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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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价格未确认★ 893 GitHub Stars目录更新于 · 2026年9月4日agent-skill

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以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

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)

查看来源 ↗

文件元数据

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".

查看原始文本

---
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)

查看并核实来源

获取价格与运行成本

获取 Skill: 价格未确认
运行 Skill: 尚未确认运行要求，请查看来源中的 Agent、API 和服务费用。
许可证: GPL-3.0
价格未确认: 我们尚未确认此 Skill 的价格，现有来源与安装入口仍可使用。

免费获取不代表免费运行，价格标签不代表安全评级。提交价格信息 →

已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: 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

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: data-goblin/power-bi-agentic-development
许可证: GPL-3.0
版本: 1.0.0
最近 GitHub 推送: 2026年8月8日
目录更新于: 2026年9月4日
技能指令路径: plugins/etl/skills/executing-spark/SKILL.md @ f8495e767930

版本来自目录元数据，使用前请核实来源发布记录。

质量

73/100

强

信任

57/100

Do not auto-install

审计

74/100

需审查

打开完整审计查看评估报告

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: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

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  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "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": [
    "teams that need a vendor-supported SLA",
    "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"
    ],
    "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"
  }
}

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创作者: data-goblin
来源: data-goblin/power-bi-agentic-development
收录方: OpenAgentSkill 社区索引

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