Understanding errors from Tabular Analysis Services

Despite its many advantages Tabular Analysis Services represents a step backwards in developer experience, particularly in the feedback following an exception.

This is a short list of some of the more head-scratching error messages that occur when processing a Tabular model (Version 1500). These are messages shown in the designer dialogs but the same messages are returned when processing on a server.

“key didn’t match any rows in the table”

Full error message (always the same):

Failed to save modifications to the server. Error returned: ‘OLE DB or ODBC error: [Expression.Error] The key didn’t match any rows in the table..

Meaning The table or view does not exist in the source.

Perhaps the table or view was renamed or dropped or Analysis Services does not have select permission on it.


The term “key”, which is prevalent in data warehousing, and the phrase “rows in the table” strongly suggest that the problem is some mismatched value in the data. Well, it isn’t: the “key” and “rows” are something internal to Analysis Services – and Tabular does not check referential integrity in any case. The source table or view itself cannot be found.

Which table or view, though? Although the processing dialog box lists each item on its own line, if one item fails then all items are marked as failures, which gives no hint as to which one is the root cause. Each item has an “Error description link”, but the message behind each link is exactly the same (see below).

Analysis Services Tabular. A table near the end of the list doesn’t exist in the source… but none of the copious error paraphernalia in these dialogs tells you that.

It is left to the user to find which item is unavailable. This is a particularly poor experience. The only way to get the designer interface to reveal which table it is is to attempt to process each item individually until the miscreant is found.

“The xxx column does not exist in the rowset”

Failed to save modifications to the server. Error returned: ‘The ‘Amount’ column does not exist in the rowset.

Meaning: one of the referenced columns could not be found in the source table or view.

This at least states which column is missing but it doesn’t say which table which can still lead to a bit of hunting.

A column is missing from one of these tables… but which? All tables repeat the same error message

“Type mismatch”

Failed to save modifications to the server. Error returned: ‘OLE DB or ODBC error: Type mismatch. (Exception from HRESULT: 0x80020005 (DISP_E_TYPEMISMATCH))

Meaning Some column of some table contains data of the wrong type


As usual, there is no indication of which column or which table. Note that Tabular does not check source data types from column metadata, hence the emphasis above. It only checks the incoming data, if any (according to my experiments). If a column has the wrong data type but has no data (only nulls) there will be no error.

If the incoming data can be coerced into the destination type there will also be no error. The exception is only raised once Analysis Services finds some data that it cannot manipulate to fit the destination data type. In this example the processing failed when character values were supplied for a numeric destination column in the Tabular model. On the other hand, incoming date-time values will be coerced into a numeric column without error.

Blank values not allowed for…

The full message is

Failed to save modifications to the server. Error returned: ‘Column ‘Date’ in Table ‘Date’ contains blank values and this is not allowed for columns on the one side of a many-to-one relationship or for columns that are used as the primary key of a table.

and yet the column mentioned is not involved in a relationship and is not a primary key (nor marked as unique).

There is another reason not mentioned in the error message: the table is marked as a date table and the column mentioned is the date column.


Analysis Services Tabular does its best to make enterprise development difficult. But enterprise techniques help. Good source control of both source database and the Analysis Services project helps to track what changed to cause the problems and allows reversion to last known good.

After a while you become conditioned to the way it works and I should say that development is not normally as frustrating as this article might suggest (although Tabular has plenty of other annoyances to offer). It is in those moments, normally after a busy day, when you develop a temporary blindness to something basic that these inadequate messages take their toll, and you end up longing for the more complete and verbose exception messages of a more mature product.

SSISDB getting large – failure of Retention Window Cleanup

The SSIS Catalog Rentention Period (days) defaults to 365.   The SSIS database can quickly become very large (as a function of SSIS activity and logging levels).

A stored procedure SSISDB.internal.cleanup_server_retention_window is executed by a job SSIS Server Maintenance Job on a nightly schedule at 00:00:00.

(All defaults observed in 2012, v.11.0.5058.0)


This stored procedure deletes rows from internal.operations table that are older than the retention period.  One row in internal.operations represents a package (?) invocation – this table won’t grow too large,  but related detail tables can;  internal.event_messages, internal.event_message_context, internal.operation_messages, internal.executable_statistics being the largest.

The cleanup stored procedure deletes rows from internal.operations tables.  Cascading foreign key constraints delete rows from other tables – some indirectly via internal.executions.  This is operational part of the procedure:

 WHILE (@rows_affected = @delete_batch_size)
     DELETE TOP (@delete_batch_size)
       FROM [internal].[operations] 
       WHERE ( [end_time] <= @temp_date
       OR ([end_time] IS NULL AND [status] = 1 AND [created_time] <= @temp_date ))
     SET @rows_affected = @@ROWCOUNT

Procedure continually fails to complete

The default for @delete_batch_size is 1,000 – and herein lies the problem.  The deletion of a single internal.operations row will cascade to many thousands of rows.  But internal.operations itself is relatively low cardinality.  Assembling such a large deletion set is a time consuming, log-expanding and blocking operation which can fail – and fail continually thereafter.  Although the deletion is sensibly in a loop, 1000 is a large proportion of  internal.operations  rows. As can be seen from the 16GB example above there are only 2.8K rows in the table to start with, so the loop would barely exceed a couple of iterations.    Naturally all this depends on the activity and retention period of the SSIS instance.  But 1,000 seems a lot in any case.

Some posts state that  @delete_batch_size is 10, though not in the versions I have checked.   If the procedure continually fails the solution is to run a copy the code with a smaller batch size to clear the backlog.   I am tempted to reduce the setting in the stored procedure itself – should a backlog of deletions accrue again for whatever reason the deletions will again fail.

The following chart shows the table sizes once the retention period has been reached – in this case, 2.8K rows in internal.operations were reduced to 827.  The projected results on the other large tables is significant.

View Change Tracking Settings

Statements to reveal Change Tracking settings.  This query does not require VIEW DATABASE STATE permission, which is required to display the database properties page in SSMS.

	[database_name] = DB_NAME(database_id) 
from  sys.change_tracking_databases


Populating Date Dimension with Fiscal Periods

A standard method for populating a date table with fiscal periods will always come to grief over exceptions.  This post includes a procedure that should be customisable enough to cater for most variations on fiscal periods – provided all periods contain a whole number of weeks.

Fiscal Periods

… do vary.

  • A 4-4-5 week pattern is standard… except when adjustments are made for busy months.
  • Normally a quarter contains three months… except when a company is adjusting its financial year end.

The procedure detailed below takes a few parameters which specify the fiscal period schema:

  • FirstDayOfFiscalYear  : the day it all starts, which also determines the first-day-of-week.
  • WeeksPerMonthPattern : a csv list stating the number of weeks in each month.  e.g.  4,4,5,4,4,5,4,4,5,4,5,4.    The procedure defaults to 4-4-5 throughout the year.
  • MonthsPerQuarterPattern : a csv list stating the number of months per quarter.  defaults to 3,3,3,3 so only needs overriding in those exceptional circumstances.

There is some validation to ensure that the numbers of months, quarters and weeks align.  There’s a “checksum” NbrOfWeeks parameter which defaults to 52; set a value if 53 weeks applies – or for fiscal years with other numbers of weeks.

Columns to Update

A date table might have any number of specialised fiscal columns to update – week and month numbers, start and end date keys, overall period counts.  Therefore the procedure provides a simple but flexible way of specifying an update clause using placeholders for values to be inserted.

  • TableToUpdate : name of table
  • DateKeyColumn : The Date-key column of the TableToUpdate. Column must be integer conversion of date format 112 (yyyymmdd).
  • ColumnUpdatePattern :  The contents of a SET clause as it would appear in a an UPDATE statement, with placeholders.  E.g.  “FiscalMonthNumber={m}, FiscalQuarterNumber={q},… FiscalMonthStart={mst},…”. Available placeholders:
    • {year} : the value of @FiscalYearName
    • {d}, {w}, {m}, {q}, {s} : day, week, month, quarter and semester numbers.
    • {wst}, {mst}, {qst}, {sst} : week, month, quarter, semester start date key
    • {wend}, {mend}, {qend}, {send} : week, month, quarter, semester end date key

Procedure ai_GenerateFiscalCalendar

This procedure generates table update statements for a single financial year.

Fiscal Calendar Update Generator.sql

Example Call

This call uses a specialised week-per-month pattern.

DECLARE @ColumnUpdatePattern varchar(1000) = 'Fiscal_year={year}, Fiscal_quarter={q}, ' +
    'Fiscal_period={m}, Fiscal_Week={w}, ' +
	'Fiscal_week_overall_nbr=(1161 + {w}), Fiscal_period_overall_nbr=(1037 + {m}), ' +
	'[quarter_Fiscal_of_year_end_date_key] = {qend}';
EXEC ai_GenerateFiscalCalendar    
	@FiscalYearName = '2017',
	@TableToUpdate = 'dbo.DimDate',
	@DateKeyColumn = 'date_key',
	@ColumnUpdatePattern = @ColumnUpdatePattern,
	@WeeksPerMonthPattern = '4,4,5,4,4,5,4,5,4,4,4,5';

A summary is output to the messages tab so that the periods can be checked.

START DATE: 2017-04-30 Sunday
 1  2017-04-30 Sun  28 days  4 weeks  Qtr: 1  Sem: 1
 2  2017-05-28 Sun  28 days  4 weeks  Qtr: 1  Sem: 1
 3  2017-06-25 Sun  35 days  5 weeks  Qtr: 1  Sem: 1
 4  2017-07-30 Sun  28 days  4 weeks  Qtr: 2  Sem: 1
 5  2017-08-27 Sun  28 days  4 weeks  Qtr: 2  Sem: 1
 6  2017-09-24 Sun  35 days  5 weeks  Qtr: 2  Sem: 1
 7  2017-10-29 Sun  28 days  4 weeks  Qtr: 3  Sem: 2
 8  2017-11-26 Sun  35 days  5 weeks  Qtr: 3  Sem: 2
 9  2017-12-31 Sun  28 days  4 weeks  Qtr: 3  Sem: 2
10  2018-01-28 Sun  28 days  4 weeks  Qtr: 4  Sem: 2
11  2018-02-25 Sun  28 days  4 weeks  Qtr: 4  Sem: 2
12  2018-03-25 Sun  35 days  5 weeks  Qtr: 4  Sem: 2

The update statements (output as rows) are created by simple concatenation of the TableToUpdate, ColumnUpdatePattern and DateKeyColumns specified.   One update is created for each day in the fiscal year.

UPDATE dbo.DimDate SET Fiscal_year=2017, Fiscal_quarter=1, Fiscal_period=1, Fiscal_Week=1, Fiscal_week_overall_nbr=(1161 + 1), Fiscal_period_overall_nbr=(1037 + 1), [quarter_Fiscal_of_year_end_date_key] = 20170729 WHERE date_key = 20170430;

-- 364 update statements generated.


A Procedure for generating Data Warehouse Update code


Transactional tables exist in Database A.   These tables have partner archive tables in the same or another database.  For each insert or change of a value in the transactional database a timestamped new row is entered into the counterpart history table.  Standard archiving, similar to the temporal archiving offered in SQL Server 2016.

The difficulty is that writing the code to populate the archive table (or a Type 2 Slowly Changing Dimension data warehouse table) is complex and error prone.  Particularly since a Merge statement cannot on its own achieve this, so a temporary table has to populated from the Merge Output clause, and so on.

Code Generator

The procedure UTIL_GenerateArchiveProcedureDDL will generate the code required to populate an archive table.  The procedure is in the file attached at the end of this article.

It can generate code for a single specified table or all tables in a schema or the database.

The source table(s) must have an explicit primary key.

The archive table(s) must

  • have same name as the source tables (TODO add option to add _Archive to the name)
  • have temporal date columns named StartDate and EndDate (TODO allow other naming schemes to be used)
  • have the schema as the source tables with the addition of two date columns StartDate (not null) and EndDate (nullable).

The generator and generated code requires this Error reporting procedure.


Usage and description.

Standard Table (single Primary Key)

-- Typical table with a single primary key.
CREATE TABLE dbo.Products(
	ProductName varchar(30) NOT NULL,
	AvailableFromDate smalldatetime NOT NULL,
	IntroducerId int NULL);

Run this with results to grid (it may be necessary to set the SSMS option to display carriage returns in the output grid view).

-- Generate Archive procedure for this table only by specifying table name
-- (if there is a similarly named table in another schema, provide @SourceSchema too)
EXEC UTIL_GenerateArchiveProcedureDDL
	@WarehouseDatabaseName = Archive,
	@SourceTable = 'Products';

This is the generated DDL.

  • The main action occurs in the MERGE statement. This joins all rows in the LIVE table with all current rows, i.e. rows where the EndDate is Null, in the Warehouse table (selected by CTE TargetCTE).
  • The Live and Warehouse tables are joined on all columns of the Primary Key (this example has only one).
  • If there is a match it means the same record (“same” here means having the same Primary Key value) exists in live and warehouse databases.
    • The procedure then compares the sets of non-PK columns in each table using the EXCEPT set operator. If a row is returned, there is a difference in one of the non-PK columns.
    • The THEN clause UPDATES the EndDate column of the warehouse table to obsolete the row in the warehouse table.
    • Note that at this stage no new row can be inserted into the warehouse to represent the new value; that is handled after the MERGE statement.
  • The remaining two MERGE clauses are simpler
    • Not matched by SOURCE:  the record no longer exists in live so the warehouse row is obsoleted.
    • Not matched by TARGET: the record doesn’t exist in warehouse, so a row is inserted.
  • All the SOURCE columns are OUTPUT-ed to a temporary table, together with the MERGE action key.
  • We still need to insert rows for records that have changed value since the warehouse was last updated.  The Merge Output will contain all rows from the full join of the source and target tables.  These can be filtered to only the records that resulted in an UPDATE operation, but even that is too wide since it includes records which have been deleted from the source; we don’t want to insert those again.  Therefore we filter to insert only rows that exist in the merge Source – that is, those where the ouput-ed Source columns are not null.  To test this, the code chooses the primary key (or the first PK column of a compound key) since this cannot be null if the row exists.
  • The Merge and changed-row Insert statements are wrapped in a transaction.
  • Row counts follow.
CREATE PROCEDURE [dbo].[UpdateDataWarehouse]
	** Auto-generated by procedure UTIL_GenerateArchiveProcedureDDL ON Jan 22 2017  3:35AM.

		This procedure updates an archive warehouse database with the current values in the source database.
		The warehouse database must contain the same tables with the same columns plus temporal (StartDate, 
		EndDate) columns.		

		18/01/2017 Andrew Ing 1.02.


DECLARE @EffectiveDate datetime;
DECLARE @ObsoletedRowCount int;
DECLARE @InsertedRowCount int;
DECLARE @UpdatedRowCount int;
DECLARE @TableActionSummary varchar(1000);
DECLARE @DebugInfo varchar(2000);

SET @EffectiveDate = GETDATE();

-- TABLE: [dbo].[Products]

  SET @DebugInfo = 'Create Temp Table [#MergeOutput_Products]';
  CREATE TABLE [#MergeOutput_Products] (
    MergeAction nvarchar(10)
   ,[ProductId] int NULL
   ,[ProductName] varchar(30) NULL
   ,[AvailableFromDate] smalldatetime NULL
   ,[IntroducerId] int NULL

  SET @DebugInfo = 'Merge [dbo].[Products]';
  -- Select only Current (EndDate IS NULL) warehouse rows for the comparison.
    (SELECT * 
     FROM Archive.[dbo].[Products]
     WHERE EndDate IS NULL
  MERGE  TargetCTE AS [Target]
  USING  [dbo].[Products] AS [Source]
         [Target].[ProductId] = [Source].[ProductId]
    THEN  -- Record has changed.
      SET [EndDate] = @EffectiveDate
    THEN  -- Record deleted from source.
      SET [EndDate] = @EffectiveDate
    THEN  -- New Record.
      INSERT (
       ) VALUES (
     $Action AS MergeAction
  INTO [#MergeOutput_Products];

  -- Insert new rows for records that have changed (old rows were marked as expired by
  -- the first MATCHED clause. #MergeOutput_ has been populated only with Source columns
  -- but MERGE is a full outer join so exclude rows with a Null primary key column.
  SET @DebugInfo = 'Insert for changed rows [dbo].[Products]';
  INSERT INTO Archive.[dbo].[Products]
    FROM [#MergeOutput_Products]
      MergeAction = 'UPDATE'
      AND [ProductId] IS NOT NULL;
    SELECT @UpdatedRowCount = @@ROWCOUNT;


  SET @DebugInfo = 'Get Row Counts [dbo].[Products]';
    @ObsoletedRowCount = SUM(CASE WHEN MergeAction = 'UPDATE' THEN 1 ELSE 0 END),
    @InsertedRowCount = SUM(CASE WHEN MergeAction = 'INSERT' THEN 1 ELSE 0 END)

  SET @TableActionSummary = CONVERT(varchar(19), GETDATE(), 121) + ' [dbo].[Products]
			 ' + CAST(ISNULL(@ObsoletedRowCount, 0) AS varchar(10)) + ' Rows Obsoleted, ' + 
			 CAST(ISNULL(@InsertedRowCount, 0) AS varchar(10)) + ' New Rows Inserted, ' +
			 CAST(@UpdatedRowCount AS varchar(10)) + ' Update Rows Inserted.';
  PRINT @TableActionSummary;
  D ROP TABLE [#MergeOutput_Products];

-- END OF TABLE [dbo].[Products]

  EXECUTE dbo.ERROR_HandleCaughtError @DebugInfo;
  RETURN 5555;

Many to Many tables

Or any table with just primary key columns.

CREATE TABLE dbo.StudentCourses(
	StudentId int NOT NULL,
	CourseId int NOT NULL,
	CONSTRAINT PK_StudentCourses PRIMARY KEY (StudentId, CourseId)

If the table only has a primary key then it is not possible to have changed values in the same record. The record either exists in both live and warehouse tables (in which case no action is needed) or it exists in only one, in which case it must be inserted or obsoleted in the warehouse accordingly.

If no non-primary key columns are found, the WHEN MATCHED AND EXISTS… clause is omitted from generated code.  Note in the section below that both primary key columns are used in the Merge join statement, and that the Matched And Exists clause is not generated.

    (SELECT * 
     FROM Archive.[dbo].[StudentCourses]
     WHERE EndDate IS NULL
  MERGE  TargetCTE AS [Target]
  USING  [dbo].[StudentCourses] AS [Source]
         [Target].[StudentId] = [Source].[StudentId]
     AND [Target].[CourseId] = [Source].[CourseId]
    THEN  -- Record deleted from source.
      SET [EndDate] = @EffectiveDate
    THEN  -- New Record.
      INSERT (

Generating Updates for all Tables

Omitting the SourceTable and SourceSchema parameters will generate DDL for a procedure that will include all tables in the database.   This can be restricted by providing SourceSchema (which will include all tables in the named schema) or SourceTable.


UTIL_GenerateArchiveProcedure.sql (3)

Mobile Reports in SSRS 2016

A tiled, dashboard style data presentation derived from DataZen and presentable from SQL Server Reporting Services (traditional rdl reports are differentiated as “Paginated Reports”).

Verdict:  it looks very nice, scales to different displays well, and can be presented in a browser via SSRS.   It lacks any update features, so usage as a dashboard is limited.

SSRS Mobile Report

These are initial comments on Build 1.0.3911.0.


  • There is no help or instructions.


  • developed in a basic application called “SQL Server Mobile Report Publisher” which is not integrated into Visual Studio.
  • The app can publish direct to report server or to file system as a .rsmobile file.
  • .rsmobile is a zipped file containing several xml files (sources, assets, metadata, definition), thumbnail images, a resources subfolder with colour specifications, and a data subfolder.
    • The data subfolder only exists if Enable client data caching is ticked in Settings.  Does this mean that data is fixed at design time and will not refresh?  If not, how long is data cached?
    • If present, the data subfolder contains a file for each data set defined containing the full data set returned, as far as I can see.  The format is JSON – encrypted if Encrypt data stored on the client is selected in Settings.


  • Takes data from standard SSRS Datasets or from Excel.  Presumably the Excel data has to be cached in the .rsmobile file.
  • Parameterised Datasets (without defaults) cannot be used.  However MS stated that these would be supported by general release.   However, there would seem to be little value in supporting parameterised datasets unless the parameters could be exposed to the mobile report users – as query string parameters, for example.  Maybe that is what is being considered.
  • Enable client data caching defaults to ON, Encryption defaults OFF.

Auto Refresh

It would make an attractive dashboard if it could refresh automatically.

  • There is no refresh option in the UI.  The full browser page can be refreshed, which a fair amount of distracting animation.
  • An auto-refresh browser plugin could be used.
  • From tests I can confirm that setting Dataset cache expiry on a scheduled doesn’t cause the report automatically to update.
  • Is there a refresh button in the mobile rendering of it?

Duration Dimension

For measuring short durations, such as telephone calls or handling times.

Q1 Linear Grain?

The question I haven’t answered is whether the dimension should be at the finest grain for all durations.  The grain I am using is seconds but probably after 30 minutes that’s finer than is needed. That’s 86,400 rows for a day.   An alternative would be using a log (ish) scale for DimDurationKey:  seconds up to 1 hour; minutes up to 24 hours, hours up to ? 7 days, then days… weeks… months.  This would allow a single Duration Dimension to cover a wide range of periods without becoming huge.  DimDimensionKey would have to be calculated during load.

Q2 Range Titles

What is a clear way of creating range titles:  0-1 minutes, 1-5 minutes, etc.   Strictly these are 0-59 secs, 1min – 1:59, 2min – 4:59, although users probably think of them as 1min, 2min, 5min.

  • 0:00 – 0:59
  • 1:00 – 1:59
  • 2:00 – 4:59
  • 5:00 – 59:59


  • 0 – 59 secs
  • 1 min – 1:59
  • 2 min – 4:59
  • 5 min – 59:59

Neither of those is clear or elegant.  The second emphasises the start of each range whereas the information is that it’s in the “5 minute” band, whereas “2 min – 4:59” most salient value is “2 min”.

Based on this I looked at rounding the end of each range.

  • 0 – 60 secs
  • 1:01- 2 min
  • 2:01 – 5 min
  • 5:01 – 1 hour

That looks better but it gets complicated once you move beyond 1 hour.  How to express the next segment up to say 1.5 hours:

  • 1:00:01 to 1 hour 3o mins
  • 1:00:01 to 1:30:00
  • 1:00:01 to 1 and a half hours

All are ugly mixes of units.

I think for short durations I am resigned to accept the one-second inaccuracy of “0-1 minute”, “2-5 minutes” for the sake of clarity and fitting in with the way that people actually look at these figures.  So long as the second in question is not double counted I don’t think it matters that it’s location is indeterminate. ( In contrast this wouldn’t be acceptable for age ranges (0-65, 65-70).)

In doing this I revert to rounding the start of each round. That accords then with the MinuteOfHour value which looks more logical when viewing the table as a whole.

So that leaves

  • 0 – 59 secs
  • 1 – 2 mins   [60 – 119 secs]
  • 2 – 5 mins
  • 45 – 60 mins
  • 60 – 90 mins
  • 90 mins – 1 hour

Creation Script

IF ( (SELECT COUNT(*) AS C FROM dbo.DimDuration) = 0 ) 
	print 'Seed DimDuration...';

		INSERT DimDuration
			0, 0, 0, 0, '0 secs', 0

	  Pass0 as (select 1 as C union all select 1), --2 rows
	  Pass1 as (select 1 as C from Pass0 as A, Pass0 as B),--4 rows
	  Pass2 as (select 1 as C from Pass1 as A, Pass1 as B),--16 rows
	  Pass3 as (select 1 as C from Pass2 as A, Pass2 as B),--256 rows
	  Pass4 as (select 1 as C from Pass3 as A, Pass3 as B),--65536 rows
	  Tally as (select row_number() over(order by C) as Number from Pass4)

	INSERT DimDuration

		SELECT TOP(15000) 
			Number / 60,
			Number / (60 * 60),
			(Number / 60) % 60,
				WHEN (Number)       < 60	    THEN '1 - 59 secs'
				WHEN (Number)       < 120	    THEN '1 - 2 mins'
				WHEN (Number / 60)  < 3		    THEN '2 - 3 mins'
				WHEN (Number / 60)  < 4		    THEN '3 - 4 mins'
				WHEN (Number / 60)  < 5		    THEN '4 - 5 mins'
				WHEN (Number / 60)  < 6		    THEN '5 - 6 mins'
				WHEN (Number / 60)  < 7		    THEN '6 - 7 mins'
				WHEN (Number / 60)  < 8		    THEN '7 - 8 mins'
				WHEN (Number / 60)  < 9		    THEN '8 - 9 mins'
				WHEN (Number / 60)  < 10	    THEN '9 - 10 mins'
				WHEN (Number / 60)  < 15	    THEN '10 - 15 mins'
				WHEN (Number / 60)  < 20	    THEN '15 - 20 mins'
				WHEN (Number / 60)  < 30	    THEN '20 - 30 mins'
				WHEN (Number / 60)  < 40	    THEN '30 - 40 mins'
				WHEN (Number / 60)  < 50	    THEN '40 - 50 mins'
				WHEN (Number / 60)  < 60	    THEN '50 - 60 mins'
				WHEN (Number / 60)  < 90	    THEN '60 - 90 mins'
				WHEN (Number / 60)  < 120	    THEN '90 mins - 2 hours'
				WHEN (Number / (60 * 60)) < 3	THEN '2 - 3 hours'
				WHEN (Number / (60 * 60)) < 4	THEN '3 - 4 hours'
				WHEN (Number / (60 * 60)) < 10	THEN '4 - 10 hours'
				WHEN (Number / (60 * 60)) < 15	THEN '10 - 15 hours'
				WHEN (Number / (60 * 60)) < 24	THEN '15 - 24 hours'
				ELSE  'over 24 hours'
			FROM Tally;

	-- Add a column that can be used as a sort-key for ranges.

			SELECT DurationAnalysis1,
				DurationAnalysis1_MaxSeconds  = MAX(Seconds)
			FROM dbo.DimDuration
			GROUP BY DurationAnalysis1
			DurationAnalysis1_MaxSeconds = CTE.DurationAnalysis1_MaxSeconds
			DimDuration DD 
			INNER JOIN CTE ON DD.DurationAnalysis1 = CTE.DurationAnalysis1;

	print 'Seed DimDuration complete.';