如何用AppEngine Python脚本将API数据流式导入Google Cloud BigQuery？

Hey there, let's break down your questions one by one and help you find a smoother path forward!

The Socrata API you're targeting supports streaming, but you might need to use specific parameters or libraries to make it work reliably. Here are two practical approaches:

• Python with sodapy (official SDK): This library handles streaming natively, making it easier than raw HTTP calls:

  from sodapy import Socrata
  
  # Initialize client (leave app_token as None if you don't have one, add it for higher rate limits)
  client = Socrata("data.cityofchicago.org", None)
  # Stream data in chunks of 1000 records
  results = client.get("8v9j-bter", limit=1000, stream=True)
  
  for chunk in results:
      # Process each record chunk here (e.g., save to storage, transform data)
      print(chunk)
  

• Shell with curl: Use the -N flag to disable buffering for real-time streaming:

  curl -N "https://data.cityofchicago.org/resource/8v9j-bter.json?$limit=1000"
  

Instead of clunky Shell scripts or full Airflow setups, consider these lighter alternatives:

• Export Datalab Notebooks to Python scripts: Strip out magic commands (more on this below) and run the script on Cloud Run or Cloud Functions, triggered by Cloud Scheduler for periodic tasks. This avoids heavy orchestration overhead.
• Use Cloud Workflows: If you need simple sequence orchestration (e.g., fetch data → transform → save), Cloud Workflows is a low-code alternative to Airflow for Google Cloud environments.

First, the bad news: Datalab's %context magic commands are environment-specific and won't work in regular Python scripts or App Engine. But the good news is you can replace them with official Google Cloud client libraries:

• Access project ID/storage: Use google.auth and google-cloud-storage to replicate what %context does:

  from google.auth import default
  from google.cloud import storage
  
  # Get current project ID
  credentials, project_id = default()
  # Access Cloud Storage buckets
  storage_client = storage.Client(project=project_id)
  bucket = storage_client.get_bucket("your-target-bucket")
  

As for App Engine: Yes, your solution is fully feasible! Just keep these considerations in mind:

• Use App Engine Flexible Environment if your streaming task runs longer than 10 minutes (Standard Environment has request timeouts).
• Ensure all dependencies are listed in a requirements.txt file.
• Avoid any Datalab-specific syntax (magic commands, UI-related code).

Here's a minimal setup to get you started on App Engine:

1. requirements.txt (list all dependencies):

   sodapy==2.1.0
   google-cloud-storage==2.15.0
   google-auth==2.23.4
   

2. app.yaml (App Engine configuration for Python 3.11):

   runtime: python311
   entrypoint: python main.py
   
   env_variables:
     # Optional: Add your Socrata app token for higher rate limits
     SOCRATA_APP_TOKEN: "your-socrata-app-token"
   

3. main.py (core processing script):

   from sodapy import Socrata
   from google.cloud import storage
   from google.auth import default
   import json
   
   def process_socrata_stream():
       credentials, project_id = default()
       # Initialize Socrata client
       client = Socrata("data.cityofchicago.org", None)
       # Stream data in chunks
       stream = client.get("8v9j-bter", limit=1000, stream=True)
   
       # Save chunks to Cloud Storage (example action)
       storage_client = storage.Client(project=project_id)
       bucket = storage_client.bucket("your-data-bucket")
   
       for idx, chunk in enumerate(stream):
           blob = bucket.blob(f"socrata_chunk_{idx}.json")
           blob.upload_from_string(json.dumps(chunk))
       print("Streaming and processing completed!")
   
   if __name__ == "__main__":
       process_socrata_stream()
   

4. Deploy command:

   gcloud app deploy app.yaml
   

内容的提问来源于stack exchange，提问作者just_sung