Developer Implementation Guide
Automate retention jobs and TTLs
Keep only what you need, no longer than necessary. Turn retention rules into code with time-bound deletes, legal holds, and tamper-evident audits across databases, object storage, logs, and analytics.
Automate Retention Jobs and TTLs
Keep only what you need, no longer than necessary. Turn retention rules into code with time-bound deletes, legal holds, and tamper-evident audits across databases, object storage, logs, and analytics.
Strategy and governance
Define a retention schedule per dataset: purpose, system, selector, duration, action (delete, anonymize, aggregate).
Prefer event time over ingestion time for retention cutoffs.
Enforce exceptions: legal hold, disputes, fraud investigations, accounting requirements.
Relational stores (Postgres example)
Prefer partitioning by time and drop whole partitions. Fall back to targeted deletes with small batches.
Document stores
MongoDB TTL index
Redis
Object storage lifecycle
Amazon S3
Google Cloud Storage
Logs and search
Elasticsearch/OpenSearch ILM
Analytics warehouses
BigQuery
Snowflake
Use tasks to purge old rows based on event time.
Set Time Travel and Fail-safe appropriately to avoid retaining data beyond policy.
Orchestration and safety
Run deletes off-hours with small batches and backoff.
Use idempotent jobs keyed by dataset and window.
Add a dry-run mode that reports counts and sampled IDs before execution.
Maintain a quarantine path for mistaken deletes and ensure restores re-apply retention rules.
Coordination with rights requests
Deletion pipelines should mark records with
delete_by <timestamp>so retention jobs can clean stragglers.Retention jobs must respect per-subject legal holds and regulator-mandated retention.
Monitoring and SLAs
Track rows deleted per run, runtime, errors, and skipped due to holds.
Alert on job failures, zero-activity anomalies, or unexpected spikes.
Prove enforcement with monthly retention reports from
retention_audit.
Quick retention checklist
Single source of truth for retention policies and legal holds
Time-based partitions or TTL indexes wherever possible
Batch deletes with dry-run and audit logs
Storage lifecycle rules for buckets and indices
Warehouse tasks tied to event time, not load time
Per-subject holds and coordination with deletion requests
Metrics, alerts, and monthly evidence reports
Conclusion
Retention is a control, not a note in a policy. By codifying durations, automating TTLs and partition drops, honoring legal holds, and auditing every run, you reduce risk, control costs, and align with GDPR and CPRA storage limitation requirements.