关于Dataprep导入限制的技术咨询：超15个JSON文件致Dataflow任务失败

Having dealt with similar silent failure headaches when working with Dataprep and large batches of JSON files, let’s walk through the likely causes and actionable fixes:

What’s Probably Going Wrong #

• Implicit File Count Limit in Direct Merges: Dataprep has unstated limits on how many individual files you can merge in a single initial step, especially for semi-structured formats like JSON. Once you cross ~15 files, the Dataflow job hits a metadata handling bottleneck and crashes before it can log any errors.
• Underprovisioned Dataflow Resources: Default Dataflow worker settings (small instances, limited memory) can’t handle the overhead of parsing and merging dozens of JSON files at once. The job fails silently because workers crash before writing error logs.
• Hidden JSON Formatting Issues: With fewer files, you might not have noticed invalid JSON (like unescaped quotes or missing commas) in some entries. When scaling to more files, these edge cases trigger a failure that doesn’t show up in Dataprep’s UI.

Fixes to Try (In Order of Ease) #

1. Batch Files Before Dataprep Ingestion #

Instead of feeding 15+ individual JSON files into Dataprep, pre-batch them into larger files:

• Use a simple script or cloud function to concatenate files. For Google Cloud Storage, you can run a command like:

  gsutil cat gs://your-bucket/json-inputs/*.json > gs://your-bucket/batched/json-batch-01.json
  

• Split your files into batches of 10-12 each. Point Dataprep to these batches instead of individual files—this stays under the implicit merge limit.

2. Boost Dataflow Job Resources #

Customize your Dataflow job settings to give it more breathing room:

• Upgrade the worker machine type (e.g., from n1-standard-1 to n1-standard-2) for more CPU and memory.
• Enable autoscaling so Dataflow adds workers dynamically as needed.
• Extend the job timeout—sometimes the failure is just the job taking longer than the default limit to initialize with more files.

3. Validate All JSON Files at Scale #

Silent failures often come from invalid JSON that slips through with smaller batches:

• Use jq to validate every file in bulk. For example:

  for file in gs://your-bucket/json-inputs/*.json; do
    jq . "$file" > /dev/null || echo "Invalid JSON found: $file"
  done
  

• Fix any problematic files before sending them to Dataprep.

4. Use Wildcard Inputs Instead of Selecting Individual Files #

Instead of manually picking each JSON file, use a wildcard pattern (e.g., gs://your-bucket/json-inputs/*.json) to let Dataprep ingest all files at once. This uses a different underlying ingestion mechanism that often bypasses the 15-file limit.

Pro Tip: Check Dataflow Logs #

Even if Dataprep shows no error messages, head to your cloud provider’s console (like Google Cloud Console for Dataflow) and check the job logs. Look for entries about out-of-memory errors or file parsing failures—those will give you the exact root cause.

内容的提问来源于stack exchange，提问作者B Delfos