[aerogear-dev] Use of Differential Synchronization for data sync
supittma at redhat.com
Mon Aug 4 21:17:01 EDT 2014
On Mon 04 Aug 2014 01:19:17 PM EDT, Randall Hauch wrote:
> Perhaps we’re looking at this from different perspectives. It’s great
> that you guys are trying to better understand DS so that you can
> compare it to other techniques, including OT. That certainly needs to
> be done. I guess I was looking at DS from the perspective of how a
> data service might need to implement it, knowing that the choice of
> how data sync is ultimately done will be influenced in part by how the
> data service would implement each approach and the impact on
> scalability and performance. Perhaps it’s too early to provide my
> thoughts along those lines.
> On Aug 4, 2014, at 9:34 AM, Summers Pittman <supittma at redhat.com
> <mailto:supittma at redhat.com>> wrote:
>> I am speaking from the perspective of the algorithm and less from my
>> opinions of the way the system should work as a whole.
>> On 08/01/2014 03:02 PM, Randall Hauch wrote:
>>> I’ve really enjoyed learning about what AeroGear has been doing with
>>> data sync. This is a tough problem, but finding a solution is really
>>> important. Both data sync POCs appear to use Differential
>>> Synchronization, or DS . I was not familiar with the paper until
>>> today, but after reading it I do have a few questions/comments. Bear
>>> with me; this is a long post.
>>> DS is clearly targeted for use within a collaborative document
>>> editor, where there are multiple clients concurrently editing the
>>> same document, and at any one time there are a relatively small
>>> number of documents being edited; you can get a feel for this by
>>> looking at figures 5 and 7 in the paper  — look at the amount of
>>> server memory and CPU required to perform DS on just one document
>>> being edited by a half-dozen clients. Also, in a collaborative
>>> document editor, clients are often continually making changes even
>>> as they attempt to synchronize with the server.
>> It doesn't actually make any claims about CPU or memory usage. A
>> shadow document is needed for each connection. For documents which
>> are infrequently edited, the shadow doc can easily be frozen to disk
>> until an edit comes in.
> Sure, you don’t have to keep it in memory. But it does have to be in
> memory to do anything with it in an efficient way. And, yes, you
> certainly can build a data service that uses this technique. My point
> was that having multiple copies of a document being edited will reduce
> the scalability of the data service compared to other techniques.
>>> (It’s interesting that Google Docs, and Google Wave before it,
>>> appear to use Operational Transformation  rather than DS. OT
>>> might also make it easier to implement undo/redo, which works really
>>> well in Google Docs.)
>> That is probably because OT, Docs, and Apache Wave are all older then
>> Diff-sync. OT is also a much more complicated algorithm in my
>> experience (and from browsing around on wikipedia)
>>> An MBaaS or any other database-like service is very different. It
>>> has to host multiple applications (i.e., databases), each with
>>> multiple collections containing potentially millions of entities
>>> (e.g., JSON documents). The entities themselves are more
>>> fine-grained and smaller than collaborative documents (though
>>> probably a bit coarser-grained and larger than a single record in a
>>> RDBMS). Many clients might be reading and updating lots of documents
>>> at once, and the data service has to coordinate those changes. A
>>> single batch update from one client might request changes to dozens
>>> of entities. And the clients can/will always wait for confirmation
>>> that the server made the requested changes before continuing (unless
>>> the client is offline); or at a minimum can enqueue the requested
>> Two quick things. A document is just a collection of entities and
>> can be structured to reduce this problem (especially is we are faking
>> it on a RDBMS with particularly sadistic abuses to an ORM).
> Yes, a document might be a JSON document that is an aggregate of
> multiple objects, and not just a flat map of key-value pairs. The use
> of aggregate data structures and denormalization are some of the ways
> that eventually-consistent data stores work. The goal is to reduce the
> scope of a set of operations to a single aggregate. Other data stores
> (like graph and hierarchical databases) require strong consistency and
> transactions because operations necessarily span multiple objects. But
> limiting operations to a single aggregate is also quite constraining
> w/r/t app development, since you can’t always denormalize all data to
> separate aggregates.
> So even if a collection (in the MongoDB sense) contains documents that
> are aggregates of multiple “entities” (in the Hibernate sense of the
> word), my point still stands that generally any given JSON document
> will still be smaller than a document used in a collaborative document
> editor. Also, I would not be surprised if the sheer number of
> documents in a MongoDB collection is orders of magnitude larger than
> the number of documents stored by a collaborative editor app.
>> Clients don't have to wait for the edits to be merged on the server
>> and the nature of diff-sync gives us batching for free.
> Hmm… even if you could do it this way, do you not want to be able to
> give feedback to the user that the changes might not have been
> Do you have some scenarios that describe the kinds of applications
> you’re considering? I’m wondering if I’m envisioning a different kind
> of app.
>>> Given these characteristics, using DS within the data service might
>>> be extremely expensive in terms of CPU and memory
>> or it might not be. We need data, use cases, etc to test and see
>> what happens.
>>> , and difficult for a DS-based service to implement all of the
>>> features necessary.
>> Which features? Features of the algorithm of features of the
>> application? The algorithm is really REALLY simple for what we get
>> out of it.
> I was referring to features of the data service, and especially how
> the data service’s implementation can satisfy the difficult
> non-functional requirements like scalability and performance. While
> the algorithm might be really simple, that doesn’t mean implementing
> it on the server is efficient. What I’ve read so far makes me think
> that it’s could very well be less efficient and scalable than other
> techniques used in data services.
>>> First, the data service doesn’t really know which entities are
>>> being“edited”; instead, connected clients read entities, make
>>> changes locally, then request the service make those changes.
>> I disagree. The service knows documents which the client has a
>> connection to/active session for. It most certainly knows which
>> entities are being edited.
> I guess I was hoping that the client can manipulate documents locally
> without having to coordinate that with the server. Again, I’m
> concerned about server scalability.
>>> Secondly, every time a change comes in, to compute the diff the
>>> service would have to read the persisted entity; this not only is
>>> inefficient, but this also makes it more difficult to scale and
>>> handle the concurrency, consistency, atomicity, and serializability
>> See earlier comment about sadistic abuses of an ORM. Yes we have to
>> be aware of the RDB underneath the sync server, but I don't think
>> this is a problem with the algorithm.
> I agree, it’s not a problem with the algorithm. It’s a problem insofar
> as it would mandate what the server has to support.
>>> Thirdly, what would the data service need to do when a client
>>> connects and asks for the changes since it was last connected?
>> Send it the diff of the clients serverside shadow and the server's
>> current document. This diff will get sent to the client, merged with
>> the clients shadow, and the diff of that will get sent back to the
>> server. Repeat until the client is in sync.
>>> The data service might be able to quickly find out which entities
>>> were modified since then, but computing the diffs (relative to the
>>> time the client last connected) for all of those changed entities
>>> would be very complicated.
>> It isn’t.
> Perhaps I should have said “expensive” rather than “complicated”.
>>> It may be easier and better for the data service to record the
>>> individual changes (edits) made by each transaction, and then to use
>>> that information to compute the effective diffs from some period of
>>> time. In fact, these recorded edits might also be useful to
>>> implement other features within the data service; see CQRS  and .
>>> What is really required by the client when trying to synchronize its
>>> data after being disconnected? Assuming the client can say which
>>> subset of entities it’s interested in when it reconnects (via some
>>> criteria in a subscription), does the client want:
>>> 1. the new versions of those entities that changed;
> This is actually what a number of MBaaS offerings do, although it’s
> often hidden by the client SDKs. It may not be ideal because it places
> more work onto the client SDK, but the benefit is that a good portion
> of the work is done on the client, and the load on the server is
> reduced (and scalability increased). It’s also trivially easy for the
> data service to implement.
>>> 1. the deltas in the entities; and/or
>>> 1. all of the events describing the individual changes made to all
>>> of those entities?
>>> It may not matter for clients that don’t allow local offline
>>> changes, but what might the preferred approach be for clients that
>>> do allow offline changes? Option 1 is clearly the easiest from the
>>> perspective of the data service, but options #2 and #3 can certainly
>>> be handled. With option #1, can the client do something like DS and
>>> maintain copies of each original (unmodified) entity so that it can
>>> compute the differences? Does this (perhaps with a journal of edits
>>> made while offline) provide enough info for the client to properly
>>> merge the local changes, or does the client really need the
>>> individual events in #3 so that it can, for example, know that some
>>> local changes were made to now-out-date data?
>> Except in the case of a merge error, the algorithm handles long
>> offline periods with edits just fine. If there is a merge error the
>> user/application will have to manually merge the documents somehow.
>> One of the things to keep in mind is on mobile devices the radio is
>> the most expensive thing you can control as an application. Any
>> decision we make should err toward only sending as little data as
>> possible as few times as possible.
> I completely agree.
>>> Will the same option work for online notifications? After all, it’d
>>> be great if the same mechanism was used for data-sync, offline
>>> (push) notifications, and online notifications (events).
>> I don't understand your question.
> Only that it seems beneficial that the same mechanism be used for
> “events" (while connected) and both online and offline data-sync.
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> aerogear-dev at lists.jboss.org
I don't think it is too early for service minded people to point out
the big honking problems we will face. At the very least it helps us
figure out the use cases we will support for the initial releases as to
POC morphs into an actual project.
From a mobile client developer perspective, this algorithm feels very
easy to understand and it handles a lot of annoying corner cases very
well (e.g. offline, operation batching, conflict handling, document
synchronization, collaboration etc.) . Additionally, in my
experiments, plugging in different diff-merge-synch operations allows
for the general framework to be adapted to other use cases and data
types. (i.e. if the server is just batching a list of changed Object
IDs instead of the actual changes for binary files).
One of the things we need to do is determine which problems we are
going to solve/support (single user multi device sync, multi user
collaboration, binary files etc), which we are going to short circuit
(limiting numbers of collaborators, limiting file size, etc), and which
we are just going to document as best practices to use or to avoid.
Maybe I'm just stuck seeing my hammer (DS) and everything is nails and
if someone can give a demo of something else (OT, etc) then I will be
really interested to compare.
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>>Java is my crack.
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