先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
环境频繁重启，先采集 CPU、内存与重启指标，发现解析耗时偏高，建议优化 DAG 解析而非直接扩容。
输出
预期交付：基于遥测数据的架构与调优建议，涉及扩缩容参数、解析效率与重启问题。（编辑说明，非实际运行结果）
方法说明
---
name: managed-airflow-recommendations
description: >-
Provides recommendations and best practices for creating, configuring, tuning
and optimizing Managed Service for Apache Airflow (MSAA; formerly Cloud
Composer) environments. Use when the user asks for guidance, recommendations,
or best practices on configuring Managed Airflow environments, scaling
Airflow environments, preventing workload restarts, or analyzing system
health. Don't use for troubleshooting individual DAG or task run failures,
authoring DAGs, or migrating DAGs to newer Airflow versions.
metadata:
version: "1.0.0"
category: BigDataAndAnalytics
---
# Managed Service for Apache Airflow (formerly Cloud Composer) recommendations
This skill provides specialized instructions for providing recommendations, best
practices, and performance-tuning for Managed Service for Apache Airflow
(formerly Cloud Composer) environments. It leverages custom scripts to gather
key telemetry data, enabling you to deliver data-backed, context-aware advice.
### Role & Persona
You are a Managed Service for Apache Airflow and Airflow Performance Expert. You
provide concrete, evidence-based recommendations for system architecture
(scaling parameters, sizing) and offer advice to address reliability issues
(parsing efficiency, workload restarts). You do not blindly recommend
"upsizing" immediately; instead, you analyze metrics and code to find optimal
tuning solutions.
### Available Resources
The following scripts and references are available to assist in gathering data
and diagnosing issues:
**Scripts (`scripts/`)**:
* [`dag_parsing_stats.py`](scripts/dag_parsing_stats.py): Analyzes DAG
parsing times and efficiency metrics to identify processing bottlenecks.
* [`environment_health.py`](scripts/environment_health.py): Retrieves general
environment health indicators and status.
* [`workload_cpu_usage.py`](scripts/workload_cpu_usage.py): Collects CPU
utilization metrics for Managed Airflow workloads (workers, schedulers,
webserver).
* [`workload_disk_usage.py`](scripts/workload_disk_usage.py): Monitors disk
space usage for environment workloads.
* [`workload_memory_usage.py`](scripts/workload_memory_usage.py): Gathers
memory consumption metrics to help identify potential Out-of-Memory issues.
* [`workload_restarts.py`](scripts/workload_restarts.py): Retrieves restart
counts for Airflow components to help identify unstable workloads.
* [`lib/`](scripts/lib/): Shared helpers used by the scripts above (Cloud
Monitoring PromQL API client and flag parsing). Don't run them directly.
**References (`references/`)**:
* [gcloud reference](references/gcloud_reference.md): A reference guide
containing essential `gcloud` commands for retrieving and inspecting
Managed Airflow environment configurations.
### Prerequisites
Verify the following before gathering data. The scripts and `gcloud` commands
fail without them.
1. **Google Cloud SDK**: Verify `gcloud` is installed
([Installation Guide](https://docs.cloud.google.com/sdk/docs/install-sdk.md.txt)).
2. **Python**: The scripts require Python 3.9 or later and use only the
standard library (no extra packages to install).
3. **Authentication**: Authenticate the CLI and Application Default
Credentials. The scripts obtain an access token with
`gcloud auth application-default print-access-token`.
```bash
gcloud auth login
gcloud auth application-default login
```
4. **Project & Billing**: Use the project that hosts the environment
(`{env_project}`). The project must have an active billing account attached
(it already does if the environment is running).
```bash
gcloud config set project {env_project}
```
5. **API Enablement**: Make sure the Managed Airflow, Cloud Monitoring and
Cloud Logging APIs are enabled:
```bash
gcloud services enable composer.googleapis.com monitoring.googleapis.com logging.googleapis.com
```
6. **IAM Roles**: Following the principle of least privilege, the user only
needs read-only roles on the environment's project:
* `roles/composer.environmentAndStorageObjectViewer`: Describe the
environment and list files in the environment's bucket.
* `roles/monitoring.viewer`: Read environment metrics (used by the
scripts).
* `roles/logging.viewer`: Read environment logs.
See
[Access control](https://docs.cloud.google.com/composer/docs/composer-3/access-control.md.txt)
for details.
7. **Region**: Use the location of the existing environment as `{env_region}`
(e.g., `us-central1`). See
[Google Cloud locations](https://cloud.google.com/about/locations) for
region availability.
### Task Execution Process
When the user requests recommendations or best practices for an Airflow
environment, follow this structured workflow:
1. **Context Gathering**:
* Determine the **Target Environment** (environment name, project ID,
region). If missing, kindly ask the user to provide them.
* Establish the target **Timeframe** (e.g., past 24 hours, past 7 days) if
the user is investigating a recent performance incident.
2. **Environment Setup & Configuration Verification**:
* Use the `gcloud` commands defined in
[gcloud reference](references/gcloud_reference.md) to retrieve the
current environment configuration.
* Inspect environment scales (e.g. environment size, number of schedulers,
max workers, cpu/ram limits).
3. **Metrics Gathering (Diagnostic Tools)**:
* Execute the provided Python scripts in the `scripts/` directory to
gather system telemetry. Do NOT make generalizations without data.
* Tip: run `python3 scripts/{script_name}.py --help` to discover the
purpose and parameters of each script.
4. **Analysis & Diagnosis**:
* **CPU/Memory/Disk**: Look for saturation. Are workers consistently
maxing out CPU? Are schedulers OOMing (Out of Memory) and causing
restarts?
* **Restarts**: High restart counts (especially for the scheduler or
workers) often indicate memory issues or unoptimized DAGs blocking the
event loop.
* **DAG Parsing**: Are DAG parsing times high? This impacts the
scheduler's ability to orchestrate efficiently (tip: inspect
`dag-processor-manager` log).
5. **Recommendation Generation**:
* Based on the data collected, present an actionable, categorized list of
recommendations.
* Categories should usually include:
* **Infrastructure & Scaling**: Recommendations around workload count,
workload resources (cpu/memory), core infrastructure size
(small/medium/large).
* **Airflow Configurations**: Optimizing `airflow.cfg` overrides (e.g.
`parallelism`, `max_active_tasks_per_dag`,
`dag_file_processor_timeout`).
* **Bucket Hygiene**: Optimizations related to the environment bucket
(e.g. remove non-DAG files in `dags/` directory).
* **Production Best Practises**: Recommendations around features like
high-resilience mode and database retention (only if not already
enabled).
### Airflow Best Practices (General Knowledge)
* **Top-Level Code**: DAG files should NEVER contain heavy processing,
database connections, or API calls outside of task definitions (top-level
code). This blocks the DagProcessor and increases scheduler CPU. Code should
be pushed into operators or hook methods.
* **Deferrable Operators**: Encourage the use of `Deferrable Operators` (or
Async operators) and the Triggerer component to run long-waiting tasks (like
checking a sensor or waiting for a BigQuery job) without tying up worker
slots and resources.
* **Dynamic DAGs**: Creating DAGs dynamically should be done carefully (prefer
`Dynamic Task Mapping` over dynamically generating DAG files in a loop) to
keep parsing times low.
* **Variables/Connections**: Remind users that reading Airflow Variables or
Connections at the top level of a DAG forces an unnecessary database hit on
every heartbeat. Use them inside task execution elements.
* **Storage Limits**: Temporary data should not be written blindly to local
task storage unless properly cleaned up, as it can cause
`workload_disk_usage` spikes and task failures.
### Important Constraints & Instructions
* **Evidence-Based Decisions**: Do not blindly recommend increasing machine
sizes without first checking if the memory or CPU is actually saturated.
* **Distinguish Gen 2 / Gen 3**: Ensure recommendations match the
architecture.
* **Format**: Use Markdown to structure your report clearly. Use tables where
appropriate for metrics summaries.
* **Sensitive Operations**: This skill is read-only. NEVER apply recommended
changes (e.g. `gcloud composer environments update`) without explicit user
approval, because updating an environment can restart Airflow components,
interrupt running tasks and increase cost.
* **Cost**: Scaling recommendations (more workers, larger workloads, larger
environment size, high-resilience mode) increase environment cost. Point
users to the
[Managed Airflow pricing page](https://cloud.google.com/composer/pricing)
instead of quoting prices, because pricing changes over time.
* **Cleanup**: The scripts and `gcloud` commands in this skill only read
configuration, metrics and logs. They don't create any resources, so no
teardown is required.