If you look at the script the compaction is only performed at the end and not on each iteration.
On Mon, May 31, 2010 at 16:58, Nicholas Orr <[email protected]> wrote: > This all makes sense except the OP says a compaction step is being > performed. > A compaction is essentially a copy/paste/delete/rename operation, so the on > disk size should be fairly constant as the data copied is just the info > required isn't it? > > Nick > > On Tue, Jun 1, 2010 at 9:39 AM, Randall Leeds <[email protected]>wrote: > >> Hi Konrad, >> >> I'll take a stab at this and if I'm wrong hopefully someone will correct >> me. >> >> The on disk BTree is written in an append only fashion rather than >> modified in place. Append only updates mean that every inner node of >> the BTree along the path from the root to the new update has to be >> re-written each time. Initially, when there are very few inner nodes, >> the amount of disk space used for each new update is relatively >> constant. Since the tree has a large fan-out the depth does not change >> much at first. In the second graph you are seeing a tree that has a >> depth of 1 (just the root) being written over an over again to disk >> and the corresponding expected linear growth results. However, when >> you have a higher revision limit the old revisions are kept in the >> tree and the tree grows taller and fatter with each update. As you >> make more updates more inner nodes need to be rewritten for each >> update which causes the growth to accelerate. Eventually, you hit the >> revision limit and old revisions are discarded, the tree stops getting >> any taller or fatter and the number of inner nodes that need to be >> changed for each update remains relatively constant (but greater than >> in the case of rev_limit=1). I suspect that the first graph becomes >> linear above 1000 updates and does not continue to accelerate. >> >> Cheers, >> Randall >> >> 2010/5/31 Konrad Förstner <[email protected]>: >> > Hi, >> > >> > I have an issue with CouchDB and posted the question on stackoverflow >> > [1] but did not get any helpful answer. I would be great if somebody >> > could answer this here or a stackoverflow (there I also had a problem >> > with the compaction which was just a timing issue as explaint in the >> > comment) >> > >> > I was wondering why my CouchDB database was growing to fast so I wrote >> > a little test script [2]. This script changes an attributed of a CouchDB >> > document 1200 times and takes the size of the database after each >> > change. After performing these 1200 writing steps the database is >> > doing a compaction step and the db size is measured again. In the end >> > the script plots the databases size against the revision numbers. The >> > benchmarking is run twice: >> > >> > * The first time the default number of document revision (=1000) is used >> (_revs_limit). >> > >> > * The second time the number of document revisions is set to 1. >> > >> > The first run produces the following plot >> > http://www.flickr.com/photos/konradfoerstner/4656011444/ >> > >> > The second run produces this plot second run >> > http://www.flickr.com/photos/konradfoerstner/4656012732/ >> > >> > For me this is quite an unexpected behavior. In the first run I would >> > have expected a linear growth as every change produces a new >> > revision. When the 1000 revisions are reached the size value should be >> > constant as the older revisions are discarded. >> > >> > In the second run the first revision should result in certain database >> > size that is then keeps during the following writing steps as every >> > new revision leads to the deletion of the previous one. >> > >> > I could understand if there is a little bit of overhead needed to >> > manage the changes but this growth behavior seems weird to me. Can >> > anybody explain this phenomenon or correct my assumptions that lead to >> > the wrong expectations? >> > >> > Many thanks in advance >> > >> > Konrad >> > >> > [1] >> http://stackoverflow.com/questions/2921151/why-do-my-couchdb-databases-grow-so-fast >> > [2] http://github.com/konrad/couchdb-benchmarking >> > >> > >> > >> > >> > >> > >> >
