ORM Performance

Note

Status: This page is under development.

Performance characteristics and optimization techniques for the ORM API.

Overview

This page focuses on performance aspects of the ORM API, including:

  • Serialization overhead

  • Index lookup performance

  • Lazy loading benefits

  • Batch operation patterns

For low-level LMDB performance, see LMDB API Performance. For overall comparisons, see Performance.

Coming Soon

This page will cover:

  1. Serialization Performance - JSON vs CBOR vs FlatBuffers comparison - Serialization overhead measurements - Zero-copy FlatBuffers benefits - NumPy array performance

  2. Lazy Loading - FlatBuffers lazy deserialization - Property access patterns - Memory efficiency - CPU trade-offs

  3. Index Performance - Index lookup overhead - Composite index efficiency - Index update cost - When indexes help vs hurt

  4. Batch Operations - Transaction batching strategies - Optimal batch sizes - Example: pydefi batch inserts - Throughput measurements

  5. Background Writer Pattern - Decoupling capture from persistence - Queue-based architecture - Performance gains - Example: pydefi order book replica

  6. Memory Efficiency - Schema overhead - Index memory usage - FlatBuffers vs CBOR memory footprint - Large object strategies

  7. Optimization Techniques - Choosing serialization formats - Index design for performance - Transaction sizing - Read vs write optimization

Benchmark Results

(Real-world benchmarks from Crossbar.io and pydefi to be added)

Serialization Overhead:

  • JSON: baseline

  • CBOR: X% faster

  • FlatBuffers: X% faster

  • NumPy: X% faster

Index Lookups:

  • Primary key: X ops/sec

  • Simple index: X ops/sec

  • Composite index: X ops/sec

Batch Writes:

  • Single-record transactions: X writes/sec

  • Batched (100): X writes/sec

  • Batched (1000): X writes/sec

Comparison with Low-Level API

When ORM overhead matters and when it doesn’t.

Real-World Performance

Crossbar.io (via cfxdb):

  • Event history: X events/sec with FlatBuffers

  • Cookie lookups: X lookups/sec with indexes

  • Session tracking: X sessions/sec

pydefi:

  • Trade ingestion: X trades/sec with batch writes

  • Order book updates: X updates/sec with background writer

  • Real-time market data: X snapshots/sec

See Also