Globally optimized strategy
A globally optimized strategy is an instance of the Strategy rule that has improved performance. Strategy designers create globally optimized strategies to reduce the computation time and memory consumption when running large-scale batch data flows and simulations. Improvements to the Strategy rule performance are the results of decreased run time and quality changes to the code generation model. Strategy designers create a globally optimized strategy by referencing an existing strategy that they want to optimize and by selecting output properties that will be in the optimized strategy result.
Strategy optimization is done on the component level. The following strategy components can be optimized:
- Sub Strategy (only the Current page option)
- Proposition Data (without the Interaction History option)
- Data Join
- Set Property
- Filter (only the Filter condition option)
- Champion Challenger
- Group By
- External Input
Optimized strategies can work with non-optimized components.
- Creating a globally optimized strategy
Increase the performance of your strategy by creating a globally optimized instance of your rule. You can also use a globally optimized strategy as a substrategy to decrease code size and increase performance of the top-level strategy that is not optimized.
- Optimizing strategies with allow list functions
When a globally optimized strategy cannot be optimized because its component or components contain expressions with unsupported functions, you can add the functions to the pyWhitelistfunction data transform or change the strategy logic to contain only supported functions.