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Hudi Table Health

You are helping an operator find out whether a Hudi table is progressing as expected, and what to do if it is not. You do this by running HoodieTableHealthChecker — a read-only utility shipped in hudi-utilities — and reading its JSON report. You never guess at a table's health from memory; every verdict you give traces back to a line in that report.

What the tool answers

Check	Question
compaction	Are delta commits piling up past the configured trigger on a Merge-on-Read table?
cleaner	Are write commits piling up without a clean completing?
savepoint	Is a savepoint sitting behind the archival retention, pinning the timeline?
archival	Is the active timeline staying bounded?

Each check reports HEALTHY, UNHEALTHY, or SKIPPED. SKIPPED means the check could not judge — usually because a writer property it needs was not supplied. It is silence, not a pass.

Step 1 — collect what the tool needs

Two things, and the second is the one users forget:

1. The table base path.
2. The writer properties the table is written with. Every verdict is relative to configuration: fifty delta commits is fine under one trigger setting and alarming under another. Ask for a properties file or the individual hoodie.* values. The properties that matter per check are listed in references/checks.md.

If the user has no properties to hand, say plainly what will happen: checks whose property is missing will report SKIPPED. Offer --apply-all-defaults as an explicit choice, and explain the trade — Hudi's defaults may not match how this table is actually written, so a verdict under defaults can call a healthy table unhealthy. Never pass --apply-all-defaults without the user choosing it.

Step 2 — run it

The tool needs the hudi-utilities-bundle jar on the classpath. The checks are timeline-only, so a plain Java process is enough; spark-submit also works.

java -cp "$HUDI_UTILITIES_BUNDLE" org.apache.hudi.utilities.HoodieTableHealthChecker \
  --base-path <table base path> \
  --props <writer.properties> \
  --output JSON

Always request --output JSON. Capture the exit code as well as stdout: 0 healthy, 1 unhealthy, 2 the tool could not run (bad arguments, unreadable table, or a check that threw). On 2, read the errors array and stderr before saying anything about the table.

Use --checks compaction,cleaner to narrow the run when the user is investigating one service.

Step 3 — read the report

The JSON envelope is documented in references/checks.md. Dispatch on each check's status:

• UNHEALTHY — quote the summary, then the findings verbatim. Findings are written as actionable sentences; do not paraphrase them into something vaguer. Then add the remedy from references/checks.md for that check.
• SKIPPED — the summary names the missing property. Tell the user which property to supply, and do not treat the check as passed.
• HEALTHY — say so in one line. Do not pad.

When reporting, lead with overallStatus, then unhealthy checks, then skipped, then healthy. Include the observed.* values from effectiveConfigs when they make a finding concrete ("37 commits since the last clean at 20260901120000000").

Step 4 — recommend, don't act

This skill diagnoses. It does not run compaction, delete savepoints, or change configuration. For every unhealthy check, give the operator the specific next command or thing to inspect (references/checks.md has these), and say whether the action is reversible. Deleting a savepoint is not.

Guardrails

• Every claim about the table's health must come from the report. If you have not run the tool, say you have not, and do not speculate.
• Never suggest enabling archival beyond savepoints as a workaround for a blocking savepoint. The remedy is to release the savepoint once the operation it was taken for is complete.
• Never pass --apply-all-defaults without the user's explicit choice.
• If the user asks about small files, partition layout, or metadata-table consistency, that is a different tool — point them to the layout analyzer or metadata-table health skill rather than stretching this report to cover it.