The title pretty much asks what I want -- an example of using predicates that don't involve iterating over a collection, using IEnumerable, or anything like that.
I don't care what language you use, or even if you provide a code example. I'd be grateful if you only described a simple example which illustrates predicates without the usual uses.
I'm working on an article and I'm stumped, so any examples will be gratefully received and credited.
In one project I have manually implemented cache dependencies using predicates.
Whenever a new item is put in the cache that depends on another cached item, a predicate delegate is created which will check whether the dependency is still valid or whether the cached item has to be invalidated.
Whenever a cached item was accessed, and it had a dependency check predicate assigned, then it was checked before the cached item was returned.
If course that probably could be solved without using predicates, but that solution was pretty elegant and enabled implementing the cache dependencies by easily injecting the depency checking method.
Related
I use a StatefulService with a IReliableDictionary.
Currently, I call StateManager.GetOrAddAsync<IReliableDictionary> everywhere I need this dictionary.
Is it best practice to call one time only StateManager.GetOrAddAsync<IReliableDictionary> in the OnOpenAsync method of StatefulService and to store the return in a member ?
It does not matter much. I've asked it to the product team an got this response:
You can cache the result of GetOrAddAsync locally but it doesn't matter since the statemanager does that for you automatically. Some folks think it's easier to keep a local, but I never do because now you have some lifecycle things to deal with (you have a ref to the thing not protected by state manger acquisition locks so you can see some different exceptions, but nothing you wouldn't have to deal with anyway).
Italic text inserted by me.
As per the official documentation here, it's not recommended to store the reliable collection references.
We don't recommended that you save references to reliable collection
instances in class member variables or properties. Special care must
be taken to ensure that the reference is set to an instance at all
times in the service lifecycle. The Reliable State Manager handles
this work for you, and it's optimized for repeat visits.
I need an array-like data structure with the fastest possible functional update. I've seen a few different implementation of flexible arrays that provide me with this property (Braun, Random Access Lists) but I'm wondering if there is an implementation that is specifically optimized for the case when we are not interested in append or prepend - just updates.
Jean-Cristophe Filliâtre has a very nice implementation of persistent arrays, that is described in the paper linked at the same page (which is about persistent union-find, of which persistent arrays are a core component). The code is directly available there.
The idea is that "the last version" of the array is represented as an usual array, with O(1) access and update operations, and previous versions are represented as this last version, plus a list of the differences. If you try to access a previous version of the structure, the array is "rerooted" to apply the list of differences and present you again the efficient representation.
This will of course not be O(1) under all workflows (if you constantly access and modify unrelated versions of the structure, you will pay rerooting costs frequently), but for the common workflow of mainly working with one version, and occasionally backtracking to an older version that becomes the "last version" again and gets the updates, this is very efficient. A very nice use of mutability hidden under a observationally pure interface.
I have a very good experience with repa (nice demo). Very good performance, automatic parallelism, multidimensional, polymorphic. Recommended to try.
Which language are you using? In Haskell you can use mutable arrays with the state monad, and in Mercury you can use mutable arrays by threading the IO state. Ocaml also has an array module, which unfortunately does not maintain referential transparency, if that is what you are after.
I also needed functional arrays and felt on this SO question some days ago. I was not satisfied with the solution proposed by Gasche as creating a new array is a costly operation and I need to access older versions of array quite frequently (I plan to use this for an AI alpha/beta implementation playing on an array).
(When I say costly, I guess it is O(n*h) where h is the history size because in the worst case only one cell was updated repeatedly and it is needed to go through the whole update list for each cell. I also expect most of the cells not being updated when I need to reroute the array).
This is why I propose another approach, maybe I can get some feedback here. My idea is to store the array like in a B-Tree, except that as it is not mutable, I can access and update any value by index quite easily.
I wrote a small introduction on the project's repository : https://github.com/shepard8/ocaml-ptarray. The order is chosen so as to even depth and order of the tree, so I can get nice complexities depending only on the order for get/set operations, namely O(k^2).
With k = 10 I can store up to 10^10 values. Actually my arrays should not contain more than 200 values but this is to show how robust my solution is intended to be.
Any advice welcome!
Does anyone have an example of how to efficiently provide a UITableView with data from a Core Data model, preferable including the use of sections (via a referenced property), without the use of NSFetchedResultsController?
How was this done before NSFetchedResultsController became available? Ideally the sample should only get the data that's being viewed and make extra requests when necessary.
Thanks,
Tim
For the record, I agree with CommaToast that there's at best a very limited set of reasons to implement an alternative version of NSFetchedResultsController. Indeed I'm unable to think of an occasion when I would advocate doing so.
That being said, for the purpose of education, I'd imagine that:
upon creation, NSFetchedResultsController runs the relevant NSFetchRequest against the managed object context to create the initial result set;
subsequently — if it has a delegate — it listens for NSManagedObjectContextObjectsDidChangeNotification from the managed object context. Upon receiving that notification it updates its result set.
Fetch requests sit atop predicates and predicates can't always be broken down into the keys they reference (eg, if you create one via predicateWithBlock:). Furthermore although the inserted and deleted lists are quite explicit, the list of changed objects doesn't provide clues as to how those objects have changed. So I'd imagine it just reruns the predicate supplied in the fetch request against the combined set of changed and inserted records, then suitably accumulates the results, dropping anything from the deleted set that it did previously consider a result.
There are probably more efficient things you could do whenever dealing with a fetch request with a fetch limit. Obvious observations, straight off the top of my head:
if you already had enough objects, none of those were deleted or modified and none of the newly inserted or modified objects have a higher sort position than the objects you had then there's obviously no changes to propagate and you needn't run a new query;
even if you've lost some of the objects you had, if you kept whichever was lowest then you've got an upper bound for everything that didn't change, so if the changed and inserted ones together with those you already had make more then enough then you can also avoid a new query.
The logical extension would seem to be that you need re-interrogate the managed object context only if you come out in a position where the deletions, insertions and changes modify your sorted list so that — before you chop it down to the given fetch limit — the bottom object isn't one you had from last time. The reasoning being that you don't already know anything about the stored objects you don't have hold of versus the insertions and modifications; you only know about how those you don't have hold of compare to those you previously had.
Summary
Is there something like http://en.wikipedia.org/wiki/E_programming_language as a DSL inside of Clojure?
Background
I'm aware of:
http://bit.ly/N4jnTI and http://bit.ly/Lm3SSD
However, neither provides what I want.
Context
I'm a big fan of both capability systems and information flow. And I'm wondering if anyone has developed Clojure DSLs for these two techniques. The following would be ideal:
all objects have some tag (say in it's meta table) that lists who has read access to the object
when I want to run a query as user "foo", I set some context var saying "now, use only the capabilities of foo" -- then the function, when it tries to reach objects, either gets the object (if foo has access to it) or nil (if foo does not have access to it). Leaking information bout the existence of objects is not a big deal to me at the moment.
Question
So the question is -- is this something easy to do as a Clojure DSL? Where each object has some capability tag, and we can execute pieces of function/code under certain tags, and the runtime system makes sure that no one gets access to things they're not supposed to access.
Thanks!
you can do this with metadata and preconditions and then create macros to add a DSL/syntax to it, though I would recommend skipping the macros and going for just preconditions and metadata.
Each object would have a piece of metadata with a list of it's capabilities.
Each function would have a precondition that checked the metadata.
Multithreading is hard. The only this you can do is program very carefully and follow good advice. One great advice I got from the answers on this forum is to avoid mutable state. I understand this is even enforced in the Erlang language. However, I fail to see how this can be done without a severe performance hit and huge amounts of caches.
For example. You have a big list of objects, each containing quite a lot of properties; in other words: a large datastructure. Suppose you have got a bunch of threads and they all need to access and modify the list. How can this be done without shared memory without having to cache the whole datastructure in each of the threads?
Update: After reading the reactions so far, I would like to put some more emphasis on performance. Don't you think that copying the same data around will make the program slower than with shared memory?
Not each algorithm can be parallelized in a successful manner.
If your program doesn't exhibit any "parallel structure", then you're pretty doomed to use locking and shared, mutable structures.
If your algorithm exhibit structure, then you can express your computation in terms of some patterns or formalism (for ex., a macro dataflow graph) that makes the choice of an immutable datastruct trivial.
So: think in term of the structure of the algorithm and just not in term of the properties of the datastructure to use.
You can get a great start in thinking about immutable collections, where they are applicable, how they can actually work without requiring lots of copying, etc. by looking through Eric Lippert's articles tagged with immutability:
http://blogs.msdn.com/ericlippert/archive/tags/Immutability/default.aspx
I guess the first question is: why do they need to modify the list? Would it be possible for them to return their changes as a list of modifications rather than actually modifying the shared list? Could they work with a list which looks like it's a mutable version of the original list, but is actually only locally mutable? Are you changing which elements are in the list, or just the properties of those elements?
These are just questions rather than answers, but I'm trying to encourage you to think about your problem in a different way. Look at the bigger picture as the task you want to achieve instead of thinking about the way you'd tackle it in a normal imperative, mutable fashion. Changing the way you think about problems is very difficult, but you may find you get some great "aha!" moments :)
There are many pitfalls when working with multiple threads and large sets of data. The advice to avoid mutable state is meant to try to make life easier for you if you can manage to follow the guideline (i.e. if you have no mutable state then multi-threading will be much easier).
If you have a large amount of data that does need to be modified then you perhaps cannot avoid mutable state. An alternative though would be to partition the data into blocks, each of which is passed to a thread for manipulation. The block can be processed and then passed back, and the controller can then perform the updates where necessary. In this scenario you have removed the mutable state from out of the the thread.
If this cannot be done and each thread needs update access to the full list (i.e. it could update any item on the list at any time) then you are going to have a lot of fun trying to make sure you have got your locking strategies and concurrency issues sorted. I'm sure there are scenarios where this is required, and the design pattern of avoiding mutable state may not apply.
Just using immutable data-objects is a big help.
Modifying lists sounds like a constructed argument, but consider granular methods that are unaware of lists.
If you really need to update the structure one way to do this is have a single worker thread which picks up update requests from a fixed area prtected by a mutex.
If you are clever you can update the structure in place without affecting any "reading"
threads (e.g. If you are adding to the end of an array you do all the work to add the new structure but only as the very last instruction do you increment the NoOfMembers count -- the reading threads should not see the new entry until you do this - or - arrange your data as an array of references to structures -- when you want to update a structure you copy the current member, update it, then as the last operation replace the reference in the array)
The other threads then only need to check a single simple "update in progess" mutex only when they activly want to update.