bag_unpack plugin

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

The bag_unpack plugin unpacks a single column of type dynamic, by treating each property bag top-level slot as a column. The plugin is invoked with the evaluate operator.

Syntax

T | evaluate bag_unpack( Column [, OutputColumnPrefix ] [, columnsConflict ] [, ignoredProperties ] ) [: OutputSchema]

Learn more about syntax conventions.

Parameters

Name	Type	Required	Description
T	string	✔️	The tabular input whose column Column is to be unpacked.
Column	dynamic	✔️	The column of T to unpack.
OutputColumnPrefix	string		A common prefix to add to all columns produced by the plugin.
columnsConflict	string		The direction for column conflict resolution. Valid values: error - Query produces an error (default) replace_source - Source column is replaced keep_source - Source column is kept
ignoredProperties	dynamic		An optional set of bag properties to be ignored. }
OutputSchema			Specify the column names and types for the bag_unpack plugin output. For syntax information, see Output schema syntax, and to understand the implications, see Performance considerations.

Output schema syntax

( ColumnName : ColumnType [, ...] )

Use a wildcard * as the first parameter to include all columns of the source table in the output, as follows:

( * , ColumnName : ColumnType [, ...] )

Performance considerations

Using the plugin without an OutputSchema can have severe performance implications in large datasets and should be avoided.

Providing an OutputSchema allows the query engine to optimize the query execution, as it can determine the output schema without needing to parse and analyze the input data. This is beneficial when the input data is large or complex. See the Examples with performance implications of using the plugin with and without a defined OutputSchema.

Returns

The bag_unpack plugin returns a table with as many records as its tabular input (T). The schema of the table is the same as the schema of its tabular input with the following modifications:

• The specified input column (Column) is removed.
• The name of each column corresponds to the name of each slot, optionally prefixed by OutputColumnPrefix.
• The type of each column is either the type of the slot, if all values of the same slot have the same type, or dynamic, if the values differ in type.
• The schema is extended with as many columns as there are distinct slots in the top-level property bag values of T.

Note

• If you don't specify the OutputSchema, the plugin output schema varies based on the input data values. Multiple executions of the plugin with different data inputs can produce different output schemas.
• If an OutputSchema is specified, the plugin returns only the columns defined in the Output schema syntax, unless a wildcard * is used.
• To return all columns of the input data, and the columns defined in the OutputSchema, use a wildcard * in the OutputSchema.

Tabular schema rules apply to the input data. In particular:

• An output column name can't be the same as an existing column in the tabular input T, unless it's the column to unpack (Column). Otherwise, the output includes two columns with the same name.
• All slot names, when prefixed by OutputColumnPrefix, must be valid entity names and follow the identifier naming rules.

The plugin ignores null values.

Examples

The examples in this section show how to use the syntax to help you get started.

Expand a bag:

Run the query

datatable(d:dynamic)
[
    dynamic({"Name": "John", "Age":20}),
    dynamic({"Name": "Dave", "Age":40}),
    dynamic({"Name": "Jasmine", "Age":30}),
]
| evaluate bag_unpack(d)

Output

Age	Name
20	John
40	Dave
30	Jasmine

Expand a bag and use the OutputColumnPrefix option to produce column names with a prefix:

Run the query

datatable(d:dynamic)
[
    dynamic({"Name": "John", "Age":20}),
    dynamic({"Name": "Dave", "Age":40}),
    dynamic({"Name": "Jasmine", "Age":30}),
]
| evaluate bag_unpack(d, 'Property_')

Output

Property_Age	Property_Name
20	John
40	Dave
30	Jasmine

Expand a bag and use the columnsConflict option to resolve a column conflict between the dynamic column and the existing column:

Run the query

datatable(Name:string, d:dynamic)
[
    'Old_name', dynamic({"Name": "John", "Age":20}),
    'Old_name', dynamic({"Name": "Dave", "Age":40}),
    'Old_name', dynamic({"Name": "Jasmine", "Age":30}),
]
| evaluate bag_unpack(d, columnsConflict='replace_source') // Use new name

Output

Age	Name
20	John
40	Dave
30	Jasmine

Run the query

datatable(Name:string, d:dynamic)
[
    'Old_name', dynamic({"Name": "John", "Age":20}),
    'Old_name', dynamic({"Name": "Dave", "Age":40}),
    'Old_name', dynamic({"Name": "Jasmine", "Age":30}),
]
| evaluate bag_unpack(d, columnsConflict='keep_source') // Keep old name

Output

Age	Name
20	Old_name
40	Old_name
30	Old_name

Expand a bag and use the ignoredProperties option to ignore 2 of the properties in the property bag:

Run the query

datatable(d:dynamic)
[
    dynamic({"Name": "John", "Age":20, "Address": "Address-1" }),
    dynamic({"Name": "Dave", "Age":40, "Address": "Address-2"}),
    dynamic({"Name": "Jasmine", "Age":30, "Address": "Address-3"}),
]
// Ignore 'Age' and 'Address' properties
| evaluate bag_unpack(d, ignoredProperties=dynamic(['Address', 'Age']))

Output

Name
John
Dave
Jasmine

Expand a bag and use the OutputSchema option:

Run the query

datatable(d:dynamic)
[
    dynamic({"Name": "John", "Age":20}),
    dynamic({"Name": "Dave", "Age":40}),
    dynamic({"Name": "Jasmine", "Age":30}),
]
| evaluate bag_unpack(d) : (Name:string, Age:long)

Output

Name	Age
John	20
Dave	40
Jasmine	30

Expand a bag with an OutputSchema and use the wildcard * option:

This query returns the original slot Description and the columns defined in the OutputSchema.

Run the query

datatable(d:dynamic, Description: string)
[
    dynamic({"Name": "John", "Age":20, "height":180}), "Student",
    dynamic({"Name": "Dave", "Age":40, "height":160}), "Teacher",
    dynamic({"Name": "Jasmine", "Age":30, "height":172}), "Student",
]
| evaluate bag_unpack(d) : (*, Name:string, Age:long)

Output

Description	Name	Age
Student	John	20
Teacher	Dave	40
Student	Jasmine	30

Examples with performance implications

Expand a bag with and without a defined OutputSchema to compare performance implications:

This example uses a publicly available table in the help cluster. In the ContosoSales database, there's a table called SalesDynamic. The table contains sales data and includes a dynamic column named Customer_Properties.

• Example with no output schema: The first query doesn't define an OutputSchema. The query takes 5.84 seconds of CPU and scans 36.39 MB of data.

  Run the query

  SalesDynamic
  | evaluate bag_unpack(Customer_Properties) 
  | summarize Sales=sum(SalesAmount) by Country, State
  

• Example with output schema: The second query does provide an OutputSchema. The query takes 0.45 seconds of CPU and scans 19.31 MB of data. The query doesn't have to analyze the input table, saving on processing time.

  Run the query

  SalesDynamic
  | evaluate bag_unpack(Customer_Properties) :  (*, Country:string, State:string, City:string)
  | summarize Sales=sum(SalesAmount) by Country, State
  

Output

The output is the same for both queries. The first 10 rows of the output are shown below.

Canada	British Columbia	56,101,083
United Kingdom	England	77,288,747
Australia	Victoria	31,242,423
Australia	Queensland	27,617,822
Australia	South Australia	8,530,537
Australia	New South Wales	54,765,786
Australia	Tasmania	3,704,648
Canada	Alberta	375,061
Canada	Ontario	38,282
United States	Washington	80,544,870
...	...	...

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