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Performance Considerations

Performance is a critical aspect of any rule engine, and Bonsai is designed with performance in mind. This section covers various performance considerations and optimization techniques for Bonsai.

Topics Covered

Performance Characteristics

Bonsai's performance characteristics depend on several factors:

  • Tree Depth: The depth of the tree affects traversal time
  • Number of Edges: The number of edges per knot affects evaluation time
  • Filter Complexity: The complexity of filter conditions affects evaluation time
  • Context Size: The size of the context data affects evaluation time
  • Storage Implementation: The performance of the storage implementation affects operation time

General Performance Tips

Here are some general tips for optimizing Bonsai performance:

Design Efficient Trees

  • Keep trees shallow: Minimize the depth of your trees to reduce traversal time
  • Limit the number of edges: Keep the number of edges per knot reasonable
  • Order edges effectively: Put the most likely matches first to reduce the number of evaluations
  • Use simple filters: Keep filter conditions simple and focused
  • Avoid complex JsonPath expressions: Complex expressions can be expensive to evaluate

Optimize Storage

  • Use in-memory storage for read-heavy workloads: In-memory storage provides the best read performance
  • Implement caching: Cache frequently accessed knots and edges
  • Use efficient serialization: Choose an efficient serialization format for persistent storage
  • Consider read/write separation: Use separate instances for read and write operations

Optimize Context

  • Keep context data small: Include only the necessary data in the context
  • Use custom context implementations: Implement custom context classes for specific use cases
  • Consider caching context evaluation results: Cache the results of expensive evaluations

Optimize Evaluation

  • Use contextual preferences for frequently accessed keys: Bypass tree traversal for frequently accessed keys
  • Batch evaluations: Evaluate multiple keys in a single operation when possible
  • Implement result caching: Cache evaluation results for frequently used key-context combinations

Monitoring and Profiling

To identify performance bottlenecks, consider:

  • Instrumenting your code: Add timing metrics to key operations
  • Using profiling tools: Use profiling tools to identify hotspots
  • Monitoring memory usage: Track memory usage to identify potential issues
  • Logging performance metrics: Log performance metrics for analysis

Scaling Considerations

For high-throughput applications, consider:

  • Horizontal scaling: Deploy multiple instances of your application
  • Vertical scaling: Increase the resources available to your application
  • Caching: Implement caching at various levels
  • Asynchronous processing: Use asynchronous processing for non-critical operations