DBRaven
Workload · oltp

High-Throughput OLTP

balanced

Summary

High-volume transactional workload with many concurrent short-lived operations, requiring low latency, strong consistency, and high write throughput.

Example Systems

  • ·E-commerce order processing
  • ·Financial transaction ledger
  • ·Ride-sharing trip lifecycle
  • ·SaaS subscription billing
  • ·Gaming leaderboard writes

Characteristics

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

Capacity

Typical RPS5,000
Peak RPS25,000
Typical data volume500 GB
Growth rate20-100 GB/month depending on retention policy
Seasonal spikes: End-of-day batch processing, promotional events, product launches (5× multiplier)

Access Patterns

point lookuprange scan

Recommended Patterns

single primaryread replicaconnection pooling

Patterns to Avoid

eventual consistency storeleaderless replication

Basis

Well-characterized workload type with consistent industry patterns

Related Architecture Knowledge

Outbound: this entity affects

Benefits FromPattern
rate limiting
Grounded

High-throughput OLTP workloads benefit from rate limiting to prevent individual tenants or clients from consuming the entire database write capacity during traffic spikes.

Full relationship →
Vulnerable ToFailure Mode
hot shard unbalanced writes
Grounded

High-throughput OLTP workloads are vulnerable to hot shard problems when the shard key is a sequential ID or timestamp, concentrating all new writes on the highest-range shard.

Full relationship →
Vulnerable ToFailure Mode
schema drift
Grounded

High-throughput OLTP workloads are vulnerable to schema drift when migrations are applied in different orders across environments, causing queries to fail in production but succeed in staging.

Full relationship →

Used In Architecture Scenarios

API Gateway Platformhigh

Multi-Tenant SaaS

A multi-tenant API gateway providing authentication, distributed rate limiting, request routing, payload transformation, and per-tenant usage analytics for API publishers. The hot path: authentication check, rate limit evaluation, and routing decision: must complete in under 1ms using Redis-only data structures to avoid proxying latency dominating upstream service response time. PostgreSQL stores tenant configuration, subscription plans, and API key definitions. Kafka receives API usage events for downstream billing and analytics. Configuration changes (rate limit updates, routing rule edits) must propagate to all gateway replicas without restart.

Analytics Data Platformhigh

Analytics Pipeline

An OLAP-oriented analytics architecture that ingests operational changes from PostgreSQL via WAL-based CDC into Kafka, then routes them to a columnar analytics store (ClickHouse or Snowflake) for product analytics, business intelligence, and operational reporting. The CQRS separation ensures analytical queries never degrade transactional write performance, and materialized views provide pre-aggregated query acceleration for the most expensive analytical patterns.

Event-Driven Analytics Pipelinehigh

Event-Driven System

A streaming architecture that captures database changes via WAL-based CDC, publishes them to an event stream (Kafka), and routes them to analytics consumers. Decouples the write path from the read path while maintaining a durable, replayable event log.

Gaming Backend Platformhigh

Realtime Collaboration

An online multiplayer game backend built around authoritative server game state synchronization. Game rooms run as distributed state machines where the server is the single source of truth for game state: client predictions are reconciled against the server state at every tick. WebSocket connections provide low-latency bidirectional communication for state deltas. Redis stores active game room state (in-flight, with sub-millisecond access), session affinity tokens (routing players to the same server instance as their game room), and matchmaking queues. PostgreSQL is the durable store for player profiles, persistent inventory, leaderboards, and achievement records. Kafka carries post-game event streams for analytics, anti-cheat processing, and achievement evaluation. NATS provides low-latency pub/sub for intra-cluster game state broadcasting when multiple game server instances must coordinate on shared state. Event sourcing records every game action as an immutable event for replay, dispute resolution, and anti-cheat audit.

Realtime Collaborative Editorexpert

Realtime Collaboration

An architecture for multi-user document editing where users see each other's changes in near real-time. PostgreSQL provides durable state persistence, Redis coordinates ephemeral session state and pub/sub for live change propagation, and connection pooling protects the database from WebSocket-induced connection churn.

Write-Heavy Transactional Platformhigh

Write-Heavy Application

A high-volume transactional write architecture anchored on PostgreSQL, where write throughput, durability guarantees, and audit completeness must coexist. The outbox pattern ensures reliable event publishing to Kafka without two-phase commit, and WAL-based CDC provides a durable change log that can reconstruct system state. Connection pooling via PgBouncer bounds connection overhead at the database layer.