Adventures with large RGW buckets

Hi, we are seeing a trend towards rather large RGW S3 buckets lately. we've worked on several clusters with 100 - 500 million objects in a single bucket, and we've been asked about the possibilities of buckets with several billion objects more than once. From our experience: buckets with tens of million objects work just fine with no big problems usually. Buckets with hundreds of million objects require some attention. Buckets with billions of objects? "How about indexless buckets?" - "No, we need to list them". A few stories and some questions: 1. The recommended number of objects per shard is 100k. Why? How was this default configuration derived? It doesn't really match my experiences. We know a few clusters running with larger shards because resharding isn't possible for various reasons at the moment. They sometimes work better than buckets with lots of shards. So we've been considering to at least double that 100k target shard size for large buckets, that would make the following point far less annoying. 2. Many shards + ordered object listing = lots of IO Unfortunately telling people to not use ordered listings when they don't really need them doesn't really work as their software usually just doesn't support that :( A listing request for X objects will retrieve up to X objects from each shard for ordering them. That will lead to quite a lot of traffic between the OSDs and the radosgw instances, even for relatively innocent simple queries as X defaults to 1000 usually. Simple example: just getting the first page of a bucket listing with 4096 shards fetches around 1 GB of data from the OSD to return ~300kb or so to the S3 client. I've got two clusters here that are only used for some relatively low-bandwidth backup use case here. However, there are a few buckets with hundreds of millions of objects that are sometimes being listed by the backup system. The result is that this cluster has an average read IO of 1-2 GB/s, all going to the index pool. Not a big deal since that's coming from SSDs and goes over 80 Gbit/s LACP bonds. But it does pose the question about scalability as the user- visible load created by the S3 clients is quite low. 3. Deleting large buckets Someone accidentaly put 450 million small objects into a bucket and only noticed when the cluster ran full. The bucket isn't needed, so just delete it and case closed? Deleting is unfortunately far slower than adding objects, also radosgw-admin leaks memory during deletion: https://tracker.ceph.com/issues/40700 Increasing --max-concurrent-ios helps with deletion speed (option does effect deletion concurrency, documentation says it's only for other specific commands). Since the deletion is going faster than new data is being added to that cluster the "solution" was to run the deletion command in a memory-limited cgroup and restart it automatically after it gets killed due to leaking. How could the bucket deletion of the future look like? Would it be possible to put all objects in buckets into RADOS namespaces and implement some kind of efficient namespace deletion on the OSD level similar to how pool deletions are handled at a lower level? 4. Common prefixes could filtered in the rgw class on the OSD instead of in radosgw Consider a bucket with 100 folders with 1000 objects in each and only one shard /p1/1, /p1/2, ..., /p1/1000, /p2/1, /p2/2, ..., /p2/1000, ... /p100/1000 Now a user wants to list / with aggregating common prefixes on the delimiter / and wants up to 1000 results. So there'll be 100 results returned to the client: the common prefixes p1 to p100. How much data will be transfered between the OSDs and radosgw for this request? How many omap entries does the OSD scan? radosgw will ask the (single) index object to list the first 1000 objects. It'll return 1000 objects in a quite unhelpful way: /p1/1, /p1/2, ...., /p1/1000 radosgw will discard 999 of these and detect one common prefix and continue the iteration at /p1/\xFF to skip the remaining entries in /p1/ if there are any. The OSD will then return everything in /p2/ in that next request and so on. So it'll internally list every single object in that bucket. That will be a problem for large buckets and having lots of shards doesn't help either. This shouldn't be too hard to fix: add an option "aggregate prefixes" to the RGW class method and duplicate the fast-forward logic from radosgw in cls_rgw. It doesn't even need to change the response type or anything, it just needs to limit entries in common prefixes to one result. Is this a good idea or am I missing something? IO would be reduced by a factor of 100 for that particular pathological case. I've unfortunately seen a real-world setup that I think hits a case like that. Paul -- Paul Emmerich Looking for help with your Ceph cluster? Contact us at https://croit.io croit GmbH Freseniusstr. 31h 81247 München www.croit.io Tel: +49 89 1896585 90