Performance Architecture Overview¶

Substation implements a comprehensive performance architecture designed for high-throughput OpenStack operations. The system is designed to provide intelligent caching, parallel processing, and real-time performance monitoring.

Or: How we designed a system to make OpenStack management not suck, despite OpenStack's best efforts.

The Core Problem: OpenStack APIs are slow. Like, "watching paint dry" slow. Like "is this thing even running?" slow.

Our Solution: Cache everything aggressively, parallelize ruthlessly, and monitor obsessively.

Performance Targets vs Actual Results¶

The performance characteristics described in this document represent design targets and expected behavior based on the architecture. Actual performance will vary based on your OpenStack deployment's API response times, network latency, resource count, and system resources. We recommend using the built-in performance monitor (:health or :h) to measure actual performance in your environment.

Performance Architecture¶

graph TB
    subgraph "Performance Layer"
        BenchmarkSystem[Performance Benchmark System]
        Metrics[Metrics Collector]
        Telemetry[Telemetry Manager]
    end

    subgraph "Caching Layer"
        CacheManager[Cache Manager]
        ResourceTTL[Resource-Specific TTLs]
        Cleanup[Intelligent Cleanup]
        MultiLevel[Multi-Level Cache]
    end

    subgraph "Search Layer"
        ParallelSearch[Parallel Search Engine]
        QueryOptimizer[Query Optimizer]
        ResultAggregator[Result Aggregator]
    end

    subgraph "Monitoring Layer"
        HealthCheck[Health Checker]
        MemoryTracking[Memory Tracking]
        PerformanceMonitor[Performance Monitor]
    end

    BenchmarkSystem --> CacheManager
    BenchmarkSystem --> ParallelSearch
    BenchmarkSystem --> PerformanceMonitor

    Metrics --> Telemetry
    CacheManager --> MultiLevel
    ParallelSearch --> QueryOptimizer
    ParallelSearch --> ResultAggregator
    PerformanceMonitor --> HealthCheck
    PerformanceMonitor --> MemoryTracking

Key Performance Components¶

1. Intelligent Caching System¶

MemoryKit: Multi-level caching system in /Sources/MemoryKit/

The cache manager implements multi-level caching with resource-specific TTL strategies because:

1. Your OpenStack API is slow (2+ seconds per call)
2. Your OpenStack API is slower than you think (seriously, measure it)
3. Your OpenStack API sometimes just breaks (500 errors, timeouts, the usual)

See: Caching Concepts for detailed caching architecture and TTL strategies.

Key features:

• Multi-level cache hierarchy (L1/L2/L3)
• Resource-specific TTL configuration
• Designed for up to 60-80% API call reduction
• Memory pressure handling
• Hit/miss tracking with real-time metrics

2. Parallel Search Engine¶

Location: /Sources/Substation/Search/SearchEngine.swift

High-performance search across multiple OpenStack services simultaneously because:

• Sequential search = 6 services x 2 seconds each = 12 seconds (unacceptable)
• Parallel search = 6 services in parallel = 2 seconds max (acceptable)

Key features:

• Concurrent execution across up to 6 services
• Query optimization and field selection
• Result aggregation with relevance scoring
• 5-second timeout with graceful degradation

3. Performance Monitoring System¶

Location: /Sources/Substation/PerformanceMonitor.swift

Comprehensive performance monitoring with automated metrics collection and tracking.

Benchmark categories:

• Cache performance (hit rates, response times)
• Search performance (cross-service speed)
• Memory management (allocation, cleanup)
• System integration (component interaction)
• Rendering performance (TUI frame rates)

See: Performance Benchmarks for detailed metrics and scoring.

4. Telemetry and Metrics Collection¶

Location: /Sources/OSClient/Enterprise/Telemetry/

Real-time performance monitoring with minimal overhead.

Metric categories:

• Performance metrics (timing, throughput, latency)
• User behavior (feature usage, navigation flows)
• Resource usage (memory, cache utilization)
• OpenStack health (service availability, API response times)
• Caching metrics (hit rates, eviction patterns)
• Networking metrics (connection states, timeout rates)

Performance Targets¶

Response Time Targets¶

Throughput Targets¶

Memory Efficiency Targets¶

What We Control vs. What We Don't¶

What We Control¶

• Caching strategy: Aggressive, multi-level
• Parallelization: 6 concurrent searches
• Memory efficiency: < 200MB target
• Retry logic: Exponential backoff
• Error handling: Graceful degradation

What We Don't Control¶

• OpenStack API performance: Usually the bottleneck
• Network latency: Between you and OpenStack
• Database performance: On OpenStack controllers
• Service availability: When OpenStack is down

The Hard Truth: OpenStack APIs are slow. This is a known, documented, years-old issue. Multiple OpenStack summits have discussed it. Countless patches have attempted to fix it. It's still slow.

Substation does everything possible to mitigate this:

• Aggressive caching (L1/L2/L3 hierarchy)
• Parallel operations (search, batch requests)
• HTTP/2 connection pooling
• Intelligent retry logic
• Memory-efficient data structures

But if the OpenStack API takes 5 seconds to list servers, we can't make it instant. The bottleneck is OpenStack, not Substation.

That said: With our caching design, we target 80% of operations to be < 1ms. The remaining 20% that hit the API directly will reflect your OpenStack API's actual performance.

Next Steps¶

• Performance Benchmarks - Detailed metrics, scoring, and regression detection
• Performance Tuning - Configuration, monitoring, optimization best practices
• Troubleshooting - Common performance problems and solutions
• Caching Concepts - Deep dive into the caching architecture

Note: All performance metrics and benchmarks represent design targets based on the architecture implemented in /Sources/Substation/PerformanceMonitor.swift, /Sources/MemoryKit/, and /Sources/OSClient/Enterprise/Telemetry/. The system provides comprehensive performance monitoring and optimization capabilities designed for production OpenStack environments.

Targets are based on testing with real OpenStack clusters with 10K+ resources. Your actual performance will vary based on your specific deployment, network conditions, and system resources.