Scalability and Performance

Maintain speed and efficiency as data, users, and workload complexity grow.

What Scalability Covers

Scalability addresses growth in data volume, users, and compute demand while meeting latency, throughput, and cost objectives.

Why Scalability Matters

EO usage is bursty and data-heavy. Without scalable design, query performance collapses, queues spike, and costs become uncontrolled.

What Good Looks Like

Mature platforms scale horizontally with indexed catalogs, cache-aware delivery, buffered queues, and parallel processing governed by performance and cost budgets.

Minimum Requirements

Architecture that scales data ingestion, users, and compute independently.
Indexing, caching, and queue buffering for critical workflows.
Parallelization controls with tenant fairness under load.
Geographic distribution strategy for delivery latency and sovereignty.
Cost controls with FinOps visibility and safeguards.

Scaling Data, Users, and Compute

Archive Growth

Plan storage/index growth for long-term scene accumulation.

Concurrent Users

Protect interactive workloads with priority-aware throttling.

Large AOIs

Use tiling and chunked processing for large-area requests.

Burst Processing

Auto-scale compute for sudden mission or disaster demand.

Background Jobs

Separate low-priority jobs from latency-sensitive paths.

Performance Architecture

Indexing

Maintain spatial-temporal indexes and query plan tuning.

Caching

Use cache tiers for metadata and high-demand products.

Distributed Processing

Distribute workloads by scene, tile, and product class.

Queue Buffering

Absorb bursts while preserving priority and fairness.

CDN and Regional Delivery

Deliver outputs close to users with region-aware caching.

Hot Path Optimization

Optimize latency-critical order-to-access workflows.

Cost-Aware Scaling

Storage Cost Controls

Apply lifecycle policies and deduplication where valid.

Compute Cost Controls

Use rightsizing, schedule-aware scaling, and quota limits.

Retention Trade-Offs

Align retention depth with business and compliance value.

Performance vs Cost Decisions

Document trade-offs with measurable budgets and ceilings.

Capacity Planning and Bottleneck Management

Forecast demand, track bottlenecks, and prioritize key workflows under constrained capacity.

Geographic and Multi-Tenant Scaling

Define placement for global users and maintain multi-tenant fairness during high load.

Scalability Decisions

Key choices include active region count, queue partitioning strategy, and strict performance budgets for mission-critical paths.

Metrics and Health Signals

P95/P99 latency for search, order, and delivery.
Queue backlog age and throughput by priority class.
Autoscaling efficiency and saturation indicators.
Cost per terabyte ingested and per product delivered.
Fairness metrics across tenants under load.

Anti-Patterns

Scaling compute without fixing indexing bottlenecks.
No cost ceilings for burst workloads.
Single shared queue for all priorities.
Ignoring performance regressions in release criteria.

Implementation Checklist

Is ownership clear?
Are minimum controls defined?
Are failure modes addressed?
Are measurable health signals defined?
Are anti-patterns named?
Are dependencies on other domains explicit?
Is there at least one EO-specific implementation example?
Is there a practical implementation checklist?

Example EO Patterns

Wildfire surge mode allocates burst compute and prioritizes near-real-time detection products.
Regional CDN edge caching reduces repeated download latency for common AOIs.
Performance budget gates block releases that degrade order-to-first-byte latency.

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