Sync job is very slow

  • Is this your first time deploying Airbyte?: Yes
  • OS Version / Instance: Linux (ubuntu 20.04) (Azure VM)
  • Memory / Disk: 8Gb / 32 Gb
  • Deployment: Docker
  • Airbyte Version: 0.40.26
  • Source name/version: S3 version 0.1.27
  • Destination name/version: Databricks version 0.3.1
  • Step: sync
  • Description:


I am currently trying to set up a connection between AWS S3 and Databricks on Azure. I got the sync to work but it is very slow and reads much more data than there is to begin with.

  1. The sync takes approx. 140s for ca. 300MB and 200 000 rows which means a transfer rate of ~2.15MB/s and ~1500rows/s. I also tried a larger file with 2GB which took approx. 1h 15min which is a similar rate. Is this in the order of the expected transfer speed? It seems very slow to me. I also tried to speed it up by increasing the block size but I didn’t notice any change in speed when trying different block sizes. The reported speeds used a block size of 1 000 000. Is there anything else I could try to speed up the transfer?

  2. I am trying to transfer 200 000 entries from some demo data. The CSV-file on S3 is 71.4MB but according to the logs airbyte reads 297MB. As far as I understand airbyte this is because the data is internally converted to JSON which could result in a larger file size. But I did not expect it to be more than 4x as large. Is this how it is designed?

I attached the log of the mentioned sync run but removed the last part of the sync job because it fails afterwards but I already submitted another issue for this and I don’t think it is relevant here. (A complete version of the log is attached to the issue: S3 -> Databricks: Datatypes missing in SQL CREATE-statement · Issue #21193 · airbytehq/airbyte · GitHub)
airbyte_log_s3_to_databricks (2).txt (71.6 KB)

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Hello MoritzLampert, it’s been a while without an update from us. Are you still having problems or did you find a solution?

no, I do not have any answers to my problems yet. One thing I did find was that the problem is not caused by my setup because the transfer rates when using Airbyte Cloud are comparable:

S3 → Azure Blob: 2,54 MB/s (Almost 220GB per 24h)
S3 → Databricks: 2,27 MB/s (Almost 200GB per 24h)

The problem with the increasing file size also remains so the transfer rates are actually even slower since I calculated the transfer rate with the file size given by Airbyte and not the actual size of the CSV initially on S3.

It would be great if someone could comment on this because I think the bottleneck here is the Airbyte software design. Are there currently any efforts to make this more efficient?

There are some performance improvements the team will working in Q1-2023 Moritz. One example is: