Top 10 Use Cases for Temporal Cleaner in Modern Applications

Getting Started with Temporal Cleaner: Setup, Tips, and Best Practices

Temporal Cleaner is a tool for managing and maintaining time-based data—archiving, pruning, compacting, and ensuring retention policies are enforced. This guide walks through a straightforward setup, practical tips, and best practices to help you integrate Temporal Cleaner into your workflow quickly and safely.

1. Quick overview

• Purpose: Automate cleanup of time-series records, logs, events, or any temporal dataset to control storage, improve query performance, and enforce retention policies.
• Common uses: Log rotation, metrics retention, event-store pruning, snapshot cleanup, and database partition management.

2. Prerequisites

• Access to the storage or database containing your temporal data (e.g., PostgreSQL, ClickHouse, S3, Elasticsearch).
• Read/write permissions for cleanup operations and configuration deployment.
• Backup strategy: a tested backup or snapshot mechanism before running destructive cleanup tasks.
• Monitoring/alerting in place (prometheus, CloudWatch, Datadog, etc.) to observe effects.

3. Installation and basic setup

Assuming Temporal Cleaner is distributed as a CLI and/or service:

1. Install the binary or container image:

   • Binary: download the latest release and place it on your PATH.
   • Docker: pull the image and run with necessary mounts and environment variables.

2. Create a configuration file (YAML or JSON). Minimal fields:

   • target: connection details for the database or storage.
   • retention: time window to keep (e.g., 90d).
   • mode: dry-run | execute
   • schedule: cron expression or interval for periodic runs
   • filters: optional rules for selective cleanup (by tag, tenant, severity)

Example (YAML-style, adapt to your format):

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target: type: postgres   host: db.example.local   port: 5432   database: events   user: cleaner retention: 90d mode: dry-run schedule: “0 3 * * *” filters:

 

tag: analytics 

3. Validate configuration with the built-in validator (if available) or run a dry-run to preview deletions:

• Run: temporal-cleaner validate –config /path/to/config.yml
• Dry-run: temporal-cleaner run –config /path/to/config.yml –mode dry-run

4. Deploy as a scheduled job:

• Kubernetes CronJob, systemd timer, or hosted cron with appropriate permissions.
• Ensure the job runs in a network environment that can reach your target storage.

4. Safety-first workflow

• Always start with dry-run to list records that would be removed.
• Run cleanup on a small tenant or test environment first.
• Maintain recent backups for at least one retention cycle beyond the target retention.
• Use role-separated credentials limiting cleanup scope to only necessary tables/paths.

5. Performance considerations

• Batch deletes: prefer batched/partitioned deletes to avoid long-running transactions.
• Use partition drops where possible (e.g., time-partitioned tables) instead of row-level deletes—they’re faster and safer.
• Rate limit cleanup operations to avoid overloading the database during peak hours.
• Monitor query plans and lock contention; prefer non-blocking operations.

6. Scheduling and coordination

• Schedule runs during off-peak hours and coordinate across services to avoid simultaneous heavy jobs.
• Stagger cleanup across tenants or shards to smooth resource usage.
• If multiple cleaner instances run, implement leader election or distributed locking to avoid duplicate work.

7. Retention strategies

• Fixed window retention: delete anything older than N days—simple and predictable.
• Tiered retention: keep high-resolution recent data (7–30 days), downsample to lower resolution for mid-term (30–365 days), and archive beyond that.
• Per-tenant or per-tag retention for business-critical vs. ephemeral data.
• Legal/compliance exceptions: ensure retention settings respect regulatory requirements.

8. Monitoring and alerting

• Track key metrics: records deleted, bytes freed, run duration, errors, and rate of deletions.
• Alert on spikes in runtime, failure rate, or unexpected volume changes.
• Log actions with enough metadata to audit what was removed and why.

9. Common troubleshooting

• Long-running transactions: switch to partition/epoch-based cleanup or smaller batch sizes.
• Permission errors: confirm service credentials and network access.
• High lock contention: reduce concurrency, lower batch sizes, and align cleanup times with low-traffic windows.
• Unexpected deletions: immediately stop execution, restore from backup if needed, and audit logs to determine cause.

10. Best practices checklist

• Backup: verify backups before enabling destructive runs.
• Dry-run: always validate what will be removed.
• Least privilege: use scoped credentials.
• Partitioning: leverage time-partitioned storage when possible.
• Staggering: avoid running heavy jobs concurrently.
• Monitoring: export metrics and set alerts.
• Documentation: keep retention policies documented and approved.

11. Example workflow (90-day retention)

1. Configure cleaner with retention: 90d and mode: dry-run.
2. Validate config and run dry-run; review output.
3. Schedule weekly dry-run reports for stakeholders.
4. After approval, switch to execute mode with conservative batch sizing.
5. Monitor first runs closely; verify backup restores in test before relying on automation.
6. Move to scheduled runs once stable and observed over multiple cycles.

If you want, I can generate a sample config file tailored to a specific backend (Postgres, ClickHouse, S3, or Elasticsearch) — tell me which backend to target and any constraints (retention period, tenant model).