Real-World SSIS
A Survival Guide
Tim Mitchell
What we’ll cover today
•
•
•
•
Lessons I’ve learned the hard way
Methodologies to solve real problems in SSIS
Tools to help out
Solutions for SQL 2012 as well as earlier
versions
• Demos
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What we won’t cover
• No intro to SSIS
• Books Online
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Housekeeping
• Presentation materials
• Lunch / breaks
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Housekeeping
•
•
•
•
Let’s keep it informal
Ask questions
Ask questions
Ask questions
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PSA: Community
•
•
•
•
•
Survival is easier in groups
Local user groups
Events (SQL Saturday, SQL Bits, PASS Summit)
Online communities
Twitter (#sqlhelp)
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About me
•
•
•
•
Business intelligence consultant
Group Principal, Linchpin People
SQL Server MVP
TimMitchell.net / @Tim_Mitchell
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Texas Dictionary
• Whole mess: Bountiful amounts of something,
usually referring to excess
• More than one way to skin a cat: A petunfriendly phrase to indicate that there are
usually multiple ways to solve the same
problem
• Ya’ll: A subgroup of the current group
• All ya’ll: The whole of the current group
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survival (noun)
The state or fact of continuing to live
or exist, typically in spite of an
accident, ordeal, or difficult
circumstances.
Reference: Dictionary.com (http://dictionary.reference.com/browse/survival)
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survival (noun)
Survival is simply the state
of existing. It’s just a small
step above being dead.
-- Me
Photo credit: Elvis Ripley (http://www.flickr.com/photos/elvisripley/315439191/). Used under Creative Commons license.
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Elements of Survival
The dangers:
• The elements
• Predators
• Foolishness of fellow survivors
• The unexpected
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Elements of Survival
The dangers:
• Dirty data
• Complex or poorly defined ETL
requirements
• Unexpected metadata changes
• Unstable sources/destinations
• Project managers
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Elements of Survival
Means of survival:
• Common sense of self preservation
• Tools
• Leaning on others
• Learning from others’ mistakes
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Elements of Survival
Means of survival:
• Best practices
• Consistency
• Document
• Tools (buy/build)
• Community
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Survival Tip #1:
Plan to Fail
Planning to Fail
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Planning to Fail
Data failures:
• Missing or offline sources
• Changed metadata
• Partial loads
• Validation issues
• Unexpected domain values
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X
If it happens…
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Planning to Fail
Planning for failure in the wild:
• Build your shelter before it rains
• Layers
• Leaves
• Bread crumbs
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Planning to Fail
Planning for failure, the ETL way:
• Be a pessimist!
• Fail gracefully
• Capture error/warning data on failure
• Build for restartability (where appropriate)
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Failing
Gracefully
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Planning to Fail
Why graceful failure?
• Avoid leaving affected systems in an
inconsistent state
• Avoid repeating wholesale operations
• Timely notifications to allow proper response
from dev/admin staff
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Planning to Fail
Graceful failures in SSIS
• Control flow:
– Event handlers
– Precedence constraints
• Data flow:
– Error row redirection
– Lookup failure redirection
– Conditional split
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Planning to Fail
Graceful failures in SSIS
• Restartability
– SSIS Checkpoints
– SSIS transactions
– Both methods have shortcomings
– Custom restartability can be an option
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Planning to Fail
Natural failures
• Simply stop processing on error
• Default behavior
• In some cases, can be the right pattern
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Demo
Designing for failure
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Survival Tip #2:
Take Notes
Take Notes
What to note?
• Trails, paths, and shortcuts
• Water sources
• Hazards
• Enemy positions
• Weather and wildlife patterns
• Sunrise/sunset time
http://www.flickr.com/photos/defenceimages/4993131844
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Take Notes
What to note?
• Success and failure of operations
• Row counts
• Run times
• Validation information
• Warnings
http://www.flickr.com/photos/mezdeathhead/3057797092/
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Take Notes
Why?
• Know what to expect
• Plan for growth
• Cover your assets
http://www.flickr.com/photos/mezdeathhead/3057797092/
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Take Notes
It’s all about the log.
• SSIS logging
• SQL Server log
• Custom logging
http://www.flickr.com/photos/rwphoto/3108504086/
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Take Notes
SSIS Package Logging
• It’s already there
• Easy to start
• Flexible events and
destinations
• Can be unwieldy
http://www.flickr.com/photos/raver_mikey/4285637209/
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Take Notes
SSIS Catalog Logging
• Version 2012 only
• Easiest to configure
– Design time or runtime
• Least flexible
http://www.flickr.com/photos/raver_mikey/4285637209/
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Take Notes
Custom Logging
• Roll your own
• Most difficult to set up
• Infinitely flexible
http://www.flickr.com/photos/raver_mikey/4285637209/
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Take Notes
Server/engine logging
• SQL Engine error log
• DMVs
• Third party tools
• Windows log
• PerfMon
http://www.flickr.com/photos/raver_mikey/4285637209/
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Demo
Take Notes
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Survival Tip #3:
Perform at
your best
Perform at your Best
http://www.flickr.com/photos/defenceimages/5786942640/
http://www.flickr.com/photos/defenceimages/4627096866/
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Perform at your Best
Soldier up!
• Recognize and avoid quicksand
• React appropriately when you’re stuck
• Know your environment
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Perform at your Best
Soldier up!
• Isolate and eliminate the things that slow you
down
• Recognize design patterns that are detrimental
to performance
• Look *outside* SSIS (gasp!)
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Perform at your Best
It’s not just SSIS
• The majority of SSIS performance problems
have nothing to do with SSIS
• Limitations on sources and destinations
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Perform at your Best
It’s not just SSIS
• Don’t just ‘pass the buck’, but do consider
other factors:
– SQL engine configuration
– Disk configuration
– Network speed/latency
– Physical machine capabilities
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Perform at your Best
It’s not just SSIS
• Proper query techniques for relational sources
• Effective indexing for sources and destinations
• Using OPTION (FAST <n>)
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Perform at your Best
Streamline your data flows
• Transformations matter!
• Know how the blocking properties of
transformations
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Perform at your Best
Streamline your data flows
• Nonblocking transforms do not hold buffers
– Derived Column
– Conditional Split
– Row Count
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Perform at your Best
Streamline your data flows
• Partially blocking transforms will queue up
buffers as needed
– Merge Join
– Lookup
– Union All
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Perform at your Best
Streamline your data flows
• Fully blocking transforms will not pass any data
through until all of the data has been buffered
at that transformation
– Sort
– Aggregate
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Perform at your Best
Streamline your data flows
• Be aware of memory use!
– LOB (large object) columns will always spool to disk
rather than staying in memory.
– [N]VARCHAR(MAX)
– Memory buffers may spill over to disk
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Perform at your Best
Streamline your data flows
• Manage your sources
– Don’t use table drop down list – specify your query
including only the necessary columns
– Be mindful of indexes when writing data retrieval
queries
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Perform at your Best
Streamline your data flows
• Manage your destinations
– Use FAST LOAD for SQL Server destinations
– Index management (drop?)
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Perform at your Best
Go Parallel!
• Parallel operations can yield faster data flows
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Demo
Parallel data flow
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Perform at your Best
Streamline your data flows
• Using lookups
– Pay attention to lookup cache mode
• Full cache
• Partial cache
• No cache
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Perform at your Best
Streamline your data flows
• Using lookups
– Two-phase lookup strategy:
• Commonly accessed data in full cache
• Remaining data in a subsequent partial cache
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Perform at your Best
Streamline your data flows
• Using lookups
– Cache connection manager
• Allow reuse of lookup information across data flows
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Demo
Lookups
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Survival Tip #4:
Clean it up
Clean it up
The greatest danger is in the elements
• Chances are that unsanitary conditions will kill
you before a predator does
– Infection
– Spoiled food or water
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Clean it up
In ETL, the greatest dangers often lie in the small
things
• Like an infection, bad data can fester for a
while until it’s too late
• Caught early, problems with dirty data are
more easily solved
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What is dirty data?
Types of dirty data:
• Data type mismatches
• Domain violations
• Semantic violations
• Technical errors
• Simple inaccuracies
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What is dirty data?
Data type mismatches
• Non-numeric data in numeric fields
• Decimal data in integer fields
• Incorrect precision / rounding
• Truncation
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What is dirty data?
Domain violations
• Invalid dates
• Incorrect addresses
Semantic violations
• Data outside of a reasonable range (such as a
person’s age in the thousands of years)
• Inconsistent use of NULL, blanks, and zeroes
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What is dirty data?
Technical errors
• Improperly formatted dates
• Out-of-alignment flat files
• Too many/too few delimiters
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What is dirty data?
Simple inaccuracies
• Misspellings
• Duplications
• Improper formatting (email addresses, phone
numbers)
• Case
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What causes
dirty data?
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What is dirty data?
Causes of dirty data:
• Internal:
• Unvalidated user input
• Lack of proper database constraints and/or
application logic
• External:
• Import bad data from other systems
• ETL errors
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Now What?
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Clean it up
• Test your cleansing logic in stage/test/QA
first
• Cleanse directly in production
• Don’t cleanse at all
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Clean it up
What to do with unresolvable bad data?
• Delete
• Update to NULL or unknown member
• Mark as suspect
• Write to triage
• Stop the ETL
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Data Cleansing
in SSIS
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Data Cleansing in SSIS
Tools of the trade
• Native SSIS components
• POTS (Plain Old Transact-SQL)
• SQL Server DQS
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Data Cleansing in SSIS
SSIS Native Components
• A versatile approach with more
transformation options
• A much better choice when data cleansing
operations involve multiple and/or non-SQL
Server data sources
• Extensible through custom code
• Third party add-ons
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Data Cleansing in SSIS
SSIS Native Components
• Precision tools include Lookup
Transformation, Merge Join
• Flexible/inexact cleansing through
Conditional Split, Derived Columns
transformation, fuzzy tools
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Data Cleansing in SSIS
Transact-SQL
• Fast, simple, effective way to do some
cleanup operations
• Requires no additional software or
configuration
• Extensible through the use of UDFs or CLR
functions
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Data Cleansing in SSIS
Data Quality Services
• A tool specifically designed for data cleansing
• Has its own client interface, or can be used
within SSIS for cleansing operations
• Limited set of operations in SSIS
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Demo
Data Cleansing
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Survival Tip #5:
The Swiss Army
Knife
Swiss Army Knife
When unexpected situations arise, an allpurpose tool can literally be a lifesaver.
• Cut up small firewood
• Can opener
• Make a game trap
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Swiss Army Knife
Scripting and coding tools
• SSIS Expressions
• Script task/script component
• PowerShell
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Swiss Army Knife
SSIS Expressions
• Built into SSIS
• Can be used in most any component or task
• No extra moving parts required
• Useful for declarative statements
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Swiss Army Knife
Pros:
• Easy to get started – just start expressing
yourself
• Ubiquity
• Relatively easy to use
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Swiss Army Knife
Cons:
• Syntax is <polite> unique </polite>
• Complex expressions are difficult
• Troubleshooting
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Swiss Army Knife
SSIS Scripting
• .NET Framework
• VB.NET or C#
• Can use existing external assemblies
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Swiss Army Knife
Pros:
• Swiss Army knife of SSIS
• Works great for operations where native
SSIS tasks/components can’t easily
accomplish goal
• Does not require in-depth programming
knowledge
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Swiss Army Knife
Cons:
• Does require some familiarity with
programming or scripting
• Not as simple as native components
• Performance (sometimes)
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Swiss Army Knife
Script Task
• Used in the Control Flow
• Variety of uses:
• Interact with OS
• Filesystem operations (archiving)
• Manipulate SSIS variables
• Call external programs
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Swiss Army Knife
Script Component
• Data Flow pane
• Data flow/manipulation
• Used for:
• Data manipulation in the pipeline that can’t be
accomplished otherwise
• Advanced branching logic
• Shred unconventional input files
• Create custom output files
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Swiss Army Knife
Script Component
• Synchronous or asynchronous
• Types
• Source
• Transformation
• Destination
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Swiss Army Knife
•
•
•
•
•
•
Semi-structured files
Nonlinear files
Multiple lines of text per output row
Varying number of columns
Dissimilar data types
“Record Type” format
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Other Scripting Uses
• Wait for a file or connection to be available
• Set and enforce thresholds for maximum
execution time
• Custom logging
• Custom notifications
• Cross-package variable sharing
• ?????
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Demo
Expressions and
scripting
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Survival Tip #6:
Know what’s
coming next
Know what’s coming next
Survivors keep an eye on what to expect in the
days/months/years ahead
• Weather forecasts
• Changing of seasons
• Wildlife patterns
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Know what’s coming next
Know the technical/business landscape
• New versions of software
• Emerging design patterns
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What’s new in SSIS for
SQL Server 2012
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Logging Changes
Back in the day…
• Logging configured at the
package level
• Inconsistent
• Difficult to add logging
afterward
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Logging Changes
… and now:
• Logging is configured at the server level (SSIS
catalog)
• Can be added, changed, or removed at
runtime
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Logging Changes
… and now:
• Logging levels:
–
–
–
–
Basic
Performance
Verbose
None
• Native row count logging
(and everyone said “Amen”)
• Logs to table in SSISDB
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Logging Changes
… and now:
• Built-in reports
– Included with SSMS
– Detail and aggregate data
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102
Undo/Redo
When I was your age…
• Package changes are
immediate
• Undo = close without saving
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Undo/Redo
… and now
• Full support of Undo and
Redo in the designer
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Package Parameters
Prior Versions:
• Sharing of values between
packages required the
inheritance of parent package
variables
• Parent packages had no
knowledge of expected variables
in child packages
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Package Parameters
Prior Versions:
• There was no practical way to
configure variables as required
(other than failing the
package)
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Package Parameters
SQL Server 2012:
• Package parameters!
• Required or optional
• Accessible through the Execute Package Task
in parent packages
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Package Parameters
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DQS and SSIS
Then:
• Data quality routines were
everywhere, but also
completely manual
• No standard means of
implementation
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DQS and SSIS
Now:
• SSIS has a transformation to
leverage DQS (also new) for
data cleansing operations
• Consumes reusable
knowledge base data for
reliable, consistent cleansing
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DQS and SSIS
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Flat File Improvements
Old school:
• Irregularly shaped flat files could not be
natively processed in SSIS
• Scripting was usually required to process
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Flat File Improvements
New school:
• New flat file connection allows native
processing of files with missing columns
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Flat File Improvements
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Shared Data Sources
In days of yore:
• “Shared” connections meant configuring a
connection in each package, and using
package configs for the connection string
• Still requires setting up and maintaining
connections at the package level
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Shared Data Sources
Here and now:
• Native shared connections allow SSIS
projects to use connections common to the
entire project
• Package-level connections still supported
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Shared Data Sources
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Script Component Debugging
Remember when:
• MessageBox.Show()
• MessageBox.Show()
• MessageBox.Show()
• MessageBox.Show()
• MessageBox.Show()
• MessageBox.Show()
• MessageBox.Show()
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Script Component
Debugging
… and now:
• Integrated debugging in the
script component
• Step through code line by line
to find issues and test
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Demo
Script Component Debugging
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Name-based metadata
mapping
Then:
• Changing upstream components often causes
runtime errors in downstream components
• The longest 4-letter word in the English
language:
VS_NEEDSNEWMETADATA
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Name-based metadata
mapping
Now:
• Metadata mapping is based on name
• Easier to remap upstream components
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Demo
Name-based metadata mapping
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CDC in SSIS
The old:
• CDC (Change Data Capture) was present in
the DB engine, but required manual T-SQL
coding to implement
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CDC in SSIS
The new:
• SSIS now has new task and
components to handle CDC
processing
• CDC Task – metadata (start/end
initial load, etc.)
• CDC Source – retrieve CDC data
• CDC Splitter – break apart
results
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Environments
Environment replace configurations
• Collections of related values (ex: Production
connection strings, Dev connection strings,
etc.)
• Multiple environments can be associated
with each project or package
• Specify for automated job, or easily choose
at runtime
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10+. Designer Improvements
Package annotations
• In prior versions, annotations were difficult
• SSIS 2012 improvements
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Designer Improvements
Sort packages by name
• Sometimes it’s the little things that matter
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Designer Improvements
Simplified data viewer
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Designer Improvements
Universal status indicators
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Designer Improvements
Variable management
• Scope default
• Expression management
• Static values vs. expression
• Expression indicator
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Survival Tip #7:
Have a bag
of tricks
Have a bag of tricks
Be lazy!
• Code once, reuse many
• Create a portable system for reusing familiar
patterns
– Database?
– Documentation?
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Have a bag of tricks
Be lazy!
• ETL Framework
– Managed execution for multipackage ETL
processes
– Restartability, consolidated error handling and
logging
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Have a bag of tricks
Be lazy!
• Custom SSIS components
– Create custom components for commonly used
design patterns
– Parameterized script packages may substitute in
SQL 2012
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Have a bag of tricks
Be lazy!
• Third party tools
– BIDS Helper
– SSIS Reporting Pack
– SQL Sentry Plan Explorer
– Brent Ozar’s SQLBlitz
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Have a bag of tricks
Be lazy!
• Biml
– Business Intelligence Markup Language
– Package generation tool
– Included with BIDS Helper (free)
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Demo
Biml package generation
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Questions?
Comments?
Standing ovation?
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Thanks!
TimMitchell.net
@Tim_Mitchell
tdmitch@gmail.com
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