Mastering Efficient File Uploads to Google Cloud Storage Using Parallel Composite Objects

Uploading large files to the cloud can be a time-consuming and frustrating experience, especially when using straightforward upload methods that don’t scale well with file size. If you’ve ever waited minutes or even hours for a massive file to upload, you know how much of a bottleneck this can be for your workflows or applications.

In this post, I’ll show you how to supercharge your uploads to Google Cloud Storage (GCS) by leveraging Parallel Composite Objects — a powerful technique that breaks big files into chunks uploaded in parallel, then seamlessly composes them on the backend. This approach speeds up transfers, improves reliability, and can even save you money.

Why Simple Uploads Fall Short for Large Files

The default way most developers upload files to GCS is by performing a single, linear upload — sending bytes from start to finish in one go. While this works fine for small or medium-sized files, it struggles with:

• High latency: One slow segment stalls the whole upload.
• Failures & retries: Network hiccups mean starting over or handling complex resumptions.
• Long waits: Upload speed is capped by your single connection’s bandwidth.

All these issues cause bottlenecks — and that’s why many apps hit limits under load.

Enter Parallel Composite Objects: What Are They?

Parallel Composite Objects (PCOs) enable you to:

• Split a large file into smaller component objects.
• Upload these parts in parallel, leveraging multiple connections.
• Use GCS’s compose API to merge these parts into one final object.

This not only speeds uploads but also distributes risk — if one chunk fails, only that part needs retrying.

How Parallel Composite Uploads Work Step-by-Step

1. Split your large file locally into smaller chunks (e.g., 100MB parts).
2. Upload all chunks concurrently as separate objects with temporary names.
3. Once all chunks are uploaded successfully, call GCS’s compose method to stitch them into a single object.
4. Optionally delete the temporary chunk objects now that composition is complete.

Practical Example Using the Python Client Library

Here’s an example workflow showing how you can handle this in Python:

from google.cloud import storage
import os
import concurrent.futures

# Initialize client and bucket
storage_client = storage.Client()
bucket_name = "your-gcs-bucket"
bucket = storage_client.bucket(bucket_name)

def upload_chunk(chunk_path, chunk_name):
    blob = bucket.blob(chunk_name)
    blob.upload_from_filename(chunk_path)
    print(f"Uploaded chunk {chunk_name}")

def parallel_composite_upload(file_path, destination_blob_name, chunk_size=100 * 1024 * 1024):
    file_size = os.path.getsize(file_path)
    chunks = []
    
    # Split file into chunks
    with open(file_path, 'rb') as f:
        chunk_index = 0
        while True:
            data = f.read(chunk_size)
            if not data:
                break
            chunk_filename = f"/tmp/chunk_{chunk_index}"
            with open(chunk_filename, 'wb') as cf:
                cf.write(data)
            chunks.append((chunk_filename, f"{destination_blob_name}_part_{chunk_index}"))
            chunk_index += 1

    # Upload chunks in parallel
    with concurrent.futures.ThreadPoolExecutor() as executor:
        futures = [executor.submit(upload_chunk, path, name) for path, name in chunks]
        concurrent.futures.wait(futures)

    # Compose uploaded chunks
    blob_names = [name for _, name in chunks]
    source_blobs = [bucket.blob(name) for name in blob_names]
    destination_blob = bucket.blob(destination_blob_name)
    
    destination_blob.compose(source_blobs)
    print(f"Composed {len(blob_names)} parts into {destination_blob_name}")

    # Clean up temp files and temporary blobs
    for path, name in chunks:
        os.remove(path)
        bucket.blob(name).delete()
        print(f"Deleted temp chunk and blob {name}")

if __name__ == "__main__":
    local_file_path = "/path/to/your/large_file.zip"
    final_blob_name = "uploads/large_file.zip"
    
    parallel_composite_upload(local_file_path, final_blob_name)

Notes & Best Practices

• The max component count for composing objects is currently 32 per compose operation. For very large uploads split into more than 32 parts, you'll need recursive composition (i.e., compose groups of parts first).
• Use chunk sizes according to your network bandwidth and latency — too small adds overhead; too large limits parallelism benefits.
• Parallel composite uploads are especially helpful if you're on a network prone to intermittent issues; retrying a smaller chunk is faster than restarting an entire file upload.
• Pricing note: each component object counts as a separate object until composition; delete them after successful compose to avoid storage costs.
• If you’re using Google Cloud CLI or gsutil tool instead of code: gsutil -o "GSUtil:parallel_composite_upload_threshold=150M" cp largefile gs://my-bucket enables parallel composite uploads automatically over the threshold size.

Wrapping Up

Leveraging parallel composite objects can transform your Google Cloud Storage uploads from slow and fragile chores into fast and robust tasks — critical for enterprises handling big data or apps needing seamless file ingestion at scale.

Try out the example above or integrate this logic into your existing pipelines and watch those upload times shrink dramatically!

If you found this useful or have questions about implementing this in your language of choice or framework, feel free to drop a comment below or reach out on Twitter.

Happy uploading! 🚀

References & Further Reading

• Google Cloud Storage - Parallel Composite Uploads
• GCS Python Client Library Documentation
• gsutil Parallel Composite Uploads

This post was written with practical developer experience in mind—helping you conquer large file uploads on Google Cloud.