we're using react-admin with a jsonServerRestClient from ra-data-json-server. We've encapsulated it to handle GET_MANY a little differently, so we already have a touchpoint there to control what goes to the server.
Going forward we would like to be able to send a delta of changes when an item is modified, as some items (in our case, groups) have 4k+ members in them.
While we could save the raw objects to local storage when they come into our REST client and use that info to create a delta upon save, the state and reducers should have that info already somewhere else, right?
So what files should I look at to see how to modify what gets sent to the REST client during modify events?
Figured it out, React-Admin actually is nice enough to give us the previous version of whatever data it wants to save. When you're writing a restClient you will be given type, resource and params. Inside params is both params.data and params.previousData. You can do your deltas there, comparing both.
I'm developing a plugin that will pull data from a third party API. The user user inputs a number of options in a normal settings form for the plugin (used Reduz Framework - that uses WP Settings API).
The user provided options will then be used to generate a request to the third party API.
Now to my problem / question: How can I store the data that's returned from that API? Is there a built in way to do this in Wordpress - or will I have to install a database table of my own? Seems to be a bit overkill... Is there any way to "hack" in to the Settings API and set custom settings without having to display them in a form on front end?
Thank you - and happy holidays to everyone!
It sounds like what you want to do is actually just store the data from the remote API request, rather than "options". If you don't want to create a table for them, I can think of three simple approaches.
Transients API
Save the data returned from the API as transients, i.e. temporary cached data. This is generally good for data that's going to expire anyway and thus will need to be refreshed. Set an expiry time! Even if you want to hang onto the data "for ever", set an expiry time or the data will be autoloaded on every page load and thus consume memory even if you don't need them. You can then easily retrieve them with get_transient; if expired, you'll get false and that is your trigger to make your API call again.
NB: on hosts with memcached or other object caches, there's a good chance that your transients will be pushed out of the object cache sooner than you intend, thus forcing your plugin to retrieve the data again from the API. Transients really are about caching, not "data storage" per se.
Options
Save your data as custom options using add_option -- and specify autoload="no" so that they don't fill up script memory when they aren't needed! Beware the update_option will add the data with autoload="yes" if it doesn't already exist, so I recommend you delete and then add rather than update. You can then retrieve your data easily.
Custom Post Type
You can easily store your data in the wp_posts table by registering a custom post type, and then you can use wp_insert to save them and the usual WordPress post queries to retrieve them. Great for long-term data that you want to hang onto. You can make use of the post_title, post_content, post_excerpt and other standard post fields to store some of your data, and if you need more, you can add post meta fields.
I have a Web Service and sqlite database. In this, web service will be used to store data inside database. Now I want to include sync functionality as - Whenever application starts at that time the database will start to load its table's data through web service.
Now after some time when I update my my web service the database will be updated accordingly. My question is that what are the best practices that I must follow for this update. Should I clear whole DB and start adding all rows again(I know this will take a lot time) but If not this then how do my database will add only particular data from the web service?
Thank you.
What I suggest you is:
store all your webservice content into db first when the app starts.
display your content on the screen from db only.
again when you need to refresh or recall your data just update the database.
Thus, you will always find all your fresh data into database.
Downloading and updating the entire server data will prove expensive. It will use more bandwidth and prove costly to your customer. Rather than pushing the entire load (even for minor update), send a delta. I will suggest you to maintain version information.
When application downloads the data from web service for a said version and store it successfully in the database, set the current updated version as well in the DB.
When app starts the next time, make a light weight header request to get just the version info from the server. The server should respond to this header request with the latest data version number.
Check the version from WS with the current application data version stored in the DB. If the server has an updated version, start the sync.
The version change information should be delta i.e.
For new version, server should send only the information that is changed since the version available with the device.
You server should have capability to calculate the delta between two versions.
Delta information will typically have sections like, new data, updated data, deleted data etc.
Based on this, the iOS app will make the necessary CRUD(Create, Read, Update and Delete) operations on the DB data.
Once the iOS app updates itself, then you can update the DB version to the latest received version from server. Until then let it remain dirty for proper error handling.
Hope that helps.
I would recommend you use RestKit's superb Core Data support.
By using RKEntityMapping you can map your remote objects from JSON or XML directly to Core Data entities in your database.
RestKit will automatically maintain the database for you, inserting and updating entries as appropriate from your web service. (In my experience, I've found deleting objects requires a tiny bit of extra work depending on how RESTful your web service is).
RestKit definitely does have a learning curve attached, but it's well worth it: having deployed it a couple of times now, is definitely a much better solution than manually writing your own SQLite/Web Service syncing code.
First you need to set all webservice content into your SQLITE.and what you want to display get that data from SQLITE.and perform opertaion into that sqlite table and when once all this done you need to changes made are saved it into webservice.
Follow this way.
I am very new to backbone.js (and MVC with javascript), and while reading several resources about backbone.js to adopt it in my project, I now have a question: how can I detect when multiple users(browsers) attempt to update? (and prevent it?)
My project is a tool for editing surveys/polls for users who want to create and distribute their own surveys. So far, my web app maintains a list of edit-commands fired by browser, sends it to the server, and the server does batch update.
What I did was, each survey maintains a version number and browser must request update with that version number, and if the request's version number does not match with the one in the server, the request fails and the user must reload his page (you know, implementing concurrent editing is not easy for everyone). Of course, when the browser's update was successful, it gets new version number from the server as ajax response, and one browser can request update to server only when its past update request is done.
Now, I am interested in RESTful APIs and MV* patterns, but having a hard time to solve this issue. What is the best / common approach for this?
There is a common trick instead of using versions, use TIMESTAMPS in your DB and then try to UPDATE WHERE timestamp = model.timestamp. If it returns zero result count - use appropriate HTTP 409 (conflict) response and ask the user to update the page in save() error callback. You can even use the local storage to merge changes, and compare the non-equivalent side by side.
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I have been working on a method to sync core data stored in an iPhone application between multiple devices, such as an iPad or a Mac. There are not many (if any at all) sync frameworks for use with Core Data on iOS. However, I have been thinking about the following concept:
A change is made to the local core data store, and the change is saved. (a) If the device is online, it tries to send the changeset to the server, including the device ID of the device which sent the changeset. (b) If the changeset does not reach the server, or if the device is not online, the app will add the change set to a queue to send when it does come online.
The server, sitting in the cloud, merges the specific change sets it receives with its master database.
After a change set (or a queue of change sets) is merged on the cloud server, the server pushes all of those change sets to the other devices registered with the server using some sort of polling system. (I thought to use Apple's Push services, but apparently according to the comments this is not a workable system.)
Is there anything fancy that I need to be thinking about? I have looked at REST frameworks such as ObjectiveResource, Core Resource, and RestfulCoreData. Of course, these are all working with Ruby on Rails, which I am not tied to, but it's a place to start. The main requirements I have for my solution are:
Any changes should be sent in the background without pausing the main thread.
It should use as little bandwidth as possible.
I have thought about a number of the challenges:
Making sure that the object IDs for the different data stores on different devices are attached on the server. That is to say, I will have a table of object IDs and device IDs, which are tied via a reference to the object stored in the database. I will have a record (DatabaseId [unique to this table], ObjectId [unique to the item in the whole database], Datafield1, Datafield2), the ObjectId field will reference another table, AllObjects: (ObjectId, DeviceId, DeviceObjectId). Then, when the device pushes up a change set, it will pass along the device Id and the objectId from the core data object in the local data store. Then my cloud server will check against the objectId and device Id in the AllObjects table, and find the record to change in the initial table.
All changes should be timestamped, so that they can be merged.
The device will have to poll the server, without using up too much battery.
The local devices will also need to update anything held in memory if/when changes are received from the server.
Is there anything else I am missing here? What kinds of frameworks should I look at to make this possible?
I've done something similar to what you're trying to do. Let me tell you what I've learned and how I did it.
I assume you have a one-to-one relationship between your Core Data object and the model (or db schema) on the server. You simply want to keep the server contents in sync with the clients, but clients can also modify and add data. If I got that right, then keep reading.
I added four fields to assist with synchronization:
sync_status - Add this field to your core data model only. It's used by the app to determine if you have a pending change on the item. I use the following codes: 0 means no changes, 1 means it's queued to be synchronized to the server, and 2 means it's a temporary object and can be purged.
is_deleted - Add this to the server and core data model. Delete event shouldn't actually delete a row from the database or from your client model because it leaves you with nothing to synchronize back. By having this simple boolean flag, you can set is_deleted to 1, synchronize it, and everyone will be happy. You must also modify the code on the server and client to query non deleted items with "is_deleted=0".
last_modified - Add this to the server and core data model. This field should automatically be updated with the current date and time by the server whenever anything changes on that record. It should never be modified by the client.
guid - Add a globally unique id (see http://en.wikipedia.org/wiki/Globally_unique_identifier) field to the server and core data model. This field becomes the primary key and becomes important when creating new records on the client. Normally your primary key is an incrementing integer on the server, but we have to keep in mind that content could be created offline and synchronized later. The GUID allows us to create a key while being offline.
On the client, add code to set sync_status to 1 on your model object whenever something changes and needs to be synchronized to the server. New model objects must generate a GUID.
Synchronization is a single request. The request contains:
The MAX last_modified time stamp of your model objects. This tells the server you only want changes after this time stamp.
A JSON array containing all items with sync_status=1.
The server gets the request and does this:
It takes the contents from the JSON array and modifies or adds the records it contains. The last_modified field is automatically updated.
The server returns a JSON array containing all objects with a last_modified time stamp greater than the time stamp sent in the request. This will include the objects it just received, which serves as an acknowledgment that the record was successfully synchronized to the server.
The app receives the response and does this:
It takes the contents from the JSON array and modifies or adds the records it contains. Each record get set a sync_status of 0.
I used the word record and model interchangeably, but I think you get the idea.
I suggest carefully reading and implementing the sync strategy discussed by Dan Grover at iPhone 2009 conference, available here as a pdf document.
This is a viable solution and is not that difficult to implement (Dan implemented this in several of its applications), overlapping the solution described by Chris. For an in-depth, theoretical discussion of syncing, see the paper from Russ Cox (MIT) and William Josephson (Princeton):
File Synchronization with Vector Time Pairs
which applies equally well to core data with some obvious modifications. This provides an overall much more robust and reliable sync strategy, but requires more effort to be implemented correctly.
EDIT:
It seems that the Grover's pdf file is no longer available (broken link, March 2015). UPDATE: the link is available through the Way Back Machine here
The Objective-C framework called ZSync and developed by Marcus Zarra has been deprecated, given that iCloud finally seems to support correct core data synchronization.
If you are still looking for a way to go, look into the Couchbase mobile. This basically does all you want. (http://www.couchbase.com/nosql-databases/couchbase-mobile)
Similar like #Cris I've implemented class for synchronization between client and server and solved all known problems so far (send/receive data to/from server, merge conflicts based on timestamps, removed duplicate entries in unreliable network conditions, synchronize nested data and files etc .. )
You just tell the class which entity and which columns should it sync and where is your server.
M3Synchronization * syncEntity = [[M3Synchronization alloc] initForClass: #"Car"
andContext: context
andServerUrl: kWebsiteUrl
andServerReceiverScriptName: kServerReceiverScript
andServerFetcherScriptName: kServerFetcherScript
ansSyncedTableFields:#[#"licenceNumber", #"manufacturer", #"model"]
andUniqueTableFields:#[#"licenceNumber"]];
syncEntity.delegate = self; // delegate should implement onComplete and onError methods
syncEntity.additionalPostParamsDictionary = ... // add some POST params to authenticate current user
[syncEntity sync];
You can find source, working example and more instructions here: github.com/knagode/M3Synchronization.
Notice user to update data via push notification.
Use a background thread in the app to check the local data and the data on the cloud server,while change happens on server,change the local data,vice versa.
So I think the most difficult part is to estimate data in which side is invalidate.
Hope this can help u
I have just posted the first version of my new Core Data Cloud Syncing API, known as SynCloud.
SynCloud has a lot of differences with iCloud because it allows for Multi-user sync interface. It is also different from other syncing api's because it allows for multi-table, relational data.
Please find out more at http://www.syncloudapi.com
Build with iOS 6 SDK, it is very up to date as of 9/27/2012.
I think a good solution to the GUID issue is "distributed ID system". I'm not sure what the correct term is, but I think that's what MS SQL server docs used to call it (SQL uses/used this method for distributed/sync'ed databases). It's pretty simple:
The server assigns all IDs. Each time a sync is done, the first thing that is checked are "How many IDs do I have left on this client?" If the client is running low, it asks the server for a new block of IDs. The client then uses IDs in that range for new records. This works great for most needs, if you can assign a block large enough that it should "never" run out before the next sync, but not so large that the server runs out over time. If the client ever does run out, the handling can be pretty simple, just tell the user "sorry you cannot add more items until you sync"... if they are adding that many items, shouldn't they sync to avoid stale data issues anyway?
I think this is superior to using random GUIDs because random GUIDs are not 100% safe, and usually need to be much longer than a standard ID (128-bits vs 32-bits). You usually have indexes by ID and often keep ID numbers in memory, so it is important to keep them small.
Didn't really want to post as answer, but I don't know that anyone would see as a comment, and I think it's important to this topic and not included in other answers.
First you should rethink how many data, tables and relations you will have. In my solution I’ve implemented syncing through Dropbox files. I observe changes in main MOC and save these data to files (each row is saved as gzipped json). If there is an internet connection working, I check if there are any changes on Dropbox (Dropbox gives me delta changes), download them and merge (latest wins), and finally put changed files. Before sync I put lock file on Dropbox to prevent other clients syncing incomplete data. When downloading changes it’s safe that only partial data is downloaded (eg lost internet connection). When downloading is finished (fully or partial) it starts to load files into Core Data. When there are unresolved relations (not all files are downloaded) it stops loading files and tries to finish downloading later. Relations are stored only as GUID, so I can easly check which files to load to have full data integrity.
Syncing is starting after changes to core data are made. If there are no changes, than it checks for changes on Dropbox every few minutes and on app startup. Additionaly when changes are sent to server I send a broadcast to other devices to inform them about changes, so they can sync faster.
Each synced entity has GUID property (guid is used also as a filename for exchange files). I have also Sync database where I store Dropbox revision of each file (I can compare it when Dropbox delta resets it’s state). Files also contain entity name, state (deleted/not deleted), guid (same as filename), database revision (to detect data migrations or to avoid syncing with never app versions) and of course the data (if row is not deleted).
This solution is working for thousands of files and about 30 entities. Instead of Dropbox I could use key/value store as REST web service which I want to do later, but have no time for this :) For now, in my opinion, my solution is more reliable than iCloud and, which is very important, I have full control on how it’s working (mainly because it’s my own code).
Another solution is to save MOC changes as transactions - there will be much less files exchanged with server, but it’s harder to do initial load in proper order into empty core data. iCloud is working this way, and also other syncing solutions have similar approach, eg TICoreDataSync.
--
UPDATE
After a while, I migrated to Ensembles - I recommend this solution over reinventing the wheel.