@kapa.ai does writing to GCS destination takes more time than to Bigquery

The provided knowledge sources do not contain direct benchmark comparisons or specific metrics comparing the write performance of the standalone GCS Destination (

destination-gcs

) versus the BigQuery Destination (

destination-bigquery

). However, based on how these destinations operate in Airbyte: 1. GCS Destination (

destination-gcs

): ◦ Modern versions run on the Bulk Load CDK, which uses parallel multipart uploads to write files directly to Google Cloud Storage [destination-gcs Migrate to Bulk Load CDK; GCS Performance Docs]. ◦ Once the files are uploaded to GCS, the write process for the destination is finished. 1. BigQuery Destination (

destination-bigquery

): ◦ Supports two main loading methods: Batched Standard Inserts (direct loading using BigQuery SQL inserts / TableDataWriteChannel) and GCS Staging [BigquerySpecification.kt; BigqueryBatchStandardInsertLoader.kt]. ◦ When configured with GCS Staging, the connector performs a multi-step process: it formats/compresses data, uploads temporary files to a GCS bucket, executes a BigQuery Load Job (

LoadJobConfiguration

) to ingest the files into BigQuery tables, waits for that job to complete, and optionally cleans up the temporary files [BigQueryBulkLoader.kt]. Because GCS staging in BigQuery involves uploading data to GCS plus the additional step of running and waiting for BigQuery load/merge jobs, loading into BigQuery via staging naturally includes more operations than writing directly to GCS alone [BigQueryBulkLoader.kt]. Overall sync performance depends heavily on connector versions, resource allocations (CPU/memory), stream formats, and network throughput [GCS Performance Docs]. 👍 0 button 👎 0 button ℹ️ Tip: Mention @kapa.ai in the thread for follow-ups or DM the app for 1-to-1 help., with interactive elements