Data Skew and how it is impacting the performance of your Salesforce instance

Data skew in Salesforce refers to an uneven distribution of records across the database, particularly when a large number of records are associated with a single record in another table, or a small number of users. This imbalance can lead to various performance issues, especially when the skewed data involves standard Salesforce objects like Accounts, Contacts, Cases, or custom objects. For example, when discussing data skew in the context of the Case object, we are looking at scenarios where a disproportionate number of Case records are owned by a single user or are related to a single Account or other parent record.

Impact of Data Skew

  1. Performance Degradation: Salesforce’s multi-tenant architecture relies on shared resources. Data skew can lead to longer processing times for operations involving skewed records, such as queries, updates, and reports. This is because the database has to handle a larger volume of data associated with certain operations, impacting the overall system performance.
  2. Lock Contention: In Salesforce, when records are updated, Salesforce locks the record and sometimes related records to prevent other operations from modifying them simultaneously, ensuring data integrity. In cases of data skew, if multiple operations target skewed records, it can lead to lock contention, causing delays or failures in data processing.
  3. Governor Limits: Salesforce enforces governor limits to maintain system performance and ensure fair usage. Operations on skewed data can quickly hit these limits, resulting in errors or incomplete transactions. For example, queries retrieving a vast number of skewed records may exceed the maximum number of records allowed to be processed in a single transaction.

Best Practices and Mitigation Strategies

  1. Distribute Ownership: Avoid assigning a large number of records to a single user. Instead, distribute record ownership across multiple users to balance the data load.
  2. Use Public Groups and Queues for Cases: Instead of assigning cases to individual users, use public groups or queues. This helps in distributing cases more evenly and also facilitates easier management and redistribution of workload.
  3. Archiving Old Data: Regularly archive old or infrequently accessed records. This reduces the volume of data the database needs to handle during operations, mitigating the impact of data skew.
  4. Custom Indexing: Request Salesforce to create custom indexes on fields that are frequently used in queries, especially in skewed objects. This can improve query performance by making data retrieval more efficient.
  5. Optimize Sharing Rules: Excessive sharing rules on skewed objects can exacerbate performance issues. Optimize sharing rules by reducing unnecessary sharing and considering criteria-based sharing to limit the amount of data shared.
  6. Batch Processing: For operations affecting a large number of records, use batch processing. This helps in managing resource utilization more effectively and reduces the risk of hitting governor limits.
  7. Monitoring and Analysis: Regularly monitor data distribution and system performance. Salesforce provides tools like the Data Distribution Viewer and Health Check to identify and analyze data skew and its impact on your org.
  8. Consult Salesforce Support: In cases of severe data skew, reaching out to Salesforce support can provide additional insights and solutions. Salesforce may offer advice or tools specific to your situation, including potential backend optimizations.

Addressing data skew requires a proactive approach, involving planning, monitoring, and ongoing management. By implementing these best practices, organizations can mitigate the adverse effects of data skew on Salesforce performance, ensuring a smoother and more efficient operation.

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