NOTE: Transforms are a part of the underlying language that is not directly accessible to users. This content is maintained for reference purposes only.
windowtransform enables you to perform summations and calculations based on a rolling window of data relative to the current row.
For example, you can compute the rolling average for a specified column for the current row value and the three preceding rows. This transform is particularly useful for processing time or otherwise sequential data.
You can apply one or more functions to your
window transform step.
- For more information on window functions, see Window Functions.
- You can also use the aggregation functions with this transform. See Aggregate Functions.
NOTE: Be careful applying this transform across a large number of rows. In some cases, the application can run out of memory generating the results, and your results can fail.
Output: Generates a new column called,
window, which contains the result of the
ROLLINGAVERAGE function applied from the current row in the
myValues column across the
3 rows forward, ordered by
MyDate and grouped by
|window||Y||transform||Name of the transform|
|value||Y||string||Expression that evaluates to the window function call and its parameters|
|order||Y||string||Column or column names by which to sort the dataset before the |
|group||N||string||Column name or names containing the values by which to group for calculation|
For more information on syntax standards, see Language Documentation Syntax Notes.
window transform, the
value parameter contains the function call or calls, which define the set of rows to which the function is applied.
You can specify multiple window functions for the value. Each function reference must be separated by a comma. The transform generates a new column for each window function.
This transform uses a special set of functions. For more information on the available functions, see Window Functions.
window transform, this parameter specifies the column on which to sort the dataset before applying the specified function. For combination sort keys, you can add multiple comma-separated columns.
NOTE: The order parameter must unambiguously specify an ordering for the data, or the generated results may vary between job executions.
NOTE: If you are applying a window function, it requires a primary key to identify rows in the output. Otherwise, results can be ambiguous. For more information on defining a primary key, see Window Functions.
NOTE: If it is present, the dataset is first grouped by the
group value before it is ordered by the values in the
order column does not need to be sorted before the
window transform is executed on it.
Tip: To sort in reverse order, prepend the column name with a dash (
|Yes||String (column name)|
window transform, this parameter specifies the column whose values are used to group the dataset prior to applying the specified function. For combination grouping, you can specify multiple comma-separated column names.
groupparameter can result in non-deterministic re-ordering in the data grid. However, if you're running your job on the Spark running environment, you should apply the
groupparameter, or your job may run out of memory and fail. To avoid this issue and to enforce row ordering, use the
sorttransform. For more information, see Sort Transform.
|No||String (column name)|
See the individual functions for examples. See Window Functions.
This page has no comments.