter and then the entity-id, a service may implement this algorithm with \(A=B\) ordered like this index.
The order of the output set can be influenced with a subsequent orderby
transformation.
bottomcount
and topcount
The first parameter MUST evaluate to a positive integer \(c\). The second parameter MUST evaluate to a primitive type whose values are totally ordered. In step 5, exit the loop if the cardinality of the output set equals \(c\).
Example 25:
GET /service/Sales?$apply=bottomcount(2,Amount)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 1, "Amount": 1 },
{ "ID": 7, "Amount": 1 }
]
}
Example 26:
GET /service/Sales?$apply=topcount(2,Amount)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 }
]
}
Note that two Sales
entities with the second highest amount 4 exist in the input set; the entity with ID
3 is included in the result, because the service chose to use the ID
property for imposing a stable ordering in step 1. Such a logic needs to be in place even with a preceding orderby
since it cannot be ensured that it creates a stable order of the instances on the expressions of the second parameter.
bottompercent
and toppercent
The first parameter MUST evaluate to a positive number \(p\) less than or equal to 100. The second parameter MUST evaluate to a number. In step 5, exit the loop if the ratio of the sum of the numbers addressed by the second parameter in the output set to their sum in the input set equals or exceeds \(p\) percent.
Example 27:
GET /service/Sales?$apply=bottompercent(50,Amount)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 1, "Amount": 1 },
{ "ID": 2, "Amount": 2 },
{ "ID": 5, "Amount": 4 },
{ "ID": 6, "Amount": 2 },
{ "ID": 7, "Amount": 1 },
{ "ID": 8, "Amount": 2 }
]
}
Example 28:
GET /service/Sales?$apply=toppercent(50,Amount)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 }
]
}
bottomsum
and topsum
The first parameter MUST evaluate to a number \(s\). The second parameter MUST be an aggregatable expression that evaluates to a number. In step 5, exit the loop if the sum of the numbers addressed by the second parameter in the output set is greater than or equal to \(s\).
Example 29:
GET /service/Sales?$apply=bottomsum(7,Amount)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 1, "Amount": 1 },
{ "ID": 2, "Amount": 2 },
{ "ID": 6, "Amount": 2 },
{ "ID": 7, "Amount": 1 },
{ "ID": 8, "Amount": 2 }
]
}
Example 30:
GET /service/Sales?$apply=topsum(15,Amount)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 },
{ "ID": 5, "Amount": 4 }
]
}
filter
The filter
transformation takes a Boolean expression that could also be passed as a $filter
system query option. Its output set is the subset of the input set containing all instances (possibly with repetitions) for which this expression, evaluated relative to the instance, yields true. No order is defined on the output set.
Example 31:
GET /service/Sales?$apply=filter(Amount gt 3)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 },
{ "ID": 5, "Amount": 4 }
]
}
orderby
The orderby
transformation takes a list of expressions that could also be passed as a $orderby
system query option. Its output set consists of the instances of the input set in the same order $orderby
would produce for the given expressions, but keeping the relative order from the input set if the given expressions do not distinguish between two instances. The orderby transformation thereby performs a stable-sort. A service supporting this transformation MUST at least offer sorting by values addressed by property paths, including dynamic properties, with both suffixes asc
and desc
.
Example 32:
GET /service/Sales?$apply=groupby((Product/Name),
aggregate(Amount with sum as Total))
/orderby(Total desc)
results in
{
"@context": "$metadata#Sales(Product(Name),Total)",
"value": [
{ "Product": { "Name": "Coffee" },
"Total@type": "Decimal", "Total": 12 },
{ "Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 8 },
{ "Product": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 4 }
]
}
search
The search
transformation takes a search expression that could also be passed as a $search
system query option. Its output set is the subset of the input set containing all instances (possibly with repetitions) that match this search expression. Closing parentheses in search expressions must be within single or double quotes in order to avoid syntax errors like search())
. No order is defined on the output set.
Example 33: assuming that free-text search on Sales
takes the related product name into account,
GET /service/Sales?$apply=search(coffee)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 }
]
}
skip
The skip
transformation takes a non-negative integer \(c\) as argument. Let \(A\) be a copy of the input set with a total order that extends any existing order of the input set but is otherwise chosen by the service. The total order MUST be stable across requests.
The transformation excludes from the output set the first \(c\) occurrences in \(A\). It keeps all remaining instances in the same order as they occur in \(A\).
Example 34:
GET /service/Sales?$apply=orderby(Customer/Name desc)/skip(2)/top(2)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 6, "Amount": 2 },
{ "ID": 7, "Amount": 1 }
]
}
top
The top
transformation takes a non-negative integer \(c\) as argument. Let \(A\) be a copy of the input set with a total order that extends any existing order of the input set but is otherwise chosen by the service. The total order MUST be stable across requests.
If \(A\) contains more than \(c\) instances, the output set consists of the first \(c\) occurrences in \(A\). Otherwise, the output set equals \(A\). The instances in the output set are in the same order as they occur in \(A\).
Note the transformation top(0)
produces an empty output set.
Example 35:
GET /service/Sales?$apply=orderby(Customer/Name desc)/top(2)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 4, "Amount": 8 },
{ "ID": 5, "Amount": 4 }
]
}
$skip
and $top
When the system query options $top
and $skip
OData-Protocol, section 11.2.6.3 are executed after the system query option $apply
and after $filter
and $orderby
, if applicable, they operate on a collection with a total order that extends any existing order but is otherwise chosen by the service. The total order MUST be stable across requests.
These transformations produce an output set in one-to-one correspondence with their input set. The output set is initially a clone of the input set, then dynamic properties are added to the output set. The values of properties copied from the input set are not changed, nor is the order of instances changed.
identity
The output set of the identity
transformation is its input set in unchanged order.
Example 36: Add a grand total row to the Sales
result set
GET /service/Sales?$apply=concat(identity,aggregate(Amount with sum as Total))
compute
The compute
transformation takes a comma-separated list of one or more compute expressions as parameters.
A compute expression is a common expression followed by the as
keyword, followed by an alias.
The output set is constructed by copying the instances of the input set and adding one dynamic property per compute expression to each occurrence in the output set. The name of each added dynamic property is the alias of the corresponding compute expression. The value of each added dynamic property is computed relative to the corresponding instance. Services MAY support expressions that address dynamic properties added by other expressions within the same compute
transformation, provided that the service can determine an evaluation sequence. The type of the property is determined by the rules for evaluating common expressions and numeric promotion defined in OData-URL, section 5.1.1.
Example 37:
GET /service/Sales?$apply=compute(Amount mul Product/TaxRate as Tax)
results in
{
"@context": "$metadata#Sales(*,Tax)",
"value": [
{ "ID": 1, "Amount": 1, "Tax@type": "Decimal", "Tax": 0.14 },
{ "ID": 2, "Amount": 2, "Tax@type": "Decimal", "Tax": 0.12 },
{ "ID": 3, "Amount": 4, "Tax@type": "Decimal", "Tax": 0.24 },
{ "ID": 4, "Amount": 8, "Tax@type": "Decimal", "Tax": 0.48 },
{ "ID": 5, "Amount": 4, "Tax@type": "Decimal", "Tax": 0.56 },
{ "ID": 6, "Amount": 2, "Tax@type": "Decimal", "Tax": 0.12 },
{ "ID": 7, "Amount": 1, "Tax@type": "Decimal", "Tax": 0.14 },
{ "ID": 8, "Amount": 2, "Tax@type": "Decimal", "Tax": 0.28 }
]
}
addnested
The addnested
transformation expands a path relative to the input set, applies one or more transformation sequences to the addressed resources, and adds the transformed resources as dynamic (navigation) properties to the output set. The output set \(A\) is initially a clone of the input set.
The first parameter of the addnested
transformation is a path \(p\) or a concatenated path \(p/q\). Here, \(p=p_1/…/p_k\) with \(k≥1\) is a data aggregation path with single- or collection-valued segments. The path \(p\) MUST NOT contain any navigation properties prior to the last segment \(p_k\), which MUST either be a navigation or a complex structural property. If the optional \(q\) is present, it MUST be a type-cast segment. This is an extension of the definition in OData-URL, section 5.1.3 in that the first parameter need not contain a navigation property.
Further parameters are one or more transformation sequences followed by the as
keyword followed by an alias whose name need not differ from names in the input set but MUST differ from names already in \(\Gamma(A,p_1/…/p_{k-1})\) (using the \(\Gamma\) notation) as well as from aliases for other transformation sequences.
If \(p_k\) is single-valued, the transformation sequences MUST consist of only identity
or compute
or addnested
transformations, because these transform one-element collections into one-element collections. This makes it meaningful to speak (in this section only) of a transformation sequence applied to a single instance; this means applying it to a collection containing the single instance and taking as result the single instance from the output set.
For each occurrence \(u\) in \(\Gamma(A,p_1/…/p_{k-1})\), let \(B=γ(u,p_k/q)\) and let the resource \(v\) be
If \(v\) is defined, then for each transformation sequence, a dynamic property is added to \(u\) as follows: If \(p_k\) is a navigation property, the added property is a dynamic navigation property, which is expanded by default, otherwise it is a dynamic structural property. Its name is the alias of the transformation sequence. The value of the added property is the result of the transformation sequence applied to \(v\). The dynamic property carries as control information the context URL of \(v\).
Example 38:
GET /service/Customers?$apply=addnested(Sales,
filter(Amount gt 3) as FilteredSales)
results in
{
"@context": "$metadata#Customers(FilteredSales())",
"value": [
{ "ID": "C1", "Name": "Joe", "Country": "USA",
"FilteredSales@context": "#Sales",
"FilteredSales": [{ "ID": "3", "Amount": 4 }]},
{ "ID": "C2", "Name": "Sue", "Country": "USA",
"FilteredSales@context": "#Sales",
"FilteredSales": [{ "ID": "4", "Amount": 8 },
{ "ID": "5", "Amount": 4 }]},
{ "ID": "C3", "Name": "Sue", "Country": "Netherlands",
"FilteredSales@context": "#Sales",
"FilteredSales": []},
{ "ID": "C4", "Name": "Luc", "Country": "France",
"FilteredSales@context": "#Sales",
"FilteredSales": []}
]
}
If Sales
was a collection-valued complex property of type SalesModel.SalesComplexType
, the context would be "FilteredSales@context": "#Collection(SalesModel.SalesComplexType)"
.
The output set of the join transformations differs from their input set in the number of instances as well as in their structure, but reflects the order of the input set. Transformation nest
produces a one-instance output set.
join
and outerjoin
The join
and outerjoin
transformations take as their first parameter \(p\) a collection-valued complex or navigation property, optionally followed by a type-cast segment to address only instances of that derived type or one of its sub-types, followed by the as
keyword, followed by an alias. The optional second parameter specifies a transformation sequence \(T\).
For each occurrence \(u\) in an order-preserving loop over the input set
outerjoin
, if \(A\) is empty, a null instance is added to it.Example 39: all links between products and sales instances
GET /service/Products?$apply=join(Sales as Sale)&$select=ID&$expand=Sale
results in
{
"@context": "$metadata#Products(ID,Sale())",
"value": [
{ "ID": "P1",
"Sale": {
"@context": "#Sales/$entity",
"ID": 2, "Amount": 2 } },
{ "ID": "P1",
"Sale": {
"@context": "#Sales/$entity",
"ID": 6, "Amount": 2 } },
{ "ID": "P2",
"Sale": {
"@context": "#Sales/$entity",
"ID": 3, "Amount": 4 } },
{ "ID": "P2",
"Sale": {
"@context": "#Sales/$entity",
"ID": 4, "Amount": 8 } },
{ "ID": "P3",
"Sale": {
"@context": "#Sales/$entity",
"ID": 1, "Amount": 1 } },
{ "ID": "P3",
"Sale": {
"@context": "#Sales/$entity",
"ID": 5, "Amount": 4 } },
{ "ID": "P3",
"Sale": {
"@context": "#Sales/$entity",
"ID": 7, "Amount": 1 } },
{ "ID": "P3",
"Sale": {
"@context": "#Sales/$entity",
"ID": 8, "Amount": 2 } }
]
}
In this example, $expand=Sale
is used to include the target entities in the result. There are no subsequent transformations like groupby
that would cause it to be expanded by default. If the first parameter Sales
was a collection-valued complex property of type SalesModel.SalesComplexType
, the complex property Sale
would be in the result regardless, and its context would be "@context": "#SalesModel.SalesComplexType"
.
Applying outerjoin
instead would return an additional instance for product with "ID": "P4"
and Sale
having a null value.
nest
The nest
transformation takes as parameters one or more transformation sequences followed by the as
keyword followed by an alias.
The output set consists of a single instance of the input type without entity-id having one dynamic property per transformation sequence. The name of the dynamic property is the alias for this transformation sequence. The value of the dynamic property is the collection resulting from the transformation sequence applied to the input set. The dynamic property carries as control information the context URL of the transformed input set.
Example 40:
GET /service/Sales?$apply=nest(groupby((Customer/ID)) as Customers)
results in
{
"@context": "$metadata#Sales(Customers())",
"value": [
{ "Customers@context": "#Sales(Customer(ID))",
"Customers": [ { "Customer": { "ID": "C1" } },
{ "Customer": { "ID": "C2" } },
{ "Customer": { "ID": "C3" } } ] }
]
}
The following two subsections introduce two new types of expression that are evaluated relative to a collection, called the input collection.
These expressions are
$these
followed by a forward slash, the input collection is then the current collection defined as follows:
$apply
, possibly nested within $expand
or $select
, the current collection is the collection that is the subject of the system query option.$apply
transformation, the current collection is the input set of the transformation.aggregate
The aggregate
function allows the use of aggregated values in expressions. It takes a single parameter accepting an aggregate expression and returns the aggregated value of type Edm.PrimitiveType
as the result from applying the aggregate expression on its input collection.
More precisely, if \(α\) is an aggregate expression, the function \(p/{\tt aggregate}(α)\) or \({\tt\$these}/{\tt aggregate}(α)\) evaluates to the value of the property \(D\) in the single instance of the output set that is produced when the transformation \({\tt aggregate}(α{\tt\ as\ }D)\) is applied with the input collection as input set.
Example 41: Sales making up at least a third of the total sales amount.
GET /service/Sales?$filter=Amount mul 3 ge $these/aggregate(Amount with sum)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": "4", "Amount": 8 }
]
}
Example 42: Products with more than 1.00 sales tax. The aggregate expression of type 2 combines paths with and without $it
prefix (compare this with example 8).
GET /service/Products?$filter=Sales/aggregate(Amount mul $it/TaxRate with sum)
gt 1
⚠ Example 43: products with a single sale of at least twice the average sales amount
GET /service/Products?$filter=Sales/any(s:s/Amount ge
Sales/aggregate(Amount with average) mul 2)
Both examples result in
{
"@context": "$metadata#Products",
"value": [
{ "ID": "P3", "Name": "Paper", "Color": "White", "TaxRate": 0.14 }
]
}
$count
The expression $count
evaluates to the cardinality of the input collection.
Example 44: The input collection for $count
consists of all sales entities, the top third of sales entities by amount form the result.
GET /service/Sales?$apply=topcount($these/$count div 3,Amount)
results in 2 (a third of 8, rounded down) entities. (This differs from toppercent(33.3,Amount)
, which returns only the sales entity with ID
4, because that already makes up a third of the total amount.)
{
"@context": "$metadata#Sales",
"value": [
{ "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 }
]
}
A definition that is equivalent to a $count
expression after a collection-valued path was made in OData-URL, section 4.8.
isdefined
Properties that are not explicitly mentioned in aggregate
or groupby
are considered to have been aggregated away. Since they are treated as having the null value in $filter
expressions OData-URL, section 5.1.1.15, the $filter
expression Product eq null
cannot distinguish between an instance containing the value for the null product and the instance containing the aggregated value across all products (where the Product
has been aggregated away).
The function isdefined
can be used to determine whether a property is present or absent in an instance. It takes a single-valued property path as its only parameter and returns true if the property is present in the instance for which the expression containing the isdefined
function call is evaluated. A present property can still have the null value; it can represent a grouping of null values, or an aggregation that results in a null value.
Example 45: Product
has been aggregated away, causing an empty result
GET /service/Sales?$apply=aggregate(Amount with sum as Total)
&$filter=isdefined(Product)
results in
{
"@context": "$metadata#Sales(Total)",
"value": []
}
$apply
as an Expand and Select OptionThe new system query option $apply
can be used as an expand or select option to inline the result of aggregating related entities or nested instances. The rules for evaluating $apply
are applied in the context of the related collection of entities or the selected collection of instances, meaning this context defines the input set of the first transformation. Furthermore, $apply
is evaluated first, and other expand or select options on the same (navigation) property are evaluated on the result of $apply
.
Example 46: products with aggregated sales
GET /service/Products
?$expand=Sales($apply=aggregate(Amount with sum as Total))
results in
{
"@context": "$metadata#Products(Sales(Total))",
"value": [
{ "ID": "P2", "Name": "Coffee", "Color": "Brown", "TaxRate": 0.06,
"Sales": [ { "Total@type": "Decimal", "Total": 12 } ] },
{ "ID": "P3", "Name": "Paper", "Color": "White", "TaxRate": 0.14,
"Sales": [ { "Total@type": "Decimal", "Total": 8 } ] },
{ "ID": "P4", "Name": "Pencil", "Color": "Black", "TaxRate": 0.14,
"Sales": [ { "Total": null } ] },
{ "ID": "P1", "Name": "Sugar", "Color": "White", "TaxRate": 0.06,
"Sales": [ { "Total@type": "Decimal", "Total": 4 } ] }
]
}
The normative ABNF construction rules for this specification are defined in OData-Agg-ABNF. They incrementally extend the rules defined in OData-ABNF.
OData supports querying related entities through defining navigation properties in the data model. These navigation paths help guide simple consumers in understanding and navigating relationships.
In some cases, however, requests need to span entity sets with no predefined associations. Such requests can be sent to the special resource $crossjoin
instead of an individual entity set. The cross join of a list of entity sets is the Cartesian product of the listed entity sets, represented as a collection of complex type instances that have a navigation property with cardinality to-one for each participating entity set, and queries across entity sets can be formulated using these navigation properties. See OData-URL, section 4.15 for details.
Where useful navigations exist it is beneficial to expose those as explicit navigation properties in the model, but the ability to pose queries that span entity sets not related by an association provides a mechanism for advanced consumers to use more flexible join conditions.
Example 47: if Sale
had a string property ProductID
instead of the navigation property Product
, a “join” between Sales
and Products
could be accessed via the $crossjoin
resource
GET /service/$crossjoin(Products,Sales)
?$expand=Products($select=Name),Sales($select=Amount)
&$filter=Products/ID eq Sales/ProductID
results in
{
"@context": "$metadata#Collection(Edm.ComplexType)",
"value": [
{ "Products": { "Name": "Paper" }, "Sales": { "Amount": 1 } },
{ "Products": { "Name": "Sugar" }, "Sales": { "Amount": 2 } },
…
]
}
Example 48: using the $crossjoin
resource for aggregate queries
GET /service/$crossjoin(Products,Sales)
?$apply=filter(Products/ID eq Sales/ProductID)
/groupby((Products/Name),
aggregate(Sales/Amount with sum as Total))
results in
{
"@context": "$metadata#Collection(Edm.ComplexType)",
"value": [
{ "Products": { "Name": "Coffee" },
"Total@type": "Decimal", "Total": 12 },
{ "Products": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 8 },
{ "Products": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 4 }
]
}
The entity container may be annotated in the same way as entity sets to express which aggregate queries are supported, see section 5.
The following terms are defined in the vocabulary for data aggregation OData-VocAggr.
The term ApplySupported
can be applied to an entity set, an entity type, or a collection if the target expression of the annotation starts with an entity container (see example 50). It describes the aggregation capabilities of the annotated target. If present, it implies that instances of the annotated target can contain dynamic properties as an effect of $apply
even if they do not specify the OpenType
attribute, see OData-CSDL, section 6.3. The term has a complex type with the following properties:
Transformations
collection lists all supported set transformations. Allowed values are the names of the standard transformations introduced in sections 3 and 6, and namespace-qualified names identifying a service-defined bindable function. If Transformations
is omitted the server supports all transformations defined by this specification.CustomAggregationMethods
collection lists supported custom aggregation methods. Allowed values are namespace-qualified names identifying service-specific aggregation methods. If omitted, no custom aggregation methods are supported.Rollup
specifies whether the service supports no rollup, only a single rollup hierarchy, or multiple rollup hierarchies in a groupby
transformation. If omitted, multiple rollup hierarchies are supported.GroupableProperties
indicates that only the listed properties of the annotated target can be used in groupby
.AggregatableProperties
indicates that only the listed properties of the annotated target can be used in aggregate
, optionally restricted to the specified aggregation methods.All properties of ApplySupported
are optional, so it can be used as a tagging annotation to signal unlimited support of aggregation.
The term ApplySupportedDefaults
can be applied to an entity container. It allows to specify default support for aggregation capabilities Transformations
, CustomAggregationMethods
and Rollup
that propagate to all collection-valued resources in the container. Annotating a specific collection-valued resource with the term ApplySupported
overrides the default support with the specified properties using PATCH
semantics:
ApplySupported
replace the corresponding properties specified in ApplySupportedDefaults
.ApplySupported
override the corresponding properties specified in ApplySupportedDefaults using PATCH
semantics recursively.ApplySupported
nor in ApplySupportedDefault
have their default value.Example 49: an entity container with default support for everything defined in this specification
EntityContainer Name="SalesData">
<Annotation Term="Aggregation.ApplySupportedDefaults" />
<
…EntityContainer> </
Example 50: Define aggregation support only for the products of a given category
Annotations Target="SalesModel.SalesData/Categories/Products">
<Annotation Term="Aggregation.ApplySupported">
<
…Annotation>
</Annotations> </
The term CustomAggregate
allows defining dynamic properties that can be used in aggregate
. No assumptions can be made on how the values of these custom aggregates are calculated, whether they are null, and which input values are used.
When applied to an entity set, an entity type, or a collection if the target expression of the annotation starts with an entity container, the annotation specifies custom aggregates that are available for its instances and for aggregated instances resulting from these instances. When applied to an entity container, the annotation specifies custom aggregates whose input set may span multiple entity sets within the container.
A custom aggregate is identified by the value of the Qualifier
attribute when applying the term. The value of the Qualifier
attribute is the name of the dynamic property. The name MUST NOT collide with the names of other custom aggregates of the same model element.
The value of the annotation is a string with the qualified name of a primitive type or type definition in scope that specifies the type returned by the custom aggregate.
If the custom aggregate is associated with an entity set, entity type, or collection, the value of the Qualifier
attribute MAY be identical to the name of a declared property of the instances in this set or collection. In these cases, the value of the annotation MUST have the same value as the Type
attribute of the declared property. This is typically done when the custom aggregate is used as a default aggregate for that property. In this case the name refers to the custom aggregate within an aggregate expression without a with
clause, and to the property in all other cases.
If the custom aggregate is associated with an entity container, the value of the Qualifier
attribute MUST NOT collide with the names of any entity container children.
Example 51: Sales forecasts are modeled as a custom aggregate of the Sale entity type because it belongs there. For the budget, there is no appropriate structured type, so it is modeled as a custom aggregate of the SalesData
entity container.
Annotations Target="SalesModel.SalesData/Sales">
<Annotation Term="Aggregation.CustomAggregate" Qualifier="Forecast"
< String="Edm.Decimal" />
Annotations>
</Annotations Target="SalesModel.SalesData">
<Annotation Term="Aggregation.CustomAggregate" Qualifier="Budget"
< String="Edm.Decimal" />
Annotations> </
These custom aggregates can be used in the aggregate
transformation:
GET /service/Sales?$apply=groupby((Time/Month),aggregate(Forecast))
and:
GET /service/$crossjoin(Time)?$apply=groupby((Time/Year),aggregate(Budget))
Sometimes the value of a property or custom aggregate is only well-defined within the context given by values of other properties, e.g. a postal code together with its country, or a monetary amount together with its currency unit. These context-defining properties can be listed with the term ContextDefiningProperties
whose type is a collection of property paths.
If present, the context-defining properties SHOULD be used as grouping properties when aggregating the annotated property or custom aggregate, or alternatively be restricted to a single value by a pre-filter operation. Services MAY respond with 400 Bad Request
if the context-defining properties are not sufficiently specified for calculating a meaningful aggregate value.
Example 52: This simplified Sales
entity set has a single aggregatable property Amount
whose context is defined by the Code
property of the related Currency
, and a custom aggregate Forecast
with the same context. The Code
property of Currencies
is groupable. All other properties are neither groupable nor aggregatable.
EntityType Name="Currency">
<Key>
<PropertyRef Name="Code" />
<Key>
</Property Name="Code" Type="Edm.String" />
<Property Name="Name" Type="Edm.String">
<Annotation Term="Core.IsLanguageDependent" />
<Property>
</EntityType>
</
EntityType Name="Sale">
<Key>
<PropertyRef Name="ID" />
<Key>
</Property Name="ID" Type="Edm.String" Nullable="false" />
<Property Name="Amount" Type="Edm.Decimal" Scale="variable">
<Annotation Term="Aggregation.ContextDefiningProperties">
<Collection>
<PropertyPath>Currency/Code</PropertyPath>
<Collection>
</Annotation>
</Property>
</NavigationProperty Name="Currency" Type="SalesModel.Currency"
< Nullable="false" />
EntityType>
</
EntityContainer Name="SalesData">
<EntitySet Name="Sales" EntityType="SalesModel.Sale">
<Annotation Term="Aggregation.ApplySupported">
<Record>
<PropertyValue Property="AggregatableProperties">
<Collection>
<Record>
<PropertyValue Property="Property" PropertyPath="Amount" />
<Record>
</Collection>
</PropertyValue>
</PropertyValue Property="GroupableProperties">
<Collection>
<PropertyPath>Currency</PropertyPath>
<Collection>
</PropertyValue>
</Record>
</Annotation>
</
Annotation Term="Aggregation.CustomAggregate" Qualifier="Forecast"
< String="Edm.Decimal">
Annotation Term="Aggregation.ContextDefiningProperties">
<Collection>
<PropertyPath>Currency/Code</PropertyPath>
<Collection>
</Annotation>
</Annotation>
</EntitySet>
</
EntitySet Name="Currencies" EntityType="SalesModel.Currency">
<Annotation Term="Aggregation.ApplySupported">
<Record>
<PropertyValue Property="GroupableProperties">
<Collection>
<PropertyPath>Code</PropertyPath>
<Collection>
</PropertyValue>
</Record>
</Annotation>
</EntitySet>
</EntityContainer> </
A hierarchy is an arrangement of entities whose values are represented as being “above”, “below”, or “at the same level as” one another. A hierarchy can be leveled or recursive.
A leveled hierarchy has a fixed number of levels each of which is represented by a grouping property. The values of a lower-level property depend on the property value of the level above.
A leveled hierarchy can be defined for a collection of instances of an entity or complex type and is described with the term LeveledHierarchy
that lists the properties used to form the hierarchy.
The order of the collection is significant: it lists paths from the entity or complex type where the term is applied to groupable properties representing the levels, starting with the root level (coarsest granularity) down to the lowest (finest-grained) level of the hierarchy.
The term LeveledHierarchy
MUST be applied with a qualifier that can be used to reference the hierarchy in grouping with rollup
.
A recursive hierarchy is defined on a collection of entities by
The recursive hierarchy is described in the model by an annotation of the entity type with the complex term RecursiveHierarchy
with these properties:
NodeProperty
MUST be a path with single-valued segments ending in a primitive property. This property holds the node identifier of an entity that is a node in the hierarchy.ParentNavigationProperty
MUST be a collection-valued or nullable single-valued navigation property path that addresses the entity type annotated with this term. It navigates from an entity that is a node in the hierarchy to its parent nodes.The term RecursiveHierarchy
can only be applied to entity types, and MUST be applied with a qualifier, which is used to reference the hierarchy in transformations operating on recursive hierarchies, in grouping with rolluprecursive
, and in hierarchy functions. The same entity can serve as nodes in different recursive hierarchies, given different qualifiers.
A root node is a node without parent nodes. A recursive hierarchy can have one or more root nodes. A node is a child node of its parent nodes, a node without child nodes is a leaf node. Two nodes with a common parent node are sibling nodes and so are two root nodes.
The descendants with maximum distance \(d≥1\) of a node are its child nodes and, if \(d>1\), the descendants of these child nodes with maximum distance \(d-1\). The descendants are the descendants with maximum distance \(d=∞\). A node together with its descendants forms a sub-hierarchy of the hierarchy.
The ancestors with maximum distance \(d≥1\) of a node are its parent nodes and, if \(d>1\), the ancestors of these parent nodes with maximum distance \(d-1\). The ancestors are the ancestors with maximum distance \(d=∞\). The ParentNavigationProperty
MUST be such that no node is an ancestor of itself, in other words: cycles are forbidden.
The term UpPath
can be used in hierarchical result sets to associate with each instance one of its ancestors, one ancestor of that ancestor and so on. This instance annotation is introduced in section 6.2.2.
For testing the position of a given entity in a recursive hierarchy, the Aggregation
vocabulary OData-VocAggr defines unbound functions. These have
HierarchyNodes
, HierarchyQualifier
where HierarchyNodes
is a collection and HierarchyQualifier
is the qualifier of a RecursiveHierarchy
annotation on its common entity type. The node identifiers in this collection define the recursive hierarchy.Node
that contains the node identifier of the entity to be tested. Note that the test result depends only on this node identifier, not on any other property of the given entityThe following functions are defined:
isnode
tests if the given entity is a node of the hierarchy.isroot
tests if the given entity is a root node of the hierarchy.isdescendant
tests if the given entity is a descendant with maximum distance MaxDistance
of an ancestor node (whose node identifier is given in a parameter Ancestor
), or equals the ancestor if IncludeSelf
is true.isancestor
tests if the given entity is an ancestor with maximum distance MaxDistance
of a descendant node (whose node identifier is given in a parameter Descendant
), or equals the descendant if IncludeSelf
is true.issibling
tests if the given entity and another entity (whose node identifier is given in a parameter Other
) are sibling nodes.isleaf
tests if the given entity is a leaf node.Another function rollupnode
is defined that can only be used in connection with rolluprecursive
.
The hierarchy terms can be applied to the Example Data Model.
⚠ Example 53: leveled hierarchies for products and time, and a recursive hierarchy for the sales organizations:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
edmx:Edmx xmlns:edmx="http://docs.oasis-open.org/odata/ns/edmx"
< Version="4.0">
edmx:Reference Uri="https://docs.oasis-open.org/odata/odata-data-
< aggregation-ext/v4.0/cs03/vocabularies/Org.OData.Aggregation.V1.xml">
edmx:Include Alias="Aggregation"
< Namespace="Org.OData.Aggregation.V1" />
edmx:Reference>
</edmx:DataServices>
<Schema xmlns="http://docs.oasis-open.org/odata/ns/edm"
< Alias="SalesModel" Namespace="org.example.odata.salesservice">
Annotations Target="SalesModel.Product">
<Annotation Term="Aggregation.LeveledHierarchy"
< Qualifier="ProductHierarchy">
Collection>
<PropertyPath>Category/Name</PropertyPath>
<PropertyPath>Name</PropertyPath>
<Collection>
</Annotation>
</Annotations>
</
Annotations Target="SalesModel.Time">
<Annotation Term="Aggregation.LeveledHierarchy"
< Qualifier="TimeHierarchy">
Collection>
<PropertyPath>Year</PropertyPath>
<PropertyPath>Quarter</PropertyPath>
<PropertyPath>Month</PropertyPath>
<Collection>
</Annotation>
</Annotations>
</
Annotations Target="SalesModel.SalesOrganization">
<Annotation Term="Aggregation.RecursiveHierarchy"
< Qualifier="SalesOrgHierarchy">
Record>
<PropertyValue Property="NodeProperty"
< PropertyPath="ID" />
PropertyValue Property="ParentNavigationProperty"
< PropertyPath="Superordinate" />
Record>
</Annotation>
</Annotations>
</Schema>
</edmx:DataServices>
</edmx:Edmx> </
The recursive hierarchy SalesOrgHierarchy
can be used in functions with the $filter
system query option.
Example 54: requesting all organizations below EMEA
GET /service/SalesOrganizations?$filter=Aggregation.isdescendant(
HierarchyNodes=$root/SalesOrganizations,
HierarchyQualifier='SalesOrgHierarchy',
Node=ID,
Ancestor='EMEA')
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
{ "ID": "EMEA Central", "Name": "EMEA Central" },
{ "ID": "Sales Netherlands", "Name": "Sales Netherlands" },
{ "ID": "Sales Germany", "Name": "Sales Germany" },
{ "ID": "EMEA South", "Name": "EMEA South" },
…
{ "ID": "EMEA North", "Name": "EMEA North" },
…
]
}
Example 55: requesting just those organizations directly below EMEA
GET /service/SalesOrganizations?$filter=Aggregation.isdescendant(
HierarchyNodes=$root/SalesOrganizations,
HierarchyQualifier='SalesOrgHierarchy',
Node=ID,
Ancestor='EMEA',
MaxDistance=1)
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
{ "ID": "EMEA Central", "Name": "EMEA Central" },
{ "ID": "EMEA South", "Name": "EMEA South" },
{ "ID": "EMEA North", "Name": "EMEA North" },
…
]
}
Example 56: just the lowest-level organizations
GET /service/SalesOrganizations?$filter=Aggregation.isleaf(
HierarchyNodes=$root/SalesOrganizations,
HierarchyQualifier='SalesOrgHierarchy',
Node=ID)
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
{ "ID": "Sales Office London", "Name": "Sales Office London" },
{ "ID": "Sales Office New York", "Name": "Sales Office New York" },
…
]
}
Example 57: the lowest-level organizations including their superordinate’s ID
GET /service/SalesOrganizations?$filter=Aggregation.isleaf(
HierarchyNodes=$root/SalesOrganizations,
HierarchyQualifier='SalesOrgHierarchy',
Node=ID)
&$expand=Superordinate($select=ID)
results in
{
"@context": "$metadata#SalesOrganizations(*,Superordinate(ID))",
"value": [
{ "ID": "Sales Office London", "Name": "Sales Office London",
"Superordinate": { "ID": "EMEA United Kingdom" } },
{ "ID": "Sales Office New York", "Name": "Sales Office New York",
"Superordinate": { "ID": "US East" } },
…
]
}
Example 58: the sales ID
s involving sales organizations from EMEA
GET /service/Sales?$select=ID&$filter=Aggregation.isdescendant(
HierarchyNodes=$root/SalesOrganizations,
HierarchyQualifier='SalesOrgHierarchy',
Node=SalesOrganization/ID,
Ancestor='EMEA')
results in
{
"@context": "$metadata#Sales(ID)",
"value": [
{ "ID": 6 },
{ "ID": 7 },
{ "ID": 8 }
]
}
Further examples for recursive hierarchies using transformations operating on the hierarchy structure are provided in section 7.9.
Service-defined bound functions that serve as set transformations MAY be annotated with the term AvailableOnAggregates
to indicate that they are applicable to aggregated entities under specific conditions:
RequiredProperties
collection lists all properties that must be available in the aggregated entities; otherwise, the annotated function will be inapplicable.Example 59: assume the product is an implicit input for a function bound to a collection of Sales
, then aggregating away the product makes this function inapplicable.
The transformations and the rolluprecursive
operator defined in this section are called hierarchical, because they make use of a recursive hierarchy and are defined in terms of hierarchy functions introduced in the previous section.
The transformations ancestors
and descendants
do not define an order on the output set. An order can be imposed by a subsequent orderby
or traverse
transformation or a $orderby
. The output set of traverse
is in preorder or postorder, and grouping with rolluprecursive
orders its output set in analogy with simple grouping.
The algorithmic descriptions of the transformations make use of a union of collections, this is defined as an unordered collection containing the items from all these collections and from which duplicates have been removed.
The notation \(u[t]\) is used to denote the value of a property \(t\), possibly preceded by a type-cast segment, in an instance \(u\). It is also used to denote the value of a single-valued data aggregation path \(t\), evaluated relative to \(u\). The value of a collection-valued data aggregation path is denoted in the \(\Gamma\) notation by \(γ(u,t)\).
The notations introduced here are used throughout the following subsections.
The parameter lists defined in the following subsections have three mandatory parameters in common.
The recursive hierarchy is defined by a parameter pair \((H,Q)\), where \(H\) and \(Q\) MUST be specified as the first and second parameter. Here, \(H\) MUST be an expression of type Collection(Edm.EntityType)
starting with $root
that has no multiple occurrences of the same entity. \(H\) identifies the collection of node entities forming a recursive hierarchy based on an annotation of their common entity type with term RecursiveHierarchy
with a Qualifier
attribute whose value MUST be provided in \(Q\). The property paths referenced by NodeProperty
and ParentNavigationProperty
in the RecursiveHierarchy
annotation must be evaluable for the nodes in the recursive hierarchy, otherwise the service MUST reject the request. The NodeProperty
is denoted by \(q\) in this section.
The third parameter MUST be a data aggregation path \(p\) with single- or collection-valued segments whose last segment MUST be a primitive property. The node identifier(s) of an instance \(u\) in the input set are the primitive values in \(γ(u,p)\), they are reached via \(p\) starting from \(u\). Let \(p=p_1/…/p_k/r\) with \(k≥0\) be the concatenation where each sub-path \(p_1,…,p_k\) consists of a collection-valued segment that is preceded by zero or more single-valued segments, and either \(r\) consists of one or more single-valued segments or \(k≥1\) and \({}/r\) is absent. Each segment can be prefixed with a type cast.
Some parameter lists allow as optional fourth or fifth parameter a non-empty sequence \(S\) of transformations. The transformation sequence \(S\) will be applied to the node collection \(H\). It MUST consist of transformations listed in section 3.3 or section 6.2 or service-defined bound functions whose output set is a subset of their input set.
These transformations produce an output set that consists of certain instances from their input set, possibly with repetitions or in a different order.
ancestors
and descendants
In the simple case, the ancestors
transformation takes an input set consisting of instances that belong to a recursive hierarchy \((H,Q)\). It determines a subset \(A\) of the input set and then determines the set of ancestors of \(A\) that were already contained in the input set. Its output set is the ancestors set, optionally including \(A\).
In the more complex case, the instances in the input set are instead related to nodes in a recursive hierarchy. Then the ancestors
transformation determines a subset \(A\) of the input set consisting of instances that are related to certain nodes in the hierarchy, called start nodes. The ancestors of these start nodes are then determined, and the output set consists of instances of the input set that are related to the ancestors, or optionally to the start nodes.
The descendants
transformation works analogously, but with descendants.
\(H\), \(Q\) and \(p\) are the first three parameters defined above.
The fourth parameter is a transformation sequence \(T\) composed of transformations listed section 3.3 or section 6.2.1 and of service-defined bound functions whose output set is a subset of their input set. \(A\) is the output set of this sequence applied to the input set.
The fifth parameter \(d\) is optional and takes an integer greater than or equal to 1 that specifies the maximum distance between start nodes and ancestors or descendants to be considered. An optional final keep start
parameter drives the optional inclusion of the subset or start nodes.
The output set of the transformation \({\tt ancestors}(H,Q,p,T,d,{\tt keep\ start})\) or \({\tt descendants}(H,Q,p,T,d,{\tt keep\ start})\) is defined as the union of the output sets of transformations \(F(u)\) applied to the input set for all \(u\) in \(A\). For a given instance \(u\), the transformation \(F(u)\) determines all instances of the input set whose node identifier is an ancestor or descendant of the node identifier of \(u\):
If \(p\) contains only single-valued segments, then, for ancestors
, \[\matrix{
F(u)={\tt filter}(\hbox{\tt Aggregation.isancestor}(\hfill\\
\quad {\tt HierarchyNodes}=H,\;{\tt HierarchyQualifier}=\hbox{\tt{'$Q$'}},\hfill\\
\quad {\tt Node}=p,\;{\tt Descendant}=u[p],\;{\tt MaxDistance}=d,\;{\tt IncludeSelf}={\tt true}))\hfill
}\] or, for descendants
, \[\matrix{
F(u)={\tt filter}(\hbox{\tt Aggregation.isdescendant}(\hfill\\
\quad {\tt HierarchyNodes}=H,\;{\tt HierarchyQualifier}=\hbox{\tt{'$Q$'}},\hfill\\
\quad {\tt Node}=p,\;{\tt Ancestor}=u[p],\;{\tt MaxDistance}=d,\;{\tt IncludeSelf}={\tt true})).\hfill
}\]
Otherwise \(p=p_1/…/p_k/r\) with \(k≥1\), in this case the output set of the transformation \(F(u)\) is defined as the union of the output sets of transformations \(G(n)\) applied to the input set for all \(n\) in \(γ(u,p)\). The output set of \(G(n)\) consists of the instances of the input set whose node identifier is an ancestor or descendant of the node identifier \(n\):
For ancestors
, \[\matrix{
G(n)={\tt filter}(\hfill\\
\hskip1pc p_1/{\tt any}(y_1:\hfill\\
\hskip2pc y_1/p_2/{\tt any}(y_2:\hfill\\
\hskip3pc ⋱\hfill\\
\hskip4pc y_{k-1}/p_k/{\tt any}(y_k:\hfill\\
\hskip5pc \hbox{\tt Aggregation.isancestor}(\hfill\\
\hskip6pc {\tt HierarchyNodes}=H,\;{\tt HierarchyQualifier}=\hbox{\tt{'$Q$'}},\hfill\\
\hskip6pc {\tt Node}=y_k/r,\;{\tt Descendant}=n,\;{\tt MaxDistance}=d,\;{\tt IncludeSelf}={\tt true}\hfill\\
\hskip5pc )\hfill\\
\hskip4pc )\hfill\\
\hskip3pc ⋰\hfill\\
\hskip2pc )\hfill\\
\hskip1pc )\hfill\\
)\hfill
}\] or, for descendants
, \[\matrix{
G(n)={\tt filter}(\hfill\\
\hskip1pc p_1/{\tt any}(y_1:\hfill\\
\hskip2pc y_1/p_2/{\tt any}(y_2:\hfill\\
\hskip3pc ⋱\hfill\\
\hskip4pc y_{k-1}/p_k/{\tt any}(y_k:\hfill\\
\hskip5pc \hbox{\tt Aggregation.isdescendant}(\hfill\\
\hskip6pc {\tt HierarchyNodes}=H,\;{\tt HierarchyQualifier}=\hbox{\tt{'$Q$'}},\hfill\\
\hskip6pc {\tt Node}=y_k/r,\;{\tt Ancestor}=n,\;{\tt MaxDistance}=d,\;{\tt IncludeSelf}={\tt true}\hfill\\
\hskip5pc )\hfill\\
\hskip4pc )\hfill\\
\hskip3pc ⋰\hfill\\
\hskip2pc )\hfill\\
\hskip1pc )\hfill\\
)\hfill
}\] where \(y_1,…,y_k\) denote lambdaVariableExpr
s as defined in OData-ABNF and \({}/r\) may be absent.
If parameter \(d\) is absent, the parameter \({\tt MaxDistance}=d\) is omitted. If keep start
is absent, the parameter \({\tt IncludeSelf}={\tt true}\) is omitted.
Since the output set of ancestors
is constructed as a union, no instance from the input set will occur more than once in it, even if, for example, a sale is related to both a sales organization and one of its ancestor organizations. For descendants
, analogously.
Example 60: Request based on the SalesOrgHierarchy
defined in Hierarchy Examples, with Superordinate/$ref
expanded to illustrate the hierarchy relation
GET /service/SalesOrganizations?$apply=
ancestors($root/SalesOrganizations,SalesOrgHierarchy,ID,
filter(contains(Name,'East') or contains(Name,'Central')))
&$expand=Superordinate/$ref
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
{ "ID": "EMEA", "Name": "EMEA",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } },
{ "ID": "US", "Name": "US",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } },
{ "ID": "Sales", "Name": "Sales",
"Superordinate": null }
]
}
Example 61: Request based on the SalesOrgHierarchy
defined in Hierarchy Examples, with Superordinate/$ref
expanded to illustrate the hierarchy relation
GET /service/SalesOrganizations?$apply=
descendants($root/SalesOrganizations,SalesOrgHierarchy,ID,
filter(Name eq 'US'),keep start)
&$expand=Superordinate/$ref
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
{ "ID": "US West", "Name": "US West",
"Superordinate": { "@id": "SalesOrganizations('US')" } },
{ "ID": "US", "Name": "US",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } },
{ "ID": "US East", "Name": "US East",
"Superordinate": { "@id": "SalesOrganizations('US')" } }
]
}
⚠ Example 62: Input set and recursive hierarchy from two different entity sets
GET /service/Sales?$apply=
ancestors($root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID,
filter(contains(SalesOrganization/Name,'East')
or contains(SalesOrganization/Name,'Central')),
keep start)
results in
{
"@context": "$metadata#Sales",
"value": [
{ "ID": "4", "Amount": 8,
"SalesOrganization": { "ID": "US East", "Name": "US East" } },
{ "ID": "5", "Amount": 4,
"SalesOrganization": { "ID": "US East", "Name": "US East" } },
{ "ID": "6", "Amount": 2,
"SalesOrganization": { "ID": "EMEA Central", "Name": "EMEA Central" } },
{ "ID": "7", "Amount": 1,
"SalesOrganization": { "ID": "EMEA Central", "Name": "EMEA Central" } },
{ "ID": "8", "Amount": 2,
"SalesOrganization": { "ID": "EMEA Central", "Name": "EMEA Central" } }
]
}
traverse
The traverse
transformation returns instances of the input set that are or are related to nodes of a given recursive hierarchy in a specified tree order.
\(H\), \(Q\) and \(p\) are the first three parameters defined above.
The fourth parameter \(h\) of the traverse
transformation is either preorder
or postorder
. \(S\) is an optional fifth parameter as defined above. Let \(H'\) be the output set of the transformation sequence \(S\) applied to \(H\), or let \(H'\) be the collection of root nodes in the recursive hierarchy \((H,Q)\) if \(S\) is not specified. Nodes in \(H'\) are called start nodes in this subsection (see example 117).
All following parameters are optional and form a list \(o\) of expressions that could also be passed as a $orderby
system query option. If \(o\) is present, the transformation stable-sorts \(H'\) by \(o\).
The instances in the input set are related to one node (if \(p\) is single-valued) or multiple nodes (if \(p\) is collection-valued) in the recursive hierarchy. Given a node \(x\), denote by \(\hat F(x)\) the collection of all instances in the input set that are related to \(x\); these collections can overlap. For each \(u\) in \(\hat F(x)\), the output set contains one instance that comprises the properties of \(u\) and additional properties that identify the node \(x\). These additional properties are independent of \(u\) and are bundled into an instance called \(σ(x)\). For example, if a sale \(u\) is related to two sales organizations and hence contained in both \(\hat F(x_1)\) and \(\hat F(x_2)\), the output set will contain two instances \((u,σ(x_1))\) and \((u,σ(x_2))\) and \(σ(x_i)\) contributes a navigation property SalesOrganization
.
A transformation \(F(x)\) is defined below such that \(\hat F(x)\) is the output set of \(F(x)\) applied to the input set of the traverse
transformation.
Given a node \(x\), the formulas below contain the transformation \(\Pi_G(σ(x))\) in order to inject the properties of \(σ(x)\) into the instances in \(\hat F(x)\); this uses the function \(\Pi_G\) that is defined in the simple grouping section. Further, \(G\) is a list of data aggregation paths that shall be present in the output set, and \(σ\) is a function that maps each hierarchy node \(x\) to an instance of the input type containing the paths from \(G\). As a consequence of the following definitions, only single-valued properties and “final segments from \(G\)” are nested into \(σ(x)\), therefore the behavior of \(\Pi_G(σ(x))\) is well-defined.
The definition of \(σ(x)\) makes use of a function \(a(ε,t,x)\), which returns a sparsely populated instance \(u\) in which only the path \(t\) has a value, namely \(u[t]=x\).
Three cases are distinguished:
Here paths are considered equal if their non-type-cast segments refer to the same model elements when evaluated relative to the input set (see example 68).
The function \(a(u,t,x)\) takes an instance, a path and another instance as arguments and is defined recursively as follows:
(See example 112.)
traverse
The algorithm is first given for the standard case where RecursiveHierarchy/ParentNavigationProperty
is single-valued and the optional parameter \(S\) is not specified. In this standard case, start nodes are root nodes and \(σ(x)\) is computed exactly once for every node \(x\), as part of the recursive formula for \(R(x)\) given below. The general case follows later.
Let \(r_1,…,r_n\) be a sequence of the start nodes in \(H'\) preserving the order of \(H'\) stable-sorted by \(o\). Then the transformation \({\tt traverse}(H,Q,p,h,o)\) is defined as equivalent to \[{\tt concat}(R(r_1),…,R(r_n)).\]
\(R(x)\) is a transformation producing the specified tree order for a sub-hierarchy of \(H\) with root node \(x\). Let \(c_1,…,c_m\) with \(m≥0\) be an order-preserving sequence of the children of \(x\) in \((H,Q)\). The recursive formula for \(R(x)\) is as follows:
If \(h={\tt preorder}\), then \[R(x)={\tt concat}(F(x)/\Pi_G(σ(x)),R(c_1),…,R(c_m)).\]
If \(h={\tt postorder}\), then \[R(x)={\tt concat}(R(c_1),…,R(c_m),F(x)/\Pi_G(σ(x))).\]
The absence of cycles guarantees that the recursion terminates.
\(F(x)\) is a transformation that determines for the specified node \(x\) the instances of the input set having the same node identifier as \(x\).
If \(p\) contains only single-valued segments, then \[F(x)={\tt filter}(p{\tt\ eq\ }x[q]).\]
Otherwise \(p=p_1/…/p_k/r\) with \(k≥1\) and \[\matrix{
F(x)={\tt filter}(\hfill\\
\hskip1pc p_1/{\tt any}(y_1:\hfill\\
\hskip2pc y_1/p_2/{\tt any}(y_2:\hfill\\
\hskip3pc ⋱\hfill\\
\hskip4pc y_{k-1}/p_k/{\tt any}(y_k:\hfill\\
\hskip5pc y_k/r{\tt\ eq\ }x[q]\hfill\\
\hskip4pc )\hfill\\
\hskip3pc ⋰\hfill\\
\hskip2pc )\hfill\\
\hskip1pc )\hfill\\
)\hfill
}\] where \(y_1,…,y_k\) denote lambdaVariableExpr
s and \({}/r\) may be absent.
Example 63: Based on the SalesOrgHierarchy
defined in Hierarchy Examples
GET /service/SalesOrganizations?$apply=
descendants($root/SalesOrganizations,SalesOrgHierarchy,ID,
Name eq 'US',keep start)
/ancestors($root/SalesOrganizations,SalesOrgHierarchy,ID,
contains(Name,'East'),keep start)
/traverse($root/SalesOrganizations,SalesOrgHierarchy,ID,preorder)
&$expand=Superordinate/$ref
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
{ "ID": "US", "Name": "US",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } },
{ "ID": "US East", "Name": "US East",
"Superordinate": { "@id": "SalesOrganizations('US')" } }
]
}
traverse
In the general case, the recursive algorithm can reach a node \(x\) multiple times, via different parents or ancestors, or because \(x\) is a start node and a descendant of another start node. Then the algorithm computes \(R(x)\) and hence \(σ(x)\) multiple times. In order to distinguish these computation results, information about the ancestors up to the start node is injected into each \(σ(x)\) by annotating \(x\) differently before each \(σ(x)\) is computed. On the other hand, certain nodes can be unreachable from any start node, these are called orphans of the traversal (see example 117).
More precisely, in the general case every node \(y\) is annotated with the term UpPath
from the Aggregation
vocabulary. The annotation has \(Q\) as qualifier and the annotation value is a collection of string values of node identifiers. The first member of that collection is the node identifier of the parent node \(x\) such that \(R(y)\) appears on the right-hand side of the recursive formula for \(R(x)\). The following members are the members of the Aggregation.UpPath
collection of \(x\). Every instance in the output set of traverse
is related to one node with Aggregation.UpPath
annotation. Start nodes appear annotated with an empty collection.
⚠ Example 64: A sales organization Atlantis with two parents US and EMEA would occur twice in the result of a traverse
transformation:
GET /service/SalesOrganizations?$apply=
traverse($root/SalesOrganizations,MultiParentHierarchy,ID,preorder)
results in
{
"@context": "$metadata#SalesOrganizations",
"value": [
…
{ "ID": "Atlantis", "Name": "Atlantis",
"@Aggregation.UpPath#MultiParentHierarchy":
[ "US", "Sales" ] },
{ "ID": "AtlantisChild", "Name": "Child of Atlantis",
"@Aggregation.UpPath#MultiParentHierarchy":
[ "Atlantis", "US", "Sales" ] },
…
{ "ID": "Atlantis", "Name": "Atlantis",
"@Aggregation.UpPath#MultiParentHierarchy":
[ "EMEA", "Sales" ] },
{ "ID": "AtlantisChild", "Name": "Child of Atlantis",
"@Aggregation.UpPath#MultiParentHierarchy":
[ "Atlantis", "EMEA", "Sales" ] },
…
]
}
Given a start node \(x\), let \(ρ_0(x)\) be the node \(x\) with the annotation \(ρ_0(x)/@\hbox{\tt Aggregation.UpPath}\#Q=[]\) set to an empty collection.
Given a node \(x\) annotated with \(x/@\hbox{\tt Aggregation.UpPath}\#Q=[x_1,…,x_d]\), where \(d≥0\), and given a child \(y\) of \(x\), let \(ρ(y,x)\) be the node \(y\) with the annotation \[ρ(y,x)/@\hbox{\tt Aggregation.UpPath}\#Q=[{\tt cast}(x[q],\hbox{\tt Edm.String}),x_1,…,x_d].\]
Like structural and navigation properties, these instance annotations are considered part of the node \(x\) and are copied over to \(σ(x)\). For them to be included in the transformation \(\Pi_G(σ(x))\), an additional step is inserted between steps 2 and 3 of the function \(a_G(u,s,p)\) as defined in the simple grouping section:
Aggregation.UpPath
and qualifier \(Q\), copy this annotation from \(s\) to \(u\).Recall that instance annotations never appear in data aggregation paths or aggregatable expressions. They are not considered when determining whether instances of structured types are the same, they do not cause conflicting representations and are absent from merged representations.
Let \(r_1,…,r_n\) be the start nodes in \(H'\) as above, then the transformation \({\tt traverse}(H,Q,p,h,S,o)\) is defined as equivalent to \[{\tt concat}(R(ρ_0(r_1)),…,R(ρ_0(r_n))\] where the function \(R(x)\) takes as argument a node with Aggregation.UpPath
annotation. With \(F(x)\) and \(c_1,…,c_m\) as above, if \(h={\tt preorder}\), then \[R(x)={\tt concat}(F(x)/\Pi_G(σ(x)),R(ρ(c_1,x)),…,R(ρ(c_m,x))),\] and if \(h={\tt postorder}\), then \[R(x)={\tt concat}(R(ρ(c_1,x)),…,R(ρ(c_m,x)),F(x)/\Pi_G(σ(x))).\]
The absence of cycles guarantees that the recursion terminates.
In the general case, servers MUST include the Aggregation.UpPath
annotations in the result of $apply
but MAY omit them if RecursiveHierarchy/ParentNavigationProperty
is single-valued and all start nodes are root nodes.
If RecursiveHierarchy/ParentNavigationProperty
is collection-valued but the parent collection never contains more than one parent and the optional parameter \(S\) is not specified, then the result is effectively like in the standard case, except for the presence of the Aggregation.UpPath
annotations.
rolluprecursive
Recall that simple grouping partitions the input set and applies a transformation sequence to each partition. By contrast, grouping with rolluprecursive
, informally speaking, transforms the input set into overlapping portions (like “US” and “US East”), one for each node \(x\) of a recursive hierarchy. The transformation \(F(x)\), defined below, outputs the portion whose node identifiers are among the descendants of \(x\) (including \(x\) itself). A transformation sequence is then applied to each portion, and they are made distinguishable in the output set through injection of information about the node \(x\), which is achieved through the transformation \(\Pi_G(σ(x))\) defined in the traverse
section.
As defined above, \(H\), \(Q\) and \(p\) are the first three parameters of rolluprecursive
, \(S\) is an optional fourth parameter. Let \(H'\) be the output set of the transformation sequence \(S\) applied to \(H\), or \(H'=H\) if \(S\) is not specified.
Navigation properties specified in \(p\) are expanded by default.
Let \(T\) be a transformation sequence, \(P_1\) stand in for zero or more property paths and \(P_2\) for zero or more rollup
or rolluprecursive
operators or property paths. The transformation \({\tt groupby}((P_1,{\tt rolluprecursive}(H,Q,p,S),P_2),T)\) is computed by the following algorithm, which invokes itself recursively if the number of rolluprecursive
operators in the first argument of the groupby
transformation, which is called \(M\), is greater than one. Let \(N\) be the recursion depth of the algorithm, starting with 1.
The rolluprecursive
algorithm:
A property \(χ_N\) appears in the algorithm, but is not present in the output set. It is explained later (see example 66). \(Z_N\) is a transformation whose output set is its input set with property \(χ_N\) removed.
Let \(x_1,…,x_n\) be the nodes in \(H'\), possibly with repetitions. If the optional transformation sequence \(S\) ends with a traverse
transformation, as in example 118, the sequence \(x_1,…,x_n\) MUST have the preorder or postorder established by that traversal, and the transformation \({\tt groupby}((P_1,{\tt rolluprecursive}(H,Q,p,S),P_2),T)\) is defined as equivalent to \[{\tt concat}(R(x_1),…,R(x_n)).\]
Otherwise, if \(S\) is not specified or does not end with a traverse
transformation, the output set of the transformation \({\tt groupby}((P_1,{\tt rolluprecursive}(H,Q,p,S),P_2),T)\) is the concatenation of \(R(x_1),…,R(x_n)\). The order of occurrences from the same \(R(x_i)\) remains the same, and no order is defined between occurrences from different \(R(x_i)\) and \(R(x_j)\).
\(R(x)\) is a transformation that processes the entire sub-hierarchy rooted at \(x\), which is the output set of \(F(x)\). The output set of \(R(x)\) is a collection of aggregated instances for all rollup results.
If at least one of \(P_1\) or \(P_2\) is non-empty, then \[R(x)=F(x)/{\tt compute}(x{\tt\ as\ }χ_N)/{\tt groupby}((P_1,P_2),T/Z_N/\Pi_G(σ(x))).\]
The property \(χ_N=x\) is present during the evaluation of \(T\), but not afterwards. If \(P_2\) contains a rolluprecursive
operator, the evaluation of the formula involves a recursive invocation (with \(N\) increased by 1) of the rolluprecursive
algorithm.
Otherwise if \(P_1\) and \(P_2\) are empty, then \[R(x)=F(x)/{\tt compute}(x{\tt\ as\ }χ_N)/T/Z_N/\Pi_G(σ(x)).\]
\(F(x)\) is defined as follows: If \(p\) contains only single-valued segments, then \[\matrix{ F(x)={\tt filter}(\hbox{\tt Aggregation.isdescendant}(\hfill\\ \quad {\tt HierarchyNodes}=H,\;{\tt HierarchyQualifier}=\hbox{\tt{'$Q$'}},\hfill\\ \quad {\tt Node}=p,\;{\tt Ancestor}=x[q],\;{\tt IncludeSelf}={\tt true})).\hfill }\]
Otherwise \(p=p_1/…/p_k/r\) with \(k≥1\) and \[\matrix{
F(x)={\tt filter}(\hfill\\
\hskip1pc p_1/{\tt any}(y_1:\hfill\\
\hskip2pc y_1/p_2/{\tt any}(y_2:\hfill\\
\hskip3pc ⋱\hfill\\
\hskip4pc y_{k-1}/p_k/{\tt any}(y_k:\hfill\\
\hskip5pc \hbox{\tt Aggregation.isdescendant}(\hfill\\
\hskip6pc {\tt HierarchyNodes}=H,\;{\tt HierarchyQualifier}=\hbox{\tt{'$Q$'}},\hfill\\
\hskip6pc {\tt Node}=y_k/r,\;{\tt Ancestor}=x[q],\;{\tt IncludeSelf}={\tt true}\hfill\\
\hskip5pc )\hfill\\
\hskip4pc )\hfill\\
\hskip3pc ⋰\hfill\\
\hskip2pc )\hfill\\
\hskip1pc )\hfill\\
)\hfill
}\] where \(y_1,…,y_k\) denote lambdaVariableExpr
s and \({}/r\) may be absent. (See example 113 for a case with \(k=1\).)
Informatively speaking, the effect of the algorithm can be summarized as follows: If \(M≥1\) and \(\hat F_N(x)\) denotes the collection of all instances that are related to a node \(x\) as determined by \(F(x)\) in the recursive hierarchy of the \(N\)-th rolluprecursive
operator, then \(T\) is applied to each of the intersections of \(\hat F_1(χ_1),…,\hat F_M(χ_M)\), as \(χ_N\) runs over all nodes of the \(N\)-th recursive hierarchy for \(1≤N≤M\). Into the instances of the resulting output sets the \(\Pi_G\) transformations inject information about the nodes \(χ_1,…,χ_M\).
Example 65: Total number of sub-organizations for all organizations in the hierarchy defined in Hierarchy Examples with \(p=q={\tt ID}\) (case 1 of the definition of \(σ(x)\)). In this case \(\Pi_G(σ(x))\) writes back the entire node into the output set of \(T\), aggregates must have an alias to avoid overwriting by a property of the node with the same name.
GET /service/SalesOrganizations?$apply=
groupby((rolluprecursive(
$root/SalesOrganizations,SalesOrgHierarchy,ID)),
aggregate($count as OrgCnt)/compute(OrgCnt sub 1 as SubOrgCnt))
&$select=ID,Name,SubOrgCnt
&$expand=Superordinate($select=ID)
results in
{
"@context":
"$metadata#SalesOrganizations(ID,Name,SubOrgCnt,Superordinate(ID))",
"value": [
{ "ID": "US West", "Name": "US West",
"SubOrgCount": 0, "Superordinate": { "ID": "US" } },
{ "ID": "US East", "Name": "US East",
"SubOrgCount": 0, "Superordinate": { "ID": "US" } },
{ "ID": "US", "Name": "US",
"SubOrgCount": 2, "Superordinate": { "ID": "Sales" } },
{ "ID": "EMEA Central", "Name": "EMEA Central",
"SubOrgCount": 0, "Superordinate": { "ID": "EMEA" } },
{ "ID": "EMEA", "Name": "EMEA",
"SubOrgCount": 1, "Superordinate": { "ID": "Sales" } },
{ "ID": "Sales", "Name": "Sales",
"SubOrgCount": 5, "Superordinate": null }
]
}
The value of the property \(χ_N\) in the rolluprecursive
algorithm is the node \(x\) at recursion level \(N\). In a common expression, \(χ_N\) cannot be accessed by its name, but can only be read as the return value of the unbound function \({\tt rollupnode}({\tt Position}=N)\) defined in the Aggregation
vocabulary, with \(1≤N≤M\), and only during the application of the transformation sequence \(T\) in the formula for \(R(x)\) above (the function is undefined otherwise). If \(N=1\), the Position
parameter can be omitted.
⚠ Example 66: Total sales amounts per organization, both including and excluding sub-organizations, in the US sub-hierarchy defined in Hierarchy Examples with \(p=p'/q={\tt SalesOrganization}/{\tt ID}\) and \(p'={\tt SalesOrganization}\) (case 2 of the definition of \(σ(x)\)). The Boolean expression \(p'\hbox{\tt\ eq Aggregation.rollupnode}()\) is true for sales in the organization for which the aggregate is computed, but not for sales in sub-organizations.
GET /service/Sales?$apply=groupby(
(rolluprecursive(
$root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID,
descendants($root/SalesOrganizations,
SalesOrgHierarchy,
ID, filter(ID eq 'US'), keep start))),
compute(case(SalesOrganization eq Aggregation.rollupnode():Amount)
as AmountExcl)
/aggregate(Amount with sum as TotalAmountIncl,
AmountExcl with sum as TotalAmountExcl))
results in
{
"@context": "$metadata#Sales(SalesOrganization(),
TotalAmountIncl,TotalAmountExcl)",
"value": [
{ "SalesOrganization": { "ID": "US West", "Name": "US West" },
"TotalAmountIncl@type": "Decimal", "TotalAmountIncl": 7,
"TotalAmountExcl@type": "Decimal" ,"TotalAmountExcl": 7 },
{ "SalesOrganization": { "ID": "US", "Name": "US" },
"TotalAmountIncl@type": "Decimal", "TotalAmountIncl": 19,
"TotalAmountExcl": null },
{ "SalesOrganization": { "ID": "US East", "Name": "US East" },
"TotalAmountIncl@type": "Decimal", "TotalAmountIncl": 12,
"TotalAmountExcl@type": "Decimal", "TotalAmountExcl": 12 }
]
}
⚠ Example 67: When requesting a sub-hierarchy consisting of the US East sales organization and its ancestors, the total sales amounts can either include the descendants outside this sub-hierarchy (“actual totals”) or can exclude them (“visual totals”).
Actual totals are computed when rolluprecursive
is restricted to the sub-hierarchy by setting the optional parameter \(S\) to an ancestors
transformation:
GET /service/Sales?$apply=groupby((rolluprecursive(
$root/SalesOrganizations,SalesOrgHierarchy,SalesOrganization/ID,
ancestors($root/SalesOrganizations,SalesOrgHierarchy,ID,
filter(ID eq 'US East'),keep start))),
aggregate(Amount with sum as Total))
results in
{
"@context": "$metadata#Sales(SalesOrganization(),Total)",
"value": [
{ "SalesOrganization": { "ID": "US East", "Name": "US East" },
"Total@type": "Decimal", "Total": 12 },
{ "SalesOrganization": { "ID": "US", "Name": "US" },
"Total@type": "Decimal", "Total": 19 },
{ "SalesOrganization": { "ID": "Sales", "Name": "Sales" },
"Total@type": "Decimal", "Total": 24 }
]
}
Visual totals are computed when the ancestors
transformation is additionally carried out before the rolluprecursive
:
GET /service/Sales?$apply=
ancestors($root/SalesOrganizations,SalesOrgHierarchy,SalesOrganization/ID,
filter(SalesOrganization/ID eq 'US East'),keep start))),
/groupby((rolluprecursive(
$root/SalesOrganizations,SalesOrgHierarchy,SalesOrganization/ID,
ancestors($root/SalesOrganizations,SalesOrgHierarchy,ID,
filter(ID eq 'US East'),keep start))),
aggregate(Amount with sum as Total))
results in
{
"@context": "$metadata#Sales(SalesOrganization(),Total)",
"value": [
{ "SalesOrganization": { "ID": "US East", "Name": "US East" },
"Total@type": "Decimal", "Total": 12 },
{ "SalesOrganization": { "ID": "US", "Name": "US" },
"Total@type": "Decimal", "Total": 12 },
{ "SalesOrganization": { "ID": "Sales", "Name": "Sales" },
"Total@type": "Decimal", "Total": 12 }
]
}
⚠ Example 68: Although \(p={\tt ID}\) and \(q={\tt ID}\), they are not equal in the sense of case 1, because they are evaluated relative to different entity sets. Hence, this is an example of case 3 of the definition of \(σ(x)\), where no Sales/ID
matches a SalesOrganizations/ID
, that is, all \(F(x)\) have empty output sets.
GET /service/Sales?$apply=
groupby((rolluprecursive(
$root/SalesOrganizations,SalesOrgHierarchy,ID))),
aggregate(Amount with sum as TotalAmount))
results in
{
"@context": "$metadata#Sales(SalesOrganization(),TotalAmount)",
"value": [
{ "SalesOrganization": { "ID": "Sales", "Name": "Corporate Sales" },
"TotalAmount": null },
{ "SalesOrganization": { "ID": "EMEA", "Name": "EMEA" },
"TotalAmount": null },
{ "SalesOrganization": { "ID": "US", "Name": "US" },
"TotalAmount": null },
…
]
}
The following examples show some common aggregation-related questions that can be answered by combining the transformations defined in sections 3 and 6.
Grouping without specifying a set transformation returns the distinct combination of the grouping properties.
Example 69:
GET /service/Customers?$apply=groupby((Name))
results in
{
"@context": "$metadata#Customers(Name)",
"value": [
{ "Name": "Luc" },
{ "Name": "Joe" },
{ "Name": "Sue" }
]
}
Note that “Sue” appears only once although the customer base contains two different Sues.
Aggregation is also possible across related entities.
Example 70: customers that bought something
GET /service/Sales?$apply=groupby((Customer/Name))
results in
{
"@context": "$metadata#Sales(Customer(Name))",
"value": [
{ "Customer": { "Name": "Joe" } },
{ "Customer": { "Name": "Sue" } }
]
}
Since groupby
expands navigation properties in grouping properties by default, this is the same result as if the request would include a $expand=Customer($select=Name)
. The groupby
removes all other properties.
Note that “Luc” does not appear in the aggregated result as he hasn’t bought anything and therefore there are no sales entities that refer/navigate to Luc.
However, even though both Sues bought products, only one “Sue” appears in the aggregate result. Including properties that guarantee the right level of uniqueness in the grouping can repair that.
Example 71:
GET /service/Sales?$apply=groupby((Customer/Name,Customer/ID))
results in
{
"@context": "$metadata#Sales(Customer(Name,ID))",
"value": [
{ "Customer": { "Name": "Joe", "ID": "C1" } },
{ "Customer": { "Name": "Sue", "ID": "C2" } },
{ "Customer": { "Name": "Sue", "ID": "C3" } }
]
}
This could also have been formulated as
GET /service/Sales?$apply=groupby((Customer))
&$expand=Customer($select=Name,ID)
Example 72: Grouping by navigation property Customer
GET /service/Sales?$apply=groupby((Customer))
results in
{
"@context": "$metadata#Sales(Customer())",
"value": [
{ "Customer": { "ID": "C1", "Name": "Joe", "Country": "USA" } },
{ "Customer": { "ID": "C2", "Name": "Sue", "Country": "USA" } },
{ "Customer": { "ID": "C3", "Name": "Sue", "Country": "Netherlands" } }
]
}
Example 73: the first question in the motivating example in section 2.3, which customers bought which products, can now be expressed as
GET /service/Sales?$apply=groupby((Customer/Name,Customer/ID,Product/Name))
and results in
{
"@context": "$metadata#Sales(Customer(Name,ID),Product(Name))",
"value": [
{ "Customer": { "Name": "Joe", "ID": "C1" },
"Product": { "Name": "Coffee"} },
{ "Customer": { "Name": "Joe", "ID": "C1" },
"Product": { "Name": "Paper" } },
{ "Customer": { "Name": "Joe", "ID": "C1" },
"Product": { "Name": "Sugar" } },
{ "Customer": { "Name": "Sue", "ID": "C2" },
"Product": { "Name": "Coffee"} },
{ "Customer": { "Name": "Sue", "ID": "C2" },
"Product": { "Name": "Paper" } },
{ "Customer": { "Name": "Sue", "ID": "C3" },
"Product": { "Name": "Paper" } },
{ "Customer": { "Name": "Sue", "ID": "C3" },
"Product": { "Name": "Sugar" } }
]
}
⚠ Example 74: grouping by properties of subtypes
GET /service/Products?$apply=groupby((SalesModel.FoodProduct/Rating,
SalesModel.NonFoodProduct/RatingClass))
results in
{
"@context": "$metadata#Products(SalesModel.FoodProduct/Rating,
SalesModel.NonFoodProduct/RatingClass)",
"value": [
{ "@type": "#SalesModel.FoodProduct", "Rating": 5 },
{ "@type": "#SalesModel.FoodProduct", "Rating": null },
{ "@type": "#SalesModel.NonFoodProduct", "RatingClass": "average" },
{ "@type": "#SalesModel.NonFoodProduct", "RatingClass": null }
]
}
⚠ Example 75: grouping by a property of a subtype
GET /service/Products?$apply=groupby((SalesModel.FoodProduct/Rating))
results in a third group representing entities with no SalesModel.FoodProduct/Rating
, including the SalesModel.NonFoodProduct
s:
{
"@context": "$metadata#Products(@Core.AnyStructure)",
"value": [
{ "@type": "#SalesModel.FoodProduct", "Rating": 5 },
{ "@type": "#SalesModel.FoodProduct", "Rating": null },
{ }
]
}
The client may specify one of the predefined aggregation methods min
, max
, sum
, average
, and countdistinct
, or a custom aggregation method, to aggregate an aggregatable expression. Expressions defining an aggregate method specify an alias. The aggregated values are returned in a dynamic property whose name is determined by the alias.
Example 76:
GET /service/Products?$apply=groupby((Name),
aggregate(Sales/Amount with sum as Total))
results in
{
"@context": "$metadata#Products(Name,Total)",
"value": [
{ "Name": "Coffee", "Total@type": "Decimal", "Total": 12 },
{ "Name": "Paper", "Total@type": "Decimal", "Total": 8 },
{ "Name": "Pencil", "Total": null },
{ "Name": "Sugar", "Total@type": "Decimal", "Total": 4 }
]
}
Note that the base set of the request is Products
, so there is a result item for product Pencil
even though there are no sales items. The input set for the aggregation in the third row is \(I\) consisting of the pencil, \(p=q/r={\tt Sales}/{\tt Amount}\), \(E=\Gamma(I,q)\) is empty and \(A=\Gamma(E,r)\) is also empty. The sum over the empty collection is null.
Example 77: Alternatively, the request could ask for the aggregated amount to be nested inside a clone of Sales
GET /service/Products?$apply=addnested(Sales,
aggregate(Amount with sum as Total) as AggregatedSales)
results in
{
"@context": "$metadata#Products(AggregatedSales())",
"value": [
{ "ID": "P2", "Name": "Coffee", "Color": "Brown", "TaxRate": 0.06,
"AggregatedSales@context": "#Sales(Total)",
"AggregatedSales": [ { "Total@type": "Decimal", "Total": 12 } ] },
{ "ID": "P3", "Name": "Paper", "Color": "White", "TaxRate": 0.14,
"AggregatedSales@context": "#Sales(Total)",
"AggregatedSales": [ { "Total@type": "Decimal", "Total": 8 } ] },
{ "ID": "P4", "Name": "Pencil", "Color": "Black", "TaxRate": 0.14,
"AggregatedSales@context": "#Sales(Total)",
"AggregatedSales": [ { "Total": null } ] },
{ "ID": "P1", "Name": "Sugar", "Color": "White", "TaxRate": 0.06,
"AggregatedSales@context": "#Sales(Total)",
"AggregatedSales": [ { "Total@type": "Decimal", "Total": 4 } ] }
]
}
Example 78: To compute the aggregate as a property without nesting, use the aggregate function in $compute
rather than the aggregate transformation in $apply
:
GET /service/Products?$compute=Sales/aggregate(Amount with sum) as Total
results in
{
"@context": "$metadata#Products(*,Total)",
"value": [
{ "ID": "P2", "Name": "Coffee", "Color": "Brown", "TaxRate": 0.06,
"Total@type": "Decimal", "Total": 12 },
{ "ID": "P3", "Name": "Paper", "Color": "White", "TaxRate": 0.14,
"Total@type": "Decimal", "Total": 8 },
{ "ID": "P4", "Name": "Pencil", "Color": "Black", "TaxRate": 0.14,
"Total": null },
{ "ID": "P1", "Name": "Sugar", "Color": "White", "TaxRate": 0.06,
"Total@type": "Decimal", "Total": 4 }
]
}
The expression $it/Sales
refers to the sales of the current product. Without $it
, all sales of all products would be aggregated, because the input collection for the aggregate
function consists of all products.
Example 79: Alternatively, join
could be applied to yield a flat structure:
GET /service/Products?$apply=
join(Sales as TotalSales,aggregate(Amount with sum as Total))
/groupby((Name,TotalSales/Total))
results in
{
"@context": "$metadata#Products(Name,TotalSales())",
"value": [
{ "Name": "Coffee",
"TotalSales@context": "#Sales(Total)/$entity",
"TotalSales": { "Total@type": "Decimal", "Total": 12 } },
{ "Name": "Paper",
"TotalSales@context": "#Sales(Total)/$entity",
"TotalSales": { "Total@type": "Decimal", "Total": 8 } },
{ "Name": "Sugar",
"TotalSales@context": "#Sales(Total)/$entity",
"TotalSales": { "Total@type": "Decimal", "Total": 4 } }
]
}
Applying outerjoin
instead would return an additional entity for product with ID
“Pencil” and TotalSales
having a null value.
Example 80:
GET /service/Sales?$apply=groupby((Customer/Country),
aggregate(Amount with average as AverageAmount))
results in
{
"@context": "$metadata#Sales(Customer(Country),AverageAmount)",
"value": [
{ "Customer": { "Country": "Netherlands" },
"AverageAmount": 1.6666666666666667 },
{ "Customer": { "Country": "USA" },
"AverageAmount": 3.8 }
]
}
Here the AverageAmount
is of type Edm.Double
.
Example 81: $count
after navigation property
GET /service/Products?$apply=groupby((Name),
aggregate(Sales/$count as SalesCount))
results in
{
"@context": "$metadata#Products(Name,SalesCount)",
"value": [
{ "Name": "Coffee", "SalesCount@type": "Decimal", "SalesCount": 2 },
{ "Name": "Paper", "SalesCount@type": "Decimal", "SalesCount": 4 },
{ "Name": "Pencil", "SalesCount@type": "Decimal", "SalesCount": 0 },
{ "Name": "Sugar", "SalesCount@type": "Decimal", "SalesCount": 2 }
]
}
To place the number of instances in a group next to other aggregated values, the aggregate expression $count
can be used:
⚠ Example 82: The effect of the groupby
is to create transient entities and avoid in the result structural properties other than Name
.
GET /service/Products?$apply=groupby((Name),addnested(Sales,
aggregate($count as SalesCount,
Amount with sum as TotalAmount) as AggregatedSales))
results in
{
"@context": "$metadata#Products(Name,AggregatedSales())",
"value": [
{ "Name": "Coffee",
"AggregatedSales@context": "#Sales(SalesCount,TotalAmount)",
"AggregatedSales": [ { "SalesCount": 2,
"TotalAmount@type": "Decimal", "TotalAmount": 12 } ] },
{ "Name": "Paper",
"AggregatedSales@context": "#Sales(SalesCount,TotalAmount)",
"AggregatedSales": [ { "SalesCount": 4,
"TotalAmount@type": "Decimal", "TotalAmount": 8 } ] },
{ "Name": "Pencil",
"AggregatedSales@context": "#Sales(SalesCount,TotalAmount)",
"AggregatedSales": [ { "SalesCount": 0, "TotalAmount": null } ] },
{ "Name": "Sugar",
"AggregatedSales@context": "#Sales(SalesCount,TotalAmount)",
"AggregatedSales": [ { "SalesCount": 2,
"TotalAmount@type": "Decimal", "TotalAmount": 4 } ] }
]
}
The aggregate
function can not only be used in $compute
but also in $filter
and $orderby
:
Example 83: Products with an aggregated sales volume of ten or more
GET /service/Products?$filter=Sales/aggregate(Amount with sum) ge 10
results in
{
"@context": "$metadata#Products",
"value": [
{ "ID": "P2", "Name": "Coffee", "Color": "Brown", "TaxRate": 0.06 },
{ "ID": "P3", "Name": "Paper", "Color": "White", "TaxRate": 0.14 }
]
}
Example 84: Customers in descending order of their aggregated sales volume
GET /service/Customers?$orderby=Sales/aggregate(Amount with sum) desc
results in
{
"@context": "$metadata#Customers",
"value": [
{ "ID": "C2", "Name": "Sue", "Country": "USA" },
{ "ID": "C1", "Name": "Joe", "Country": "USA" },
{ "ID": "C3", "Name": "Sue", "Country": "Netherlands" },
{ "ID": "C4", "Name": "Luc", "Country": "France" }
]
}
Example 85: Contribution of each sales to grand total sales amount
GET /service/Sales?$compute=Amount divby $these/aggregate(Amount with sum)
as Contribution
results in
{
"@context": "$metadata#Sales(*,Contribution)",
"value": [
{ "ID": 1, "Amount": 1, "Contribution@type": "Decimal",
"Contribution": 0.0416666666666667 },
{ "ID": 2, "Amount": 2, "Contribution@type": "Decimal",
"Contribution": 0.0833333333333333 },
{ "ID": 3, "Amount": 4, "Contribution@type": "Decimal",
"Contribution": 0.1666666666666667 },
{ "ID": 4, "Amount": 8, "Contribution@type": "Decimal",
"Contribution": 0.3333333333333333 },
{ "ID": 5, "Amount": 4, "Contribution@type": "Decimal",
"Contribution": 0.1666666666666667 },
{ "ID": 6, "Amount": 2, "Contribution@type": "Decimal",
"Contribution": 0.0833333333333333 },
{ "ID": 7, "Amount": 1, "Contribution@type": "Decimal",
"Contribution": 0.0416666666666667 },
{ "ID": 8, "Amount": 2, "Contribution@type": "Decimal",
"Contribution": 0.0833333333333333 }
]
}
Example 86: Product categories with at least one product having an aggregated sales amount greater than 10
GET /service/Categories?$filter=Products/any(
p:p/Sales/aggregate(Amount with sum) gt 10)
results in
{
"@context": "$metadata#Categories",
"value": [
{ "ID": "PG1", "Name": "Food" }
]
}
The aggregate
function can also be applied inside $apply
:
Example 87: Sales volume per customer in relation to total volume
GET /service/Sales?$apply=
groupby((Customer),aggregate(Amount with sum as CustomerAmount))
/compute(CustomerAmount divby $these/aggregate(CustomerAmount with sum)
as Contribution)
&$expand=Customer/$ref
results in
{
"@context": "$metadata#Sales(Customer(),CustomerAmount,Contribution)",
"value": [
{ "Customer": { "@id": "Customers('C1')" },
"Contribution@type": "Decimal", "Contribution": 0.2916667 },
{ "Customer": { "@id": "Customers('C2')" },
"Contribution@type": "Decimal", "Contribution": 0.5 },
{ "Customer": { "@id": "Customers('C3')" },
"Contribution@type": "Decimal", "Contribution": 0.2083333 }
]
}
Example 88: rule 1 for keyword from
applied repeatedly
GET /service/Sales?$apply=aggregate(Amount with sum
from Time with average
from Customer/Country with max
as MaxDailyAveragePerCountry)
is equivalent to (with nested groupby
transformations)
GET /service/Sales?$apply=
groupby((Customer/Country),
groupby((Time),aggregate(Amount with sum as D1))
/aggregate(D1 with average as D2))
/aggregate(D2 with max as MaxDailyAveragePerCountry)
and is equivalent to (with consecutive groupby
transformations)
GET /service/Sales?$apply=
groupby((Customer/Country,Time),aggregate(Amount with sum as D1))
/groupby((Customer/Country),aggregate(D1 with average as D2))
/aggregate(D2 with max as MaxDailyAveragePerCountry)
Example 89: Assuming an extension of the data model where Customer
contains an additional collection-valued complex property Addresses
and these contain a single-valued navigation property ResponsibleSalesOrganization
, addnested
can be used to compute a nested dynamic property:
GET /service/Customers?$apply=
addnested(Addresses/ResponsibleSalesOrganization,
compute(Superordinate/Name as SalesRegion)
as AugmentedSalesOrganization)
results in
{
"@context": "$metadata#Customers(Addresses(AugmentedSalesOrganization())",
"value": [
{ "ID": "C1", "Name": "Joe", "Country": "US",
"Addresses": [
{ "Locality": "Seattle",
"AugmentedSalesOrganization":
{ "@context": "#SalesOrganizations/$entity",
"ID": "US West", "SalesRegion": "US" } },
{ "Locality": "DC",
"AugmentedSalesOrganization":
{ "@context": "#SalesOrganizations/$entity",
"ID": "US", "SalesRegion": "Corporate Sales" } },
]
}, …
]
}
addnested
transformations can be nested.
Example 90: nested addnested
transformations
GET /service/Categories?$apply=
addnested(Products,
addnested(Sales,filter(Amount gt 3) as FilteredSales)
as FilteredProducts)
results in
{
"@context": "$metadata#Categories(FilteredProducts()",
"value": [
{ "ID": "PG1", "Name": "Food",
"FilteredProducts@context": "#Products(FilteredSales())",
"FilteredProducts": [
{ "ID": "P1", "Name": "Sugar", "Color": "White",
"FilteredSales@context": "#Sales",
"FilteredSales": [] },
{ "ID": "P2", "Name": "Coffee", "Color": "Brown",
"FilteredSales@context": "#Sales",
"FilteredSales": [ { "ID": 3, "Amount": 4 },
{ "ID": 4, "Amount": 8 } ] }
]
},
{ "ID": "PG2", "Name": "Non-Food",
"FilteredProducts@context": "#Products(FilteredSales())",
"FilteredProducts": [
{ "ID": "P3", "Name": "Paper", "Color": "White",
"FilteredSales@context": "#Sales",
"FilteredSales": [ { "ID": 5, "Amount": 4 } ] },
{ "ID": "P4", "Name": "Pencil", "Color": "Black",
"FilteredSales@context": "#Sales",
"FilteredSales": [] }
]
}
]
}
Instead of keeping all related entities from navigation properties that addnested
expanded by default, an explicit $expand
controls which of them to include in the response:
GET /service/Categories?$apply=
addnested(Products,
addnested(Sales,filter(Amount gt 3) as FilteredSales)
as FilteredProducts)
&$expand=FilteredProducts
results in the response before without the FilteredSales dynamic navigation properties expanded in the result.
Example 91: Here only the GroupedSales
are expanded, because they are named in $expand
, the related Product
entity is not:
GET /service/Customers?$apply=addnested(Sales,
groupby((Product/Name)) as GroupedSales)
&$expand=GroupedSales
results in
{
"@context": "$metadata#Customers(GroupedSales())",
"value": [
{ "ID": "C1", "Name": "Joe", "Country": "USA",
"GroupedSales@context": "#Sales(@Core.AnyStructure)",
"GroupedSales": [
{ },
{ },
{ }
] },
{ "ID": "C2", "Name": "Sue", "Country": "USA",
"GroupedSales@context": "#Sales(@Core.AnyStructure)",
"GroupedSales": [
{ },
{ }
] },
{ "ID": "C3", "Name": "Joe", "Country": "Netherlands",
"GroupedSales@context": "#Sales(@Core.AnyStructure)",
"GroupedSales": [
{ },
{ }
] },
{ "ID": "C4", "Name": "Luc", "Country": "France",
"GroupedSales@context": "#Sales(@Core.AnyStructure)",
"GroupedSales": [ ] }
]
}
Example 92: use outerjoin
to split up collection-valued navigation properties for grouping
GET /service/Customers?$apply=outerjoin(Sales as ProductSales)
/groupby((Country,ProductSales/Product/Name))
returns the different combinations of products sold per country:
{
"@context": "$metadata#Customers(Country,ProductSales())",
"value": [
{ "Country": "Netherlands",
"ProductSales@context": "#Sales(Product(Name))/$entity",
"ProductSales": { "Product": { "Name": "Paper" } } },
{ "Country": "Netherlands",
"ProductSales@context": "#Sales(Product(Name))/$entity",
"ProductSales": { "Product": { "Name": "Sugar" } } },
{ "Country": "USA",
"ProductSales@context": "#Sales(Product(Name))/$entity",
"ProductSales": { "Product": { "Name": "Coffee" } } },
{ "Country": "USA",
"ProductSales@context": "#Sales(Product(Name))/$entity",
"ProductSales": { "Product": { "Name": "Paper" } } },
{ "Country": "USA",
"ProductSales@context": "#Sales(Product(Name))/$entity",
"ProductSales": { "Product": { "Name": "Sugar" } } },
{ "Country": "France", "ProductSales": null }
]
}
Custom aggregates are defined through the CustomAggregate
annotation. They can be associated with an entity set, a collection or an entity container.
A custom aggregate can be used by specifying the name of the custom aggregate in the aggregate
clause.
Example 93:
GET /service/Sales?$apply=groupby((Customer/Country),
aggregate(Amount with sum as Actual,Forecast))
results in
{
"@context": "$metadata#Sales(Customer(Country),Actual,Forecast)",
"value": [
{ "Customer": { "Country": "Netherlands" },
"Actual@type": "Decimal", "Actual": 5,
"Forecast@type": "Decimal", "Forecast": 4 },
{ "Customer": { "Country": "USA" },
"Actual@type": "Decimal", "Actual": 19,
"Forecast@type": "Decimal", "Forecast": 21 }
]
}
When associated with an entity set a custom aggregate MAY have the same name as a property of the underlying entity type with the same type as the type returned by the custom aggregate. This is typically done when the aggregate is used as a default aggregate for that property.
Example 94: A custom aggregate can be defined with the same name as a property of the same type in order to define a default aggregate for that property.
GET /service/Sales?$apply=groupby((Customer/Country),aggregate(Amount))
results in
{
"@context": "$metadata#Sales(Customer(Country),Amount)",
"value": [
{ "Customer": { "Country": "Netherlands" }, "Amount": 5 },
{ "Customer": { "Country": "USA" }, "Amount": 19 }
]
}
Example 95: illustrates rule 1 for keyword from
: maximal sales forecast for a product
GET /service/Sales?$apply=aggregate(Forecast from Product with max
as MaxProductForecast)
is equivalent to
GET /service/Sales?$apply=
groupby((Product),aggregate(Forecast))
/aggregate(Forecast with max as MaxProductForecast)
Example 96: illustrates rule 2 for keyword from
: the forecast is computed in two steps
GET /service/Sales?$apply=aggregate(Forecast from Product as ProductForecast)
is equivalent to the following (except that the property name is Forecast
instead of ProductForecast
)
GET /service/Sales?$apply=
groupby((Product),aggregate(Forecast))
/aggregate(Forecast)
Example 97: illustrates rule 1 followed by rule 2 for keyword from
: a forecast based on the average daily forecasts per country
GET /service/Sales?$apply=aggregate(Forecast from Time with average
from Customer/Country
as CountryForecast)
is equivalent to the following (except that the property name is Forecast
instead of CountryForecast
). Note that Forecast
appears as a property and as a custom aggregate.
GET /service/Sales?$apply=
groupby((Customer/Country),
groupby((Time),aggregate(Forecast))
/aggregate(Forecast with average as D1))
/aggregate(Forecast)
A property can be aggregated in multiple ways, each with a different alias.
Example 98:
GET /service/Sales?$apply=groupby((Customer/Country),
aggregate(Amount with sum as Total,
Amount with average as AvgAmt))
results in
{
"@context": "$metadata#Sales(Customer(Country),Total,AvgAmt)",
"value": [
{ "Customer": { "Country": "Netherlands" },
"Total@type": "Decimal", "Total": 5,
"AvgAmt@type": "Decimal", "AvgAmt": 1.6666667 },
{ "Customer": { "Country": "USA" },
"Total@type": "Decimal", "Total": 19,
"AvgAmt@type": "Decimal", "AvgAmt": 3.8 }
]
}
The introduced dynamic property is added to the context where the aggregate expression is applied to:
Example 99:
GET /service/Products?$apply=groupby((Name),
aggregate(Sales/Amount with sum as Total))
/groupby((Name),
addnested(Sales,aggregate(Amount with average as AvgAmt)
as AggregatedSales))
results in
{
"@context": "$metadata#Products(Name,Total,AggregatedSales())",
"value": [
{ "Name": "Coffee", "Total": 12,
"AggregatedSales@context": "#Sales(AvgAmt)",
"AggregatedSales": [ { "AvgAmt@type": "Decimal",
"AvgAmt": 6 } ] },
{ "Name": "Paper", "Total": 8,
"AggregatedSales@context": "#Sales(AvgAmt)",
"AggregatedSales": [ { "AvgAmt@type": "Decimal",
"AvgAmt": 2 } ] },
{ "Name": "Pencil", "Total": null,
"AggregatedSales@context": "#Sales(AvgAmt)",
"AggregatedSales": [ { "AvgAmt": null } ] },
{ "Name": "Sugar", "Total": 4,
"AggregatedSales@context": "#Sales(AvgAmt)",
"AggregatedSales": [ { "AvgAmt@type": "Decimal",
"AvgAmt": 2 } ] }
]
}
There is no hard distinction between groupable and aggregatable properties: the same property can be aggregated and used to group the aggregated results.
Example 100:
GET /service/Sales?$apply=groupby((Amount),aggregate(Amount with sum as Total))
will return all distinct amounts appearing in sales orders and how much money was made with deals of this amount
{
"@context": "$metadata#Sales(Amount,Total)",
"value": [
{ "Amount": 1, "Total@type": "Decimal", "Total": 2 },
{ "Amount": 2, "Total@type": "Decimal", "Total": 6 },
{ "Amount": 4, "Total@type": "Decimal", "Total": 8 },
{ "Amount": 8, "Total@type": "Decimal", "Total": 8 }
]
}
Dynamic property names may be reused in different transformation sequences passed to concat
.
Example 101: to get the best-selling product per country with sub-totals for every country, the partial results of a transformation sequence and a groupby
transformation are concatenated:
GET /service/Sales?$apply=concat(
groupby((Customer/Country,Product/Name),
aggregate(Amount with sum as Total))
/groupby((Customer/Country),topcount(1,Total)),
groupby((Customer/Country),
aggregate(Amount with sum as Total)))
results in
{
"@context": "$metadata#Sales(Customer(Country),Total)",
"value": [
{ "Customer": { "Country": "USA" }, "Product": { "Name": "Coffee" },
"Total@type": "Decimal", "Total": 12
},
{ "Customer": { "Country": "Netherlands" }, "Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 3
},
{ "Customer": { "Country": "USA" },
"Total@type": "Decimal", "Total": 19
},
{ "Customer": { "Country": "Netherlands" },
"Total@type": "Decimal", "Total": 5
}
]
}
Example 102: transformation sequences are also useful inside groupby
: Aggregate the amount by only considering the top two sales amounts per product and country:
GET /service/Sales?$apply=groupby((Customer/Country,Product/Name),
topcount(2,Amount)/aggregate(Amount with sum as Total))
results in
{
"@context": "$metadata#Sales(Customer(Country),Product(Name),Total)",
"value": [
{ "Customer": { "Country": "Netherlands" }, "Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 3
},
{ "Customer": { "Country": "Netherlands" }, "Product": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 2
},
{ "Customer": { "Country": "USA" }, "Product": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 2
},
{ "Customer": { "Country": "USA" }, "Product": { "Name": "Coffee" },
"Total@type": "Decimal", "Total": 12
},
{ "Customer": { "Country": "USA" }, "Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 5
}
]
}
Example 103: concatenation of two different groupings “biggest sale per customer” and “biggest sale per product”, made distinguishable by a dynamic property:
GET /service/Sales?$apply=concat(
groupby((Customer),topcount(1,Amount))/compute('Customer' as per),
groupby((Product),topcount(1,Amount))/compute('Product' as per))
&$expand=Customer($select=ID),Product($select=ID)
In the result, Sales
entities 4 and 6 occur twice each with contradictory values of the dynamic property per
. If a UI consuming the response presents the two groupings in separate columns based on the per
property, no contradiction effectively arises.
{
"@context": "$metadata#Sales(*,per,Customer(ID),Product(ID))",
"value": [
{ "Customer": { "ID": "C1" }, "Product": { "ID": "P2" },
"ID": "3", "Amount": 4, "per": "Customer" },
{ "Customer": { "ID": "C2" }, "Product": { "ID": "P2" },
"ID": "4", "Amount": 8, "per": "Customer" },
{ "Customer": { "ID": "C3" }, "Product": { "ID": "P1" },
"ID": "6", "Amount": 2, "per": "Customer" },
{ "Customer": { "ID": "C3" }, "Product": { "ID": "P1" },
"ID": "6", "Amount": 2, "per": "Product" },
{ "Customer": { "ID": "C2" }, "Product": { "ID": "P2" },
"ID": "4", "Amount": 8, "per": "Product" },
{ "Customer": { "ID": "C2" }, "Product": { "ID": "P3" },
"ID": "5", "Amount": 4, "per": "Product" }
]
}
Example 104: As a variation of example 101, a query for returning the best-selling product per country and the total amount of the remaining products can be formulated with the help of a model function.
For this purpose, the model includes a definition of a TopCountAndRemainder
function that accepts a count and a numeric property for the top entities:
edm:Function Name="TopCountAndRemainder"
< IsBound="true">
edm:Parameter Name="EntityCollection"
< Type="Collection(Edm.EntityType)" />
edm:Parameter Name="Count" Type="Edm.Int16" />
<edm:Parameter Name="Property" Type="Edm.String" />
<edm:ReturnType Type="Collection(Edm.EntityType)" />
<edm:Function> </
The function retains those entities that topcount
also would retain, and replaces the remaining entities by a single aggregated entity, where only the numeric property has a value, which is the sum over those remaining entities:
GET /service/Sales?$apply=
groupby((Customer/Country,Product/Name),
aggregate(Amount with sum as Total))
/groupby((Customer/Country),
Self.TopCountAndRemainder(Count=1,Property='Total'))
results in
{
"@context": "$metadata#Sales(Customer(Country),Total)",
"value": [
{ "Customer": { "Country": "Netherlands" },
"Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 3 },
{ "Customer": { "Country": "Netherlands" },
"Total@type": "Decimal", "Total": 2 },
{ "Customer": { "Country": "USA" },
"Product": { "Name": "Coffee" },
"Total@type": "Decimal", "Total": 12 },
{ "Customer": { "Country": "USA" },
"Total@type": "Decimal", "Total": 7 }
]
}
Note that these two entities get their values for the Country property from the groupby transformation, which ensures that they contain all grouping properties with the correct values.
For a leveled hierarchy, consumers may specify a different aggregation method per level for every property passed to rollup
as a hierarchy level below the root level.
Example 105: get the average of the overall amount by month per product.
Using a transformation sequence:
GET /service/Sales?$apply=groupby((Product/ID,Product/Name,Time/Month),
aggregate(Amount with sum) as Total))
/groupby((Product/ID,Product/Name),
aggregate(Total with average as MonthlyAverage))
Using from
:
GET /service/Sales?$apply=groupby((Product/ID,Product/Name),
aggregate(Amount with sum
from Time/Month with average
as MonthlyAverage))
Example 106: get the total amount per customer, the average of the total customer amounts per country, and the overall average of these averages
GET /service/Sales?$apply=concat(
groupby((rollup(Customer/Country,Customer/ID)),
aggregate(Amount with sum
from Customer/ID with average
as CustomerCountryAverage)),
aggregate(Amount with sum
from Customer/ID with average
from Customer/Country with average
as CustomerCountryAverage)))
results in
{
"@context": "$metadata#Sales(CustomerCountryAverage)",
"value": [
{ "Customer": { "Country": "USA", "ID": "C1" },
"CustomerCountryAverage@type": "Decimal",
"CustomerCountryAverage": 7 },
{ "Customer": { "Country": "USA", "ID": "C2" },
"CustomerCountryAverage@type": "Decimal",
"CustomerCountryAverage": 12 },
{ "Customer": { "Country": "USA" },
"CustomerCountryAverage@type": "Decimal",
"CustomerCountryAverage": 9.5 },
{ "Customer": { "Country": "Netherlands", "ID": "C3" },
"CustomerCountryAverage@type": "Decimal",
"CustomerCountryAverage": 5 },
{ "Customer": { "Country": "Netherlands" },
"CustomerCountryAverage@type": "Decimal",
"CustomerCountryAverage": 5 },
{ "CustomerCountryAverage@type": "Decimal",
"CustomerCountryAverage": 7.25 }
]
}
Note that this example extends the result of rollup
with concat
and aggregate
to append the overall average.
If aggregation along a recursive hierarchy does not apply to the entire hierarchy, transformations ancestors
and descendants
may be used to restrict it as needed.
Example 107: Total sales amounts for sales orgs in ‘US’ in the SalesOrgHierarchy
defined in Hierarchy Examples
GET /service/Sales?$apply=
descendants(
$root/SalesOrganizations,SalesOrgHierarchy,SalesOrganization/ID,
filter(SalesOrganization/Name eq 'US'),keep start)
/groupby((rolluprecursive(
$root/SalesOrganizations,SalesOrgHierarchy,SalesOrganization/ID)),
aggregate(Amount with sum as TotalAmount))
&$expand=SalesOrganization($expand=Superordinate/$ref)
results in
{
"@context": "$metadata#Sales(TotalAmount,SalesOrganization())",
"value": [
{ "TotalAmount@type": "Decimal", "TotalAmount": 19,
"SalesOrganization": { "ID": "US", "Name": "US",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } } },
{ "TotalAmount@type": "Decimal", "TotalAmount": 12,
"SalesOrganization": { "ID": "US East", "Name": "US East",
"Superordinate": { "@id": "SalesOrganizations('US')" } } },
{ "TotalAmount@type": "Decimal", "TotalAmount": 7,
"SalesOrganization": { "ID": "US West", "Name": "US West",
"Superordinate": { "@id": "SalesOrganizations('US')" } } }
]
}
Note that this example returns the actual total sums regardless of whether the descendants
transformation comes before or after the groupby
with rolluprecursive
.
The order of transformations becomes relevant if groupby
with rolluprecursive
shall aggregate over a thinned-out hierarchy, like here:
Example 108: Number of Paper sales per sales org aggregated along the the SalesOrgHierarchy
defined in Hierarchy Examples
GET /service/Sales?$apply=
filter(Product/Name eq 'Paper')
/groupby((rolluprecursive((
$root/SalesOrganizations,SalesOrgHierarchy,SalesOrganization/ID)),
aggregate($count as PaperSalesCount))
&$expand=SalesOrganization($expand=Superordinate/$ref)
results in
{
"@context": "$metadata#Sales(PaperSalesCount,SalesOrganization())",
"value": [
{ "PaperSalesCount@type": "Decimal", "PaperSalesCount": 2,
"SalesOrganization": { "ID": "US", "Name": "US",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } } },
{ "PaperSalesCount@type": "Decimal", "PaperSalesCount": 1,
"SalesOrganization": { "ID": "US East", "Name": "US East",
"Superordinate": { "@id": "SalesOrganizations('US')" } } },
{ "PaperSalesCount@type": "Decimal", "PaperSalesCount": 1,
"SalesOrganization": { "ID": "US West", "Name": "US West",
"Superordinate": { "@id": "SalesOrganizations('US')" } } },
{ "PaperSalesCount@type": "Decimal", "PaperSalesCount": 2,
"SalesOrganization": { "ID": "EMEA", "Name": "EMEA",
"Superordinate": { "@id": "SalesOrganizations('Sales')" } } },
{ "PaperSalesCount@type": "Decimal", "PaperSalesCount": 2,
"SalesOrganization": { "ID": "EMEA Central", "Name": "EMEA Central",
"Superordinate": { "@id": "SalesOrganizations('EMEA')" } } },
{ "PaperSalesCount@type": "Decimal", "PaperSalesCount": 4,
"SalesOrganization": { "ID": "Sales", "Name": "Sales",
"Superordinate": null } }
]
}
⚠ Example 109: The input set Sales
is filtered along a hierarchy on a related entity (navigation property SalesOrganization
) before an aggregation
GET /service/Sales?$apply=
descendants($root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID,
filter(SalesOrganization/Name eq 'US'),
keep start)
/aggregate(Amount with sum as TotalAmount)
The same aggregate value is computed if the input set is the hierarchical entity SalesOrganizations
and an assumed partner navigation property Sales
of SalesOrganization
appears in the aggregate
transformation
GET /service/SalesOrganizations?$apply=
descendants($root/SalesOrganizations,
SalesOrgHierarchy,
ID,
filter(Name eq 'US'),
keep start)
/aggregate(Sales/Amount with sum as TotalAmount)
⚠ Example 110: total sales amount aggregated along the sales organization sub-hierarchy with root EMEA restricted to 3 levels
GET /service/Sales?$apply=
groupby((rolluprecursive($root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID)),
aggregate(Amount with sum as Total))
/filter(Aggregation.isdescendant(
HierarchyNodes=$root/SalesOrganizations,
HierarchyQualifier='SalesOrgHierarchy',
Node=SalesOrganization/ID,
Ancestor='EMEA',
MaxDistance=2,
IncludeSelf=true))
/orderby(SalesOrganization/Name)
/traverse($root/SalesOrganizations,
SalesOrgHierarchy,SalesOrganization/ID,preorder)
or, equivalently
GET /service/Sales?$apply=
groupby((rolluprecursive(
$root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID,
descendants(
$root/SalesOrganizations,
SalesOrgHierarchy,
ID,
filter(ID eq 'EMEA'),
2, keep start))),
aggregate(Amount with sum as Total))
/orderby(SalesOrganization/Name)
/traverse($root/SalesOrganizations,
SalesOrgHierarchy,SalesOrganization/ID,preorder)
Example 111: Return the result of example 66 in preorder
GET /service/Sales?$apply=groupby(
(rolluprecursive(
$root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID,
descendants(
$root/SalesOrganizations,
SalesOrgHierarchy,
ID, filter(ID eq 'US'), keep start))),
compute(case(SalesOrganization eq Aggregation.rollupnode():Amount)
as AmountExcl)
/aggregate(Amount with sum as TotalAmountIncl,
AmountExcl with sum as TotalAmountExcl))
/traverse($root/SalesOrganizations,
SalesOrgHierarchy,
SalesOrganization/ID,
preorder,
Name asc)
results in
{
"@context": "$metadata#Sales(SalesOrganization(ID),
TotalAmountIncl,TotalAmountExcl)",
"value": [
{ "SalesOrganization": { "ID": "US", "Name": "US" },
"TotalAmountIncl@type": "Decimal", "TotalAmountIncl": 19,
"TotalAmountExcl": null },
{ "SalesOrganization": { "ID": "US East", "Name": "US East" },
"TotalAmountIncl@type": "Decimal", "TotalAmountIncl": 12,
"TotalAmountExcl@type": "Decimal", "TotalAmountExcl": 12 },
{ "SalesOrganization": { "ID": "US West", "Name": "US West" },
"TotalAmountIncl@type": "Decimal", "TotalAmountIncl": 7,
"TotalAmountExcl@type": "Decimal" ,"TotalAmountExcl": 7 }
]
}
Example 112: Preorder traversal of a hierarchy with 1:N relationship with collection-valued segment \(p_1={\tt Sales}\) and \(r={\tt SalesOrganization}/{\tt ID}\).
GET /service/Products?$apply=traverse(
$root/SalesOrganizations,
SalesOrgHierarchy,
Sales/SalesOrganization/ID,
preorder,
Name asc)
&$select=ID
The result contains multiple instances of the same Product
that differ in their Sales
navigation property even though they agree in their ID
key property. The node \(x\) with \(x/{\tt ID}={}\)"US"
has \(σ(x)={}\){"Sales": [{"SalesOrganization": {"ID": "US"}}]}
.
{
"@context":
"$metadata#Products(ID,Sales(SalesOrganization(ID)))",
"value": [
{ "ID": "P1", "Sales": [ { "SalesOrganization": { "ID": "Sales" } } ] },
{ "ID": "P2", "Sales": [ { "SalesOrganization": { "ID": "Sales" } } ] },
{ "ID": "P3", "Sales": [ { "SalesOrganization": { "ID": "Sales" } } ] },
{ "ID": "P1", "Sales": [ { "SalesOrganization": { "ID": "EMEA" } } ] },
{ "ID": "P3", "Sales": [ { "SalesOrganization": { "ID": "EMEA" } } ] },
{ "ID": "P1",
"Sales": [ { "SalesOrganization": { "ID": "EMEA Central" } } ] },
{ "ID": "P3",
"Sales": [ { "SalesOrganization": { "ID": "EMEA Central" } } ] },
{ "ID": "P1", "Sales": [ { "SalesOrganization": { "ID": "US" } } ] },
{ "ID": "P2", "Sales": [ { "SalesOrganization": { "ID": "US" } } ] },
{ "ID": "P3", "Sales": [ { "SalesOrganization": { "ID": "US" } } ] },
{ "ID": "P2", "Sales": [ { "SalesOrganization": { "ID": "US East" } } ] },
{ "ID": "P3", "Sales": [ { "SalesOrganization": { "ID": "US East" } } ] },
{ "ID": "P1", "Sales": [ { "SalesOrganization": { "ID": "US West" } } ] },
{ "ID": "P2", "Sales": [ { "SalesOrganization": { "ID": "US West" } } ] },
{ "ID": "P3", "Sales": [ { "SalesOrganization": { "ID": "US West" } } ] }
]
}
Example 113: Aggregation along a hierarchy with 1:N relationship: Sold products per sales organization
GET /service/Products?$apply=
groupby((rolluprecursive(
$root/SalesOrganizations,
SalesOrgHierarchy,
Sales/SalesOrganization/ID)),
aggregate(ID with Custom.concat as SoldProducts)
results in
{
"@context": "$metadata#Products(Sales(SalesOrganization(ID)),SoldProducts)",
"value": [
{ "Sales": [ { "SalesOrganization": { "ID": "Sales" } } ],
"SoldProducts": "P1,P2,P3" },
{ "Sales": [ { "SalesOrganization": { "ID": "EMEA" } } ],
"SoldProducts": "P1,P3" },
{ "Sales": [ { "SalesOrganization": { "ID": "EMEA Central" } } ],
"SoldProducts": "P1,P3" },
{ "Sales": [ { "SalesOrganization": { "ID": "US" } } ],
"SoldProducts": "P1,P2,P3" },
{ "Sales": [ { "SalesOrganization": { "ID": "US East" } } ],
"SoldProducts": "P2,P3" },
{ "Sales": [ { "SalesOrganization": { "ID": "US West" } } ],
"SoldProducts": "P1,P2,P3" }
]
}
⚠ Example 114: Assume an extension of the data model where a SalesOrganization
is associated with one or more instances of ProductCategory
, and ProductCategory
also organizes categories in a recursive hierarchy:
ProductCategory | parent ProductCategory | associated SalesOrganizations |
---|---|---|
Food | US, EMEA | |
Cereals | Food | US |
Organic cereals | Cereals | US West |
Aggregation of sales amounts along the sales organization hierarchy could be restricted to those organizations linked with product category “Cereals” or a descendant of it, and the ancestors of those organizations:
GET /service/Sales?$apply=groupby((rolluprecursive(
$root/SalesOrganizations,SalesOrgHierarchy,
SalesOrganization/ID,
ancestors(
$root/SalesOrganizations,SalesOrgHierarchy,
ID,
traverse(
$root/ProductCategories,ProductCategoryHierarchy,
ProductCategories/ID,
preorder,
filter(Name eq 'Cereals')),
keep start)
)),
aggregate(Amount with sum as TotalAmount))
&$expand=SalesOrganization($select=ID,$expand=ProductCategories/$ref)
results in
{
"@context": "$metadata#Sales(SalesOrganization(ID),TotalAmount)",
"value": [
{ "SalesOrganization": { "ID": "Sales", "ProductCategories": [ ] },
"TotalAmount@type": "Decimal", "TotalAmount": 24 },
{ "SalesOrganization": { "ID": "US", "ProductCategories": [
{ "@id": "ProductCategories('Food')" },
{ "@id": "ProductCategories('Cereals')" } ] },
"TotalAmount@type": "Decimal", "TotalAmount": 19 },
{ "SalesOrganization": { "ID": "US West", "ProductCategories": [
{ "@id": "ProductCategories('Organic cereals')" } ] },
"TotalAmount@type": "Decimal", "TotalAmount": 7 }
]
}
traverse
acts here as a filter, hence preorder
could be changed to postorder
without changing the result. filter
is the parameter \(S\) of traverse
and operates on the product category hierarchy being traversed.
Replacing the traverse
transformation with a descendants
transformation, as in
ancestors(
$root/SalesOrganizations,SalesOrgHierarchy,
ID,
descendants(
$root/ProductCategories,ProductCategoryHierarchy,
ProductCategories/ID,
filter(ProductCategories/any(c:c/Name eq 'Cereals')),
keep start),
keep start)
works differently: descendants
is the parameter \(T\) of ancestors
and operates on its input set of sales organizations. This would determine descendants of sales organizations for “Cereals” and their ancestor sales organizations, so US East would appear in the result.
Besides changes to the structural properties of the entities in a hierarchical collection, hierarchy maintenance involves changes to the parent-child relationships.
Example 115: Move a sales organization Switzerland under the parent EMEA Central by binding the parent navigation property to EMEA Central OData-JSON, section 8.5:
PATCH /service/SalesOrganizations('Switzerland')
Content-Type: application/json
{ "Superordinate": { "@id": "SalesOrganizations('EMEA Central')" } }
results in 204 No Content
.
Deleting the parent from the sales organization Switzerland (making it a root) can be achieved either with:
PATCH /service/SalesOrganizations('Switzerland')
Content-Type: application/json
{ "Superordinate": { "@id": null } }
or with:
DELETE /service/SalesOrganizations('Switzerland')/Superordinate/$ref
Example 116: If the parent navigation property contained a referential constraint for the key of the target OData-CSDL, section 8.5,
EntityType Name="SalesOrganization">
<Key>
<PropertyRef Name="ID" />
<Key>
</Property Name="ID" Type="Edm.String" Nullable="false" />
<Property Name="Name" Type="Edm.String" />
<Property Name="SuperordinateID" Type="Edm.String" />
<NavigationProperty Name="Superordinate"
< Type="SalesModel.SalesOrganization">
ReferentialConstraint Property="SuperordinateID"
< ReferencedProperty="ID" />
NavigationProperty>
</EntityType> </
then alternatively the property taking part in the referential constraint OData-Protocol, section 11.4.9.1 could be changed to EMEA Central:
PATCH /service/SalesOrganizations('Switzerland')
Content-Type: application/json
{ "SuperordinateID": "EMEA Central" }
If the parent-child relationship between sales organizations is maintained in a separate entity set, a node can have multiple parents, with additional information on each parent-child relationship.
⚠ Example 117: Assume the relation from a node to its parent nodes contains a weight:
EntityType Name="SalesOrganizationRelation">
<Key>
<PropertyRef Name="Superordinate/ID" Alias="SuperordinateID" />
<Key>
</Property Name="Weight" Type="Edm.Decimal"
< Nullable="false" DefaultValue="1" />
NavigationProperty Name="Superordinate"
< Type="SalesModel.SalesOrganization" Nullable="false" />
EntityType>
</EntityType Name="SalesOrganization">
<Key>
<PropertyRef Name="ID" />
<Key>
</Property Name="ID" Type="Edm.String" Nullable="false" />
<Property Name="Name" Type="Edm.String" />
<NavigationProperty Name="Relations"
< Type="Collection(SalesModel.SalesOrganizationRelation)"
Nullable="false" ContainsTarget="true" />
Annotation Term="Aggregation.RecursiveHierarchy"
< Qualifier="MultiParentHierarchy">
Record>
<PropertyValue Property="NodeProperty"
< PropertyPath="ID" />
PropertyValue Property="ParentNavigationProperty"
< NavigationPropertyPath="Relations/Superordinate" />
Record>
</Annotation>
</EntityType> </
Further assume the following relationships between sales organizations:
ID | Relations/SuperordinateID | Relations/Weight |
---|---|---|
US | Sales | 1 |
EMEA | Sales | 1 |
EMEA Central | EMEA | 1 |
Atlantis | US | 0.6 |
Atlantis | EMEA | 0.4 |
Phobos | Mars | 1 |
Then Atlantis is a node with two parents. The standard hierarchical transformations disregard the weight property and consider both parents equally valid (but see example 118).
In a traversal with start node Sales only:
GET /service/SalesOrganizations?$apply=
traverse($root/SalesOrganizations,MultiParentHierarchy,ID,preorder,
filter(ID eq 'Sales'))
Mars and Phobos cannot be reached and hence are orphans. But they can be made descendants of the start node Sales by adding a relationship. Note the collection-valued segment of the ParentNavigationProperty
appears at the end of the resource path and the subsequent single-valued segment appears in the payload:
POST /service/SalesOrganizations('Mars')/Relations
Content-Type: application/json
{ "Superordinate": { "@id": "SalesOrganizations('Sales')" } }
Since this example contains no referential constraint, there is no analogy to example 116. The alias SuperordinateID
cannot be used in the payload, the following request is invalid:
POST /service/SalesOrganizations('Mars')/Relations
Content-Type: application/json
{ "SuperordinateID": "Sales" }
The alias SuperordinateID
is used in the request to delete the added relationship again:
DELETE /service/SalesOrganizations('Mars')/Relations('Sales')
⚠ Example 118: Continuing example 117, assume a custom aggregate MultiParentWeightedTotal
that computes the total sales amount weighted by the SalesOrganizationRelation/Weight
properties along the @Aggregation.UpPath#MultiParentHierarchy
of a sales organization:
Annotations Target="SalesData.Sales">
<Annotation Term="Aggregation.CustomAggregate"
< Qualifier="MultiParentWeightedTotal" String="Edm.Decimal" />
Annotations> </
Then rolluprecursive
can be used to aggregate the weighted sales amounts with the request below. The traverse
transformation produces an output set \(H'\) in which sales organizations with multiple parents occur multiple times. For each occurrence \(x\) in \(H'\), the rolluprecursive
algorithm determines a sales collection \(F(x)\) and the custom aggregate MultiParentWeightedTotal
evaluates the path SalesOrganization/@Aggregation.UpPath#MultiParentHierarchy
relative to that collection:
GET /service/Sales?$apply=groupby(
(rolluprecursive(
$root/SalesOrganizations,
MultiParentHierarchy,
SalesOrganization/ID,
traverse(
$root/SalesOrganizations,
MultiParentHierarchy,
SalesOrganization/ID,
preorder))),
aggregate(MultiParentWeightedTotal))
Assume that in addition to the sales in the example data there are sales of 10 in Atlantis. Then 60% of them would contribute to the US sales organization and 40% to the EMEA sales organization. Without the weights, all duplicate nodes would contribute the same aggregate result, therefore this example only makes sense in connection with a custom aggregate that considers the weights.
Note that rolluprecursive
must preserve the preorder established by traverse
:
{
"@context": "$metadata#Sales(SalesOrganization(),MultiParentWeightedTotal)",
"value": [
{ "SalesOrganization": { "ID": "Sales", "Name": "Corporate Sales",
"@Aggregation.UpPath#MultiParentHierarchy": [ ] },
"MultiParentWeightedTotal": 34 },
{ "SalesOrganization": { "ID": "US", "Name": "US",
"@Aggregation.UpPath#MultiParentHierarchy": [ "Sales" ] },
"MultiParentWeightedTotal": 25 },
{ "SalesOrganization": { "ID": "Atlantis", "Name": "Atlantis",
"@Aggregation.UpPath#MultiParentHierarchy": [ "US", "Sales" ] },
"MultiParentWeightedTotal": 6 },
…
{ "SalesOrganization": { "ID": "EMEA", "Name": "EMEA",
"@Aggregation.UpPath#MultiParentHierarchy": [ "Sales" ] },
"MultiParentWeightedTotal": 9 },
{ "SalesOrganization": { "ID": "Atlantis", "Name": "Atlantis",
"@Aggregation.UpPath#MultiParentHierarchy": [ "EMEA", "Sales" ] },
"MultiParentWeightedTotal": 4 },
…
]
}
Applying aggregation first covers the most prominent use cases. The slightly more sophisticated question “how much money is earned with small sales” requires filtering the base set before applying the aggregation. To enable this type of question several transformations can be specified in $apply
in the order they are to be applied, separated by a forward slash.
Example 119:
GET /service/Sales?$apply=filter(Amount le 1)
/aggregate(Amount with sum as Total)
means “filter first, then aggregate”, and results in
{
"@context": "$metadata#Sales(Total)",
"value": [
{ "Total@type": "Decimal", "Total": 2 }
]
}
Using filter
within $apply
does not preclude using it as a normal system query option.
Example 120:
GET /service/Sales?$apply=filter(Amount le 2)/groupby((Product/Name),
aggregate(Amount with sum as Total))
&$filter=Total ge 4
results in
{
"@context": "$metadata#Sales(Product(Name),Total)",
"value": [
{ "Product": { "Name": "Paper" },
"Total@type": "Decimal", "Total": 4 },
{ "Product": { "Name": "Sugar" },
"Total@type": "Decimal", "Total": 4 }
]
}
Example 121: Revisiting example 16 for using the from
keyword with the aggregate
function, the request
GET /service/Sales?$apply=aggregate(Amount from Time with average
as DailyAverage)
could be rewritten in a more procedural way using a transformation sequence returning the same result
GET /service/Sales?$apply=groupby((Time),aggregate(Amount with sum as Total))
/aggregate(Total with average as DailyAverage)
For further examples, consider another data model containing entity sets for cities, countries and continents and the obvious associations between them.
Example 122: getting the population per country with
GET /service/Cities?$apply=groupby((Continent/Name,Country/Name),
aggregate(Population with sum as TotalPopulation))
results in
{
"@context": "$metadata#Cities(Continent(Name),Country(Name),
TotalPopulation)",
"value": [
{ "Continent": { "Name": "Asia" }, "Country": { "Name": "China" },
"TotalPopulation@type": "Int32", "TotalPopulation": 1412000000 },
{ "Continent": { "Name": "Asia" }, "Country": { "Name": "India" },
"TotalPopulation@type": "Int32", "TotalPopulation": 1408000000 },
…
]
}
Example 123: all countries with megacities and their continents
GET /service/Cities?$apply=filter(Population ge 10000000)
/groupby((Continent/Name,Country/Name),
aggregate(Population with sum as TotalPopulation))
Example 124: all countries with tens of millions of city dwellers and the continents only for these countries
GET /service/Cities?$apply=groupby((Continent/Name,Country/Name),
aggregate(Population with sum as CountryPopulation))
/filter(CountryPopulation ge 10000000)
/concat(identity,
groupby((Continent/Name),
aggregate(CountryPopulation with sum
as TotalPopulation)))
or
GET /service/Cities?$apply=groupby((Continent/Name,Country/Name),
aggregate(Population with sum as CountryPopulation))
/filter(CountryPopulation ge 10000000)
/groupby((rollup(Continent/Name,Country/Name)),
aggregate(CountryPopulation with sum
as TotalPopulation))
Example 125: all countries with tens of millions of city dwellers and all continents with cities independent of their size
GET /service/Cities?$apply=groupby((Continent/Name,Country/Name),
aggregate(Population with sum as CountryPopulation))
/concat(filter(CountryPopulation ge 10000000),
groupby((Continent/Name),
aggregate(CountryPopulation with sum
as TotalPopulation)))
Example 126: assuming the data model includes a sales order entity set with related sets for order items and customers, the base set as well as the related items can be filtered before aggregation
GET /service/SalesOrders?$apply=filter(Status eq 'incomplete')
/addnested(Items,filter(not Shipped) as FilteredItems)
/groupby((Customer/Country),
aggregate(FilteredItems/Amount with sum as ItemAmount))
Example 127: assuming that Amount
is a custom aggregate in addition to the property, determine the total for countries with an Amount
greater than 1000
GET /service/SalesOrders?$apply=
groupby((Customer/Country),aggregate(Amount))
/filter(Amount gt 1000)
/aggregate(Amount)
Example 128: The output set of the concat
transformation contains Sales
entities multiple times with conflicting related AugmentedProduct
entities that cannot be aggregated by the second transformation.
GET /service/Sales?$apply=
concat(addnested(Product,compute(0.1 as Discount) as AugmentedProduct),
addnested(Product,compute(0.2 as Discount) as AugmentedProduct))
/aggregate(AugmentedProduct/Discount with max as MaxDiscount)
results in an error.
Example 129: The nest
transformation can be used inside groupby
to produce one or more collection-valued properties per group.
GET /service/Sales?$apply=groupby((Product/Category/ID),
nest(groupby((Customer/ID)) as Customers))
results in
{
"@context": "$metadata#Sales(Product(Category(ID)),Customers())",
"value": [
{ "Product": { "Category": { "ID": "PG1" } },
"Customers@context": "#Sales(Customer(ID))",
"Customers": [ { "Customer": { "ID": "C1" } },
{ "Customer": { "ID": "C2" } },
{ "Customer": { "ID": "C3" } } ] },
{ "Product": { "Category": { "ID": "PG2" } },
"Customers@context": "#Sales(Customer(ID))",
"Customers": [ { "Customer": { "ID": "C1" } },
{ "Customer": { "ID": "C2" } },
{ "Customer": { "ID": "C3" } } ] }
]
}
Conforming services MUST follow all rules of this specification for the set transformations and aggregation methods they support. They MUST implement all set transformations and aggregation methods they advertise via the annotation ApplySupported
.
Conforming clients MUST be prepared to consume a model that uses any or all of the constructs defined in this specification, including custom aggregation methods defined by the service, and MUST ignore any constructs not defined in this version of the specification.
This appendix contains the normative references that are used in this document.
While any hyperlinks included in this appendix were valid at the time of publication, OASIS cannot guarantee their long-term validity.
The following documents are referenced in such a way that some or all of their content constitutes requirements of this document.
ABNF components: OData ABNF Construction Rules Version 4.01 and OData ABNF Test Cases.
See link in “Related work” section on cover page.
OData Aggregation ABNF Construction Rules Version 4.0.
See link in “Additional artifacts” section on cover page.
OData Common Schema Definition Language (CSDL) JSON Representation Version 4.01.
See link in “Related work” section on cover page.
OData Common Schema Definition Language (CSDL) XML Representation Version 4.01.
See link in “Related work” section on cover page.
OData JSON Format Version 4.01.
See link in “Related work” section on cover page.
OData Version 4.01. Part 1: Protocol.
See link in “Related work” section on cover page.
OData Version 4.01. Part 2: URL Conventions.
See link in “Related work” section on cover page.
OData Aggregation Vocabulary.
See link in “Additional artifacts” section on cover page.
OData Core Vocabulary.
See link in “Related work” section on cover page.
Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels”, BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997
https://www.rfc-editor.org/info/rfc2119.
Leiba, B., “Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words”, BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017
https://www.rfc-editor.org/info/rfc8174.
The contributions of the OASIS OData Technical Committee members, enumerated in OData-Protocol, section C.2, are gratefully acknowledged.
OData TC Members:
First Name | Last Name | Company |
---|---|---|
George | Ericson | Dell |
Hubert | Heijkers | IBM |
Ling | Jin | IBM |
Stefan | Hagen | Individual |
Michael | Pizzo | Microsoft |
Christof | Sprenger | Microsoft |
Ralf | Handl | SAP SE |
Gerald | Krause | SAP SE |
Heiko | Theißen | SAP SE |
Martin | Zurmuehl | SAP SE |
Revision | Date | Editor | Changes Made |
---|---|---|---|
Working Draft 01 | 2012-11-12 | Ralf Handl | Translated contribution into OASIS format |
Committee Specification Draft 01 | 2013-07-25 | Ralf Handl Hubert Heijkers Gerald Krause Michael Pizzo Martin Zurmuehl |
Switched to pipe-and-filter-style query language based on composable set transformations Fleshed out examples and addressed numerous editorial and technical issues processed through the TC Added Conformance section |
Committee Specification Draft 02 | 2014-01-09 | Ralf Handl Hubert Heijkers Gerald Krause Michael Pizzo Martin Zurmuehl |
Dynamic properties used all aggregated values either via aliases or via custom aggregates Refactored annotations |
Committee Specification Draft 03 | 2015-07-16 | Ralf Handl Hubert Heijkers Gerald Krause Michael Pizzo Martin Zurmuehl |
Added compute transformation Minor clean-up |
Committee Specification Draft 04 | 2023-07-05 | Ralf Handl Hubert Heijkers Gerald Krause Michael Pizzo Heiko Theißen |
Added section about fundamentals of input and output sets Algorithmic descriptions of transformations Added join and outerjoin transformations, replaced expand by addnested Added transformations orderby, skip, top, nest Added transformations for recursive hierarchies, updated related filter functions Added functions evaluable on a collection, introduced keyword $these Merged section 4 “Representation of Aggregated Instances” into section 3 Remove actions and functions (except set transformations) on aggregated entities, adapted section “Actions and Functions on Aggregated Entities” |
Committee Specification 03 | 2023-09-19 | Ralf Handl Gerald Krause Heiko Theißen |
Non-material changes from public review feedback |
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