Window Transform

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.

Example

Explore the effects of using different frames and windowed aggregation functions.

Transform Parameters

Property Type Description
sort Compare A comparator 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).
groupby Field[ ] The data fields to by which to partition data objects into separate windows. If not specified, a single group containing all data objects will be used.
ops String[ ] The window or aggregation operations to apply within a window, including rank, lead, sum, average or count. See the window operation reference for more.
fields Field[ ] The data fields for which to compute aggregate or window functions. This array should align with the ops, as, and params arrays. Field values can be null for operations that do not operate over a specific data field, including count, rank, and dense_rank.
params Array Parameter values for window functions. This array should align with the ops array. Parameter values can be null for operations that do not accept a parameter (such as aggregation operations).
as String[ ] The output field names to use for each operation in ops. If not specified, names will be automatically generated based on the operation and field names (e.g., rank, sum_field, average_field).
frame 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.
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.

Window Operation Reference

The valid operations include all valid 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.

Usage

For the following input data:

[
  {"key":0, "value":1},
  {"key":1, "value":3},
  {"key":2, "value":2},
  {"key":2, "value":4},
  {"key":3, "value":3}
]

The window transform

{
  "type": "window",
  "sort": {"field": "key", "order": "ascending"},
  "ops": ["rank", "dense_rank", "sum", "mean"],
  "fields": [null, null, "value", "value"],
  "as": ["rank", "drank", "sum", "mean"]
}

produces as output the augmented input stream:

[
  {"key":0, "value":1, "rank":1, "drank":1, "sum":1, "mean":1},
  {"key":1, "value":3, "rank":2, "drank":2, "sum":4, "mean":2},
  {"key":2, "value":2, "rank":3, "drank":3, "sum":10, "mean":2.5},
  {"key":2, "value":4, "rank":3, "drank":3, "sum":10, "mean":2.5},
  {"key":3, "value":3, "rank":5, "drank":4, "sum":13, "mean":2.6}
]