High-cardinality data: Performance design

Defines workload assumptions, budgets, capacity boundaries, bottlenecks, and scaling strategy for high-cardinality data. It fixes adoption boundaries and observable failure evidence while keeping environment-specific limits configurable.

Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.

Decision

For High-cardinality data, adopt a measured workload model and explicit resource budget. Use one owned boundary, explicit state model, and versioned interfaces; keep policy decisions separate from adapters and side effects. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.

Scope

• The High-cardinality data actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this architecture block.
• Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
• Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.

Outside this block

• Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
• Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.

Contract

• State representative request shape, concurrency, data volume, distribution, environment, warm-up, and measurement window with every result.
• Use percentile latency, error, throughput, saturation, and cost together; an average alone cannot authorize a capacity claim.
• Bound queues, batches, concurrency, memory, payloads, cardinality, and retries, including overload admission and degradation behavior.
• Compare regressions against a versioned baseline and require investigation or an explicit budget change before release.
• Document component ownership and allowed dependency direction, including which data may cross each boundary.
• Make synchronous, asynchronous, and batch paths implement the same authority and state invariants.
• Set per-signal dimension allowlists and cardinality budgets; move unrestricted identifiers to logs or traces with access and retention controls.
• Detect series growth before ingestion collapse and degrade optional dimensions rather than dropping all operational visibility.

Implementation guidance

• Start with the domain state and interface contract, then attach adapters, storage, queues, and provider-specific code.
• Model High-cardinality data inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable High-cardinality data contract or policy version with material state so migrations, replay, and support decisions remain attributable.
• Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.

Failure handling and safeguards

• At saturation, apply declared backpressure or rejection while preserving accepted-work identity and system recovery capacity.
• Invalid or contradictory High-cardinality data state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
• Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.

Verification and operations

• Exercise the contract through each adapter and prove that a failed dependency cannot bypass the authoritative boundary.
• Monitor High-cardinality data success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
• Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.

Adoption assumptions

• Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
• The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.

The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.