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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 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

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

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

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 @

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