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  • Row Versioning Concurrency in SQL Server

    The optimistic concurrency model assumes that several concurrent transactions can usually complete without interfering with each other, and therefore do not require draconian locking on the resources they access. SQL Server 2005, and later, implements a form of this model called row versioning concurrency. It works by remembering the value of the data at the start of the transaction and checking that no other transaction has modified it before committing. If this optimism is justified for the pattern of activity within a database, it can improve performance by greatly reducing blocking. Kalen Delaney explains how it works in SQL Server.

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  • Free eBook: Understanding SQL Server Concurrency

    When you can’t get to your data because another application has it locked, a thorough knowledge of SQL Server concurrency will give you the confidence to decide what to do. Get your SQL Server database under version control now!Version control is standard for applications, but databases haven’t caught up. So how can you bring database development up to speed? Why should you start? Find out…

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  • Suggest a open source project which heavily uses java concurrency utilities?

    - by user49767
    I have done good amount of Java programming, but yet to master Threading & Concurrency. I would like to become an expert programmer in threading & concurrency. I have also took a short at Tomcat code, I was able to understand, but looking even more complex project. Could you suggest any open source project which heavily uses java threading & concurrency utilities? Note : I have also reading java.util.concurrent package source code, but eager to learn from Application perspective, than creating my own threading utilities.

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  • concurrency::accelerator_view

    - by Daniel Moth
    Overview We saw previously that accelerator represents a target for our C++ AMP computation or memory allocation and that there is a notion of a default accelerator. We ended that post by introducing how one can obtain accelerator_view objects from an accelerator object through the accelerator class's default_view property and the create_view method. The accelerator_view objects can be thought of as handles to an accelerator. You can also construct an accelerator_view given another accelerator_view (through the copy constructor or the assignment operator overload). Speaking of operator overloading, you can also compare (for equality and inequality) two accelerator_view objects between them to determine if they refer to the same underlying accelerator. We'll see later that when we use concurrency::array objects, the allocation of data takes place on an accelerator at array construction time, so there is a constructor overload that accepts an accelerator_view object. We'll also see later that a new concurrency::parallel_for_each function overload can take an accelerator_view object, so it knows on what target to execute the computation (represented by a lambda that the parallel_for_each also accepts). Beyond normal usage, accelerator_view is a quality of service concept that offers isolation to multiple "consumers" of an accelerator. If in your code you are accessing the accelerator from multiple threads (or, in general, from different parts of your app), then you'll want to create separate accelerator_view objects for each thread. flush, wait, and queuing_mode When you create an accelerator_view via the create_view method of the accelerator, you pass in an option of immediate or deferred, which are the two members of the queuing_mode enum. At any point you can access this value from the queuing_mode property of the accelerator_view. When the queuing_mode value is immediate (which is the default), any commands sent to the device such as kernel invocations and data transfers (e.g. parallel_for_each and copy, as we'll see in future posts), will get submitted as soon as the runtime sees fit (that is the definition of immediate). When the value of queuing_mode is deferred, the commands will be batched up. To send all buffered commands to the device for execution, there is a non-blocking flush method that you can call. If you wish to block until all the commands have been sent, there is a wait method you can call. Deferring is a more advanced scenario aimed at performance gains when you are submitting many device commands and you want to avoid the tiny overhead of flushing/submitting each command separately. Querying information Just like accelerator, accelerator_view exposes the is_debug and version properties. In fact, you can always access the accelerator object from the accelerator property on the accelerator_view class to access the accelerator interface we looked at previously. Interop with D3D (aka DX) In a later post I'll show an example of an app that uses C++ AMP to compute data that is used in pixel shaders. In those scenarios, you can benefit by integrating C++ AMP into your graphics pipeline and one of the building blocks for that is being able to use the same device context from both the compute kernel and the other shaders. You can do that by going from accelerator_view to device context (and vice versa), through part of our interop API in amp.h: *get_device, create_accelerator_view. More on those in a later post. Comments about this post by Daniel Moth welcome at the original blog.

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  • How would you practice concurrency and multi-threading?

    - by Xavier Nodet
    I've been reading about concurrency, multi-threading, and how "the free lunch is over". But I've not yet had the possibility to use MT in my job. I'm thus looking for suggestions about what I could do to get some practice of CPU heavy MT through exercises or participation in some open-source projects. Thanks. Edit: I'm more interested in open-source projects that use MT for CPU-bound tasks, or simply algorithms that are interesting to implement using MT, rather than books or papers about the tools like threads, mutexes and locks...

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  • Actor based concurrency and cancellation

    - by Akash
    I'm reading about actor based concurrency and I appreciate the simplicity of actors sequentially processing messages on a single thread. However there is one scenario that doesn't seen possible. Suppose that actor A sends a message to actor B, who then performs some long running task and returns a completion message to actor A. How can actor A force actor B to cancel the long running task after it has started? If actor B is running the task in its message queue thread, it won't pick up the cancellation message until it had completed the task; if actor B runs the task in a background thread then it seems to be violating the principle of actors. Is there a common way that this scenario is handled with actors? Or does each actor language/framework take a different approach? Or is this not a suitable problem to tackle via actors?

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  • Great Java EE Concurrency Write-up!

    - by reza_rahman
    As you are aware JSR-236, Concurrency Utilities for the Java EE platform, is now a candidate for addition into Java EE 7. While it is a critical enabling API it is not necessarily obvious why it is so important. This is especially true with existing features like EJB 3 @Asynchronous, Servlet 3 async and JAX-RS 2 async. On his blog DZone MVB Sander Mak does an excellent job of explaining the motivation and importance of JSR-236. Perhaps even more importantly, he discusses potential issues with the API such alignment with CDI and Java SE Fork/Join. Read the excellent write-up here!

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  • concurrency::accelerator

    - by Daniel Moth
    Overview An accelerator represents a "target" on which C++ AMP code can execute and where data can reside. Typically (but not necessarily) an accelerator is a GPU device. Accelerators are represented in C++ AMP as objects of the accelerator class. For many scenarios, you do not need to obtain an accelerator object, since the runtime has a notion of a default accelerator, which is what it thinks is the best one in the system. Examples where you need to deal with accelerator objects are if you need to pick your own accelerator (based on your specific criteria), or if you need to use more than one accelerators from your app. Construction and operator usage You can query and obtain a std::vector of all the accelerators on your system, which the runtime discovers on startup. Beyond enumerating accelerators, you can also create one directly by passing to the constructor a system-wide unique path to a device if you know it (i.e. the “Device Instance Path” property for the device in Device Manager), e.g. accelerator acc(L"PCI\\VEN_1002&DEV_6898&SUBSYS_0B001002etc"); There are some predefined strings (for predefined accelerators) that you can pass to the accelerator constructor (and there are corresponding constants for those on the accelerator class itself, so you don’t have to hardcode them every time). Examples are the following: accelerator::default_accelerator represents the default accelerator that the C++ AMP runtime picks for you if you don’t pick one (the heuristics of how it picks one will be covered in a future post). Example: accelerator acc; accelerator::direct3d_ref represents the reference rasterizer emulator that simulates a direct3d device on the CPU (in a very slow manner). This emulator is available on systems with Visual Studio installed and is useful for debugging. More on debugging in general in future posts. Example: accelerator acc(accelerator::direct3d_ref); accelerator::direct3d_warp represents a target that I will cover in future blog posts. Example: accelerator acc(accelerator::direct3d_warp); accelerator::cpu_accelerator represents the CPU. In this first release the only use of this accelerator is for using the staging arrays technique that I'll cover separately. Example: accelerator acc(accelerator::cpu_accelerator); You can also create an accelerator by shallow copying another accelerator instance (via the corresponding constructor) or simply assigning it to another accelerator instance (via the operator overloading of =). Speaking of operator overloading, you can also compare (for equality and inequality) two accelerator objects between them to determine if they refer to the same underlying device. Querying accelerator characteristics Given an accelerator object, you can access its description, version, device path, size of dedicated memory in KB, whether it is some kind of emulator, whether it has a display attached, whether it supports double precision, and whether it was created with the debugging layer enabled for extensive error reporting. Below is example code that accesses some of the properties; in your real code you'd probably be checking one or more of them in order to pick an accelerator (or check that the default one is good enough for your specific workload): void inspect_accelerator(concurrency::accelerator acc) { std::wcout << "New accelerator: " << acc.description << std::endl; std::wcout << "is_debug = " << acc.is_debug << std::endl; std::wcout << "is_emulated = " << acc.is_emulated << std::endl; std::wcout << "dedicated_memory = " << acc.dedicated_memory << std::endl; std::wcout << "device_path = " << acc.device_path << std::endl; std::wcout << "has_display = " << acc.has_display << std::endl; std::wcout << "version = " << (acc.version >> 16) << '.' << (acc.version & 0xFFFF) << std::endl; } accelerator_view In my next blog post I'll cover a related class: accelerator_view. Suffice to say here that each accelerator may have from 1..n related accelerator_view objects. You can get the accelerator_view from an accelerator via the default_view property, or create new ones by invoking the create_view method that creates an accelerator_view object for you (by also accepting a queuing_mode enum value of deferred or immediate that we'll also explore in the next blog post). Comments about this post by Daniel Moth welcome at the original blog.

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  • Optimistic work sharing on sparsely distributed systems

    - by Asti
    What would a system like BOINC look like if it were written today? At the time BOINC was written, databases were the primary choice for maintaining a shared state and concurrency among nodes. Since then, many approaches have been developed for tasking with optimistic concurrency (OT, partial synchronization primitives, shared iterators etc.) Is there an optimal paradigm for optimistically distributing units of work on sparsely distributing systems which communicate through message passing? Sorry if this is a bit vague. P.S. The concept of Tuple-spaces is great, but locking is inherent to its definition. Edit: I already have a federation system which works very well. I have a reactive OT system is implemented on top of it. I'm looking to extend it to get clients to do units of work.

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  • How are you using CFThread in ColdFusion Applications?

    - by marc esher
    I'm presenting on Concurrency in ColdFusion at CFObjective this year, and I'd like to hear how you're using CFThread in your ColdFusion applications. In addition, what problems have you had while using it, and how (if at all) have you solved them? What do you dislike about CFThread? Have you run into significant weaknesses with CFThread or other problems where it simply could not do what you wanted to do? Finally, if there's anything you'd like to add related to concurrency in CF, not specifically related to CFThread, please do tell.

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  • Why C++ people loves multithreading when it comes to performances?

    - by user1849534
    I have a question, it's about why programmers seems to love concurrency and multi-threaded programs in general. I'm considering 2 main approach here: an async approach basically based on signals, or just an async approach as called by many papers and languages like the new C# 5.0 for example, and a "companion thread" that maanges the policy of your pipeline a concurrent approach or multi-threading approach I will just say that I'm thinking about the hardware here and the worst case scenario, and I have tested this 2 paradigms myself, the async paradigm is a winner at the point that I don't get why people 90% of the time talk about concurrency when they wont to speed up things or make a good use of their resources. I have tested multi-threaded programs and async program on an old machine with an Intel quad-core that doesn't offer a memory controller inside the CPU, the memory is managed entirely by the motherboard, well in this case performances are horrible with a multi-threaded application, even a relatively low number of threads like 3-4-5 can be a problem, the application is unresponsive and is just slow and unpleasant. A good async approach is, on the other hand, probably not faster but it's not worst either, my application just waits for the result and doesn't hangs, it's responsive and there is a much better scaling going on. I have also discovered that a context change in the threading world it's not that cheap in real world scenario, it's infact quite expensive especially when you have more than 2 threads that need to cycle and swap among each other to be computed. On modern CPUs the situation it's not really that different, the memory controller it's integrated but my point is that an x86 CPUs is basically a serial machine and the memory controller works the same way as with the old machine with an external memory controller on the motherboard. The context switch is still a relevant cost in my application and the fact that the memory controller it's integrated or that the newer CPU have more than 2 core it's not bargain for me. For what i have experienced the concurrent approach is good in theory but not that good in practice, with the memory model imposed by the hardware, it's hard to make a good use of this paradigm, also it introduces a lot of issues ranging from the use of my data structures to the join of multiple threads. Also both paradigms do not offer any security abut when the task or the job will be done in a certain point in time, making them really similar from a functional point of view. According to the X86 memory model, why the majority of people suggest to use concurrency with C++ and not just an async aproach ? Also why not considering the worst case scenario of a computer where the context switch is probably more expensive than the computation itself ?

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  • How can I return a Future object with Spring without writing concurrency logic?

    - by Johan
    How can I return a java.util.concurrent.Future object with a Receipt object and only use the @javax.ejb.Asynchronous annotation? And do I need any extra configuration to let Spring handle ejb annotations? I don't want to write any concurrency logic myself. Here's my attempt that doesn't work: @Asynchronous public Future<Receipt> execute(Job job) { Receipt receipt = timeConsumingWork(job); return receipt; }

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  • concurrency::index<N> from amp.h

    - by Daniel Moth
    Overview C++ AMP introduces a new template class index<N>, where N can be any value greater than zero, that represents a unique point in N-dimensional space, e.g. if N=2 then an index<2> object represents a point in 2-dimensional space. This class is essentially a coordinate vector of N integers representing a position in space relative to the origin of that space. It is ordered from most-significant to least-significant (so, if the 2-dimensional space is rows and columns, the first component represents the rows). The underlying type is a signed 32-bit integer, and component values can be negative. The rank field returns N. Creating an index The default parameterless constructor returns an index with each dimension set to zero, e.g. index<3> idx; //represents point (0,0,0) An index can also be created from another index through the copy constructor or assignment, e.g. index<3> idx2(idx); //or index<3> idx2 = idx; To create an index representing something other than 0, you call its constructor as per the following 4-dimensional example: int temp[4] = {2,4,-2,0}; index<4> idx(temp); Note that there are convenience constructors (that don’t require an array argument) for creating index objects of rank 1, 2, and 3, since those are the most common dimensions used, e.g. index<1> idx(3); index<2> idx(3, 6); index<3> idx(3, 6, 12); Accessing the component values You can access each component using the familiar subscript operator, e.g. One-dimensional example: index<1> idx(4); int i = idx[0]; // i=4 Two-dimensional example: index<2> idx(4,5); int i = idx[0]; // i=4 int j = idx[1]; // j=5 Three-dimensional example: index<3> idx(4,5,6); int i = idx[0]; // i=4 int j = idx[1]; // j=5 int k = idx[2]; // k=6 Basic operations Once you have your multi-dimensional point represented in the index, you can now treat it as a single entity, including performing common operations between it and an integer (through operator overloading): -- (pre- and post- decrement), ++ (pre- and post- increment), %=, *=, /=, +=, -=,%, *, /, +, -. There are also operator overloads for operations between index objects, i.e. ==, !=, +=, -=, +, –. Here is an example (where no assertions are broken): index<2> idx_a; index<2> idx_b(0, 0); index<2> idx_c(6, 9); _ASSERT(idx_a.rank == 2); _ASSERT(idx_a == idx_b); _ASSERT(idx_a != idx_c); idx_a += 5; idx_a[1] += 3; idx_a++; _ASSERT(idx_a != idx_b); _ASSERT(idx_a == idx_c); idx_b = idx_b + 10; idx_b -= index<2>(4, 1); _ASSERT(idx_a == idx_b); Usage You'll most commonly use index<N> objects to index into data types that we'll cover in future posts (namely array and array_view). Also when we look at the new parallel_for_each function we'll see that an index<N> object is the single parameter to the lambda, representing the (multi-dimensional) thread index… In the next post we'll go beyond being able to represent an N-dimensional point in space, and we'll see how to define the N-dimensional space itself through the extent<N> class. Comments about this post by Daniel Moth welcome at the original blog.

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  • concurrency::extent<N> from amp.h

    - by Daniel Moth
    Overview We saw in a previous post how index<N> represents a point in N-dimensional space and in this post we'll see how to define the N-dimensional space itself. With C++ AMP, an N-dimensional space can be specified with the template class extent<N> where you define the size of each dimension. From a look and feel perspective, you'd expect the programmatic interface of a point type and size type to be similar (even though the concepts are different). Indeed, exactly like index<N>, extent<N> is essentially a coordinate vector of N integers ordered from most- to least- significant, BUT each integer represents the size for that dimension (and hence cannot be negative). So, if you read the description of index, you won't be surprised with the below description of extent<N> There is the rank field returning the value of N you passed as the template parameter. You can construct one extent from another (via the copy constructor or the assignment operator), you can construct it by passing an integer array, or via convenience constructor overloads for 1- 2- and 3- dimension extents. Note that the parameterless constructor creates an extent of the specified rank with all bounds initialized to 0. You can access the components of the extent through the subscript operator (passing it an integer). You can perform some arithmetic operations between extent objects through operator overloading, i.e. ==, !=, +=, -=, +, -. There are operator overloads so that you can perform operations between an extent and an integer: -- (pre- and post- decrement), ++ (pre- and post- increment), %=, *=, /=, +=, –= and, finally, there are additional overloads for plus and minus (+,-) between extent<N> and index<N> objects, returning a new extent object as the result. In addition to the usual suspects, extent offers a contains function that tests if an index is within the bounds of the extent (assuming an origin of zero). It also has a size function that returns the total linear size of this extent<N> in units of elements. Example code extent<2> e(3, 4); _ASSERT(e.rank == 2); _ASSERT(e.size() == 3 * 4); e += 3; e[1] += 6; e = e + index<2>(3,-4); _ASSERT(e == extent<2>(9, 9)); _ASSERT( e.contains(index<2>(8, 8))); _ASSERT(!e.contains(index<2>(8, 9))); grid<N> Our upcoming pre-release bits also have a similar type to extent, grid<N>. The way you create a grid is by passing it an extent, e.g. extent<3> e(4,2,6); grid<3> g(e); I am not going to dive deeper into grid, suffice for now to think of grid<N> simply as an alias for the extent<N> object, that you create when you encounter a function that expects a grid object instead of an extent object. Usage The extent class on its own simply defines the size of the N-dimensional space. We'll see in future posts that when you create containers (arrays) and wrappers (array_views) for your data, it is an extent<N> object that you'll need to use to create those (and use an index<N> object to index into them). We'll also see that it is a grid<N> object that you pass to the new parallel_for_each function that I'll cover in the next post. Comments about this post by Daniel Moth welcome at the original blog.

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  • Concurrency pattern of logger in multithreaded application

    - by Dipan Mehta
    The context: We are working on a multi-threaded (Linux-C) application that follows a pipeline model. Each module has a private thread and encapsulated objects which do processing of data; and each stage has a standard form of exchanging data with next unit. The application is free from memory leak and is threadsafe using locks at the point where they exchange data. Total number of threads is about 15- and each thread can have from 1 to 4 objects. Making about 25 - 30 odd objects which all have some critical logging to do. Most discussion I have seen about different levels as in Log4J and it's other translations. The real big questions is about how the overall logging should really happen? One approach is all local logging does fprintf to stderr. The stderr is redirected to some file. This approach is very bad when logs become too big. If all object instantiate their individual loggers - (about 30-40 of them) there will be too many files. And unlike above, one won't have the idea of true order of events. Timestamping is one possibility - but it is still a mess to collate. If there is a single global logger (singleton) pattern - it indirectly blocks so many threads while one is busy putting up logs. This is unacceptable when processing of the threads are heavy. So what should be the ideal way to structure the logging objects? What are some of the best practices in actual large scale applications? I would also love to learn from some of the real designs of large scale applications to get inspirations from!

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  • Unintentional run-in with C# thread concurrency

    - by geekrutherford
    For the first time today we began conducting load testing on a ASP.NET application already in production. Obviously you would normally want to load test prior to releasing to a production environment, but that isn't the point here.   We ran a test which simulated 5 users hitting the application doing the same actions simultaneously. The first few pages visited seemed fine and then things just hung for a while before the test failed. While the test was running I was viewing the performance counters on the server noting that the CPU was consistently pegged at 100% until the testing tool gave up.   Fortunately the application logs all exceptions including those unhandled to the database (thanks to log4net). I checked the log and low and behold the error was:   System.ArgumentException: An item with the same key has already been added. (The rest of the stack trace intentionally omitted)   Since the code was running with debug on the line number where the exception occured was also provided. I began inspecting the code and almost immediately it hit me, the section of code responsible for the exception is trying to initialize a static class. My next question was how is this code being hit multiple times when I have a rudimentary check already in place to prevent this kind of thing (i.e. a check on a public variable of the static class before entering the initializing routine). The answer...the check fails because the value is not set before other threads have already made it through.   Not being one who consistently works with threading I wasn't quite sure how to handle this problem. Fortunately a co-worker recalled having to lock a section of code in the past but couldn't recall exactly how. After a quick search on Google the solution is as follows:   Object objLock = new Object(); lock(objLock) { //logic requiring lock }   The lock statement takes an object and tells the .NET runtime that the current thread has exclusive access while the code within brackets is executing. Once the code completes, the lock is released for another thread to utilize.   In my case, I only need to execute the inner code once to initialize my static class. So within the brackets I have a check on a public variable to prevent it from being initialized again.

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  • How to implement blocking request-reply using Java concurrency primitives?

    - by Uri
    My system consists of a "proxy" class that receives "request" packets, marshals them and sends them over the network to a server, which unmarshals them, processes, and returns some "response packet". My "submit" method on the proxy side should block until a reply is received to the request (packets have ids for identification and referencing purposes) or until a timeout is reached. If I was building this in early versions of Java, I would likely implement in my proxy a collection of "pending messages ids", where I would submit a message, and wait() on the corresponding id (with a timeout). When a reply was received, the handling thread would notify() on the corresponding id. Is there a better way to achieve this using an existing library class, perhaps in java.util.concurrency? If I went with the solution described above, what is the correct way to deal with the potential race condition where a reply arrives before wait() is invoked?

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  • How to handle concurrency control in ASP.NET Dynamic Data?

    - by Andrew
    I've been quite impressed with dynamic data and how easy and quick it is to get a simple site up and running. I'm planning on using it for a simple internal HR admin site for registering people's skills/degrees/etc. I've been watching the intro videos at www.asp.net/dynamicdata and one thing they never mention is how to handle concurrency control. It seems that DD does not handle it right out of the box (unless there is some setting I haven't seen) as I manually generated a change conflict exception and the app failed without any user friendly message. Anybody know if DD handles it out of the box? Or do you have to somehow build it into the site?

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  • What are the best settings of the H2 database for high concurrency?

    - by dexter
    There are a lot of settings that can be used in H2 database. AUTO_SERVER, MVCC, LOCK_MODE, FILE_LOCK and MULTI_THREADED. I wonder what combination works best for high concurrency setup e.g. one thread is doing INSERTs and another connection does some UPDATEs and SELECTs? I tried MVCC=TRUE;LOCK_MODE=3lFILE_LOCK=NO but whenever I do some UPDATEs in one connection, the other connection does not see it even though I commit it. By the way the connections are from different processes e.g. separate program.

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  • Is Akka a good solution for a concurrent pipeline/workflow problem?

    - by herpylderp
    Disclaimer: I am brand new to Akka and the concept of Actors/Event-Driven Architectures in general. I have to implement a fairly complex problem where users can configure a "concurrent pipeline": Pipeline: consists of 1+ Stages; all Stages execute sequentially Stage: consists of 1+ Tasks; all Tasks execute in parallel Task: essentially a Java Runnable As you can see above, a Task is a Runnable that does some unit of work. Tasks are organized into Stages, which execute their Tasks in parallel. Stages are organized into the Pipeline, which executes its Stages sequentially. Hence if a user specifies the following Pipeline: CrossTheRoadSafelyPipeline Stage 1: Look Left Task 1: Turn your head to the left and look for cars Task 2: Listen for cars Stage 2: Look right Task 1: Turn your head to the right and look for cars Task 2: Listen for cars Then, Stage 1 will execute, and then Stage 2 will execute. However, while each Stage is executing, it's individual Tasks are executing in parallel/at the same time. In reality Pipelines will become very complicated, and with hundreds of Stages, dozens of Tasks per Stage (again, executing at the same time). To implement this Pipeline I can only think of several solutions: ESB/Apache Camel Guava Event Bus Java 5 Concurrency Actors/Akka Camel doesn't seem right because its core competency is integration not synchrony and orchestration across worker threads. Guava is great, but this doesn't really feel like a subscriber/publisher-type of problem. And Java 5 Concurrency (ExecutorService, etc.) just feels too low-level and painful. So I ask: is Akka a strong candidate for this type of problem? If so, how? If not, then why, and what is a good candidate?

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  • In Java Concurrency In Practice by Brian Goetz, why is the Memoizer class not annotated with @ThreadSafe?

    - by dig_dug
    Java Concurrency In Practice by Brian Goetz provides an example of a efficient scalable cache for concurrent use. The final version of the example showing the implementation for class Memoizer (pg 108) shows such a cache. I am wondering why the class is not annotated with @ThreadSafe? The client, class Factorizer, of the cache is properly annotated with @ThreadSafe. The appendix states that if a class is not annotated with either @ThreadSafe or @Immutable that it should be assumed that it isn't thread safe. Memoizer seems thread-safe though. Here is the code for Memoizer: public class Memoizer<A, V> implements Computable<A, V> { private final ConcurrentMap<A, Future<V>> cache = new ConcurrentHashMap<A, Future<V>>(); private final Computable<A, V> c; public Memoizer(Computable<A, V> c) { this.c = c; } public V compute(final A arg) throws InterruptedException { while (true) { Future<V> f = cache.get(arg); if (f == null) { Callable<V> eval = new Callable<V>() { public V call() throws InterruptedException { return c.compute(arg); } }; FutureTask<V> ft = new FutureTask<V>(eval); f = cache.putIfAbsent(arg, ft); if (f == null) { f = ft; ft.run(); } } try { return f.get(); } catch (CancellationException e) { cache.remove(arg, f); } catch (ExecutionException e) { throw launderThrowable(e.getCause()); } } } }

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