Daily DAX : DAY 253 INFO.PARQUETSTORAGES
The INFO.PARQUETFILESTORAGES DAX function in Power BI is part of the INFO family of functions, which provide metadata about a semantic model. Specifically, INFO.PARQUETFILESTORAGES retrieves information about the Parquet file storage used in a Power BI semantic model, particularly when leveraging Direct Lake mode in Microsoft Fabric. This function is useful for administrators or developers who need to inspect or audit the underlying storage structure of their data model.
Syntax
dax
INFO.PARQUETFILESTORAGES()
No arguments: The function does not take any parameters and returns a table with metadata about Parquet file storage.
Return Value
The function returns a table containing details about the Parquet files used in the semantic model. The columns in the returned table may include:
TableID: The identifier for the table in the model.
TableName: The name of the table in the model.
ParquetFileStoragePath: The storage path or location of the Parquet file (e.g., in Azure Data Lake Storage Gen2).
PartitionKey: Information about the partition key, if partitioning is used.
RowGroupCount: The number of row groups in the Parquet file.
FileSize: The size of the Parquet file (in bytes or another unit).
CompressionType: The compression algorithm used (e.g., GZip, Snappy, Brotli, LZ4, or ZStd).
LastModifiedDate: The date the Parquet file was last modified.
Other metadata related to the Parquet file structure or storage configuration.
Note: The exact columns may vary depending on the Power BI version and the underlying storage implementation. Always refer to the latest Microsoft documentation for precise details.
Key Characteristics
Output Type: Returns a table, which can be used with other DAX functions like FILTER, SELECTCOLUMNS, or ADDCOLUMNS for further manipulation.
Permissions: Requires semantic model admin permissions to access, similar to other INFO DAX functions.
Context: Executes in the context of the semantic model and is particularly relevant for models using Direct Lake mode, where data is stored in Parquet files in a data lake (e.g., Azure Data Lake Storage Gen2).
DirectQuery Compatibility: The function may have limitations in DirectQuery mode, depending on the data source and Power BI version.
Use Case
The INFO.PARQUETFILESTORAGES function is primarily used for model introspection and optimization in scenarios involving large-scale data storage, particularly in Microsoft Fabric's Direct Lake mode. Here are some practical use cases:
Auditing and Documentation:
Scenario: A data engineer needs to document the storage structure of a Power BI semantic model to ensure compliance or share details with stakeholders.
Use: Use INFO.PARQUETFILESTORAGES to generate a report listing all Parquet files, their sizes, compression types, and partition details. This can be exported to Excel or visualized in a Power BI report for auditing purposes.
Example:
dax
EVALUATE
SELECTCOLUMNS(
INFO.PARQUETFILESTORAGES(),
"Table Name", [TableName],
"File Path", [ParquetFileStoragePath],
"File Size (MB)", [FileSize] / 1024 / 1024,
"Compression", [CompressionType]
)
This query retrieves key storage details and converts file size to megabytes for readability.
Performance Optimization:
Scenario: A Power BI developer notices slow query performance in a Direct Lake model and suspects inefficient Parquet file configurations (e.g., too many small files or suboptimal row group sizes).
Use: Query INFO.PARQUETFILESTORAGES to analyze file sizes, row group counts, and partitioning strategies. This can help identify whether files need to be repartitioned or consolidated to improve query performance.
Example:
dax
EVALUATE
FILTER(
INFO.PARQUETFILESTORAGES(),
[RowGroupCount] < 10 || [FileSize] < 1000000
)
This query identifies Parquet files with too few row groups or small file sizes, which could indicate optimization opportunities.
Monitoring Data Freshness:
Scenario: A data team needs to verify when Parquet files were last updated to ensure data freshness in reports.
Use: Use the LastModifiedDate column to check the recency of data in the Parquet files and trigger alerts or refreshes if files are outdated.
Example:
dax
EVALUATE
ADDCOLUMNS(
INFO.PARQUETFILESTORAGES(),
"Days Since Last Modified", DATEDIFF([LastModifiedDate], TODAY(), DAY)
)
This query calculates how many days have passed since each Parquet file was last modified.
Storage Cost Analysis:
Scenario: A cloud architect wants to estimate storage costs for a Power BI model hosted in Azure Data Lake Storage.
Use: Sum the FileSize column to calculate total storage usage and combine with cloud pricing data for cost analysis.
Example:
dax
EVALUATE
SUMMARIZE(
INFO.PARQUETFILESTORAGES(),
[TableName],
"Total Size (GB)", SUM([FileSize]) / 1024 / 1024 / 1024
)
This query aggregates file sizes by table and converts them to gigabytes.
Practical Considerations
Direct Lake Mode: The function is most relevant in Microsoft Fabric’s Direct Lake mode, where Power BI directly queries Parquet files in a data lake without importing data into memory (unlike Import mode). This makes it critical for optimizing performance in big data scenarios.
Performance Optimization Tips:
Ensure Parquet files are partitioned by key columns (e.g., date or region) to reduce scan times.
Aim for optimal file sizes (e.g., 512 MB to 1 GB per file) and row group sizes to balance performance and memory usage.
Use compression types like Snappy or ZStd for a good balance of compression ratio and read performance.
Limitations:
The function does not support predicate pushdown (e.g., filtering rows at the storage level), so query performance depends heavily on columnar storage and partitioning.
It is not supported in SQL Server Analysis Services, Azure Analysis Services, or PowerPivot models—only in Power BI semantic models.
Access Requirements: Model admin permissions are required, so ensure the user has appropriate access to the semantic model.
Related Functions
INFO.VIEW.TABLES: Provides friendly names and additional details about tables, which can complement INFO.PARQUETFILESTORAGES for model documentation.
INFO.MEASURES: Retrieves metadata about measures in the model.
INFO.RELATIONSHIPS: Provides information about relationships, useful for understanding model structure alongside storage details.
Example in Practice
Suppose you’re managing a large sales dataset in Direct Lake mode, stored as Parquet files in Azure Data Lake Storage Gen2. You want to optimize query performance by identifying tables with inefficient file configurations. You could run:
dax
EVALUATE
SELECTCOLUMNS(
FILTER(
INFO.PARQUETFILESTORAGES(),
[FileSize] < 52428800 // 50 MB
),
"Table Name", [TableName],
"File Path", [ParquetFileStoragePath],
"File Size (MB)", [FileSize] / 1024 / 1024,
"Row Groups", [RowGroupCount]
)
This query identifies Parquet files smaller than 50 MB, which may indicate fragmentation, and includes row group counts to assess whether files need repartitioning.
Conclusion
The INFO.PARQUETFILESTORAGES DAX function is a powerful tool for Power BI developers and administrators working with Direct Lake mode in Microsoft Fabric. It enables detailed inspection of Parquet file storage, supporting tasks like performance optimization, storage cost analysis, and data freshness monitoring. By combining this function with other DAX functions and best practices for Parquet file management, you can ensure efficient and scalable data models for large datasets. For further details, check Microsoft’s official DAX documentation.
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