I made a code this summer holidays and today I look for the first time at my code again, and I am strugging on one thing I did.
My system is a system with multiple types (pages, newsletters etc.) and multiple subtypes (items, archive, concepts etc.). The idea now I have an object like this:
object { 1: { normal: { 1: { content: 'somecontent', title: 'sometitle' } } } }
Another example:
object { 1: { normal: { 1: { content: 'somecontent', title: 'sometitle' } }, archive: {} }, 2: { normal: {} } }
The data originally comes from the database. I'm making a system to edit pages on the website and other things like newsletters. Because I have multiple types and subtypes.
I made a cache for the reason I don't want to get all items from the database every time. But now the problem is if I add an item, edit an item and remove an item I have to delete it from the cache / edit / add.
My question: is this a good way? I thought it is because you don't have to call an AJAX file to get the data from the database.
I'm sorry if I'm not allowed to ask this here.
My question: is this a good way? I thought it is because you don't
have to call an AJAX file to get the data from the database.
The answer is that "it depends". There is no always right and always wrong answer for caching because caching is a tradeoff between efficiency and timeliness of data.
If you want maximum efficiency, you cache like crazy, but your data may not be perfectly up to date because you're using old data from the cache.
If you want the most up-to-date data, you don't cache anything so you always get the latest data, but obviously efficiency may suffer if you are regular requesting the same data over and over.
So, it's a tradeoff and the tradeoff depends entirely upon the application, its needs, how often the data is modified and what the consequences are for having stale data or for not caching. There is no single right or wrong answer for that tradeoff. It depends entirely upon the particular situation for your application and the tradeoff may even be different for some types of data vs. others within the same application.
For example, let's supposed you were writing an online bidding site that offered some functionality like eBay. You would probably be fine caching the item description for at least several hours because that almost never changes and even if it does, the consequences of being a bit tardy on seeing a new item description are fairly low. But, you could never cache the data on the current bid because the timeliness of that information is critical. The user needs to always see the latest info on the current bid, even if you have to make some sacrifices in efficiency.
Also, remember that caching isn't completely all or none. You can set a lifetime for a cached value such that it can only be used for a certain period of time that is appropriate for the type of data. For example, you might cache an item description in the above auction for up to 2 hours. This allows you to achieve some efficiency gains, but also to eventually see the new data if it happens to change.
In general, you have to review the consequences of showing stale data. If the consequences for having data that is even minutes out of date are high (like the latest price in a live auction), then you can't cache that data at all.
If the consequences of having data that is even hours out of date are low, then you can likely cache that value for at least several hours - maybe even longer.
And, when considering what to cache, you obviously want to first look at the items that are most requested and are the most expensive on your server to retrieve. Some analysis of the usage pattern on your server would give you a prioritized list of candidates to consider for caching.
My question: is this a good way? I thought it is because you don't
have to call an AJAX file to get the data from the database.
This is fine if
1) You want to provide offline reading continuity to the user. User doesn't have to wait for internet connection to be available so that they can read at any time.
2) Your data-service is quite heavy and you want to avoid multiple/frequent visits to the server to get the same data over and over again.
3) You want your app to be bundled with a native package (like phonegap) to become a hybrid app and give a complete offline experience to the user.
This is not a comprehensive list, but just to get your started in terms of when to go for offline and when to keep totally offline
So, on the other hand, this is a bad idea if
1) Your local storage structure is going to change frequently for user to require re-install (unless you can figure out auto-upgrate of local storage)
2) All your features are transactional and require synch with other users also.
Nothing wrong with your approach, just make sure you have kept these points in mind while managing client-side cache
You have one variable 'version' maintained, this version is to be increased whenever there's any change in structure, this version will be sent to client every time, client is responsible for comparison of versions and empty client cache if server version is greater than client version.
You can implement or find any open-sources to handle your ajax responses, this one might be useful - https://github.com/SaneMethod/jquery-ajax-localstorage-cache.
you can set proper expiry tag from server, which can also help, browser to cache response for you, if it is 'get' request.
You can also implement server-side cache, which will not make calls to database, it will cache response against request-url, Note - if different users are supposed to receive different response than this approach wont work. You can delete the cache if any changes happens related to that particular data set - delete/update
In your case you can also maintain flags on server, which simply tells if data has been updated or not the time of article update, if stored version is older you can make server-request or just use local version.
I hope it helps.
Related
In the design stage for an app that collects large amounts of data...
Ideally, I want it to be an offline-first app and was looking to Pouchdb/Counchdb - However, the data needs to be kept for years for legal reasons, and my concern is that this is going to consume too much local storage over time.
My thoughts were:
handle sync between pouchdb and couchdb myself, allowing me to purge inactive documents from the local store without impacting the couchdb. This feels messy and probably a lot of work
Build a local store using dexie.js and completely write the sync function. It also looks hard work, but may be less as I'm not trying to mess with a sync function
Search harder :)
Conceptually, I guess I'm looking for a 'DB cache' - holding active json document versions and removing documents that have not been touched for X period. It might be that 'offline' mode is handled separate to the DB cache..
Not sure yet if this is the correct answer..
setup a filter on couchdb to screen out old documents (lets say we have a 'date_modified' field in the doc and we filter out any docs with date_modified older than one month)
have a local routine on the client that deletes documents from the local pouchdb that are older than one month ( actually using the remove() method against the local pouchdb, not updating it with _deleted:true) - from https://pouchdb.com/2015/04/05/filtered-replication.html it appears removed documents don't sync.
docs updated on the Pouchdb will replicate normally
there might be a race condition here for replication, we'll see
Are there any patterns for cases when something (in my case it's a filters) is stored on client (e.g. localStorage) and you need to run a script once per user/version to migrate data you store. For example, initially there is a filter saved in localStorage with a key myFilter after some time you decide that you need to separate filters per environment, so you need separate dev-myFilter, train-myFilter, etc. You update your code to work with environment-dependant filters, but there are users who have old myFilter and you want with next deployed version to run script which will update the key of saved filter if there is one.
Question is - what are patterns/best practices for that?
I don't know about "best practices", but the obvious technical solution, just like with any API or storage format, is to store a version number alongside the data. If you didn't do so from the start, assume version == 1 when absent.
You may be able to avoid this if the data structure is so unique between versions that the version can be determined simply by examining it.
Either way, you simply perform the translation whenever you spot that the user's data is in the old format.
The downside of this is that you have to keep checking; for a web application this is unlikely to be a bottleneck, but if you can make your data forward-compatible from the outset then you may save a bit of processing time on each request. But for the data to be useful you've got to read it anyway, so a little branching for as long as you wish to maintain backward-compatibility is, again, unlikely to be a big problem.
We are investigating using Breeze for field deployment of some tools. The scenario is this -- an auditor will visit sites in the field, where most of the time there will be no -- or very degraded -- internet access. Rather than replicate our SQL database on all the laptops and tablets (if that's even possible), we are hoping to use Breeze to cache the data and then store it locally so it is accessible when there is not a usable connection.
Unfortunately, Breeze seems to choke when caching any significant amount of data. Generally on Chrome it's somewhere between 8 and 13MB worth of entities (as measured by the HTTPResponse headers). This can change a bit depending on how many tabs I have open and such, but I have not been able to move that more than 10%. the error I get is the Chrome tab crashes and tells me to reload. The error is replicable (I download the data in 100K chunks and it fails on the same read every time and works fine if I stop it after the previous read) When I change the page size, it always fails within the same range.
Is this a limitation of Breeze, or Chrome? Or windows? I tried it on Firefox, and it handles even less data before the whole browser crashes. IE fares a little better, but none of them do great.
Looking at performance in task manager, I get the following:
IE goes from 250M memory usage to 1.7G of memory usage during the caching process and caches a total of about 14MB before throwing an out-of-memory error.
Chrome goes from 206B memory usage to about 850M while caching a total of around 9MB
Firefox goes from around 400M to about 750M and manages to cache about 5MB before the whole program crashes.
I can calculate how much will be downloaded with any selection criteria, but I cannot find a way to calculate how much data can be handled by any specific browser instance. This makes using Breeze for offline auditing close to useless.
Has anyone else tackled this problem yet? What are the best approaches to handling something like this. I've thought of several things, but none of them are ideal. Any ideas would be appreciated.
ADDED At Steve Schmitt's request:
Here are some helpful links:
Metadata
Entity Diagram (pdf) (and html and edmx)
The first query, just to populate the tags on the page runs quickly and downloads minimal data:
var query = breeze.EntityQuery
.from("Countries")
.orderBy("Name")
.expand("Regions.Districts.Seasons, Regions.Districts.Sites");
Once the user has select the Sites s/he wishes to cache, the following two queries are kicked off (used to be one query, but I broke it into two hoping it would be less of a burden on resources -- it didn't help). The first query (usually 2-3K entities and about 2MB) runs as expected. Some combination of the predicates listed are used to filter the data.
var qry = breeze.EntityQuery
.from("SeasonClients")
.expand("Client,Group.Site,Season,VSeasonClientCredit")
.orderBy("DistrictId,SeasonId,GroupId,ClientId")
var p = breeze.Predicate("District.Region.CountryId", "==", CountryId);
var p1 = breeze.Predicate("SeasonId", "==", SeasonId);
var p2 = breeze.Predicate("DistrictId", "==", DistrictId);
var p3 = breeze.Predicate("Group.Site.SiteId", "in", SiteIds);
After the first query runs, the second query (below) runs (also using some combination of the predicates listed to filter the data. At about 9MB, it will have about 50K rows to download). When the total download burden between the two queries is between 10MB and 13MB, browsers will crash.
var qry = breeze.EntityQuery
.from("Repayments")
.orderBy('SeasonId,ClientId,RepaymentDate');
var p1 = breeze.Predicate("District.Region.CountryId", "==", CountryId);
var p2 = breeze.Predicate("SeasonId", "==", SeasonId);
var p3 = breeze.Predicate("DistrictId", "==", DistrictId);
var p4 = breeze.Predicate("SiteId", "in", SiteIds);
Thanks for the interest, Steve. You should know that the Entity Relationships are inherited and currently in production supporting the majority of the organization's operations, so as few changes as possible to that would be best. Also, the hope is to grow this from a reporting application to one with which data entry can be done in the field (so, as I understand it, using projections to limit the data wouldn't work).
Thanks for the interest, and let me know if there is anything else you need.
Here are some suggestions based on my experience building on an offline capable web application using breeze. Some or all of these might not make sense for your use cases...
Identify which entity types need to be editable vs which are used to fill drop-downs etc. Load non-editable data using the noTracking query option and cache them in localStorage yourself using JSON.stringify. This avoids the overhead of coercing the data into entities, change tracking, etc. Good candidates for this approach in your model might be entity types like Country, Region, District, Site, etc.
If possible, provide a facility in your application for users to identify which records they want to "take offline". This way you don't need to load and cache everything, which can get quite expensive depending on the number of relationships, entities, properties, etc.
In conjunction with suggestion #2, avoid loading all the editable data at once and avoid using the same EntityManager instance to load each set of data. For example, if the Client entity is something that needs to be editable out in the field without a connection, create a new EntityManager, load a single client (expanding any children that also need to be editable) and cache this data separately from other clients.
Cache the breeze metadata once. When calling exportEntities the includeMetadata argument should be false. More info on this here.
To create new EntityManager instances make use of the createEmptyCopy method.
EDIT:
I want to respond to this comment:
Say I have a client who has bills and payments. That client is in a
group, in a site, in a region, in a country. Are you saying that the
client, payment, and bill information might each have their own EM,
while the location hierarchy might be in a 4th EM with no-tracking?
Then when I refer to them, I wire up the relationships as needed using
LINQs on the different EMs (give me all the bills for customer A, give
me all the payments for customer A)?
It's a bit of a judgement call in terms of deciding how to separate things out. Some of what I'm suggesting might be overkill, it really depends on the amount of data and the way your application is used.
Assuming you don't need to edit groups, sites, regions and countries while offline, the first thing I'd do would be to load the list of groups using the noTracking option and cache them in localStorage for offline use. Then do the same for sites, regions and countries. Keep in mind, entities loaded with the noTracking option aren't cached in the entity manager so you'll need to grab the query result, JSON.stringify it and then call localStorage.setItem. The intent here is to make sure your application always has access to the list of groups, sites, regions, etc so that when you display a form to edit a client entity you'll have the data you need to populate the group, site, region and country select/combobox/dropdown.
Assuming the user has identified the subset of clients they want to work with while offline, I'd then load each of these clients one at a time (including their payment and bill information but not expanding their group, site, region, country) and cache each client+payments+bills set using entityManager.exportEntities. Reasoning here is it doesn't make sense to load several clients plus their payments and bills into the same EntityManager each time you want to edit a particular client. That could be a lot of unnecessary overhead, but again, this is a bit of a judgement call.
#Jeremy's answer was excellent and very helpful, but didn't actually answer the question, which I was starting to think was unanswerable, or at least the wrong question. However #Steve in the comments gave me the most appropriate information for this question.
It is neither Breeze nor the Browser, but rather Knockout. Apparently the knockout wrapper around the breeze entities uses all that memory (at least while loading the entities and in my environment). As described above, Knockout/Breeze would crap out after reading around 5MB of data, causing Chrome to crash with over 1.7GB of memory usage (from a pre-download memory usage around 300MB). Rewriting the app in ANgularJS eliminated the problem. So far I have been able to download over 50MB from the exact same EF6 model into Breeze/Angular, total Chrome memory usage never went above 625MB.
I will be testing larger payloads, but 50 MB more than satisfies my needs for the moment. Thanks everyone for your help.
The question is about the general approach. For me, I try to present the problem using AngularJS with ngStorage.
Let's say I have something like this saved in local storage:
$scope.$storage = $localStorage;
$scope.$storage.food = { type: 'candy', eaten: false }
With this way, I've saved this in local storage. So next time a user visit my page, I know if he/she has eaten the candy. However, in the future, I change my app and change the structure of food.
So how should I update this? Two things must be took care of are:
Notify client of new structure for storing.
Integrate that change with the old storage.
My approach is using a version field to indicate changes, and upon seeing that, reset all clients storage.
This process is called "data migration" (i.e. upgrading a data structure as the application evolves). It's a well-known problem from the database world (and before that for config/preferences files).
The usual approach is to add a version in the header of the data structure. That means the header is always the same (or just changes in backwards-compatible ways) while the payload (the actual data) can change as much as it needs.
A simple solution just checks the version and uses defaults when the version doesn't match. More elaborate schemes contain migration code which can upgrade a data structure from version N to N+1. Control code will then apply all the migration steps necessary to upgrade all data structures to the latest version.
I have a web page that shows a large amount of data from the server. The communication is done via ajax.
Every time the user interacts and changes this data (Say user A renames something) it tells the server to do the action and the server returns the new changed data.
If user B accesses the page at the same time and creates a new data object it will again tell the server via ajax and the server will return with the new object for the user.
On A's page we have the data with a renamed object. And on B's page we have the data with a new object. On the server the data has both a renamed object and a new object.
What are my options for keeping the page in sync with the server when multiple users are using it concurrently?
Such options as locking the entire page or dumping the entire state to the user on every change are rather avoided.
If it helps, in this specific example the webpage calls a static webmethod that runs a stored procedure on the database. The stored procedure will return any data it has changed and no more. The static webmethod then forwards the return of the stored procedure to the client.
Bounty Edit:
How do you design a multi-user web application which uses Ajax to communicate with the server but avoids problems with concurrency?
I.e. concurrent access to functionality and to data on a database without any risk of data or state corruption
Overview:
Intro
Server architecture
Client architecture
Update case
Commit case
Conflict case
Performance & scalability
Hi Raynos,
I will not discuss any particular product here. What others mentioned is a good toolset to have a look at already (maybe add node.js to that list).
From an architectural viewpoint, you seem to have the same problem that can be seen in version control software. One user checks in a change to an object, another user wants to alter the same object in another way => conflict. You have to integrate users changes to objects while at the same time being able to deliver updates timely and efficiently, detecting and resolving conflicts like the one above.
If I was in your shoes I would develop something like this:
1. Server-Side:
Determine a reasonable level at which you would define what I'd call "atomic artifacts" (the page? Objects on the page? Values inside objects?). This will depend on your webservers, database & caching hardware, # of user, # of objects, etc. Not an easy decision to make.
For each atomic artifact have:
an application-wide unique-id
an incrementing version-id
a locking mechanism for write-access (mutex maybe)
a small history or "changelog" inside a ringbuffer (shared memory works well for those). A single key-value pair might be OK too though less extendable. see http://en.wikipedia.org/wiki/Circular_buffer
A server or pseudo-server component that is able to deliver relevant changelogs to a connected user efficiently. Observer-Pattern is your friend for this.
2. Client-Side:
A javascript client that is able to have a long-running HTTP-Connection to said server above, or uses lightweight polling.
A javascript artifact-updater component that refreshes the sites content when the connected javascript client notifies of changes in the watched artifacts-history. (again an observer pattern might be a good choice)
A javascript artifact-committer component that may request to change an atomic artifact, trying to acquire mutex lock. It will detect if the state of the artifact had been changed by another user just seconds before (latancy of javascript client and commit process factors in) by comparing known clientside artifact-version-id and current serverside artifact-version-id.
A javascript conflict-solver allowing for a human which-change-is-the-right decision. You may not want to just tell the user "Someone was faster than you. I deleted your change. Go cry.". Many options from rather technical diffs or more user-friendly solutions seem possible.
So how would it roll ...
Case 1: kind-of-sequence-diagram for updating:
Browser renders page
javascript "sees" artifacts which each having at least one value field, unique- and a version-id
javascript client gets started, requesting to "watch" the found artifacts history starting from their found versions (older changes are not interesting)
Server process notes the request and continuously checks and/or sends the history
History entries may contain simple notifications "artifact x has changed, client pls request data" allowing the client to poll independently or full datasets "artifact x has changed to value foo"
javascript artifact-updater does what it can to fetch new values as soon as they become known to have updated. It executes new ajax requests or gets feeded by the javascript client.
The pages DOM-content is updated, the user is optionally notified. History-watching continues.
Case 2: Now for committing:
artifact-committer knows the desired new value from user input and sends a change-request to the server
serverside mutex is acquired
Server receives "Hey, I know artifact x's state from version 123, let me set it to value foo pls."
If the Serverside version of artifact x is equal (can not be less) than 123 the new value is accepted, a new version id of 124 generated.
The new state-information "updated to version 124" and optionally new value foo are put at the beginning of the artifact x's ringbuffer (changelog/history)
serverside mutex is released
requesting artifact committer is happy to receive a commit-confirmation together with the new id.
meanwhile serverside server component keeps polling/pushing the ringbuffers to connected clients. All clients watching the buffer of artifact x will get the new state information and value within their usual latency (See case 1.)
Case 3: for conflicts:
artifact committer knows desired new value from user input and sends a change-request to the server
in the meanwhile another user updated the same artifact successfully (see case 2.) but due to various latencies this is yet unknown to our other user.
So a serverside mutex is acquired (or waited on until the "faster" user committed his change)
Server receives "Hey, I know artifact x's state from version 123, let me set it to value foo."
On the Serverside the version of artifact x now is 124 already. The requesting client can not know the value he would be overwriting.
Obviously the Server has to reject the change request (not counting in god-intervening overwrite priorities), releases the mutex and is kind enough to send back the new version-id and new value directly to the client.
confronted with a rejected commit request and a value the change-requesting user did not yet know, the javascript artifact committer refers to the conflict resolver which displays and explains the issue to the user.
The user, being presented with some options by the smart conflict-resolver JS, is allowed another attempt to change the value.
Once the user selected a value he deems right, the process starts over from case 2 (or case 3 if someone else was faster, again)
Some words on Performance & Scalability
HTTP Polling vs. HTTP "pushing"
Polling creates requests, one per second, 5 per second, whatever you regard as an acceptable latency. This can be rather cruel to your infrastructure if you do not configure your (Apache?) and (php?) well enough to be "lightweight" starters. It is desirable to optimize the polling request on the serverside so that it runs for far less time than the length of the polling interval. Splitting that runtime in half might well mean lowering your whole system load by up to 50%,
Pushing via HTTP (assuming webworkers are too far off to support them) will require you to have one apache/lighthttpd process available for each user all the time. The resident memory reserved for each of these processes and your systems total memory will be one very certain scaling limit that you will encounter. Reducing the memory footprint of the connection will be necessary, as well as limiting the amount continuous CPU and I/O work done in each of these (you want lots of sleep/idle time)
backend scaling
Forget database and filesystem, you will need some sort of shared memory based backend for the frequent polling (if the client does not poll directly then each running server process will)
if you go for memcache you can scale better, but its still expensive
The mutex for commits has to work globaly even if you want to have multiple frontend servers to loadbalance.
frontend scaling
regardless if you are polling or receiving "pushes", try to get information for all watched artifacts in one step.
"creative" tweaks
If clients are polling and many users tend to watch the same artifacts, you could try to publish the history of those artifacts as a static file, allowing apache to cache it, nevertheless refreshing it on the serverside when artifacts change. This takes PHP/memcache out of the game some for requests. Lighthttpd is verry efficent at serving static files.
use a content delivery network like cotendo.com to push artifact history there. The push-latency will be bigger but scalability's a dream
write a real server (not using HTTP) that users connect to using java or flash(?). You have to deal with serving many users in one server-thread. Cycling through open sockets, doing (or delegating) the work required. Can scale via forking processes or starting more servers. Mutexes have to remain globaly unique though.
Depending on load scenarios group your frontend- and backend-servers by artifact-id ranges. This will allow for better usage of persistent memory (no database has all the data) and makes it possible to scale the mutexing. Your javascript has to maintain connections to multiple servers at the same time though.
Well I hope this can be a start for your own ideas. I am sure there are plenty more possibilities.
I am more than welcoming any criticism or enhancements to this post, wiki is enabled.
Christoph Strasen
I know this is an old question, but I thought I'd just chime in.
OT (operational transforms) seem like a good fit for your requirement for concurrent and consistent multi-user editing. It's a technique used in Google Docs (and was also used in Google Wave):
There's a JS-based library for using Operational Transforms - ShareJS (http://sharejs.org/), written by a member from the Google Wave team.
And if you want, there's a full MVC web-framework - DerbyJS (http://derbyjs.com/) built on ShareJS that does it all for you.
It uses BrowserChannel for communication between the server and clients (and I believe WebSockets support should be in the works - it was in there previously via Socket.IO, but was taken out due to the developer's issues with Socket.io) Beginner docs are a bit sparse at the moment, however.
I would consider adding time-based modified stamp for each dataset. So, if you're updating db tables, you would change the modified timestamp accordingly. Using AJAX, you can compare the client's modified timestamp with the data source's timestamp - if the user is ever behind, update the display. Similar to how this site checks a question periodically to see if anyone else has answered while you're typing an answer.
You need to use push techniques (also known as Comet or reverse Ajax) to propagate changes to the user as soon as they are made to the db. The best technique currently available for this seems to be Ajax long polling, but it isn't supported by every browser, so you need fallbacks. Fortunately there are already solutions that handle this for you. Among them are: orbited.org and the already mentioned socket.io.
In the future there will be an easier way to do this which is called WebSockets, but it isn't sure yet when that standard will be ready for prime time as there are security concerns about the current state of the standard.
There shouldn't be concurrency problems in the database with new objects. But when a user edits an object the server needs to have some logic that checks whether the object has been edited or deleted in the meantime. If the object has been deleted the solution is, again, simple: Just discard the edit.
But the most difficult problem appears, when multiple users are editing the same object at the same time. If User 1 and 2 start editing an object at the same time, they will both make their edits on the same data. Let's say the changes User 1 made are sent to the server first while User 2 is still editing the data. You then have two options: You could try to merge User 1's changes into the data of User 2 or you could tell User 2 that his data is out of date and display him an error message as soon as his data gets send to the server. The latter isn't very user friendly option here, but the former is very hard to implement.
One of the few implementations that really got this right for the first time was EtherPad, which was acquired by Google. I believe they then used some of EtherPad's technologies in Google Docs and Google Wave, but I can't tell that for sure. Google also opensourced EtherPad, so maybe that's worth a look, depending on what you're trying to do.
It's really not easy to do this simultaneously editing stuff, because it's not possible to do atomic operations on the web because of the latency. Maybe this article will help you to learn more about the topic.
Trying to write all this yourself is a big job, and it's very difficult to get it right. One option is to use a framework that's built to keep clients in sync with the database, and with each other, in realtime.
I've found that the Meteor framework does this well (http://docs.meteor.com/#reactivity).
"Meteor embraces the concept of reactive programming. This means that you can write your code in a simple imperative style, and the result will be automatically recalculated whenever data changes that your code depends on."
"This simple pattern (reactive computation + reactive data source) has wide applicability. The programmer is saved from writing unsubscribe/resubscribe calls and making sure they are called at the right time, eliminating whole classes of data propagation code which would otherwise clog up your application with error-prone logic."
I can't believe that nobody has mentioned Meteor. It's a new and immature framework for sure (and only officially supports one DB), but it takes all the grunt work and thinking out of a multi-user app like the poster is describing. In fact, you can't NOT build a mult-user live-updating app. Here's a quick summary:
Everything is in node.js (JavaScript or CoffeeScript), so you can share stuff like validations between the client and server.
It uses websockets, but can fall back for older browsers
It focuses on immediate updates to local object (i.e. the UI feels snappy), with changes sent to the server in the background. Only atomic updates are allowed to make mixing updates simpler. Updates rejected on the server are rolled back.
As a bonus, it handles live code reloads for you, and will preserves user state even when the app changes radically.
Meteor is simple enough that I would suggest you at least take a look at it for ideas to steal.
These Wikipedia pages may help add perspective to learning about concurrency and concurrent computing for designing an ajax web application that either pulls or is pushed state event (EDA) messages in a messaging pattern. Basically, messages are replicated out to channel subscribers which respond to change events and synchronization requests.
https://en.wikipedia.org/wiki/Category:Concurrency_control
https://en.wikipedia.org/wiki/Distributed_concurrency_control
https://en.wikipedia.org/wiki/CAP_theorem
https://en.wikipedia.org/wiki/Operational_transformation
https://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing
There are many forms of concurrent web-based collaborative software.
There are a number of HTTP API client libraries for etherpad-lite, a collaborative real-time editor.
django-realtime-playground implements a realtime chat app in Django with various real-time technologies like Socket.io.
Both AppEngine and AppScale implement the AppEngine Channel API; which is distinct from the Google Realtime API, which is demonstrated by googledrive/realtime-playground.
Server-side push techniques are the way to go here. Comet is (or was?) a buzz word.
The particular direction you take depends heavily on your server stack, and how flexible you/it is. If you can, I would take a look at socket.io, which provides a cross-browser implementation of websockets, which provide a very streamline way to have bidirectional communication with the server, allowing the server to push updates to the clients.
In particular, see this demonstration by the library's author, which demonstrates almost exactly the situation you describe.