Data Skew in Salesforce

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Data skew in Salesforce refers to a condition where the distribution of related records is uneven or imbalanced across parent records. This can occur within standard or custom objects when many records are associated with a single account, user, or parent record. 

Two primary types of skew can happen in Salesforce:

Ownership Data Skew: This happens when a single user owns many records. For example, suppose one user owns tens of thousands of account or opportunity records. In that case, it can cause performance issues because the system optimizes sharing calculations based on a more balanced record ownership model.

Parent-Child Data Skew: This is a situation where a large number of child records are linked to a single parent record. A typical example is having thousands of contact records associated with a single account. When a child record is created, updated, or deleted, Salesforce must lock the parent record to maintain data integrity. This can lead to contention and performance issues, particularly when the data is heavily skewed. It's crucial for administrators to be aware of this potential issue and take proactive measures to manage it.

Here's a simple diagram illustrating the concept of data skew in Salesforce:










In this diagram:

Salesforce Org: Represents the Salesforce environment.

Accounts: Represents the Salesforce object containing Account records.

User A, User B, ..., User Z: Represent different users in the Salesforce org.

Ownership Percentage: Indicates the percentage of Account records owned by each user.

This diagram illustrates ownership data skew, where User A owns most of the Account records (90%), while User B and other users own only a small fraction (5%). As a result, User A may experience slower performance and increased contention for resources compared to other users.

Data skew issues like this can lead to performance degradation, particularly when processing sharing calculations, access permissions, or reports involving the affected records. Salesforce administrators and developers must address data skew issues to maintain optimal system performance and scalability.

Here's how data skew can impact Salesforce performance:

Slower Record Access: When a single user accesses a record with a high volume of associated records, Salesforce must filter through more data, resulting in slower response times.

Longer Save Times: As Salesforce maintains referential integrity, having a high volume of child records can extend the time needed to create or update records due to the need to acquire locks on the parent record.

Sharing Calculation Delays: In the presence of data skew, the sharing calculations for access permission can take longer, which may delay the visibility of records to users.

Increased Risk of Deadlocks: A heavily skewed data model is more prone to deadlocks where two or more operations are waiting on each other to release locks, leading to errors and rollbacks.

To manage data skew, Salesforce recommends the following practices:

Balancing Record Distribution: Encourage users to distribute records more evenly and avoid assigning too many records to a single owner.It is recommended to avoid having a large number of child records (more than 10,000) in a master/detail relationship. This can have negative impacts on performance and cause potential issues.

Archiving Old Data: Implement archiving strategies to move old and infrequently accessed records to external storage.

Using Indexes Properly: Ensure that fields used in WHERE clauses and joins are indexed, and understand how Salesforce indexes work, including the limitations.

Custom Index Requests: Sometimes, Salesforce support can add custom indexes to objects to help with skewed data issues.

Monitoring and Planning: Regularly monitor data distribution and plan for potential skew in the system design process by incorporating features like

Resources to consider for more information 

Managing Lookup Skew in Salesforce to Avoid Record Lock Exceptions

Overview of Object Relationships

Best Practices for Deployments with Large Data Volumes

Account Data Skew

Ownership Skew

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