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executing-spark

Execute arbitrary Python or PySpark code on Fabric Spark compute without creating a notebook artifact; ephemeral Livy sessions with full Delta table access. Automatically invoke when the user asks to "run PySpark in Fabric", "create a Livy session", "execute Python on Fabric comp

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Preis unbestätigt★ 893 GitHub-StarsVerzeichnis aktualisiert · 4. Sept. 2026agent-skill

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Executing Spark Code in Fabric (No Notebook)

Run arbitrary PySpark or Python code on Fabric Spark compute via the Livy API. No notebook artifact is created or persisted; sessions are ephemeral. Full read/write access to lakehouse Delta tables via Spark SQL.

Prerequisites

Azure CLI authenticated (az login)
A lakehouse in the target workspace (the Livy session runs against it)
Fabric capacity (F or trial)

Critical: Authentication

The Livy API requires a token from az account get-access-token --resource https://api.fabric.microsoft.com. Tokens from fab auth do not work for OneLake storage access inside the Spark session.

import subprocess, json

result = subprocess.run(
    ["az", "account", "get-access-token", "--resource", "https://api.fabric.microsoft.com"],
    capture_output=True, text=True
)
token = json.loads(result.stdout)["accessToken"]

Do not output or log the token. Pass it directly to the API call.

Lifecycle

1. Create session   POST .../sessions              {"kind": "pyspark"}
2. Wait for idle    GET  .../sessions/{id}          poll until state: "idle" (~30-90s)
3. Submit code      POST .../sessions/{id}/statements   {"code": "...", "kind": "pyspark"}
4. Get result       GET  .../sessions/{id}/statements/{n}   poll until state: "available"
5. Delete session   DELETE .../sessions/{id}        ALWAYS do this

Base URL: https://api.fabric.microsoft.com/v1/workspaces/{wsId}/lakehouses/{lhId}/livyapi/versions/2023-12-01

CRITICAL: Always delete sessions when done. Idle sessions consume Fabric capacity units (CUs). A forgotten session burns compute until it times out (default: 20 minutes). In automation, wrap cleanup in a finally block.

Getting IDs

WS_ID=$(fab get "Workspace.Workspace" -q "id" | tr -d '"')
LH_ID=$(fab get "Workspace.Workspace/Lakehouse.Lakehouse" -q "id" | tr -d '"')

Submitting Code

Submit PySpark or pure Python as statements. The spark object is available automatically.

# Statement payload
{"code": "df = spark.sql('SELECT * FROM products LIMIT 10')\ndf.show()", "kind": "pyspark"}

Results are in output.data["text/plain"] when state: "available" and output.status: "ok".

What Works

spark.sql("SELECT ...") ; full Spark SQL against lakehouse tables
spark.sql("SHOW TABLES") ; metastore access
df.write.mode("overwrite").saveAsTable(...) ; write Delta tables
Pure Python (pandas, numpy, pyarrow); runs on Spark container
In-memory Spark DataFrames and transformations
Multiple sequential statements in one session

What Does Not Work

deltalake (delta-rs) is not pre-installed; use Spark SQL instead
notebookutils has limited functionality (no FUSE mount at /lakehouse/default/)
Tokens from fab auth ; must use az CLI token
Tokens expire after ~60 minutes; long sessions need token refresh

When to Use This vs Alternatives

Scenario	Approach
Quick read-only exploration	DuckDB locally (fastest; see `using-duckdb` skill)
Write data back to lakehouse	Livy session or notebook
Ephemeral transform; no artifact	Livy session (this skill)
Complex multi-cell workflow	Notebook (`nb exec` or portal)
Scheduled ETL	Notebook via `fab job run`
Agent-driven compute (Dagster, orchestrators)	Livy session

Persisting code as a notebook: poll the definition LRO tightly

This skill is for ephemeral execution with no artifact. When you instead want to persist or change a notebook (deploy new code, iterate on an existing one), that is an item-definition change, and the poll interval is the single biggest performance lever. fab import, nb create, and nb cell edit take 25-60s because they poll the create/update long-running operation at the server's advertised Retry-After: 20; the work itself finishes in ~1s, and neither CLI lets you change that interval. Poll the LRO at ~0.3s and the same deploy takes ~1-2s. The fabric-cli skill ships scripts/deploy_notebook.py which does this (auto-detects create vs update, --poll-interval default 0.3s); strongly prefer it over fab import / nb for any notebook definition change.

Sessions vs Batch Jobs

A Livy session (this skill) is interactive: create it, submit statements, read output as it runs, delete it. It stays alive and you pay for idle time until you delete it or it times out (~20 min).

A Livy batch is one-shot: submit a single job (a file or inline job spec), poll it to a terminal state, done. No idle-CU footgun, nothing to remember to delete. For scheduled or fire-and-forget agent ETL, prefer a batch over a session; keep sessions for interactive, multi-statement work. Same base URL, /batches instead of /sessions -- see references/livy-api.md.

Livy vs Notebook Jobs: reading the outcome

A Livy statement returns its result directly in the response (output.status = ok/error), so you always know whether it worked. A notebook run via fab job run does not -- its job status reports Completed even when the notebook caught an exception and exited a failure payload. If you run notebooks as batch jobs instead of Livy, you must read the notebook's exit value to get its real verdict. The fabric-cli skill (in the fabric-cli plugin) documents that endpoint and ships scripts/run_notebook_checked.py for it.

References

references/livy-api.md -- Full API reference with endpoints (sessions + batches), request/response formats, and error handling
references/example-script.md -- Complete working script that creates a session, queries data, writes results, and cleans up

using-duckdb skill (same etl plugin) -- read-only Delta querying, local or in-notebook, when you don't need Spark compute
fabric-cli skill (fabric-cli plugin) -- nb exec / fab job run for notebooks, reading a notebook's exit value, the SQL-endpoint metadata sync after a Spark write, and scripts/deploy_notebook.py for fast notebook definition changes (tight LRO polling)

Quelle ansehen ↗

Dateimetadaten

name: executing-spark
description: Execute arbitrary Python or PySpark code on Fabric Spark compute without creating a notebook artifact; ephemeral Livy sessions with full Delta table access. Automatically invoke when the user asks to "run PySpark in Fabric", "create a Livy session", "execute Python on Fabric compute", "run Spark without a notebook", "submit code to Fabric", "ephemeral Spark execution", "run ETL in Fabric".

Originaltext anzeigen

---
name: executing-spark
description: Execute arbitrary Python or PySpark code on Fabric Spark compute without creating a notebook artifact; ephemeral Livy sessions with full Delta table access. Automatically invoke when the user asks to "run PySpark in Fabric", "create a Livy session", "execute Python on Fabric compute", "run Spark without a notebook", "submit code to Fabric", "ephemeral Spark execution", "run ETL in Fabric".
---

# Executing Spark Code in Fabric (No Notebook)

Run arbitrary PySpark or Python code on Fabric Spark compute via the Livy API. No notebook artifact is created or persisted; sessions are ephemeral. Full read/write access to lakehouse Delta tables via Spark SQL.

## Prerequisites

- Azure CLI authenticated (`az login`)
- A lakehouse in the target workspace (the Livy session runs against it)
- Fabric capacity (F or trial)

## Critical: Authentication

The Livy API requires a token from `az account get-access-token --resource https://api.fabric.microsoft.com`. Tokens from `fab auth` do **not** work for OneLake storage access inside the Spark session.

```python
import subprocess, json

result = subprocess.run(
    ["az", "account", "get-access-token", "--resource", "https://api.fabric.microsoft.com"],
    capture_output=True, text=True
)
token = json.loads(result.stdout)["accessToken"]
```

Do not output or log the token. Pass it directly to the API call.

## Lifecycle

```
1. Create session   POST .../sessions              {"kind": "pyspark"}
2. Wait for idle    GET  .../sessions/{id}          poll until state: "idle" (~30-90s)
3. Submit code      POST .../sessions/{id}/statements   {"code": "...", "kind": "pyspark"}
4. Get result       GET  .../sessions/{id}/statements/{n}   poll until state: "available"
5. Delete session   DELETE .../sessions/{id}        ALWAYS do this
```

Base URL: `https://api.fabric.microsoft.com/v1/workspaces/{wsId}/lakehouses/{lhId}/livyapi/versions/2023-12-01`

**CRITICAL: Always delete sessions when done.** Idle sessions consume Fabric capacity units (CUs). A forgotten session burns compute until it times out (default: 20 minutes). In automation, wrap cleanup in a `finally` block.

## Getting IDs

```bash
WS_ID=$(fab get "Workspace.Workspace" -q "id" | tr -d '"')
LH_ID=$(fab get "Workspace.Workspace/Lakehouse.Lakehouse" -q "id" | tr -d '"')
```

## Submitting Code

Submit PySpark or pure Python as statements. The `spark` object is available automatically.

```python
# Statement payload
{"code": "df = spark.sql('SELECT * FROM products LIMIT 10')\ndf.show()", "kind": "pyspark"}
```

Results are in `output.data["text/plain"]` when `state: "available"` and `output.status: "ok"`.

## What Works

- `spark.sql("SELECT ...")` ; full Spark SQL against lakehouse tables
- `spark.sql("SHOW TABLES")` ; metastore access
- `df.write.mode("overwrite").saveAsTable(...)` ; write Delta tables
- Pure Python (pandas, numpy, pyarrow); runs on Spark container
- In-memory Spark DataFrames and transformations
- Multiple sequential statements in one session

## What Does Not Work

- `deltalake` (delta-rs) is not pre-installed; use Spark SQL instead
- `notebookutils` has limited functionality (no FUSE mount at `/lakehouse/default/`)
- Tokens from `fab auth` ; must use `az` CLI token
- Tokens expire after ~60 minutes; long sessions need token refresh

## When to Use This vs Alternatives

| Scenario | Approach |
|----------|----------|
| Quick read-only exploration | DuckDB locally (fastest; see `using-duckdb` skill) |
| Write data back to lakehouse | Livy session or notebook |
| Ephemeral transform; no artifact | Livy session (this skill) |
| Complex multi-cell workflow | Notebook (`nb exec` or portal) |
| Scheduled ETL | Notebook via `fab job run` |
| Agent-driven compute (Dagster, orchestrators) | Livy session |

## Persisting code as a notebook: poll the definition LRO tightly

This skill is for ephemeral execution with no artifact. When you instead want to **persist or change** a notebook (deploy new code, iterate on an existing one), that is an item-definition change, and the poll interval is the single biggest performance lever. `fab import`, `nb create`, and `nb cell edit` take 25-60s because they poll the create/update long-running operation at the server's advertised `Retry-After: 20`; the work itself finishes in ~1s, and neither CLI lets you change that interval. Poll the LRO at ~0.3s and the same deploy takes ~1-2s. The `fabric-cli` skill ships [`scripts/deploy_notebook.py`](../../../fabric-cli/skills/fabric-cli/scripts/deploy_notebook.py) which does this (auto-detects create vs update, `--poll-interval` default 0.3s); strongly prefer it over `fab import` / `nb` for any notebook definition change.

## Sessions vs Batch Jobs

A Livy **session** (this skill) is interactive: create it, submit statements, read output as it runs, delete it. It stays alive and you pay for idle time until you delete it or it times out (~20 min).

A Livy **batch** is one-shot: submit a single job (a file or inline job spec), poll it to a terminal state, done. No idle-CU footgun, nothing to remember to delete. For scheduled or fire-and-forget agent ETL, prefer a batch over a session; keep sessions for interactive, multi-statement work. Same base URL, `/batches` instead of `/sessions` -- see [`references/livy-api.md`](./references/livy-api.md#batch-jobs-one-shot).

## Livy vs Notebook Jobs: reading the outcome

A Livy statement returns its result **directly** in the response (`output.status` = `ok`/`error`), so you always know whether it worked. A notebook run via `fab job run` does not -- its job status reports `Completed` even when the notebook caught an exception and exited a failure payload. If you run notebooks as batch jobs instead of Livy, you must read the notebook's **exit value** to get its real verdict. The `fabric-cli` skill (in the `fabric-cli` plugin) documents that endpoint and ships `scripts/run_notebook_checked.py` for it.

## References

- **`references/livy-api.md`** -- Full API reference with endpoints (sessions + batches), request/response formats, and error handling
- **`references/example-script.md`** -- Complete working script that creates a session, queries data, writes results, and cleans up

## Related

- `using-duckdb` skill (same `etl` plugin) -- read-only Delta querying, local or in-notebook, when you don't need Spark compute
- `fabric-cli` skill (`fabric-cli` plugin) -- `nb exec` / `fab job run` for notebooks, reading a notebook's exit value, the SQL-endpoint metadata sync after a Spark write, and `scripts/deploy_notebook.py` for fast notebook definition changes (tight LRO polling)

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: GPL-3.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: GPL-3.0

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill relies on the Azure CLI being installed and authenticated, which is a prerequisite but not always available in agent environments.
The SKILL.md does not explicitly mention that the agent should verify the user's intent before executing arbitrary code, though this is implied by the skill's purpose.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: data-goblin/power-bi-agentic-development
Lizenz: GPL-3.0
Version: 1.0.0
Letzter GitHub-Push: 8. Aug. 2026
Verzeichnis aktualisiert: 4. Sept. 2026
Anleitungspfad: plugins/etl/skills/executing-spark/SKILL.md @ f8495e767930

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

73/100

Stark

Vertrauen

57/100

Do not auto-install

Audit

74/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill relies on the Azure CLI being installed and authenticated, which is a prerequisite but not always available in agent environments.
The SKILL.md does not explicitly mention that the agent should verify the user's intent before executing arbitrary code, though this is implied by the skill's purpose.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

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Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 74,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The skill relies on the Azure CLI being installed and authenticated, which is a prerequisite but not always available in agent environments.",
      "The SKILL.md does not explicitly mention that the agent should verify the user's intent before executing arbitrary code, though this is implied by the skill's purpose.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 73,
    "label": "Strong"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "mattpocock-implement",
      "name": "Implement",
      "url": "https://www.openagentskill.com/skills/mattpocock-implement",
      "stars": 175741,
      "install_command": "",
      "trust_score": 89,
      "audit_score": 91
    }
  ],
  "do_not_use_when": [
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    "production agents without a repository review",
    "The skill relies on the Azure CLI being installed and authenticated, which is a prerequisite but not always available in agent environments.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The SKILL.md does not explicitly mention that the agent should verify the user's intent before executing arbitrary code, though this is implied by the skill's purpose.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use executing-spark in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 74/100 Needs review",
      "Safety: 30/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "data-goblin-executing-spark (executing-spark)",
      "install_command": "npx skills add data-goblin/power-bi-agentic-development --skill executing-spark",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
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    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "data-goblin-executing-spark",
      "task": "Use executing-spark in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/data-goblin-executing-spark",
    "api": "https://www.openagentskill.com/api/agent/skills/data-goblin-executing-spark",
    "audit": "https://www.openagentskill.com/skills/data-goblin-executing-spark/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=data-goblin-executing-spark&task=Use%20executing-spark%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20executing-spark%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20executing-spark%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/data-goblin-executing-spark/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/data-goblin-executing-spark"
  }
}

Für Ersteller

Quelle des Eintrags

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: data-goblin
Quelle: data-goblin/power-bi-agentic-development
Indexiert von: OpenAgentSkill Community-Index

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