Search Results

Search found 134 results on 6 pages for 'bounded'.

Page 6/6 | < Previous Page | 2 3 4 5 6 

  • C#: Does an IDisposable in a Halted Iterator Dispose?

    - by James Michael Hare
    If that sounds confusing, let me give you an example. Let's say you expose a method to read a database of products, and instead of returning a List<Product> you return an IEnumerable<Product> in iterator form (yield return). This accomplishes several good things: The IDataReader is not passed out of the Data Access Layer which prevents abstraction leak and resource leak potentials. You don't need to construct a full List<Product> in memory (which could be very big) if you just want to forward iterate once. If you only want to consume up to a certain point in the list, you won't incur the database cost of looking up the other items. This could give us an example like: 1: // a sample data access object class to do standard CRUD operations. 2: public class ProductDao 3: { 4: private DbProviderFactory _factory = SqlClientFactory.Instance 5:  6: // a method that would retrieve all available products 7: public IEnumerable<Product> GetAvailableProducts() 8: { 9: // must create the connection 10: using (var con = _factory.CreateConnection()) 11: { 12: con.ConnectionString = _productsConnectionString; 13: con.Open(); 14:  15: // create the command 16: using (var cmd = _factory.CreateCommand()) 17: { 18: cmd.Connection = con; 19: cmd.CommandText = _getAllProductsStoredProc; 20: cmd.CommandType = CommandType.StoredProcedure; 21:  22: // get a reader and pass back all results 23: using (var reader = cmd.ExecuteReader()) 24: { 25: while(reader.Read()) 26: { 27: yield return new Product 28: { 29: Name = reader["product_name"].ToString(), 30: ... 31: }; 32: } 33: } 34: } 35: } 36: } 37: } The database details themselves are irrelevant. I will say, though, that I'm a big fan of using the System.Data.Common classes instead of your provider specific counterparts directly (SqlCommand, OracleCommand, etc). This lets you mock your data sources easily in unit testing and also allows you to swap out your provider in one line of code. In fact, one of the shared components I'm most proud of implementing was our group's DatabaseUtility library that simplifies all the database access above into one line of code in a thread-safe and provider-neutral way. I went with my own flavor instead of the EL due to the fact I didn't want to force internal company consumers to use the EL if they didn't want to, and it made it easy to allow them to mock their database for unit testing by providing a MockCommand, MockConnection, etc that followed the System.Data.Common model. One of these days I'll blog on that if anyone's interested. Regardless, you often have situations like the above where you are consuming and iterating through a resource that must be closed once you are finished iterating. For the reasons stated above, I didn't want to return IDataReader (that would force them to remember to Dispose it), and I didn't want to return List<Product> (that would force them to hold all products in memory) -- but the first time I wrote this, I was worried. What if you never consume the last item and exit the loop? Are the reader, command, and connection all disposed correctly? Of course, I was 99.999999% sure the creators of C# had already thought of this and taken care of it, but inspection in Reflector was difficult due to the nature of the state machines yield return generates, so I decided to try a quick example program to verify whether or not Dispose() will be called when an iterator is broken from outside the iterator itself -- i.e. before the iterator reports there are no more items. So I wrote a quick Sequencer class with a Dispose() method and an iterator for it. Yes, it is COMPLETELY contrived: 1: // A disposable sequence of int -- yes this is completely contrived... 2: internal class Sequencer : IDisposable 3: { 4: private int _i = 0; 5: private readonly object _mutex = new object(); 6:  7: // Constructs an int sequence. 8: public Sequencer(int start) 9: { 10: _i = start; 11: } 12:  13: // Gets the next integer 14: public int GetNext() 15: { 16: lock (_mutex) 17: { 18: return _i++; 19: } 20: } 21:  22: // Dispose the sequence of integers. 23: public void Dispose() 24: { 25: // force output immediately (flush the buffer) 26: Console.WriteLine("Disposed with last sequence number of {0}!", _i); 27: Console.Out.Flush(); 28: } 29: } And then I created a generator (infinite-loop iterator) that did the using block for auto-Disposal: 1: // simply defines an extension method off of an int to start a sequence 2: public static class SequencerExtensions 3: { 4: // generates an infinite sequence starting at the specified number 5: public static IEnumerable<int> GetSequence(this int starter) 6: { 7: // note the using here, will call Dispose() when block terminated. 8: using (var seq = new Sequencer(starter)) 9: { 10: // infinite loop on this generator, means must be bounded by caller! 11: while(true) 12: { 13: yield return seq.GetNext(); 14: } 15: } 16: } 17: } This is really the same conundrum as the database problem originally posed. Here we are using iteration (yield return) over a large collection (infinite sequence of integers). If we cut the sequence short by breaking iteration, will that using block exit and hence, Dispose be called? Well, let's see: 1: // The test program class 2: public class IteratorTest 3: { 4: // The main test method. 5: public static void Main() 6: { 7: Console.WriteLine("Going to consume 10 of infinite items"); 8: Console.Out.Flush(); 9:  10: foreach(var i in 0.GetSequence()) 11: { 12: // could use TakeWhile, but wanted to output right at break... 13: if(i >= 10) 14: { 15: Console.WriteLine("Breaking now!"); 16: Console.Out.Flush(); 17: break; 18: } 19:  20: Console.WriteLine(i); 21: Console.Out.Flush(); 22: } 23:  24: Console.WriteLine("Done with loop."); 25: Console.Out.Flush(); 26: } 27: } So, what do we see? Do we see the "Disposed" message from our dispose, or did the Dispose get skipped because from an "eyeball" perspective we should be locked in that infinite generator loop? Here's the results: 1: Going to consume 10 of infinite items 2: 0 3: 1 4: 2 5: 3 6: 4 7: 5 8: 6 9: 7 10: 8 11: 9 12: Breaking now! 13: Disposed with last sequence number of 11! 14: Done with loop. Yes indeed, when we break the loop, the state machine that C# generates for yield iterate exits the iteration through the using blocks and auto-disposes the IDisposable correctly. I must admit, though, the first time I wrote one, I began to wonder and that led to this test. If you've never seen iterators before (I wrote a previous entry here) the infinite loop may throw you, but you have to keep in mind it is not a linear piece of code, that every time you hit a "yield return" it cedes control back to the state machine generated for the iterator. And this state machine, I'm happy to say, is smart enough to clean up the using blocks correctly. I suspected those wily guys and gals at Microsoft engineered it well, and I wasn't disappointed. But, I've been bitten by assumptions before, so it's good to test and see. Yes, maybe you knew it would or figured it would, but isn't it nice to know? And as those campy 80s G.I. Joe cartoon public service reminders always taught us, "Knowing is half the battle...". Technorati Tags: C#,.NET

    Read the article

  • Changes to the LINQ-to-StreamInsight Dialect

    - by Roman Schindlauer
    In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

    Read the article

  • Changes to the LINQ-to-StreamInsight Dialect

    - by Roman Schindlauer
    In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

    Read the article

  • Converting OCaml to F#: F# equivelent of Pervasives at_exit

    - by Guy Coder
    I am converting the OCaml Format module to F# and tracked a problem back to a use of the OCaml Pervasives at_exit. val at_exit : (unit -> unit) -> unit Register the given function to be called at program termination time. The functions registered with at_exit will be called when the program executes exit, or terminates, either normally or because of an uncaught exception. The functions are called in "last in, first out" order: the function most recently added with at_exit is called first. In the process of conversion I commented out the line as the compiler did not flag it as being needed and I was not expecting an event in the code. I checked the FSharp.PowerPack.Compatibility.PervasivesModule for at_exit using VS Object Browser and found none. I did find how to run code "at_exit"? and How do I write an exit handler for an F# application? The OCaml line is at_exit print_flush with print_flush signature: val print_flush : (unit -> unit) Also in looking at the use of it during a debug session of the OCaml code, it looks like at_exit is called both at the end of initialization and at the end of each use of a call to the module. Any suggestions, hints on how to do this. This will be my first event in F#. EDIT Here is some of what I have learned about the Format module that should shed some light on the problem. The Format module is a library of functions for basic pretty printer commands of simple OCaml values such as int, bool, string. The format module has commands like print_string, but also some commands to say put the next line in a bounded box, think new set of left and right margins. So one could write: print_string "Hello" or open_box 0; print_string "<<"; open_box 0; print_string "p \/ q ==> r"; close_box(); print_string ">>"; close_box() The commands such as open_box and print_string are handled by a loop that interprets the commands and then decides wither to print on the current line or advance to the next line. The commands are held in a queue and there is a state record to hold mutable values such as left and right margin. The queue and state needs to be primed, which from debugging the test cases against working OCaml code appears to be done at the end of initialization of the module but before the first call is made to any function in the Format module. The queue and state is cleaned up and primed again for the next set of commands by the use of mechanisms for at_exit that recognize that the last matching frame for the initial call to the format modules has been removed thus triggering the call to at_exit which pushes out any remaining command in the queue and re-initializes the queue and state. So the sequencing of the calls to print_flush is critical and appears to be at more than what the OCaml documentation states.

    Read the article

  • C#/.NET Fundamentals: Choosing the Right Collection Class

    - by James Michael Hare
    The .NET Base Class Library (BCL) has a wide array of collection classes at your disposal which make it easy to manage collections of objects. While it's great to have so many classes available, it can be daunting to choose the right collection to use for any given situation. As hard as it may be, choosing the right collection can be absolutely key to the performance and maintainability of your application! This post will look at breaking down any confusion between each collection and the situations in which they excel. We will be spending most of our time looking at the System.Collections.Generic namespace, which is the recommended set of collections. The Generic Collections: System.Collections.Generic namespace The generic collections were introduced in .NET 2.0 in the System.Collections.Generic namespace. This is the main body of collections you should tend to focus on first, as they will tend to suit 99% of your needs right up front. It is important to note that the generic collections are unsynchronized. This decision was made for performance reasons because depending on how you are using the collections its completely possible that synchronization may not be required or may be needed on a higher level than simple method-level synchronization. Furthermore, concurrent read access (all writes done at beginning and never again) is always safe, but for concurrent mixed access you should either synchronize the collection or use one of the concurrent collections. So let's look at each of the collections in turn and its various pros and cons, at the end we'll summarize with a table to help make it easier to compare and contrast the different collections. The Associative Collection Classes Associative collections store a value in the collection by providing a key that is used to add/remove/lookup the item. Hence, the container associates the value with the key. These collections are most useful when you need to lookup/manipulate a collection using a key value. For example, if you wanted to look up an order in a collection of orders by an order id, you might have an associative collection where they key is the order id and the value is the order. The Dictionary<TKey,TVale> is probably the most used associative container class. The Dictionary<TKey,TValue> is the fastest class for associative lookups/inserts/deletes because it uses a hash table under the covers. Because the keys are hashed, the key type should correctly implement GetHashCode() and Equals() appropriately or you should provide an external IEqualityComparer to the dictionary on construction. The insert/delete/lookup time of items in the dictionary is amortized constant time - O(1) - which means no matter how big the dictionary gets, the time it takes to find something remains relatively constant. This is highly desirable for high-speed lookups. The only downside is that the dictionary, by nature of using a hash table, is unordered, so you cannot easily traverse the items in a Dictionary in order. The SortedDictionary<TKey,TValue> is similar to the Dictionary<TKey,TValue> in usage but very different in implementation. The SortedDictionary<TKey,TValye> uses a binary tree under the covers to maintain the items in order by the key. As a consequence of sorting, the type used for the key must correctly implement IComparable<TKey> so that the keys can be correctly sorted. The sorted dictionary trades a little bit of lookup time for the ability to maintain the items in order, thus insert/delete/lookup times in a sorted dictionary are logarithmic - O(log n). Generally speaking, with logarithmic time, you can double the size of the collection and it only has to perform one extra comparison to find the item. Use the SortedDictionary<TKey,TValue> when you want fast lookups but also want to be able to maintain the collection in order by the key. The SortedList<TKey,TValue> is the other ordered associative container class in the generic containers. Once again SortedList<TKey,TValue>, like SortedDictionary<TKey,TValue>, uses a key to sort key-value pairs. Unlike SortedDictionary, however, items in a SortedList are stored as an ordered array of items. This means that insertions and deletions are linear - O(n) - because deleting or adding an item may involve shifting all items up or down in the list. Lookup time, however is O(log n) because the SortedList can use a binary search to find any item in the list by its key. So why would you ever want to do this? Well, the answer is that if you are going to load the SortedList up-front, the insertions will be slower, but because array indexing is faster than following object links, lookups are marginally faster than a SortedDictionary. Once again I'd use this in situations where you want fast lookups and want to maintain the collection in order by the key, and where insertions and deletions are rare. The Non-Associative Containers The other container classes are non-associative. They don't use keys to manipulate the collection but rely on the object itself being stored or some other means (such as index) to manipulate the collection. The List<T> is a basic contiguous storage container. Some people may call this a vector or dynamic array. Essentially it is an array of items that grow once its current capacity is exceeded. Because the items are stored contiguously as an array, you can access items in the List<T> by index very quickly. However inserting and removing in the beginning or middle of the List<T> are very costly because you must shift all the items up or down as you delete or insert respectively. However, adding and removing at the end of a List<T> is an amortized constant operation - O(1). Typically List<T> is the standard go-to collection when you don't have any other constraints, and typically we favor a List<T> even over arrays unless we are sure the size will remain absolutely fixed. The LinkedList<T> is a basic implementation of a doubly-linked list. This means that you can add or remove items in the middle of a linked list very quickly (because there's no items to move up or down in contiguous memory), but you also lose the ability to index items by position quickly. Most of the time we tend to favor List<T> over LinkedList<T> unless you are doing a lot of adding and removing from the collection, in which case a LinkedList<T> may make more sense. The HashSet<T> is an unordered collection of unique items. This means that the collection cannot have duplicates and no order is maintained. Logically, this is very similar to having a Dictionary<TKey,TValue> where the TKey and TValue both refer to the same object. This collection is very useful for maintaining a collection of items you wish to check membership against. For example, if you receive an order for a given vendor code, you may want to check to make sure the vendor code belongs to the set of vendor codes you handle. In these cases a HashSet<T> is useful for super-quick lookups where order is not important. Once again, like in Dictionary, the type T should have a valid implementation of GetHashCode() and Equals(), or you should provide an appropriate IEqualityComparer<T> to the HashSet<T> on construction. The SortedSet<T> is to HashSet<T> what the SortedDictionary<TKey,TValue> is to Dictionary<TKey,TValue>. That is, the SortedSet<T> is a binary tree where the key and value are the same object. This once again means that adding/removing/lookups are logarithmic - O(log n) - but you gain the ability to iterate over the items in order. For this collection to be effective, type T must implement IComparable<T> or you need to supply an external IComparer<T>. Finally, the Stack<T> and Queue<T> are two very specific collections that allow you to handle a sequential collection of objects in very specific ways. The Stack<T> is a last-in-first-out (LIFO) container where items are added and removed from the top of the stack. Typically this is useful in situations where you want to stack actions and then be able to undo those actions in reverse order as needed. The Queue<T> on the other hand is a first-in-first-out container which adds items at the end of the queue and removes items from the front. This is useful for situations where you need to process items in the order in which they came, such as a print spooler or waiting lines. So that's the basic collections. Let's summarize what we've learned in a quick reference table.  Collection Ordered? Contiguous Storage? Direct Access? Lookup Efficiency Manipulate Efficiency Notes Dictionary No Yes Via Key Key: O(1) O(1) Best for high performance lookups. SortedDictionary Yes No Via Key Key: O(log n) O(log n) Compromise of Dictionary speed and ordering, uses binary search tree. SortedList Yes Yes Via Key Key: O(log n) O(n) Very similar to SortedDictionary, except tree is implemented in an array, so has faster lookup on preloaded data, but slower loads. List No Yes Via Index Index: O(1) Value: O(n) O(n) Best for smaller lists where direct access required and no ordering. LinkedList No No No Value: O(n) O(1) Best for lists where inserting/deleting in middle is common and no direct access required. HashSet No Yes Via Key Key: O(1) O(1) Unique unordered collection, like a Dictionary except key and value are same object. SortedSet Yes No Via Key Key: O(log n) O(log n) Unique ordered collection, like SortedDictionary except key and value are same object. Stack No Yes Only Top Top: O(1) O(1)* Essentially same as List<T> except only process as LIFO Queue No Yes Only Front Front: O(1) O(1) Essentially same as List<T> except only process as FIFO   The Original Collections: System.Collections namespace The original collection classes are largely considered deprecated by developers and by Microsoft itself. In fact they indicate that for the most part you should always favor the generic or concurrent collections, and only use the original collections when you are dealing with legacy .NET code. Because these collections are out of vogue, let's just briefly mention the original collection and their generic equivalents: ArrayList A dynamic, contiguous collection of objects. Favor the generic collection List<T> instead. Hashtable Associative, unordered collection of key-value pairs of objects. Favor the generic collection Dictionary<TKey,TValue> instead. Queue First-in-first-out (FIFO) collection of objects. Favor the generic collection Queue<T> instead. SortedList Associative, ordered collection of key-value pairs of objects. Favor the generic collection SortedList<T> instead. Stack Last-in-first-out (LIFO) collection of objects. Favor the generic collection Stack<T> instead. In general, the older collections are non-type-safe and in some cases less performant than their generic counterparts. Once again, the only reason you should fall back on these older collections is for backward compatibility with legacy code and libraries only. The Concurrent Collections: System.Collections.Concurrent namespace The concurrent collections are new as of .NET 4.0 and are included in the System.Collections.Concurrent namespace. These collections are optimized for use in situations where multi-threaded read and write access of a collection is desired. The concurrent queue, stack, and dictionary work much as you'd expect. The bag and blocking collection are more unique. Below is the summary of each with a link to a blog post I did on each of them. ConcurrentQueue Thread-safe version of a queue (FIFO). For more information see: C#/.NET Little Wonders: The ConcurrentStack and ConcurrentQueue ConcurrentStack Thread-safe version of a stack (LIFO). For more information see: C#/.NET Little Wonders: The ConcurrentStack and ConcurrentQueue ConcurrentBag Thread-safe unordered collection of objects. Optimized for situations where a thread may be bother reader and writer. For more information see: C#/.NET Little Wonders: The ConcurrentBag and BlockingCollection ConcurrentDictionary Thread-safe version of a dictionary. Optimized for multiple readers (allows multiple readers under same lock). For more information see C#/.NET Little Wonders: The ConcurrentDictionary BlockingCollection Wrapper collection that implement producers & consumers paradigm. Readers can block until items are available to read. Writers can block until space is available to write (if bounded). For more information see C#/.NET Little Wonders: The ConcurrentBag and BlockingCollection Summary The .NET BCL has lots of collections built in to help you store and manipulate collections of data. Understanding how these collections work and knowing in which situations each container is best is one of the key skills necessary to build more performant code. Choosing the wrong collection for the job can make your code much slower or even harder to maintain if you choose one that doesn’t perform as well or otherwise doesn’t exactly fit the situation. Remember to avoid the original collections and stick with the generic collections.  If you need concurrent access, you can use the generic collections if the data is read-only, or consider the concurrent collections for mixed-access if you are running on .NET 4.0 or higher.   Tweet Technorati Tags: C#,.NET,Collecitons,Generic,Concurrent,Dictionary,List,Stack,Queue,SortedList,SortedDictionary,HashSet,SortedSet

    Read the article

  • Another Marketing Conference, part one – the best morning sessions.

    - by Roger Hart
    Yesterday I went to Another Marketing Conference. I honestly can’t tell if the title is just tipping over into smug, but in the balance of things that doesn’t matter, because it was a good conference. There was an enjoyable blend of theoretical and practical, and enough inter-disciplinary spread to keep my inner dilettante grinning from ear to ear. Sure, there was a bumpy bit in the middle, with two back-to-back sales pitches and a rather thin overview of the state of the web. But the signal:noise ratio at AMC2012 was impressively high. Here’s the first part of my write-up of the sessions. It’s a bit of a mammoth. It’s also a bit of a mash-up of what was said and what I thought about it. I’ll add links to the videos and slides from the sessions as they become available. Although it was in the morning session, I’ve not included Vanessa Northam’s session on the power of internal comms to build brand ambassadors. It’ll be in the next roundup, as this is already pushing 2.5k words. First, the important stuff. I was keeping a tally, and nobody said “synergy” or “leverage”. I did, however, hear the term “marketeers” six times. Shame on you – you know who you are. 1 – Branding in a post-digital world, Graham Hales This initially looked like being a sales presentation for Interbrand, but Graham pulled it out of the bag a few minutes in. He introduced a model for brand management that was essentially Plan >> Do >> Check >> Act, with Do and Check rolled up together, and went on to stress that this looks like on overall business management model for a reason. Brand has to be part of your overall business strategy and metrics if you’re going to care about it at all. This was the first iteration of what proved to be one of the event’s emergent themes: do it throughout the stack or don’t bother. Graham went on to remind us that brands, in so far as they are owned at all, are owned by and co-created with our customers. Advertising can offer a message to customers, but they provide the expression of a brand. This was a preface to talking about an increasingly chaotic marketplace, with increasingly hard-to-manage purchase processes. Services like Amazon reviews and TripAdvisor (four presenters would make this point) saturate customers with information, and give them a kind of vigilante power to comment on and define brands. Consequentially, they experience a number of “moments of deflection” in our sales funnels. Our control is lessened, and failure to engage can negatively-impact buying decisions increasingly poorly. The clearest example given was the failure of NatWest’s “caring bank” campaign, where staff in branches, customer support, and online presences didn’t align. A discontinuity of experience basically made the campaign worthless, and disgruntled customers talked about it loudly on social media. This in turn presented an opportunity to engage and show caring, but that wasn’t taken. What I took away was that brand (co)creation is ongoing and needs monitoring and metrics. But reciprocally, given you get what you measure, strategy and metrics must include brand if any kind of branding is to work at all. Campaigns and messages must permeate product and service design. What that doesn’t mean (and Graham didn’t say it did) is putting Marketing at the top of the pyramid, and having them bawl demands at Product Management, Support, and Development like an entitled toddler. It’s going to have to be collaborative, and session 6 on internal comms handled this really well. The main thing missing here was substantiating data, and the main question I found myself chewing on was: if we’re building brands collaboratively and in the open, what about the cultural politics of trolling? 2 – Challenging our core beliefs about human behaviour, Mark Earls This was definitely the best show of the day. It was also some of the best content. Mark talked us through nudging, behavioural economics, and some key misconceptions around decision making. Basically, people aren’t rational, they’re petty, reactive, emotional sacks of meat, and they’ll go where they’re led. Comforting stuff. Examples given were the spread of the London Riots and the “discovery” of the mountains of Kong, and the popularity of Susan Boyle, which, in turn made me think about Per Mollerup’s concept of “social wayshowing”. Mark boiled his thoughts down into four key points which I completely failed to write down word for word: People do, then think – Changing minds to change behaviour doesn’t work. Post-rationalization rules the day. See also: mere exposure effects. Spock < Kirk - Emotional/intuitive comes first, then we rationalize impulses. The non-thinking, emotive, reactive processes run much faster than the deliberative ones. People are not really rational decision makers, so  intervening with information may not be appropriate. Maximisers or satisficers? – Related to the last point. People do not consistently, rationally, maximise. When faced with an abundance of choice, they prefer to satisfice than evaluate, and will often follow social leads rather than think. Things tend to converge – Behaviour trends to a consensus normal. When faced with choices people overwhelmingly just do what they see others doing. Humans are extraordinarily good at mirroring behaviours and receiving influence. People “outsource the cognitive load” of choices to the crowd. Mark’s headline quote was probably “the real influence happens at the table next to you”. Reference examples, word of mouth, and social influence are tremendously important, and so talking about product experiences may be more important than talking about products. This reminded me of Kathy Sierra’s “creating bad-ass users” concept of designing to make people more awesome rather than products they like. If we can expose user-awesome, and make sharing easy, we can normalise the behaviours we want. If we normalize the behaviours we want, people should make and post-rationalize the buying decisions we want.  Where we need to be: “A bigger boy made me do it” Where we are: “a wizard did it and ran away” However, it’s worth bearing in mind that some purchasing decisions are personal and informed rather than social and reactive. There’s a quadrant diagram, in fact. What was really interesting, though, towards the end of the talk, was some advice for working out how social your products might be. The standard technology adoption lifecycle graph is essentially about social product diffusion. So this idea isn’t really new. Geoffrey Moore’s “chasm” idea may not strictly apply. However, his concepts of beachheads and reference segments are exactly what is required to normalize and thus enable purchase decisions (behaviour change). The final thing is that in only very few categories does a better product actually affect purchase decision. Where the choice is personal and informed, this is true. But where it’s personal and impulsive, or in any way social, “better” is trumped by popularity, endorsement, or “point of sale salience”. UX, UCD, and e-commerce know this to be true. A better (and easier) experience will always beat “more features”. Easy to use, and easy to observe being used will beat “what the user says they want”. This made me think about the astounding stickiness of rational fallacies, “common sense” and the pathological willful simplifications of the media. Rational fallacies seem like they’re basically the heuristics we use for post-rationalization. If I were profoundly grimy and cynical, I’d suggest deploying a boat-load in our messaging, to see if they’re really as sticky and appealing as they look. 4 – Changing behaviour through communication, Stephen Donajgrodzki This was a fantastic follow up to Mark’s session. Stephen basically talked us through some tactics used in public information/health comms that implement the kind of behavioural theory Mark introduced. The session was largely about how to get people to do (good) things they’re predisposed not to do, and how communication can (and can’t) make positive interventions. A couple of things stood out, in particular “implementation intentions” and how they can be linked to goals. For example, in order to get people to check and test their smoke alarms (a goal intention, rarely actualized  an information campaign will attempt to link this activity to the clocks going back or forward (a strong implementation intention, well-actualized). The talk reinforced the idea that making behaviour changes easy and visible normalizes them and makes them more likely to succeed. To do this, they have to be embodied throughout a product and service cycle. Experiential disconnects undermine the normalization. So campaigns, products, and customer interactions must be aligned. This is underscored by the second section of the presentation, which talked about interventions and pre-conditions for change. Taking the examples of drug addiction and stopping smoking, Stephen showed us a framework for attempting (and succeeding or failing in) behaviour change. He noted that when the change is something people fundamentally want to do, and that is easy, this gets a to simpler. Coordinated, easily-observed environmental pressures create preconditions for change and build motivation. (price, pub smoking ban, ad campaigns, friend quitting, declining social acceptability) A triggering even leads to a change attempt. (getting a cold and panicking about how bad the cough is) Interventions can be made to enable an attempt (NHS services, public information, nicotine patches) If it succeeds – yay. If it fails, there’s strong negative enforcement. Triggering events seem largely personal, but messaging can intervene in the creation of preconditions and in supporting decisions. Stephen talked more about systems of thinking and “bounded rationality”. The idea being that to enable change you need to break through “automatic” thinking into “reflective” thinking. Disruption and emotion are great tools for this, but that is only the start of the process. It occurs to me that a great deal of market research is focused on determining triggers rather than analysing necessary preconditions. Although they are presumably related. The final section talked about setting goals. Marketing goals are often seen as deriving directly from business goals. However, marketing may be unable to deliver on these directly where decision and behaviour-change processes are involved. In those cases, marketing and communication goals should be to create preconditions. They should also consider priming and norms. Content marketing and brand awareness are good first steps here, as brands can be heuristics in decision making for choice-saturated consumers, or those seeking education. 5 – The power of engaged communities and how to build them, Harriet Minter (the Guardian) The meat of this was that you need to let communities define and establish themselves, and be quick to react to their needs. Harriet had been in charge of building the Guardian’s community sites, and learned a lot about how they come together, stabilize  grow, and react. Crucially, they can’t be about sales or push messaging. A community is not just an audience. It’s essential to start with what this particular segment or tribe are interested in, then what they want to hear. Eventually you can consider – in light of this – what they might want to buy, but you can’t start with the product. A community won’t cohere around one you’re pushing. Her tips for community building were (again, sorry, not verbatim): Set goals Have some targets. Community building sounds vague and fluffy, but you can have (and adjust) concrete goals. Think like a start-up This is the “lean” stuff. Try things, fail quickly, respond. Don’t restrict platforms Let the audience choose them, and be aware of their differences. For example, LinkedIn is very different to Twitter. Track your stats Related to the first point. Keeping an eye on the numbers lets you respond. They should be qualified, however. If you want a community of enterprise decision makers, headcount alone may be a bad metric – have you got CIOs, or just people who want to get jobs by mingling with CIOs? Build brand advocates Do things to involve people and make them awesome, and they’ll cheer-lead for you. The last part really got my attention. Little bits of drive-by kindness go a long way. But more than that, genuinely helping people turns them into powerful advocates. Harriet gave an example of the Guardian engaging with an aspiring journalist on its Q&A forums. Through a series of serendipitous encounters he became a BBC producer, and now enthusiastically speaks up for the Guardian community sites. Cultivating many small, authentic, influential voices may have a better pay-off than schmoozing the big guys. This could be particularly important in the context of Mark and Stephen’s models of social, endorsement-led, and example-led decision making. There’s a lot here I haven’t covered, and it may be worth some follow-up on community building. Thoughts I was quite sceptical of nudge theory and behavioural economics. First off it sounds too good to be true, and second it sounds too sinister to permit. But I haven’t done the background reading. So I’m going to, and if it seems to hold real water, and if it’s possible to do it ethically (Stephen’s presentations suggests it may be) then it’s probably worth exploring. The message seemed to be: change what people do, and they’ll work out why afterwards. Moreover, the people around them will do it too. Make the things you want them to do extraordinarily easy and very, very visible. Normalize and support the decisions you want them to make, and they’ll make them. In practice this means not talking about the thing, but showing the user-awesome. Glib? Perhaps. But it feels worth considering. Also, if I ever run a marketing conference, I’m going to ban speakers from using examples from Apple. Quite apart from not being consistently generalizable, it’s becoming an irritating cliché.

    Read the article

  • Scala n00b: Critique my code

    - by Peter
    G'day everyone, I'm a Scala n00b (but am experienced with other languages) and am learning the language as I find time - very much enjoying it so far! Usually when learning a new language the first thing I do is implement Conway's Game of Life, since it's just complex enough to give a good sense of the language, but small enough in scope to be able to whip up in a couple of hours (most of which is spent wrestling with syntax). Anyhoo, having gone through this exercise with Scala I was hoping the Scala gurus out there might take a look at the code I've ended up with and provide feedback on it. I'm after anything - algorithmic improvements (particularly concurrent solutions!), stylistic improvements, alternative APIs or language constructs, disgust at the length of my function names - whatever feedback you've got, I'm keen to hear it! You should be able to run the following script via "scala GameOfLife.scala" - by default it will run a 20x20 board with a single glider on it - please feel free to experiment. // CONWAY'S GAME OF LIFE (SCALA) abstract class GameOfLifeBoard(val aliveCells : Set[Tuple2[Int, Int]]) { // Executes a "time tick" - returns a new board containing the next generation def tick : GameOfLifeBoard // Is the board empty? def empty : Boolean = aliveCells.size == 0 // Is the given cell alive? protected def alive(cell : Tuple2[Int, Int]) : Boolean = aliveCells contains cell // Is the given cell dead? protected def dead(cell : Tuple2[Int, Int]) : Boolean = !alive(cell) } class InfiniteGameOfLifeBoard(aliveCells : Set[Tuple2[Int, Int]]) extends GameOfLifeBoard(aliveCells) { // Executes a "time tick" - returns a new board containing the next generation override def tick : GameOfLifeBoard = new InfiniteGameOfLifeBoard(nextGeneration) // The next generation of this board protected def nextGeneration : Set[Tuple2[Int, Int]] = aliveCells flatMap neighbours filter shouldCellLiveInNextGeneration // Should the given cell should live in the next generation? protected def shouldCellLiveInNextGeneration(cell : Tuple2[Int, Int]) : Boolean = (alive(cell) && (numberOfAliveNeighbours(cell) == 2 || numberOfAliveNeighbours(cell) == 3)) || (dead(cell) && numberOfAliveNeighbours(cell) == 3) // The number of alive neighbours for the given cell protected def numberOfAliveNeighbours(cell : Tuple2[Int, Int]) : Int = aliveNeighbours(cell) size // Returns the alive neighbours for the given cell protected def aliveNeighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = aliveCells intersect neighbours(cell) // Returns all neighbours (whether dead or alive) for the given cell protected def neighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = Set((cell._1-1, cell._2-1), (cell._1, cell._2-1), (cell._1+1, cell._2-1), (cell._1-1, cell._2), (cell._1+1, cell._2), (cell._1-1, cell._2+1), (cell._1, cell._2+1), (cell._1+1, cell._2+1)) // Information on where the currently live cells are protected def xVals = aliveCells map { cell => cell._1 } protected def xMin = (xVals reduceLeft (_ min _)) - 1 protected def xMax = (xVals reduceLeft (_ max _)) + 1 protected def xRange = xMin until xMax + 1 protected def yVals = aliveCells map { cell => cell._2 } protected def yMin = (yVals reduceLeft (_ min _)) - 1 protected def yMax = (yVals reduceLeft (_ max _)) + 1 protected def yRange = yMin until yMax + 1 // Returns a simple graphical representation of this board override def toString : String = { var result = "" for (y <- yRange) { for (x <- xRange) { if (alive (x,y)) result += "# " else result += ". " } result += "\n" } result } // Equality stuff override def equals(other : Any) : Boolean = { other match { case that : InfiniteGameOfLifeBoard => (that canEqual this) && that.aliveCells == this.aliveCells case _ => false } } def canEqual(other : Any) : Boolean = other.isInstanceOf[InfiniteGameOfLifeBoard] override def hashCode = aliveCells.hashCode } class FiniteGameOfLifeBoard(val boardWidth : Int, val boardHeight : Int, aliveCells : Set[Tuple2[Int, Int]]) extends InfiniteGameOfLifeBoard(aliveCells) { override def tick : GameOfLifeBoard = new FiniteGameOfLifeBoard(boardWidth, boardHeight, nextGeneration) // Determines the coordinates of all of the neighbours of the given cell override protected def neighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = super.neighbours(cell) filter { cell => cell._1 >= 0 && cell._1 < boardWidth && cell._2 >= 0 && cell._2 < boardHeight } // Information on where the currently live cells are override protected def xRange = 0 until boardWidth override protected def yRange = 0 until boardHeight // Equality stuff override def equals(other : Any) : Boolean = { other match { case that : FiniteGameOfLifeBoard => (that canEqual this) && that.boardWidth == this.boardWidth && that.boardHeight == this.boardHeight && that.aliveCells == this.aliveCells case _ => false } } override def canEqual(other : Any) : Boolean = other.isInstanceOf[FiniteGameOfLifeBoard] override def hashCode : Int = { 41 * ( 41 * ( 41 + super.hashCode ) + boardHeight.hashCode ) + boardWidth.hashCode } } class GameOfLife(initialBoard: GameOfLifeBoard) { // Run the game of life until the board is empty or the exact same board is seen twice // Important note: this method does NOT necessarily terminate!! def go : Unit = { var currentBoard = initialBoard var previousBoards = List[GameOfLifeBoard]() while (!currentBoard.empty && !(previousBoards contains currentBoard)) { print(27.toChar + "[2J") // ANSI: clear screen print(27.toChar + "[;H") // ANSI: move cursor to top left corner of screen println(currentBoard.toString) Thread.sleep(75) // Warning: unbounded list concatenation can result in OutOfMemoryExceptions ####TODO: replace with LRU bounded list previousBoards = List(currentBoard) ::: previousBoards currentBoard = currentBoard tick } // Print the final board print(27.toChar + "[2J") // ANSI: clear screen print(27.toChar + "[;H") // ANSI: move cursor to top left corner of screen println(currentBoard.toString) } } // Script starts here val simple = Set((1,1)) val square = Set((4,4), (4,5), (5,4), (5,5)) val glider = Set((2,1), (3,2), (1,3), (2,3), (3,3)) val initialBoard = glider (new GameOfLife(new FiniteGameOfLifeBoard(20, 20, initialBoard))).go //(new GameOfLife(new InfiniteGameOfLifeBoard(initialBoard))).go // COPYRIGHT PETER MONKS 2010 Thanks! Peter

    Read the article

  • Unknown error in Producer/Consumer program, believe it to be an infinite loop.

    - by ray2k
    Hello, I am writing a program that is solving the producer/consumer problem, specifically the bounded-buffer version(i believe they mean the same thing). The producer will be generating x number of random numbers, where x is a command line parameter to my program. At the current moment, I believe my program is entering an infinite loop, but I'm not sure why it is occurring. I believe I am executing the semaphores correctly. You compile it like this: gcc -o prodcon prodcon.cpp -lpthread -lrt Then to run, ./prodcon 100(the number of randum nums to produce) This is my code. typedef int buffer_item; #include <stdlib.h> #include <stdio.h> #include <pthread.h> #include <semaphore.h> #include <unistd.h> #define BUFF_SIZE 10 #define RAND_DIVISOR 100000000 #define TRUE 1 //two threads void *Producer(void *param); void *Consumer(void *param); int insert_item(buffer_item item); int remove_item(buffer_item *item); int returnRandom(); //the global semaphores sem_t empty, full, mutex; //the buffer buffer_item buf[BUFF_SIZE]; //buffer counter int counter; //number of random numbers to produce int numRand; int main(int argc, char** argv) { /* thread ids and attributes */ pthread_t pid, cid; pthread_attr_t attr; pthread_attr_init(&attr); pthread_attr_setscope(&attr, PTHREAD_SCOPE_SYSTEM); numRand = atoi(argv[1]); sem_init(&empty,0,BUFF_SIZE); sem_init(&full,0,0); sem_init(&mutex,0,0); printf("main started\n"); pthread_create(&pid, &attr, Producer, NULL); pthread_create(&cid, &attr, Consumer, NULL); printf("main gets here"); pthread_join(pid, NULL); pthread_join(cid, NULL); printf("main done\n"); return 0; } //generates a randum number between 1 and 100 int returnRandom() { int num; srand(time(NULL)); num = rand() % 100 + 1; return num; } //begin producing items void *Producer(void *param) { buffer_item item; int i; for(i = 0; i < numRand; i++) { //sleep for a random period of time int rNum = rand() / RAND_DIVISOR; sleep(rNum); //generate a random number item = returnRandom(); //acquire the empty lock sem_wait(&empty); //acquire the mutex lock sem_wait(&mutex); if(insert_item(item)) { fprintf(stderr, " Producer report error condition\n"); } else { printf("producer produced %d\n", item); } /* release the mutex lock */ sem_post(&mutex); /* signal full */ sem_post(&full); } return NULL; } /* Consumer Thread */ void *Consumer(void *param) { buffer_item item; int i; for(i = 0; i < numRand; i++) { /* sleep for a random period of time */ int rNum = rand() / RAND_DIVISOR; sleep(rNum); /* aquire the full lock */ sem_wait(&full); /* aquire the mutex lock */ sem_wait(&mutex); if(remove_item(&item)) { fprintf(stderr, "Consumer report error condition\n"); } else { printf("consumer consumed %d\n", item); } /* release the mutex lock */ sem_post(&mutex); /* signal empty */ sem_post(&empty); } return NULL; } /* Add an item to the buffer */ int insert_item(buffer_item item) { /* When the buffer is not full add the item and increment the counter*/ if(counter < BUFF_SIZE) { buf[counter] = item; counter++; return 0; } else { /* Error the buffer is full */ return -1; } } /* Remove an item from the buffer */ int remove_item(buffer_item *item) { /* When the buffer is not empty remove the item and decrement the counter */ if(counter > 0) { *item = buf[(counter-1)]; counter--; return 0; } else { /* Error buffer empty */ return -1; } }

    Read the article

  • Producer and Consumer Threads Hang

    - by user972425
    So this is my first foray into threads and thus far it is driving me insane. My problem seems to be some kind of synchronization error that causes my consumer thread to hang. I've looked at other code and just about everything I could find and I can't find what my error is. There also seems to be a discrepancy between the code being executed in Eclipse and via javac in the command line. Intention - Using a bounded buffer (with 1000 slots) create and consume 1,000,000 doubles. Use only notify and wait. Problem - In Eclipse the consumer thread will occasionally hang around 940,000 iterations, but other times completes. In the command line the consumer thread always hangs. Output - Eclipse - Successful Producer has produced 100000 doubles. Consumer has consumed 100000 doubles. Producer has produced 200000 doubles. Consumer has consumed 200000 doubles. Producer has produced 300000 doubles. Consumer has consumed 300000 doubles. Producer has produced 400000 doubles. Consumer has consumed 400000 doubles. Producer has produced 500000 doubles. Consumer has consumed 500000 doubles. Producer has produced 600000 doubles. Consumer has consumed 600000 doubles. Producer has produced 700000 doubles. Consumer has consumed 700000 doubles. Producer has produced 800000 doubles. Consumer has consumed 800000 doubles. Producer has produced 900000 doubles. Consumer has consumed 900000 doubles. Producer has produced 1000000 doubles. Producer has produced all items. Consumer has consumed 1000000 doubles. Consumer has consumed all items. Exitting Output - Command Line/Eclipse - Unsuccessful Producer has produced 100000 doubles. Consumer has consumed 100000 doubles. Producer has produced 200000 doubles. Consumer has consumed 200000 doubles. Producer has produced 300000 doubles. Consumer has consumed 300000 doubles. Producer has produced 400000 doubles. Consumer has consumed 400000 doubles. Producer has produced 500000 doubles. Consumer has consumed 500000 doubles. Producer has produced 600000 doubles. Consumer has consumed 600000 doubles. Producer has produced 700000 doubles. Consumer has consumed 700000 doubles. Producer has produced 800000 doubles. Consumer has consumed 800000 doubles. Producer has produced 900000 doubles. Consumer has consumed 900000 doubles. Producer has produced 1000000 doubles. Producer has produced all items. At this point it just sits and hangs. Any help you can provide about where I might have misstepped is greatly appreciated. Code - Producer thread import java.text.DecimalFormat;+ " doubles. Cumulative value of generated items= " + temp) import java.util.*; import java.io.*; public class producer implements Runnable{ private buffer produceBuff; public producer (buffer buff){ produceBuff = buff; } public void run(){ Random random = new Random(); double temp = 0, randomElem; DecimalFormat df = new DecimalFormat("#.###"); for(int i = 1; i<=1000000; i++) { randomElem = (Double.parseDouble( df.format(random.nextDouble() * 100.0))); try { produceBuff.add(randomElem); } catch (InterruptedException e) { // TODO Auto-generated catch block e.printStackTrace(); } temp+= randomElem; if(i%100000 == 0) {produceBuff.print("Producer has produced "+ i ); } } produceBuff.print("Producer has produced all items."); } } Consumer thread import java.util.*; import java.io.*; public class consumer implements Runnable{ private buffer consumBuff; public consumer (buffer buff){ consumBuff = buff; } public void run(){ double temp = 0; for(int i = 1; i<=1000000; i++) { try { temp += consumBuff.get(); } catch (InterruptedException e) { // TODO Auto-generated catch block e.printStackTrace(); } if(i%100000 == 0) {consumBuff.print("Consumer has consumed "+ i ); //if(i>999000) //{System.out.println("Consuming item " + i);} } consumBuff.print("Consumer has consumed all items."); } } Buffer/Main import java.util.*; import java.io.*; public class buffer { private double buff[]; private int addPlace; private int getPlace; public buffer(){ buff = new double[1000]; addPlace = 0; getPlace = 0; } public synchronized void add(double add) throws InterruptedException{ if((addPlace+1 == getPlace) ) { try { wait(); } catch (InterruptedException e) {throw e;} } buff[addPlace] = add; addPlace = (addPlace+1)%1000; notify(); } public synchronized double get()throws InterruptedException{ if(getPlace == addPlace) { try { wait(); } catch (InterruptedException e) {throw e;} } double temp = buff[getPlace]; getPlace = (getPlace+1)%1000; notify(); return temp; } public synchronized void print(String view) { System.out.println(view); } public static void main(String args[]){ buffer buf = new buffer(); Thread produce = new Thread(new producer(buf)); Thread consume = new Thread(new consumer(buf)); produce.start(); consume.start(); try { produce.join(); consume.join(); } catch (InterruptedException e) {return;} System.out.println("Exitting"); } }

    Read the article

< Previous Page | 2 3 4 5 6