Good morning peeps,
I would like to open this thread to discuss some ideas about how to
improve the current build on Push server. Lukas have been doing a
stellar job improving it and I think we can help.
Yesterday I spent some time trying to build a developer environment for
UPS — was a good exercise to realize how people feel trying to contribute.
The goal was to build the environment from scratch.
Here comes some feedback (keep in mind that I'm not that good on Node.js)
- Build our dependencies takes a considerable time. For fair
reasons, we are running mvn install, npm install and bower install
for the first time. Maybe we can reduce one step?
- Java developers don't have their environment ready for Node — it can
be a blocker. For example, was necessary to install gcc, libpng and
libpng-devel. I already saw team members struggling with it, like me.
- Maybe we should run -Pdev by default and run the complete build only
- Maybe we can minify some JS dependencies and don't build
I built an image, because developers willing to just use UPS with the
latest bits might struggle to configure their environment and maybe it
can be helpful.
The image is not perfect and soon will be moved to jboss/dockerfiles.
 - https://github.com/abstractj/docker/tree/master/aerogear-unifiedpush-dev
 - https://registry.hub.docker.com/u/abstractj/unifiedpush-dev/
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’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.)
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 changes.
Given these characteristics, using DS within the data service might be extremely expensive in terms of CPU and memory, and difficult for a DS-based service to implement all of the features necessary. 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. 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 guarantees. Thirdly, what would the data service need to do when a client connects and asks for the changes since it was last connected? 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 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:
the new versions of those entities that changed;
the deltas in the entities; and/or
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?
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).
Finally, the data sync APIs of the data service should support the use of local client storage, but it should not require it.