Thanks for great feedback, Randall!

ad) Scalability

Sure, I'm also worried about DiffSync being an algorithm that implies statefulness.

But we must consider that only way how to make synchronization process efficient always involves holding some state on server (be it e.g. revision control).

ad) AeroGear Data Sync == Differential Synchronization?

DiffSync is not only solution we are working on, it is just the most advanced one.
We have started its prototype early to give it a chance to settle down (which may also mean figuring out it is totally inappropriate for any use case we can about and abandoning the idea).

We are considering several approaches as outlined in the Data Sync module roadmap from Dan [1]:

1. offline storage (we alreave have that) + conflict detection + conflict resolution
2. realtime updates
3. realtime synchronization

It seems that most of the concerns you've mentioned are covered by (1) and that you highlight the importance to get the APIs we are working on in (1) right.
That's right and that's exactly the feedback we need at this stage!

Solution (2) is extension to (1) that makes sure that clients are updated periodically and so that conflicts happen less often.

Solution (3) is extension to (1) and (2) that allows framework to implement efficient synchronization and realtime collaboration.

While DiffSync makes conflicts less often, there will always be cases when API for (1) will matter a lot.
The API for conflict resolution will be a crucial part, especially considering UI feedback when conflict need to be resolved by an app user himself.

ad) Relevance for various use cases

I completely agree that Differential Synchronization seems a most valid in a case of collaboration on bigger documents.
(Note: that's exactly how, lets say Firebase works - you have a huge unstructured document that all of the connected parties can freely modify, while rest of the clients are notified about changes in realtime)

DiffSync gives us perfect case for studying a JSON patching strategy that may be base for similar, but better scalable solutions.

We have to balance between several concerns Data Sync is connected with (not necessarily complete list):

1. efficiency of synchronization
2. statefulness / scalability
3. first sync after longer period of disconnection (aka batches)
4. prioritization of sync after reconnect (aka slow network connection)
5. simplicity of adoption

DiffSync ensures (1) and (3). Slow sync (simple conflict detection + resolution) ensures (2) out of the box.

The huge variable part here is (5). I'm very interested in (5).

To satisfy all these needs we may build several different, handshake / data-exchange protocols that will make sure all the use cases are covered,
and it may be a hybrid approach that will make AeroGear Data Sync relevant in all cases. One protocol can be fallback to another.

Such a hybrid solution depends on the application developer what approach he will choose to take (it can be even multiple approaches in one app).
We will leave a space for customization by configuration (and plugins).
E.g. let's configure how long the document synced with DiffSync (say JSON array of company employees) will be held on the server before it will expire and you fallback to Slow Sync (solution (1) on the roadmap).

Thanks for the feedback!


~ Lukas


On Fri, Sep 5, 2014 at 8:32 PM, Randall Hauch <> wrote:
This does sounds quite interesting, Lukáš. If AeroGear uses Differential Synchronization, this approach might make it more efficient for the server to implement the behavior.

But I’m still bothered by the thought that AeroGear’s Data Sync feature uses any algorithm that requires the server to maintain client-specific state. 

First of all, servers that maintain no client state across requests will *always* scale much better than servers that maintain client-specific state across requests. Maintaining client-specific state requires extra work, consumes more memory, and complicates clustering, failover, and fault tolerance.

Secondly, if a server is maintaining client-specific state, how long does that client state have to be maintained before assuming the client is no longer there? What happens when a client on a mobile device loses its connection for a short period of time and then reconnects? What if load balancing cause the client to reconnect to a different process in the cluster? Bottom line is that it adds quite a bit of complexity.

Finally, I completely understand the benefits of using Differential Synchronization where many clients are collaborating on a single document. That’s the “collaborative editor” scenario, and surely there are apps that need this functionality. I’m just not convinced that this is the most common use case. In fact, I think it’s relatively uncommon, and I don’t think LiveOak will support it in the near term. (BTW, please tell me if the whole purpose of AeroGear's Data Sync feature is to satisfy this and only this scenario. If so, then I apologize for being a distraction.)

My understanding of the Data Sync feature, though, is that it is should be applicable to other scenarios. IMO, the far more common scenario is:

  • The server manages (many) millions of small entities, each of which is a domain-specific aggregate JSON document. Examples of different kinds of entities include: customers, books, insurance claims, catalog items, restaurants, purchases, tasks, etc. A single customer entity would aggregate most/all the information about the customer, including the name, phone numbers, addresses, profile information, domain-specific privileges (e.g., authorized to log issues or call support), etc. Note that these aggregate entities do not correspond to the entities in a traditional RDBMS/JPA/Hibernate app, where you’d have much finer-grained records (separate tables for customer, customer addresses, customer phone numbers, customer privileges, etc.).
  • At any point in time there may be 1Ks or 10Ks of clients reading dozens of documents/entities and sending changes to some of them (likely in batch operations). IOW, any one document might be concurrently modified by a relatively small number of clients.

I think it’s possible to support Data Sync in this scenario while maintaining no client-specific state on the server (other than while processing a single request).

The Data Sync functionality of the SDK used in the clients would work with local persistence and enable the app to function online or offline. This includes making it possible to edit entities and create new entities while offline, though it does not matter to Data Sync which subset of entities is persisted locally. If the client goes offline and then back online, the Data Sync functionality in the SDK would figure out which of the local entities the client has changed, and request from the server the latest revisions for each of them. Data Sync then merges the local changes onto the latest revisions (perhaps asking the user to manually resolve anything that cannot be automatically resolved), computes the new effective changes, and then sends a single batch request to the server to apply them. Some, all or none changes are accepted, and likely the application then has to decide whether to continue the process again, revert to the server’s versions, etc.  (The SDK would also make use of subscriptions/notifications so that it can be told immediately when entities are changed.)

What the batch operations entail is dependent upon the needs of the client app and capabilities of the server. If a server supports batch operations that contain the entire entity, then the server will almost certainly need to put revision identifiers in each entity sent to the client, so that when the client sends back a modified representation entity, the server can tell whether that document has since been modified by another client. If so, the server might rely upon client preferences (see below) to know whether to merge the changes (using a simple algorithm or more useful application-specific logic), overwrite, or fail to update the entity.

On the other hand, the server might support batch operations with partial updates, where the client only sends for each entity only the set of fields that are to be changed or removed. Conflicts and merging with this approach are easier and less-likely to be application specific, though client preferences (see below) might still dictate whether or not to merge based upon revision numbers. It also might make it easier for Data Sync, since the SDK simply has to record for each modified entity only those modified fields (no matter how long the client was offline). To keep the SDK simple, a best practice might be to ensure clients always modify together those fields that must be consistent. For example, in a contacts application, if a user modifies the first name of a contact, the app might tell the SDK to modify the first name and last name fields, even though the user didn’t modify the last name. When synchronized, both the first name and last name fields would be sent to the server as a single update. This helps ensure that even when multiple clients are concurrently updating the same set of fields to different values, the result of applying all of those changes will exactly match the state as set by one of the clients.

It’s also likely that for batch operations to work well for many different kinds of applications, the server may support multiple policies that specify for a given batch operation the atomicity, consistency, and isolation guarantees. One policy might ensure that the entire batch either completely succeeds only when there are no revision conflicts, otherwise it completely fails. Another policy might be eventually-consistent in the sense that the changes in every batch operations will eventually be applied, though this may require the server to have application-specific conflict resolution logic. And there are policies that are somewhere in-between. For example, imagine a server that supports “public” collections whose entities can be read/updated by any user, and “private” collections that expose only the entities that are readable and editable by that user. The likelihood of a concurrent update conflict on a private entity is quite small, since it’s possible only when different changes made on different devices are submitted at exactly the same time. On the other hand, the likelihood of a concurrent update conflict on a public entity is much higher. The server may allow updates to both public and private entities within a single batch operation, and a save policy that might update all private entities atomically (they all succeed or they all fail) while updates to *each* public entity is atomic (some might succeed while others might fail).

Any given server will likely support only a few of these policies. But either way, the server has to report back to the client the outcome of the batch request: which entities were updated, which were not, and the reason why each of the rejected updates failed (because other entity changes were rejected, because of a conflict, etc.). The SDK’s Data Sync functionality would use those results to know how to update the local persisted representations, and to start trying to resolve any failures.

The kinds of requests needed to support data sync in this scenario are fairly basic, so the server doesn’t have to be that complicated. Best of all, the server is not required to maintain any client-specific state.

Thoughts? Am I way off-base?

Best regards,


On Sep 4, 2014, at 10:38 AM, Lukáš Fryč <> wrote:

Hey guys,

TL;DR: there are ways how to make the memory footprint of Differential Synchronization (DS) very low; if we assume that JSON patches are reversible and we create cumulative patches from series of individual patches, we can use (smart, garbage-collactable) revision control for storage of documents that server need to maintain.


I had an idea in my head that for couple of days as I was becoming familiar with how Differential Synchronization (DS) works and studying Dan's and Luke's impl.

I share the concern that Randall expressed in earlier thread - the DS in its pure version doesn't scale for huge amount of users when connected to one server.

Sure, the algorithm can be scaled trivially by adding new nodes that uses very same algorithm as is used for client<->server. But that doesn't mean it is something that we should do regularly to get more memory available.

Instead, I was thinking about limiting a memory that server needs to maintain in any point in time.

In pure DS, server have to maintain all copies of documents that clients ever requested. Even worse, it has to maintain two copies - "shadow" and "backup",

So, in worst case, 2 x N copies of document will be maintained by the server for N clients.


The DS algorithm doesn't tell us how the "shadow" and "backup" documents should be stored.

We can transparently plug in a storage that will be clever about how to use "backups" and "shadow".




In order to limit a number of documents we need to maintain in server's memory, we can come with a system where just last known state is remembered in full version. Together with last known version, we remember also history of the document for each revision as it was patched by clients.

But server doesn't have to maintain full history of the document, it maintains just revisions that are known to its clients (that revision is known by DS for each client out of the box).


Server trivially knows what versions are mantained by client, so it can get rid of those revisions that are no longer needed.

First, it can cut all the history that is older than the oldest version of document throughout all its clients.

Secondly, it can accumulate the serious of patches between states so that it doesn't have to maintain full history, but just revisions that is known to any of those clients.



I've attached a picture for illustration of the revision control / garbage collection, it does not illustrate algorithm itself (Dan did awesome job describing DS here [3]).

In the attached diagram, you can see four clients with different revisions:

Client 1: revision X
Client 2: revision X+3
Client 3: revision X+3
Client 4: revision X+4

If any of the clients reaches server, server can reconstruct the document (shadow and backup) for given client revision and the DS algorithm then can operate as normally (that's why it is transparent from algorithmic point of view).

If server comes to garbage collection, it can scan through available client revisions. As it identifies, that the oldest known revision is X, it can remove patch for X-1 and less as they are no longer needed.

Later, it can even more optimize, and accumulate patches so that the only information he knows is how to get to the revision for each participating client. In the picture, he can compute cumulative patch for getting from state X to state X+3, as X+1 and X+2 themselves are not needed.

(Patch accumulation is actually something that Google Wave does in its Operational Transformation implementation, just there it is called cumulative Operations).


Then it comes to caching strategies, you can maintain caches of things like last recently used revisions (they don't need to be computed each time). You can store some documents and their revision history to the disk (fully or partially), etc.



1000 employees maintain one document - Contact List - on their smartphones

400 of those employees has mobile data plan, so they want to synchronize almost immediately as they are notified about changes. The rest of the devices is connected rather sparely.

If one employee changes data, 600 devices are notified about the change immediately (some on data plan, some through Wifi, etc.) Those clients all have version say X+3, because they are synchronized proactively.

These clients reach the server in say <10 seconds after that employee did the change.

First client reaches server and ask for version X+3 (it probably is still in the memory, but if it isn't, it is deconstructed and placed into MRU cache). All of the others reaches the server and re-synchronize themselves against the one copy of backup/shadow that is in the memory already.

The other clients will reach the server later, as they are connecting to Wifi, say in 5 minutes to 24 hours. They will have even older versions, such as X. Those clients will require more computation, but at the end the server can resolve them and through their revisions out of the window.

At the end, there are clients that didn't connected for days, maybe months - I suggest we don't actually use DS here and fallback to e.g. slow synchronization (show me what you have, I will show you mine), because maintaining full history for longer period of days may be too resource demanding (configurable?).

Obviously, 1000 is not much, but this can scale to pretty decent numbers if we use hybrid approach (fallback to other sync strategies if revision is already forgotten by a server).



we still have to maintain all the document revisions, but practically it won't happen :-)



1. patches are reversible (we are able to compute reverse patch for each patch sent by client)
2. patches are cumulative (we are able to compute aggregated patch from series of patches)

For 1, it seems many of JSON-patch libraries actually implement it (Java, JavaScript) [1, 2]

For 2, it again seems some libraries implement it and if not, we can implement it or even use brute-force - compute a patch between two documents


It's not the only way how to make it scale, but it does solve the problem pretty independently of the DS algorithm and still leaves the algorithm clean and simple.


~ Lukas

aerogear-dev mailing list

aerogear-dev mailing list