Does anyone know how to check if a key exists using Membase Client (Enyim)? I dont want to pull the entire object from the cache, just check if its in there.
Unfortunately there is no "exists" operation in memcached, but there are some hacks you can do:
do a client.Store(StoreMode.Add, keyToCheck, null, new Date(2000, 1, 1)); Add fails if the item already exists, and will return false in this case.
you can client.Append(keyToCheck, new ArraySegment<byte>(new byte[0])) This will return true if the key exists, but its value will not change.
or client.Cas(StoreMode.Set, keyToCheck, null, UInt64.MaxValue) This will also fail if the item exists.
Please keep in mind that all three operations will create an item if the key does not exist, so you need to clean up after them.
Also, if multiple threads are checking for the same item, then it's possible that one thread will treat another threads temp item as the real one.
Related
We have a breeze client solution in which we show parent entities with lists of their children. We do hard deletes on some child entities. Now when the user is the one doing the deletes, there is no problem, but when someone else does, there seems to be no way to invalidate the children already loaded in cache. We do a new query with the parent and expanding to children, but breeze attaches all the other children it has already heard of, even if the database did not return them.
My question: shouldn't breeze realize we are loading through expand and thus completely remove all children from cache before loading back the results from the db? How else can we accomplish this if that is not the case?
Thank you
Yup, that's a really good point.
Deletion is simply a horrible complication to every data management effort. This is true no matter whether you use Breeze or not. It just causes heartache up and down the line. Which is why I recommend soft deletes instead of hard deletes.
But you don't care what I think ... so I will continue.
Let me be straight about this. There is no easy way for you to implement a cache cleanup scheme properly. I'm going to describe how we might do it (with some details neglected I'm sure) and you'll see why it is difficult and, in perverse cases, fruitless.
Of course the most efficient, brute force approach is to blow away the cache before querying. You might as well not have caching if you do that but I thought I'd mention it.
The "Detached" entity problem
Before I continue, remember the technique I just mentioned and indeed all possible solutions are useless if your UI (or anything else) is holding references to the entities that you want to remove.
Oh, you'll remove them from cache alright. But whatever is holding references to them now will continue to have a reference to an entity object which is in a "Detached" state - a ghost. Making sure that doesn't happen is your responsibility; Breeze can't know and couldn't do anything about it if it did know.
Second attempt
A second, less blunt approach (suggested by Jay) is to
apply the query to the cache first
iterate over the results and for each one
detach every child entity along the "expand" paths.
detach that top level entity
Now when the query succeeds, you have a clear road for it to fill the cache.
Here is a simple example of the code as it relates to a query of TodoLists and their TodoItems:
var query = breeze.EntityQuery.from('TodoLists').expand('TodoItems');
var inCache = manager.executeQueryLocally(query);
inCache.slice().forEach(function(e) {
inCache = inCache.concat(e.TodoItems);
});
inCache.slice().forEach(function(e) {
manager.detachEntity(e);
});
There are at least four problems with this approach:
Every queried entity is a ghost. If your UI is displaying any of the queried entities, it will be displaying ghosts. This is true even when the entity was not touched on the server at all (99% of the time). Too bad. You have to repaint the entire page.
You may be able to do that. But in many respects this technique is almost as impractical as the first. It means that ever view is in a potentially invalid state after any query takes place anywhere.
Detaching an entity has side-effects. All other entities that depend on the one you detached are instantly (a) changed and (b) orphaned. There is no easy recovery from this, as explained in the "orphans" section below.
This technique wipes out all pending changes among the entities that you are querying. We'll see how to deal with that shortly.
If the query fails for some reason (lost connection?), you've got nothing to show. Unless you remember what you removed ... in which case you could put those entities back in cache if the query fails.
Why mention a technique that may have limited practical value? Because it is a step along the way to approach #3 that could work
Attempt #3 - this might actually work
The approach I'm about to describe is often referred to as "Mark and Sweep".
Run the query locally and calculate theinCache list of entities as just described. This time, do not remove those entities from cache. We WILL remove the entities that remain in this list after the query succeeds ... but not just yet.
If the query's MergeOption is "PreserveChanges" (which it is by default), remove every entity from the inCache list (not from the manager's cache!) that has pending changes. We do this because such entities must stay in cache no matter what the state of the entity on the server. That's what "PreserveChanges" means.
We could have done this in our second approach to avoid removing entities with unsaved changes.
Subscribe to the EntityManager.entityChanged event. In your handler, remove the "entity that changed" from the inCache list because the fact that this entity was returned by the query and merged into the cache tells you it still exists on the server. Here is some code for that:
var handlerId = manager.entityChanged.subscribe(trackQueryResults);
function trackQueryResults(changeArgs) {
var action = changeArgs.entityAction;
if (action === breeze.EntityAction.AttachOnQuery ||
action === breeze.EntityAction.MergeOnQuery) {
var ix = inCache.indexOf(changeArgs.entity);
if (ix > -1) {
inCache.splice(ix, 1);
}
}
}
If the query fails, forget all of this
If the query succeeds
unsubscribe: manager.entityChanged.unsubscribe(handlerId);
subscribe with orphan detection handler
var handlerId = manager.entityChanged.subscribe(orphanDetector);
function orphanDetector(changeArgs) {
var action = changeArgs.entityAction;
if (action === breeze.EntityAction.PropertyChange) {
var orphan = changeArgs.entity;
// do something about this orphan
}
}
detach every entity that remains in the inCache list.
inCache.slice().forEach(function(e) {
manager.detachEntity(e);
});
unsubscribe the orphan detection handler
Orphan Detector?
Detaching an entity can have side-effects. Suppose we have Products and every product has a Color. Some other user hates "red". She deletes some of the red products and changes the rest to "blue". Then she deletes the "red" Color.
You know nothing about this and innocently re-query the Colors. The "red" color is gone and your cleanup process detaches it from cache. Instantly every Product in cache is modified. Breeze doesn't know what the new Color should be so it sets the FK, Product.colorId, to zero for every formerly "red" product.
There is no Color with id=0 so all of these products are in an invalid state (violating referential integrity constraint). They have no Color parent. They are orphans.
Two questions: how do you know this happened to you and what do your do?
Detection
Breeze updates the affected products when you detach the "red" color.
You could listen for a PropertyChanged event raised during the detach process. That's what I did in my code sample. In theory (and I think "in fact"), the only thing that could trigger the PropertyChanged event during the detach process is the "orphan" side-effect.
What do you do?
leave the orphan in an invalid, modified state?
revert to the equally invalid former colorId for the deleted "red" color?
refresh the orphan to get its new color state (or discover that it was deleted)?
There is no good answer. You have your pick of evils with the first two options. I'd probably go with the second as it seems least disruptive. This would leave the products in "Unchanged" state, pointing to a non-existent Color.
It's not much worse then when you query for the latest products and one of them refers to a new Color ("banana") that you don't have in cache.
The "refresh" option seems technically the best. It is unwieldy. It could easily cascade into a long chain of asynchronous queries that could take a long time to finish.
The perfect solution escapes our grasp.
What about the ghosts?
Oh right ... your UI could still be displaying the (fewer) entities that you detached because you believe they were deleted on the server. You've got to remove these "ghosts" from the UI.
I'm sure you can figure out how to remove them. But you have to learn what they are first.
You could iterate over every entity that you are displaying and see if it is in a "Detached" state. YUCK!
Better I think if the cleanup mechanism published a (custom?) event with the list of entities you detached during cleanup ... and that list is inCache. Your subscriber(s) then know which entities have to be removed from the display ... and can respond appropriately.
Whew! I'm sure I've forgotten something. But now you understand the dimensions of the problem.
What about server notification?
That has real possibilities. If you can arrange for the server to notify the client when any entity has been deleted, that information can be shared across your UI and you can take steps to remove the deadwood.
It's a valid point but for now we don't ever remove entities from the local cache as a result of a query. But.. this is a reasonable request, so please add this to the breeze User Voice. https://breezejs.uservoice.com/forums/173093-breeze-feature-suggestions
In the meantime, you can always create a method that removes the related entities from the cache before the query executes and have the query (with expand) add them back.
I'm writing a financial app with Firebase and for an receipt to be submitted, a number of other objects also need to be updated. For the data to be valid, all data updates need to be completed successfully. If there's an error in one of the writes, all updates must be rolled back.
For example:
If the user submits a receipt, the receipt object must be updated as well as an invoice object as well as other general ledger objects.
If the update started but the user lost internet connection half way through, all changes should be rolled back.
What's the best way to achieve this in Firebase?
First, let's chat for a minute about why someone might want to do commit/rollback on multiple data paths...
Do you need this?
Generally, you do not need this if:
you are not writing with high concurrency (hundreds of write opes per minute to the SAME record by DIFFERENT users)
your dependencies are straightforward (B depends on A, and C depends on A, but A does not depend on B or C)
your data can be merged into a single path
Developers are a bit too worried about orphaned records appearing in their data.
The chance of a web socket failing between one write and the other is probably trivial and somewhere on the order of collisions between
timestamp based IDs. That’s not to say it’s impossible, but it's generally low consequency, highly unlikely, and shouldn’t be your primary concern.
Also, orphans are extremely easy to clean up with a script or even just by typing a few lines of code into the JS console. So again,
they tend to be very low consequence.
What can you do instead of this?
Put all the data that must be written atomically into a single path. Then you can write it as a single set or a transaction if necessary.
Or in the case where one record is the primary and the others depend on this, simply write the primary first, then write the others in the callback. Add security rules to enforce this, so that the primary record always exists before the others are allowed to write.
If you are denormalizing data simply to make it easy and fast to iterate (e.g. to obtain a list of names for users), then simply index that data in a separate path.
Then you can have the complete data record in a single path and the names, emails, etc in a fast, query/sort-friendly list.
When is this useful?
This is an appropriate tool to use if you have a denormalized set of records that:
cannot be merged practically into one path in a practical way
have complex dependencies (A depends on C, and C depends on B, and B depends on A)
records are written with high concurrency (i.e. possibly hundreds of write ops per minute to the SAME record by DIFFERENT users)
How do you do this?
The idea is to use update counters to ensure all paths stay at the same revision.
1) Create an update counter which is incremented using transactions:
function updateCounter(counterRef, next) {
counterRef.transaction(function(current_value) {
return (current_value||0)+1;
}, function(err, committed, ss) {
if( err ) console.error(err)
else if( committed ) next(ss.val());
}, false);
}
2) Give it some security rules
"counters": {
"$counter": {
".read": true,
".write": "newData.isNumber() && ( (!data.exists() && newData.val() === 1) || newData.val() === data.val() + 1 )"
}
},
3) Give your records security rules to enforce the update_counter
"$atomic_path": {
".read": true,
// .validate allows these records to be deleted, use .write to prevent deletions
".validate": "newData.hasChildren(['update_counter', 'update_key']) && root.child('counters/'+newData.child('update_key').val()).val() === newData.child('update_counter').val()",
"update_counter": {
".validate": "newData.isNumber()"
},
"update_key": {
".validate": "newData.isString()"
}
}
4) Write the data with the update_counter
Since you have security rules in place, records can only successfully write if the counter does not move. If it does move, then the records have been overwritten by a concurrent change, so they no longer matter (they are no longer the latest and greatest).
var fb = new Firebase(URL);
updateCounter(function(newCounter) {
var data = { foo: 'bar', update_counter: newCounter, update_key: 'myKey' };
fb.child('pathA').set(data);
fb.child('pathB').set(/* some other data */);
// depending on your use case, you may want transactions here
// to check data state before write, but they aren't strictly necessary
});
5) Rollbacks
Rollbacks are a bit more involved, but can be built off this principle:
store the old values before calling set
monitor each set op for failures
set back to old values on any committed changes, but keep the new counter
A pre-built library
I wrote up a lib today that does this and stuffed it on GitHub. Feel free to use it, but please be sure you aren't making your life complicated by reading "Do you need this?" above.
I'm learning about concurrency in conjunction with EF4.0 and have a question about the locking pattern used.
Say I configure a fixed concurrency mode on a version number property.
Now say I fetch a record (entity) from the database (context) and edit some property. Version gets incremented and when SaveChanges is called on its context. If the current database (context) version matches the version of the original record (entity) the save continues, otherwise an OptimisticConcurrencyException gets thrown by EF.
Now, my point of interest is the following: between the check of the versions there's always a small period of time, however small, it is there. So in theory someone else could've just updated the record between the comparison and the actual save, thus possibly corrupting the data.
How does this get solved? It feels as if the problem just gets pushed forward.
There is no period of time between checking versions and updating record because the database command looks like:
UPDATE SomeTable
SET SomeColumn = 'SomeValue'
WHERE Id = #Id AND Version = #OldVersion
SELECT ##ROWCOUNT
The check and update is one atomic operation. Rowcount will return 0 if no record with Id = #Id and Version = #OldVersion exists and that zero is translated to the exception.
This can (and probably is) solved using locking hints.
For SQL Server, EF can query (SELECT) from the database WITH UPDLOCK.
This tells the Database Engine that, you want to read a/several records, and nobody else can change those records until you perform an update thereafter.
If you want to see this for yourself, check out the Sql Server Profiler which will show you the queries in real-time.
Hope that helps.
CAVEAT: I can't say for sure that this is the way EF handles this scenario because I haven't checked myself but, certainly if you were going to do it yourself, this is one way to do it.
What is wrong in this code?
I was expected "titi" in person.name but I still have "toto"!
More explicitly, how to modify a record in a function?
init1()->
S=#person{name="toto"}, %record creation and field setting
fct(S),
io:format("~s~n",[S#person.name]).
fct(R)->
R#person{name="titi"}. %record updating
You need to get a result of fct():
init1()->
S=#person{name="toto"}, %record creation and field setting
S2 = fct(S), % Get updated record
io:format("~s~n",[S2#person.name]).
fct(R)->
R#person{name="titi"}. %record updating
Bertaud, I think you are getting ahead of yourself a bit. You really need to understand the basics of immutability before you write any more code. (i.e. "variables" do not vary : you can only assign a value to them once.) I suggest you read the free online guide "Learn You Some Erlang For Great Good", at http://learnyousomeerlang.com/. The section that covers the basics of variables is http://learnyousomeerlang.com/starting-out-for-real#invariable-variables.
It is impossible to stress too much that all data in Erlang is immutable. So to do something like in your original question you need to modify it like #hdima did. The record is not updated but rewritten. In the same way there is no global data in Erlang, all data belongs to a process. This is even true of ETS tables as they basically behave like a process, albeit a built-in one without explicit communication.
So if you use the process dictionary or an ETS table the data itself can never be updated, only the dictionary/table. This means that to modify some data in the dictionary/table you basically have to:
"Read" the data
Update the data making new data
"Write" the new back into the dictionary/table
Without writing the new data back into the dictionary/table it will be lost, as your new data was.
Within fct(), you're not mutating the record, but you're returning a new value for the record, which needs to be used further. If you're calling fct(S), without handling the return value, then you'll lose that new value ("titi").
preface note: I'm just starting to learn Grails, so I'm sure there are many other problems and room for optimization.
I've got two domains, a parent (Collection) and child (Event), in a one-to-many mapping. I'm trying to code an integration test for the deletion of children. Prior to the code in question, I've successfully created a parent and three children. The point where I'm having problems is getting a single child in preparation to delete it. The first line of my sample code is only there because of my rudimentary attempt to troubleshoot.
// lines 95-100 of my EventIntegrationTests.groovy file
// delete a single event
assertEquals("2nd Event", event2.title) // passes
def foundEvent = Event.get(event2.id) // no apparent problems
assertEquals("2nd Event", foundEvent.title) // FAILS (line #98)
foundEvent.delete()
assertFalse Event.exists(foundEvent.id)
The error message I'm getting is:
Cannot get property 'title' on null object
java.lang.NullPointerException: Cannot get property 'title' on null object
at edu.learninggrails.EventIntegrationTests.testEventsDelete(EventIntegrationTests.groovy:98)
What should my next troubleshooting steps be? (Since the first assertEquals passes, event2 is clearly not null, so at this point I have no idea how to troubleshoot the failure of the second assertEquals.)
This is not evident from the code: did you persist event2 by calling save()? Get will try to retrieve it from the persistent storage (the in-memory database for example) and if the event wasn't saved, the retrieved instance will be null.
If you did save it, did the save go through OK? Calling event.save() will return false if there was something wrong while saving the item (like a validation error). Lastly, you might try calling event.save(flush:true) in case the Hibernate session doesn't handle this case as you might expect (I'm not entirely sure about this one, but it can't hurt to try).
Try to print or inspect the event2.id on line 97 and check if you actually have an id, if so check if you actually get an Event object on line 97.
I dont think you saved the parent and its children successfully. after you save, you should make sure that every object that was persisted has a non null id, in your test.
What you are seeing is you created the event2 with a title, but didnt save it. It passes the first assertion because you created it. When you do the get, null is returned because your save failed.
in general for DAO integration tests i do the following
Setup -- create all objects Ill use in the test.
Save -- assert that all ids on saved objects are NOT null.
Clear the hibernate session -- this is important because if you don't do it, objects can be in the session from the previous operations. In your real world scenario, you are probably going to start with a find, i.e. an empty session. In other words, you are not going to start with anything in the session. If you are you need to adjust this rule so that the session in the test, when you start the actual testing part, is the same as the session of the code in the wild
Load the objects on which you want to operate and do what you need to do.