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  • Is there added overhead to looking up a column in a DataTable by name rather than by index?

    - by Ben McCormack
    In a DataTable object, is there added overhead to looking up a column value by name thisRow("ColumnA") rather than by the column index thisRow(0)? In which scenarios might this be an issue. I work on a team that has lots of experience writing VB6 code and I noticed that didn't do column lookups by name for DataTable objects or data grids. Even in .NET code, we use a set of integer constants to reference column names in these types of objects. I asked our team lead why this was so, and he mentioned that in VB6, there was a lot of overhead in looking up data by column name rather than by index. Is this still true for .NET? Example code (in VB.NET, but same applies to C#): Public Sub TestADOData() Dim dt As New DataTable 'Set up the columns in the DataTable ' dt.Columns.Add(New DataColumn("ID", GetType(Integer))) dt.Columns.Add(New DataColumn("Name", GetType(String))) dt.Columns.Add(New DataColumn("Description", GetType(String))) 'Add some data to the data table ' dt.Rows.Add(1, "Fred", "Pitcher") dt.Rows.Add(3, "Hank", "Center Field") 'Method 1: By Column Name ' For Each r As DataRow In dt.Rows Console.WriteLine( _ "{0,-2} {1,-10} {2,-30}", r("ID"), r("Name"), r("Description")) Next Console.WriteLine() 'Method 2: By Column Name ' For Each r As DataRow In dt.Rows Console.WriteLine("{0,-2} {1,-10} {2,-30}", r(0), r(1), r(2)) Next End Sub Is there an case where method 2 provides a performance advantage over method 1?

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  • Infinite detail inside Perlin noise procedural mapping

    - by Dave Jellison
    I am very new to game development but I was able to scour the internet to figure out Perlin noise enough to implement a very simple 2D tile infinite procedural world. Here's the question and it's more conceptual than code-based in answer, I think. I understand the concept of "I plug in (x, y) and get back from Perlin noise p" (I'll call it p). P will always be the same value for the same (x, y) (as long as the Perlin algorithm parameters haven't changed, like altering number of octaves, et cetera). What I want to do is be able to zoom into a square and be able to generate smaller squares inside of the already generated overhead tile of terrain. Let's say I have a jungle tile for overhead terrain but I want to zoom in and maybe see a small river tile that would only be a creek and not large enough to be a full "big tile" of water in the overhead. Of course, I want the same net effect as a Perlin equation inside a Perlin equation if that makes sense? (aka. I want two people playing the game with the same settings to get the same terrain and details every time). I can conceptually wrap my head around the large tile being based on an "zoomed out" coordinate leaving enough room to drill into but this approach doesn't make sense in my head (maybe I'm wrong). I'm guessing with this approach my overhead terrain would lose all of the cohesiveness delivered by the Perlin. Imagine I calculate (0, 0) as overhead tile 1 and then to the east of that I plug in (50, 0). OK, great, I now have 49 pixels of detail I could then "drill down" into. The issue I have in my head with this approach (without attempting it) is that there's no guarantee from my Perlin noise that (0,0) would be a good neighbor to (50,0) as they could have wildly different "elevations" or p/resultant values returning from the Perlin equation when I generate the overhead map. I think I can conceive of using the Perlin noise for the overhead tile to then reuse the p value as a seed for the "detail" level of noise once I zoom in. That would ensure my detail Perlin is always the same configuration for (0,0), (1,0), etc. ad nauseam but I'm not sure if there are better approaches out there or if this is a sound approach at all.

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  • Why do I have such high overhead when creating tables in MSSQL7?

    - by scotty2012
    I have an old system running MSSQL7. It takes about 10.5 seconds to create the table below and another 30 seconds to add the index. Is there anything I can do to decrease these times? CREATE TABLE [dbo].[MyTable] ( [queue] [int] NOT NULL , [seqNum] [numeric](12, 0) NOT NULL , [cTime] [char] (14) NOT NULL , [msg] [char] (255) NULL , [status] [int] NOT NULL , [socket] [int] NULL ) ON [PRIMARY] GO CREATE INDEX [search] ON [dbo].[MyTable]([queue], [seqNum], [status]) ON [PRIMARY] GO CREATE INDEX [new] ON [dbo].[MyTable]([queue], [status]) ON [PRIMARY]

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  • What might cause the big overhead of making a HttpWebRequest call?

    - by Dimitri C.
    When I send/receive data using HttpWebRequest (on Silverlight, using the HTTP POST method) in small blocks, I measure the very small throughput of 500 bytes/s over a "localhost" connection. When sending the data in large blocks, I get 2 MB/s, which is some 5000 times faster. Does anyone know what could cause this incredibly big overhead? Update: I did the performance measurement on both Firefox 3.6 and Internet Explorer 7. Both showed similar results. Update: The Silverlight client-side code I use is essentially my own implementation of the WebClient class. The reason I wrote it is because I noticed the same performance problem with WebClient, and I thought that the HttpWebRequest would allow to tweak the performance issue. Regrettably, this did not work. The implementation is as follows: public class HttpCommChannel { public delegate void ResponseArrivedCallback(object requestContext, BinaryDataBuffer response); public HttpCommChannel(ResponseArrivedCallback responseArrivedCallback) { this.responseArrivedCallback = responseArrivedCallback; this.requestSentEvent = new ManualResetEvent(false); this.responseArrivedEvent = new ManualResetEvent(true); } public void MakeRequest(object requestContext, string url, BinaryDataBuffer requestPacket) { responseArrivedEvent.WaitOne(); responseArrivedEvent.Reset(); this.requestMsg = requestPacket; this.requestContext = requestContext; this.webRequest = WebRequest.Create(url) as HttpWebRequest; this.webRequest.AllowReadStreamBuffering = true; this.webRequest.ContentType = "text/plain"; this.webRequest.Method = "POST"; this.webRequest.BeginGetRequestStream(new AsyncCallback(this.GetRequestStreamCallback), null); this.requestSentEvent.WaitOne(); } void GetRequestStreamCallback(IAsyncResult asynchronousResult) { System.IO.Stream postStream = webRequest.EndGetRequestStream(asynchronousResult); postStream.Write(requestMsg.Data, 0, (int)requestMsg.Size); postStream.Close(); requestSentEvent.Set(); webRequest.BeginGetResponse(new AsyncCallback(this.GetResponseCallback), null); } void GetResponseCallback(IAsyncResult asynchronousResult) { HttpWebResponse response = (HttpWebResponse)webRequest.EndGetResponse(asynchronousResult); Stream streamResponse = response.GetResponseStream(); Dim.Ensure(streamResponse.CanRead); byte[] readData = new byte[streamResponse.Length]; Dim.Ensure(streamResponse.Read(readData, 0, (int)streamResponse.Length) == streamResponse.Length); streamResponse.Close(); response.Close(); webRequest = null; responseArrivedEvent.Set(); responseArrivedCallback(requestContext, new BinaryDataBuffer(readData)); } HttpWebRequest webRequest; ManualResetEvent requestSentEvent; BinaryDataBuffer requestMsg; object requestContext; ManualResetEvent responseArrivedEvent; ResponseArrivedCallback responseArrivedCallback; } I use this code to send data back and forth to an HTTP server.

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  • Overhead of serving pages - JSPs vs. PHP vs. ASPXs vs. C

    - by John Shedletsky
    I am interested in writing my own internet ad server. I want to serve billions of impressions with as little hardware possible. Which server-side technologies are best suited for this task? I am asking about the relative overhead of serving my ad pages as either pages rendered by PHP, or Java, or .net, or coding Http responses directly in C and writing some multi-socket IO monster to serve requests (I assume this one wins, but if my assumption is wrong, that would actually be most interesting). Obviously all the most efficient optimizations are done at the algorithm level, but I figure there has got to be some speed differences at the end of the day that makes one method of serving ads better than another. How much overhead does something like apache or IIS introduce? There's got to be a ton of extra junk in there I don't need. At some point I guess this is more a question of which platform/language combo is best suited - please excuse the in-adroitly posed question, hopefully you understand what I am trying to get at.

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  • Oracle Schema Design: Seperate Schema with I/O Overhead?

    - by Guru
    We are designing database schema for a new system based on Oracle 11gR1. We have identified a main schema which would have close to 100 tables, these will be accessed from the front end Java application. We have a requirement to audit the values which got changed in close to 50 tables, this has to be done every row. Which means, it is possible that, for a single row in MYSYS.T1 there might be 50 (or more) rows in MYSYS_AUDIT.T1_AUD table. We might be having old values of every column entry and new values available from T1. DBA gave an observation, advising against this method, because he said, separate schema meant an extra I/O for every operation. Basically AUDIT schema would be used only to do some analyse and enter values (thus SELECT and INSERT). Is it true that, "a separate schema means an extra I/O" ? I could not find justification. It appears logical to me, as the AUDIT data should not be tampered with, so a separate schema. Also, we designed a separate schema for archiving some tables from MYSYS. From MYSYS_ARC the table might be backed up into tapes or deleted after sufficient time. Few stats: Few tables (close to 20, 30) in MYSYS schema could grow to around 50M rows. We have asked for a total disk space of 4 TB. MYSYS_AUDIT schema might be having 10 times that of MYSYS but we wont keep them more than 3 months. Questions Given all these, can you suggest me any improvements? Separate schema affects disc I/O? (one extra I/O for every schema ?) Any general suggestions? Figure: +-------------------+ +-------------------+ | MYSYS | | MYSYS_AUDIT | | | | | | 1. T1 | | 1. T1_AUD | | 2. T2 | | 2. T2_AUD | | 3. T3 |--------->| 3. T3_AUD | | 4. T4 |(SELECT, | 4. T4_AUD | | . | INSERT) | . | | . | | . | | . | | . | | 100. T100 | | 50. T50_AUD | +-------------------+ +-------------------+ | | | | |(INSERT) | | | * +-------------------+ | MYSYS_ARC | | | | 1. T1_ARC | | 2. T2_ARC | | 3. T3_ARC | | 4. T4_ARC | | . | | . | | . | | 100. T100_ARC | +-------------------+ Apart from this, we have two more schemas with only read only rights, but mainly they are for adhoc purpose and we dont mind the performance on them.

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  • How much network overhead does TLS add compared to a non-encrypted connection?

    - by Daniel Sterling
    (Approximately) how many more bits of data must be transferred over the network during an encrypted connection compared to an unencrypted connection? IIUC, once the TLS handshake has completed, the number of bits transferred is equal to those transferred during an unencrypted connection. Is this accurate? As a follow up, is transferring a large file over https significantly slower than transferring that file over http, given fast processors and the same (ideal) network conditions?

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  • Getting a nicely formatted timestamp without lots of overhead?

    - by Brad Hein
    In my app I have a textView which contains real-time messages from my app, as things happen, messages get printed to this text box. Each message is time-stamped with HH:MM:SS. Up to now, I had also been chasing what seemed to be a memory leak, but as it turns out, it's just my time-stamp formatting method (see below), It apparently produces thousands of objects that later get gc'd. For 1-10 messages per second, I was seeing 500k-2MB of garbage collected every second by the GC while this method was in place. After removing it, no more garbage problem (its back to a nice interval of about 30 seconds, and only a few k of junk typically) So I'm looking for a new, more lightweight method for producing a HH:MM:SS timestamp string :) Old code: /** * Returns a string containing the current time stamp. * @return - a string. */ public static String currentTimeStamp() { String ret = ""; Date d = new Date(); SimpleDateFormat timeStampFormatter = new SimpleDateFormat("hh:mm:ss"); ret = timeStampFormatter.format(d); return ret; }

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  • Overhead of calling tiny functions from a tight inner loop? [C++]

    - by John
    Say you see a loop like this one: for(int i=0; i<thing.getParent().getObjectModel().getElements(SOME_TYPE).count(); ++i) { thing.getData().insert( thing.GetData().Count(), thing.getParent().getObjectModel().getElements(SOME_TYPE)[i].getName() ); } if this was Java I'd probably not think twice. But in performance-critical sections of C++, it makes me want to tinker with it... however I don't know if the compiler is smart enough to make it futile. This is a made up example but all it's doing is inserting strings into a container. Please don't assume any of these are STL types, think in general terms about the following: Is having a messy condition in the for loop going to get evaluated each time, or only once? If those get methods are simply returning references to member variables on the objects, will they be inlined away? Would you expect custom [] operators to get optimized at all? In other words is it worth the time (in performance only, not readability) to convert it to something like: ElementContainer &source = thing.getParent().getObjectModel().getElements(SOME_TYPE); int num = source.count(); Store &destination = thing.getData(); for(int i=0;i<num;++i) { destination.insert(thing.GetData().Count(), source[i].getName(); } Remember, this is a tight loop, called millions of times a second. What I wonder is if all this will shave a couple of cycles per loop or something more substantial? Yes I know the quote about "premature optimisation". And I know that profiling is important. But this is a more general question about modern compilers, Visual Studio in particular.

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  • Low-overhead way to access the memory space of a traced process?

    - by vovick
    Hello all. I'm looking for an efficient way to access(for both read and write operations) the memory space of my ptraced child process. The size of blocks being accessed may vary from several bytes up to several megabytes in size, so using the ptrace call with PTRACE_PEEKDATA and PTRACE_POKEDATA which read only one word at a time and switch context every time they're called seems like a pointless waste of resources. The only one alternative solution I could find, though, was the /proc/<pid>/mem file, but it has long since been made read only. Is there any other (relatively simple) way to do that job? The ideal solution would be to somehow share the address space of my child process with its parent and then use the simple memcpy call to copy data I need in both directions, but I have no clues how to do it and where to begin. Any ideas?

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  • Understanding LINQ to SQL (11) Performance

    - by Dixin
    [LINQ via C# series] LINQ to SQL has a lot of great features like strong typing query compilation deferred execution declarative paradigm etc., which are very productive. Of course, these cannot be free, and one price is the performance. O/R mapping overhead Because LINQ to SQL is based on O/R mapping, one obvious overhead is, data changing usually requires data retrieving:private static void UpdateProductUnitPrice(int id, decimal unitPrice) { using (NorthwindDataContext database = new NorthwindDataContext()) { Product product = database.Products.Single(item => item.ProductID == id); // SELECT... product.UnitPrice = unitPrice; // UPDATE... database.SubmitChanges(); } } Before updating an entity, that entity has to be retrieved by an extra SELECT query. This is slower than direct data update via ADO.NET:private static void UpdateProductUnitPrice(int id, decimal unitPrice) { using (SqlConnection connection = new SqlConnection( "Data Source=localhost;Initial Catalog=Northwind;Integrated Security=True")) using (SqlCommand command = new SqlCommand( @"UPDATE [dbo].[Products] SET [UnitPrice] = @UnitPrice WHERE [ProductID] = @ProductID", connection)) { command.Parameters.Add("@ProductID", SqlDbType.Int).Value = id; command.Parameters.Add("@UnitPrice", SqlDbType.Money).Value = unitPrice; connection.Open(); command.Transaction = connection.BeginTransaction(); command.ExecuteNonQuery(); // UPDATE... command.Transaction.Commit(); } } The above imperative code specifies the “how to do” details with better performance. For the same reason, some articles from Internet insist that, when updating data via LINQ to SQL, the above declarative code should be replaced by:private static void UpdateProductUnitPrice(int id, decimal unitPrice) { using (NorthwindDataContext database = new NorthwindDataContext()) { database.ExecuteCommand( "UPDATE [dbo].[Products] SET [UnitPrice] = {0} WHERE [ProductID] = {1}", id, unitPrice); } } Or just create a stored procedure:CREATE PROCEDURE [dbo].[UpdateProductUnitPrice] ( @ProductID INT, @UnitPrice MONEY ) AS BEGIN BEGIN TRANSACTION UPDATE [dbo].[Products] SET [UnitPrice] = @UnitPrice WHERE [ProductID] = @ProductID COMMIT TRANSACTION END and map it as a method of NorthwindDataContext (explained in this post):private static void UpdateProductUnitPrice(int id, decimal unitPrice) { using (NorthwindDataContext database = new NorthwindDataContext()) { database.UpdateProductUnitPrice(id, unitPrice); } } As a normal trade off for O/R mapping, a decision has to be made between performance overhead and programming productivity according to the case. In a developer’s perspective, if O/R mapping is chosen, I consistently choose the declarative LINQ code, unless this kind of overhead is unacceptable. Data retrieving overhead After talking about the O/R mapping specific issue. Now look into the LINQ to SQL specific issues, for example, performance in the data retrieving process. The previous post has explained that the SQL translating and executing is complex. Actually, the LINQ to SQL pipeline is similar to the compiler pipeline. It consists of about 15 steps to translate an C# expression tree to SQL statement, which can be categorized as: Convert: Invoke SqlProvider.BuildQuery() to convert the tree of Expression nodes into a tree of SqlNode nodes; Bind: Used visitor pattern to figure out the meanings of names according to the mapping info, like a property for a column, etc.; Flatten: Figure out the hierarchy of the query; Rewrite: for SQL Server 2000, if needed Reduce: Remove the unnecessary information from the tree. Parameterize Format: Generate the SQL statement string; Parameterize: Figure out the parameters, for example, a reference to a local variable should be a parameter in SQL; Materialize: Executes the reader and convert the result back into typed objects. So for each data retrieving, even for data retrieving which looks simple: private static Product[] RetrieveProducts(int productId) { using (NorthwindDataContext database = new NorthwindDataContext()) { return database.Products.Where(product => product.ProductID == productId) .ToArray(); } } LINQ to SQL goes through above steps to translate and execute the query. Fortunately, there is a built-in way to cache the translated query. Compiled query When such a LINQ to SQL query is executed repeatedly, The CompiledQuery can be used to translate query for one time, and execute for multiple times:internal static class CompiledQueries { private static readonly Func<NorthwindDataContext, int, Product[]> _retrieveProducts = CompiledQuery.Compile((NorthwindDataContext database, int productId) => database.Products.Where(product => product.ProductID == productId).ToArray()); internal static Product[] RetrieveProducts( this NorthwindDataContext database, int productId) { return _retrieveProducts(database, productId); } } The new version of RetrieveProducts() gets better performance, because only when _retrieveProducts is first time invoked, it internally invokes SqlProvider.Compile() to translate the query expression. And it also uses lock to make sure translating once in multi-threading scenarios. Static SQL / stored procedures without translating Another way to avoid the translating overhead is to use static SQL or stored procedures, just as the above examples. Because this is a functional programming series, this article not dive into. For the details, Scott Guthrie already has some excellent articles: LINQ to SQL (Part 6: Retrieving Data Using Stored Procedures) LINQ to SQL (Part 7: Updating our Database using Stored Procedures) LINQ to SQL (Part 8: Executing Custom SQL Expressions) Data changing overhead By looking into the data updating process, it also needs a lot of work: Begins transaction Processes the changes (ChangeProcessor) Walks through the objects to identify the changes Determines the order of the changes Executes the changings LINQ queries may be needed to execute the changings, like the first example in this article, an object needs to be retrieved before changed, then the above whole process of data retrieving will be went through If there is user customization, it will be executed, for example, a table’s INSERT / UPDATE / DELETE can be customized in the O/R designer It is important to keep these overhead in mind. Bulk deleting / updating Another thing to be aware is the bulk deleting:private static void DeleteProducts(int categoryId) { using (NorthwindDataContext database = new NorthwindDataContext()) { database.Products.DeleteAllOnSubmit( database.Products.Where(product => product.CategoryID == categoryId)); database.SubmitChanges(); } } The expected SQL should be like:BEGIN TRANSACTION exec sp_executesql N'DELETE FROM [dbo].[Products] AS [t0] WHERE [t0].[CategoryID] = @p0',N'@p0 int',@p0=9 COMMIT TRANSACTION Hoverer, as fore mentioned, the actual SQL is to retrieving the entities, and then delete them one by one:-- Retrieves the entities to be deleted: exec sp_executesql N'SELECT [t0].[ProductID], [t0].[ProductName], [t0].[SupplierID], [t0].[CategoryID], [t0].[QuantityPerUnit], [t0].[UnitPrice], [t0].[UnitsInStock], [t0].[UnitsOnOrder], [t0].[ReorderLevel], [t0].[Discontinued] FROM [dbo].[Products] AS [t0] WHERE [t0].[CategoryID] = @p0',N'@p0 int',@p0=9 -- Deletes the retrieved entities one by one: BEGIN TRANSACTION exec sp_executesql N'DELETE FROM [dbo].[Products] WHERE ([ProductID] = @p0) AND ([ProductName] = @p1) AND ([SupplierID] IS NULL) AND ([CategoryID] = @p2) AND ([QuantityPerUnit] IS NULL) AND ([UnitPrice] = @p3) AND ([UnitsInStock] = @p4) AND ([UnitsOnOrder] = @p5) AND ([ReorderLevel] = @p6) AND (NOT ([Discontinued] = 1))',N'@p0 int,@p1 nvarchar(4000),@p2 int,@p3 money,@p4 smallint,@p5 smallint,@p6 smallint',@p0=78,@p1=N'Optimus Prime',@p2=9,@p3=$0.0000,@p4=0,@p5=0,@p6=0 exec sp_executesql N'DELETE FROM [dbo].[Products] WHERE ([ProductID] = @p0) AND ([ProductName] = @p1) AND ([SupplierID] IS NULL) AND ([CategoryID] = @p2) AND ([QuantityPerUnit] IS NULL) AND ([UnitPrice] = @p3) AND ([UnitsInStock] = @p4) AND ([UnitsOnOrder] = @p5) AND ([ReorderLevel] = @p6) AND (NOT ([Discontinued] = 1))',N'@p0 int,@p1 nvarchar(4000),@p2 int,@p3 money,@p4 smallint,@p5 smallint,@p6 smallint',@p0=79,@p1=N'Bumble Bee',@p2=9,@p3=$0.0000,@p4=0,@p5=0,@p6=0 -- ... COMMIT TRANSACTION And the same to the bulk updating. This is really not effective and need to be aware. Here is already some solutions from the Internet, like this one. The idea is wrap the above SELECT statement into a INNER JOIN:exec sp_executesql N'DELETE [dbo].[Products] FROM [dbo].[Products] AS [j0] INNER JOIN ( SELECT [t0].[ProductID], [t0].[ProductName], [t0].[SupplierID], [t0].[CategoryID], [t0].[QuantityPerUnit], [t0].[UnitPrice], [t0].[UnitsInStock], [t0].[UnitsOnOrder], [t0].[ReorderLevel], [t0].[Discontinued] FROM [dbo].[Products] AS [t0] WHERE [t0].[CategoryID] = @p0) AS [j1] ON ([j0].[ProductID] = [j1].[[Products])', -- The Primary Key N'@p0 int',@p0=9 Query plan overhead The last thing is about the SQL Server query plan. Before .NET 4.0, LINQ to SQL has an issue (not sure if it is a bug). LINQ to SQL internally uses ADO.NET, but it does not set the SqlParameter.Size for a variable-length argument, like argument of NVARCHAR type, etc. So for two queries with the same SQL but different argument length:using (NorthwindDataContext database = new NorthwindDataContext()) { database.Products.Where(product => product.ProductName == "A") .Select(product => product.ProductID).ToArray(); // The same SQL and argument type, different argument length. database.Products.Where(product => product.ProductName == "AA") .Select(product => product.ProductID).ToArray(); } Pay attention to the argument length in the translated SQL:exec sp_executesql N'SELECT [t0].[ProductID] FROM [dbo].[Products] AS [t0] WHERE [t0].[ProductName] = @p0',N'@p0 nvarchar(1)',@p0=N'A' exec sp_executesql N'SELECT [t0].[ProductID] FROM [dbo].[Products] AS [t0] WHERE [t0].[ProductName] = @p0',N'@p0 nvarchar(2)',@p0=N'AA' Here is the overhead: The first query’s query plan cache is not reused by the second one:SELECT sys.syscacheobjects.cacheobjtype, sys.dm_exec_cached_plans.usecounts, sys.syscacheobjects.[sql] FROM sys.syscacheobjects INNER JOIN sys.dm_exec_cached_plans ON sys.syscacheobjects.bucketid = sys.dm_exec_cached_plans.bucketid; They actually use different query plans. Again, pay attention to the argument length in the [sql] column (@p0 nvarchar(2) / @p0 nvarchar(1)). Fortunately, in .NET 4.0 this is fixed:internal static class SqlTypeSystem { private abstract class ProviderBase : TypeSystemProvider { protected int? GetLargestDeclarableSize(SqlType declaredType) { SqlDbType sqlDbType = declaredType.SqlDbType; if (sqlDbType <= SqlDbType.Image) { switch (sqlDbType) { case SqlDbType.Binary: case SqlDbType.Image: return 8000; } return null; } if (sqlDbType == SqlDbType.NVarChar) { return 4000; // Max length for NVARCHAR. } if (sqlDbType != SqlDbType.VarChar) { return null; } return 8000; } } } In this above example, the translated SQL becomes:exec sp_executesql N'SELECT [t0].[ProductID] FROM [dbo].[Products] AS [t0] WHERE [t0].[ProductName] = @p0',N'@p0 nvarchar(4000)',@p0=N'A' exec sp_executesql N'SELECT [t0].[ProductID] FROM [dbo].[Products] AS [t0] WHERE [t0].[ProductName] = @p0',N'@p0 nvarchar(4000)',@p0=N'AA' So that they reuses the same query plan cache: Now the [usecounts] column is 2.

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  • Engineering as a Service

    - by jgelhaus
    Oracle Exadata Database Machine is known for great compute performance, and over the past few years, it has also become known as a great platform for any type of Oracle Database workload, from data warehousing to online transaction processing (OLTP). But now organizations are turning to Oracle Exadata for business efficiencies and private cloud solutions—for consolidation and database as a service (DBaaS). University of Minnesota For an inside look at how DBaaS is working in the real world, it’s worth checking into the University of Minnesota’s database hotel.  With more than 50,000 students, the University of Minnesota in Minneapolis is one of the largest universities in the United States. The university’s centralized IT group not only has to support all those students but also must provide support and services to more than 40 departments and colleges within the university. They have two Exadata Database Machine X2-2 half-rack systems from Oracle, with four database nodes each and roughly 30 terabytes of usable disk space for each of the Oracle Exadata systems. The university is using Oracle Real Application Clusters (Oracle RAC) for high availability and the Data Guard feature of Oracle Database, Enterprise Edition, for disaster recovery capabilities. The deployment has been live in production since May 2011. Overhead Door When it comes to overhead, revolving, sliding, or other specialty residential and commercial doors, Overhead Door is the worldwide leader. But when they needed to open doors with their customers through a better, faster, and more agile IT infrastructure, Overhead Door turned to Oracle and Oracle Exadata. Oracle Exadata Database Machine plays an important part in Overhead Door’s IT and business strategy. The organization has two Exadata Database Machine X2-2s deployed, one in production and one in development and testing Read the full Oracle Magazine article Engineering as a Service

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  • C99 variable length automatic array performance

    - by aaa
    Is there significant cpu/memory overhead associated with using automatic arrays with g++/Intel on 64-bit x86 linux platform? int function(int N) { double array[N]; overhead compared to allocating array before hand (assuming function is called multiple times) overhead compared to using new overhead compared to using malloc The range of N may be from 1kb to 16kb roughly, stack overrun is not a problem.

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  • C/C++ variable length automatic array performance

    - by aaa
    hello. Is there significant cpu/memory overhead associated with using automatic arrays with g++/Intel on 64-bit x86 linux platform? int function(int N) { double array[N]; overhead compared to allocating array before hand (assuming function is called multiple times) overhead compared to using new overhead compared to using malloc range of N maybe from 1kb to 16kb roughly, stack overrun is not a problem Thank you

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  • Does OO, TDD, and Refactoring to Smaller Functions affect Speed of Code?

    - by Dennis
    In Computer Science field, I have noticed a notable shift in thinking when it comes to programming. The advice as it stands now is write smaller, more testable code refactor existing code into smaller and smaller chunks of code until most of your methods/functions are just a few lines long write functions that only do one thing (which makes them smaller again) This is a change compared to the "old" or "bad" code practices where you have methods spanning 2500 lines, and big classes doing everything. My question is this: when it call comes down to machine code, to 1s and 0s, to assembly instructions, should I be at all concerned that my class-separated code with variety of small-to-tiny functions generates too much extra overhead? While I am not exactly familiar with how OO code and function calls are handled in ASM in the end, I do have some idea. I assume that each extra function call, object call, or include call (in some languages), generate an extra set of instructions, thereby increasing code's volume and adding various overhead, without adding actual "useful" code. I also imagine that good optimizations can be done to ASM before it is actually ran on the hardware, but that optimization can only do so much too. Hence, my question -- how much overhead (in space and speed) does well-separated code (split up across hundreds of files, classes, and methods) actually introduce compared to having "one big method that contains everything", due to this overhead? UPDATE for clarity: I am assuming that adding more and more functions and more and more objects and classes in a code will result in more and more parameter passing between smaller code pieces. It was said somewhere (quote TBD) that up to 70% of all code is made up of ASM's MOV instruction - loading CPU registers with proper variables, not the actual computation being done. In my case, you load up CPU's time with PUSH/POP instructions to provide linkage and parameter passing between various pieces of code. The smaller you make your pieces of code, the more overhead "linkage" is required. I am concerned that this linkage adds to software bloat and slow-down and I am wondering if I should be concerned about this, and how much, if any at all, because current and future generations of programmers who are building software for the next century, will have to live with and consume software built using these practices. UPDATE: Multiple files I am writing new code now that is slowly replacing old code. In particular I've noted that one of the old classes was a ~3000 line file (as mentioned earlier). Now it is becoming a set of 15-20 files located across various directories, including test files and not including PHP framework I am using to bind some things together. More files are coming as well. When it comes to disk I/O, loading multiple files is slower than loading one large file. Of course not all files are loaded, they are loaded as needed, and disk caching and memory caching options exist, and yet still I believe that loading multiple files takes more processing than loading a single file into memory. I am adding that to my concern.

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  • Performance impact of Zones.

    - by nospam(at)example.com (Joerg Moellenkamp)
    I was really astonished when i saw this question. Because this question was a old acquaintance from years ago, that i didn't heard for a long time. However there was it again. The question: "What's the overhead of Zones?". Sun was and Oracle is not saying "zero". We saying saying minimal. However during all the performance analysis gigs on customer systems i made since the introduction of Zones i failed to measure any overhead caused by zones. What i saw however, was additional load intoduced by processes that wouldn't be there when you would use only one zone Like additional monitoring daemons, like additional daemons having a controlling or supervising job for the application that resulted in slighly longer runtimes of processes, because such additional daemons wanted some cycles on the CPU as well. So i ask when someone wants to tell me that he measured a slight slowdown, if he or she has really measured the impact of the virtualization layer or of a side effect described above. It seems to be a little bit hard to believe, that a virtualisation technology has no overhead, however keep in mind that there is no hypervisor and just one kernel running that looks and behaves like many operating system instances to apps and users. While this imposes some limits to the technology (because there is just one kernel running you can't have zones with different kernels versions running ... obvious even to the cursory observer), but that is key to it's lightweightness and thus to the low overhead. Continue reading "Performance impact of Zones."

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  • Automated testing tool development challenges (for embedded software)

    - by Karthi prime
    My boss want to come up with the proposal for the following tool: An IDE: Able to build, compile, debug, via JTAG programming for the micro-controller. A Test Suite, reads the code in the IDE, auto generates the test cases, and it gives the in-target unit testing results(which is done by controlling code execution in the micro-controller via IDE). A no-overhead code coverage tool which interacts with the test suite and IDE. My work is to obtain the high level architecture of this tool, so as to proceed further. My current knowledge: There are tool-chains available from the chip manufacturer for the micro-controllers which can be utilized along with an open-source IDE like Eclipse, and along with an open-source burner, a complete IDE for a micro-controller can be done. Test cases can be auto-generated by reading the source file through the process of parsing, scripting, based on keywords. Test suite must be able to command the IDE to control, through breakpoints, and read the register contents from the microcontroller - This enables the in-target unit testing. An no-overhead code coverage should be done by no-overhead code instrumentation so as to execute those in the resource constraint environment of the micro-controller. I have the following questions: Any advice on the validity of my understanding? What are the challenges I will have during the development? What are the helpful open-source tools regarding this? What is the development time for this software? Thanks

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  • Can it be a good idea to lease a house rather than a standard office-space for a software development shop? [closed]

    - by hamlin11
    Our lease is up on our US-based office-space in July, so it's back on my radar to evaluate our office-space situation. Two of our partners rather like the idea of leasing a house rather than standard office-space. We have 4 partners and one employee. I'm against the idea at this moment in time. Pros, as I see them Easier to get a good location (minimize commutes) All partners/employees have dogs. Easier to work longer hours without dog-duties pulling people back home More comfortable bathroom situation Residential Internet Rate Control of the thermostat Clients don't come to our office, so this would not change our image The additional comfort-level should facilitate a significantly higher-percentage of time "in the zone" for programmers and artists. Cons, as I see them Additional bills to pay (house-cleaning, yard, util, gas, electric) Additional time-overhead in dealing with bills (house-cleaning, yard, util, gas, electric) Additional overhead required to deal with issues that maintenance would have dealt with in a standard office-space Residential neighbors to contend with The equation starts to look a little nasty when factoring in potential time-overhead, especially on issues that a maintenance crew would deal with at a standard office complex. Can this be a good thing for a software development shop?

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  • SQL SERVER – Select the Most Optimal Backup Methods for Server

    - by pinaldave
    Backup and Restore are very interesting concepts and one should be very much with the concept if you are dealing with production database. One never knows when a natural disaster or user error will surface and the first thing everybody wants is to get back on point in time when things were all fine. Well, in this article I have attempted to answer a few of the common questions related to Backup methodology. How to Select a SQL Server Backup Type In order to select a proper SQL Server backup type, a SQL Server administrator needs to understand the difference between the major backup types clearly. Since a picture is worth a thousand words, let me offer it to you below. Select a Recovery Model First The very first question that you should ask yourself is: Can I afford to lose at least a little (15 min, 1 hour, 1 day) worth of data? Resist the temptation to save it all as it comes with the overhead – majority of businesses outside finances can actually afford to lose a bit of data. If your answer is YES, I can afford to lose some data – select a SIMPLE (default) recovery model in the properties of your database, otherwise you need to select a FULL recovery model. The additional advantage of the Full recovery model is that it allows you to restore the data to a specific point in time vs to only last backup time in the Simple recovery model, but it exceeds the scope of this article Backups in SIMPLE Recovery Model In SIMPLE recovery model you can select to do just Full backups or Full + Differential. Full Backup This is the simplest type of backup that contains all information needed to restore the database and should be your first choice. It is often sufficient for small databases, but note that it makes a big impact on the performance of your database Full + Differential Backup After Full, Differential backup picks up all of the changes since the last Full backup. This means if you made Full, Diff, Diff backup – the last Diff backup contains all of the changes and you don’t need the previous Differential backup. Differential backup is obviously smaller and carries less performance overhead Backups in FULL Recovery Model In FULL recovery model you can select Full + Transaction Log or Full + Differential + Transaction Log backup. You have to create Transaction Log backup, because at that time the log is being truncated. Otherwise your Transaction Log will grow uncontrollably. Full + Transaction Log Backup You would always need to perform a Full backup first. Then a series of Transaction log backup. Note that (in contrast to Differential) you need ALL transactions to log since the last Full of Diff backup to properly restore. Transaction log backups have the smallest performance overhead and can be performed often. Full + Differential + Transaction Log Backup If you want to ease the performance overhead on your server, you can replace some of the Full backup in the previous scenario with Differential. You restore scenario would start from Full, then the Last Differential, then all of the remaining transactions log backups Typical backup Scenarios You may say “Well, it is all nice – give me the examples now”. As you may already know, my favorite SQL backup software is SQLBackupAndFTP. If you go to Advanced Backup Schedule form in this program and click “Load a typical backup plan…” link, it will give you these scenarios that I think are quite common – see the image below. The Simplest Way to Schedule SQL Backups I hate to repeat myself, but backup scheduling in SQL agent leaves a lot to be desired. I do not know the simple way to schedule your SQL server backups than in SQLBackupAndFTP – see the image below. The whole backup scheduling with compression, encryption and upload to a Network Folder / HDD / NAS Drive / FTP / Dropbox / Google Drive / Amazon S3 takes just a few minutes – see my previous post for the review. Final Words This post offered an explanation for major backup types only. For more complicated scenarios or to research other options as usually go to MSDN. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • C++: is it safe to work with std::vectors as if they were arrays?

    - by peoro
    I need to have a fixed-size array of elements and to call on them functions that require to know about how they're placed in memory, in particular: functions like glVertexPointer, that needs to know where the vertices are, how distant they are one from the other and so on. In my case vertices would be members of the elements to store. to get the index of an element within this array, I'd prefer to avoid having an index field within my elements, but would rather play with pointers arithmetic (ie: index of Element *x will be x - & array[0]) -- btw, this sounds dirty to me: is it good practice or should I do something else? Is it safe to use std::vector for this? Something makes me think that an std::array would be more appropriate but: Constructor and destructor for my structure will be rarely called: I don't mind about such overhead. I'm going to set the std::vector capacity to size I need (the size that would use for an std::array, thus won't take any overhead due to sporadic reallocation. I don't mind a little space overhead for std::vector's internal structure. I could use the ability to resize the vector (or better: to have a size chosen during setup), and I think there's no way to do this with std::array, since its size is a template parameter (that's too bad: I could do that even with an old C-like array, just dynamically allocating it on the heap). If std::vector is fine for my purpose I'd like to know into details if it will have some runtime overhead with respect to std::array (or to a plain C array): I know that it'll call the default constructor for any element once I increase its size (but I guess this won't cost anything if my data has got an empty default constructor?), same for destructor. Anything else?

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  • Parallelism in .NET – Part 5, Partitioning of Work

    - by Reed
    When parallelizing any routine, we start by decomposing the problem.  Once the problem is understood, we need to break our work into separate tasks, so each task can be run on a different processing element.  This process is called partitioning. Partitioning our tasks is a challenging feat.  There are opposing forces at work here: too many partitions adds overhead, too few partitions leaves processors idle.  Trying to work the perfect balance between the two extremes is the goal for which we should aim.  Luckily, the Task Parallel Library automatically handles much of this process.  However, there are situations where the default partitioning may not be appropriate, and knowledge of our routines may allow us to guide the framework to making better decisions. First off, I’d like to say that this is a more advanced topic.  It is perfectly acceptable to use the parallel constructs in the framework without considering the partitioning taking place.  The default behavior in the Task Parallel Library is very well-behaved, even for unusual work loads, and should rarely be adjusted.  I have found few situations where the default partitioning behavior in the TPL is not as good or better than my own hand-written partitioning routines, and recommend using the defaults unless there is a strong, measured, and profiled reason to avoid using them.  However, understanding partitioning, and how the TPL partitions your data, helps in understanding the proper usage of the TPL. I indirectly mentioned partitioning while discussing aggregation.  Typically, our systems will have a limited number of Processing Elements (PE), which is the terminology used for hardware capable of processing a stream of instructions.  For example, in a standard Intel i7 system, there are four processor cores, each of which has two potential hardware threads due to Hyperthreading.  This gives us a total of 8 PEs – theoretically, we can have up to eight operations occurring concurrently within our system. In order to fully exploit this power, we need to partition our work into Tasks.  A task is a simple set of instructions that can be run on a PE.  Ideally, we want to have at least one task per PE in the system, since fewer tasks means that some of our processing power will be sitting idle.  A naive implementation would be to just take our data, and partition it with one element in our collection being treated as one task.  When we loop through our collection in parallel, using this approach, we’d just process one item at a time, then reuse that thread to process the next, etc.  There’s a flaw in this approach, however.  It will tend to be slower than necessary, often slower than processing the data serially. The problem is that there is overhead associated with each task.  When we take a simple foreach loop body and implement it using the TPL, we add overhead.  First, we change the body from a simple statement to a delegate, which must be invoked.  In order to invoke the delegate on a separate thread, the delegate gets added to the ThreadPool’s current work queue, and the ThreadPool must pull this off the queue, assign it to a free thread, then execute it.  If our collection had one million elements, the overhead of trying to spawn one million tasks would destroy our performance. The answer, here, is to partition our collection into groups, and have each group of elements treated as a single task.  By adding a partitioning step, we can break our total work into small enough tasks to keep our processors busy, but large enough tasks to avoid overburdening the ThreadPool.  There are two clear, opposing goals here: Always try to keep each processor working, but also try to keep the individual partitions as large as possible. When using Parallel.For, the partitioning is always handled automatically.  At first, partitioning here seems simple.  A naive implementation would merely split the total element count up by the number of PEs in the system, and assign a chunk of data to each processor.  Many hand-written partitioning schemes work in this exactly manner.  This perfectly balanced, static partitioning scheme works very well if the amount of work is constant for each element.  However, this is rarely the case.  Often, the length of time required to process an element grows as we progress through the collection, especially if we’re doing numerical computations.  In this case, the first PEs will finish early, and sit idle waiting on the last chunks to finish.  Sometimes, work can decrease as we progress, since previous computations may be used to speed up later computations.  In this situation, the first chunks will be working far longer than the last chunks.  In order to balance the workload, many implementations create many small chunks, and reuse threads.  This adds overhead, but does provide better load balancing, which in turn improves performance. The Task Parallel Library handles this more elaborately.  Chunks are determined at runtime, and start small.  They grow slowly over time, getting larger and larger.  This tends to lead to a near optimum load balancing, even in odd cases such as increasing or decreasing workloads.  Parallel.ForEach is a bit more complicated, however. When working with a generic IEnumerable<T>, the number of items required for processing is not known in advance, and must be discovered at runtime.  In addition, since we don’t have direct access to each element, the scheduler must enumerate the collection to process it.  Since IEnumerable<T> is not thread safe, it must lock on elements as it enumerates, create temporary collections for each chunk to process, and schedule this out.  By default, it uses a partitioning method similar to the one described above.  We can see this directly by looking at the Visual Partitioning sample shipped by the Task Parallel Library team, and available as part of the Samples for Parallel Programming.  When we run the sample, with four cores and the default, Load Balancing partitioning scheme, we see this: The colored bands represent each processing core.  You can see that, when we started (at the top), we begin with very small bands of color.  As the routine progresses through the Parallel.ForEach, the chunks get larger and larger (seen by larger and larger stripes). Most of the time, this is fantastic behavior, and most likely will out perform any custom written partitioning.  However, if your routine is not scaling well, it may be due to a failure in the default partitioning to handle your specific case.  With prior knowledge about your work, it may be possible to partition data more meaningfully than the default Partitioner. There is the option to use an overload of Parallel.ForEach which takes a Partitioner<T> instance.  The Partitioner<T> class is an abstract class which allows for both static and dynamic partitioning.  By overriding Partitioner<T>.SupportsDynamicPartitions, you can specify whether a dynamic approach is available.  If not, your custom Partitioner<T> subclass would override GetPartitions(int), which returns a list of IEnumerator<T> instances.  These are then used by the Parallel class to split work up amongst processors.  When dynamic partitioning is available, GetDynamicPartitions() is used, which returns an IEnumerable<T> for each partition.  If you do decide to implement your own Partitioner<T>, keep in mind the goals and tradeoffs of different partitioning strategies, and design appropriately. The Samples for Parallel Programming project includes a ChunkPartitioner class in the ParallelExtensionsExtras project.  This provides example code for implementing your own, custom allocation strategies, including a static allocator of a given chunk size.  Although implementing your own Partitioner<T> is possible, as I mentioned above, this is rarely required or useful in practice.  The default behavior of the TPL is very good, often better than any hand written partitioning strategy.

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  • Resource consumption of FreeBSD's jails

    - by Juan Francisco Cantero Hurtado
    Just for curiosity. An example machine: an dedicated amd64 server with the last stable version of FreeBSD and UFS for the partitions. How much resources consume FreeBSD for each empty jail? I mean, I don't want know what is the resource consumption of a jailed server or whatever, just the overhead of each jail. I'm especially interested on CPU, memory and IO. For a few jails the overhead is negligible but imagine a server with 100 jails.

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  • MySQL not respond when overheaded

    - by Michal Gow
    I have few Drupal 6 websites on webhosting, which causes this strange problem: some tables, especially Cache and Watchdog, tend to overhead, when overhead is bigger than some amount of kB, MySQL server is refusing connection to given Drupal database or connection is broken during query execution, Optimizing table (just overheaded rows) in phpMyAdmin is putting all back to normal. But - until database is optimized, site is showing just MySQL errors, which is ugly... Where is a problem? Thank you for any hints I could pass back to the hosting admins!

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