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Monday, 4 March 2013

Nonblocking, Partial Blocking and Blocking Transformations In SSIS


Classifying Data Flow Transformations on performance considerations:

The three classifications of Data Flow transformations that are based mainly on
performance considerations.

Nonblocking Synchronous Row-Based Transformations:

These transformations work on a row-by-row basis and modify the data by changing
the data in columns, adding new columns, or removing columns from the data rows,
but these components do not add new rows to the data flow. The rows that arrive at the
input are those that leave at the output of these transformations. These transformations
have synchronous outputs and make data rows available to the downstream components
straightaway. In fact, these transformations do not cause data to move from one buffer
to another; instead, they traverse over the data buffer and make the changes to the data
columns. So these transformations are efficient and lightweight from the process point
of view. The transformations that readily pass the data to the downstream components are classified as Row Transformations.

The following Row Transformations are examples of Nonblocking synchronous row-based transformations:

Ø  Audit transformation
Ø  Character Map transformation
Ø  Conditional Split transformation
Ø  Copy Column transformation
Ø  Data Conversion transformation
Ø  Derived Column transformation
Ø  Export Column transformation
Ø  Import Column transformation
Ø  Lookup transformation
Ø  Multicast transformation
Ø  OLE DB Command transformation
Ø  Percentage Sampling transformation
Ø  Row Count transformation
Ø  Row Sampling transformation
Ø  Script Component transformation configured as Nonblocking Synchronous
Row–based transformation
Ø  Slowly Changing Dimension transformation

Partially Blocking Asynchronous Row-Set-Based Transformations:

Integration Services provides some transformations that essentially add new rows in the
data flow and hold on to the data buffers before they can perform the transformation.
The nature of such transformations is defined as Partially Blocking because these
transformations hold on to data for a while before they start releasing buffers. As
these transformations add new rows in the data flow, they are asynchronous in
nature. For example, a Merge transformation combines two sorted data sets that may
be the same data, but it essentially adds new rows in the data flow. The time taken
by this transformation before starting to release the buffers depends on when these
components receive matching data from both inputs.

Following is a list of Partially Blocking asynchronous row set-based transformations:

Ø  Data Mining Query transformation
Ø  Merge Join transformation
Ø  Merge transformation
Ø  Pivot transformation
Ø  Term Lookup transformation
Ø  UnPivot transformation
Ø  Union All transformation
Ø  Script Component transformation configured as Partially Blocking Asynchronous
Row-set-based transformation

Blocking Asynchronous Full Row-Set-Based Transformations:

These transformations require all the rows to be assembled before they can perform
their operation. These transformations can also change the number of rows in the
data flow and have asynchronous outputs. For example, the Aggregate transformation
needs to see each and every row to perform aggregations such as summation or
finding an average. Similarly, the Sort transformation needs to see all the rows to
sort them in proper order. This requirement of collecting all rows before performing
a transformation operation puts a heavy load on both processor and memory of the
server. You can understand from the nature of the function they perform that these
transformations block data until they have seen all the rows and do not pass the data to
the downstream component until they have finished their operation.

The following are Blocking asynchronous full row-set-based transformations:

Ø  Aggregate transformation
Ø  Fuzzy Grouping transformation
Ø  Fuzzy Lookup transformation
Ø  Sort transformation
Ø  Term Extraction transformation



Resources:
For more detailed information regarding this study, please see
Ø  Microsoft SQL Server 2008 Integration Services by Ashwani Nanda
Ø  Microsoft Books Online




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