Adding to this conversation, I found this set of slides by Chris explaining the append-only index update format: http://www.slideshare.net/jchrisa/btree-nosql-oak?from=embed
Specifically slides 16, 17 and 18. Using this example tree, rewriting the updated path (in reverse order) appended to the end of the file makes sense... you see how index queries can simply read backwards from the end of the file and not only find the latest revisions of docs, but also every other doc that wasn't touched (it will just seek into the existing inner nodes of the b+ tree for searching). What I am hoping for clarification on is the following pain-point that I perceive with this approach: 1. In a sufficiently shallow B+ tree (like CouchDB), the paths themselves to elements are short (typically no more than 3 to 5 levels deep) as opposed to a trie or some other construct that would have much longer paths to elements. 2. Because the depth of the tree is so shallow, the breadth of it becomes large to compensate... more specifically, each internal node can have 100s, 1000s or more children. Using the example slides, consider the nodes [A...M] and [R...Z] -- in a good sized CouchDB database, those internal index nodes would have 100s (or more) elements in them pointing at deeper internal nodes that themselves had thousands of elements; instead of the 13 or so as implied by [A...M]. 3. Looking at slide 17 and 18, where you see the direct B+ tree path to the update node getting appended to the end of the file after the revision is written (leaf to root ordering: [J' M] -> [A M] -> [A Z]) it implies that those internal nodes with *all* their child elements are getting rewritten as well. In this example tree, it is isn't such a big issue... but in a sufficiently large CouchDB database, these nodes denoted by [A...M] and [A...Z] could be quite large... I don't know the format of the node elements in the B+ tree, but it would be whatever the size of a node is times however many elements are contained at each level (1 for root, say 100 for level 2, 1000 for level 3 and 10,000 for level 4 -- there is a lot of hand-waving going on here, of course it depends on the size of the data store). Am I missing something or is CouchDB really rewriting that much index information between document revisions on every update? What was previously confusing me is I thought it was *only* rewriting a direct path to the updated revision, like [B]>[E]>[J'] and Couch was some-how patching in that updated path info to the B+ index at runtime. If couch is rewriting entire node paths with all their elements then I am no longer confused about the B+ index updates, but am curious about the on-disk cost of this. In my own rough insertion testing, that would explain why I see my collections absolutely explode in size until they are compacted (not using bulk insert, but intentionally doing single inserts for a million(s) of docs to see what kind of cost the index path duplication would be like). Can anyone confirm/deny/correct this assessment? I want to make sure I am on the right track understanding this. Best wishes, Riyad On Tue, Dec 20, 2011 at 6:13 PM, Riyad Kalla <rka...@gmail.com> wrote: > @Filipe - I was just not clear on how CouchDB operated; you and Robert > cleared that up for me. Thank you. > > @Robert - The writeup is excellent so far (I am not familiar with erlang, > so there is a bit of stickiness there), thank you for taking the time to > put this together! > > At this point I am curious how the _id and _seq indices are read as their > data is continually appended to the end of the data file in small > diff-trees for every updated doc. > > If CouchDB kept all the indices in-memory and simply patched-in the > updated paths at runtime (maybe something akin to memory-mapped indices in > MongoDB) I would be fairly clear on the operation... but as I understand > it, CouchDB keeps such a small memory footprint by doing no in-memory > caching and relying on the intelligence of the OS and filesystem (and/or > drives) to cache frequently accessed data. > > I am trying to understand the logic used by CouchDB to answer a query > using the index once updates to the tree have been appended to the data > file... for example, consider a CouchDB datastore like the one Filipe > has... 10 million documents and let's say it is freshly compacted. > > If I send in a request to that Couch instance, it hits the header of the > data file along with the index and walks the B+ tree to the leaf node, > where it finds the offset into the data file where the actual doc lives... > let's say 1,000,000 bytes away. > > These B+ trees are shallow, so it might look something like this: > > Level 1: 1 node, root node. > Level 2: 100 nodes, inner child nodes > Level 3: 10,000 nodes, inner child nodes > Level 4: 1,000,000, leaf nodes... all with pointers to the data offsets in > the data file. > > Now let's say I write 10 updates to documents in that file. There are 10 > new revisions appended to the end of the data file *each one* separated by > a rewritten B+ path to a leaf node with it's new location at the end of the > file. Each of those paths written between each doc revision (say roughly 2k > like Filipe mentioned) are just 4 item paths... root -> level1 -> level2 -> > level3 --> level4... showing the discrete path from the root to that > individual updated doc. The intermediary levels (l1, 2, 3) are not fully > flushed out with all the OTHER children from the original b+ tree index. > > [[ is this correct so far? If not, please point out my mistakes...] > > Now I issue a query for a document that WAS NOT updated... > > **** this is where I get confused on the logic *** > > this would mean I need to access the original B+ tree index at the root of > the data file, because the revised B+ paths that are written between each > of the updated doc revisions at the end of the file are not full indices. > > NOW consider I want to query for one of the changed docs... now I suddenly > need to scan backwards from the data file's end to find the updated path to > the new revision of that document. > > (obviously) this isn't what Couch is actually doing... it's doing > something more elegant, I just can't figure out what or how and that is > what I was hoping for help with. > > Much thanks guys, I know this is a heavy question to ask. > > Best wishes, > R > > > On Tue, Dec 20, 2011 at 1:35 PM, Robert Dionne < > dio...@dionne-associates.com> wrote: > >> >> Robert Dionne >> Computer Programmer >> dio...@dionne-associates.com >> 203.231.9961 >> >> >> >> >> On Dec 20, 2011, at 3:27 PM, Riyad Kalla wrote: >> >> > Filipe, >> > >> > Thank you for the reply. >> > >> > Maybe I am misunderstanding exactly what couch is writing out; the docs >> > I've read say that it "rewrites the root node" -- I can't tell if the >> docs >> > mean the parent node of the child doc that was changed (as one of the b+ >> > leaves) or if it means the direct path, from the root, to that >> individual >> > doc... or if it means the *entire* index... >> > >> > In the case of even rewriting the single parent, with such a shallow >> tree, >> > each internal leaf will have a huge fan of nodes; let's say 1-10k in a >> > decent sized data set. >> > >> > If you are seeing a few K of extra written out after each changed doc >> then >> > that cannot be write... I almost assumed my understanding was wrong >> because >> > the sheer volume of data would make performance abysmal if it was true. >> > >> > Given that... is it just the changed path, from the root to the new leaf >> > that is rewritten? >> >> Hi Riyad, >> >> You are correct, it's only the changed path. Interestingly I've just >> started to document all these internals[1] along with links to the code and >> other references available. >> >> Cheers, >> >> Bob >> >> >> [1] http://bdionne.github.com/couchdb/ >> >> > That makes me all sorts of curious as to how Couch >> > updates/searches the new modified index with the small diff that is >> written >> > out. >> > >> > Any pointers to reading that will help me dig down on this (even early >> bugs >> > in JIRA?) would be appreciated. I've tried skimming back in 2007/08 on >> > Damien's blog to see if it wrote about it in depth and so far haven't >> found >> > anything as detailed as I am hoping for on this architecture. >> > >> > Best, >> > Riyad >> > >> > On Tue, Dec 20, 2011 at 1:07 PM, Filipe David Manana < >> fdman...@apache.org>wrote: >> > >> >> On Tue, Dec 20, 2011 at 6:24 PM, Riyad Kalla <rka...@gmail.com> wrote: >> >>> I've been reading everything I can find on the CouchDB file format[1] >> and >> >>> am getting bits and pieces here and there, but not a great, concrete, >> >>> step-by-step explanation of the process. >> >>> >> >>> I'm clear on the use of B+ trees and after reading a few papers on the >> >>> benefits of log-structured file formats, I understand the benefits of >> >>> inlining the B+ tree indices directly into the data file as well >> >> (locality >> >>> + sequential I/O)... what I'm flummoxed about is how much of the B+ >> >> tree's >> >>> index is rewritten after every modified document. >> >>> >> >>> Consider a CouchDB file that looks more or less like this: >> >>> >> >>> [idx/header][doc1, rev1][idx/header][doc1, rev2].... >> >>> >> >>> After each revised doc is written and the "b-tree root" is rewritten >> >> after >> >>> that, is that just a modified root node of the B+ tree or the entire >> B+ >> >>> tree? >> >>> >> >>> The reason I ask is because regardless of the answer to my previous >> >>> question, for a *huge* database will millions of records, that seems >> like >> >>> an enormous amount of data to rewrite after every modification. Say >> the >> >>> root node had a fanning factor of 133; that would still be alot of >> data >> >> to >> >>> rewrite. >> >> >> >> Hi Riyad, >> >> >> >> Have you observed that in practice? >> >> >> >> Typically the depth of database btrees is not that high even for >> >> millions of documents. For example I have one around with about 10 >> >> million documents which doesn't have more than 5 or 6 levels if I >> >> recall correctly. >> >> >> >> So updating a doc, for that particular case, means rewriting 5 or 6 >> >> new nodes plus the document itself. Each node is normally not much >> >> bigger than 1.2Kb. >> >> >> >> I've written once a tool to analyze database files which reports btree >> >> depths, however it's not updated to work with recent changes on >> >> master/1.2.x such as snappy compression and btree sizes: >> >> >> >> https://github.com/fdmanana/couchfoo >> >> >> >> It should work with CouchDB 1.1 (and older) database files. >> >> >> >>> >> >>> I am certain I am missing the boat on this; if anyone can pull me out >> of >> >>> the water and point me to dry land I'd appreciate it. >> >>> >> >>> Best, >> >>> R >> >>> >> >>> >> >>> >> >>> [1] >> >>> -- >> >>> >> >> >> http://jchrisa.net/drl/_design/sofa/_list/post/post-page?startkey=%5B%22CouchDB-Implements-a-Fundamental-Algorithm%22%5D >> >>> -- http://horicky.blogspot.com/2008/10/couchdb-implementation.html >> >>> -- http://blog.kodekabuki.com/post/132952897/couchdb-naked >> >>> -- http://guide.couchdb.org/editions/1/en/btree.html >> >>> -- http://ayende.com/blog/* (Over my head) >> >> >> >> >> >> >> >> -- >> >> Filipe David Manana, >> >> >> >> "Reasonable men adapt themselves to the world. >> >> Unreasonable men adapt the world to themselves. >> >> That's why all progress depends on unreasonable men." >> >> >> >> >
