Tag Archives: Best Practices

Articles related to performance and usability best practices in Microsoft SQL Server.

The OLE DB destination in "Fast Load" configuration with "Table Lock" checked (default)

#0391 – SQL Server – SSIS – OLE DB Destination – Table Fast Load – Performance – Table Lock option

Developing SSIS packages is quite easy – it’s mostly drag and drop and some minor configuration, which is a really boon for someone who is new to SSIS. However, when it comes to tuning the package, one needs to understand the finer points of each task on the control flow.

The OLE DB Destination

In one of my previous posts, I started to explore the OLE DB destination. In order to load data as quickly into the destination as possible, the  OLE DB destination allows us to use a “Fast Load” mode. The “Fast Load” option allows the data team to configure various options that affect the speed of the data load:

  1. Keep Identity
  2. Keep NULLs
  3. Table Lock
  4. Check Constraints
  5. Rows per Batch
  6. Maximum Insert Commit Size

I looked at the “Keep NULLs” and the “Keep Identity” options earlier, and today I will go over the “Table Lock” option.

Because the option is part of the OLE DB destination task, the “Table Lock” option indicates whether the database engine should request a more wider lock on the entire table (i.e. use TABLOCK) rather than trying to get granular locks one each row/page and then follow lock escalation to block the table.

Theoretically, when moving extremely large amounts of data compared to the number of records already available in the destination table, the database engine would deem the granular locks (at the row/page level) too cost prohibitive and escalate to the table anyway. In this case, it would be better to specify the wider TABLOCK.

Allow me to present a brief demo.

Creating the package with logging for comparing execution time

As always, I have created a simple package that creates a table and inserts data into it. The table is identical to the [Sales].[SalesOrderDetail] table in the [AdventureWorks2014] sample database. The table creation script used in the Execute SQL task on the package is provided below:

USE [tempdb];

IF OBJECT_ID('[dbo].[SalesOrderDetail]','U') IS NOT NULL
    DROP TABLE [dbo].[SalesOrderDetail];

CREATE TABLE [dbo].[SalesOrderDetail]
    [SalesOrderID]           [INT]              NOT NULL,
    [SalesOrderDetailID]     [INT]              NOT NULL,
    [CarrierTrackingNumber]  [NVARCHAR](25)         NULL,
    [OrderQty]               [SMALLINT]         NOT NULL,
    [ProductID]              [INT]              NOT NULL,
    [SpecialOfferID]         [INT]              NOT NULL,
    [UnitPrice]              [MONEY]            NOT NULL,
    [UnitPriceDiscount]      [MONEY]            NOT NULL 
                             CONSTRAINT [DF_sodUnitPriceDiscount]  DEFAULT ((0.0)),
    [LineTotal]              DECIMAL(38, 6),
    [rowguid]                [UNIQUEIDENTIFIER] NOT NULL,
    [ModifiedDate]           [DATETIME]         NOT NULL 
                             CONSTRAINT [DF_sodModifiedDate]  DEFAULT (GETDATE())

Once the table is created, the package “flows” to the Data Flow Task. Inside the data flow, essentially I simply select about 4 times the data from the [AdventureWorks2014].[Sales].[SalesOrderDetail] table (approximately 485268 rows) using an OLE DB source and pump it to the newly created target table via an OLE DB destination with the “Table Lock” option checked (default).

I then configure logging on the package to log the package activity for the Data Flow Task for the OnError, OnPreExecute and OnPostExecute events (Configuring package logging is out of scope for this blog post).

The script used in the OLEDB source is presented here:

USE AdventureWorks2014;
SELECT sod.SalesOrderID,
FROM Sales.SalesOrderDetail AS sod
CROSS JOIN (VALUES (1), (2), (3), (4)) AS Combinations (ComboValue)

A set of screenshots showing the package configuration described above are shown below.


An “Execute SQL Task” creates the test table


The OLE DB destination in “Fast Load” configuration with “Table Lock” checked (default)

Package Log configuration enabled for the OLEDB destination

Package Log configuration enabled for the OLEDB destination

Event configuration done to log task activities as part of package logging

Event configuration done to log task activities as part of package logging

Once the package is executed, I will compare the difference between the OnPreExecute and OnPostExecute times for both configurations of the “Table Lock” option to get an idea of the performance difference between them.

NOTE: The packages were executed after it was confirmed that the databases involved (in this case tempdb) had grown sufficiently to accommodate the inserted data.

Confirming that we are indeed taking a Table Lock

While the package is executing in SQL Server Data Tools (SSDT, erstwhile BIDS), I run the following query on the SQL Server to check the locks occupied on our test table.

USE [tempdb]
SELECT tl.resource_associated_entity_id AS ObjectId,
       OBJECT_NAME(tl.resource_associated_entity_id) AS ObjectName,
       tl.request_mode AS LockRequestMode
FROM [sys].[dm_tran_locks] AS tl
WHERE tl.resource_database_id = DB_ID()
  AND tl.resource_associated_entity_id = OBJECT_ID('[dbo].[SalesOrderDetail]','U');

Based on the results, we can confirm that an exclusive Bulk Update (BU) lock has indeed been requested and granted on the table – indicating that the TABLOCK option was used as part of the insert.

Bulk Update (BU) lock used on the table, indicating use of TABLOCK (Table Lock)

Bulk Update (BU) lock used on the table, indicating use of TABLOCK (Table Lock)

Running the package with “Table Lock” turned Off

If I check the locks on the table while running the package with the “Table Lock” option unchecked, I see that either an “Intent Exclusive” (IX) or an “Exclusive” (X) lock have been used. This indicates that SQL Server is actually using Exclusive locks on lower level allocation units (page/row).


When “Table Lock” is unchecked, an Intent Exclusive Lock is acquired on the table, indicating granular exclusive locks in use

Please note that between each run, the data buffers were cleaned and procedure cache was cleared out to get a “cold” state performance of the database engine.


Comparing package execution performance

Because I had turned on logging on the SSIS package, I ran the following query against the [dbo].[sysssislog] table which gives me the time difference (in seconds) between the “OnPreExecute” and “OnPostExecute” events for both the packages. The query and the results are available below:

USE [tempdb];
SELECT [PivotedTbl].,
               ) AS PackageRunTimeInSeconds
      FROM [dbo].[sysssislog] AS sl
     ) AS [SourceTbl]
PIVOT (MAX([SourceTbl].[starttime]) 
       FOR [SourceTbl].[event] IN ([OnPreExecute],[OnPostExecute])
      ) AS [PivotedTbl];

Execution time summary showing data flow with OLE DB destination using Table locks completes faster than one using granular locks

We can clearly see that the table load with “Table lock” checkbox turned on is comparatively faster.


The OLE DB destination task is a very powerful way to load data into SQL Server table. It also provides the flexibility to boost the rate of data insertion when used wisely.

  • When loading data into an empty table or when system is under an outage window, evaluate keeping the “Table Lock” checkbox checked
  • When it is important to keep the tables accessible during the data load, evaluate keeping the “Table Lock” checkbox unchecked
    • This will ensure that if possible, the SELECT queries are not blocked from being executed as long as they do not refer to the same page(s) being inserted/updated
  • Using a higher-level lock (in case of requesting a Table Lock/TABLOCK) does provide a reduced data “load” time due to reduced overhead of maintaining granular locks and can be used as a means to speed up the data inserts for large data sets


  • Lock Modes in SQL Server [MSDN Link]
  • Lock Compatibility [MSDN Link]
  • Performance – Best Practice – Create Index Before or After a data insert? [Blog Link]
  • Performance – Best Practice – Create Index Before or After a data insert – I/O, Fragmentation, CPU and Elapsed Time [Blog Link]

Until we meet next time,

Be courteous. Drive responsibly.

Data Flow Task used to demonstrate case-sensitivity of Lookup transformation

#0388 – SQL Server – SSIS – Lookup transformations are case-sensitive

I have been working with SQL Server Integration Services (SSIS) recently. In many ways, SSIS is counter-intuitive if you have been working with the database engine for long (more than a decade in my case). Working with SSIS is more like working with any other .net framework based programming language (C# or VB.net). Over the next couple of days, I will be writing about some of the salient aspects of SSIS which should be kept in mind in case you are working on multiple SQL Server technologies.

Lookup Transformations – A key to successful system integrations

Cross-referencing of Enumerations

One of the key challenges for any system integration is to ensure that the enumerations  and “default” values used in the source system (e.g. sales statuses, product categories, etc) align between the “source” & “target” systems.

Once the values aligned during business, high-level and low-level designs, implementation of this cross-referencing in SQL Server Integration Services (SSIS) is done by a data flow component called the “Lookup Transformation“. The Lookup transformation effectively performs a join between the input data with a reference data set. If values match, they are available in what is called the “Match Output” whereas values that do not match can be made available as a “No Match Output”. However, this comes with a tiny counter-intuitive behaviour that I learnt about the hard way.

The lookup performed by the lookup transformation is case-sensitive.


In order to demonstrate the case-sensitivity of lookup transformations, I have developed a SSIS package that does the following in a simple data-flow task:

  1. Get some static data from an OLEDB data source, basically some rows with text representation of numbers (One, Two, Three, and so on)
  2. The Lookup transform has a static mapping between the numeric and text values of various numbers – 1 through 10
  3. As the input data passes through the lookup transformation, we try to map the text values in the source data with the values available in the lookup transformation so that we can get the appropriate numeric representation
    • In my demo, records that find a valid lookup are written to a recordset destination (it could be any valid destination), whereas records that do not match are written to another destination
    • I have placed data viewers on the output pipelines to visually see the data being moved, which is what I will show below

The query used to generate the static data in the OLE DB source is provided below.

SELECT srcValues.RowName,
FROM (VALUES ('Row1','One'),
     ) AS srcValues (RowName, RowValue);

The query used to generate the lookup values for the lookup transform is provided below:

SELECT lookUpValues.Id,
FROM (VALUES (1, 'One'),
             (2, 'Two'),
             (3, 'Three'),
             (4, 'Four'),
             (5, 'Five'),
             (6, 'Six'),
             (7, 'Seven'),
             (8, 'Eight'),
             (9, 'Nine'),
             (10, 'Ten')
     ) AS lookUpValues (Id, RowValue);

Observe that in the static source data, not all values have a consistent case – some are in sentence case, whereas some are in small case.

The screenshots below show the overall setup of the SSIS package.

Data Flow Task used to demonstrate case-sensitivity of Lookup transformation

Data Flow Task used to demonstrate case-sensitivity of Lookup transformation

LookUp Transformation - General configuration (Notice redirection to no match output)

LookUp Transformation – General configuration (Notice redirection to no match output)

Lookup Transformation - Connection tab showing reference values

Lookup Transformation – Connection tab showing reference values

Lookup Transformation - Columns configuration showing "RowValue" used for matching whereas the reference Id is fetched to include in output

Lookup Transformation – Columns configuration

Notice here that we have used the text value from the source data (“RowValue” column) for matching/lookup to the reference data set. The reference Id is fetched to include in output.

If a match is found the “Match Output” should contain the matching row from the source combined with the Id from the reference/lookup data. This is seen in the data viewer output below.

Lookup Transformation - Match Output (source rows with the Id from the reference data)

Lookup Transformation – Match Output (source rows with the Id from the reference data)

If a match is not found (which would be the case for the values with lower case in the source data), the “No Match Output” will contain the row from the source data that failed the lookup (since failures were redirected to the “No Match” output in the general configuration). Notice we do not get the Id from the reference because no match to the reference was found.

Lookup Transformation - No Match Output

Lookup Transformation – No Match Output


When working with a case insensitive database, we often tend to take data quality with respect to case of the data for granted. Having data with inconsistent case has multiple repercussions (especially with data grouping in front end applications), but the biggest negative impact due to inconsistent case of text data is the inaccurate cross-referencing during a master data cleanup, system integration or data migration exercise.

Call to action

Do take a few cycles in your development to take a look at your data quality, and if necessary, implement required data cleansing to ensure that your lookup data, enumerations and master data are using a case that is correct and consistent with the domain and business requirements.

Until we meet next time,

Be courteous. Drive responsibly.