DBRaven
Workload · oltp

Mixed OLTP (SaaS Core)

balanced

Summary

Balanced read/write SaaS core workload covering user actions, content CRUD, and moderate read caching. Reads slightly outpace writes; the bottleneck shifts between connection pool saturation during peaks and read replica lag during write bursts.

Example Systems

  • ·SaaS dashboard API
  • ·Project management backend (Linear, Notion)
  • ·CRM core API
  • ·LMS course platform
  • ·Ticketing system

Characteristics

CategoryOLTP
Read / write patternbalanced
Latency requirementlow
Consistency requirementsession
Durability requiredYes
Ordering requiredNo

Capacity

Typical RPS8,000
Peak RPS30,000
Typical data volume300 GB
Growth rate10-50 GB/month depending on attachment and audit log retention
Seasonal spikes: Monday morning logins, end-of-quarter reporting bursts, US business hours peak (4× multiplier)

Access Patterns

point lookuprange scan

Recommended Patterns

connection poolingread replicacache asidecircuit breaker

Patterns to Avoid

two phase commitsharding

Basis

Most SaaS backends fit this profile; well-documented patterns from Shopify, GitHub, and Basecamp engineering blogs

Used In Architecture Scenarios

Content Management Platformmoderate

Read-Heavy Application

A CMS for publishing and serving structured content: articles, documentation, product pages, and localized variants: where read APIs serve 50–100x more traffic than editorial write APIs. PostgreSQL stores the content graph (articles, authors, categories, taxonomy) and workflow state (draft, in-review, scheduled, published). Redis caches published content objects for read APIs. Elasticsearch powers full-text content search with faceting and relevance ranking. Cache invalidation on publish must be fast and complete; N+1 query patterns on content relationship traversal are the dominant database performance risk during reads.

Developer Tools Platformhigh

Multi-Tenant SaaS

A multi-tenant developer tooling platform providing CI/CD pipeline execution, log aggregation, code analysis, and dependency scanning across isolated tenant organizations. Tenant isolation is the primary correctness constraint: a security boundary violation between tenants is a critical incident, not a performance event. PostgreSQL row-level security enforces data isolation; Redis manages job queues and distributed locks; Elasticsearch indexes pipeline log output for search; Kafka delivers webhook events to tenant-registered endpoints; MinIO stores pipeline artifacts. Resource quota enforcement prevents any single tenant's burst from affecting others.

Healthcare Records Platformexpert

Financial Ledger

An electronic health record (EHR) architecture built around strict auditability, HIPAA compliance, and append-only correctness. Clinical records are mutable by design (amendments, addenda) but corrections must be explicitly attributed, not silently overwritten. Event sourcing provides a reconstructable audit log; PostgreSQL row-level security enforces patient-level access control at the database layer; Kafka streams HL7 FHIR events to downstream clinical systems. The architecture must support breach detection, access auditing, and state reconstruction at any historical point: not just current state retrieval.

Multi-Tenant SaaS Platformmoderate

Multi-Tenant SaaS

A multi-tenant SaaS architecture where multiple customers are served from a shared deployment, with PostgreSQL row-level security providing logical tenant isolation, Redis delivering per-tenant caching, and connection pooling managing the aggregate connection demand across tenant workloads. Tenant isolation, resource fairness, and operational simplicity are the three competing forces this architecture must balance.