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From Adam Kocoloski <>
Subject Re: # [DISCUSS] : things we need to solve/decide : storage of edit conflicts
Date Mon, 04 Feb 2019 23:22:09 GMT
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


    “_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

(“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 properties:

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,


> 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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