Hi Charles,
Basically the goal is to reduce write-amplification as much as
possible. The deeper that the rocksdb hierarchy gets, the worse the
write-amplifcation for compaction is going to be. If you look at the
OSD logs you'll see the write-amp factors for compaction in the rocksdb
compaction summary sections that periodically pop up. There's a couple
of things we are trying to see if we can improve things on our end:
1) Adam has been working on experimenting with sharding data across
multiple column families. The idea here is that it might be better to
hav multiple L0 and L1 levels rather than L0, L1, L2 and L3. I'm not
sure if this will pan out of not, but that was one of the goals behind
trying this.
2) Toshiba recently released trocksdb which could have a really big
impact on compaction write amplification:
Code: https://github.com/ToshibaMemoryAmerica/trocksdb/tree/TRocksRel
Wiki: https://github.com/ToshibaMemoryAmerica/trocksdb/wiki
I recently took a look to see if our key/value size distribution would
work well with the approach that trocksdb is taking to reduce
write-amplification:
https://docs.google.com/spreadsheets/d/1fNFI8U-JRkU5uaRJzgg5rNxqhgRJFlDB4TsTAVsuYkk/edit?usp=sharing
The good news is that it sounds like the "Trocks Ratio" for the data we
put in rocksdb is sufficiently high that we'd see some benefit since it
should greatly reduce write-amplification during compaction for data
(but not keys). This doesn't help your immediate problem, but I wanted
you to know that you aren't the only one and we are thinking about ways
to reduce the compaction impact.
Mark
On 4/10/19 2:07 AM, Charles Alva wrote:
Hi Ceph Users,
Is there a way around to minimize rocksdb compacting event so that it
won't use all the spinning disk IO utilization and avoid it being
marked as down due to fail to send heartbeat to others?
Right now we have frequent high IO disk utilization for every 20-25
minutes where the rocksdb reaches level 4 with 67GB data to compact.
Kind regards,
Charles Alva
Sent from Gmail Mobile
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