Window

Edit this page

The window transform performs calculations over sorted groups of data objects. These calculations including ranking, lead/lag analysis, and aggregates such as running sums and averages. Calculated values are written back to the input data stream.

Documentation Overview

Window Field Definition

// A View Specification
{
  ...
  "transform": [
    {
      // Window Transform
      "window": [{
          "op": ...,
          "field": ...,
          "param": ...
          "as": ...
      }],
      "sort": [
        {"field": ..., "order": ...}
      ],
      "ignorePeers": ...,
      "groupby": [
        "..."
      ],
      "frame": [...,...]
    }
     ...
  ],
  ...
}

Window Transform Definition

Property Type Description
window WindowFieldDef[]

Required. The definition of the fields in the window, and what calculations to use.

frame (Null | Number)[]

A frame specification as a two-element array indicating how the sliding window should proceed. The array entries should either be a number indicating the offset from the current data object, or null to indicate unbounded rows preceding or following the current data object. The default value is [null, 0], indicating that the sliding window includes the current object and all preceding objects. The value [-5, 5] indicates that the window should include five objects preceding and five objects following the current object. Finally, [null, null] indicates that the window frame should always include all data objects. The only operators affected are the aggregation operations and the first_value, last_value, and nth_value window operations. The other window operations are not affected by this.

Default value:: [null, 0] (includes the current object and all preceding objects)

ignorePeers Boolean

Indicates if the sliding window frame should ignore peer values. (Peer values are those considered identical by the sort criteria). The default is false, causing the window frame to expand to include all peer values. If set to true, the window frame will be defined by offset values only. This setting only affects those operations that depend on the window frame, namely aggregation operations and the first_value, last_value, and nth_value window operations.

Default value: false

groupby String[]

The data fields for partitioning the data objects into separate windows. If unspecified, all data points will be in a single group.

sort SortField[]

A sort field definition for sorting data objects within a window. If two data objects are considered equal by the comparator, they are considered “peer” values of equal rank. If sort is not specified, the order is undefined: data objects are processed in the order they are observed and none are considered peers (the ignorePeers parameter is ignored and treated as if set to true).

Window Transform Field Definition

Property Type Description
op String | String

Required. The window or aggregation operations to apply within a window, including rank, lead, sum, average or count. See the list of all supported operations here.

param Number

Parameter values for the window functions. Parameter values can be omitted for operations that do not accept a parameter.

See the list of all supported operations and their parameters here.

field String

The data field for which to compute the aggregate or window function. This can be omitted for window functions that do not operate over a field such as count, rank, dense_rank.

as String

Required. The output name for the window operation.

Sort Field Definition

Property Type Description
field String

Required. The name of the field to sort.

order String

Whether to sort the field in ascending or descending order.

Window Only Operation Reference

The valid operations include all aggregate operations plus the following window operations.

Operation Parameter Description
row_number None Assigns each data object a consecutive row number, starting from 1.
rank None Assigns a rank order value to each data object in a window, starting from 1. Peer values are assigned the same rank. Subsequent rank scores incorporate the number of prior values. For example, if the first two values tie for rank 1, the third value is assigned rank 3.
dense_rank None Assigns dense rank order values to each data object in a window, starting from 1. Peer values are assigned the same rank. Subsequent rank scores do not incorporate the number of prior values. For example, if the first two values tie for rank 1, the third value is assigned rank 2.
percent_rank None Assigns a percentage rank order value to each data object in a window. The percent is calculated as (rank - 1) / (group_size - 1).
cume_dist None Assigns a cumulative distribution value between 0 and 1 to each data object in a window.
ntile Number Assigns a quantile (e.g., percentile) value to each data object in a window. Accepts an integer parameter indicating the number of buckets to use (e.g., 100 for percentiles, 5 for quintiles).
lag Number Assigns a value from the data object that precedes the current object by a specified number of positions. If no such object exists, assigns null. Accepts an offset parameter (default 1) that indicates the number of positions. This operation must have a corresponding entry in the fields parameter array.
lead Number Assigns a value from the data object that follows the current object by a specified number of positions. If no such object exists, assigns null. Accepts an offset parameter (default 1) that indicates the number of positions. This operation must have a corresponding entry in the fields parameter array.
first_value None Assigns a value from the first data object in the current sliding window frame. This operation must have a corresponding entry in the fields parameter array.
last_value None Assigns a value from the last data object in the current sliding window frame. This operation must have a corresponding entry in the fields parameter array.
nth_value Number Assigns a value from the nth data object in the current sliding window frame. If no such object exists, assigns null. Requires a non-negative integer parameter that indicates the offset from the start of the window frame. This operation must have a corresponding entry in the fields parameter array.

Examples

Below are some common use cases for the window transform.

Cumulative Frequency Distribution

Here we use the window transform with frame: [null, 0] to accumulate count in a cumulative frequency distribution plot.

See also: layered histogram and cumulative histogram

Percent of Total

Here we use the window transform to derive the global sum so that we can calculate percentage.

Difference from Mean

One example is to show the “exemplar” movies from a movie collection. Here “exemplar” is defined by having a score of 2.5 points higher than the global average.

Another example is to show the “exemplar” movies based on the release year average. Here “exemplar” is defined by having a score 2.5 points higher than the annual average for its release year (instead of the global average).

Rather than filtering the above two examples we can also calculate a residual by deriving the mean using the window transform first.

Rank Chart

We can also use rank operator to calculate ranks over time.

Top K

Here we use window transform to derive the total number of students along with the rank of the current student to determine the top K students and display their score.

Cumulative Running Average

Here we use window transform to visualize how the average MPG for vehicles have changed over the years.