debug

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更新时间2026年6月10日 12:49

Debug with root-cause analysis before fixes. Use for bugs, failing tests, CI failures, runtime errors, Spark/SQL performance, schema drift, data quality incidents, logs, metrics, and unexplained behavior.

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来源

datacoolie

datacoolie/dekit

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Debug

Root cause first. No speculative fixes.

Process

Capture the exact symptom: command, error, logs, failing assertion, bad data sample.
Map affected scope: files, jobs, tables, configs, recent changes.
Form 2-3 plausible hypotheses.
Test each hypothesis with observable evidence.
Fix the confirmed root cause, not the symptom.
Re-run the original failing check.
Add prevention: regression test, validation, monitor, guardrail, or contract.

Evidence Checklist

Raw error output or failing data captured.
Timeline connects trigger, symptom, and impact.
Recent code/config/source data changes checked.
Alternatives ruled out are named.
Root cause has an evidence chain.
Fix is verified by the original reproduction path.

Data Pipeline Debugging

Schema drift: compare actual vs expected schema at ingestion.
Missing data: check source freshness, file manifest, watermark, partition filters.
Duplicates: inspect replay behavior, merge keys, dedup ordering.
Stale data: check cache/materialization refresh and orchestration.
Spark OOM/skew: inspect collect/toPandas, partition sizes, join strategy, explain plan.
SQL slowness: inspect plan, predicates, implicit casts, partition/index pruning.

Red Flags

"Probably", "seems fixed", or "tests pass" without original reproduction rerun.
Fixing production data manually instead of replaying pipeline.
Adding retries for deterministic data failures.
Suppressing failing tests or quality gates.

Output

Root cause: <one sentence>
Evidence:
- ...
Fix:
- ...
Verification:
- <command/check/result>
Prevention:
- ...
Open questions:
- ...

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research

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plan

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Create implementation plans, architecture decisions, and phased roadmaps. Use for Standard or Complex changes, new pipelines, schema changes, migrations, multi-file features, technology choices, or when acceptance criteria and verification strategy are unclear.

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code-review

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Review code and data pipeline changes for correctness, security, performance, maintainability, contract breaks, and missing verification. Use before merge, after implementation, for PRs, commits, pending diffs, or codebase risk scans.

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data-ingestion

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Design data ingestion into landing/bronze layers. Use for source onboarding, transfer pattern selection, full/incremental/CDC scope, change detection, landing zones, schema drift, idempotent file or batch processing, and ingestion quality gates.

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name	debug
description	Debug with root-cause analysis before fixes. Use for bugs, failing tests, CI failures, runtime errors, Spark/SQL performance, schema drift, data quality incidents, logs, metrics, and unexplained behavior.

Debug

Root cause first. No speculative fixes.

Process

Capture the exact symptom: command, error, logs, failing assertion, bad data sample.
Map affected scope: files, jobs, tables, configs, recent changes.
Form 2-3 plausible hypotheses.
Test each hypothesis with observable evidence.
Fix the confirmed root cause, not the symptom.
Re-run the original failing check.
Add prevention: regression test, validation, monitor, guardrail, or contract.

Evidence Checklist

Raw error output or failing data captured.
Timeline connects trigger, symptom, and impact.
Recent code/config/source data changes checked.
Alternatives ruled out are named.
Root cause has an evidence chain.
Fix is verified by the original reproduction path.

Data Pipeline Debugging

Schema drift: compare actual vs expected schema at ingestion.
Missing data: check source freshness, file manifest, watermark, partition filters.
Duplicates: inspect replay behavior, merge keys, dedup ordering.
Stale data: check cache/materialization refresh and orchestration.
Spark OOM/skew: inspect collect/toPandas, partition sizes, join strategy, explain plan.
SQL slowness: inspect plan, predicates, implicit casts, partition/index pruning.

Red Flags

"Probably", "seems fixed", or "tests pass" without original reproduction rerun.
Fixing production data manually instead of replaying pipeline.
Adding retries for deterministic data failures.
Suppressing failing tests or quality gates.

Output

Root cause: <one sentence>
Evidence:
- ...
Fix:
- ...
Verification:
- <command/check/result>
Prevention:
- ...
Open questions:
- ...