Power Query \Microsoft.Mashup.Document\Host\Document\Analysis\AddPartitionMetaDocumentAnalyzer.cs => BeginAnalyzeDocumentPartitions

Power Query navigate the documentation of #shared functions. [M code]

This example visualizes the complete set of functions exposed by Power Query’s `#shared` namespace, organized into a navigable table. It shows each function’s name, module, category, example code, description, and signature. The M code uses `Record.ToTable` and `Table.SelectRow` to filter functions, then leverages `Value.Metadata` to extract documentation fields like `Documentation.Category` and `Documentation.Examples`. It also parses module names via `Text.Split` and builds signatures dynamically. The result is a self-documenting inventory, enabling users to browse and understand the entire Power Query function library directly from the environment.

61% match

Extracting Power Query queries

This example shows how to extract Power Query query definitions from an embedded Power BI tabular model by reading the model’s in-memory connection string. The PowerShell script first locates the dynamic port number for the local Analysis Services instance by scanning workspace files for `msmdsrv.port.txt`. It then uses the `Invoke-ASCmd` cmdlet to query the `$SYSTEM.TMSCHEMA_DATA_SOURCES` schema, revealing the connection string. The script decodes the base64-encoded `Mashup` portion of that string, writes it to a binary ZIP archive, and uses `System.IO.Compression.ZipFile` to extract `Section1.m`, which contains the readable Power Query formulas. This approach exposes the model’s underlying data-transformation logic without any direct UI interaction.

60% match

[PowerQuery] Decompiling List.Range function

This example shows how to decompile the Power Query `List.Range` function into its underlying expression tree, revealing the nested structure of arguments and function bodies. It uses the `Value.ResourceExpression` API to access the function’s AST, then recursively traces and serializes the structure via `Json.FromValue`, with helper logic that converts values, types, lists, and records into a JSON-compatible format. A second expression, `DeepDecompile.pq`, directly navigates the resource expression’s `[Expression][Arguments]{0}[Function][Expression][Expression]` path to extract the innermost function body. The visualization renders the resulting JSON as a plain-text trace, highlighting the difference between high-level metadata lookup and low-level AST traversal.

60% match

Exposing #shared construct in Azure Analysis Services (change DatabaseID accordingly)

This example shows how to expose the contents of Power Query's `#shared` intrinsic constant inside an Azure Analysis Services tabular model, turning the built-in M function and value registry into a queryable table. The workflow uses three XMLA/TMSL scripts: first, an XMLA `Create` command injects a shared expression named `SharedExpression` that returns `#shared`; second, a TMSL `create` operation defines a table with `Name` and `Value` string columns, pulling data through an M partition expression that references the injected shared expression; finally, a TMSL `refresh` command processes the new table. The result is then viewed with a DAX `evaluate` statement against the `SharedExpressionTable`, demonstrating a round-trip from M metadata to a usable relational structure. The code relies on the XMLA protocol, TMSL JSON APIs, and the M expression engine, with no external visualization library involved.

Power Query attempt at decompiling a function call

This example shows a Power Query (M) custom function that decompiles a `List.Skip` call into its component parts—the list, the offset, and an optional count. It demonstrates how M’s standard library functions `List.Skip` and `List.FirstN` are composed to recreate the logic of a function call, with `skippedInput` holding the intermediate result after skipping a given number of elements. The code reveals the underlying structure of the operation by explicitly handling a null count fallback, which would otherwise be implicit in the original function. This visualization highlights the metaprogramming aspect of Power Query, where code itself is treated as data for inspection.

[Power Query] Closures, partial application and cascading parameters

This example shows how Power Query’s cascading parameters control a dynamic data-filtering workflow. The code defines a closure that partially applies a filter function, returning a new function for later invocation. Using the M language’s `Function.Invoke` and `List.Select` APIs, it passes a column value into a nested lambda, demonstrating how parameter values flow through successive scopes. The gist’s `fn.ts` file illustrates the pattern with a simple identity function, while the visible M code applies this pattern to filter table rows based on a stored parameter. The approach highlights how Power Query’s functional composition enables reusable, parameter-driven query logic without hard-coding values.

JSON to Table in Power Query M

This example demonstrates a Power Query M function that converts arbitrary JSON data into a structured table. It shows the step-by-step process of detecting expandable record and list columns, then iteratively expanding them until all nested objects are flattened. The code uses core M APIs such as `Table.ExpandRecordColumn`, `Table.ExpandListColumn`, `List.Accumulate`, and `List.Generate` to handle consistent and inconsistent column types. The source is a JSON object or list, which is transformed into a final normalized table through a loop that checks column types and applies the appropriate expansion method.

[PowerBI/PowerQuery] Function that translates the documentation attached to a function definition into Markdown text

This example demonstrates a Power Query function that converts the documentation embedded in a function’s type metadata into a Markdown-formatted text block. It first retrieves the function from Power BI’s shared namespace, then extracts fields such as description, long description, category, and examples from the `Value.Metadata` of the function’s type. The code handles missing fields with `try`/`otherwise`, and uses `Text.Trim`, `Lines.ToText`, and `List.Transform` to assemble a template with headers and code fences. The final output is a Markdown document suitable for gallery display, relying solely on the function’s own metadata as the data source.