1. 6/12/2018 2:19 PM
BRK3245
DirectQuery in Analysis Services: best practices, performance, and use cases
Marco Russo
SQLBI
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Agenda DirectQuery in SSAS 2016/2017 (and Azure AS)
What’s changed since 2012/2014
How to create a data model in DirectQuery mode
Querying data models in DirectQuery mode
What happens when you use DirectQuery
Differences between clients: Excel and Power BI
Security models
Optimizing DirectQuery
Best practices for DAX, data modeling, and data sources
Leveraging columnstore indexes
Final considerations
When to use DirectQuery, Q & A

3. Whitepaper DirectQuery in Analysis Services 2016
Include demos and further details about the content of this presentation
All the considerations are still valid in Analysis Services 2017 and Azure Analysis Services as of September 2017

4. Storage engines in SSAS Tabular
In-Memory (VertiPaq)
Create a copy of data in memory
Single access to data source
Queries run on a “snapshot” of data
DirectQuery
Queries sent to data source, SSAS Tabular does not “cache” data
No processing time
Real-time queries
Higher workload on data source

5. DirectQuery vs In-Memory (VertiPaq)

6. VertiPaq pros/cons Pros Cons Data compressed in memory, faster queries
Different data sources for one model
Single scan of source tables
Cons
Processing takes time (latency of updates)
Processing requires CPU time
Limited querying capacity during process
All data have to fit in memory
Copy data from data source to SSAS
Network could be one bottleneck

7. DirectQuery pros/cons
No processing time
No limits of data size related to memory
No data duplication
No latency in updates
Cons
Data slower to query
Specific optimizations on data source
Only one data source for one model
Larger workload on data source for queries

8. DirectQuery evolution
SSAS 2012/2014
Support only for SQL Server, no other RDBMS
Support only for DAX queries, no MDX
One DAX query equal to one SQL query
No cache in SSAS
No formula engine in SSAS
Limited support to DAX language
No security roles
SSAS 2016/2017 – Azure AS
Support for several RDBMS (also non-MS)
Support for DAX and MDX queries
Multiple SQL queries for one MDX/DAX query
Partial cache in SSAS
Formula engine support in SSAS
Full DAX language in query/measures
Security roles supported

9. Supported data sources
Microsoft
Microsoft SQL Server 2008 and later
Microsoft Azure SQL Database
Microsoft Azure SQL Data Warehouse
Microsoft Azure SQL Analytics Platform System (APS)
Oracle
Oracle 9i and later
Teradata
Teradata V2R6 and later
List of specific providers supported for DirectQuery:

10. Demo
Create a Tabular model in DirectQuery mode

11. Set DirectQuery Mode property

12. Set DirectQuery Mode property

13. Use DirectQuery connection mode

14. Developing for DirectQuery
Using default partitions
Switch between On/Off for DirectQuery Mode
Features unlimited when DirectQuery = Off
You cannot switch to On if the model uses unsupported features
Using sample data
Create one or more partitions with sample data
Default partition is DirectQuery only – no preview in SSDT
Switch to DirectQuery after deployment (SSMS)
You can change the DirectQuery mode after deployment
Use Database Properties in SSMS, or XMLA, or PowerShell

15. Demo
Sample data in SSDT Switch DirectQuery in SSMS

16. Sample partition in SSDT

17. Switch DirectQuery in SSMS

18. Limitations in Tabular models for DirectQuery
Data modeling
No calculated tables
DAX
All functions supported in measures and queries
Restrictions in calculated columns and row-level security (RLS) filters
Detailed list of functions available at
MDX
No relative object names
No session-scope MDX statements
No tuple with member from different levels in subselects
No used-defined hierarchies
Detailed list of MDX limitations available at

19. What happens in DirectQuery mode
Optimizing DirectQuery  optimizing RDBMS
Understand how SQL queries are generated by SSAS Tabular
Create indexes to improve performance
Design data model and DAX expressions to improve efficiency of SQL code
Tools
SQL Profiler
DAX Studio

20. Demo
Analyze queries using SQL Profiler and DAX Studio

21. Differences between clients
Excel PivotTable
Generates MDX queries
No cache at the SSAS layer
Repeating the same drilldown repeats the SQL queries
Multiple subqueries repeat same queries at different granularities
PivotTable in Excel
Power BI
Multiple DAX queries in the same request
Strong caching at Power BI application level
Repeating the same drilldown reuse previous results
Explicit Refresh required to empty client cache enforcing SQL queries to read data
More efficient DAX queries
Reduced number of SQL queries sent for the same report

22. Tuning query limits in DirectQuery
Limit results in SQL queries
Default: 1 million rows
Avoid long running queries and memory issues
Only affects queries returning large intermediate results to SSAS (it depends on DAX expressions)
Can be configured through MaxIntermediateRowsetSize setting in msmdsrv.ini
<ConfigurationSettings> <DAX> <DQ> <MaxIntermediateRowsetSize> </MaxIntermediateRowsetSize> </DQ> </DAX> . . .

23. Security Role based security Impersonation with DirectQuery
Same rules defined by DAX filter expressions, one for each table
Dynamic security using windows username
DAX filters translated in corresponding SQL filters (WHERE conditions)
Performance depends on data source optimization
Every SQL query repeats the security filter
Impersonation with DirectQuery
SQL queries sent to RDBMS using the credentials of user connected to SSAS
SQL Server 2016 has its own row-level security
RDBMS must support Windows Integrated authentication
Can replace role based security in SSAS, but you can also use both at the same time

24. Demo
Role based security
Impersonation with DirectQuery

25. Optimizing DirectQuery
Optimize DAX
Use optimized DAX functions in DAX measures and queries
Reduce complex calculations in aggregations (materialize calculated columns in RDBMS)
Optimize data model
Enable referential integrity in relationships
It will generate INNER JOIN instead of LEFT JOIN
Careful consider bidirectional filters
Optimize RDBMS
Columnstore index
Indexed views

26. Demo
Optimize data model and RDBMS

27. Scenarios for DirectQuery
Small database updated often
DirectQuery removes latency caused by data refresh
Data is always up-to-date
Large database that cannot fit in memory
Removes processing time
Does not require large hardware for Analysis Services (reduced memory requirements)
VertiPaq is usually faster than DirectQuery, but you need SSAS memory to store the entire database

28. Recap and call to action
DirectQuery in SSAS 2016/2017 and Azure AS
Huge improvements over 2012/2014
Larger number of use cases where it can be used (now it also supports Excel as a client)
Design models for DirectQuery mode
Consolidate calculated columns on RDBMS for performance
Use referential integrity assumption in relationships
Choose between different security models
Optimizing DirectQuery
You have to optimize the RDBMS
Columnstore indexes are a primary tool for SQL Server
Use cases for DirectQuery
Small database updated often
Large database that cannot fit in memory

29. Whitepaper DirectQuery in Analysis Services 2016

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The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

31. 6/12/2018 2:19 PM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.