1. Extreme Salesforce Data Volumes
Force.com Architecture Best Practices
From salesforce.com’s Customer Centric Engineering – Technical Enablement team
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3. Speakers
Bud Vieira Steve Bobrowski
Architect Evangelist Architect Evangelist
@aavra @sbob909
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7. Today s Learning Goal
AWARENESS
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8. How much data can the Salesforce Platform handle?
You might be surprised!
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9. Best practices
Design Load Configure Maintain
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13. Understand application and user requirements
Ho w m uc h
How many o perational data
records make is really
sense in each list necessar y?
view an d report?
Can yo u deem
certain data
historical or not
useful for views
an d reports?
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14. Example: Design list views that make sense
How many pages of data records can a human process?
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16. Estimate the growth and volume of data
Can yo u eliminate
At what rate w ill certain types of
the data volu me data fro m yo ur
increase fro m schema? Fro m
transactions? yo ur data loa ds?
Fro m data loa ds?
Can yo u offset
the data growth
by archiving
data? Hard-
deletes? Recycle
bin empties?
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17. Example: Project monthly and annual growth
Large Object X (Monthly)
Transaction growth rate
• Records
• GB
Data load growth rate
• Records
• GB
Archival offset
• Records
• GB
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18. Key success factor: Minimize data volume
Devise a data Devise a data
loa ding plan offloa ding plan
that meets, but that archives
doesn t excee d, unnecessar y data
requirements
After archiving,
hard- delete rows
or empty the
recycle bin
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20. Learn how to design efficient queries
Un derstan d
Docu ment SOQL quer y
available o ptimization
in dexes
Design selective
list views,
reports, an d
SOQL queries
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25. Learn how to design efficient searches
Docu ment Un derstan d
available SOSL quer y
in dexes o ptimization
Design selective
list views,
reports, an d
SOSL queries
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27. Cheat Sheet:
Search Fields
http://developer.force.com/architect
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28. Learn how Search retrieves rows
INDEX
Millions
of
DB
records
Query
across entire
index for
matches to Record IDs of DB processing
search string matches to includes sharing
query are sent access check
to DB for
processing
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29. Review: Design phase best practices
ü Know your requirements
ü Estimate data growth rate
ü Minimize data volume
ü Design efficient operations
ü Plan an efficient sharing model
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30. Data load best practices
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31. Data load best practices
Clean an d Check o ut
organize data AppExchange
before loa ding to ols
Use Bulk APIs for Disable an d defer
better what yo u can
thro ughput
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32. Prepare your data to avoid overhead
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33. Disable actions that fire on insert
Validation Rules
Workflow Rules
Triggers
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38. Review: Data load phase best practices
ü Load cleansed data
ü Use the Bulk API
ü Consider loading utilities that use the Bulk API
ü Disable triggers, validations, and workflow
ü Defer sharing calculations
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40. Learn how to create an efficient sharing model
Don t o verprotect Streamline gro up
yo ur data nesting an d yo ur
role hierarchy
Distribute Beware of locking
ownership an d on updates
parenting of
records
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42. Streamline your role Hierarchy
Complex hierarchies = longer
sharing calculations
Keep nesting below 10 levels
Simplify sales branch if using territory
management
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43. Avoid skewed data configurations
Ownership Parent-child
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44. Beware of locking on updates
Organization Lock Record Lock
X
X
Single thread group updates Avoid parent child skew
Use Granular Locking Sequence updates by ParentID
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45. Review: Configuration phase best practices
ü Keep data public when possible
ü Use a lean role hierarchy
ü Avoid data skews
ü Minimize record lock traps
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46. Maintenance
best practices
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47. Maintenance phase best practices
Custo m Data
in dexing partitioning
Application
Skinny tables partitioning
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