tech-matrix – skill Tài liệu & Office cho agent AI

MONOPOLY — Technology Decision Matrix

When to Use

• Use this skill when the task matches this description: Reference document for monopoly tech-matrix.

Use this as a qualitative candidate list. Verify exact editions, versions, service quotas and pricing with current primary docs; fixed throughput, memory, storage, edge counts and prices are not established capacity evidence. Decide from measured workload, operations and failure tests rather than user/team counts.

Table of Contents

1. Database Selection

2. Cache Selection

3. Message Queue / Event Streaming

4. API Protocol

5. Search Engine

6. Object Storage

7. Container Orchestration

8. Load Balancer

9. Observability Stack

10. CDN

---

1. Database Selection

Relational (SQL)

| 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 | Check current product/engine support for required joins and foreign keys | Very high — Vitess based |

| Amazon Aurora | AWS-native apps, managed PostgreSQL/MySQL, high availability | Non-AWS environments | Up to 128TB, 15 replicas |

NoSQL

| Database | Best For | Avoid When | Scale Ceiling |

|----------|----------|------------|---------------|

| MongoDB | Flexible schema, document model, prototyping | Requires validating transaction/isolation and distributed-operation costs; MongoDB supports transactions | Petabyte-scale with sharding |

| DynamoDB | Key-value at massive scale, AWS-native, serverless, predictable latency | Complex queries, ad-hoc analytics, JOINs | Subject to account/table/partition quotas |

| 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 |

Decision Framework

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

---

2. Cache Selection

| Technology | Best For | Max Single Node | Cluster Support |

|------------|----------|----------------|----------------|

| Redis | Sessions, leaderboards, pub/sub, complex data structures, Lua scripting | ~1TB RAM | Cluster sharding; Sentinel provides non-cluster failover |

| 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.

---

3. Message Queue / Event Streaming

| 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) | Configurable; verify current topic/subscription retention limits |

| 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 |

Decision Matrix

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 a managed AWS-native task queue with explicit retry/latency objectives?
  SQS

Volume > 1 million messages/second?
  Kafka (self-hosted) or Kinesis (managed)

---

4. API Protocol

| 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:

• External / public: REST
• Internal service-to-service: gRPC
• Real-time features: WebSocket or SSE

---

5. Search Engine

| 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 |

Selection: Benchmark relevance, query mix, indexes, language support, data size and update latency. Document count alone does not determine when to leave PostgreSQL FTS.

---

6. Object Storage

Compare AWS S3, Cloudflare R2, GCS, Azure Blob, Backblaze B2 and self-hosted S3-compatible systems against region/residency, consistency/API compatibility, operations, availability, replication and object lifecycle needs. Build a dated cost model including storage, requests, retrieval, inter-region transfer, internet egress and connected compute. Zero egress for one service does not make the full delivery path free. Use current calculators; this reference supplies no price quote.

---

7. Container Orchestration

| 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 |

**Star

… (nội dung đầy đủ có trong gói tải về)