Performance Best Practices

This document outlines performance optimization patterns used throughout the Eidetica codebase.

Identify and optimize performance-critical code paths. Common hot paths in Eidetica include CRDT state computation, entry storage/retrieval, authentication verification, bulk operations, and string conversions.

2. Memory Efficiency

Minimize allocations through appropriate string parameter types, pre-allocation of collections, stack allocation preference, and efficient caching strategies.

3. Algorithmic Efficiency

Choose algorithms that scale well with data size by using appropriate data structures, implementing caching for expensive computations, and preferring direct iteration over complex iterator chains in hot paths.

String Parameter Optimization

1. Parameter Type Selection

Use Into<String> for stored parameters and AsRef<str> for lookup operations to minimize allocations and conversions.

2. Bulk Operation Optimization

Convert parameters upfront for bulk operations rather than converting on each iteration to reduce overhead.

Memory Allocation Patterns

1. Pre-allocation Strategies

Allocate collections with known or estimated capacity to reduce reallocation overhead. Pre-allocate strings when building keys or compound values.

2. Memory-Efficient Data Structures

Choose data structures based on access patterns: BTreeMap for ordered iteration and range queries, HashMap for fast lookups, and Vec for dense indexed access.

3. Avoiding Unnecessary Clones

Use references and borrowing effectively. Work with references when possible and clone only when ownership transfer is required.

CRDT Performance Patterns

1. State Computation Caching

Cache expensive CRDT state computations using entry ID and store name as cache keys. Immutable entries eliminate cache invalidation concerns.

2. Efficient Merge Operations

Optimize merge algorithms by pre-allocating capacity, performing in-place merges when possible, and cloning only when adding new keys.

3. Lazy Computation Patterns

Defer expensive computations until needed using lazy initialization patterns to avoid unnecessary work.

Backend Performance Patterns

1. Batch Operations

Optimize backend operations for bulk access by implementing batch retrieval and storage methods that leverage backend-specific bulk operations.

2. Connection Pooling and Resource Management

Use connection pooling for expensive resources and implement read caching with bounded LRU caches to reduce backend load.

Algorithm Optimization

1. Direct Loops vs Iterator Chains

Prefer direct loops over complex iterator chains in hot paths for better performance and clearer control flow.

2. Efficient Graph Traversal

Use iterative traversal with explicit stacks to avoid recursion overhead and maintain visited sets to prevent redundant processing in DAG traversal.

Profiling and Measurement

1. Benchmark-Driven Development

Use criterion for performance testing with varied data sizes to understand scaling characteristics.

2. Performance Monitoring

Track operation timings in critical paths to identify bottlenecks and measure optimization effectiveness.

Memory Profiling

1. Memory Usage Tracking

Implement allocation tracking for operations to identify memory-intensive code paths and optimize accordingly.

2. Memory-Efficient Collections

Use adaptive collection types that switch between Vec for small collections and HashMap for larger ones to optimize memory usage patterns.

Common Performance Anti-Patterns

Avoid unnecessary string allocations through repeated concatenation, repeated expensive computations that could be cached, and unbounded cache growth that leads to memory exhaustion.

Summary

Effective performance optimization in Eidetica focuses on string parameter optimization, memory-efficient patterns, hot path optimization, CRDT performance with caching, backend optimization with batch operations, and algorithm efficiency. Following these patterns ensures the system maintains good performance characteristics as it scales.

Eidetica Documentation