spark-cli

name

spark-cli

description

Author, run and diagnose Fabric Spark: notebook cell code (%%configure, %%sql, PySpark, notebookutils), named notebook runs, Livy-session ad-hoc calculations, Spark failure triage, and the whole Materialized Lake View lifecycle -- definition, incremental-refresh readiness review, schedules, refresh jobs, run history and refresh-failure diagnosis. KQL materialized views are eventhouse-cli. Triggers:write notebook code,run notebook,notebookutils,create a Livy session,failed notebook,Spark OOM,create materialized lake view,schedule MLV,diagnose MLV refresh failure

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: spark-cli (az rest: --headers "x-ms-fabric-skill=spark-cli"), including every LRO poll, fabric_lro and retry. Snippets omit it — add it anyway.

CRITICAL NOTES

1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering
3. Skill disambiguation: use spark-cli for notebook cell code (including %%sql cells), named notebook runs, Livy sessions, Spark failure triage, and everything about a Materialized Lake View -- writing the definition, reviewing a query for incremental-refresh readiness, and scheduling, refreshing, monitoring or diagnosing an existing one. A KQL materialized view in an Eventhouse is eventhouse-cli; plain read-only T-SQL against a Warehouse or Lakehouse SQL endpoint is sqldw-cli.
4. Hard routing boundary: never execute an Eventhouse/KQL materialized-view request from this skill. Route it to ; if that skill is unavailable, state that the request cannot be completed in the current skill context and stop without calling Fabric APIs or creating artifacts.