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https://issues.apache.org/jira/browse/COUCHDB-620?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12799129#action_12799129
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Brian Candler commented on COUCHDB-620:
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"the CouchDB process took between 70% and 130% of a CPU, usually in the 110%
range"
That looks like one CPU fully used for CouchDB (with afterburners to get to
110% :-)
"The couchjs process was hovering around 25% of a CPU" - which suggests that
couchjs is waiting on couchdb to issue it with more work, so parallelising
couchjs wouldn't help.
For comparison, have you tried the erlang view server? That would eliminate
JSON serialisation/deserialisation overhead, plus much of the message-passing
overhead. It would be very useful to have this comparison on your large dataset.
If measurement shows that the json serialisation overhead is large, maybe
there's a fairly simple improvement: in couch_os_process.erl, make writejson
and readjson execute in separate erlang processes, so they can execute on
another core. You would need to pipeline requests to the view server to get the
full benefit though.
> Generating views is extremely slow - makes CouchDB hard to use with
> non-trivial number of docs
> ----------------------------------------------------------------------------------------------
>
> Key: COUCHDB-620
> URL: https://issues.apache.org/jira/browse/COUCHDB-620
> Project: CouchDB
> Issue Type: Improvement
> Components: Infrastructure
> Affects Versions: 0.10
> Environment: Ubuntu 9.10 64 bit, CouchDB 0.10
> Reporter: Roger Binns
> Assignee: Damien Katz
>
> Generating views is extremely slow. For example adding 10 million documents
> takes less than 10 minutes but generating some simple views on the same docs
> takes over 4 hours.
> Using top you can see that CouchDB (erlang) and couchjs between them cannot
> even saturate a single CPU let alone the I/O system. Under ideal conditions
> performance should be limited by cpu, disk or memory. This implies that the
> processes are doing simple things in lockstep accumulating latencies in each
> process as well as the communication between them which when multiplied by
> the number of documents can amount to a lot.
> Some suggestions:
> * Run as many couchjs instances as there are processor cores and scatter work
> amongst them
> * Have some sort of pipelining in the erlang so that the moment the first
> byte of response is received from couchjs the data is sent for the next
> request (the JSON conversion, HTTP headers etc should all have been assembled
> already) to reduce latencies. Do whatever is most similar in couchjs (eg use
> separate threads to read requests, process them and write responses).
> * Use the equivalent of HTTP pipelining when talking to couchjs so that it
> always has a doc ready to work on rather than having to transmit an entire
> response and then wait for erlang to think and provide an entire new request
> A simple test of success is to have a database with a million or so documents
> with a trivial view and have view creation max out the CPU,. memory or disk.
> Some things in CouchDB make this a particularly nasty problem. View data is
> not replicated so replicating documents can lead the view data by a large
> margin on the recipient database. This can lead to inconsistencies. You
> also can't expect users to then wait minutes (or hours) for a request to
> complete because the view generation got that far behind. (My own plans now
> are to not use replication and instead create the database file on another
> couchdb instance and then rsync the binary database file over instead!)
> Although stale=ok is available, you still have no idea if the response will
> be quick or take however long view generation does. (Sure I could add some
> sort of timeout and complicate the code but then what value do I pick? If I
> have a user waiting I want an answer ASAP or I have to give them some
> horrible error message. Taking a long wait and then giving a timeout is even
> worse!)
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