Increasing the number of threads used to upload files to Google Cloud Storage (GCS) can help improve the write speed. This can be achieved by using parallel uploads, which is a feature provided by the Google Cloud Storage client libraries.
Here's a general example of how you can do this in Python using the google-cloud-storage library:
from google.cloud import storage
from multiprocessing.dummy import Pool
def upload_blob(bucket_name, source_file_name, destination_blob_name):
"""Uploads a file to the bucket."""
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name)
print(
"File {} uploaded to {}.".format(
source_file_name, destination_blob_name
)
)
# Get a list of all files to upload
files_to_upload = [...] # replace with your list of files
bucket_name = 'your-bucket-name'
# Create a thread pool
pool = Pool(processes=20) # replace 20 with the number of threads you want
# Use the thread pool to upload files in parallel
for file in files_to_upload:
pool.apply_async(upload_blob, (bucket_name, file, file))
pool.close()
pool.join()
In this example, a pool of threads is created using the multiprocessing.dummy.Pool class. The Pool.apply_async method is then used to upload each file in a separate thread.
Please note that you should adjust the number of threads based on your specific use case and the resources available on your machine. Too many threads could overwhelm your system, while too few might not fully utilize your resources.
Also, remember to handle exceptions properly in your upload_blob function. If an upload fails, you might want to retry it or log the failure so you can investigate it later.
This is a very basic example and might need to be adjusted based on your specific use case. For example, if you're uploading large files, you might want to use the Blob.upload_from_file method instead, which allows you to upload a file object and could be more memory-efficient for large files.
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