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  • Does this prove a network bandwidth bottleneck?

    - by Yuji Tomita
    I've incorrectly assumed that my internal AB testing means my server can handle 1k concurrency @3k hits per second. My theory at at the moment is that the network is the bottleneck. The server can't send enough data fast enough. External testing from blitz.io at 1k concurrency shows my hits/s capping off at 180, with pages taking longer and longer to respond as the server is only able to return 180 per second. I've served a blank file from nginx and benched it: it scales 1:1 with concurrency. Now to rule out IO / memcached bottlenecks (nginx normally pulls from memcached), I serve up a static version of the cached page from the filesystem. The results are very similar to my original test; I'm capped at around 180 RPS. Splitting the HTML page in half gives me double the RPS, so it's definitely limited by the size of the page. If I internally ApacheBench from the local server, I get consistent results of around 4k RPS on both the Full Page and the Half Page, at high transfer rates. Transfer rate: 62586.14 [Kbytes/sec] received If I AB from an external server, I get around 180RPS - same as the blitz.io results. How do I know it's not intentional throttling? If I benchmark from multiple external servers, all results become poor which leads me to believe the problem is in MY servers outbound traffic, not a download speed issue with my benchmarking servers / blitz.io. So I'm back to my conclusion that my server can't send data fast enough. Am I right? Are there other ways to interpret this data? Is the solution/optimization to set up multiple servers + load balancing that can each serve 180 hits per second? I'm quite new to server optimization, so I'd appreciate any confirmation interpreting this data. Outbound traffic Here's more information about the outbound bandwidth: The network graph shows a maximum output of 16 Mb/s: 16 megabits per second. Doesn't sound like much at all. Due to a suggestion about throttling, I looked into this and found that linode has a 50mbps cap (which I'm not even close to hitting, apparently). I had it raised to 100mbps. Since linode caps my traffic, and I'm not even hitting it, does this mean that my server should indeed be capable of outputting up to 100mbps but is limited by some other internal bottleneck? I just don't understand how networks at this large of a scale work; can they literally send data as fast as they can read from the HDD? Is the network pipe that big? In conclusion 1: Based on the above, I'm thinking I can definitely raise my 180RPS by adding an nginx load balancer on top of a multi nginx server setup at exactly 180RPS per server behind the LB. 2: If linode has a 50/100mbit limit that I'm not hitting at all, there must be something I can do to hit that limit with my single server setup. If I can read / transmit data fast enough locally, and linode even bothers to have a 50mbit/100mbit cap, there must be an internal bottleneck that's not allowing me to hit those caps that I'm not sure how to detect. Correct? I realize the question is huge and vague now, but I'm not sure how to condense it. Any input is appreciated on any conclusion I've made.

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  • Persisting complex data between postbacks in ASP.NET MVC

    - by Robert Wagner
    I'm developing an ASP.NET MVC 2 application that connects to some services to do data retrieval and update. The services require that I provide the original entity along with the updated entity when updating data. This is so it can do change tracking and optimistic concurrency. The services cannot be changed. My problem is that I need to somehow store the original entity between postbacks. In WebForms, I would have used ViewState, but from what I have read, that is out for MVC. The original values do not have to be tamper proof as the services treat them as untrusted. The entities would be (max) 1k and it is an intranet app. The options I have come up are: Session - Ruled out - Store the entity in the Session, but I don't like this idea as there are no plans to share session between URL - Ruled out - Data is too big HiddenField - Store the serialized entity in a hidden field, perhaps with encryption/encoding HiddenVersion - The entities have a (SQL) version field on them, which I could put into a hidden field. Then on a save I get "original" entity from the services and compare the versions, doing my own optimistic concurrency. Cookies - Like 3 or 4, but using a cookie instead of a hidden field I'm leaning towards option 4, although 3 would be simpler. Are these valid options or am I going down the wrong track? Is there a better way of doing this?

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  • Using ember-resource with couchdb - how can i save my documents?

    - by Thomas Herrmann
    I am implementing an application using ember.js and couchdb. I choose ember-resource as database access layer because it nicely supports nested JSON documents. Since couchdb uses the attribute _rev for optimistic locking in every document, this attribute has to be updated in my application after saving the data to the couchdb. My idea to implement this is to reload the data right after saving to the database and get the new _rev back with the rest of the document. Here is my code for this: // Since we use CouchDB, we have to make sure that we invalidate and re-fetch // every document right after saving it. CouchDB uses an optimistic locking // scheme based on the attribute "_rev" in the documents, so we reload it in // order to have the correct _rev value. didSave: function() { this._super.apply(this, arguments); this.forceReload(); }, // reload resource after save is done, expire to make reload really do something forceReload: function() { this.expire(); // Everything OK up to this location Ember.run.next(this, function() { this.fetch() // Sub-Document is reset here, and *not* refetched! .fail(function(error) { App.displayError(error); }) .done(function() { App.log("App.Resource.forceReload fetch done, got revision " + self.get('_rev')); }); }); } This works for most cases, but if i have a nested model, the sub-model is replaced with the old version of the data just before the fetch is executed! Interestingly enough, the correct (updated) data is stored in the database and the wrong (old) data is in the memory model after the fetch, although the _rev attribut is correct (as well as all attributes of the main object). Here is a part of my object definition: App.TaskDefinition = App.Resource.define({ url: App.dbPrefix + 'courseware', schema: { id: String, _rev: String, type: String, name: String, comment: String, task: { type: 'App.Task', nested: true } } }); App.Task = App.Resource.define({ schema: { id: String, title: String, description: String, startImmediate: Boolean, holdOnComment: Boolean, ..... // other attributes and sub-objects } }); Any ideas where the problem might be? Thank's a lot for any suggestion! Kind regards, Thomas

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  • C# 4: The Curious ConcurrentDictionary

    - by James Michael Hare
    In my previous post (here) I did a comparison of the new ConcurrentQueue versus the old standard of a System.Collections.Generic Queue with simple locking.  The results were exactly what I would have hoped, that the ConcurrentQueue was faster with multi-threading for most all situations.  In addition, concurrent collections have the added benefit that you can enumerate them even if they're being modified. So I set out to see what the improvements would be for the ConcurrentDictionary, would it have the same performance benefits as the ConcurrentQueue did?  Well, after running some tests and multiple tweaks and tunes, I have good and bad news. But first, let's look at the tests.  Obviously there's many things we can do with a dictionary.  One of the most notable uses, of course, in a multi-threaded environment is for a small, local in-memory cache.  So I set about to do a very simple simulation of a cache where I would create a test class that I'll just call an Accessor.  This accessor will attempt to look up a key in the dictionary, and if the key exists, it stops (i.e. a cache "hit").  However, if the lookup fails, it will then try to add the key and value to the dictionary (i.e. a cache "miss").  So here's the Accessor that will run the tests: 1: internal class Accessor 2: { 3: public int Hits { get; set; } 4: public int Misses { get; set; } 5: public Func<int, string> GetDelegate { get; set; } 6: public Action<int, string> AddDelegate { get; set; } 7: public int Iterations { get; set; } 8: public int MaxRange { get; set; } 9: public int Seed { get; set; } 10:  11: public void Access() 12: { 13: var randomGenerator = new Random(Seed); 14:  15: for (int i=0; i<Iterations; i++) 16: { 17: // give a wide spread so will have some duplicates and some unique 18: var target = randomGenerator.Next(1, MaxRange); 19:  20: // attempt to grab the item from the cache 21: var result = GetDelegate(target); 22:  23: // if the item doesn't exist, add it 24: if(result == null) 25: { 26: AddDelegate(target, target.ToString()); 27: Misses++; 28: } 29: else 30: { 31: Hits++; 32: } 33: } 34: } 35: } Note that so I could test different implementations, I defined a GetDelegate and AddDelegate that will call the appropriate dictionary methods to add or retrieve items in the cache using various techniques. So let's examine the three techniques I decided to test: Dictionary with mutex - Just your standard generic Dictionary with a simple lock construct on an internal object. Dictionary with ReaderWriterLockSlim - Same Dictionary, but now using a lock designed to let multiple readers access simultaneously and then locked when a writer needs access. ConcurrentDictionary - The new ConcurrentDictionary from System.Collections.Concurrent that is supposed to be optimized to allow multiple threads to access safely. So the approach to each of these is also fairly straight-forward.  Let's look at the GetDelegate and AddDelegate implementations for the Dictionary with mutex lock: 1: var addDelegate = (key,val) => 2: { 3: lock (_mutex) 4: { 5: _dictionary[key] = val; 6: } 7: }; 8: var getDelegate = (key) => 9: { 10: lock (_mutex) 11: { 12: string val; 13: return _dictionary.TryGetValue(key, out val) ? val : null; 14: } 15: }; Nothing new or fancy here, just your basic lock on a private object and then query/insert into the Dictionary. Now, for the Dictionary with ReadWriteLockSlim it's a little more complex: 1: var addDelegate = (key,val) => 2: { 3: _readerWriterLock.EnterWriteLock(); 4: _dictionary[key] = val; 5: _readerWriterLock.ExitWriteLock(); 6: }; 7: var getDelegate = (key) => 8: { 9: string val; 10: _readerWriterLock.EnterReadLock(); 11: if(!_dictionary.TryGetValue(key, out val)) 12: { 13: val = null; 14: } 15: _readerWriterLock.ExitReadLock(); 16: return val; 17: }; And finally, the ConcurrentDictionary, which since it does all it's own concurrency control, is remarkably elegant and simple: 1: var addDelegate = (key,val) => 2: { 3: _concurrentDictionary[key] = val; 4: }; 5: var getDelegate = (key) => 6: { 7: string s; 8: return _concurrentDictionary.TryGetValue(key, out s) ? s : null; 9: };                    Then, I set up a test harness that would simply ask the user for the number of concurrent Accessors to attempt to Access the cache (as specified in Accessor.Access() above) and then let them fly and see how long it took them all to complete.  Each of these tests was run with 10,000,000 cache accesses divided among the available Accessor instances.  All times are in milliseconds. 1: Dictionary with Mutex Locking 2: --------------------------------------------------- 3: Accessors Mostly Misses Mostly Hits 4: 1 7916 3285 5: 10 8293 3481 6: 100 8799 3532 7: 1000 8815 3584 8:  9:  10: Dictionary with ReaderWriterLockSlim Locking 11: --------------------------------------------------- 12: Accessors Mostly Misses Mostly Hits 13: 1 8445 3624 14: 10 11002 4119 15: 100 11076 3992 16: 1000 14794 4861 17:  18:  19: Concurrent Dictionary 20: --------------------------------------------------- 21: Accessors Mostly Misses Mostly Hits 22: 1 17443 3726 23: 10 14181 1897 24: 100 15141 1994 25: 1000 17209 2128 The first test I did across the board is the Mostly Misses category.  The mostly misses (more adds because data requested was not in the dictionary) shows an interesting trend.  In both cases the Dictionary with the simple mutex lock is much faster, and the ConcurrentDictionary is the slowest solution.  But this got me thinking, and a little research seemed to confirm it, maybe the ConcurrentDictionary is more optimized to concurrent "gets" than "adds".  So since the ratio of misses to hits were 2 to 1, I decided to reverse that and see the results. So I tweaked the data so that the number of keys were much smaller than the number of iterations to give me about a 2 to 1 ration of hits to misses (twice as likely to already find the item in the cache than to need to add it).  And yes, indeed here we see that the ConcurrentDictionary is indeed faster than the standard Dictionary here.  I have a strong feeling that as the ration of hits-to-misses gets higher and higher these number gets even better as well.  This makes sense since the ConcurrentDictionary is read-optimized. Also note that I tried the tests with capacity and concurrency hints on the ConcurrentDictionary but saw very little improvement, I think this is largely because on the 10,000,000 hit test it quickly ramped up to the correct capacity and concurrency and thus the impact was limited to the first few milliseconds of the run. So what does this tell us?  Well, as in all things, ConcurrentDictionary is not a panacea.  It won't solve all your woes and it shouldn't be the only Dictionary you ever use.  So when should we use each? Use System.Collections.Generic.Dictionary when: You need a single-threaded Dictionary (no locking needed). You need a multi-threaded Dictionary that is loaded only once at creation and never modified (no locking needed). You need a multi-threaded Dictionary to store items where writes are far more prevalent than reads (locking needed). And use System.Collections.Concurrent.ConcurrentDictionary when: You need a multi-threaded Dictionary where the writes are far more prevalent than reads. You need to be able to iterate over the collection without locking it even if its being modified. Both Dictionaries have their strong suits, I have a feeling this is just one where you need to know from design what you hope to use it for and make your decision based on that criteria.

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  • Good practices - database programming, unit testing

    - by Piotr Rodak
    Jason Brimhal wrote today on his blog that new book, Defensive Database Programming , written by Alex Kuznetsov ( blog ) is coming to bookstores. Alex writes about various techniques that make your code safer to run. SQL injection is not the only one vulnerability the code may be exposed to. Some other include inconsistent search patterns, unsupported character sets, locale settings, issues that may occur during high concurrency conditions, logic that breaks when certain conditions are not met. The...(read more)

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  • Structuring multi-threaded programs

    - by davidk01
    Are there any canonical sources for learning how to structure multi-threaded programs? Even with all the concurrency utility classes that Java provides I'm having a hard time properly structuring multi-threaded programs. Whenever threads are involved my code becomes very brittle, any little change can potentially break the program because the code that jumps back and forth between the threads tends to be very convoluted.

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  • How would you rank these programming skills in order of learning them? [closed]

    - by mumtaz
    As a general purpose programmer, what should you learn first and what should you learn later on? Here are some skills I wonder about... SQL Regular Expressions Multi-threading / Concurrency Functional Programming Graphics The mastery of your mother programming language's syntax/semantics/featureset The mastery of your base class framework libraries Version Control System Unit Testing XML Do you know other important ones? Please specify them... On which skills should I focus first?

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  • What is the diffference between "data hiding" and "encapsulation"?

    - by john smith optional
    I'm reading "Java concurrency in practice" and there is said: "Fortunately, the same object-oriented techniques that help you write well-organized, maintainable classes - such as encapsulation and data hiding -can also help you crate thread-safe classes." The problem #1 - I never heard about data hiding and don't know what it is. The problem #2 - I always thought that encapsulation is using private vs public, and is actually the data hiding. Can you please explain what data hiding is and how it differs from encapsulation?

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  • Do you need to know Java before trying Scala

    - by gizgok
    I'm interested in learning Scala. I've been reading a lot about it, but a lot of people value it because it has an actor model which is better for concurrency, it handles xml in a much better way, solves the problem of first class functions. My question is do you need to know Java to understand/appreciate the way things work in Scala? Is it better to first take a stab at Java and then try Scala or you can start Scala with absolutely no java backround?

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  • parallel_for_each from amp.h – part 1

    - by Daniel Moth
    This posts assumes that you've read my other C++ AMP posts on index<N> and extent<N>, as well as about the restrict modifier. It also assumes you are familiar with C++ lambdas (if not, follow my links to C++ documentation). Basic structure and parameters Now we are ready for part 1 of the description of the new overload for the concurrency::parallel_for_each function. The basic new parallel_for_each method signature returns void and accepts two parameters: a grid<N> (think of it as an alias to extent) a restrict(direct3d) lambda, whose signature is such that it returns void and accepts an index of the same rank as the grid So it looks something like this (with generous returns for more palatable formatting) assuming we are dealing with a 2-dimensional space: // some_code_A parallel_for_each( g, // g is of type grid<2> [ ](index<2> idx) restrict(direct3d) { // kernel code } ); // some_code_B The parallel_for_each will execute the body of the lambda (which must have the restrict modifier), on the GPU. We also call the lambda body the "kernel". The kernel will be executed multiple times, once per scheduled GPU thread. The only difference in each execution is the value of the index object (aka as the GPU thread ID in this context) that gets passed to your kernel code. The number of GPU threads (and the values of each index) is determined by the grid object you pass, as described next. You know that grid is simply a wrapper on extent. In this context, one way to think about it is that the extent generates a number of index objects. So for the example above, if your grid was setup by some_code_A as follows: extent<2> e(2,3); grid<2> g(e); ...then given that: e.size()==6, e[0]==2, and e[1]=3 ...the six index<2> objects it generates (and hence the values that your lambda would receive) are:    (0,0) (1,0) (0,1) (1,1) (0,2) (1,2) So what the above means is that the lambda body with the algorithm that you wrote will get executed 6 times and the index<2> object you receive each time will have one of the values just listed above (of course, each one will only appear once, the order is indeterminate, and they are likely to call your code at the same exact time). Obviously, in real GPU programming, you'd typically be scheduling thousands if not millions of threads, not just 6. If you've been following along you should be thinking: "that is all fine and makes sense, but what can I do in the kernel since I passed nothing else meaningful to it, and it is not returning any values out to me?" Passing data in and out It is a good question, and in data parallel algorithms indeed you typically want to pass some data in, perform some operation, and then typically return some results out. The way you pass data into the kernel, is by capturing variables in the lambda (again, if you are not familiar with them, follow the links about C++ lambdas), and the way you use data after the kernel is done executing is simply by using those same variables. In the example above, the lambda was written in a fairly useless way with an empty capture list: [ ](index<2> idx) restrict(direct3d), where the empty square brackets means that no variables were captured. If instead I write it like this [&](index<2> idx) restrict(direct3d), then all variables in the some_code_A region are made available to the lambda by reference, but as soon as I try to use any of those variables in the lambda, I will receive a compiler error. This has to do with one of the direct3d restrictions, where only one type can be capture by reference: objects of the new concurrency::array class that I'll introduce in the next post (suffice for now to think of it as a container of data). If I write the lambda line like this [=](index<2> idx) restrict(direct3d), all variables in the some_code_A region are made available to the lambda by value. This works for some types (e.g. an integer), but not for all, as per the restrictions for direct3d. In particular, no useful data classes work except for one new type we introduce with C++ AMP: objects of the new concurrency::array_view class, that I'll introduce in the post after next. Also note that if you capture some variable by value, you could use it as input to your algorithm, but you wouldn’t be able to observe changes to it after the parallel_for_each call (e.g. in some_code_B region since it was passed by value) – the exception to this rule is the array_view since (as we'll see in a future post) it is a wrapper for data, not a container. Finally, for completeness, you can write your lambda, e.g. like this [av, &ar](index<2> idx) restrict(direct3d) where av is a variable of type array_view and ar is a variable of type array - the point being you can be very specific about what variables you capture and how. So it looks like from a large data perspective you can only capture array and array_view objects in the lambda (that is how you pass data to your kernel) and then use the many threads that call your code (each with a unique index) to perform some operation. You can also capture some limited types by value, as input only. When the last thread completes execution of your lambda, the data in the array_view or array are ready to be used in the some_code_B region. We'll talk more about all this in future posts… (a)synchronous Please note that the parallel_for_each executes as if synchronous to the calling code, but in reality, it is asynchronous. I.e. once the parallel_for_each call is made and the kernel has been passed to the runtime, the some_code_B region continues to execute immediately by the CPU thread, while in parallel the kernel is executed by the GPU threads. However, if you try to access the (array or array_view) data that you captured in the lambda in the some_code_B region, your code will block until the results become available. Hence the correct statement: the parallel_for_each is as-if synchronous in terms of visible side-effects, but asynchronous in reality.   That's all for now, we'll revisit the parallel_for_each description, once we introduce properly array and array_view – coming next. Comments about this post by Daniel Moth welcome at the original blog.

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  • Is there a canonical resource on multi-tenancy web applications using ruby + rails

    - by AlexC
    Is there a canonical resource on multi-tenancy web applications using ruby + rails. There are a number of ways to develop rails apps using cloud capabilities with real elastic properties but there seems to be a lack of clarity with how to achieve multitenancy, specifically at the model / data level. Is there a canonical resource on options to developing multitenancy rails applications with the required characteristics of data seperation, security, concurrency and contention required by an enterprise level cloud application.

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  • Parallel Computing Features Tour in VS2010

    Just realized that I have not linked from here to a screencast I recorded a couple weeks ago that shows the API, parallel debugger and concurrency visualizer in VS2010. Take a few minutes to watch the VS2010 Parallel Computing Features Tour. Comments about this post welcome at the original blog.

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  • What is the canonical resource on multi-tenancy web applications using ruby + rails

    - by AlexC
    What is the canonical resource on multi-tenancy web applications using ruby + rails. There are a number of ways to develop rails apps using cloud capabilities with real elastic properties but there seems to be a lack of clarity with how to achieve multitenancy, specifically at the model / data level. Is there a canonical resource on options to developing multitenancy rails applications with the required characteristics of data seperation, security, concurrency and contention required by an enterprise level cloud application.

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  • Are my actual worker threads exceeding the sp_configure 'max worker threads' value?

    Tom Stringer (@SQLife) was working on some HADR testing for a customer to simulate many availability groups and introduce significant load into the system to measure overhead and such. In his quest to do that he was seeing behavior that he couldn’t really explain and so worked with him to uncover what was happening under the covers. Understand Locking, Blocking & Row VersioningRead Kalen Delaney's eBook to understand SQL Server concurrency, and use SQL Monitor to pinpoint excessive blocking and deadlocking. Download free resources.

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  • NUMA-aware constructs for java.util.concurrent

    - by Dave
    The constructs in the java.util.concurrent JSR-166 "JUC" concurrency library are currently NUMA-oblivious. That's because we currently don't have the topology discovery infrastructure and underpinnings in place that would allow and enable NUMA-awareness. But some quick throw-away prototypes show that it's possible to write NUMA-aware library code. I happened to use JUC Exchanger as a research vehicle. Another interesting idea is to adapt fork-join work-stealing to favor stealing from queues associated with 'nearby' threads.

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  • What is the difference between "data hiding" and "encapsulation"?

    - by Software Engeneering Learner
    I'm reading "Java concurrency in practice" and there is said: "Fortunately, the same object-oriented techniques that help you write well-organized, maintainable classes - such as encapsulation and data hiding -can also help you create thread-safe classes." The problem #1 - I never heard about data hiding and don't know what it is. The problem #2 - I always thought that encapsulation is using private vs public, and is actually the data hiding. Can you please explain what data hiding is and how it differs from encapsulation?

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  • What is the actual problem with a prototype based design?

    - by WindScar
    I feel like anything that can be developed using OO/functional languages can be generally made 'better' using a prototype based language, because they appaer to have the best of them all: high order functions, flexibility to simulate any OO structure, productivity (low verbosity) and scalability because of concurrency. But it seems like they are avoided for the creation of executable applications and of bigger projects in general. Why that?

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  • Game engine development in C++ [closed]

    - by Chris Cochran
    I am arriving at completion on a multithreaded concurrency framework designed for high-performance computing. Though I am not a gamer, it has occurred to me that this stand-alone software core could be an ideal basis for a multiprocessor game engine (64-bit native C++, 5000+ entry points). Are there any websites I could visit to discuss this technology with programmers and developers who could really benefit from it?

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  • Parallel Computing Features Tour in VS2010

    Just realized that I have not linked from here to a screencast I recorded a couple weeks ago that shows the API, parallel debugger and concurrency visualizer in VS2010. Take a few minutes to watch the VS2010 Parallel Computing Features Tour. Comments about this post welcome at the original blog.

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  • Basics of SQL Server 2008 Locking

    Relational databases are designed for multiple simultaneous users, and Microsoft SQL Server is no different. However, supporting multiple users requires some form of concurrency control, which in SQL Server's case means transaction isolation and locking. Read on to learn how SQL Server 2008 implements locking.

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  • Basics of SQL Server 2008 Locking

    Relational databases are designed for multiple simultaneous users, and Microsoft SQL Server is no different. However, supporting multiple users requires some form of concurrency control, which in SQL Server's case means transaction isolation and locking. Read on to learn how SQL Server 2008 implements locking.

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