couchdb-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Apache Wiki <wikidi...@apache.org>
Subject [Couchdb Wiki] Update of "How to handle stats aggregation" by PaulDavis
Date Fri, 15 May 2009 00:00:09 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Couchdb Wiki" for change notification.

The following page has been changed by PaulDavis:
http://wiki.apache.org/couchdb/How_to_handle_stats_aggregation

New page:
I got an email today explaining an approach to stats logging that quite suprised me in terms
of its complete obviousness (that I had failed to see) and its complete awesomeness.

The basic idea: pre-reduce. The use case as described below is for stats reporting and the
idea is that after we've figured out to use _bulk_docs and buffer some number or time period
of log data before inserting, we can go one step further and pre-reduce that data down into
a single doc for insertion. I can already see people implementing support via their favorite
language binding support to even use the same map/reduce functions from CouchDB as a pre-processor
for creating summary docs on input.

Anyway, I thought this would make a good candidate for the How-to series of wiki pages so
here it is. Forgive the Mysql on CouchDB wiki, but I didn't want to edit the original until
after it'd been read and discussed a bit. Then we can delete the reference and pretend it
never happened.

This is via apokalyptik on #couchdb:

A common thing people think of when they read how CouchDB works is "STATS CRUNCHING, THAT'LL
WORK PERFECTLY!"  I know that thats the first thing I thought.  And it will work, but you're
probably already thinking about doing it the hard way (read: in a way that won't.)  You're
thinking that you'll dump a couple hundred or thousand docs a second into Couch and let map/reduce
magically make your cares all disappear (and ponies... map/reduce should make ponies!)  
But a few hundred thousand documents into the process and it's going to fall over in a big
way leaving you pretty jaded and upset at CouchDB.  Well, since CouchDB is all about approaching
problems from a different angle, lets try looking at the problem from a different angle. 

First lets drop some assumptions that you're already likely to be working off of.

#1 this is a collect and report model and not a realtime view

#2 this kind of data almost NEVER EVER actually really has to be accessible in absolute realtime

#3 you do not need one document for every (whatever) you're recording

For assumption #1 You're thinking that map/reduce is going to go and whiz-bang through all
your data at the speed of light, and distill a terabyte of data down to a single page report
in a second... maybe two tops.  We'll thats not realistic.  And besides CouchDB views are
only updated on access (remember?) and the reduces for a single view run serially and not
in parallel (remember?) so you'll be waiting for that... one... report... for... ever because
you have 2^99 documents to process since you looked last.  Which leads to...

Assumption #2.  You think that your data needs to be available REALTIME.  After all banks
do it that way, right? Yea, think again... They don't (and can't.)  They learned a long time
ago what you're just starting to learn right now:  A little delay is OK.  Because how many
times do you check your bank balance per day? Lets pretend it was a thousand times per day
(O.C.D. much?) Thats still only once every 85ish seconds... not exactly realtime. If you cant
let go of this idea then you need to bake your own solution, because this isn't going to work
for you.

If you're still reading I'll assume you've decided that whole "REALTIME" thing was a bad idea
(or at least you're wiling to humor me while evaluating the rest of what I've got to say.)
 At this point it should be pretty obvious that if you don't actually need 1000 updates per
second that you probably don't need 1000 documents per second either. DING DING DING we have
a winner.

Ok, now that we have some assumptions out of the way lets distill the problem down into a
cute cuddly little example.  We all know that most examples are oversimplifications of real
world problems and this one is no different.  
Lets say you're running a website, and you want to track the time it takes to render pages.
 You have n types of pages being rendered, and in n languages, and on n servers.  and you
want to be able to take a look at the average render time by language, page type, or server.
 So... Every pagerender you get an insert into (insert traditional rdbms or kv store with
increment function here) like this:

 INSERT INTO `rendertimes` (`when`,`type`,`server`,`language`,`views`,`time`)
 VALUES('yyyy-mm-dd hh:mm:00', '$type', '$server', '$lang', 1, 0.4)
 ON DUPLICATE KEY UPDATE `views`=`views`+1, `time`=`time`+0.4

First off this does the job of aggregating the data quite a bit.  Then you could compress
the data even farther down into a single document per minute with the general form of.

document.time = n
document[lang1] = { time: x, views: y }
document[type1] = { time: x, views: y }
document[server1] = { time: x, views: y }
[...]

you would then insert this document into couchdb and end up with 14,400 documents per day
rather than 86,400,000 If your data is viewed hourly you could drop it down to 24 docs per
day

Mime
View raw message