Caching, Retention & Data Lifecycle

This page covers how Acceso manages performance and lifecycle. It focuses on caching, retention, and integrity boundaries.

Caching strategy (latency and load control)

Acceso uses in-memory and distributed caching to reduce upstream load. Caching also reduces latency on hot read paths.

Caching is applied selectively. It depends on:

• Data volatility.

• Request frequency.

• Consistency requirements.

Typical behavior:

• Highly dynamic data may bypass caching entirely.

• Static metadata is aggressively cached.

Cache eligibility rules (how decisions are made)

Cache is favored when:

• The data is stable across short time windows.

• The response is expensive to compute or fetch upstream.

• The correctness model tolerates bounded staleness.

Cache is avoided when:

• The data changes at high frequency.

• Stale reads would be user-visible or unsafe.

• Requests must reflect immediate writes.

Caching is a correctness decision first. Latency is the secondary benefit.

Retention policies (what is kept, and for how long)

Not all data is stored indefinitely. Retention varies by data category and usage purpose.

Examples:

• Usage and billing data retained for audit windows.

• Historical analytics retained based on plan tier.

• Temporary computation artifacts discarded immediately.

Retention rules are enforced automatically. This minimizes unnecessary data accumulation.

Retention enforcement (deep dive)

Common mechanisms include:

• Time-based TTLs on ephemeral datasets.

• Scheduled purge/compaction jobs for historical stores.

• Partitioning strategies that make deletion cheap and safe.

The goal is predictable retention. It should not depend on manual cleanup.

Data lifecycle (end-to-end shape)

Most data follows a consistent lifecycle:

1. Ingest and validate.

2. Normalize and derive indices.

3. Serve on request paths (often via cache).

4. Aggregate into analytical datasets.

5. Expire or purge per policy.

Data integrity and isolation

Each data domain is isolated to prevent cross-contamination. Identifiers are scoped to accounts and API keys. This enforces strict tenant separation.

Integrity checks and validation rules are applied during ingestion. This prevents corrupted or inconsistent data from persisting.

Integrity checks must be non-optional on ingestion paths. Fail fast beats repairing corrupt datasets later.