Many-to-many relationships

Tooling

New 1470 Compatibility Level

Memory settings for resource governance

QueryMemoryLimit

DbpropMsmdRequestMemoryLimit

RowsetSerializationLimit

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• Sales MTD, Sales QTD, Sales YTD, Sales PY, Sales YOY%, …
• Orders MTD, Orders QTD, Orders YTD, Orders PY, Orders YOY%, …

• The user experience is overwhelming because must sift through so many measures
• DAX is difficult to maintain
• Model metadata is bloated

Time-intelligence example

"Current"

SELECTEDMEASURE()

"MTD"

CALCULATE(SELECTEDMEASURE(), DATESMTD(DimDate[Date]))

"QTD"

CALCULATE(SELECTEDMEASURE(), DATESQTD(DimDate[Date]))

"YTD"

CALCULATE(SELECTEDMEASURE(), DATESYTD(DimDate[Date]))

"PY"

CALCULATE(SELECTEDMEASURE(), SAMEPERIODLASTYEAR(DimDate[Date]))

"PY MTD"

CALCULATE(
    SELECTEDMEASURE(),
    SAMEPERIODLASTYEAR(DimDate[Date]),
    'Time Intelligence'[Time Calculation] = "MTD"
)

"PY QTD"

CALCULATE(
    SELECTEDMEASURE(),
    SAMEPERIODLASTYEAR(DimDate[Date]),
    'Time Intelligence'[Time Calculation] = "QTD"
)

"PY YTD"

CALCULATE(
    SELECTEDMEASURE(),
    SAMEPERIODLASTYEAR(DimDate[Date]),
    'Time Intelligence'[Time Calculation] = "YTD"
)

"YOY"

SELECTEDMEASURE() –
CALCULATE(
    SELECTEDMEASURE(),
    'Time Intelligence'[Time Calculation] = "PY"
)

"YOY%"

DIVIDE(
    CALCULATE(
        SELECTEDMEASURE(),
        'Time Intelligence'[Time Calculation]="YOY"
    ),
    CALCULATE(
        SELECTEDMEASURE(),
        'Time Intelligence'[Time Calculation]="PY"
    ),
)

EVALUATE
CALCULATETABLE (
    SUMMARIZECOLUMNS (
        DimDate[CalendarYear],
        DimDate[EnglishMonthName],
        "Current", CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "Current" ),
        "QTD",     CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "QTD" ),
        "YTD",     CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "YTD" ),
        "PY",      CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "PY" ),
        "PY QTD",  CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "PY QTD" ),
        "PY YTD",  CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "PY YTD" )
    ),
    DimDate[CalendarYear] IN { 2012, 2013 }
)

Sideways recursion

DIVIDE(
    CALCULATE(
        SELECTEDMEASURE(),
        'Time Intelligence'[Time Calculation]="YOY"
    ),
    CALCULATE(
        SELECTEDMEASURE(),
        'Time Intelligence'[Time Calculation]="PY"
    ),
)

Single calculation item in filter context

CALCULATE(
    SELECTEDMEASURE(),
    SAMEPERIODLASTYEAR(DimDate[Date]),
    'Time Intelligence'[Time Calculation] = "YTD"
)

EVALUATE
CALCULATETABLE (
    SUMMARIZECOLUMNS (
        DimDate[CalendarYear],
        DimDate[EnglishMonthName],

        //No time intelligence applied: all calc items in filter context:
        "InternetTotalSales", [InternetTotalSales],

        //No time intelligence applied: 2 calc items in filter context:
        "PY || YTD", CALCULATE ( [InternetTotalSales],
            'Time Intelligence'[Time Calculation] = "PY" || 'Time Intelligence'[Time Calculation] = "YTD"
        ),

        //YTD applied: exactly 1 calc item in filter context:
        "YTD", CALCULATE ( [InternetTotalSales], 'Time Intelligence'[Time Calculation] = "YTD" )
    ),
    DimDate[CalendarYear] = 2012
)

Precedence

"No Average"

SELECTEDMEASURE()

"Daily Average"

DIVIDE(SELECTEDMEASURE(), COUNTROWS(DimDate))

EVALUATE
    CALCULATETABLE (
        SUMMARIZECOLUMNS (
        DimDate[CalendarYear],
        DimDate[EnglishMonthName],
        "InternetTotalSales", CALCULATE (
            [InternetTotalSales],
            'Time Intelligence'[Time Calculation] = "Current",
            'Averages'[Average Calculation] = "No Average"
        ),
        "YTD", CALCULATE (
            [InternetTotalSales],
            'Time Intelligence'[Time Calculation] = "YTD",
            'Averages'[Average Calculation] = "No Average"
        ),
        "Daily Average", CALCULATE (
            [InternetTotalSales],
            'Time Intelligence'[Time Calculation] = "Current",
            'Averages'[Average Calculation] = "Daily Average"
        ),
        "YTD Daily Average", CALCULATE (
            [InternetTotalSales],
            'Time Intelligence'[Time Calculation] = "YTD",
            'Averages'[Average Calculation] = "Daily Average"
        )
    ),
    DimDate[CalendarYear] = 2012
)

CALCULATE(SELECTEDMEASURE(), DATESYTD(DimDate[Date]))

DIVIDE(SELECTEDMEASURE(), COUNTROWS(DimDate))

CALCULATE(DIVIDE(SELECTEDMEASURE(), COUNTROWS(DimDate)), DATESYTD(DimDate[Date]))

DIVIDE(CALCULATE(SELECTEDMEASURE(), DATESYTD(DimDate[Date])), COUNTROWS(DimDate))

New DAX functions

SELECTEDMEASURE()

SELECTEDMEASURENAME()

ISSELECTEDMEASURE( M1, M2, … )

IF (
    ISSELECTEDMEASURE ( [Expense Ratio 1], [Expense Ratio 2] ),
    SELECTEDMEASURE (),
    DIVIDE ( SELECTEDMEASURE (), COUNTROWS ( DimDate ) )
)

Power BI implicit measures

DEFINE
MEASURE FactInternetSales[QueryScope] = SUM ( FactInternetSales[SalesAmount] )
EVALUATE
CALCULATETABLE (
    SUMMARIZECOLUMNS (
        DimDate[CalendarYear],
        DimDate[EnglishMonthName],

        //YTD applied successfully to model measure:
        "Model Measure", CALCULATE (
            [InternetTotalSales],
            'Time Intelligence'[Time Calculation] = "YTD"
        ),

        //YTD applied successfully to query scope measure:
        "Query Scope", CALCULATE (
            [QueryScope],
            'Time Intelligence'[Time Calculation] = "YTD"
        ),

        //YTD not applied to inline calculation:
        "Inline", CALCULATE (
            SUM ( FactInternetSales[SalesAmount] ),
            'Time Intelligence'[Time Calculation] = "YTD"
        )
    ),
    DimDate[CalendarYear] = 2012
)

DMV support

• TMSCHEMA_CALCULATION_GROUPS
• TMSCHEMA_CALCULATION_ITEMS

OLS

Planned for a forthcoming CTP

• MDX query support with calculation groups.
• RLS is not supported in CTP 2.3. The planned behavior for SSAS 2019 is that you will be able to define RLS on tables in the same model, but not on calculation groups themselves (directly or indirectly).
• Dynamic format strings. Calculation groups increase the need for dynamic format strings. For example, the YOY% calculation item needs to be displayed as a percentage, while the others should probably inherit the data type of the measure currently in context. We plan to introduce dynamic format strings in an upcoming SQL Server 2019 CTP.
• ALLSELECTED DAX function support with calculation groups.
• Detail rows support with calculation groups.

Limitations of CTP 2.3

New 1470 Compatibility Level

Differences between calculation groups in tabular and calculated members in multidimensional

Single calculation-item column

Recursion safeguards not required

Calculation items cannot be created on other column types

Tooling

Pace of delivery

• Arguably the biggest scalability feature in the history of the AS engine: aggregations
• Policy-based incremental refresh
• Opening the XMLA endpoint to bring AS to Power BI

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1. Benefits of designing your model as a “dimensional model”, a.k.a. “star schema”.
   • Pre-processing your data in this manner will enable the most scale for high data volumes, and deliver the best performance at query time.
2. Steps for optimizing Dimensions:
   • Minimize the number of columns.
   • Reduce cardinality (data type conversions).
   • Filter out unused dimension values (unless a business scenario requires them).
   • Integer Surrogate Keys (SK).
   • Ordered by SK (to maximize Value encoding).
   • Hint for VALUE encoding on numeric columns.
   • Hint for disabling hierarchies on SKs.
3. Steps for optimizing Facts:
   • Handle early arriving facts. [Facts without corresponding dimension records.]
   • Replace dimension IDs with their surrogate keys.
   • Reduce cardinality (data type conversions).
   • Consider moving calculations to the source (to use in compression evaluations).
   • Ordered by less diverse SKs first (to maximize compression).
   • Increased Tabular sample size for deciding Encoding, by considering segments and partitions.
   • Hint for VALUE encoding on numeric columns.
   • Hint for disabling hierarchies.

  {
    "name": "Sales Amount",
    "dataType": "decimal",
    "sourceColumn": "SalesAmount",
    "isAvailableInMdx": false
  }

QueryMemoryLimit

DbpropMsmdRequestMemoryLimit

RowsetSerializationLimit

AMO

ADOMD

MSI installer

NuGet versioning

MSI versioning

Windows Explorer file properties

Base URL

https://<rollout>.asazure.windows.net/servers/<serverName>/models/<resource>/

asazure://westus.asazure.windows.net/myserver

https://westus.asazure.windows.net/servers/myserver/models/AdventureWorks/

• Anything that ends in "s" is a collection.
• Anything that ends with "()" is a function.
• Anything else is a resource/object.

https://westus.asazure.windows.net/servers/myserver/models/AdventureWorks/refreshes

Authentication

• The token must be either a user token or an application service principal.
• The user or application must have sufficient permissions on the server or model to make the requested call. The permission level is determined by roles within the model or the admin group on the server.
• The token must have the correct audience set to: "https://*.asazure.windows.net".

POST /refreshes

{
    "Type": "Full",
    "CommitMode": "transactional",
    "MaxParallelism": 2,
    "RetryCount": 2,
    "Objects": [
        {
            "table": "DimCustomer",
            "partition": "DimCustomer"
        },
        {
            "table": "DimDate"
        }
    ]
}

GET /refreshes/<refreshId>

{
    "startTime": "2017-12-07T02:06:57.1838734Z",
    "endTime": "2017-12-07T02:07:00.4929675Z",
    "type": "full",
    "status": "succeeded",
    "currentRefreshType": "full",
    "objects": [
        {
            "table": "DimCustomer",
            "partition": "DimCustomer",
            "status": "succeeded"
        },
        {
            "table": "DimDate",
            "partition": "DimDate",
            "status": "succeeded"
        }
    ]
}

GET /refreshes

[
    {
        "refreshId": "1344a272-7893-4afa-a4b3-3fb87222fdac",
        "startTime": "2017-12-09T01:58:04.76",
        "endTime": "2017-12-09T01:58:12.607",
        "status": "succeeded"
    },
    {
        "refreshId": "474fc5a0-3d69-4c5d-adb4-8a846fa5580b",
        "startTime": "2017-12-07T02:05:48.32",
        "endTime": "2017-12-07T02:05:54.913",
        "status": "succeeded"
    }
]

DELETE /refreshes/<refreshId>

POST /sync

GET /sync?operationId=<operationId>

{
    "operationId": "cd5e16c6-6d4e-4347-86a0-762bdf5b4875",
    "database": "AdventureWorks2",
    "UpdatedAt": "2017-12-09T02:44:26.18",
    "StartedAt": "2017-12-09T02:44:20.743",
    "syncstate": 2,
    "details": null
}

• 0: Replicating. Database files are being replicated to a target folder.
• 1: Rehydrating. The database is being rehydrated on read-only server instance(s).
• 2: Completed. The sync operation completed successfully.
• 3: Failed. The sync operation failed.
• 4: Finalizing. The sync operation has completed but is performing clean up steps.

Code sample

1. Clone or download the repo. Open the RestApiSample solution.
2. Find the line “client.BaseAddress = …” and provide your base URL (see above).

• Interactive login or username/password
• Service principal

Interactive login or username/password

1. Select the Azure Active Directory section, click App registrations, and then New application registration.

2. In the Create blade, enter a meaningful name, select Native application type, and then enter “urn:ietf:wg:oauth:2.0:oob” for the Redirect URI. Then click the Create button.

3. Select your app from the list and take note of the Application ID.

4. In the Settings section for your app, click Required permissions, and then click Add.

5. In Select an API, type “SQL Server Analysis Services” into the search box. Then select “Azure Analysis Services (SQL Server Analysis Services Azure)”.

6. Select Read and Write all Models and then click the Select button. Then click Done to add the permissions. It may take a few minutes to propagate.

7. In the code sample, find the method UpdateToken(). Observe the contents of this method.
8. Find the line “string clientID = …” and enter the application ID you previously recorded.
9. Run the sample.

Service principal

1. Find the line “string authority = …” and enter your organization’s tenant ID in place of “common”. See code comments for further info.
2. Comment/uncomment so the ClientCredential class is used to instantiate the cred object. Ensure the <App ID> and <App Key> values are accessed in a secure way or use certificate-based authentication for service principals.
3. Run the sample.

Provider data sources versus structured data sources

Query partitions versus M partitions

Data sources and partitions in SSDT Tabular

Fully enabling the legacy UI

Windows Registry Editor Version 5.0

[HKEY_CURRENT_USER\Software\Microsoft\Microsoft SQL Server\14.0\Microsoft Analysis Services\Settings]
"Enable Legacy Import"=dword:00000001

Wrapping things up

@raw_parsed = EXTRACT child_id int,
                <list of all table columns>,
                empty string
FROM "<URI to Blob Container>/{*}_{child_id}_100.dat"
USING Extractors.Text(delimiter: '|');

@filtered_results = SELECT <list of relevant table columns>
FROM @raw_parsed
<WHERE clause to extract specific rows for output>;

OUTPUT @filtered_results
TO "/<output folder>/<filename>.csv"
USING Outputters.Csv(outputHeader:true);

@raw_parsed = EXTRACT child_id int,
                      b_income_band_sk string,
                      b_lower_bound string,
                      b_upper_bound string,
                      empty string
FROM "wasb://income-band@aasuseast2/{*}_{child_id}_100.dat"
USING Extractors.Text(delimiter: '|');

@filtered_results = SELECT b_income_band_sk,
                           b_lower_bound,
                           b_upper_bound
FROM @raw_parsed
ORDER BY child_id ASC
FETCH 100 ROWS;

$script_folder = "<Path to U-SQL Scripts>"
$adla_account = "<ADLA Account Name>"
Login-AzureRmAccount -SubscriptionName "<Windows Azure Subscription Name>"

Get-ChildItem $script_folder -Filter *.usql |
Foreach-Object {
    $job = Submit-AdlJob -Name $_.Name -AccountName $adla_account –ScriptPath $_.FullName -DegreeOfParallelism 100
    Wait-AdlJob -Account $adla_account -JobId $job.JobId
}

Write-Host "Finished processing U-SQL jobs!";

1. Start Visual Studio and check under Tools -> Extensions and Updates that you have the latest version of Microsoft Analysis Services Projects installed.
2. Create a new Tabular project at the 1400 compatibility level.
3. Open the Model menu and click on Import From Data Source.
4. Pick the Azure Data Lake Store connector, provide the storage account URL, and sign in by using an Organizational Account. Click Connect and then OK to create the data source object in the Tabular model.
5. Because you chose Import From Data Source, SSDT displays Query Editor automatically. In the Content column, click on the Table link next to the desired folder name (such as modelling) to navigate to the desired root folder where the csv files reside.
6. Right-click the Table object in the right Queries pane, and click Create Function. In the No Parameters Found dialog box, click Create.
7. In the Create Function dialog box, type GetCsvFileList, and then click OK.
8. Make sure the GetCsvFileList function is selected, and then on the View menu, click Advanced Editor.
9. In the Edit Function dialog box informing you that updates from the Table object will no longer propagate to the GetCsvFileList function if you continue, click OK.
10. In Advanced Editor, note how the GetCsvFileList function navigates to the modelling folder, enter a whitespace character at the end of the last line to modify the expression, and then click Done.
11. In the right Queries pane, select the Table object, and then in the left Applied Steps pane, delete the Navigation step, so that Source is the only remaining step.
12. Make sure the Formula Bar is displayed (View menu -> Formula Bar), and then redefine the Source step as = GetCsvFileList() and press Enter. Verify that the list of csv files is displayed in Query Editor, as in the following screenshot.
13. For each table you want to import:
    1. Right-click the existing Table object and click Duplicate.
    2. In the Content column, click on the Binary link next to the desired file name (such as call_center) and verify that Query Editor parses the columns and detects the data types correctly.
    3. Rename the table according to the csv file you selected (such as call_center).
    4. Right-click the renamed table object (such as call_center) in the Queries pane and click Create New Table.
    5. Verify that the renamed table object (such as call_center) is no longer displayed in italic, which indicates that the query will now be imported as a table into the Tabular model.
14. After you created all desired tables by using the sequence above, delete the original Table object by right-clicking on it and selecting Delete.
15. In Query Editor, click Import to add the GetCsvFileList expression and the tables to your Tabular model.

Background information

let
    Source = Sql.Database("server01", "AdventureWorksDW")
in
    Source

let
    Source = #"SQL/server01;AdventureWorksDW"
in
    Source

Accessing the same data source with different options

let
    Source1 = Sql.Database("server01", "AdventureWorksDW", [CommandTimeout=#duration(0, 0, 5, 0)]),
    Source2 = Sql.Database("server01", "AdventureWorksDW", [CommandTimeout=#duration(0, 0, 10, 0)]),
    Combined = Table.Combine({Source1, Source2})
in
    Combined

Note: This workaround of using a data source merely as a holder of a credential object is not recommended and should be considered a last-resort option. If possible, create a common set of options for all data access calls to the same source and use a single data source object to replace the data access functions. Another option might be to register the same server with multiple names in DNS and then use a different server name in each data source definition.

Accessing the same data source with different native queries

let
    Source1 = Sql.Database("server01", "AdventureWorksDW", [Query="SELECT * FROM dimCustomer WHERE LastName = 'Walker'"]),
    Source2 = Sql.Database("server01", "AdventureWorksDW", [Query="SELECT * FROM dimCustomer WHERE LastName = 'Jenkins'"]),
    Combined = Table.Combine({Source1, Source2})
in
    Combined

let
    Source = Sql.Database("server01", "AdventureWorksDW"),
    Table1 = Value.NativeQuery(Source, "SELECT * FROM dimCustomer WHERE LastName = 'Walker'"),
    Table2 = Value.NativeQuery(Source, "SELECT * FROM dimCustomer WHERE LastName = 'Jenkins'"),
    Combined = Table.Combine({Table1, Table2})
in
    Combined

Note: Even if you consolidated the queries, as in SELECT * FROM dimCustomer WHERE LastName = 'Walker' Or LastName = 'Jenkins', avoid putting this native query on the query parameter of the data source definition. The query parameter exists for full compatibility between data access functions and data source definitions. But it shouldn't be used because it narrows the data source down to a particular query. You could not import any other data from that source. As explained earlier, you cannot define a second data source object with the same address parameters in Tabular 1400. So, define the data source in broadest terms and then use the Value.NativeQuery function to submit a native query.

Handling parameterized data access functions

let
    Source = #"SQL/server01;AdventureWorksDW"
in
    Source

Dealing with Dynamic Data Access Functions

let
    #"Dynamic Function" = Sql.Database,
    Source = #"Dynamic Function"("server01", "AdventureWorksDW")
in
    Source

let
    Source = Sql.Database("server01", "Dynamic"),
    DataAccessPoints = Source{[Schema="dbo",Item="DataAccessPoints"]}[Data],
    NavigationTable = Table.AddColumn(DataAccessPoints, "Data", each Connect([FunctionName], [Parameter1], [Parameter2]))
in
    NavigationTable

let
    Source = (FunctionName, Param1, Param2) => let
        FunctionTable = Record.ToTable(#shared),
        FunctionRow = Table.SelectRows(FunctionTable, each ([Name] = FunctionName)),
        Function = FunctionRow{0}[Value],
        Result = if Param2 = null then Function(Param1) else Function(Param1, Param2)
    in
        Result
in
    Source

Looking to the future