求助：Azure Data Factory从REST API向Azure Data Lake Store复制大数据文件失败

Let’s break down the key issues in your configuration and actionable fixes to get your large file transfers working:

1. Extend HTTP Request Timeout #

Your httpRequestTimeout is set to 00:30:40 (30 minutes), which might be too short for the API to stream a large file fully before timing out. Match this value to your activity’s overall timeout (7 hours) to give the API enough time to send the data:

"source": {
    "type": "HttpSource",
    "httpRequestTimeout": "7.00:00:00"
}

2. Boost Cloud Data Movement Units (CDUs) #

Setting cloudDataMovementUnits to 0 lets ADF auto-scale, but for large files, explicitly specifying a higher value (4, 8, or 16, depending on your ADF SKU) increases throughput and reduces resource-related failures:

"cloudDataMovementUnits": 8

3. Enable Staging for Resilient Transfers #

Direct transfers (enableStaging: false) are prone to network interruptions with large files. Use Azure Blob Storage as a staging layer—ADF will first copy data to staging, then to ADLS, which adds a buffer against failures:

"enableStaging": true,
"stagingSettings": {
    "linkedServiceName": {
        "referenceName": "YourBlobStagingLinkedService",
        "type": "LinkedServiceReference"
    }
}

4. Add Retries for Transient Errors #

Your retry count is 0, so any temporary network glitch or API blip will kill the transfer. Enable retries with a reasonable interval to recover from transient issues:

"policy": {
    "timeout": "7.00:00:00",
    "retry": 3,
    "retryIntervalInSeconds": 60,
    "secureOutput": false
}

5. Validate Your Translator Setup #

Your translator config is cut off ("type": "Tabula..."). Ensure the tabular translator is correctly configured for your data format. For large datasets, check if there are batch size limits in the translator—adjusting these can prevent memory-related failures during data conversion.

6. Check API-Specific Limitations #

Many REST APIs throttle large requests or require chunked downloads:

• If the API supports range headers, add them to additionalHeaders in your HTTP source to download the file in smaller chunks
• If the API uses pagination, configure the HTTP source to fetch data incrementally instead of in one large request

内容的提问来源于stack exchange，提问作者Waqas Idrees