tested and working #3
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JordiNeil
reviewed
Apr 9, 2025
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| cmd = [ | ||
| "curl", "-s", | ||
| "-H", f"Authorization: Bearer {DATABRICKS_TOKEN}", | ||
| "-H", "Content-Type: application/json", | ||
| f"https://{DATABRICKS_HOST}/api/2.0/dbfs/list?path={path}" | ||
| ] |
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I think here we can use the request function as in the other calls. Not sure why to do it using subprocess and cmd.
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| # Build URL with query parameters directly | ||
| encoded_path = urllib.parse.quote(path) | ||
| url = f"https://{DATABRICKS_HOST}/api/2.0/dbfs/read?path={encoded_path}&length={length}" | ||
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| print(f"Requesting URL: {url}") | ||
| response = requests.get(url, headers=headers) | ||
| print(f"Status code: {response.status_code}") |
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Same here, we can reuse the databricks_api_request function
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| headers = { | ||
| "Authorization": f"Bearer {DATABRICKS_TOKEN}", | ||
| "Content-Type": "application/json" | ||
| } | ||
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| # DBFS file upload requires three steps: | ||
| # 1. Create a handle | ||
| create_url = f"https://{DATABRICKS_HOST}/api/2.0/dbfs/create" | ||
| create_data = { | ||
| "path": dbfs_path, | ||
| "overwrite": overwrite | ||
| } | ||
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| create_response = requests.post(create_url, headers=headers, json=create_data) |
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| headers = { | ||
| "Authorization": f"Bearer {DATABRICKS_TOKEN}", | ||
| "Content-Type": "application/json" | ||
| } | ||
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| # Build URL with query parameters directly | ||
| encoded_path = urllib.parse.quote(path) | ||
| url = f"https://{DATABRICKS_HOST}/api/2.0/workspace/list?path={encoded_path}" | ||
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| print(f"Requesting URL: {url}") | ||
| response = requests.get(url, headers=headers) | ||
| print(f"Status code: {response.status_code}") | ||
| print(f"Response: {response.text}") |
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| headers = { | ||
| "Authorization": f"Bearer {DATABRICKS_TOKEN}", | ||
| "Content-Type": "application/json" | ||
| } | ||
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| # Build request | ||
| export_url = f"https://{DATABRICKS_HOST}/api/2.0/workspace/export" | ||
| export_data = { | ||
| "path": path, | ||
| "format": format | ||
| } | ||
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| response = requests.get(export_url, headers=headers, params=export_data) |
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| headers = { | ||
| "Authorization": f"Bearer {DATABRICKS_TOKEN}", | ||
| "Content-Type": "application/json" | ||
| } | ||
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| # Encode the content as base64 | ||
| import base64 | ||
| content_bytes = content.encode("utf-8") | ||
| encoded_content = base64.b64encode(content_bytes).decode("utf-8") | ||
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| # Build request | ||
| import_url = f"https://{DATABRICKS_HOST}/api/2.0/workspace/import" | ||
| import_data = { | ||
| "path": path, | ||
| "content": encoded_content, | ||
| "language": language, | ||
| "format": format, | ||
| "overwrite": overwrite | ||
| } | ||
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| response = requests.post(import_url, headers=headers, json=import_data) | ||
| response.raise_for_status() |
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Thank you so much for this contribution! Let me know if you want to apply the changes, if not, I'll apply them later 😄 |
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My honest take is that i actually did a lot of this with AI to get it working faster. I actually use for my job. |
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Hey, yes, I noticed you used AI 😅 Thank you! 🙏 |
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feel free to accept or not, but it's heloping me a lot