80% Latency Reduction at 10K+ Orders/Day — Case Study

Problem

Monolithic .NET Core order platform was hitting latency limits under peak load of 10K+ daily orders, with checkout and catalog endpoints degrading under concurrent traffic.

Solution

Decomposed the monolith into Redis-backed microservices, introduced RabbitMQ for async workloads, applied cache-aside patterns and projection queries against PostgreSQL — achieving 80% latency reduction on critical endpoints.

Architecture

$ render architecture.mmd

flowchart LR
  Client[Web / Mobile] --> GW[API Gateway]
  GW --> Orders[(Orders Service<br/>.NET Core)]
  GW --> Catalog[(Catalog Service)]
  GW --> Pricing[(Pricing Service)]
  Orders --> Q{{RabbitMQ}}
  Q --> Worker[Image Workers<br/>Python · Flask]
  Orders -- cache-aside --> Redis[(Redis)]
  Catalog -- projection --> Redis
  Orders --> DB[(PostgreSQL)]
  Catalog --> DB
  Worker --> S3[(Object Storage)]
  classDef cache fill:#7f1d1d,stroke:#ef4444,color:#fff
  classDef queue fill:#78350f,stroke:#f59e0b,color:#fff
  classDef db fill:#1e3a8a,stroke:#3b82f6,color:#fff
  class Redis cache
  class Q queue
  class DB,S3 db

Technical decisions

$ git log --oneline decisions/

#01

Cache-aside over read-through

Cache-aside kept write paths simple and avoided coupling the cache layer to service code. Redis miss rates dropped below 8% on hot product reads.

#02

Async over sync for heavy work

Image processing, marketplace pushes and inventory sync moved to RabbitMQ consumers so the order request path stayed under 200ms p95.

#03

Projection queries on PostgreSQL

Wide read endpoints projected only the fields the UI needed, cutting payload and removing N+1 joins on EF Core.

#04

Onion architecture per service

Each microservice owned its domain core, with infrastructure (Redis/EF) only at the outermost shell — kept business logic testable and portable.

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