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EcommerceAPI is a sample Go-based microservices platform for e-commerce, featuring a GraphQL gateway, gRPC inter-service communication, Kafka event streaming, and a Python recommendation engine. It demonstrates modern backend architecture patterns with Docker Compose deployment and integrations for PostgreSQL, Elasticsearch, and payment processing.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | rasadov/EcommerceAPI |
| Owner | rasadov |
| Primary language | Go |
| License | Apache-2.0 — OSI-approved |
| Stars | 689 |
| Forks | 102 |
| Open issues | 1 |
| Latest release | Unknown |
| Last updated | 2026-06-26 |
| Source | https://github.com/rasadov/EcommerceAPI |
What EcommerceAPI is
Built on Go microservices (Account, Product, Order, Payment) communicating via gRPC, with a GraphQL API gateway using gqlgen. Data flows through Kafka topics to a Python-based Recommender service. PostgreSQL and Elasticsearch provide persistence; full stack runs in Docker Compose with containerized Kafka and Zookeeper.
Get the EcommerceAPI source
Clone the repository and explore it locally.
git clone https://github.com/rasadov/EcommerceAPI.gitcd EcommerceAPI# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Authenticate and authorize all gRPC calls; samples show JWT token generation but inter-service security policies are not documented.
- Configure Kafka topic retention, replication, and consumer group offsets explicitly before production; defaults may cause data loss or replay issues.
- Elasticsearch indexing strategy must be tuned for product cardinality and query patterns; no mention of mapping validation or analyzer configuration.
- Python Recommender service depends on PostgreSQL replica freshness; replication lag will degrade recommendation quality.
- API Gateway GraphQL schema stitching requires careful field resolution and error handling to avoid cascading failures from backend service timeouts.
When to avoid it — and what to weigh
- Production use without hardening — No release versions, limited issue tracking (1 open issue suggests minimal active curation), and no security audit history. Requires substantial code review and security testing before production deployment.
- Existing monolithic codebase requiring minimal refactoring — This architecture assumes microservices from the ground up. Migration costs and operational overhead (Kubernetes/orchestration not mentioned) may outweigh benefits for teams needing quick feature iteration.
- Teams without Docker/containerization experience — Deployment relies entirely on Docker Compose; no Kubernetes manifests or alternative single-server deployment paths. Requires proficiency with container networking and multi-service orchestration.
- Small teams with limited DevOps capacity — Managing PostgreSQL, Elasticsearch, Kafka, and five+ services requires monitoring, logging, and troubleshooting infrastructure. Not suitable for teams without dedicated ops or SRE support.
License & commercial use
Licensed under Apache License 2.0, a permissive OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions (attribution and license copy required).
Apache-2.0 permits commercial use and proprietary modifications. However, this is a reference implementation (sample project), not a production-ready platform. Commercial adoption requires security hardening, compliance validation, and your own liability assessment. No commercial support or SLAs are offered by the author.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
Authentication via JWT tokens is sketched but inter-service authorization (mTLS, service account tokens) is not addressed. No input validation rules, rate limiting, or CORS policies mentioned. Elasticsearch is likely exposed without authentication. PostgreSQL and Kafka require network isolation. No secrets management (env vars, vault) guidance. Recommend threat modeling and penetration testing before handling real user/payment data.
Alternatives to consider
Medusa (Node.js)
Production-ready e-commerce backend with extensible plugin system, built-in admin API, and larger community. Steeper learning curve but lower maintenance burden.
commercetools (SaaS)
Fully managed, multi-tenant e-commerce platform-as-a-service with battle-tested payment and order workflows. Eliminates infrastructure management but locks in vendor and incurs subscription costs.
Saleor (GraphQL, Python/Django)
Open-source GraphQL-first e-commerce API with built-in headless CMS, webhooks, and plugin architecture. More opinionated than EcommerceAPI but stronger ecosystem support.
Build on EcommerceAPI with DEV.co software developers
EcommerceAPI is a solid reference for modern backend patterns. For production-grade e-commerce, Devco can help you architect, harden, and deploy a customized microservices platform or evaluate commercial alternatives.
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EcommerceAPI FAQ
Can I use this in production?
What payment providers are supported?
How do I scale this beyond Docker Compose?
Is the Python Recommender service required?
Custom software development, end to end
Standing up EcommerceAPI properly takes more than a deploy. As a software development agency, DEV.co handles the open-source ecommerce architecture, the web development on top, and the operational work underneath.
Ready to build your e-commerce platform?
EcommerceAPI is a solid reference for modern backend patterns. For production-grade e-commerce, Devco can help you architect, harden, and deploy a customized microservices platform or evaluate commercial alternatives.