Extreme Salesforce Data Volumes Webinar

Extreme Salesforce Data Volumes Force.com Architecture Best Practices

From salesforce.com’s Customer Centric Engineering – Technical Enablement team

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Bud Vieira

Architect Evangelist @aavra

Steve Bobrowski

Architect Evangelist @sbob909

Speakers


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Today’s Learning Goal

AWARENESS


You might be surprised!

How much data can the Salesforce Platform handle?


Best practices Configure Maintain Load Design


Design best practices


Design phase best practices

Requirements� Data volume and growth�

Query �design�

Text �searching �


Requirements


Understand application and user requirements

How many records make

sense in each list view and report?�

How much operational data

is really necessary?�

Can you deem �certain data

historical or not useful for views

and reports?�


Example: Design list views that make sense

How many pages of data records can a human process?


Data volume and growth


Estimate the growth and volume of data

At what rate will the data volume increase from transactions?

From data loads?�

Can you offset the data growth

by archiving data? Hard-

deletes? Recycle bin empties? �

Can you eliminate certain types of data from your schema? From

your data loads?�


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


Key success factor: Minimize data volume

Devise a data loading plan �

that meets, but doesn’t exceed, requirements�

After archiving, �hard-delete rows

or empty the recycle bin�

Devise a data offloading plan �that archives

unnecessary data�


Query design


Learn how to design efficient queries

Document available �indexes�

Design selective list views,

reports, and SOQL queries�

Understand �SOQL query optimization�


Document available indexes


Cheat Sheet: Indexed Fields



Example: Design and profile selective SOQL


Full-text search


Learn how to design efficient searches

Document available �indexes�

Design selective list views,

reports, and SOSL queries�

Understand �SOSL query optimization�


Document text fields with Search indexes


Cheat Sheet: Search Fields



Learn how Search retrieves rows

DB

INDEX

DB processing includes sharing access check

Millions of

records

Query across entire index for matches to search string

Record IDs of matches to query are sent to DB for processing


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


Data load best practices


Data load best practices

Clean and organize data before loading �

Use Bulk APIs for better

throughput�

Check out AppExchange

tools�

Disable and defer what you can�


Prepare your data to avoid overhead


Disable actions that fire on insert

Triggers

Workflow Rules

Validation Rules


Understand the Force.com Bulk API


Be aware of Bulk API limits


Look for utilities to help get the job done easy


Defer sharing calculations

Or … load with Public default sharing


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


Configuration best practices


Learn how to create an efficient sharing model

Distribute ownership and parenting of

records�

Streamline group nesting and your �role hierarchy�

Beware of locking on updates�

�

Don’t overprotect your data�

�


Don’t overprotect your data


Streamline your role Hierarchy

Complex hierarchies = longer sharing calculations Keep nesting below 10 levels Simplify sales branch if using territory management


Avoid skewed data configurations

Ownership Parent-child


Beware of locking on updates

Organization Lock

X

X

Record Lock

Single thread group updates Use Granular Locking

Avoid parent child skew Sequence updates by ParentID


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


Maintenance best practices


Maintenance phase best practices

Custom indexing �

Data partitioning �

Skinny tables�Application�partitioning �


Custom indexes


Create your own indexes


Request non-standard indexes

Create a Support request to implement custom one and two-field indexes


Skinny tables


Query tuning with skinny tables


Data partitioning


Test and implement divisions


Application partitioning


Leverage Salesforce Platform components

100010010

ETL process

Heroku app & database manages historical data

Force.com Canvas facilitates seamless, transparent integration of

operational & historical data

Lean Salesforce org manages operational data


Demo

Integrated best practices


Demo: Integrated best practices

ü Lean Salesforce database ü Archive historical data

ü Hard-delete archived records

ü  Leverage external application (Heroku) for analytics ü  Integrate UIs using Force.com Canvas


Additional Resources

More Best Practices @

Architect Core Resources


Extreme Salesforce Data Volumes Webinar

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