- URL:https://<layerID-url>/queryAnalytic
- Version Introduced:11.1
Description
The queryAnalytic operation exposes the standard SQL windows functions that computes aggregate and ranking values based on a group of rows called window partitions. The windows function is applied to rows after they have been partitioned and ordered. This operation defines a window or user-specified set of rows within a query result set that can be used to compute aggregated values such as moving averages, cumulative aggregates, or running totals.
Request parameters
| Parameter | Details |
|---|---|
| where (Optional) | A WHERE clause for the query filter that defines the source rows. For more information on WHERE clauses, see the Where and AnalyticWhere section below. Example |
| analyticWhere | A WHERE clause for the query filter that applies to the result set of applying the source where clause and all other parameters. For more information on WHERE clauses, see the Where and AnalyticWhere section below. Example |
| geometry (Optional) | The geometry that will be applied as the spatial filter. The structure of the geometry is the same as the structure of the JSON geometry objects returned by the ArcGIS REST API. In addition to the JSON structures, you can specify the geometry of envelopes and points with a simple comma-separated syntax. For more information on the JSON structure of geometry objects, see the Geometry objects reference. Syntax examples Examples |
| geometryType | The type of geometry specified by the geometry parameter. The geometry type can be an envelope, a point, a line, or a polygon. The default geometry type is an envelope. Values: esriGeometryPoint | esriGeometryMultipoint | esriGeometryPolyline | esriGeometryPolygon | esriGeometryEnvelope |
| inSR | The spatial reference of the input geometry. The spatial reference can be specified as either a well-known ID or as a spatial reference JSON object. If the inSR is not specified, the geometry is assumed to be in the spatial reference of the map. |
| outSR | The spatial reference of the returned geometry. The spatial reference can be specified as either a well-known ID or as a spatial reference JSON object. If outSR is not specified, the geometry is returned in the spatial reference of the map. |
| spatialRel | The spatial relationship to be applied to the input geometry while performing the query. The supported spatial relationships include intersects, contains, envelop intersects, within, and so on. The default spatial relationship is intersects (esriSpatialRelIntersects). Values: esriSpatialRelIntersects | esriSpatialRelContains | esriSpatialRelCrosses | esriSpatialRelEnvelopIntersects | esriSpatialRelIndexIntersects | esriSpatialRelOverlaps | esriSpatialRelTouches | esriSpatialRelWithin |
| outFields | The list of fields to be included in the returned result set. This list is a comma delimited list of field names. You can also specify the wildcard "*" as the value of this parameter. In this case, the query results include all the field values. Example |
| returnGeometry | If true, the result includes the geometry associated with each feature returned. The default value is true. Values: true | false |
| outAnalytics | The definitions for one or more field-based or expression analytics to be computed. The syntax for this parameter is an array of analytic definitions. An analytic definition specifies the type of analytics, the field or expression on which it is to be computed, and the resulting output field name. For more information on the syntax properties for outAnalytics, see the outAnalytics overview section below. Starting at 10.9.1, the outAnalytics parameter also supports a syntax for computing the linear regression. The linear regression describes the best fit line when charting data values. To get the linear regression, the outAnalytics parameter takes on "analyticType": "LinReg2", and a list of two fields in the onAnalyticField parameter. The first of the two fields represents values along the x-axis while the second field represents values along the y-axis. You can't combine "analyticType": "LinRegR2" with other analyticTypes in the same call. Unlike other analyticTypes, results from linear regression includes three values which describe how to plot the best fit line. These include:
Since linear regression ("analyticType": "LinRegR2") always returns a result with three values, some parameters are not applicable, such as outSR or analyticWhere. Note:For services running on SAP HANA, the windowFrametype property's "RANGE" value is not supported. Syntax Example Linear regression example Linear regression attributes example |
| orderByFields | One or more field names on which the features/records need to be ordered. Use ASC or DESC for ascending or descending, respectively, following every field to control the ordering. orderByFields defaults to ASC (ascending order) if <ORDER> is unspecified. orderByFields is supported on only those layers/tables that indicate supportsAdvancedQueries is true. Note:If supportsOrderByOnlyLayerFields is true, only fields from the layer's fields array can be used with the orderByFields parameter. For example, the outStatisticfieldName from outStatistics can't be used if supportsOrderByOnlyOnLayerFields is true. Syntax Example |
| resultType | The resultType parameter can be used to control the number of features returned by the query operation. The title value is used when the client is using a virtual tiling scheme when querying features, which works similarly to tiles in a tiled map service layer. The standard value is used with a nontiled query where the client will send only one query for the full extent. Support for this parameter is advertised on the layer metadata in the supportsQueryWithResultType property. For additional information on the resultType parameter and how it interacts with max record counts, see the Result type and max record count. Values: none | standard | tile |
| cacheHint | Indicates if the the query results should be cached. This parameter is only supported if the layer specifies supportsQueryWithCacheHint as true. The default value for cacheHint is false. Values: true | false |
| resultRecordCount | This option can be used for fetching query results up to the resultRecordCount specified. When resultOffset is specified, but this parameter is not, the map service defaults to its maxRecordCount. The maximum value for this parameter is the value of the layer's maxRecordCount property. The minimum value entered for this parameter cannot be below 1. This parameter only applies if supportsPagination is true. |
| quantizationParameters | A JSON object used to project the geometry onto a virtual grid, likely representing pixels on the screen. The properties of the JSON object include extent, mode, originPosition, and tolerance. For more information, see the quantizationParameters JSON properties section below. Note:This parameter is only supported if the layer has the supportsCoordinatesQuantization property as true. |
| sqlFormat | The sqlFormat parameter can be either standard, using the SQL-92 standard, or it can be specified as native, using the native SQL of the underlying data store. Values: none | standard | native |
| dataFormat | The format of the changes returned in the response. Values: json | pbf |
| async | Specifies whether the operation will run synchronously (false) or asynchronously (true). Values: true | false |
| f | The response format. The default response format is html. Values: html | json | pjson | pbf |
Where and analyticWhere clauses
The source rows and output result can be controlled by using where and analyticWhere clauses. Where clauses in the queryAnalytic API are used to filter the source data used when computing the analytic type results. The analyticWhere clause can be used to filter the returned results from queryAnalytic. Example use cases include filtering to remove duplicate data or to return top rows with Row_Number. Any fields including aliases in the output including outputAnalyticField can be used in the analyticWhere clause.
quantizationParameters JSON properties
Listed below are the properties included in the quantizationParameters JSON object.
| Property | Description |
|---|---|
| extent | An extent defining the quantization grid bounds. Its spatialReference matches the input geometry spatial reference if one is specified for the query. Otherwise, the extent will be in the layer's spatial reference. |
| mode | The view value specifies that geometry coordinates should be optimized for viewing and displaying data. The edit value specifies that full-resolution geometries should be returned, which can support lossless editing. Note:The edit value can only be used when the supportsQuantizationEditMode layer property is true. Values: view | edit |
| originPosition | Integer coordinates will be returned relative to the origin position defined by this property value. The default value is upperLeft. Values: upperLeft | lowerLeft |
| tolerance | The tolerance is the size of one pixel in the outSpatialReference units. This number is used to convert the coordinates to integers by building a grid with the resolution matching the tolerance. Each coordinate is then snapped to one pixel on the grid. Consecutive coordinates snapped to the same pixel are removed to reduce the overall response size. The units of tolerance are defined by outSpatialReference. If the outSpatialReference is not specified, tolerance is assumed to be in the unit of the spatial reference of the layer. If mode is set to edit, the tolerance is always set to the full-resolution tolerance of the spatial reference. |
outAnalytics overview
The QueryAnalytic operation defines a window (a user-specified set of rows) within a query result set to compute aggregated values such as moving averages, cumulative aggregates, and running totals. The outAnalytics parameter that's passed through when calling the operation provides the definitions for one or more field-based or expression analytics that will be computed. This information is passed through as an array of analytic definitions that specifies the type of analytics, the field or expression on which it is to be computed, and the resulting output field name. The following sections break down the syntax of outAnalytics and provides examples of how it can be customized to gauge revenue growth, running totals, and filter out duplicate data.
Syntax
[
{
"analyticType": "<COUNT | SUM | MIN | MAX | AVG | STDDEV | VAR | FIRST_VALUE, LAST_VALUE, LAG, LEAD, PERCENTILE_CONT,PERCENTILE_DISC, PERCENT_RANK, RANK, NTILE, DENSE_RANK, EXPRESSION>",
"onAnalyticField": "Field1",
"outAnalyticFieldName": "Out_Field_Name1",
"analyticParameters”: {
"orderBy": "<orderBy expression",
"value": <double value>, // percentile value
"partitionBy": "<field name or expression>",
"offset": <integer>, // used by LAG/LEAD
"windowsFrame": {
"type": "ROWS" | "RANGE",
"extent": {
"extentType": "PRECEDING" | "BOUNDARY",
"PRECEDING": {
"type": <"UNBOUNDED"|"NUMERIC_CONSTANT"|"CURRENT_ROW">
"value": <numeric constant value>
},
"BOUNDARY": {
"start": "UNBOUNDED_PRECEDING", "NUMERIC_PRECEDING", "CURRENT_ROW",
"startValue": <numeric constant value>,
"end": <"UNBOUNDED_FOLLOWING"|"NUMERIC_FOLLOWING"|"CURRENT_ROW">,
"endValue": <numeric constant value>
}
}
}
}
}
]The following sections describe, in more detail, aspects of the outAnalytics properties listed above:
- analyticType: SQL windows functions
- partitionBy: Partitioning
- windowsFrame: Understanding winodwsFrame
- extent: windowsFrame extent
For example scenarios for outAnalytics, see the outAnalytics example section below.
SQL windows function
A windows function performs a calculation across a set of rows (SQL partition or window) that are related to the current row. Unlike regular aggregate functions, use of a windows function does not return a single output row. The rows retain their separate identities with each calculation appended as a new field value. As well, the windows function can access more than just the current rows of the query result.
QueryAnalytic currently supports the following windows functions:
- Aggregate functions
- Analytic functions
- Ranking functions
The values for these functions are passed through the analyticType property for outAnalytics.
Aggregate functions
Aggregate functions are deterministic and perform calculations on a set of values to returns a single value. Aggregate functions are used in the select list with an optional HAVING clause, though a GROUP BY clause can also be used to calculate the aggregation on categories of rows. Supported aggregate functions are:
- MIN
- MAX
- SUM
- COUNT
- AVG
- STDDEV
- VAR
Syntax
[
{
"analyticType": "MAX",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "Max_Value",
"analyticParameters": {
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"MAX","onAnalyticField":"POP1990","analyticParameters"{"partitionBy":"state_name"},"outAnalyticFieldName":"Max_Value"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=Analytic functions
Analytic functions compute aggregate values based on a group of rows or window partitions. Unlike aggregate functions, analytic functions can return single or multiple rows for each group. Supported analytic functions are:
- CUME_DIST
- FIRST_VALUE
- LAST_VALUE
- LEAD
- LAG
- PERCENTILE_DISC
- PERCENTILE_CONT
- PERCENT_RANK
CUME_DIST
The CUME_DIST function calculates the cumulative distribution of a value in a group of values (defined with partitionBy clause). CUME_DIST computes the relative position of a specified value in a group of values. For example, the cumulative distribution for a given row in group is equal to the number of rows with values lower than or equal to the value of r, divided by the total number of rows in groups. The range of values returned by CUME_DIST is greater than 0 and less than or equal to 1.
Syntax
[
{
"analyticType": "CUME_DIST",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "CumDistance",
"analyticParameters": {
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"CUME_DIST","onAnalyticField":"POP1990","outAnalyticFieldName":"CumDistance","analyticParameters":{"partitionBy":"state_name"}}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=jsonFIRST_VALUE
FIRST_VALUE function returns the first value in an ordered set of values.
Syntax
[
{
"analyticType": "FIRST_VALUE",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "FirstValue",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"FIRST_VALUE","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name"},"outAnalyticFieldName":"FirstValue"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=jsonLAST_VALUE
The LAST_VALUE function returns the last value in an ordered set of values defined by the partitionBy clause.
[
{
"analyticType": "LAST_VALUE",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "LastValue",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"LAST_VALUE","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","paritionBy":"state_name"},"outAnalyticFieldName":"LastValue"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=LEAD
The LEAD function accesses data from a subsequent row in the same result set without the use of a self-join. LEAD provides access to a row at a given physical offset that follows the current row. The offset value is defined in the analyticParameters JSON object.
Syntax
[
{
"analyticType": "LEAD",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "Lead_Value",
"analyticParameters": {
"orderBy": "POP1990 desc",
"partitionBy": "state_name",
"offset": 2
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"LEAD","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name","offset":2},"outAnalyticFieldName":"LEAD_Value"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=LAG
The LAG function accesses data from a previous row in the same result set without the use of a self-join. LAG provides access to a row at a given physical offset that comes before the current row. The offset value is defined in the analyticParameters JSON object. Note the first row in each partition group returns null LAG values for the first row in the group or for previous rows that are outside the range of the group.
Syntax
[
{
"analyticType": "LAG",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "Lag_Value",
"analyticParameters": {
"orderBy": "POP1990 desc",
"partitionBy": "state_name",
"offset": 2
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"LAG","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name","offset":2},"outAnalyticFieldName":"LAG_VALUE"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=PERCENTILE_CONT
The PERCENTILE_CONT analytic function calculates a percentile based on a continuous distribution of the column value, similar to finding median with percentile value. If the number of elements is even, then PERCENTILE_CONT will be calculated by adding both values from the middle and dividing by two. If the number of elements is odd, PERCENTILE_CONT will be calculated by selecting the digits from the middle.
Note:
If the value is not in the list, then the function interpolates to calculate the percentile based on the specified percentile value.
Syntax
[
{
"analyticType": "PERCENTILE_CONT",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "PERCENTILE_CVALUE",
"analyticParameters": {
"orderBy": "POP1990 desc",
"partitionBy": "state_name",
"value": 0.50
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"PERCENTILE_CONT","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name","value":0.5},"outAnalyticFieldName":"PERCENTILE_CVALUE"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=PERCENTILE_DISC
The PERCENTILE_DISC analytic function computes a specific percentile for sorted values in an entire result or within distinct partitions defined by the partition by clause. For a given percentile value P, PERCENTILE_DISC sorts the values of the expression in the ORDER BY clause and returns the value with the smallest value that is greater than or equal to P.
Syntax
[
{
"analyticType": "PERCENTILE_DISC",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "PERCENTILE_DVALUE",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name",
"value": 0.50
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"PERCENTILE_DISC","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"POP1990","value":0.50},"outAnalyticFieldName":"PERCENTILE_DVALUE"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=PERCENT_RANK
The PERCENT_RANK analytic function calculates the relative rank of a row within a group of rows defined by the partition by clause. Use PERCENT_RANK to evaluate the relative standing of a value within a query result or partition.
Syntax
[
{
"analyticType": "PERCENT_RANK",
"onAnalyticField": "POP1990",
"outAnalyticFieldName": "Percent_Rank",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"PERCENT_RANK","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name"},"outAnalyticFieldName":"Percent_Rank"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=Ranking functions
Ranking functions return a ranking value for each row in a partition. Depending on the function that is used, some rows might receive the same value as other rows. The following shows the difference between different ranking functions (based on the value column) within a single partition:
| Id | Value | RANK | DENSE_RANK | Row_Number |
|---|---|---|---|---|
1 | 1 | 1 | 1 | 1 |
2 | 2 | 2 | 2 | 2 |
3 | 2 | 2 | 2 | 3 |
4 | 5 | 4 | 3 | 4 |
5 | 8 | 5 | 4 | 5 |
Supported ranking functions are:
- RANK
- NTILE
- DENSE_RANK
- ROW_NUMBER
RANK
Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. RANK returns the ranking within the ordered partition. Ties are assigned the same rank, with the next ranking(s) skipped. For example, if there are 3 items at rank 2, the next rank listed would be ranked 5.
Syntax
[
{
"analyticType": "RANK",
"outAnalyticFieldName": "Rank_Value",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"RANK","onAnalyticField":"POP1990","outAnalyticFieldName":"Rank_Value","analyticParameters": {"orderBy":"POP1990","partitionBy":"state_name"}}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=DENSE_RANK
This function returns the rank of each row within a result set partition, with no gaps in the ranking values. The rank of a specific row is one plus the number of distinct rank values that come before that specific row. DENSE_RANK returns the ranking within your ordered partition, but the ranks are consecutive. No ranks are skipped if there are ranks with multiple items.
Syntax
[
{
"analyticType": "DENSE_RANK",
"outAnalyticFieldName": "DenseRank_Value",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"DENSE_RANK","onAnalyticField":"POP1990","analyticParameters":{"partitionBy":"state_name","orderBy":"POP1990"},"outAnalyticFieldName":"DenseRank_Value"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=NTILE
This function distributes the rows in an ordered partition into a specified number of groups. The groups are numbered, starting at one. For each row, NTILE returns the number of the group to which the row belongs.
Syntax
[
{
"analyticType": "NTILE",
"outAnalyticFieldName": "DenseRank_Value",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name",
"nTileExpression": 4
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/arcgis/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"NTILE","analyticParameters":{"partitionBy":"state_name","orderBy":"POP1990","nTileExpression":4},"outAnalyticFieldName":"NTile_Value"}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=ROW_NUMBER
The ROW_NUMBER function is used to provide consecutive numbering of the rows in the partitionBy clause. It will assign the value 1 for the first row and increase the number of the subsequent rows. The rows are ordered based on the orderBy parameter.
Syntax
[
{
"analyticType": "ROW_NUMBER",
"outAnalyticFieldName": "Row_Number",
"analyticParameters": {
"orderBy": "POP1990",
"partitionBy": "state_name"
}
}
]Example
https://services.arcgis.com/lidGgNLxw9LL0SbI/ArcGIS/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType":"ROW_NUMBER","outAnalyticFieldName":"Row_Number","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name"}}]&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=json&f=html&token=Partitioning (partitionBy)
Partitions are very powerful and have many potential uses, such as calculating the same metric over different groups of rows. You can also add partitionBy clause to your window specification to look at different groups of rows individually. The partitionBy clause divides the query result set into partitions and the SQL windows function is applied to each partition. The partitionBy clause normally refers to the column by which the result is partitioned, and can also be a value expression (column expression or function) that references any of the selected columns (not aliases).
Understanding windowsFrame
Every analytic type for QueryAnalytic supports the windowsFrame property. The windowsFrame property defines the rows around the current row within a partition, over which the analytic function or type is evaluated. This type of window frame allows both physical (ROWS) and logical (RANGE) window frame types.
Syntax
{
...
"windowsFrame": {
"type": "ROWS" | "RANGE",
"extent": {
"extentType": "PRECEDING" | "BOUNDARY",
//If extentType is PRECEDING
"PRECEDING": {
"type": < "UNBOUNDED"| "NUMERIC_CONSTANT"| "CURRENT_ROW">,
"value": <numeric constant value>
}
//If extentType is BOUNDARY
"BOUNDARY": {
"start": "UNBOUNDED_PRECEDING" | "NUMERIC_PRECEDING" | "CURRENT_ROW",
"startValue": <numeric constant value>,
"end": < "UNBOUNDED_FOLLOWING" | "NUMERIC_FOLLOWING" | "CURRENT_ROW",
"endValue": <numeric constant value>
}
}
}
}Default windowsFrame
If windowsFrame is not defined within the analyticParameter object, and if orderBy is defined within the partition, then the default windowsFrame is used. The default frame type is RANGE (logical) and the window for each current row is defined by all preceding rows. The order in each window frame is defined by the orderBy clause. The default windowsFrame configuration is shown in the code example below.
{
…
"windowsFrame": {
"type": "RANGE",
"extent": {
"extentType": "BOUNDARY",
"BOUNDARY": {
"start": "UNBOUNDED_PRECEDING",
"end": "CURRENT_ROW"
}
}
}
…
}windowsFrame Types
ROWS
A ROWS based windowsFrame type computes the window frame based on physical offsets from the current row. For example, the window frame below defines a window frame of size three (at most) around the current window.
{
…
"windowsFrame": {
"type": "ROWS",
"extent": {
"extentType": "BOUNDARY",
"BOUNDARY": {
"startValue": 3,
"endValue": 3
}
}
}
…
}RANGE
Note:
For services running on SAP HANA, the windowFrametype property's "RANGE" value is not supported.
A RANGE based windowsFrame type computes the window frame based on a logical range of rows around the current row based on the row's orderBy key value. The provided range value is added to or subtracted from the key value to define a starting or ending range boundary for the window frame.
outAnalytic examples
The SQL windows functions can be used for business analysis, such as revenue growth, running total, filter out duplicate data, or return top rows.
Revenue growth
Revenue growth in a month m1, compared to the previous month (m0) is calculated as:
100*(m1-m0)/m0The LAG SQL windows function can be used to return the previous month sales and return it as a new column in addition to the sales for each month. The expression above can be computed and returned as a new column.
Example
[
{
"analyticType": "LAG",
"onAnalyticField": "MonthlySales",
"outAnalyticFieldName": "PreviousMonthlySales",
"analyticParameters": {
"partitionBy": "ProductCatagory",
"orderBy": "Month"
}
}
]Running total
SQL windows functions can calculate running totals over some period of time. This is the sum of the current element and all previous elements. For example, the following demonstrates how the running total monthly revenue would look in the dataset outlined in the table below.
| Id | CustomerID | Month | Revenue | Running_Total |
|---|---|---|---|---|
1 | A | 2018-01-01 | 100 | 100 |
2 | A | 2018-02-01 | 1200 | 1300 |
3 | A | 2018-03-01 | 1700 | 3000 |
[
{
"analyticType": "SUM",
"onAnalyticField": "Revenue",
"outAnalyticFieldName": "Running_Total",
"analyticParameters": {
"orderBy": "Month",
"partitionBy": "CustomerId",
"windowsFrame": {
"type": "RANGE",
"extent": {
"extentType": "BOUNDARY",
"BOUNDARY": {
"start": "UNBOUNDED_PRECEDING",
"end": "CURRENT_ROW"
}
}
}
}
}
]Duplicate data or top rows
Another common use is to filter out duplicate data or to discover duplicated data as defined by the partitionBy clause.
Note:
In the following example, there is no need to define the onAnalyticFieldName property. The example below demonstrates how to specify
analyticWhere = (Row_Number = 1)| Id | CutomerId | Month | Revenue | Row_Number |
|---|---|---|---|---|
1 | A | 2018-01-01 | 100 | 1 |
2 | A | 2018-02-01 | 1200 | 2 |
3 | B | 2018-03-01 | 1700 | 1 |
[
{
"analyticType": "ROW_NUMBER",
"outAnalyticFieldName": "Row_Number",
"analyticParameters": {
"orderBy": "Month",
"partitionBy": "CustomerId",
"windowsFrame": {
"type": "RANGE",
"extent": {
"extentType": "BOUNDARY",
"BOUNDARY": {
"start": "UNBOUNDED_PRECEDING",
"end": "CURRENT_ROW"
}
}
}
}
}
]
+ analyticWhere = (Row_Number = 1)Top rows in groups (TopFilterQuery API)
SQL windows functions can also return top rows within a group as defined by the partitionBy clause.
Note:
In the following example, there is no need to define the onAnalyticFieldName property. The example below demonstrates how to specify
analyticWhere = (Row_Number <= {topRowCount})| Id | CustomerId | Month | Revenue | Row_Number |
|---|---|---|---|---|
1 | A | 2018-01-01 | 100 | 1 |
2 | A | 2018-02-01 | 1200 | 2 |
3 | B | 2018-03-01 | 1700 | 1 |
[
{
"analyticType": "ROW_NUMBER",
"outAnalyticFieldName": "Row_Number",
"analyticParameters": {
"orderBy": "Month",
"partitionBy": "CustomerId",
"windowsFrame": {
"type": "RANGE",
"extent": {
"extentType": "BOUNDARY",
"BOUNDARY": {
"start": "UNBOUNDED_PRECEDING",
"end": "CURRENT_ROW"
}
}
}
}
}
]
+ analyticWhere = (Row_Number <= 2) // Top 2 rows for each customerExample usage
The following is a sample request URL for the queryAnaltic operation:
https://orgServices.arcgis.com/ligGgNLxw9LL0SbI/ArcGIS/rest/services/counties/MapServer/0/queryAnalytic?where=1=1&analyticWhere=&outAnalytics=[{"analyticType": "FIRST_VALUE","onAnalyticField":"POP1990","analyticParameters":{"orderBy":"POP1990","partitionBy":"state_name"},"outAnalyticFieldName":"FirstValue"}]geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&returnGeometry=true&resultType=none&outSR=&outFields=*&orderByFields=&cacheHint=false&quantizationParameters=&resultOffset=&resultRecordCount=&async=false&sqlFormat=none&dataFormat=jsonJSON Response syntax
{
"objectIdFieldName": "FID",
"uniqueIdField": {
"name": "FID",
"isSystemMaintained": true
},
"globalIdFieldName": "GlobalID",
"geometryProperties": {
"shapeAreaFieldName": "Shape__Area",
"shapeLengthFieldName": "Shape__Length",
"units": "esriMeters"
},
"geometryType": "esriGeometryPolygon",
"spatialReference": {
"wkid": 102100,
"latestWkid": 3857
},
"fields": [
{
"name": "FID",
"type": "esriFieldTypeOID",
"alias": "FID",
"sqlType": "sqlTypeInteger",
"domain": null,
"defaultValue": null
},
{
"name": "NAME",
"type": "esriFieldTypeString",
"alias": "NAME",
"sqlType": "sqlTypeNVarchar",
"length": 32,
"domain": null,
"defaultValue": null
},
{
"name": "STATE_NAME",
"type": "esriFieldTypeString",
"alias": "STATE_NAME",
"sqlType": "sqlTypeNVarchar",
"length": 25,
"domain": null,
"defaultValue": null
},
{
"name": "STATE_FIPS",
"type": "esriFieldTypeString",
"alias": "STATE_FIPS",
"sqlType": "sqlTypeNVarchar",
"length": 2,
"domain": null,
"defaultValue": null
},
{
"name": "CNTY_FIPS",
"type": "esriFieldTypeString",
"alias": "CNTY_FIPS",
"sqlType": "sqlTypeNVarchar",
"length": 3,
"domain": null,
"defaultValue": null
},
{
"name": "FIPS",
"type": "esriFieldTypeString",
"alias": "FIPS",
"sqlType": "sqlTypeNVarchar",
"length": 5,
"domain": null,
"defaultValue": null
},
{
"name": "AREA",
"type": "esriFieldTypeDouble",
"alias": "AREA",
"sqlType": "sqlTypeFloat",
"domain": null,
"defaultValue": null
},
{
"name": "POP1990",
"type": "esriFieldTypeInteger",
"alias": "POP1990",
"sqlType": "sqlTypeInteger",
"domain": null,
"defaultValue": null
},
{
"name": "POP1996",
"type": "esriFieldTypeInteger",
"alias": "POP1996",
"sqlType": "sqlTypeInteger",
"domain": null,
"defaultValue": null
},
{
"name": "jn_fips",
"type": "esriFieldTypeInteger",
"alias": "jn_fips",
"sqlType": "sqlTypeInteger",
"domain": null,
"defaultValue": null
},
{
"name": "two_words1",
"type": "esriFieldTypeSmallInteger",
"alias": "two_words1",
"sqlType": "sqlTypeSmallInt",
"domain": null,
"defaultValue": null
},
{
"name": "Shape__Area",
"type": "esriFieldTypeDouble",
"alias": "Shape__Area",
"sqlType": "sqlTypeFloat",
"domain": null,
"defaultValue": null
},
{
"name": "Shape__Length",
"type": "esriFieldTypeDouble",
"alias": "Shape__Length",
"sqlType": "sqlTypeFloat",
"domain": null,
"defaultValue": null
},
{
"name": "GlobalID",
"type": "esriFieldTypeGlobalID",
"alias": "GlobalID",
"sqlType": "sqlTypeOther",
"length": 38,
"domain": null
},
{
"name": "FirstValue",
"type": "esriFieldTypeDouble",
"alias": "FirstValue",
"sqlType": "sqlTypeFloat",
"domain": null,
"defaultValue": null
}
],
"features": [
{
"attributes": {
"FID": 442,
"NAME": "Greene",
"STATE_NAME": "Alabama",
"STATE_FIPS": "01",
"CNTY_FIPS": "063",
"FIPS": "01063",
"AREA": 659.9269,
"POP1990": 10153,
"POP1996": 10092,
"jn_fips": 1063,
"two_words1": 0,
"Shape__Area": 2507773320.62891,
"Shape__Length": 340036.603117597,
"GlobalID": "a3ea8ef3-9905-4cd2-8b1d-75fecb31a2d9",
"FirstValue": 10153
},
"geometry": {
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}
},
{
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"STATE_FIPS": "01",
"CNTY_FIPS": "037",
"FIPS": "01037",
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}
},
...
],
"exceededTransferLimit": true
}JSON Response example
{
"objectIdFieldName": "FID",
"uniqueIdField": {
"name": "FID",
"isSystemMaintained": true
},
"globalIdFieldName": "GlobalID",
"geometryProperties": {
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"shapeLengthFieldName": "Shape__Length",
"units": "esriMeters"
},
"geometryType": "esriGeometryPolygon",
"spatialReference": {
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"latestWkid": 3857
},
"fields": [
{
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"alias": "FID",
"sqlType": "sqlTypeInteger",
"domain": null,
"defaultValue": null
},
{
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"domain": null,
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},
{
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},
{
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},
{
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},
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"sqlType": "sqlTypeFloat",
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},
{
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"alias": "POP1990",
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},
{
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{
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{
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},
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},
{
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},
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