Memory and Compute Considerations¶

Bonsai's memory usage and computational requirements are important factors to consider when designing and deploying applications that use Bonsai. This guide explains the memory and compute considerations for Bonsai and provides recommendations for optimizing resource usage.

Memory Usage¶

Bonsai's memory usage depends on several factors:

Tree Size¶

The size of your tree structure affects memory usage:

Number of Knots: Each Knot requires memory to store its ID, data, properties, and references to Edges
Number of Edges: Each Edge requires memory to store its ID, filters, properties, and references to Knots
KnotData Size: The size of the data stored in Knots affects memory usage
Property Size: The size of properties stored in Knots and Edges affects memory usage

For large trees with thousands of Knots and Edges, memory usage can be significant.

Storage Implementation¶

The storage implementation affects memory usage:

In-Memory Storage: Stores all Knots and Edges in memory, which provides the best performance but requires more memory
Persistent Storage: Stores Knots and Edges in a persistent store, which requires less memory but may have lower performance
Caching: Caching frequently accessed Knots and Edges can improve performance but increases memory usage

Context Size¶

The size of the Context data affects memory usage during evaluation:

Context Data Size: The size of the data stored in the Context affects memory usage
Number of Concurrent Evaluations: The number of concurrent evaluations affects the total memory usage

Compute Requirements¶

Bonsai's computational requirements depend on several factors:

Evaluation Complexity¶

The complexity of tree evaluation affects CPU usage:

Tree Depth: Deeper trees require more traversal steps
Number of Edges per Knot: More Edges per Knot require more filter evaluations
Filter Complexity: More complex filters require more computation
Context Size: Larger Context data may require more computation for JsonPath evaluation

Operation Frequency¶

The frequency of operations affects CPU usage:

Evaluation Frequency: How often trees are evaluated
Modification Frequency: How often trees are modified
Batch Size: The number of operations performed in a batch

Concurrency¶

The level of concurrency affects CPU usage:

Number of Concurrent Evaluations: More concurrent evaluations require more CPU resources
Number of Concurrent Modifications: More concurrent modifications require more CPU resources
Thread Contention: High concurrency can lead to thread contention and reduced performance

Memory Optimization Strategies¶

Here are some strategies for optimizing memory usage:

Optimize Tree Structure¶

Limit tree depth: Keep trees shallow to reduce the number of Knots and Edges
Reuse Knots: Use the same Knot in multiple places to reduce duplication
Minimize property size: Keep properties small and focused
Use appropriate KnotData types: Choose the right KnotData type for your data

Implement Efficient Storage¶

Use persistent storage for large trees: Consider using a database or other persistent storage for large trees
Implement caching strategically: Cache frequently accessed Knots and Edges, but be mindful of memory usage
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
Structure Context data efficiently: Organize data to minimize memory usage
Reuse Context objects: Reuse Context objects when possible to reduce allocation overhead

Compute Optimization Strategies¶

Here are some strategies for optimizing compute usage:

Optimize Evaluation¶

Optimize tree structure: Design trees for efficient evaluation
Order Edges effectively: Put the most likely matches first to reduce the number of evaluations
Use simple filters: Keep filter conditions simple and focused
Batch evaluations: Evaluate multiple keys in a single operation when possible

Implement Caching¶

Cache evaluation results: Cache the results of expensive evaluations
Use contextual preferences: Bypass tree traversal for frequently accessed keys
Implement result caching: Cache evaluation results for frequently used key-context combinations

Manage Concurrency¶

Limit concurrency: Set appropriate limits on the number of concurrent operations
Use thread pools: Use thread pools to manage concurrency
Implement backpressure: Implement backpressure mechanisms to prevent overload

Monitoring and Tuning¶

To optimize memory and compute usage effectively:

Monitor memory usage: Track memory usage to identify potential issues
Monitor CPU usage: Track CPU usage to identify bottlenecks
Profile your application: Use profiling tools to identify hotspots
Tune parameters: Adjust parameters based on monitoring and profiling results
Test with realistic workloads: Test with representative workloads to ensure optimal performance

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

Example: Memory Usage Estimation¶

Here's a rough estimation of memory usage for a Bonsai tree:

Knot: ~100-200 bytes per Knot (excluding KnotData)
Edge: ~100-200 bytes per Edge (excluding filters)
Filter: ~50-100 bytes per filter
String value: ~24 bytes + string length
Boolean value: ~16 bytes
Number value: ~16-24 bytes
JSON value: ~24 bytes + JSON string length

For a tree with: - 1,000 Knots - 2,000 Edges - 5,000 filters - Average of 50 bytes of string data per Knot

The estimated memory usage would be: - Knots: 1,000 * 150 bytes = 150,000 bytes - Edges: 2,000 * 150 bytes = 300,000 bytes - Filters: 5,000 * 75 bytes = 375,000 bytes - String data: 1,000 * 50 bytes = 50,000 bytes

Total: ~875,000 bytes (~875 KB)

This is a rough estimation and actual memory usage may vary based on JVM implementation, object overhead, and other factors.

Example: Compute Usage Estimation¶

Here's a rough estimation of compute usage for Bonsai operations:

Knot retrieval: O(1) with in-memory storage
Edge retrieval: O(1) with in-memory storage
Tree traversal: O(d) where d is the depth of the tree
Filter evaluation: O(f) where f is the number of filters per Edge
JsonPath evaluation: O(p) where p is the complexity of the path expression

For a tree with: - Depth of 5 - Average of 10 Edges per Knot - Average of 2 filters per Edge - Simple JsonPath expressions

The estimated compute usage for a single evaluation would be: - Tree traversal: 5 steps - Edge evaluations: 5 * 10 = 50 evaluations - Filter evaluations: 50 * 2 = 100 evaluations - JsonPath evaluations: 100 evaluations

This is a rough estimation and actual compute usage may vary based on the specific tree structure, context data, and other factors.

Best Practices¶

Start with in-memory storage: Use in-memory storage for development and testing
Monitor memory and CPU usage: Track resource usage to identify potential issues
Optimize hot paths: Focus optimization efforts on frequently used paths
Consider the full lifecycle: Balance creation, evaluation, and maintenance costs
Test with realistic workloads: Test with representative workloads to ensure optimal performance
Scale appropriately: Choose the right scaling strategy for your application