Salesforce Governor Limits: Causes, Impacts, and Solutions
Salesforce governor limits exist because the platform runs on a shared, multi-tenant architecture. Every org consumes the same underlying resources, so Salesforce enforces strict controls on SOQL limits, DML limits, CPU time, heap size, and Flow limits to ensure system stability. These Salesforce limits are a constant challenge for developers and admins, especially when automation grows over time.
Why Salesforce Governor Limits Matter
Salesforce governor limits prevent any single transaction from monopolizing resources. A transaction includes all Apex triggers, record-triggered flows, processes, and validation rules executed during one database operation. When multiple automation layers run together, they share the same SOQL limits, DML limits, and CPU time. This is why seemingly simple updates can suddenly exceed Salesforce limits.
Common Limits: SOQL, DML, CPU Time, and Heap Size
SOQL limits are hit when flows and Apex repeatedly query the same objects. A record-triggered flow may run a Get Records on a related object, then an Apex trigger performs another query, followed by another flow querying again. Each action is correct in isolation, but the combined design violates SOQL limits.
DML limits become an issue when flows or Apex update records inside loops. A subflow that updates each child record individually can easily reach the 150-DML limit. Bulkification in Salesforce is required to prevent this.
CPU time is a major hidden bottleneck. Complex record-triggered flows, large decision trees, unoptimized formulas, and chains of automation layers quickly consume the 10-second synchronous CPU limit. This commonly affects Case and Opportunity objects, where teams have built heavy automation over years.
Heap size limits appear when large collections, JSON payloads, or big queries load too much data into memory. Salesforce heap size errors often occur in Apex classes handling integrations or large data volumes.
How to Avoid Hitting Salesforce Limits
Bulkification in Salesforce is the primary strategy. Apex must process lists, not single records. Flows must avoid DML-in-loop patterns and should move Update Records elements outside loops. Queries in Apex and flows should be consolidated and selective.
Salesforce performance improves dramatically when unnecessary Get Records elements are removed. Flow optimization requires designing with minimum queries and reusing previously queried data through variables.
Asynchronous Apex—specifically Queueable Apex, Batch Apex, and Future methods—expands available limits. Heavy logic, large data operations, and complex integrations belong in async processing, not synchronous flows or triggers.
Automation Cleanup and Scalability
Many orgs hit Salesforce governor limits because they accumulate years of unmanaged automation. Multiple Process Builders, redundant record-triggered flows, unmanaged triggers, and outdated workflow rules stack together. Consolidating automation into optimized flows reduces SOQL limits usage, DML limits consumption, and CPU time.
Conclusion
Governor limits are not obstacles—they are indicators of inefficiency. When Salesforce governor limits appear, they highlight design problems in flows, Apex classes, or automation architecture. Solving these issues leads to improved Salesforce performance, greater scalability, and a more stable org.
Written w help of Chat GPT