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,Randall<DS-revision-control.png>_______________________________________________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".-----CONCEPTS:
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