Advanced M Language Techniques for Power BI

Now that you have a solid grasp of M language basics, it's time to unlock more advanced capabilities. This guide covers techniques like performance optimization, custom visual development, Python and R integration, error handling, and deployment options to take your M skills to the next level. This article is part of JBI Trainings M Language course and is intended as a support resource for our Power BI - Beyond the Basics

Optimizing M Language Performance

As M codebases grow, query performance can degrade. Some optimization techniques include:

Using proper data types - tables vs. lists, typed columns, etc.
Limiting loaded data with filtering
concatenation vs. Table.Combine()
Parallelizing with #shared keyword
Utilizing query diagnostic tools

// Filter Sales table before loading 
FilteredSales = Table.SelectRows(Sales, each [Country] = "USA")

// Concatenate with Table.Combine()
CombinedSales = Table.Combine({FilteredSales, FilteredReturns})

Profiling tools like the Power BI Performance Analyzer and M Query Profiler provide detailed query diagnostics to identify and fix bottlenecks.

Developing Custom Visuals in M

The Power BI custom visual SDK enables building visuals using M language.

// Sample bar chart custom visual
Visual = (data as table, category as text) as visual =>

  Visual.Chart(data,
    Visual.Column(category),
    "BarChart", [IsMultiSelect = true])

After developing the visual, package it into a PBIVIZ file using the Power BI Desktop exporter. Then import into reports using Visual.Import

Custom visual developed with M language

Integrating R and Python in M

R and Python scripts can be directly embedded within M code.

// Run Python script
Python.Execute(Text.FromBinary(pyScript))

// Convert R dataframe to table  
mTable = R.DataFrameToTable(rDataframe)

This enables reusing R and Python libraries while keeping M as the top-level orchestration language.

Handling Errors in M

Robust error handling prevents failures in production.

Try GetData = Source(){1},
  Otherwise Error = Exception.Message, 
  "Error getting data: " & Error

Techniques like wrapping code in try/otherwise blocks and logging detailed errors prevent query failures.

Deploying M Solutions

To scale out M usage, solutions can be deployed in Azure using:

Power BI Premium - For shared capacity
Azure Analysis Services - For enterprise BI models
Power Apps - For commenting to other services

With proper deployment governance, M can power organization-wide analytics solutions.

Conclusion

This guide presented more advanced techniques to elevate your M language skills. Focus on performance tuning, robust error handling, custom visualization, and enterprise deployment to take your M programming to the next level.

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