# Epic 17: Performance Optimizations & Scalability

**Goal**: Optimize performance for high-throughput scenarios, implement advanced caching strategies, and enable horizontal scalability for enterprise workloads.

**Value**: Supports large-scale deployments, reduces infrastructure costs through efficiency, and enables handling 10x traffic growth without redesign.

**Priority**: Medium (Post-MVP optimization)

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## Story 17.1: Advanced Caching Strategies

As a performance engineer,
I want intelligent caching with TTL and invalidation policies,
So that repeated queries are fast and cache stays consistent with RabbitMQ state.

**Acceptance Criteria:**

**Given** caching layer configured
**When** operations are executed
**Then** cacheable operations use cache: list operations (queues, exchanges, bindings), operation metadata (get-id)

**And** cache strategy: Redis for distributed cache (optional), in-memory for single instance

**And** TTL (time-to-live): queues/exchanges list (60s), operation metadata (300s, rarely changes)

**And** invalidation triggers: create/delete operations invalidate related list caches, update operations invalidate specific resource cache

**And** cache hit rate tracked: rabbitmq_mcp_cache_hit_rate metric (target: >80%)

**And** cache configuration: CACHE_BACKEND (memory/redis), CACHE_TTL_LISTS (60s), CACHE_TTL_METADATA (300s), CACHE_MAX_SIZE (1000 entries)

**And** cache warming: on startup, preload frequently used data (operation metadata)

**And** cache monitoring: track hit/miss ratio, cache size, eviction rate

**Prerequisites:** Story 7.7 (observability), Story 12.1 (metrics)

**Technical Notes:**
- Use cachetools for in-memory: from cachetools import TTLCache, LRUCache
- Cache decorator: @cached(cache=TTLCache(maxsize=1000, ttl=60))
- Redis integration: use redis-py with JSON serialization
- Invalidation: on queues.create, delete cache key: "queues:list:{vhost}"
- Cache key format: "{resource}:{operation}:{vhost}:{name}"
- Cache statistics: hits, misses, size, evictions (expose via metrics)
- Distributed cache: Redis enables multi-instance caching (shared cache)

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## Story 17.2: Connection Pool Optimization

As a performance engineer,
I want optimized connection pooling for both HTTP and AMQP,
So that we minimize connection overhead and maximize throughput.

**Acceptance Criteria:**

**Given** high-concurrency scenario (100+ concurrent operations)
**When** operations execute
**Then** HTTP connection pool reuses connections efficiently: pool size auto-scales (min 5, max 50), idle connections closed after 60s, connection reuse rate >90%

**And** AMQP connection pooling: maintain pool of channels (not connections), channel pool size configurable (default 10), thread-safe channel acquisition

**And** connection pool metrics: active_connections, idle_connections, pool_exhaustion_count, connection_wait_time

**And** pool exhaustion handling: block with timeout (10s default), fail-fast option (immediate error), queue requests (backpressure)

**And** connection health monitoring: periodic health checks, remove unhealthy connections from pool

**And** configuration: HTTP_POOL_MIN_SIZE (5), HTTP_POOL_MAX_SIZE (50), AMQP_CHANNEL_POOL_SIZE (10), CONNECTION_POOL_TIMEOUT (10s)

**Prerequisites:** Story 2.3 (HTTP client), Story 2.6 (HTTP connection pooling)

**Technical Notes:**
- httpx connection pool: httpx.AsyncClient(limits=httpx.Limits(max_connections=50, max_keepalive_connections=20))
- AMQP channel pool: pika doesn't support connection pooling natively, implement custom pool
- Channel pool pattern: create channels on connection, reuse across operations, close on return to pool
- Metrics: track pool usage over time, identify sizing needs (underutilized vs saturated)
- Auto-scaling: monitor pool_exhaustion_count, increase max_size if frequent
- Health checks: verify connection/channel is open before returning from pool

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## Story 17.3: Query Optimization & Batch Operations

As a developer,
I want batch operations for efficient bulk processing,
So that I can create/delete/update multiple resources in single API call.

**Acceptance Criteria:**

**Given** batch operation request
**When** batch executes
**Then** batch operations supported: batch_create_queues (create multiple queues), batch_delete_queues (delete multiple queues), batch_publish_messages (publish multiple messages)

**And** batch size limits: max 100 resources per batch (configurable), max 10MB payload size

**And** batch execution: parallel execution with configurable concurrency (default 10)

**And** batch results: all-or-nothing (transaction) or best-effort (continue on error), return results array: [{resource: "queue1", status: "success"}, {resource: "queue2", status: "failed", error: "..."}]

**And** batch performance: 10x faster than sequential operations (measured)

**And** batch validation: validate all resources before executing (fail fast on validation errors)

**And** batch operations logged: single audit log entry with batch details (resource count, success count, failure count)

**Prerequisites:** Epic 3 complete (topology operations), Epic 4 complete (messaging)

**Technical Notes:**
- Batch API: POST /api/batch/queues with body: [{"name": "q1", ...}, {"name": "q2", ...}]
- Parallel execution: asyncio.gather(*[create_queue(q) for q in queues])
- Transaction mode: collect all operations, execute, rollback on any failure (delete created resources)
- Best-effort mode: execute all, collect successes and failures
- Batch publish: use AMQP publisher confirms for reliability
- CLI batch: rabbitmq-mcp-server queue create --batch --file=queues.json
- Performance: benchmark batch vs sequential (expect 10x improvement for 100 operations)

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