I use two different events for the callback to respond when the IndexedDB transaction finishes or is successful:
Let's say... db : IDBDatabase object, tr : IDBTransaction object, os : IDBObjectStore object
tr = db.transaction(os_name,'readwrite');
os = tr.objectStore();
case 1 :
r = os.openCursor();
r.onsuccess = function(){
if(r.result){
callback_for_result_fetched();
r.result.continue;
}else callback_for_transaction_finish();
}
case 2:
tr.oncomplete = callback_for_transaction_finish();
It is a waste if both of them work similarly. So can you tell me, is there any difference between them?
Sorry for raising up quite an old thread, but it's questioning is a good starting point...
I've looked for a similar question but in a bit different use case and actually found no good answers or even a misleading ones.
Think of a use case when you need to make several writes into the objectStore of even into several ones. You definitely don't want to manage each single write and it's own success and error events. That is the meaning of transaction and this is the (proper) implementation of it for indexedDB:
var trx = dbInstance.transaction([storeIdA, storeIdB], 'readwrite'),
storeA = trx.objectStore(storeIdA),
storeB = trx.objectStore(storeIdB);
trx.oncomplete = function(event) {
// this code will run only when ALL of the following requests are succeed
// and only AFTER ALL of them were processed
};
trx.onerror = function(error) {
// this code will run if ANY of the following requests will fail
// and only AFTER ALL of them were processed
};
storeA.put({ key:keyA, value:valueA });
storeA.put({ key:keyB, value:valueB });
storeB.put({ key:keyA, value:valueA });
storeB.put({ key:keyB, value:valueB });
Clue to this understanding is to be found in the following statement of W3C spec:
To determine if a transaction has completed successfully, listen to the transaction’s complete event rather than the success event of a particular request, because the transaction may still fail after the success event fires.
While it's true these callbacks function similarly they are not the same: the difference between onsuccess and oncomplete is that transactions complete but requests, which are made on those transactions, are successful.
oncomplete is only defined in the spec as related to a transaction. A transaction doesn't have an onsuccess callback.
I would only caution that there is no garentee that getting a successful trx.oncomplete means the data was written to the disk/database:
We are seeing a problem with trx.oncomplete where the data is not being written to the db on disk. FireFox has an explanation of what they did that is causing this problem here: https://developer.mozilla.org/en-US/docs/Web/API/IDBTransaction/oncomplete
It seems that windows/edge is also having the same issue. Basically, there is no guarantee that your app will have data written to the database, if/when the user decides to kill or power down the device. We've even tried waiting up to 15 minutes before shutting down in some cases and haven't seen the data written. For me I'd always want to ensure that a data write completes and is committed.
Are there other solutions for a real persistent database, or enhancements to the IndexedDB beyond FF experimental add...
Related
The IndexBatchException documentation, e.g., when calling IndexAsync, states:
Thrown when some of the indexing actions failed, but other actions succeeded and modified the state of the index. This can happen when the Search Service is under heavy indexing load. It is important to explicitly catch this exception and check its IndexResult property. This property reports the status of each indexing action in the batch, making it possible to determine the state of the index after a partial failure.
Does this mean this exception can be safely ignored when there is just a single document in the IndexBatch? Since, it seems impossible for an IndexBatch with just a single document to fail partially.
I tried calling IndexAsync with a Merge batch containing a single document to update, but with a non-existing document key (as recommended by Bruce):
var nonExistingDocument = SomeDocument()
var work = IndexBatch.Merge( nonExistingDocument );
try
{
await _search.Documents.IndexAsync( work );
}
catch ( IndexBatchException e )
{
var toRetry = e.FindFailedActionsToRetry( work, d => d.Id);
}
IndexBatchException was triggered, which is different behavior than what is documented in two ways:
"Thrown when some of the indexing actions failed, but other actions succeeded and modified the state of the index." Instead, the exception is thrown when any action fails.
"This can happen when the Search Service is under heavy indexing load." This can also happen for incorrect requests.
But, FindFailedActionsToRetry is seemingly smart enough to not suggest retrying requests which have failed due to erroneous requests. The toRetry enumeration is empty in the code sample above.
In short, no, this exception cannot be safely ignored. The documentation is misleading and it would be nice if it were updated.
Tying to learn how use Akka.net Streams to process items in parallel from a Source.Queue, with the processing done in an Actor.
I've been able to get it to work with calling a function with Sink.ForEachParallel, and it works as expected.
Is it possible to process items in parallel with Sink.ActorRefWithAck (as I would prefer it utilize back-pressure)?
About to press Post, when tried to combine previous attempts and viola!
Previous attempts with ForEachParallel failed when I tried to create the actor within, but couldn't do so in an async function. If I use an single actor previous declared, then the Tell would work, but I couldn't get the parallelism I desired.
I got it to work with a router with roundrobin configuration.
var props = new RoundRobinPool(5).Props(Props.Create<MyActor>());
var actor = Context.ActorOf(props);
flow = Source.Queue<Element>(2000,OverflowStrategy.Backpressure)
.Select(x => {
return new Wrapper() { Element = x, Request = ++cnt };
})
.To(Sink.ForEachParallel<Wrapper>(5, (s) => { actor.Tell(s); }))
.Run(materializer);
The Request ++cnt is for console output to verify the requests are being processed as desired.
MyActor has a long delay on every 10th request to verify the backpressure was working.
I need to combine a catch up and a subscribe to new feed. So first I query the database for all new records I've missed, then switch to a pub sub for all new records that are coming in.
The first part is easy do your query, perhaps in batches of 500, that will give you an array and you can rx.observeFrom that.
The second part is easy you just put an rx.observe on the pubsub.
But I need to do is sequentially so I need to play all the old records before I start playing the new ones coming in.
I figure I can start the subscribe to pubsub, put those in an array, then start processing the old ones, and when I'm done either remove the dups ( or since I do a dup check ) allow the few dups, but play the accumulated records until they are gone and then one in one out.
my question is what is the best way to do this? should I create a subscribe to start building up new records in an array, then start processing old, then in the "then" of the oldrecord process subscribe to the other array?
Ok this is what I have so far. I need to build up the tests and finish up some psudo code to find out if it even works, much less is a good implementation. Feel free to stop me in my tracks before I bury myself.
var catchUpSubscription = function catchUpSubscription(startFrom) {
EventEmitter.call(this);
var subscription = this.getCurrentEventsSubscription();
// calling map to start subscription and catch in an array.
// not sure if this is right
var events = rx.Observable.fromEvent(subscription, 'event').map(x=> x);
// getPastEvents gets batches of 500 iterates over and emits each
// till no more are returned, then resolves a promise
this.getPastEvents({count:500, start:startFrom})
.then(function(){
rx.Observable.fromArray(events).forEach(x=> emit('event', x));
});
};
I don't know that this is the best way. Any thoughts?
thx
I would avoid mixing your different async strategies unnecessarily. You can use concat to join together the two sequences:
var catchUpSubscription = function catchUpSubscription(startFrom) {
var subscription = this.getCurrentEventsSubscription();
return Rx.Observable.fromPromise(this.getPastEvents({count:500, start:startFrom}))
.flatMap(x => x)
.concat(Rx.Observable.fromEvent(subscription, 'event'));
};
///Sometime later
catchUpSubscription(startTime).subscribe(x => /*handle event*/)
For one of my backbone project (in which i cannot implement REST/sync), i need to refresh a backbone collection (using backbone relational as model, if it matters ?) every X seconds.
What i've been doing is implement a function like this :
refresh: function(){
var self = this;
// clears timeout
self.timeoutRefresh && clearTimeout(self.timeoutRefresh);
// aborts request if running
self.xhrRefresh && self.xhrRefresh.abort();
// do request
self.xhrRefresh = self.options.myfunction.call(self, {}, function (data) {
self.mycollection.set(data);
// call it again in 5 seconds
self.timeoutRefresh = _.delay(function(){
self.refresh.call(self);
}, 5 * 1000);
});
},
The problem is that this block of code seems to be guilty of a big memory leak in my application.
Can it be a closure problem with the self variable ?
Should i then do the recursive call this way ?
self.timeoutRefresh = _.delay(function(context){
context.refresh.call(context);
}, 5 * 1000, self);
If not, where does it come from ?
Well after a lot of chrome debugging, i figured out backbone relational was never actually replacing my objects cause it is doing comparison over the id attribute which my objects didn't have (physically and logically).
I ended up calculating a md5 hash from the different meaningful properties of my object and using it as id so backbone relational would know it shouldn't consider the object as a new one.
Conclusion, it's not a closure problem as chrome garbage collector does its thing well.
NB: Backbone debugger helped me a lot finding where the problem came from.
I am trying to solve a problem that has been blocking me for a month
I am bulding the backend of an application using Node.js & Redis and due to our structure we have to transfer data from one redis table to another (What I mean by table is the one's that we use "select" i.e. "select 2")
We receive a lot of request and push a lot of response in a sec, and no matter how much I tried I could not stop data getting mixed. Assume we have a "teacherID" that has to be stored inside Redis table #2. And a "studentID" that has to be stored in Redis table #4. How matter what I tried (I've checked my code multiple times) I could not stop teacherID getting into studentID. The last trick I've tried was actually placing callback at each select.;
redisClient.select(4, function(err) {
if(err)
console.log("You could not select the table. Function will be aborted");
else {
// Proceed with the logic
}
});
What could be the reason that I cannot simply stop this mess ? One detail that drivers me crazy is that it works really well on local and also online however whenever multiple request reach to server it gets mixed. Any suggestions to prevent this error? (Even though I cannot share the code to NDA I can make sure that logic has been coded correctly)
I'm not sure about your statement about having to "transfer data from one redis table to another". Reading through your example it seems like you could simply have two redis clients that write to different databases (what you called "tables").
It would look similar to this:
var redis = require("redis");
var client1 = redis.createClient(6379, '127.0.0.1');
var client2 = redis.createClient(6379, '127.0.0.1');
client1.select(2, function(err){
console.log('client 1 is using database 2');
});
client2.select(4, function(err){
console.log('client 2 is using database 4');
});
Then, wherever your read/write logic is, you just use the appropriate client:
client1.set("someKey", "teacherID", function(err){
// ...
});
client2.set("someKey", "studentID", function(err){
// ...
});
You can obviously encapsulate the above into functions with callbacks, nest the operations, use some async library, etc. to make it do whatever you need it to do. If you need to transfer values from database 2 to database 4 you could do a simple client1.get() and client2.set().