# Coder System Design Perf Patterns

> System-level performance pattern selection with measurable tradeoffs and SLO alignment. Use when this capability is needed.

- Skill: `tomevault-io/coder-system-design-perf-patterns` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/coder-system-design-perf-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/coder-system-design-perf-patterns/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/coder-system-design-perf-patterns

---


<when_to_use>
  <trigger>Designing architecture under latency, throughput, or cost pressure</trigger>
  <trigger>Planning scaling strategy for growth or traffic spikes</trigger>
  <trigger>Reviewing performance bottlenecks and reliability tradeoffs</trigger>
</when_to_use>

<input_requirements>
  <required>Current bottleneck description and baseline metrics</required>
  <required>Target SLO/SLI and error budget constraints</required>
  <required>Traffic profile (steady, bursty, hot keys, read/write ratio)</required>
  <required>Operational constraints (cost, team capacity, infra limits)</required>
</input_requirements>

<pattern_catalog>
  <pattern>Cache-aside for read-heavy paths with tolerated staleness</pattern>
  <pattern>Queue-based load leveling for burst absorption</pattern>
  <pattern>Async processing for long-running tasks and user-facing responsiveness</pattern>
  <pattern>Batching to reduce per-operation overhead and network chatter</pattern>
  <pattern>Load shedding to protect core goodput under overload</pattern>
  <pattern>Horizontal scaling with autoscaling tied to meaningful saturation metrics</pattern>
  <pattern>Hotspot mitigation via key design and partition distribution</pattern>
</pattern_catalog>

<selection_rules>
  <rule importance="critical">Choose patterns from measured bottlenecks, not preference</rule>
  <rule importance="high">Define expected gain and rollback trigger before rollout</rule>
  <rule importance="high">Account for consistency, cost, and ops complexity tradeoffs</rule>
  <rule importance="high">Keep observability and SLO guardrails in same design package</rule>
</selection_rules>

<tradeoff_matrix>
  <item>Caching: lower latency, possible staleness and invalidation complexity</item>
  <item>Queues and async: smooth peaks, eventual consistency and replay complexity</item>
  <item>Batching: higher throughput, partial failure handling complexity</item>
  <item>Load shedding: preserves core capacity, intentional selective failures</item>
  <item>Horizontal scaling: capacity gain, bottleneck may shift to shared dependency</item>
</tradeoff_matrix>

<do_not>
  <item importance="critical">Do not scale on vanity metrics unrelated to user SLI</item>
  <item importance="high">Do not use unbounded retries/queues under overload</item>
  <item importance="high">Do not evaluate performance on averages only; include tail latency</item>
  <item importance="high">Do not ship performance changes without capacity and rollback plan</item>
</do_not>

<output_requirements>
  <requirement>Chosen pattern(s) with why-now rationale</requirement>
  <requirement>Expected SLI impact and acceptance thresholds</requirement>
  <requirement>Operational tradeoffs and failure modes</requirement>
  <requirement>Rollout, monitoring, and rollback plan</requirement>
</output_requirements>

<references>
  <source url="https://aws.amazon.com/builders-library/using-load-shedding-to-avoid-overload/">AWS Builders Library: Load Shedding</source>
  <source url="https://sre.google/sre-book/service-level-objectives/">Google SRE: Service Level Objectives</source>
  <source url="https://sre.google/workbook/alerting-on-slos/">Google SRE Workbook: Alerting on SLOs</source>
  <source url="https://sre.google/sre-book/addressing-cascading-failures/">Google SRE: Cascading Failures</source>
  <source url="https://learn.microsoft.com/en-us/azure/architecture/patterns/queue-based-load-leveling">Azure Queue-Based Load Leveling</source>
  <source url="https://kubernetes.io/docs/concepts/workloads/autoscaling/horizontal-pod-autoscale/">Kubernetes HPA</source>
  <source url="https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-design.html">DynamoDB Partition Key Design</source>
</references>

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/ozerohax) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-11 -->