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databricks-common-errors

by jeremylongshore

Diagnose and fix Databricks common errors and exceptions.\nUse when encountering Databricks errors, debugging failed jobs,\nor troubleshooting cluster and notebook issues.\nTrigger with phrases like \

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
jeremylongshore
Category
Security
Views
11
GitHub repo

About this skill

Diagnose and fix Databricks common errors and exceptions.\nUse when encountering Databricks errors, debugging failed jobs,\nor troubleshooting cluster and notebook issues.\nTrigger with phrases like \

How to use

  1. Upewnij się, że masz zainstalowany Databricks CLI i skonfigurowany dostęp do workspace'u. Zainstaluj również pakiet databricks-sdk poleceniem pip install databricks-sdk.

  2. Gdy napotkasz błąd Databricks (np. "ClusterNotReadyException" czy "Spark OOM"), wyzwól skill frazami takimi jak "databricks error", "fix databricks", "databricks not working" lub "debug databricks".

  3. Skill wyciągnie szczegóły nieudanego uruchomienia zadania za pomocą Databricks CLI — pobierze stan klastra, komunikat błędu i listę zadań z ich stanami.

  4. Skill dopasuje błąd do znanego typu (np. CLUSTER_NOT_READY, INVALID_STATE, OOM) i zaproponuje konkretne rozwiązanie — może to być uruchomienie klastra, zmiana konfiguracji pamięci lub naprawa uprawnień.

  5. Skorzystaj z dostarczonych fragmentów kodu Python (używających WorkspaceClient z SDK) lub poleceń SQL, aby zastosować naprawę bezpośrednio w swoim środowisku.

  6. Sprawdź logi klastra i zadania, aby potwierdzić, że błąd został rozwiązany — skill pomoże ci zinterpretować komunikaty stanu i termination_reason.

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