Best Practices for Deployments with Large Data Volumes
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Best Practices
This section lists best practices for achieving good performance in deployments with large data volumes.
The main approaches to performance tuning in large Salesforce deployments rely on reducing the number of records that the system must process. If the number of retrieved records is sufficiently small, the platform might use standard database constructs like indexes or de-normalization to speed up the retrieval of data.
- Reducing scope by writing queries that are narrow or selective
For example, if the Account object contains accounts distributed evenly across all states, then a report that summarizes accounts by cities in a single state is much broader—and takes longer to execute—than a report that summarizes accounts by a single city in a single state.
- Reducing the amount of data kept active
For example, if your volume of data is increasing, performance can degrade as time goes by. A policy of archiving or discarding data at the same rate at which it comes into the system can prevent this effect.
These tables feature major goals and the best practices to follow to achieve those goals.