On 09/05/2013 09:19 AM, Bill Omer wrote:
Thats correct. We created 65k buckets, using two hex characters as the naming convention, then stored the files in each container based on their first two characters in the file name. The end result was 20-50 files per bucket. Once all of the buckets were created and files were being loaded, we still observed an increase in latency overtime. Is there a way to disable indexing? Or are there other settings you can suggest to attempt to speed this process up?
There's been some talk recently about indexless buckets, but I don't think it's possible right now. Yehuda can probably talk about it.
If you remove objects from the bucket so it is empty does it speed up again? Anything you can tell us about when and how it slows down would be very useful!
Mark
On Wed, Sep 4, 2013 at 5:21 PM, Mark Nelson <mark.nelson@xxxxxxxxxxx <mailto:mark.nelson@xxxxxxxxxxx>> wrote: Just for clarification, distributing objects over lots of buckets isn't helping improve small object performance? The degradation over time is similar to something I've seen in the past, with higher numbers of seeks on the underlying OSD device over time. Is it always (temporarily) resolved writing to a new empty bucket? Mark On 09/04/2013 02:45 PM, Bill Omer wrote: We've actually done the same thing, creating 65k buckets and storing 20-50 objects in each. No change really, not noticeable anyway On Wed, Sep 4, 2013 at 2:43 PM, Bryan Stillwell <bstillwell@xxxxxxxxxxxxxxx <mailto:bstillwell@xxxxxxxxxxxxxxx> <mailto:bstillwell@__photobucket.com <mailto:bstillwell@xxxxxxxxxxxxxxx>>> wrote: So far I haven't seen much of a change. It's still working through removing the bucket that reached 1.5 million objects though (my guess is that'll take a few more days), so I believe that might have something to do with it. Bryan On Wed, Sep 4, 2013 at 12:14 PM, Mark Nelson <mark.nelson@xxxxxxxxxxx <mailto:mark.nelson@xxxxxxxxxxx> <mailto:mark.nelson@inktank.__com <mailto:mark.nelson@xxxxxxxxxxx>>> wrote: Bryan, Good explanation. How's performance now that you've spread the load over multiple buckets? Mark On 09/04/2013 12:39 PM, Bryan Stillwell wrote: Bill, I've run into a similar issue with objects averaging ~100KiB. The explanation I received on IRC is that there are scaling issues if you're uploading them all to the same bucket because the index isn't sharded. The recommended solution is to spread the objects out to a lot of buckets. However, that ran me into another issue once I hit 1000 buckets which is a per user limit. I switched the limit to be unlimited with this command: radosgw-admin user modify --uid=your_username --max-buckets=0 Bryan On Wed, Sep 4, 2013 at 11:27 AM, Bill Omer <bill.omer@xxxxxxxxx <mailto:bill.omer@xxxxxxxxx> <mailto:bill.omer@xxxxxxxxx <mailto:bill.omer@xxxxxxxxx>> <mailto:bill.omer@xxxxxxxxx <mailto:bill.omer@xxxxxxxxx> <mailto:bill.omer@xxxxxxxxx <mailto:bill.omer@xxxxxxxxx>>>> wrote: I'm testing ceph for storing a very large number of small files. I'm seeing some performance issues and would like to see if anyone could offer any insight as to what I could do to correct this. Some numbers: Uploaded 184111 files, with an average file size of 5KB, using 10 separate servers to upload the request using Python and the cloudfiles module. I stopped uploading after 53 minutes, which seems to average 5.7 files per second per node. My storage cluster consists of 21 OSD's across 7 servers, with their journals written to SSD drives. I've done a default installation, using ceph-deploy with the dumpling release. I'm using statsd to monitor the performance, and what's interesting is when I start with an empty bucket, performance is amazing, with average response times of 20-50ms. However as time goes on, the response times go in to the hundreds, and the average number of uploads per second drops. I've installed radosgw on all 7 ceph servers. I've tested using a load balancer to distribute the api calls, as well as pointing the 10 worker servers to a single instance. I've not seen a real different in performance with this either. Each of the ceph servers are 16 core Xeon 2.53GHz with 72GB of ram, OCZ Vertex4 SSD drives for the journals and Seagate Barracuda ES2 drives for storage. 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