Le 12/05/15 14:34, Howard Chu a écrit :
Emmanuel Lécharny wrote:
Le 11/05/15 22:17, Howard Chu a écrit :
There are two main problems:
- the AVL tree used for presentlist is still extremely inefficient
in both CPU and memory use. 2) the consumer does twice as much work for a single modification as the provider. I.e., the consumer does a write op to the backend for the modification, and then a second write op to update its contextCSN.
Updating the contextCSN is an extra operation on the consumer, but as you have to update potentially tens of indexes when updating an entry (on both teh consumer and the producer), it's not really twoce more work. It's an additianal operation, but that would not double the time it costs on the producer.
You're forgetting one very important thing - each operation is a single transaction in the backend, and transactions are synchronous by default. The main cost is not the indexing, it's the txn fsync, and yes, it is twice the cost when you're doing two txns instead of just one.
Good point.
The question would be : how do we update the contextCSN only periodically, to mitigate this extra cost, and it seems you proposed to batch the updates for this reason. By using btaches of 500 updates, this extra cost will be almost unnoticable, and one would expect the work on the consumer to be the same as on the producer side, right ?
Yes.
The provider only does the original modification, and caches the contextCSN update.
If we fix both of these issues, consumer speed should be much faster. Nothing else is worth investigating until these two areas are reworked.
Agreed in most of the case. Although for use cases of an important number of updates have occured while a consumer is off line, another strategy might work. That this other strategy is to stop the consumer, slapcat the producer, slapadd the result and restart the server, all with the command line, instead of having it implemented in the server code, was what I was suggesting, but this is another story for a corner case that is not frequent. Plus we don't know at which point this would be the correct strategy (ie, for how many updates should we consider it as a better startegy than the current implementation ?).
If the consumer has been offline for a long time, then this discussion is moot. No clients will be looking at it, so the risk of serving out-of-date information to clients is zero. In that case, it doesn't matter what strategy you use, they'll all work.
Another good point. It would require a use that send a hell lots of updates while no client is having activity, and a disconnected consumer - all three conditions at the same time - to face my scenario. Quite rare. I had in mind this user who was updating his database with millions of updates once in a while (say, once a year), during night, and who find than teh consumer is not up and running in the morning.
Not sure then it worth the effort to find a way to mitigate such corner case.
For (1) I've been considering a stripped down memory-only version of LMDB. There are plenty of existing memory-only Btree implementations out there already though, if anyone has a favorite it would probably save us some time to use an existing library. The Linux kernel has one (lib/btree.c) but it's under GPL so we can't use it directly.
Q : do you need to keep the presentList ina BTree at all ?
Good question. We process it by doing a single search over the target range, and removing presentlist entries for each entry returned by the search. Since the search order is random, we want fast search access to the presentlist.
We could alternatively do a dynamic array and walk the presentlist in order, doing (entryUUID=x) searches on each element. The overhead of doing X individual searches is worse than doing one global search though.
If the goal is to find all the entries that are not present in the DB, wouldn't it be faster to simply quick sort the entryUUID we have received? Both algorithms (AVL insertions and quickSort) are in O(n x Log(n)) - if you except the possibility that the quicksort degenerates in O(n2), of course - but Quicksort is faster than AVL when it comes to order a set of values.