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From "Robert Newson (JIRA)" <j...@apache.org>
Subject [jira] Commented: (COUCHDB-1092) Storing documents bodies as raw JSON binaries instead of serialized JSON terms
Date Wed, 16 Mar 2011 11:34:29 GMT

    [ https://issues.apache.org/jira/browse/COUCHDB-1092?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13007429#comment-13007429
] 

Robert Newson commented on COUCHDB-1092:
----------------------------------------

The main problem with considering http headers to convey metadata (a neat idea) is that, while
it's true there's no defined maximum, many proxies, accelerators, ssl offloaders, etc, do
have real and quite low limits. S3, iirc, limits custom header metadata to 8k. Perhaps that's
enough for id/rev/etc, but it sounds tight.

I like the performance boosts here though I think the example might be a little contrived
(especially given Adam's point about how poorly numbers are encoded by default). I'm also
concerned that Paul is concerned. Is there a clearer way to encode the extra metadata fields
from the user data? I was thinking we encode the metadata as a valid json doc followed by
another with just the user data (i.e, all writes are two docs, back to back). When we read,
we read both docs and take the union? Insane, broken, stupid? Discuss.


> Storing documents bodies as raw JSON binaries instead of serialized JSON terms
> ------------------------------------------------------------------------------
>
>                 Key: COUCHDB-1092
>                 URL: https://issues.apache.org/jira/browse/COUCHDB-1092
>             Project: CouchDB
>          Issue Type: Improvement
>          Components: Database Core
>            Reporter: Filipe Manana
>            Assignee: Filipe Manana
>
> Currently we store documents as Erlang serialized (via the term_to_binary/1 BIF) EJSON.
> The proposed patch changes the database file format so that instead of storing serialized
> EJSON document bodies, it stores raw JSON binaries.
> The github branch is at:  https://github.com/fdmanana/couchdb/tree/raw_json_docs
> Advantages:
> * what we write to disk is much smaller - a raw JSON binary can easily get up to 50%
smaller
>   (at least according to the tests I did)
> * when serving documents to a client we no longer need to JSON encode the document body
>   read from the disk - this applies to individual document requests, view queries with
>   ?include_docs=true, pull and push replications, and possibly other use cases.
>   We just grab its body and prepend the _id, _rev and all the necessary metadata fields
>   (this is via simple Erlang binary operations)
> * we avoid the EJSON term copying between request handlers and the db updater processes,
>   between the work queues and the view updater process, between replicator processes,
etc
> * before sending a document to the JavaScript view server, we no longer need to convert
it
>   from EJSON to JSON
> The changes done to the document write workflow are minimalist - after JSON decoding
the
> document's JSON into EJSON and removing the metadata top level fields (_id, _rev, etc),
it
> JSON encodes the resulting EJSON body into a binary - this consumes CPU of course but
it
> brings 2 advantages:
> 1) we avoid the EJSON copy between the request process and the database updater process
-
>    for any realistic document size (4kb or more) this can be very expensive, specially
>    when there are many nested structures (lists inside objects inside lists, etc)
> 2) before writing anything to the file, we do a term_to_binary([Len, Md5, TheThingToWrite])
>    and then write the result to the file. A term_to_binary call with a binary as the
input
>    is very fast compared to a term_to_binary call with EJSON as input (or some other
nested
>    structure)
> I think both compensate the JSON encoding after the separation of meta data fields and
non-meta data fields.
> The following relaximation graph, for documents with sizes of 4Kb, shows a significant
> performance increase both for writes and reads - especially reads.   
> http://graphs.mikeal.couchone.com/#/graph/698bf36b6c64dbd19aa2bef63400b94f
> I've also made a few tests to see how much the improvement is when querying a view, for
the
> first time, without ?stale=ok. The size difference of the databases (after compaction)
is
> also very significant - this change can reduce the size at least 50% in common cases.
> The test databases were created in an instance built from that experimental branch.
> Then they were replicated into a CouchDB instance built from the current trunk.
> At the end both databases were compacted (to fairly compare their final sizes).
> The databases contain the following view:
> {
>     "_id": "_design/test",
>     "language": "javascript",
>     "views": {
>         "simple": {
>             "map": "function(doc) { emit(doc.float1, doc.strings[1]); }"
>         }
>     }
> }
> ## Database with 500 000 docs of 2.5Kb each
> Document template is at:  https://github.com/fdmanana/couchdb/blob/raw_json_docs/doc_2_5k.json
> Sizes (branch vs trunk):
> $ du -m couchdb/tmp/lib/disk_json_test.couch 
> 1996	couchdb/tmp/lib/disk_json_test.couch
> $ du -m couchdb-trunk/tmp/lib/disk_ejson_test.couch 
> 2693	couchdb-trunk/tmp/lib/disk_ejson_test.couch
> Time, from a user's perpective, to build the view index from scratch:
> $ time curl http://localhost:5984/disk_json_test/_design/test/_view/simple?limit=1
> {"total_rows":500000,"offset":0,"rows":[
> {"id":"0000076a-c1ae-4999-b508-c03f4d0620c5","key":null,"value":"wfxuF3N8XEK6"}
> ]}
> real	6m6.740s
> user	0m0.016s
> sys	0m0.008s
> $ time curl http://localhost:5985/disk_ejson_test/_design/test/_view/simple?limit=1
> {"total_rows":500000,"offset":0,"rows":[
> {"id":"0000076a-c1ae-4999-b508-c03f4d0620c5","key":null,"value":"wfxuF3N8XEK6"}
> ]}
> real	15m41.439s
> user	0m0.012s
> sys	0m0.012s
> ## Database with 100 000 docs of 11Kb each
> Document template is at:  https://github.com/fdmanana/couchdb/blob/raw_json_docs/doc_11k.json
> Sizes (branch vs trunk):
> $ du -m couchdb/tmp/lib/disk_json_test_11kb.couch
> 1185	couchdb/tmp/lib/disk_json_test_11kb.couch
> $ du -m couchdb-trunk/tmp/lib/disk_ejson_test_11kb.couch
> 2202	couchdb-trunk/tmp/lib/disk_ejson_test_11kb.couch
> Time, from a user's perpective, to build the view index from scratch:
> $ time curl http://localhost:5984/disk_json_test_11kb/_design/test/_view/simple?limit=1
> {"total_rows":100000,"offset":0,"rows":[
> {"id":"00001511-831c-41ff-9753-02861bff73b3","key":null,"value":"2fQUbzRUax4A"}
> ]}
> real	4m19.306s
> user	0m0.008s
> sys	0m0.004s
> $ time curl http://localhost:5985/disk_ejson_test_11kb/_design/test/_view/simple?limit=1
> {"total_rows":100000,"offset":0,"rows":[
> {"id":"00001511-831c-41ff-9753-02861bff73b3","key":null,"value":"2fQUbzRUax4A"}
> ]}
> real	18m46.051s
> user	0m0.008s
> sys	0m0.016s
> All in all, I haven't seen yet any disadvantage with this approach. Also, the code changes
> don't bring additional complexity. I say the performance and disk space gains it gives
are
> very positive.
> This branch still needs to be polished in a few places. But I think it isn't far from
getting mature.
> Other experiments that can be done are to store view values as raw JSON binaries as well
(instead of EJSON)
> and optional compression of the stored JSON binaries (since it's pure text, the compression
ratio is very high).
> However, I would prefer to do these other 2 suggestions in separate branches/patches
- I haven't actually tested
> any of them yet, so maybe they not bring significant gains.
> Thoughts? :)

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