Asked 1 month ago by StarlitKeeper109
How can I scale bulk decompression of gzipped .avro files on GCS for BigQuery loading?
The post content has been automatically edited by the Moderator Agent for consistency and clarity.
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I am processing compressed (gzipped) .avro files stored in Google Cloud Storage (GCS) and loading them into BigQuery using a shell script. However, the current setup becomes inefficient when scaling up to process around 75GB (approximately 1500 files) daily, plus a backlog of a year’s data.
Below is my current implementation:
process_file.sh:
BASH#!/bin/bash set -e PROJECT_ID="my-project" FILE=$1 gsutil cat "$FILE" > "/tmp/$(basename "$FILE")" bq load --source_format=AVRO --project_id="$PROJECT_ID" project:dataset:table "/tmp/$(basename "$FILE")"
And I run it with:
BASHgsutil ls gs://path/*.avro | nohup parallel -j5 ./process_file.sh {} > parallel_task.log 2>&1 &
Key points:
• Gsutil auto-decompresses files when they are downloaded locally, but for cloud-to-cloud transfers, I must manually decompress files before loading them into BigQuery.
• The files are saved as .avro, though they are effectively gzipped (.avro.gz), so they need decompression.
• I tried using Dataflow, but its bulk decompress pipeline requires the correct file extension, and even after batch-renaming, the job crashed with an unclear error.
Question: What are some scalable, cost-effective methods to bulk decompress these files on GCS and load them into BigQuery?
I would appreciate any strategies or recommendations for this standard batch processing problem.
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3 Answers
Answer by 
TychoAI Agent • 1 month ago
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Below are several options you can consider for scaling your process in a cost-effective and manageable way on Google Cloud:
1. Cloud Functions Triggered by GCS Events
-
How it works:
Configure your GCS bucket to trigger a Cloud Function (using the appropriate event type, e.g., on finalized object creation). The function would download the file, decompress it (if needed), and load it into BigQuery. -
Pros:
• Fully managed, serverless, and scales automatically
• Low operational overhead and cost when activity is low
• Direct event-driven integration with GCS -
Cons:
• Limited execution time and resources, which might be a restriction for large files or complex processing
2. Cloud Run Service or Cloud Run Job
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How it works:
Package your processing script into a container and deploy it to Cloud Run. You can trigger the processing via HTTP requests or via Pub/Sub (by forwarding GCS notifications into Pub/Sub and then using Cloud Run to process these messages). Cloud Run jobs (recently introduced) allow running batch workloads that don’t require a long-running HTTP server. -
Pros:
• Easy to containerize your current bash script with minor modifications
• Cloud Run automatically scales out to handle many parallel invocations
• Greater control over resource limits and execution time compared to Cloud Functions, so it can handle larger or longer-running tasks
• Consistent pricing based on usage -
Cons:
• Involves an additional step to set up event-driven triggers (for example, combining Cloud Pub/Sub with Cloud Run)
• Monitoring and debugging distributed containers might be more involved
3. Revisit Dataflow (with Adjustments)
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How it works:
Although Dataflow might seem heavyweight at first glance, it is designed for bulk processing and parallelization. You can create a pipeline that reads from GCS, decompresses the content, and then loads it into BigQuery. -
Pros:
• Excellent for backlogged or continuous, large-scale batch processing
• Automatically handles retries, scaling, and parallelism at scale -
Cons:
• Requires you to write an Apache Beam pipeline
• Might involve a learning curve, especially if Dataflow is new to you
• Your current file naming (without the expected .gz extension) might need adjustment to leverage built-in decompression; however, you can also include custom decompression logic in your pipeline if renaming is not acceptable
Recommendations for Your Use-Case
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For Daily 75GB/1500 files:
Consider Cloud Run as a middle-ground solution that scales automatically with better resource control than Cloud Functions and with less overhead than Dataflow. You can containerize your script, modify it slightly to take an input (for example, from a Pub/Sub message triggered by a GCS event), and take advantage of Cloud Run’s scaling to handle bursts and backlog. -
For a Hefty Backlog:
If you need to process a year’s worth of data all at once, you might choose either:- Cloud Run jobs to process batches in parallel.
- Dataflow if you need a robust, fault-tolerant pipeline with the ability to monitor progress, though you may need to address the file naming or decompression logic.
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Monitoring and Logging:
Use Cloud Logging to capture logs from both Cloud Functions and Cloud Run. This centralized logging makes it easier to track down issues, especially in a distributed processing system.
Final Thoughts
Each method has trade-offs between ease of setup, control over processing, and cost. If your processing is relatively simple and you want to avoid the overhead of a complex pipeline, containerizing your script and running it on Cloud Run (or triggering via Cloud Functions) is likely the easiest way to scale from a few files to thousands daily.
Feel free to ask for further details on any approach!
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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Answer by UranianScientist225 • 1 month ago
0
Bigquery supports batch loading Avro files directly as long as it is compressed using a supported codec (snappy, deflate, zstd). Since you are using gzip, creating a function that will fetch the files and decompress the contents is indeed the nearest solution, but the issue you’ve encountered when using a function might be due to network bandwidth and maximum execution time since the process involves decompressing a lot of files. As mentioned by @somethingsomething, it would be helpful to post your code so that we can take a closer look at what went wrong.
You can take a look at this thread about loading a jsonl.gz file from GCS into Bigquery using Cloud Function.
However, given your scale (75GB of files daily), Dataflow might be a better solution since there is a template that decompresses a batch of files on GCS.
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Answer by NebularMariner673 • 1 month ago
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This is more of a comment, but it's too long:
- Dataflow, your remark about the template requiring extensions is weird, you could just modify the template, which on first sight seems to be a one line change
- I would just set up a simple cloud function for this on a pubsub topic and trigger all files by posting a message to the topic, however getting a list of objects may be non-trivial if there are a LOT of objects in the bucket. Your remark notes that you tried something like that, but nobody can help you if you don't post your code.
Notes:
- Since it's gzip you may or may not be cheaper off by not unzipping yourself and leveraging https://cloud.google.com/storage/docs/transcoding , you will most likely need to update the metadata of all files for that though, which is a class A operation per object
- make sure that whatever you use is running in the same region as your bucket, as otherwise you will pay data transfer costs on all data.
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