What Causes Home Assistant to Retain More Temporary Data Than Expected?
Home Assistant retains more temporary data than expected when backup, database, logging, update, or cache work outlives the cleanup event assumed by the operator.
Temporary does not always mean short-lived or safe to delete. A failed archive can leave staging files, SQLite may keep journal or free pages, a verbose integration can expand logs, and a running process can hold deleted storage until restart. Identify the owning path, process, creation time, and active job before removing anything; otherwise cleanup can interrupt recovery or corrupt state.
Separate Temporary Paths From Retained Application Data
Classify growth by directory and owner: database and journals, backup staging, logs, media conversion, update downloads, add-on cache, container layers, and operating-system temporary space. Compare apparent file size, allocated blocks, and filesystem free space. A path named temp may contain an active transaction, while a cache directory may intentionally survive restarts.
Home Assistant installations commonly retain precise history for a configured window and long-term statistics under different rules. This retention distinction shows why database growth should not be labeled temporary merely because older detail will eventually be purged.
If the data belongs to a documented retention policy, adjust the policy rather than deleting files. If it belongs to a completed or failed job with no active owner, it becomes a cleanup candidate. Unknown ownership is a stop condition, especially inside database, backup, or supervisor-managed paths.
Failed Jobs Can Strand Staging Data
Backups, updates, imports, and database maintenance often create a second copy while work is in progress. Success normally renames or removes the staging data; interruption, disk exhaustion, or a crashed worker may skip that cleanup. Repeated retries can then create several generations whose timestamps align with failed jobs.
A concrete failure mode is documented in the request to purge failed backup files, where unsuccessful backup runs left large supervisor temporary directories. The lesson is ownership and job state, not a universal path that every installation should delete manually.
Confirm no backup, restore, upgrade, or migration is running, preserve the job error, and use the supported cleanup mechanism for the installation type. If the same files return, fix the failing job or free-space boundary first. Deleting symptoms without changing the failure loop only resets the clock.
Database Files May Not Shrink When Rows Disappear
Recorder purging can remove logical rows while the database retains allocated pages for reuse. Write-ahead logs or journals may also expand until checkpoint conditions are met. File size therefore can remain high even after retention drops, and an abrupt manual deletion can destroy consistency rather than reclaim harmless cache.
One Home Assistant investigation of a database purge pattern distinguishes ongoing daily growth from the scheduled purge point and illustrates why a short observation window can misclassify expected behavior.
Measure logical row age, database health, journal state, and free-space trend across at least one cleanup cycle. Use supported database maintenance only with a verified backup and enough temporary headroom. Escalate if journals never converge, integrity checks fail, or Recorder repeatedly restarts during cleanup.
Run a Safe Temporary-Data Triage
Take two storage snapshots an hour apart, listing path, allocated size, modification time, owner process, and related job status. Mark each item active, retained by policy, orphaned after failure, or unknown. Stop generating new backups and updates during the comparison so growth has a traceable cause.
The ZimaSpace procedure for cache and temporary storage provides the installation-level controls after ownership is established.
Remove only items documented as disposable and not open by a process, then rerun the original job and confirm both success and cleanup. A pass is stable free space across two cycles. If ownership remains unknown or database integrity is involved, preserve the data and escalate instead of forcing reclamation.
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