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From Adam Kocoloski <>
Subject Re: # [DISCUSS] : things we need to solve/decide : storage of edit conflicts
Date Tue, 05 Feb 2019 04:09:10 GMT
I overlooked two important details when I sent this earlier email. I try not to be in the habit
of replying to my own emails but here we go ...

First, in order to update a document while reading only the winning edit branch entry (the
second property that I said I wanted to provide) we also need to include the versionstamp
associated with the current leaf in the edit branch KV, so we know which entry to clear from
the “by_seq” subspace. So a more complete example would be

(“_meta”, DocID, IsDeleted, RevPosition, RevHash) = (VersionstampForRev, [ParentRev, GrandparentRev,

Second ... that property isn’t really compatible with the compact edit conflict storage
model I proposed, is it? If you need to merge in a new edit to an existing set of KVs you
can’t very well just blindly clear the whole range.

This is an interesting balancing act. Perhaps there’s a good way for us to preserve the
“fast path” which is applicable in 99% of circumstances and then only fallback to the
“read the doc in order to update it” mode when we know there are multiple edit branches.


> On Feb 4, 2019, at 6:22 PM, Adam Kocoloski <> wrote:
> I think it’s fine to start a focused discussion here as it might help inform some of
the broader debate over in that thread.
> As a reminder, today CouchDB writes the entire body of each document revision on disk
as a separate blob. Edit conflicts that have common fields between them do not share any storage
on disk. The revision tree is encoded into a compact format and a copy of it is stored directly
in both the by_id tree and the by_seq tree. Each leaf entry in the revision tree contain a
pointer to the position of the associated doc revision on disk.
> As a further reminder, CouchDB 2.x clusters can generate edit conflict revisions just
from multiple clients concurrently updating the same document in a single cluster. This won’t
happen when FoundationDB is running under the hood, but users who deploy multiple CouchDB
or PouchDB servers and replicate between them can of course still produce conflicts just like
they could in CouchDB 1.x, so we need a solution.
> Let’s consider the two sub-topics separately: 1) storage of edit conflict bodies and
2) revision trees
> ## Edit Conflict Storage
> The simplest possible solution would be to store each document revision separately, like
we do today. We could store document bodies with (“docid”, “revid”) as the key prefix,
and each transaction could clear the key range associated with the base revision against which
the edit is being attempted. This would work, but I think we can try to be a bit more clever
and save on storage space given that we’re splitting JSON documents into multiple KV pairs.
> One thought I’d had is to introduce a special enum Value which indicates that the subtree
“beneath” the given Key is in conflict. For example, consider the documents
> {
>    “_id”: “foo”,
>    “_rev”: “1-abc”,
>    “owner”: “alice”,
>    “active”: true
> }
> and 
> {
>    “_id”: “foo”,
>    “_rev”: “1-def”,
>    “owner”: “bob”,
>    “active”: true
> }
> We could represent these using the following set of KVs:
> (“foo”, “active”) = true
> (“foo”, “owner”) = kCONFLICT
> (“foo”, “owner”, “1-abc”) = “alice”
> (“foo”, “owner”, “1-def”) = “bob”
> This approach also extends to conflicts where the two versions have different data types.
Consider a more complicated example where bob dropped the “active” field and changed the
“owner” field to an object:
> {
>  “_id”: “foo”,
>  “_rev”: “1-def”,
>  “owner”: {
>    “name”: “bob”,
>    “email”: “"
>  }
> }
> Now the set of KVs for “foo” looks like this (note that a missing field needs to
be handled explicitly):
> (“foo”, “active”) = kCONFLICT
> (“foo”, “active”, “1-abc”) = true
> (“foo”, “active”, “1-def”) = kMISSING
> (“foo”, “owner”) = kCONFLICT
> (“foo”, “owner”, “1-abc”) = “alice”
> (“foo”, “owner”, “1-def”, “name”) = “bob”
> (“foo”, “owner”, “1-def”, “email”) = “”
> I like this approach for the common case where documents share most of their data in
common but have a conflict in a very specific field or set of fields. 
> I’ve encountered one important downside, though: an edit that replicates in and conflicts
with the entire document can cause a bit of a data explosion. Consider a case where I have
10 conflicting versions of a 100KB document, but the conflicts are all related to a single
scalar value. Now I replicate in an empty document, and suddenly I have a kCONFLICT at the
root. In this model I now need to list out every path of every one of the 10 existing revisions
and I end up with a 1MB update. Yuck. That’s technically no worse in the end state than
the “zero sharing” case above, but one could easily imagine overrunning the transaction
size limit this way.
> I suspect there’s a smart path out of this. Maybe the system detects a “default”
value for each field and uses that instead of writing out the value for every revision in
a conflicted subtree. Worth some discussion.
> ## Revision Trees
> In CouchDB we currently represent revisions as a hash history tree; each revision identifier
is derived from the content of the revision including the revision identifier of its parent.
Individual edit branches are bounded in *length* (I believe the default is 1000 entries),
but the number of edit branches is technically unbounded.
> The size limits in FoundationDB preclude us from storing the entire key tree as a single
value; in pathological situations the tree could exceed 100KB. Rather, I think it would make
sense to store each edit *branch* as a separate KV. We stem the branch long before it hits
the value size limit, and in the happy case of no edit conflicts this means we store the edit
history metadata in a single KV. It also means that we can apply an interactive edit without
retrieving the entire conflicted revision tree; we need only retrieve and modify the single
branch against which the edit is being applied. The downside is that we duplicate historical
revision identifiers shared by multiple edit branches, but I think this is a worthwhile tradeoff.
> I would furthermore try to structure the keys so that it is possible to retrieve the
“winning” revision in a single limit=1 range query. Ideally I’d like to proide the following
> 1) a document read does not need to retrieve the revision tree at all, just the winning
revision identifier (which would be stored with the rest of the doc)
> 2) a document update only needs to read the edit branch of the revision tree against
which the update is being applied, and it can read that branch immediately knowing only the
content of the edit that is being attempted (i.e., it does not need to read the current version
of the document itself).
> So, I’d propose a separate subspace (maybe “_meta”?) for the revision trees, with
keys and values that look like
> (“_meta”, DocID, IsDeleted, RevPosition, RevHash) = [ParentRev, GrandparentRev, …]
> The inclusion of IsDeleted, RevPosition and RevHash in the key should be sufficient (with
the right encoding) to create a range query that automatically selects the “winner” according
to CouchDB’s arcane rules, which are something like
> 1) deleted=false beats deleted=true
> 2) longer paths (i.e. higher RevPosition) beat shorter ones
> 3) RevHashes with larger binary values beat ones with smaller values
> ===========
> OK, that’s all on this topic from me for now. I think this is a particularly exciting
area where we start to see the dividends of splitting up data into multiple KV pairs in FoundationDB
:) Cheers,
> Adam
>> On Feb 4, 2019, at 2:41 PM, Robert Newson <> wrote:
>> This one is quite tightly coupled to the other thread on data model, should we start
much conversation here before that one gets closer to a solution?
>> -- 
>> Robert Samuel Newson
>>> On Mon, 4 Feb 2019, at 19:25, Ilya Khlopotov wrote:
>>> This is a beginning of a discussion thread about storage of edit 
>>> conflicts and everything which relates to revisions.

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