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Reference document for monopoly tech-matrix.
Create or audit ECL Agent Harness infrastructure: AGENTS.md, change tracking, repository guidance, lint checks, CI gates, and agent handoff docs.
Reference document for monopoly patterns.
Reference document for monopoly scale-benchmarks.
Reference document for monopoly security-checklist.
MONOPOLY is a Senior System Design Engineer skill for architecting, reviewing, and scaling systems. Triggers on requests involving architecture, databases, scaling, microservices, or infrastructure design. Proactively engages to design resilient backend systems.
Before accepting an agent's 'done / shipped / fixed' claim, verify it against ground truth (git ancestry + the commit's own diff) using the DOS kernel's `dos verify` and `dos commit-audit` — never the agent's own narration.
| name | tech-matrix |
| description | Reference document for monopoly tech-matrix. |
| risk | safe |
| reports-to | monopoly |
| Database | Best For | Avoid When | Scale Ceiling |
|---|---|---|---|
| PostgreSQL | Complex queries, JSONB, GIS, strong consistency, most default use cases | Ultra-high write throughput (>100K writes/s) | ~10TB single node; use Citus for horizontal |
| MySQL / MariaDB | Read-heavy apps, legacy systems, WordPress/Drupal ecosystem | Complex queries, full ACID at scale | ~10TB; use Vitess for sharding |
| CockroachDB | Global distributed SQL, geo-partitioning, multi-region | Simple single-region apps (overkill) | Petabyte-scale |
| PlanetScale | MySQL-compatible, serverless, branch-based workflow | Complex JOINs (foreign keys removed by design) | Very high — Vitess based |
| Amazon Aurora | AWS-native apps, managed PostgreSQL/MySQL, high availability | Non-AWS environments | Up to 128TB, 15 replicas |
| Database | Best For | Avoid When | Scale Ceiling |
|---|---|---|---|
| MongoDB | Flexible schema, document model, prototyping | Financial transactions requiring ACID | Petabyte-scale with sharding |
| DynamoDB | Key-value at massive scale, AWS-native, serverless, predictable latency | Complex queries, ad-hoc analytics, JOINs | Unlimited (AWS-managed) |
| Cassandra | Write-heavy, time-series, wide-column, geographically distributed | Read-heavy with complex queries | Petabyte-scale; used at Apple, Netflix |
| Redis | Cache, sessions, leaderboards, pub/sub, rate limiting | Primary data store for complex models | ~1TB per node; cluster for more |
| Elasticsearch | Full-text search, log aggregation, analytics | Primary database (durability risk) | Petabyte-scale with clusters |
| InfluxDB | Time-series metrics, IoT, monitoring data | General-purpose data | Very high write throughput |
| Neo4j | Graph data, social networks, recommendation engines, fraud detection | Non-graph data (overhead not worth it) | Billions of nodes |
Is your data relational (joins, foreign keys, transactions)?
YES → Start with PostgreSQL
NO → Continue below
Is your primary access pattern key-value?
YES, need extreme scale → DynamoDB or Cassandra
YES, need speed/cache → Redis
Is your data document-shaped (nested, flexible schema)?
YES → MongoDB
Is it time-series (metrics, logs, IoT)?
YES → InfluxDB or TimescaleDB
Is it graph (relationships are the data)?
YES → Neo4j
Is it search?
YES → Elasticsearch / OpenSearch
| Technology | Best For | Max Single Node | Cluster Support |
|---|---|---|---|
| Redis | Sessions, leaderboards, pub/sub, complex data structures, Lua scripting | ~1TB RAM | Yes (Redis Cluster, Redis Sentinel) |
| Memcached | Simple key-value, multi-threaded, large object cache | ~64GB RAM | Yes (client-side sharding) |
| Varnish | HTTP reverse proxy cache, full-page caching | RAM bound | Limited |
| CloudFront / CDN | Static assets, edge caching globally | N/A (distributed) | Built-in global distribution |
Default recommendation: Redis — more features, better ecosystem, active development.
Use Memcached only when: you need multi-threading for CPU-bound caching workloads and don't need data structures beyond string.
| Technology | Model | Best For | Throughput | Retention |
|---|---|---|---|---|
| Apache Kafka | Log-based streaming | Event sourcing, high-throughput pipelines, replay, audit | Millions msg/s | Days to forever |
| RabbitMQ | AMQP message broker | Task queues, RPC, routing, fanout | 50K–100K msg/s | Until consumed |
| AWS SQS | Managed queue | AWS-native, simple task queue, serverless | Very high (managed) | Up to 14 days |
| AWS SNS | Pub/sub notification | Fan-out to many subscribers (email, SMS, Lambda, SQS) | Very high (managed) | No retention |
| Google Pub/Sub | Managed streaming | GCP-native, global, serverless | Very high (managed) | Up to 7 days |
| Redis Pub/Sub | In-memory pub/sub | Real-time notifications, low latency, fire-and-forget | Very high | None (no retention) |
| NATS | Lightweight messaging | IoT, microservices, low latency | Very high | JetStream adds retention |
Need event replay / audit trail?
YES → Kafka or Kinesis
Need simple task queue with retries and DLQ?
AWS shop → SQS
Self-hosted → RabbitMQ
Need real-time pub/sub with no persistence?
Redis Pub/Sub or NATS
Need fan-out to multiple consumers?
Kafka (consumer groups) or SNS → SQS fan-out
Need < 5 minutes guaranteed delivery, AWS-native, zero ops?
SQS
Volume > 1 million messages/second?
Kafka (self-hosted) or Kinesis (managed)
| Protocol | Best For | Avoid When |
|---|---|---|
| REST (HTTP/JSON) | Public APIs, CRUD, browser clients, simplicity | Strict typing required; high-performance internal services |
| GraphQL | Complex client data requirements, mobile (reduce over-fetching), BFF pattern | Simple CRUD; not worth the complexity |
| gRPC (HTTP/2 + Protobuf) | Internal microservice communication, low latency, strict contracts, streaming | Public browser APIs (needs gRPC-web) |
| WebSocket | Real-time bidirectional (chat, live dashboards, multiplayer games) | One-way server push (use SSE instead) |
| SSE (Server-Sent Events) | Server → client push (notifications, live feeds) | Bidirectional communication |
| GraphQL Subscriptions | Real-time with GraphQL schema consistency | Simple push scenarios |
Default recommendation:
| Technology | Best For | Avoid When |
|---|---|---|
| Elasticsearch | Full-text search, log analytics (ELK), complex aggregations | Simple lookups; operational overhead is high |
| OpenSearch | AWS-native Elasticsearch alternative | Non-AWS preferred setups |
| Typesense | Simple, fast full-text search, typo tolerance, easy ops | Complex aggregations at massive scale |
| Algolia | Managed search-as-a-service, fast setup, great UI | High volume (expensive); self-hosted preference |
| Meilisearch | Self-hosted, developer-friendly, fast relevancy | Enterprise-scale analytics |
| PostgreSQL FTS | Basic full-text search, already using PostgreSQL | High relevancy requirements or large datasets |
Rule of thumb: Use PostgreSQL FTS under 1M documents. Move to Typesense or Elasticsearch above that.
| Service | Best For | Egress Cost |
|---|---|---|
| AWS S3 | AWS-native apps, de facto standard, massive ecosystem | $0.09/GB (expensive) |
| Cloudflare R2 | S3-compatible, zero egress cost, global | $0.00 egress |
| GCS | GCP-native | $0.12/GB |
| Azure Blob | Azure-native | $0.087/GB |
| Backblaze B2 | Cost-sensitive, S3-compatible | Free with Cloudflare |
| MinIO | Self-hosted S3-compatible | Self-managed |
Cost optimization tip: Use Cloudflare R2 for user-facing media delivery (zero egress). Use S3 for internal/AWS-integrated storage.
| Technology | Best For | Avoid When |
|---|---|---|
| Kubernetes (K8s) | Large teams, complex deployments, multi-cloud, full control | Small teams (ops overhead is very high) |
| AWS ECS + Fargate | AWS-native, serverless containers, simpler than K8s | Multi-cloud or K8s ecosystem tools needed |
| AWS EKS | Managed K8s on AWS, best of both | Small teams; Fargate may be enough |
| GKE (Google) | Best managed K8s, GCP-native, Autopilot mode | Non-GCP environments |
| Docker Compose | Local dev, small single-server deployments | Production at any meaningful scale |
| Nomad | HashiCorp ecosystem, simpler than K8s, multi-workload | K8s ecosystem tools required |
Startup default: ECS + Fargate (zero cluster management). Scale default: EKS or GKE once team > 5 engineers or services > 10.
| Technology | Layer | Best For |
|---|---|---|
| AWS ALB | L7 (HTTP/HTTPS) | AWS apps, path-based routing, WebSocket, HTTP/2 |
| AWS NLB | L4 (TCP/UDP) | Ultra-low latency, static IP, non-HTTP protocols |
| GCP GLB | L7 global | GCP apps, global anycast, single IP worldwide |
| Nginx | L4/L7 | Self-hosted, reverse proxy, flexible config |
| HAProxy | L4/L7 | High performance self-hosted, advanced routing |
| Cloudflare | L7 global + DDoS | DDoS protection + CDN + load balancing combined |
| Traefik | L7 | Kubernetes-native, automatic SSL, service discovery |
| Tool | Best For |
|---|---|
| Prometheus + Grafana | Self-hosted, open-source, Kubernetes-native |
| Datadog | Managed, APM + infra + logs unified, expensive |
| CloudWatch | AWS-native, zero setup, integrated with AWS services |
| New Relic | APM-focused, good for application-level insights |
| Tool | Best For |
|---|---|
| ELK Stack (Elasticsearch + Logstash + Kibana) | Self-hosted, powerful, high volume |
| Loki + Grafana | Lightweight, Kubernetes-native, cheap |
| Splunk | Enterprise, compliance, expensive |
| AWS CloudWatch Logs | AWS-native, zero setup |
| Datadog Logs | Unified with metrics, expensive |
| Tool | Best For |
|---|---|
| Jaeger | Open-source, Kubernetes-native, OpenTelemetry |
| Zipkin | Simple, lightweight, good integrations |
| AWS X-Ray | AWS-native, integrates with Lambda, ECS |
| Datadog APM | Managed, unified with metrics and logs |
| Honeycomb | High-cardinality event-based observability |
Recommended open-source stack: Prometheus + Grafana + Loki + Jaeger (all integrate via OpenTelemetry) Recommended managed stack: Datadog (expensive but unified) or Grafana Cloud
| Technology | Best For | Edge Locations |
|---|---|---|
| Cloudflare | DDoS protection + CDN + DNS, best free tier, edge workers | 300+ |
| AWS CloudFront | AWS-native, deep S3 and API GW integration | 450+ |
| Akamai | Enterprise, highest performance, expensive | 4000+ |
| Fastly | Real-time purging, streaming, VCL customization | 90+ |
| Vercel Edge / Netlify | Jamstack, frontend-first, zero config | 100+ |
Default recommendation: Cloudflare for most use cases (best value, DDoS included, free SSL, Workers for edge compute).
| Technology | Write Throughput | Read Throughput | Notes |
|---|---|---|---|
| PostgreSQL (single) | ~10K writes/s | ~50K reads/s | With connection pooling |
| PostgreSQL (replicas) | ~10K writes/s | ~200K reads/s | 4 replicas |
| MySQL (single) | ~15K writes/s | ~60K reads/s | |
| Cassandra | ~1M writes/s | ~500K reads/s | 10-node cluster |
| Redis | ~1M ops/s | ~1M ops/s | Single node in-memory |
| Kafka | ~1M msgs/s | ~1M msgs/s | Per partition |
| Elasticsearch | ~50K docs/s | ~10K queries/s | Per node |
| MongoDB | ~50K writes/s | ~100K reads/s | Per replica set |
All benchmarks are approximate and depend heavily on hardware, payload size, and query complexity.