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Use Google’s google-cloud-storage Python client: authenticate with Application Default Credentials (ADC), choose a bucket and destination object name, then call upload_from_filename(). The example below covers a single local file; later sections explain permissions, overwrite protection, large uploads, and batches.
What you need before uploading
- A Google Cloud project with billing enabled and the Cloud Storage API enabled.
- A bucket in that project, plus the local file your program will upload.
- An identity authenticated through ADC that has permission to create objects in the bucket.
Install the client library in your Python environment:
pip install google-cloud-storage
For local development, ADC can use your developer credentials. On Google Cloud, prefer credentials from the service account attached to the compute resource. Authentication identifies the caller; IAM determines what that identity may do. Keep credentials out of source code. See Google’s Cloud Storage authentication guidance.
Upload one file
Google’s documented pattern is to create a client, select a bucket, create a blob for the destination object name, and upload the local file:
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from google.cloud import storage
client = storage.Client()
bucket = client.bucket("your-bucket-name")
blob = bucket.blob("destination/object-name")
blob.upload_from_filename("local/path/to/file")Replace the bucket name and both paths with your values. A bucket stores objects; the blob name is the object name you choose, including any slash-separated prefixes your application uses. The snippet follows Google’s official sample and is not a guarantee that your environment is configured correctly; project, API, credentials, permissions, and file path must all be valid. See the official upload sample and setup instructions.
Choose the destination and decide what happens to existing objects
If an object already exists under the destination name, an upload can replace its contents. The final behavior can depend on the bucket’s object versioning and lifecycle policies. Choose names deliberately, especially when different files must not collide.
To avoid replacing an object that changed between checking and uploading, pass a generation-match precondition. A value of 0 means the upload should proceed only if no live object with that name exists:
blob.upload_from_filename(
"local/path/to/file",
if_generation_match=0,
)This is useful for create-only behavior; it is not appropriate if replacement is intended. The API reference describes overwrite behavior and the upload parameters in the Blob class reference.
Grant only the permissions the uploader needs
For ordinary uploads, Google’s resumable-upload guidance names roles/storage.objectUser as a suitable role. Uploads that include an Object Retention Lock require roles/storage.objectAdmin according to that guidance. Actual role choice depends on the operation and bucket policy; avoid granting broad access by default. See Google’s resumable upload documentation.
Set content type when it matters
upload_from_filename() accepts an explicit content_type. If you omit it, the client uses the blob’s stored content type, then tries to infer one from the filename, and finally falls back to application/octet-stream. Supply a content type explicitly when your application needs a particular value rather than relying on filename inference. The behavior is documented in the Blob upload reference.
If uploading through upload_from_file() instead, open the file in binary mode:
with open("local/path/to/file", "rb") as file_obj:
blob.upload_from_file(file_obj, content_type="application/pdf")The method requires a bytes-mode file. The client’s checksum setting defaults to automatic selection; its documentation describes CRC32C as the usual choice and MD5 for unusual cases when the fast C extension is unavailable. Checksums help detect transfer corruption; they are not a security guarantee.
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The Python client chooses the ordinary upload mechanism based on object size. Google’s current client guidance says it uses resumable media above 8 MiB and multipart upload below that threshold; the threshold cannot be changed. For large files or slow connections, resumable transfer is recommended because an interrupted upload can resume rather than restart from the beginning.
| Approach | When it fits | Relevant behavior |
|---|---|---|
Regular upload_from_filename() |
Most single-file uploads | The client selects multipart or resumable media based on its 8 MiB threshold. |
| Resumable upload | Large files or interruption-prone connections | Can resume an interrupted transfer; a resumable session may remain active for up to one week. |
Blob.open(mode="w") or BlobWriter |
Streaming writes where resumability is required at any object size | Forces resumable upload and has a 40 MiB default buffer. |
For resumable uploads, the Python client’s documented default buffer is 100 MiB. You can configure blob.chunk_size; chunk sizes must be multiples of 256 KiB. Larger chunks can improve speed but consume more memory, so choose based on the workload’s memory limits and network conditions rather than assuming the default is ideal. Google documents these details in its resumable upload guide.
A resumable session URI can authorize uploads to its target. If your application exposes sessions to an untrusted client, treat the URI as a sensitive token and do not log or disclose it casually.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Upload several files
For a batch, use the transfer manager’s upload_many_from_filenames() rather than writing a worker pool from scratch. The method supports thread and process workers; the API reference lists a default maximum of eight workers. Select worker type and count with your memory, network, and error-handling requirements in mind.
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filenames = ["report.csv", "image.png"]
bucket = client.bucket("your-bucket-name")
results = transfer_manager.upload_many_from_filenames(
bucket,
filenames,
source_directory="local/files",
)Inspect the returned results and handle per-file failures in your application. See the transfer manager reference for parameters and worker options.
When concurrent chunks are appropriate
upload_chunks_concurrently() is an advanced option for parallel chunks of a single file. It uses the XML multipart upload API, which differs from the normal JSON API path. Failed operations can leave incomplete multipart uploads that persist indefinitely; Google recommends an AbortIncompleteMultipartUpload bucket lifecycle rule with a nonzero age as a mitigation. Use this method only when its concurrency benefits justify the extra operational and cleanup considerations, and follow the transfer manager guidance.
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