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• Understanding Java Wait and Notify methods

  - by Maddy

  Hello all: I have a following program: import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; public class SimpleWaitNotify implements Runnable { final static Object obj = new Object(); static boolean value = true; public synchronized void flag() { System.out.println("Before Wait"); try { obj.wait(); } catch (InterruptedException e) { System.out.println("Thread interrupted"); } System.out.println("After Being Notified"); } public synchronized void unflag() { System.out.println("Before Notify All"); obj.notifyAll(); System.out.println("After Notify All Method Call"); } public void run() { if (value) { flag(); } else { unflag(); } } public static void main(String[] args) throws InterruptedException { ExecutorService pool = Executors.newFixedThreadPool(4); SimpleWaitNotify sWait = new SimpleWaitNotify(); pool.execute(sWait); SimpleWaitNotify.value = false; SimpleWaitNotify sNotify = new SimpleWaitNotify(); pool.execute(sNotify); pool.shutdown(); } } When I wait on obj, I get the following exception Exception in thread "pool-1-thread-1" java.lang.IllegalMonitorStateException: current thread not owner for each of the two threads. But if I use SimpleWaitNotify's monitor then the program execution is suspended. In other words, I think it suspends current execution thread and in turn the executor. Any help towards understanding what's going on would be duly appreciated. This is an area1 where the theory and javadoc seem straightforward, and since there aren't many examples, conceptually left a big gap in me.

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• F# - Facebook Hacker Cup - Double Squares

  - by Jacob

  I'm working on strengthening my F#-fu and decided to tackle the Facebook Hacker Cup Double Squares problem. I'm having some problems with the run-time and was wondering if anyone could help me figure out why it is so much slower than my C# equivalent. There's a good description from another post; Source: Facebook Hacker Cup Qualification Round 2011 A double-square number is an integer X which can be expressed as the sum of two perfect squares. For example, 10 is a double-square because 10 = 3^2 + 1^2. Given X, how can we determine the number of ways in which it can be written as the sum of two squares? For example, 10 can only be written as 3^2 + 1^2 (we don't count 1^2 + 3^2 as being different). On the other hand, 25 can be written as 5^2 + 0^2 or as 4^2 + 3^2. You need to solve this problem for 0 = X = 2,147,483,647. Examples: 10 = 1 25 = 2 3 = 0 0 = 1 1 = 1 My basic strategy (which I'm open to critique on) is to; Create a dictionary (for memoize) of the input numbers initialzed to 0 Get the largest number (LN) and pass it to count/memo function Get the LN square root as int Calculate squares for all numbers 0 to LN and store in dict Sum squares for non repeat combinations of numbers from 0 to LN If sum is in memo dict, add 1 to memo Finally, output the counts of the original numbers. Here is the F# code (See code changes at bottom) I've written that I believe corresponds to this strategy (Runtime: ~8:10); open System open System.Collections.Generic open System.IO /// Get a sequence of values let rec range min max = seq { for num in [min .. max] do yield num } /// Get a sequence starting from 0 and going to max let rec zeroRange max = range 0 max /// Find the maximum number in a list with a starting accumulator (acc) let rec maxNum acc = function | [] -> acc | p::tail when p > acc -> maxNum p tail | p::tail -> maxNum acc tail /// A helper for finding max that sets the accumulator to 0 let rec findMax nums = maxNum 0 nums /// Build a collection of combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) let rec combos range = seq { let count = ref 0 for inner in range do for outer in Seq.skip !count range do yield (inner, outer) count := !count + 1 } let rec squares nums = let dict = new Dictionary<int, int>() for s in nums do dict.[s] <- (s * s) dict /// Counts the number of possible double squares for a given number and keeps track of other counts that are provided in the memo dict. let rec countDoubleSquares (num: int) (memo: Dictionary<int, int>) = // The highest relevent square is the square root because it squared plus 0 squared is the top most possibility let maxSquare = System.Math.Sqrt((float)num) // Our relevant squares are 0 to the highest possible square; note the cast to int which shouldn't hurt. let relSquares = range 0 ((int)maxSquare) // calculate the squares up front; let calcSquares = squares relSquares // Build up our square combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) for (sq1, sq2) in combos relSquares do let v = calcSquares.[sq1] + calcSquares.[sq2] // Memoize our relevant results if memo.ContainsKey(v) then memo.[v] <- memo.[v] + 1 // return our count for the num passed in memo.[num] // Read our numbers from file. //let lines = File.ReadAllLines("test2.txt") //let nums = [ for line in Seq.skip 1 lines -> Int32.Parse(line) ] // Optionally, read them from straight array let nums = [1740798996; 1257431873; 2147483643; 602519112; 858320077; 1048039120; 415485223; 874566596; 1022907856; 65; 421330820; 1041493518; 5; 1328649093; 1941554117; 4225; 2082925; 0; 1; 3] // Initialize our memoize dictionary let memo = new Dictionary<int, int>() for num in nums do memo.[num] <- 0 // Get the largest number in our set, all other numbers will be memoized along the way let maxN = findMax nums // Do the memoize let maxCount = countDoubleSquares maxN memo // Output our results. for num in nums do printfn "%i" memo.[num] // Have a little pause for when we debug let line = Console.Read() And here is my version in C# (Runtime: ~1:40: using System; using System.Collections.Generic; using System.Diagnostics; using System.IO; using System.Linq; using System.Text; namespace FBHack_DoubleSquares { public class TestInput { public int NumCases { get; set; } public List<int> Nums { get; set; } public TestInput() { Nums = new List<int>(); } public int MaxNum() { return Nums.Max(); } } class Program { static void Main(string[] args) { // Read input from file. //TestInput input = ReadTestInput("live.txt"); // As example, load straight. TestInput input = new TestInput { NumCases = 20, Nums = new List<int> { 1740798996, 1257431873, 2147483643, 602519112, 858320077, 1048039120, 415485223, 874566596, 1022907856, 65, 421330820, 1041493518, 5, 1328649093, 1941554117, 4225, 2082925, 0, 1, 3, } }; var maxNum = input.MaxNum(); Dictionary<int, int> memo = new Dictionary<int, int>(); foreach (var num in input.Nums) { if (!memo.ContainsKey(num)) memo.Add(num, 0); } DoMemoize(maxNum, memo); StringBuilder sb = new StringBuilder(); foreach (var num in input.Nums) { //Console.WriteLine(memo[num]); sb.AppendLine(memo[num].ToString()); } Console.Write(sb.ToString()); var blah = Console.Read(); //File.WriteAllText("out.txt", sb.ToString()); } private static int DoMemoize(int num, Dictionary<int, int> memo) { var highSquare = (int)Math.Floor(Math.Sqrt(num)); var squares = CreateSquareLookup(highSquare); var relSquares = squares.Keys.ToList(); Debug.WriteLine("Starting - " + num.ToString()); Debug.WriteLine("RelSquares.Count = {0}", relSquares.Count); int sum = 0; var index = 0; foreach (var square in relSquares) { foreach (var inner in relSquares.Skip(index)) { sum = squares[square] + squares[inner]; if (memo.ContainsKey(sum)) memo[sum]++; } index++; } if (memo.ContainsKey(num)) return memo[num]; return 0; } private static TestInput ReadTestInput(string fileName) { var lines = File.ReadAllLines(fileName); var input = new TestInput(); input.NumCases = int.Parse(lines[0]); foreach (var lin in lines.Skip(1)) { input.Nums.Add(int.Parse(lin)); } return input; } public static Dictionary<int, int> CreateSquareLookup(int maxNum) { var dict = new Dictionary<int, int>(); int square; foreach (var num in Enumerable.Range(0, maxNum)) { square = num * num; dict[num] = square; } return dict; } } } Thanks for taking a look. UPDATE Changing the combos function slightly will result in a pretty big performance boost (from 8 min to 3:45): /// Old and Busted... let rec combosOld range = seq { let rangeCache = Seq.cache range let count = ref 0 for inner in rangeCache do for outer in Seq.skip !count rangeCache do yield (inner, outer) count := !count + 1 } /// The New Hotness... let rec combos maxNum = seq { for i in 0..maxNum do for j in i..maxNum do yield i,j }

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• Is there a useDirtyFlag option for Tomcat 6 cluster configuration?

  - by kevinjansz

  In Tomcat 5.0.x you had the ability to set useDirtyFlag="false" to force replication of the session after every request rather than checking for set/removeAttribute calls. <Cluster className="org.apache.catalina.cluster.tcp.SimpleTcpCluster" managerClassName="org.apache.catalina.cluster.session.SimpleTcpReplicationManager" expireSessionsOnShutdown="false" **useDirtyFlag="false"** doClusterLog="true" clusterLogName="clusterLog"> ... The comments in the server.xml stated this may be used to make the following work: <% HashMap map = (HashMap)session.getAttribute("map"); map.put("key","value"); %> i.e. change the state of an object that has already been put in the session and you can be sure that this object still be replicated to the other nodes in the cluster. According to the Tomcat 6 documentation you only have two "Manager" options - DeltaManager & BackupManager ... neither of these seem to allow this option or anything like it. In my testing the default setup: <Cluster className="org.apache.catalina.ha.tcp.SimpleTcpCluster"/> where you get the DeltaManager by default, it's definitely behaving as useDirtyFlag="true" (as I'd expect). So my question is - is there an equivalent in Tomcat 6? Looking at the source I can see a manager implementation "org.apache.catalina.ha.session.SimpleTcpReplicationManager" which does have the useDirtyFlag but the javadoc comments in this state it's "Tomcat Session Replication for Tomcat 4.0" ... I don't know if this is ok to use - I'm guessing not as it's not mentioned in the main cluster configuration documentation.

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• Hadoop/MapReduce: Reading and writing classes generated from DDL

  - by Dave

  Hi, Can someone walk me though the basic work-flow of reading and writing data with classes generated from DDL? I have defined some struct-like records using DDL. For example: class Customer { ustring FirstName; ustring LastName; ustring CardNo; long LastPurchase; } I've compiled this to get a Customer class and included it into my project. I can easily see how to use this as input and output for mappers and reducers (the generated class implements Writable), but not how to read and write it to file. The JavaDoc for the org.apache.hadoop.record package talks about serializing these records in Binary, CSV or XML format. How do I actually do that? Say my reducer produces IntWritable keys and Customer values. What OutputFormat do I use to write the result in CSV format? What InputFormat would I use to read the resulting files in later, if I wanted to perform analysis over them?

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• Question about WeakHashMap

  - by michael

  Hi, In the Javadoc of "http://java.sun.com/j2se/1.4.2/docs/api/java/util/WeakHashMap.html", it said "Each key object in a WeakHashMap is stored indirectly as the referent of a weak reference. Therefore a key will automatically be removed only after the weak references to it, both inside and outside of the map, have been cleared by the garbage collector." And then Note that a value object may refer indirectly to its key via the WeakHashMap itself; that is, a value object may strongly refer to some other key object whose associated value object, in turn, strongly refers to the key of the first value object. But should not both Key and Value should be used weak reference in WeakHashMap? i.e. if there is low on memory, GC will free the memory held by the value object (since the value object most likely take up more memory than key object in most cases)? And if GC free the Value object, the Key Object can be free as well? Basically, I am looking for a HashMap which will reduce memory usage when there is low memory (GC collects the value and key objects if necessary). Is it possible in Java? Thank you.

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• Is it possible to pass a structure of delegates from managed to native?

  - by Veiva

  I am writing a wrapper for the game programming library "Allegro" and its less stable 4.9 branch. Now, I have done good insofar, except for when it comes to wrapping a structure of function pointers. Basically, I can't change the original code, despite having access to it, because that would require me to fork it in some manner. I need to know how I can somehow pass a structure of delegates from managed to native without causing an AccessViolationException that has occurred so far. Now, for the code. Here is the Allegro definition of the structure: typedef struct ALLEGRO_FILE_INTERFACE { AL_METHOD(ALLEGRO_FILE*, fi_fopen, (const char *path, const char *mode)); AL_METHOD(void, fi_fclose, (ALLEGRO_FILE *handle)); AL_METHOD(size_t, fi_fread, (ALLEGRO_FILE *f, void *ptr, size_t size)); AL_METHOD(size_t, fi_fwrite, (ALLEGRO_FILE *f, const void *ptr, size_t size)); AL_METHOD(bool, fi_fflush, (ALLEGRO_FILE *f)); AL_METHOD(int64_t, fi_ftell, (ALLEGRO_FILE *f)); AL_METHOD(bool, fi_fseek, (ALLEGRO_FILE *f, int64_t offset, int whence)); AL_METHOD(bool, fi_feof, (ALLEGRO_FILE *f)); AL_METHOD(bool, fi_ferror, (ALLEGRO_FILE *f)); AL_METHOD(int, fi_fungetc, (ALLEGRO_FILE *f, int c)); AL_METHOD(off_t, fi_fsize, (ALLEGRO_FILE *f)); } ALLEGRO_FILE_INTERFACE; My simple attempt at wrapping it: public delegate IntPtr AllegroInternalOpenFileDelegate(string path, string mode); public delegate void AllegroInternalCloseFileDelegate(IntPtr file); public delegate int AllegroInternalReadFileDelegate(IntPtr file, IntPtr data, int size); public delegate int AllegroInternalWriteFileDelegate(IntPtr file, IntPtr data, int size); public delegate bool AllegroInternalFlushFileDelegate(IntPtr file); public delegate long AllegroInternalTellFileDelegate(IntPtr file); public delegate bool AllegroInternalSeekFileDelegate(IntPtr file, long offset, int where); public delegate bool AllegroInternalIsEndOfFileDelegate(IntPtr file); public delegate bool AllegroInternalIsErrorFileDelegate(IntPtr file); public delegate int AllegroInternalUngetCharFileDelegate(IntPtr file, int c); public delegate long AllegroInternalFileSizeDelegate(IntPtr file); [StructLayout(LayoutKind.Sequential, Pack = 0)] public struct AllegroInternalFileInterface { [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalOpenFileDelegate fi_fopen; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalCloseFileDelegate fi_fclose; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalReadFileDelegate fi_fread; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalWriteFileDelegate fi_fwrite; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalFlushFileDelegate fi_fflush; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalTellFileDelegate fi_ftell; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalSeekFileDelegate fi_fseek; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalIsEndOfFileDelegate fi_feof; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalIsErrorFileDelegate fi_ferror; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalUngetCharFileDelegate fi_fungetc; [MarshalAs(UnmanagedType.FunctionPtr)] public AllegroInternalFileSizeDelegate fi_fsize; } I have a simple auxiliary wrapper that turns an ALLEGRO_FILE_INTERFACE into an ALLEGRO_FILE, like so: #define ALLEGRO_NO_MAGIC_MAIN #include <allegro5/allegro5.h> #include <stdlib.h> #include <string.h> #include <assert.h> __declspec(dllexport) ALLEGRO_FILE * al_aux_create_file(ALLEGRO_FILE_INTERFACE * fi) { ALLEGRO_FILE * file; assert(fi && "`fi' null"); file = (ALLEGRO_FILE *)malloc(sizeof(ALLEGRO_FILE)); if (!file) return NULL; file->vtable = (ALLEGRO_FILE_INTERFACE *)malloc(sizeof(ALLEGRO_FILE_INTERFACE)); if (!(file->vtable)) { free(file); return NULL; } memcpy(file->vtable, fi, sizeof(ALLEGRO_FILE_INTERFACE)); return file; } __declspec(dllexport) void al_aux_destroy_file(ALLEGRO_FILE * f) { assert(f && "`f' null"); assert(f->vtable && "`f->vtable' null"); free(f->vtable); free(f); } Lastly, I have a class that accepts a Stream and provides the proper methods to interact with the stream. Just to make sure, here it is: /// <summary> /// A semi-opaque data type that allows one to load fonts, etc from a stream. /// </summary> public class AllegroFile : AllegroResource, IDisposable { AllegroInternalFileInterface fileInterface; Stream fileStream; /// <summary> /// Gets the file interface. /// </summary> internal AllegroInternalFileInterface FileInterface { get { return fileInterface; } } /// <summary> /// Constructs an Allegro file from the stream provided. /// </summary> /// <param name="stream">The stream to use.</param> public AllegroFile(Stream stream) { fileStream = stream; fileInterface = new AllegroInternalFileInterface(); fileInterface.fi_fopen = Open; fileInterface.fi_fclose = Close; fileInterface.fi_fread = Read; fileInterface.fi_fwrite = Write; fileInterface.fi_fflush = Flush; fileInterface.fi_ftell = GetPosition; fileInterface.fi_fseek = Seek; fileInterface.fi_feof = GetIsEndOfFile; fileInterface.fi_ferror = GetIsError; fileInterface.fi_fungetc = UngetCharacter; fileInterface.fi_fsize = GetLength; Resource = AllegroFunctions.al_aux_create_file(ref fileInterface); if (!IsValid) throw new AllegroException("Unable to create file"); } /// <summary> /// Disposes of all resources. /// </summary> ~AllegroFile() { Dispose(); } /// <summary> /// Disposes of all resources used. /// </summary> public void Dispose() { if (IsValid) { Resource = IntPtr.Zero; // Should call AllegroFunctions.al_aux_destroy_file fileStream.Dispose(); } } IntPtr Open(string path, string mode) { return IntPtr.Zero; } void Close(IntPtr file) { fileStream.Close(); } int Read(IntPtr file, IntPtr data, int size) { byte[] d = new byte[size]; int read = fileStream.Read(d, 0, size); Marshal.Copy(d, 0, data, size); return read; } int Write(IntPtr file, IntPtr data, int size) { byte[] d = new byte[size]; Marshal.Copy(data, d, 0, size); fileStream.Write(d, 0, size); return size; } bool Flush(IntPtr file) { fileStream.Flush(); return true; } long GetPosition(IntPtr file) { return fileStream.Position; } bool Seek(IntPtr file, long offset, int whence) { SeekOrigin origin = SeekOrigin.Begin; if (whence == 1) origin = SeekOrigin.Current; else if (whence == 2) origin = SeekOrigin.End; fileStream.Seek(offset, origin); return true; } bool GetIsEndOfFile(IntPtr file) { return fileStream.Position == fileStream.Length; } bool GetIsError(IntPtr file) { return false; } int UngetCharacter(IntPtr file, int character) { return -1; } long GetLength(IntPtr file) { return fileStream.Length; } } Now, when I do something like this: AllegroFile file = new AllegroFile(new FileStream("Test.bmp", FileMode.Create, FileAccess.ReadWrite)); bitmap.SaveToFile(file, ".bmp"); ...I get an AccessViolationException. I think I understand why (the garbage collector can relocate structs and classes whenever), but I'd think that the method stub that is created by the framework would take this into consideration and route the calls to the valid classes. However, it seems obviously so that I'm wrong. So basically, is there any way I can successfully wrap that structure? (And I'm sorry for all the code! Hope it's not too much...)

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• Is there a Scala version of .irbrc or another way to define some default libraries for REPL use?

  - by Tom Morris

  I've written a little library that uses implicits to add functionality that one only needs when using the REPL in Scala. Ruby has libraries like this - for things like pretty printing, firing up text editors (like the interactive_editor gem which invokes Vim from irb - see this post), debuggers and the like. The library I am trying to write adds some methods to java.lang.Class and java.lang.reflect classes using the 'pimp my library' implicit conversion process to help you go and find documentation (initially, with Google, then later possibly with a JavaDoc/ScalaDoc viewer, and maybe the StackOverflow API eventually!). It's an itch-scratching library: I spend so much time copying and pasting classnames into Google that I figured I may as well automate the process. It is the sort of functionality that developers will want to add to their system for use only in the REPL - they shouldn't really be adding it to projects (partly because it may not be something that their fellow developers want, but also because if you are doing some exploratory development, it may be with just a Scala REPL that's not being invoked by an IDE or build tool). In my case, I want to include a few classes and set up some implicits - include a .jar on the CLASSPATH and import it, basically. In Ruby, this is the sort of thing that you'd add to your .irbrc file. Other REPLs have similar ways of setting options and importing libraries. Is there a similar file or way of doing this for the Scala REPL?

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• Would an immutable keyword in Java be a good idea?

  - by berry120

  Generally speaking, the more I use immutable objects in Java the more I'm thinking they're a great idea. They've got lots of advantages from automatically being thread-safe to not needing to worry about cloning or copy constructors. This has got me thinking, would an "immutable" keyword go amiss? Obviously there's the disadvantages with adding another reserved word to the language, and I doubt it will actually happen primarily for the above reason - but ignoring that I can't really see many disadvantages. At present great care has to be taken to make sure objects are immutable, and even then a dodgy javadoc comment claiming a component object is immutable when it's in fact not can wreck the whole thing. There's also the argument that even basic objects like string aren't truly immutable because they're easily vunerable to reflection attacks. If we had an immutable keyword the compiler could surely recursively check and give an iron clad guarantee that all instances of a class were immutable, something that can't presently be done. Especially with concurrency becoming more and more used, I personally think it'd be good to add a keyword to this effect. But are there any disadvantages or implementation details I'm missing that makes this a bad idea?

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• Most awkward/misleading method in Java Base API ?

  - by JG

  I was recently trying to convert a string literal into a boolean, when the method "boolean Boolean.getBoolean(String name)" popped out of the auto-complete window. There was also another method ("boolean Boolean.parseBoolean(String s)") appearing right after, which lead me to search to find out what were the differences between these two, as they both seemed to do the same. It turns out that what Boolean.getBoolean(String name) really does is to check if there exists a System property (!) of the given name and if its value is true. I think this is very misleading, as I'm definitely not expecting that a method of Boolean is actually making a call to System.getProperty, and just by looking at the method signature, it sure looks (at least to me) like it should be used to parse a String as a boolean. Sure, the javadoc states it clearly, but I still think the method has a misleading name and is not in the right place. Other primitive type wrappers, such as Integer also have a similar method. Also, it doesn't seem to be a very useful method to belong in the base API, as I think it's not very common to have something like -Darg=true. Maybe it's a good question for a Java position interview: "What is the output of Boolean.getBoolean("true")?". I believe a more appropriate place for those methods would be in the System class, e.g., getPropertyAsBoolean; but again, I still think it's unnecessary to have these methods in the base API. It'd make sense to have these in something like the Properties class, where it's very common to do this kind of type conversions. What do you think of all this ? Also, if there's another "awkward" method that you're aware of, please post it. N.B. I know I can use Boolean.valueOf or Boolean.parseBoolean to convert a string literal into a boolean, but I'm just looking to discuss the API design.

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• Using Maven for maintaining product documentation

  - by Waldheinz

  We are using Maven for building a Java server-style application. It consists of several modules (in a single Maven "reactor") which can be plugged together to generate a final product (essentially a .jar) with the features enabled that the customer needs. All the internals are documented using JavaDoc and all, but that's not what you can give to the customer to find out how to get the thing running. Currently we have an OpenOffice document which serves as end-user documentation. I'd like to integrate this documentation into the Maven build process where each module's documentation is maintained (hand-edited) together with the Module's sources and the final document can reference the required Module documentation sections, add some friendly foreword and, if possible at all, can reference into the JavaDocs. Ultimately, the document should be output as a PDF. Is there any experience on Maven plugins can help with this? Is DocBook the right tool? Maybe Latex? Or something completely different? A sound "stick with OpenOffice and some text blocks" could be an answer, too.

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• Mixing menuItem.setIntent with onOptionsItemSelected doesn't work

  - by superjos

  While extending a sample Android activity that fires some other activities from its menu, I came to have some menu items handled within onOptionsItemSelected, and some menu items (that just fired intents) handled by calling setIntent within onCreateOptionsMenu. Basically something like: @Override public boolean onCreateOptionsMenu(Menu menu) { super.onCreateOptionsMenu(menu); menu.add(0, MENU_ID_1, Menu.NONE, R.string.menu_text_1); menu.add(0, MENU_ID_2, Menu.NONE, R.string.menu_text_2); menu.add(0, MENU_ID_3, Menu.NONE, R.string.menu_text_3). setIntent(new Intent(this, MyActivity_3.class)); return true; } @Override public boolean onOptionsItemSelected(MenuItem item) { super.onOptionsItemSelected(item); switch (item.getItemId()) { case (MENU_ID_1): // Process menu command 1 ... return true; case (MENU_ID_2): // Process menu command 2 ... // E.g. also fire Intent for MyActivity_2 return true; default: return false; } } Apparently, in this situation the Intent set on MENU_ID_3 is never fired, or anyway the related activity is never started. Android javadoc at some point goes like <<[if you set an intent on a menu item] and nothing else handles the item, then the default behavior will be to [start the activity with the intent]. What does it actually mean "and nothing else handles the item"? Is it enough to return false from onOptionsItemSelected? I also tried not to call super.onOptionsItemSelected(item) at the beginning and only invoke it in the default switch case, but I had same results. Does anyone have any suggestion? Does Android allow to mix the two type of handling? Thanks for your time everyone.

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• Google calendar query returns at most 25 entries

  - by Dean Hill

  I'm trying to delete all calendar entries from today forward. I run a query then call getEntries() on the query result. getEntries() always returns 25 entries (or less if there are fewer than 25 entries on the calendar). Why aren't all the entries returned? I'm expecting about 80 entries. As a test, I tried running the query, deleting the 25 entries returned, running the query again, deleting again, etc. This works, but there must be a better way. Below is the Java code that only runs the query once. CalendarQuery myQuery = new CalendarQuery(feedUrl); DateFormat dfGoogle = new SimpleDateFormat("yyyy-MM-dd'T00:00:00'"); Date dt = Calendar.getInstance().getTime(); myQuery.setMinimumStartTime(DateTime.parseDateTime(dfGoogle.format(dt))); // Make the end time far into the future so we delete everything myQuery.setMaximumStartTime(DateTime.parseDateTime("2099-12-31T23:59:59")); // Execute the query and get the response CalendarEventFeed resultFeed = service.query(myQuery, CalendarEventFeed.class); // !!! This returns 25 (or less if there are fewer than 25 entries on the calendar) !!! int test = resultFeed.getEntries().size(); // Delete all the entries returned by the query for (int j = 0; j < resultFeed.getEntries().size(); j++) { CalendarEventEntry entry = resultFeed.getEntries().get(j); entry.delete(); } PS: I've looked at the Data API Developer's Guide and the Google Data API Javadoc. These sites are okay, but not great. Does anyone know of additional Google API documentation?

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• JVM terminated. Exit code = -1

  - by Lex

  Hello, I've been using Eclipse for some time already, and I didn't have any problems with it. However, when I tried to generate some javadoc, I found that I didn't have the SDK installed. I installed jdk 6u23, and from then on, Eclipse hasn't been working. At first it told me it couldn't find a JRE/SDK. After copying and placing the JRE in the Eclipse folder, the error changed to this: http://i258.photobucket.com/albums/hh261/magical_7/Eclipse.png I've searched around for some solutions, and found a few of varying nature. However, none of them seemed to work. I've tried emptying my eclipse.ini file without success. Altering some values in eclipse.ini didn't work either. Installing Java SDK 1.5 didn't work. Does anyone have other possible solutions? Update: I tried reinstalling Eclipse, but it didn't fix the problem. I did notice that it's a 32bit version, while I'm running a 64bit system. However, if this were to be the problem, I find it strange that it hasn't been an issue earlier.

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• Java Equivalent of C++ .dll?

  - by Matt D

  So, I've been programming for a while now, but since I haven't worked on many larger, modular projects, I haven't come across this issue before. I know what a .dll is in C++, and how they are used. But every time I've seen similar things in Java, they've always been packaged with source code. For instance, what would I do if I wanted to give a Java library to someone else, but not expose the source code? Instead of the source, I would just give a library as well as a Javadoc, or something along those lines, with the public methods/functions, to another programmer who could then implement them in their own Java code. For instance, if I wanted to create a SAX parser that could be "borrowed" by another programmer, but (for some reason--can't think of one in this specific example lol) I don't want to expose my source. Maybe there's a login involved that I don't want exploited--I don't know. But what would be the Java way of doing this? With C++, .dll files make it much easier, but I have never run into a Java equivalent so far. (I'm pretty new to Java, and a pretty new "real-world" programmer, in general as well)

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• Why does java.util.concurrent.ArrayBlockingQueue use 'while' loops instead of 'if' around calls to

  - by theFunkyEngineer

  I have been playing with my own version of this, using 'if', and all seems to be working fine. Of course this will break down horribly if signalAll() is used instead of signal(), but if only one thread at a time is notified, how can this go wrong? Their code here - check out the put() and take() methods; a simpler and more-to-the-point implementation can be seen at the top of the JavaDoc for Condition. Relevant portion of my implementation below. public Object get() { lock.lock(); try { if( items.size() < 1 ) hasItems.await(); Object poppedValue = items.getLast(); items.removeLast(); hasSpace.signal(); return poppedValue; } catch (InterruptedException e) { e.printStackTrace(); return null; } finally { lock.unlock(); } } public void put(Object item) { lock.lock(); try { if( items.size() >= capacity ) hasSpace.await(); items.addFirst(item); hasItems.signal(); return; } catch (InterruptedException e) { e.printStackTrace(); } finally { lock.unlock(); } } P.S. I know that generally, particularly in lib classes like this, one should let the exceptions percolate up.

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• SQL SERVER – 2008 – Introduction to Snapshot Database – Restore From Snapshot

  - by pinaldave

  Snapshot database is one of the most interesting concepts that I have used at some places recently. Here is a quick definition of the subject from Book On Line: A Database Snapshot is a read-only, static view of a database (the source database). Multiple snapshots can exist on a source database and can always reside on the same server instance as the database. Each database snapshot is consistent, in terms of transactions, with the source database as of the moment of the snapshot’s creation. A snapshot persists until it is explicitly dropped by the database owner. If you do not know how Snapshot database work, here is a quick note on the subject. However, please refer to the official description on Book-on-Line for accuracy. Snapshot database is a read-only database created from an original database called the “source database”. This database operates at page level. When Snapshot database is created, it is produced on sparse files; in fact, it does not occupy any space (or occupies very little space) in the Operating System. When any data page is modified in the source database, that data page is copied to Snapshot database, making the sparse file size increases. When an unmodified data page is read in the Snapshot database, it actually reads the pages of the original database. In other words, the changes that happen in the source database are reflected in the Snapshot database. Let us see a simple example of Snapshot. In the following exercise, we will do a few operations. Please note that this script is for demo purposes only- there are a few considerations of CPU, DISK I/O and memory, which will be discussed in the future posts. Create Snapshot Delete Data from Original DB Restore Data from Snapshot First, let us create the first Snapshot database and observe the sparse file details. USE master GO -- Create Regular Database CREATE DATABASE RegularDB GO USE RegularDB GO -- Populate Regular Database with Sample Table CREATE TABLE FirstTable (ID INT, Value VARCHAR(10)) INSERT INTO FirstTable VALUES(1, 'First'); INSERT INTO FirstTable VALUES(2, 'Second'); INSERT INTO FirstTable VALUES(3, 'Third'); GO -- Create Snapshot Database CREATE DATABASE SnapshotDB ON (Name ='RegularDB', FileName='c:\SSDB.ss1') AS SNAPSHOT OF RegularDB; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO Now let us see the resultset for the same. Now let us do delete something from the Original DB and check the same details we checked before. -- Delete from Regular Database DELETE FROM RegularDB.dbo.FirstTable; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO When we check the details of sparse file created by Snapshot database, we will find some interesting details. The details of Regular DB remain the same. It clearly shows that when we delete data from Regular/Source DB, it copies the data pages to Snapshot database. This is the reason why the size of the snapshot DB is increased. Now let us take this small exercise to  the next level and restore our deleted data from Snapshot DB to Original Source DB. -- Restore Data from Snapshot Database USE master GO RESTORE DATABASE RegularDB FROM DATABASE_SNAPSHOT = 'SnapshotDB'; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Clean up DROP DATABASE [SnapshotDB]; DROP DATABASE [RegularDB]; GO Now let us check the details of the select statement and we can see that we are successful able to restore the database from Snapshot Database. We can clearly see that this is a very useful feature in case you would encounter a good business that needs it. I would like to request the readers to suggest more details if they are using this feature in their business. Also, let me know if you think it can be potentially used to achieve any tasks. Complete Script of the afore- mentioned operation for easy reference is as follows: USE master GO -- Create Regular Database CREATE DATABASE RegularDB GO USE RegularDB GO -- Populate Regular Database with Sample Table CREATE TABLE FirstTable (ID INT, Value VARCHAR(10)) INSERT INTO FirstTable VALUES(1, 'First'); INSERT INTO FirstTable VALUES(2, 'Second'); INSERT INTO FirstTable VALUES(3, 'Third'); GO -- Create Snapshot Database CREATE DATABASE SnapshotDB ON (Name ='RegularDB', FileName='c:\SSDB.ss1') AS SNAPSHOT OF RegularDB; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Delete from Regular Database DELETE FROM RegularDB.dbo.FirstTable; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Restore Data from Snapshot Database USE master GO RESTORE DATABASE RegularDB FROM DATABASE_SNAPSHOT = 'SnapshotDB'; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Clean up DROP DATABASE [SnapshotDB]; DROP DATABASE [RegularDB]; GO Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: SQL, SQL Authority, SQL Backup and Restore, SQL Data Storage, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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• New MySQL Cluster 7.3 Previews: Foreign Keys, NoSQL Node.js API and Auto-Tuned Clusters

  - by Mat Keep

  At this weeks MySQL Connect conference, Oracle previewed an exciting new wave of developments for MySQL Cluster, further extending its simplicity and flexibility by expanding the range of use-cases, adding new NoSQL options, and automating configuration. What’s new: Development Release 1: MySQL Cluster 7.3 with Foreign Keys Early Access “Labs” Preview: MySQL Cluster NoSQL API for Node.js Early Access “Labs” Preview: MySQL Cluster GUI-Based Auto-Installer In this blog, I'll introduce you to the features being previewed. Review the blogs listed below for more detail on each of the specific features discussed. Save the date!: A live webinar is scheduled for Thursday 25th October at 0900 Pacific Time / 1600UTC where we will discuss each of these enhancements in more detail. Registration will be open soon and published to the MySQL webinars page MySQL Cluster 7.3: Development Release 1 The first MySQL Cluster 7.3 Development Milestone Release (DMR) previews Foreign Keys, bringing powerful new functionality to MySQL Cluster while eliminating development complexity. Foreign Key support has been one of the most requested enhancements to MySQL Cluster – enabling users to simplify their data models and application logic – while extending the range of use-cases for both custom projects requiring referential integrity and packaged applications, such as eCommerce, CRM, CMS, etc. Implementation The Foreign Key functionality is implemented directly within the MySQL Cluster data nodes, allowing any client API accessing the cluster to benefit from them – whether they are SQL or one of the NoSQL interfaces (Memcached, C++, Java, JPA, HTTP/REST or the new Node.js API - discussed later.) The core referential actions defined in the SQL:2003 standard are implemented: CASCADE RESTRICT NO ACTION SET NULL In addition, the MySQL Cluster implementation supports the online adding and dropping of Foreign Keys, ensuring the Cluster continues to serve both read and write requests during the operation.  This represents a further enhancement to MySQL Cluster's support for on0line schema changes, ie adding and dropping indexes, adding columns, etc.  Read this blog for a demonstration of using Foreign Keys with MySQL Cluster.  Getting Started with MySQL Cluster 7.3 DMR1: Users can download either the source or binary and evaluate the MySQL Cluster 7.3 DMR with Foreign Keys now! (Select the Development Release tab). MySQL Cluster NoSQL API for Node.js Node.js is hot! In a little over 3 years, it has become one of the most popular environments for developing next generation web, cloud, mobile and social applications. Bringing JavaScript from the browser to the server, the design goal of Node.js is to build new real-time applications supporting millions of client connections, serviced by a single CPU core. Making it simple to further extend the flexibility and power of Node.js to the database layer, we are previewing the Node.js Javascript API for MySQL Cluster as an Early Access release, available for download now from http://labs.mysql.com/. Select the following build: MySQL-Cluster-NoSQL-Connector-for-Node-js Alternatively, you can clone the project at the MySQL GitHub page.  Implemented as a module for the V8 engine, the new API provides Node.js with a native, asynchronous JavaScript interface that can be used to both query and receive results sets directly from MySQL Cluster, without transformations to SQL. Figure 1: MySQL Cluster NoSQL API for Node.js enables end-to-end JavaScript development Rather than just presenting a simple interface to the database, the Node.js module integrates the MySQL Cluster native API library directly within the web application itself, enabling developers to seamlessly couple their high performance, distributed applications with a high performance, distributed, persistence layer delivering 99.999% availability. The new Node.js API joins a rich array of NoSQL interfaces available for MySQL Cluster. Whichever API is chosen for an application, SQL and NoSQL can be used concurrently across the same data set, providing the ultimate in developer flexibility.  Get started with MySQL Cluster NoSQL API for Node.js tutorial MySQL Cluster GUI-Based Auto-Installer Compatible with both MySQL Cluster 7.2 and 7.3, the Auto-Installer makes it simple for DevOps teams to quickly configure and provision highly optimized MySQL Cluster deployments – whether on-premise or in the cloud. Implemented with a standard HTML GUI and Python-based web server back-end, the Auto-Installer intelligently configures MySQL Cluster based on application requirements and auto-discovered hardware resources Figure 2: Automated Tuning and Configuration of MySQL Cluster Developed by the same engineering team responsible for the MySQL Cluster database, the installer provides standardized configurations that make it simple, quick and easy to build stable and high performance clustered environments. The auto-installer is previewed as an Early Access release, available for download now from http://labs.mysql.com/, by selecting the MySQL-Cluster-Auto-Installer build. You can read more about getting started with the MySQL Cluster auto-installer here. Watch the YouTube video for a demonstration of using the MySQL Cluster auto-installer Getting Started with MySQL Cluster If you are new to MySQL Cluster, the Getting Started guide will walk you through installing an evaluation cluster on a singe host (these guides reflect MySQL Cluster 7.2, but apply equally well to 7.3 and the Early Access previews). Or use the new MySQL Cluster Auto-Installer! Download the Guide to Scaling Web Databases with MySQL Cluster (to learn more about its architecture, design and ideal use-cases). Post any questions to the MySQL Cluster forum where our Engineering team and the MySQL Cluster community will attempt to assist you. Post any bugs you find to the MySQL bug tracking system (select MySQL Cluster from the Category drop-down menu) And if you have any feedback, please post them to the Comments section here or in the blogs referenced in this article. Summary MySQL Cluster 7.2 is the GA, production-ready release of MySQL Cluster. The first Development Release of MySQL Cluster 7.3 and the Early Access previews give you the opportunity to preview and evaluate future developments in the MySQL Cluster database, and we are very excited to be able to share that with you. Let us know how you get along with MySQL Cluster 7.3, and other features that you want to see in future releases, by using the comments of this blog.

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• SQL SERVER – Database Dynamic Caching by Automatic SQL Server Performance Acceleration

  - by pinaldave

  My second look at SafePeak’s new version (2.1) revealed to me few additional interesting features. For those of you who hadn’t read my previous reviews SafePeak and not familiar with it, here is a quick brief: SafePeak is in business of accelerating performance of SQL Server applications, as well as their scalability, without making code changes to the applications or to the databases. SafePeak performs database dynamic caching, by caching in memory result sets of queries and stored procedures while keeping all those cache correct and up to date. Cached queries are retrieved from the SafePeak RAM in microsecond speed and not send to the SQL Server. The application gets much faster results (100-500 micro seconds), the load on the SQL Server is reduced (less CPU and IO) and the application or the infrastructure gets better scalability. SafePeak solution is hosted either within your cloud servers, hosted servers or your enterprise servers, as part of the application architecture. Connection of the application is done via change of connection strings or adding reroute line in the c:\windows\system32\drivers\etc\hosts file on all application servers. For those who would like to learn more on SafePeak architecture and how it works, I suggest to read this vendor’s webpage: SafePeak Architecture. More interesting new features in SafePeak 2.1 In my previous review of SafePeak new I covered the first 4 things I noticed in the new SafePeak (check out my article “SQLAuthority News – SafePeak Releases a Major Update: SafePeak version 2.1 for SQL Server Performance Acceleration”): Cache setup and fine-tuning – a critical part for getting good caching results Database templates Choosing which database to cache Monitoring and analysis options by SafePeak Since then I had a chance to play with SafePeak some more and here is what I found. 5. Analysis of SQL Performance (present and history): In SafePeak v.2.1 the tools for understanding of performance became more comprehensive. Every 15 minutes SafePeak creates and updates various performance statistics. Each query (or a procedure execute) that arrives to SafePeak gets a SQL pattern, and after it is used again there are statistics for such pattern. An important part of this product is that it understands the dependencies of every pattern (list of tables, views, user defined functions and procs). From this understanding SafePeak creates important analysis information on performance of every object: response time from the database, response time from SafePeak cache, average response time, percent of traffic and break down of behavior. One of the interesting things this behavior column shows is how often the object is actually pdated. The break down analysis allows knowing the above information for: queries and procedures, tables, views, databases and even instances level. The data is show now on all arriving queries, both read queries (that can be cached), but also any types of updates like DMLs, DDLs, DCLs, and even session settings queries. The stats are being updated every 15 minutes and SafePeak dashboard allows going back in time and investigating what happened within any time frame. 6. Logon trigger, for making sure nothing corrupts SafePeak cache data If you have an application with many parts, many servers many possible locations that can actually update the database, or the SQL Server is accessible to many DBAs or software engineers, each can access some database directly and do some changes without going thru SafePeak – this can create a potential corruption of the data stored in SafePeak cache. To make sure SafePeak cache is correct it needs to get all updates to arrive to SafePeak, and if a DBA will access the database directly and do some changes, for example, then SafePeak will simply not know about it and will not clean SafePeak cache. In the new version, SafePeak brought a new feature called “Logon Trigger” to solve the above challenge. By special click of a button SafePeak can deploy a special server logon trigger (with a CLR object) on your SQL Server that actually monitors all connections and informs SafePeak on any connection that is coming not from SafePeak. In SafePeak dashboard there is an interface that allows to control which logins can be ignored based on login names and IPs, while the rest will invoke cache cleanup of SafePeak and actually locks SafePeak cache until this connection will not be closed. Important to note, that this does not interrupt any logins, only informs SafePeak on such connection. On the Dashboard screen in SafePeak you will be able to see those connections and then decide what to do with them. Configuration of this feature in SafePeak dashboard can be done here: Settings -> SQL instances management -> click on instance -> Logon Trigger tab. Other features: 7. User management ability to grant permissions to someone without changing its configuration and only use SafePeak as performance analysis tool. 8. Better reports for analysis of performance using 15 minute resolution charts. 9. Caching of client cursors 10. Support for IPv6 Summary SafePeak is a great SQL Server performance acceleration solution for users who want immediate results for sites with performance, scalability and peak spikes challenges. Especially if your apps are packaged or 3rd party, since no code changes are done. SafePeak can significantly increase response times, by reducing network roundtrip to the database, decreasing CPU resource usage, eliminating I/O and storage access. SafePeak team provides a free fully functional trial www.safepeak.com/download and actually provides a one-on-one assistance during such trial. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

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• SQL Server 2012 - AlwaysOn

  - by Claus Jandausch

  Ich war nicht nur irritiert, ich war sogar regelrecht schockiert - und für einen kurzen Moment sprachlos (was nur selten der Fall ist). Gerade eben hatte mich jemand gefragt "Wann Oracle denn etwas Vergleichbares wie AlwaysOn bieten würde - und ob überhaupt?" War ich hier im falschen Film gelandet? Ich konnte nicht anders, als meinen Unmut kundzutun und zu erklären, dass die Fragestellung normalerweise anders herum läuft. Zugegeben - es mag vielleicht strittige Punkte geben im Vergleich zwischen Oracle und SQL Server - bei denen nicht unbedingt immer Oracle die Nase vorn haben muss - aber das Thema Clustering für Hochverfügbarkeit (HA), Disaster Recovery (DR) und Skalierbarkeit gehört mit Sicherheit nicht dazu. Dieses Erlebnis hakte ich am Nachgang als Einzelfall ab, der so nie wieder vorkommen würde. Bis ich kurz darauf eines Besseren belehrt wurde und genau die selbe Frage erneut zu hören bekam. Diesmal sogar im Exadata-Umfeld und einem Oracle Stretch Cluster. Einmal ist keinmal, doch zweimal ist einmal zu viel... Getreu diesem alten Motto war mir klar, dass man das so nicht länger stehen lassen konnte. Ich habe keine Ahnung, wie die Microsoft Marketing Abteilung es geschafft hat, unter dem AlwaysOn Brading eine innovative Technologie vermuten zu lassen - aber sie hat ihren Job scheinbar gut gemacht. Doch abgesehen von einem guten Marketing, stellt sich natürlich die Frage, was wirklich dahinter steckt und wie sich das Ganze mit Oracle vergleichen lässt - und ob überhaupt? Damit wären wir wieder bei der ursprünglichen Frage angelangt.  So viel zum Hintergrund dieses Blogbeitrags - von meiner Antwort handelt der restliche Blog. "Windows was the God ..." Um den wahren Unterschied zwischen Oracle und Microsoft verstehen zu können, muss man zunächst das bedeutendste Microsoft Dogma kennen. Es lässt sich schlicht und einfach auf den Punkt bringen: "Alles muss auf Windows basieren." Die Überschrift dieses Absatzes ist kein von mir erfundener Ausspruch, sondern ein Zitat. Konkret stammt es aus einem längeren Artikel von Kurt Eichenwald in der Vanity Fair aus dem August 2012. Er lautet Microsoft's Lost Decade und sei jedem ans Herz gelegt, der die "Microsoft-Maschinerie" unter Steve Ballmer und einige ihrer Kuriositäten besser verstehen möchte. "YOU TALKING TO ME?" Microsoft C.E.O. Steve Ballmer bei seiner Keynote auf der 2012 International Consumer Electronics Show in Las Vegas am 9. Januar   Manche Dinge in diesem Artikel mögen überspitzt dargestellt erscheinen - sind sie aber nicht. Vieles davon kannte ich bereits aus eigener Erfahrung und kann es nur bestätigen. Anderes hat sich mir erst so richtig erschlossen. Insbesondere die folgenden Passagen führten zum Aha-Erlebnis: “Windows was the god—everything had to work with Windows,” said Stone... “Every little thing you want to write has to build off of Windows (or other existing roducts),” one software engineer said. “It can be very confusing, …” Ich habe immer schon darauf hingewiesen, dass in einem SQL Server Failover Cluster die Microsoft Datenbank eigentlich nichts Nenneswertes zum Geschehen beiträgt, sondern sich voll und ganz auf das Windows Betriebssystem verlässt. Deshalb muss man auch die Windows Server Enterprise Edition installieren, soll ein Failover Cluster für den SQL Server eingerichtet werden. Denn hier werden die Cluster Services geliefert - nicht mit dem SQL Server. Er ist nur lediglich ein weiteres Server Produkt, für das Windows in Ausfallszenarien genutzt werden kann - so wie Microsoft Exchange beispielsweise, oder Microsoft SharePoint, oder irgendein anderes Server Produkt das auf Windows gehostet wird. Auch Oracle kann damit genutzt werden. Das Stichwort lautet hier: Oracle Failsafe. Nur - warum sollte man das tun, wenn gleichzeitig eine überlegene Technologie wie die Oracle Real Application Clusters (RAC) zur Verfügung steht, die dann auch keine Windows Enterprise Edition voraussetzen, da Oracle die eigene Clusterware liefert. Welche darüber hinaus für kürzere Failover-Zeiten sorgt, da diese Cluster-Technologie Datenbank-integriert ist und sich nicht auf "Dritte" verlässt. Wenn man sich also schon keine technischen Vorteile mit einem SQL Server Failover Cluster erkauft, sondern zusätzlich noch versteckte Lizenzkosten durch die Lizenzierung der Windows Server Enterprise Edition einhandelt, warum hat Microsoft dann in den vergangenen Jahren seit SQL Server 2000 nicht ebenfalls an einer neuen und innovativen Lösung gearbeitet, die mit Oracle RAC mithalten kann? Entwickler hat Microsoft genügend? Am Geld kann es auch nicht liegen? Lesen Sie einfach noch einmal die beiden obenstehenden Zitate und sie werden den Grund verstehen. Anders lässt es sich ja auch gar nicht mehr erklären, dass AlwaysOn aus zwei unterschiedlichen Technologien besteht, die beide jedoch wiederum auf dem Windows Server Failover Clustering (WSFC) basieren. Denn daraus ergeben sich klare Nachteile - aber dazu später mehr. Um AlwaysOn zu verstehen, sollte man sich zunächst kurz in Erinnerung rufen, was Microsoft bisher an HA/DR (High Availability/Desaster Recovery) Lösungen für SQL Server zur Verfügung gestellt hat. Replikation Basiert auf logischer Replikation und Pubisher/Subscriber Architektur Transactional Replication Merge Replication Snapshot Replication Microsoft's Replikation ist vergleichbar mit Oracle GoldenGate. Oracle GoldenGate stellt jedoch die umfassendere Technologie dar und bietet High Performance. Log Shipping Microsoft's Log Shipping stellt eine einfache Technologie dar, die vergleichbar ist mit Oracle Managed Recovery in Oracle Version 7. Das Log Shipping besitzt folgende Merkmale: Transaction Log Backups werden von Primary nach Secondary/ies geschickt Einarbeitung (z.B. Restore) auf jedem Secondary individuell Optionale dritte Server Instanz (Monitor Server) für Überwachung und Alarm Log Restore Unterbrechung möglich für Read-Only Modus (Secondary) Keine Unterstützung von Automatic Failover Database Mirroring Microsoft's Database Mirroring wurde verfügbar mit SQL Server 2005, sah aus wie Oracle Data Guard in Oracle 9i, war funktional jedoch nicht so umfassend. Für ein HA/DR Paar besteht eine 1:1 Beziehung, um die produktive Datenbank (Principle DB) abzusichern. Auf der Standby Datenbank (Mirrored DB) werden alle Insert-, Update- und Delete-Operationen nachgezogen. Modi Synchron (High-Safety Modus) Asynchron (High-Performance Modus) Automatic Failover Unterstützt im High-Safety Modus (synchron) Witness Server vorausgesetzt     Zur Frage der Kontinuität Es stellt sich die Frage, wie es um diesen Technologien nun im Zusammenhang mit SQL Server 2012 bestellt ist. Unter Fanfaren seinerzeit eingeführt, war Database Mirroring das erklärte Mittel der Wahl. Ich bin kein Produkt Manager bei Microsoft und kann hierzu nur meine Meinung äußern, aber zieht man den SQL AlwaysOn Team Blog heran, so sieht es nicht gut aus für das Database Mirroring - zumindest nicht langfristig. "Does AlwaysOn Availability Group replace Database Mirroring going forward?” “The short answer is we recommend that you migrate from the mirroring configuration or even mirroring and log shipping configuration to using Availability Group. Database Mirroring will still be available in the Denali release but will be phased out over subsequent releases. Log Shipping will continue to be available in future releases.” Damit wären wir endlich beim eigentlichen Thema angelangt. Was ist eine sogenannte Availability Group und was genau hat es mit der vielversprechend klingenden Bezeichnung AlwaysOn auf sich?   SQL Server 2012 - AlwaysOn Zwei HA-Features verstekcne sich hinter dem “AlwaysOn”-Branding. Einmal das AlwaysOn Failover Clustering aka SQL Server Failover Cluster Instances (FCI) - zum Anderen die AlwaysOn Availability Groups. Failover Cluster Instances (FCI) Entspricht ungefähr dem Stretch Cluster Konzept von Oracle Setzt auf Windows Server Failover Clustering (WSFC) auf Bietet HA auf Instanz-Ebene AlwaysOn Availability Groups (Verfügbarkeitsgruppen) Ähnlich der Idee von Consistency Groups, wie in Storage-Level Replikations-Software von z.B. EMC SRDF Abhängigkeiten zu Windows Server Failover Clustering (WSFC) Bietet HA auf Datenbank-Ebene   Hinweis: Verwechseln Sie nicht eine SQL Server Datenbank mit einer Oracle Datenbank. Und auch nicht eine Oracle Instanz mit einer SQL Server Instanz. Die gleichen Begriffe haben hier eine andere Bedeutung - nicht selten ein Grund, weshalb Oracle- und Microsoft DBAs schnell aneinander vorbei reden. Denken Sie bei einer SQL Server Datenbank eher an ein Oracle Schema, das kommt der Sache näher. So etwas wie die SQL Server Northwind Datenbank ist vergleichbar mit dem Oracle Scott Schema. Wenn Sie die genauen Unterschiede kennen möchten, finden Sie eine detaillierte Beschreibung in meinem Buch "Oracle10g Release 2 für Windows und .NET", erhältich bei Lehmanns, Amazon, etc.   Windows Server Failover Clustering (WSFC) Wie man sieht, basieren beide AlwaysOn Technologien wiederum auf dem Windows Server Failover Clustering (WSFC), um einerseits Hochverfügbarkeit auf Ebene der Instanz zu gewährleisten und andererseits auf der Datenbank-Ebene. Deshalb nun eine kurze Beschreibung der WSFC. Die WSFC sind ein mit dem Windows Betriebssystem geliefertes Infrastruktur-Feature, um HA für Server Anwendungen, wie Microsoft Exchange, SharePoint, SQL Server, etc. zu bieten. So wie jeder andere Cluster, besteht ein WSFC Cluster aus einer Gruppe unabhängiger Server, die zusammenarbeiten, um die Verfügbarkeit einer Applikation oder eines Service zu erhöhen. Falls ein Cluster-Knoten oder -Service ausfällt, kann der auf diesem Knoten bisher gehostete Service automatisch oder manuell auf einen anderen im Cluster verfügbaren Knoten transferriert werden - was allgemein als Failover bekannt ist. Unter SQL Server 2012 verwenden sowohl die AlwaysOn Avalability Groups, als auch die AlwaysOn Failover Cluster Instances die WSFC als Plattformtechnologie, um Komponenten als WSFC Cluster-Ressourcen zu registrieren. Verwandte Ressourcen werden in eine Ressource Group zusammengefasst, die in Abhängigkeit zu anderen WSFC Cluster-Ressourcen gebracht werden kann. Der WSFC Cluster Service kann jetzt die Notwendigkeit zum Neustart der SQL Server Instanz erfassen oder einen automatischen Failover zu einem anderen Server-Knoten im WSFC Cluster auslösen.   Failover Cluster Instances (FCI) Eine SQL Server Failover Cluster Instanz (FCI) ist eine einzelne SQL Server Instanz, die in einem Failover Cluster betrieben wird, der aus mehreren Windows Server Failover Clustering (WSFC) Knoten besteht und so HA (High Availability) auf Ebene der Instanz bietet. Unter Verwendung von Multi-Subnet FCI kann auch Remote DR (Disaster Recovery) unterstützt werden. Eine weitere Option für Remote DR besteht darin, eine unter FCI gehostete Datenbank in einer Availability Group zu betreiben. Hierzu später mehr. FCI und WSFC Basis FCI, das für lokale Hochverfügbarkeit der Instanzen genutzt wird, ähnelt der veralteten Architektur eines kalten Cluster (Aktiv-Passiv). Unter SQL Server 2008 wurde diese Technologie SQL Server 2008 Failover Clustering genannt. Sie nutzte den Windows Server Failover Cluster. In SQL Server 2012 hat Microsoft diese Basistechnologie unter der Bezeichnung AlwaysOn zusammengefasst. Es handelt sich aber nach wie vor um die klassische Aktiv-Passiv-Konfiguration. Der Ablauf im Failover-Fall ist wie folgt: Solange kein Hardware-oder System-Fehler auftritt, werden alle Dirty Pages im Buffer Cache auf Platte geschrieben Alle entsprechenden SQL Server Services (Dienste) in der Ressource Gruppe werden auf dem aktiven Knoten gestoppt Die Ownership der Ressource Gruppe wird auf einen anderen Knoten der FCI transferriert Der neue Owner (Besitzer) der Ressource Gruppe startet seine SQL Server Services (Dienste) Die Connection-Anforderungen einer Client-Applikation werden automatisch auf den neuen aktiven Knoten mit dem selben Virtuellen Network Namen (VNN) umgeleitet Abhängig vom Zeitpunkt des letzten Checkpoints, kann die Anzahl der Dirty Pages im Buffer Cache, die noch auf Platte geschrieben werden müssen, zu unvorhersehbar langen Failover-Zeiten führen. Um diese Anzahl zu drosseln, besitzt der SQL Server 2012 eine neue Fähigkeit, die Indirect Checkpoints genannt wird. Indirect Checkpoints ähnelt dem Fast-Start MTTR Target Feature der Oracle Datenbank, das bereits mit Oracle9i verfügbar war.   SQL Server Multi-Subnet Clustering Ein SQL Server Multi-Subnet Failover Cluster entspricht vom Konzept her einem Oracle RAC Stretch Cluster. Doch dies ist nur auf den ersten Blick der Fall. Im Gegensatz zu RAC ist in einem lokalen SQL Server Failover Cluster jeweils nur ein Knoten aktiv für eine Datenbank. Für die Datenreplikation zwischen geografisch entfernten Sites verlässt sich Microsoft auf 3rd Party Lösungen für das Storage Mirroring.     Die Verbesserung dieses Szenario mit einer SQL Server 2012 Implementierung besteht schlicht darin, dass eine VLAN-Konfiguration (Virtual Local Area Network) nun nicht mehr benötigt wird, so wie dies bisher der Fall war. Das folgende Diagramm stellt dar, wie der Ablauf mit SQL Server 2012 gehandhabt wird. In Site A und Site B wird HA jeweils durch einen lokalen Aktiv-Passiv-Cluster sichergestellt.     Besondere Aufmerksamkeit muss hier der Konfiguration und dem Tuning geschenkt werden, da ansonsten völlig inakzeptable Failover-Zeiten resultieren. Dies liegt darin begründet, weil die Downtime auf Client-Seite nun nicht mehr nur von der reinen Failover-Zeit abhängt, sondern zusätzlich von der Dauer der DNS Replikation zwischen den DNS Servern. (Rufen Sie sich in Erinnerung, dass wir gerade von Multi-Subnet Clustering sprechen). Außerdem ist zu berücksichtigen, wie schnell die Clients die aktualisierten DNS Informationen abfragen. Spezielle Konfigurationen für Node Heartbeat, HostRecordTTL (Host Record Time-to-Live) und Intersite Replication Frequeny für Active Directory Sites und Services werden notwendig. Default TTL für Windows Server 2008 R2: 20 Minuten Empfohlene Einstellung: 1 Minute DNS Update Replication Frequency in Windows Umgebung: 180 Minuten Empfohlene Einstellung: 15 Minuten (minimaler Wert)   Betrachtet man diese Werte, muss man feststellen, dass selbst eine optimale Konfiguration die rigiden SLAs (Service Level Agreements) heutiger geschäftskritischer Anwendungen für HA und DR nicht erfüllen kann. Denn dies impliziert eine auf der Client-Seite erlebte Failover-Zeit von insgesamt 16 Minuten. Hierzu ein Auszug aus der SQL Server 2012 Online Dokumentation: Cons: If a cross-subnet failover occurs, the client recovery time could be 15 minutes or longer, depending on your HostRecordTTL setting and the setting of your cross-site DNS/AD replication schedule.    Wir sind hier an einem Punkt unserer Überlegungen angelangt, an dem sich erklärt, weshalb ich zuvor das "Windows was the God ..." Zitat verwendet habe. Die unbedingte Abhängigkeit zu Windows wird zunehmend zum Problem, da sie die Komplexität einer Microsoft-basierenden Lösung erhöht, anstelle sie zu reduzieren. Und Komplexität ist das Letzte, was sich CIOs heutzutage wünschen.  Zur Ehrenrettung des SQL Server 2012 und AlwaysOn muss man sagen, dass derart lange Failover-Zeiten kein unbedingtes "Muss" darstellen, sondern ein "Kann". Doch auch ein "Kann" kann im unpassenden Moment unvorhersehbare und kostspielige Folgen haben. Die Unabsehbarkeit ist wiederum Ursache vieler an der Implementierung beteiligten Komponenten und deren Abhängigkeiten, wie beispielsweise drei Cluster-Lösungen (zwei von Microsoft, eine 3rd Party Lösung). Wie man die Sache auch dreht und wendet, kommt man an diesem Fakt also nicht vorbei - ganz unabhängig von der Dauer einer Downtime oder Failover-Zeiten. Im Gegensatz zu AlwaysOn und der hier vorgestellten Version eines Stretch-Clusters, vermeidet eine entsprechende Oracle Implementierung eine derartige Komplexität, hervorgerufen duch multiple Abhängigkeiten. Den Unterschied machen Datenbank-integrierte Mechanismen, wie Fast Application Notification (FAN) und Fast Connection Failover (FCF). Für Oracle MAA Konfigurationen (Maximum Availability Architecture) sind Inter-Site Failover-Zeiten im Bereich von Sekunden keine Seltenheit. Wenn Sie dem Link zur Oracle MAA folgen, finden Sie außerdem eine Reihe an Customer Case Studies. Auch dies ist ein wichtiges Unterscheidungsmerkmal zu AlwaysOn, denn die Oracle Technologie hat sich bereits zigfach in höchst kritischen Umgebungen bewährt.   Availability Groups (Verfügbarkeitsgruppen) Die sogenannten Availability Groups (Verfügbarkeitsgruppen) sind - neben FCI - der weitere Baustein von AlwaysOn.   Hinweis: Bevor wir uns näher damit beschäftigen, sollten Sie sich noch einmal ins Gedächtnis rufen, dass eine SQL Server Datenbank nicht die gleiche Bedeutung besitzt, wie eine Oracle Datenbank, sondern eher einem Oracle Schema entspricht. So etwas wie die SQL Server Northwind Datenbank ist vergleichbar mit dem Oracle Scott Schema.   Eine Verfügbarkeitsgruppe setzt sich zusammen aus einem Set mehrerer Benutzer-Datenbanken, die im Falle eines Failover gemeinsam als Gruppe behandelt werden. Eine Verfügbarkeitsgruppe unterstützt ein Set an primären Datenbanken (primäres Replikat) und einem bis vier Sets von entsprechenden sekundären Datenbanken (sekundäre Replikate).       Es können jedoch nicht alle SQL Server Datenbanken einer AlwaysOn Verfügbarkeitsgruppe zugeordnet werden. Der SQL Server Spezialist Michael Otey zählt in seinem SQL Server Pro Artikel folgende Anforderungen auf: Verfügbarkeitsgruppen müssen mit Benutzer-Datenbanken erstellt werden. System-Datenbanken können nicht verwendet werden Die Datenbanken müssen sich im Read-Write Modus befinden. Read-Only Datenbanken werden nicht unterstützt Die Datenbanken in einer Verfügbarkeitsgruppe müssen Multiuser Datenbanken sein Sie dürfen nicht das AUTO_CLOSE Feature verwenden Sie müssen das Full Recovery Modell nutzen und es muss ein vollständiges Backup vorhanden sein Eine gegebene Datenbank kann sich nur in einer einzigen Verfügbarkeitsgruppe befinden und diese Datenbank düerfen nicht für Database Mirroring konfiguriert sein Microsoft empfiehl außerdem, dass der Verzeichnispfad einer Datenbank auf dem primären und sekundären Server identisch sein sollte Wie man sieht, eignen sich Verfügbarkeitsgruppen nicht, um HA und DR vollständig abzubilden. Die Unterscheidung zwischen der Instanzen-Ebene (FCI) und Datenbank-Ebene (Availability Groups) ist von hoher Bedeutung. Vor kurzem wurde mir gesagt, dass man mit den Verfügbarkeitsgruppen auf Shared Storage verzichten könne und dadurch Kosten spart. So weit so gut ... Man kann natürlich eine Installation rein mit Verfügbarkeitsgruppen und ohne FCI durchführen - aber man sollte sich dann darüber bewusst sein, was man dadurch alles nicht abgesichert hat - und dies wiederum für Desaster Recovery (DR) und SLAs (Service Level Agreements) bedeutet. Kurzum, um die Kombination aus beiden AlwaysOn Produkten und der damit verbundene Komplexität kommt man wohl in der Praxis nicht herum.    Availability Groups und WSFC AlwaysOn hängt von Windows Server Failover Clustering (WSFC) ab, um die aktuellen Rollen der Verfügbarkeitsreplikate einer Verfügbarkeitsgruppe zu überwachen und zu verwalten, und darüber zu entscheiden, wie ein Failover-Ereignis die Verfügbarkeitsreplikate betrifft. Das folgende Diagramm zeigt de Beziehung zwischen Verfügbarkeitsgruppen und WSFC:   Der Verfügbarkeitsmodus ist eine Eigenschaft jedes Verfügbarkeitsreplikats. Synychron und Asynchron können also gemischt werden: Availability Modus (Verfügbarkeitsmodus) Asynchroner Commit-Modus Primäres replikat schließt Transaktionen ohne Warten auf Sekundäres Synchroner Commit-Modus Primäres Replikat wartet auf Commit von sekundärem Replikat Failover Typen Automatic Manual Forced (mit möglichem Datenverlust) Synchroner Commit-Modus Geplanter, manueller Failover ohne Datenverlust Automatischer Failover ohne Datenverlust Asynchroner Commit-Modus Nur Forced, manueller Failover mit möglichem Datenverlust   Der SQL Server kennt keinen separaten Switchover Begriff wie in Oracle Data Guard. Für SQL Server werden alle Role Transitions als Failover bezeichnet. Tatsächlich unterstützt der SQL Server keinen Switchover für asynchrone Verbindungen. Es gibt nur die Form des Forced Failover mit möglichem Datenverlust. Eine ähnliche Fähigkeit wie der Switchover unter Oracle Data Guard ist so nicht gegeben.   SQL Sever FCI mit Availability Groups (Verfügbarkeitsgruppen) Neben den Verfügbarkeitsgruppen kann eine zweite Failover-Ebene eingerichtet werden, indem SQL Server FCI (auf Shared Storage) mit WSFC implementiert wird. Ein Verfügbarkeitesreplikat kann dann auf einer Standalone Instanz gehostet werden, oder einer FCI Instanz. Zum Verständnis: Die Verfügbarkeitsgruppen selbst benötigen kein Shared Storage. Diese Kombination kann verwendet werden für lokale HA auf Ebene der Instanz und DR auf Datenbank-Ebene durch Verfügbarkeitsgruppen. Das folgende Diagramm zeigt dieses Szenario:   Achtung! Hier handelt es sich nicht um ein Pendant zu Oracle RAC plus Data Guard, auch wenn das Bild diesen Eindruck vielleicht vermitteln mag - denn alle sekundären Knoten im FCI sind rein passiv. Es existiert außerdem eine weitere und ernsthafte Einschränkung: SQL Server Failover Cluster Instanzen (FCI) unterstützen nicht das automatische AlwaysOn Failover für Verfügbarkeitsgruppen. Jedes unter FCI gehostete Verfügbarkeitsreplikat kann nur für manuelles Failover konfiguriert werden.   Lesbare Sekundäre Replikate Ein oder mehrere Verfügbarkeitsreplikate in einer Verfügbarkeitsgruppe können für den lesenden Zugriff konfiguriert werden, wenn sie als sekundäres Replikat laufen. Dies ähnelt Oracle Active Data Guard, jedoch gibt es Einschränkungen. Alle Abfragen gegen die sekundäre Datenbank werden automatisch auf das Snapshot Isolation Level abgebildet. Es handelt sich dabei um eine Versionierung der Rows. Microsoft versuchte hiermit die Oracle MVRC (Multi Version Read Consistency) nachzustellen. Tatsächlich muss man die SQL Server Snapshot Isolation eher mit Oracle Flashback vergleichen. Bei der Implementierung des Snapshot Isolation Levels handelt sich um ein nachträglich aufgesetztes Feature und nicht um einen inhärenten Teil des Datenbank-Kernels, wie im Falle Oracle. (Ich werde hierzu in Kürze einen weiteren Blogbeitrag verfassen, wenn ich mich mit der neuen SQL Server 2012 Core Lizenzierung beschäftige.) Für die Praxis entstehen aus der Abbildung auf das Snapshot Isolation Level ernsthafte Restriktionen, derer man sich für den Betrieb in der Praxis bereits vorab bewusst sein sollte: Sollte auf der primären Datenbank eine aktive Transaktion zu dem Zeitpunkt existieren, wenn ein lesbares sekundäres Replikat in die Verfügbarkeitsgruppe aufgenommen wird, werden die Row-Versionen auf der korrespondierenden sekundären Datenbank nicht sofort vollständig verfügbar sein. Eine aktive Transaktion auf dem primären Replikat muss zuerst abgeschlossen (Commit oder Rollback) und dieser Transaktions-Record auf dem sekundären Replikat verarbeitet werden. Bis dahin ist das Isolation Level Mapping auf der sekundären Datenbank unvollständig und Abfragen sind temporär geblockt. Microsoft sagt dazu: "This is needed to guarantee that row versions are available on the secondary replica before executing the query under snapshot isolation as all isolation levels are implicitly mapped to snapshot isolation." (SQL Storage Engine Blog: AlwaysOn: I just enabled Readable Secondary but my query is blocked?)  Grundlegend bedeutet dies, dass ein aktives lesbares Replikat nicht in die Verfügbarkeitsgruppe aufgenommen werden kann, ohne das primäre Replikat vorübergehend stillzulegen. Da Leseoperationen auf das Snapshot Isolation Transaction Level abgebildet werden, kann die Bereinigung von Ghost Records auf dem primären Replikat durch Transaktionen auf einem oder mehreren sekundären Replikaten geblockt werden - z.B. durch eine lang laufende Abfrage auf dem sekundären Replikat. Diese Bereinigung wird auch blockiert, wenn die Verbindung zum sekundären Replikat abbricht oder der Datenaustausch unterbrochen wird. Auch die Log Truncation wird in diesem Zustant verhindert. Wenn dieser Zustand längere Zeit anhält, empfiehlt Microsoft das sekundäre Replikat aus der Verfügbarkeitsgruppe herauszunehmen - was ein ernsthaftes Downtime-Problem darstellt. Die Read-Only Workload auf den sekundären Replikaten kann eingehende DDL Änderungen blockieren. Obwohl die Leseoperationen aufgrund der Row-Versionierung keine Shared Locks halten, führen diese Operatioen zu Sch-S Locks (Schemastabilitätssperren). DDL-Änderungen durch Redo-Operationen können dadurch blockiert werden. Falls DDL aufgrund konkurrierender Lese-Workload blockiert wird und der Schwellenwert für 'Recovery Interval' (eine SQL Server Konfigurationsoption) überschritten wird, generiert der SQL Server das Ereignis sqlserver.lock_redo_blocked, welches Microsoft zum Kill der blockierenden Leser empfiehlt. Auf die Verfügbarkeit der Anwendung wird hierbei keinerlei Rücksicht genommen.   Keine dieser Einschränkungen existiert mit Oracle Active Data Guard.   Backups auf sekundären Replikaten  Über die sekundären Replikate können Backups (BACKUP DATABASE via Transact-SQL) nur als copy-only Backups einer vollständigen Datenbank, Dateien und Dateigruppen erstellt werden. Das Erstellen inkrementeller Backups ist nicht unterstützt, was ein ernsthafter Rückstand ist gegenüber der Backup-Unterstützung physikalischer Standbys unter Oracle Data Guard. Hinweis: Ein möglicher Workaround via Snapshots, bleibt ein Workaround. Eine weitere Einschränkung dieses Features gegenüber Oracle Data Guard besteht darin, dass das Backup eines sekundären Replikats nicht ausgeführt werden kann, wenn es nicht mit dem primären Replikat kommunizieren kann. Darüber hinaus muss das sekundäre Replikat synchronisiert sein oder sich in der Synchronisation befinden, um das Beackup auf dem sekundären Replikat erstellen zu können.   Vergleich von Microsoft AlwaysOn mit der Oracle MAA Ich komme wieder zurück auf die Eingangs erwähnte, mehrfach an mich gestellte Frage "Wann denn - und ob überhaupt - Oracle etwas Vergleichbares wie AlwaysOn bieten würde?" und meine damit verbundene (kurze) Irritation. Wenn Sie diesen Blogbeitrag bis hierher gelesen haben, dann kennen Sie jetzt meine darauf gegebene Antwort. Der eine oder andere Punkt traf dabei nicht immer auf Jeden zu, was auch nicht der tiefere Sinn und Zweck meiner Antwort war. Wenn beispielsweise kein Multi-Subnet mit im Spiel ist, sind alle diesbezüglichen Kritikpunkte zunächst obsolet. Was aber nicht bedeutet, dass sie nicht bereits morgen schon wieder zum Thema werden könnten (Sag niemals "Nie"). In manch anderes Fettnäpfchen tritt man wiederum nicht unbedingt in einer Testumgebung, sondern erst im laufenden Betrieb. Erst recht nicht dann, wenn man sich potenzieller Probleme nicht bewusst ist und keine dedizierten Tests startet. Und wer AlwaysOn erfolgreich positionieren möchte, wird auch gar kein Interesse daran haben, auf mögliche Schwachstellen und den besagten Teufel im Detail aufmerksam zu machen. Das ist keine Unterstellung - es ist nur menschlich. Außerdem ist es verständlich, dass man sich in erster Linie darauf konzentriert "was geht" und "was gut läuft", anstelle auf das "was zu Problemen führen kann" oder "nicht funktioniert". Wer will schon der Miesepeter sein? Für mich selbst gesprochen, kann ich nur sagen, dass ich lieber vorab von allen möglichen Einschränkungen wissen möchte, anstelle sie dann nach einer kurzen Zeit der heilen Welt schmerzhaft am eigenen Leib erfahren zu müssen. Ich bin davon überzeugt, dass es Ihnen nicht anders geht. Nachfolgend deshalb eine Zusammenfassung all jener Punkte, die ich im Vergleich zur Oracle MAA (Maximum Availability Architecture) als unbedingt Erwähnenswert betrachte, falls man eine Evaluierung von Microsoft AlwaysOn in Betracht zieht. 1. AlwaysOn ist eine komplexe Technologie Der SQL Server AlwaysOn Stack ist zusammengesetzt aus drei verschiedenen Technlogien: Windows Server Failover Clustering (WSFC) SQL Server Failover Cluster Instances (FCI) SQL Server Availability Groups (Verfügbarkeitsgruppen) Man kann eine derartige Lösung nicht als nahtlos bezeichnen, wofür auch die vielen von Microsoft dargestellten Einschränkungen sprechen. Während sich frühere SQL Server Versionen in Richtung eigener HA/DR Technologien entwickelten (wie Database Mirroring), empfiehlt Microsoft nun die Migration. Doch weshalb dieser Schwenk? Er führt nicht zu einem konsisten und robusten Angebot an HA/DR Technologie für geschäftskritische Umgebungen.  Liegt die Antwort in meiner These begründet, nach der "Windows was the God ..." noch immer gilt und man die Nachteile der allzu engen Kopplung mit Windows nicht sehen möchte? Entscheiden Sie selbst ... 2. Failover Cluster Instanzen - Kein RAC-Pendant Die SQL Server und Windows Server Clustering Technologie basiert noch immer auf dem veralteten Aktiv-Passiv Modell und führt zu einer Verschwendung von Systemressourcen. In einer Betrachtung von lediglich zwei Knoten erschließt sich auf Anhieb noch nicht der volle Mehrwert eines Aktiv-Aktiv Clusters (wie den Real Application Clusters), wie er von Oracle bereits vor zehn Jahren entwickelt wurde. Doch kennt man die Vorzüge der Skalierbarkeit durch einfaches Hinzufügen weiterer Cluster-Knoten, die dann alle gemeinsam als ein einziges logisches System zusammenarbeiten, versteht man was hinter dem Motto "Pay-as-you-Grow" steckt. In einem Aktiv-Aktiv Cluster geht es zwar auch um Hochverfügbarkeit - und ein Failover erfolgt zudem schneller, als in einem Aktiv-Passiv Modell - aber es geht eben nicht nur darum. An dieser Stelle sei darauf hingewiesen, dass die Oracle 11g Standard Edition bereits die Nutzung von Oracle RAC bis zu vier Sockets kostenfrei beinhaltet. Möchten Sie dazu Windows nutzen, benötigen Sie keine Windows Server Enterprise Edition, da Oracle 11g die eigene Clusterware liefert. Sie kommen in den Genuss von Hochverfügbarkeit und Skalierbarkeit und können dazu die günstigere Windows Server Standard Edition nutzen. 3. SQL Server Multi-Subnet Clustering - Abhängigkeit zu 3rd Party Storage Mirroring  Die SQL Server Multi-Subnet Clustering Architektur unterstützt den Aufbau eines Stretch Clusters, basiert dabei aber auf dem Aktiv-Passiv Modell. Das eigentlich Problematische ist jedoch, dass man sich zur Absicherung der Datenbank auf 3rd Party Storage Mirroring Technologie verlässt, ohne Integration zwischen dem Windows Server Failover Clustering (WSFC) und der darunterliegenden Mirroring Technologie. Wenn nun im Cluster ein Failover auf Instanzen-Ebene erfolgt, existiert keine Koordination mit einem möglichen Failover auf Ebene des Storage-Array. 4. Availability Groups (Verfügbarkeitsgruppen) - Vier, oder doch nur Zwei? Ein primäres Replikat erlaubt bis zu vier sekundäre Replikate innerhalb einer Verfügbarkeitsgruppe, jedoch nur zwei im Synchronen Commit Modus. Während dies zwar einen Vorteil gegenüber dem stringenten 1:1 Modell unter Database Mirroring darstellt, fällt der SQL Server 2012 damit immer noch weiter zurück hinter Oracle Data Guard mit bis zu 30 direkten Stanbdy Zielen - und vielen weiteren durch kaskadierende Ziele möglichen. Damit eignet sich Oracle Active Data Guard auch für die Bereitstellung einer Reader-Farm Skalierbarkeit für Internet-basierende Unternehmen. Mit AwaysOn Verfügbarkeitsgruppen ist dies nicht möglich. 5. Availability Groups (Verfügbarkeitsgruppen) - kein asynchrones Switchover  Die Technologie der Verfügbarkeitsgruppen wird auch als geeignetes Mittel für administrative Aufgaben positioniert - wie Upgrades oder Wartungsarbeiten. Man muss sich jedoch einem gravierendem Defizit bewusst sein: Im asynchronen Verfügbarkeitsmodus besteht die einzige Möglichkeit für Role Transition im Forced Failover mit Datenverlust! Um den Verlust von Daten durch geplante Wartungsarbeiten zu vermeiden, muss man den synchronen Verfügbarkeitsmodus konfigurieren, was jedoch ernstzunehmende Auswirkungen auf WAN Deployments nach sich zieht. Spinnt man diesen Gedanken zu Ende, kommt man zu dem Schluss, dass die Technologie der Verfügbarkeitsgruppen für geplante Wartungsarbeiten in einem derartigen Umfeld nicht effektiv genutzt werden kann. 6. Automatisches Failover - Nicht immer möglich Sowohl die SQL Server FCI, als auch Verfügbarkeitsgruppen unterstützen automatisches Failover. Möchte man diese jedoch kombinieren, wird das Ergebnis kein automatisches Failover sein. Denn ihr Zusammentreffen im Failover-Fall führt zu Race Conditions (Wettlaufsituationen), weshalb diese Konfiguration nicht länger das automatische Failover zu einem Replikat in einer Verfügbarkeitsgruppe erlaubt. Auch hier bestätigt sich wieder die tiefere Problematik von AlwaysOn, mit einer Zusammensetzung aus unterschiedlichen Technologien und der Abhängigkeit zu Windows. 7. Problematische RTO (Recovery Time Objective) Microsoft postioniert die SQL Server Multi-Subnet Clustering Architektur als brauchbare HA/DR Architektur. Bedenkt man jedoch die Problematik im Zusammenhang mit DNS Replikation und den möglichen langen Wartezeiten auf Client-Seite von bis zu 16 Minuten, sind strenge RTO Anforderungen (Recovery Time Objectives) nicht erfüllbar. Im Gegensatz zu Oracle besitzt der SQL Server keine Datenbank-integrierten Technologien, wie Oracle Fast Application Notification (FAN) oder Oracle Fast Connection Failover (FCF). 8. Problematische RPO (Recovery Point Objective) SQL Server ermöglicht Forced Failover (erzwungenes Failover), bietet jedoch keine Möglichkeit zur automatischen Übertragung der letzten Datenbits von einem alten zu einem neuen primären Replikat, wenn der Verfügbarkeitsmodus asynchron war. Oracle Data Guard hingegen bietet diese Unterstützung durch das Flush Redo Feature. Dies sichert "Zero Data Loss" und beste RPO auch in erzwungenen Failover-Situationen. 9. Lesbare Sekundäre Replikate mit Einschränkungen Aufgrund des Snapshot Isolation Transaction Level für lesbare sekundäre Replikate, besitzen diese Einschränkungen mit Auswirkung auf die primäre Datenbank. Die Bereinigung von Ghost Records auf der primären Datenbank, wird beeinflusst von lang laufenden Abfragen auf der lesabaren sekundären Datenbank. Die lesbare sekundäre Datenbank kann nicht in die Verfügbarkeitsgruppe aufgenommen werden, wenn es aktive Transaktionen auf der primären Datenbank gibt. Zusätzlich können DLL Änderungen auf der primären Datenbank durch Abfragen auf der sekundären blockiert werden. Und imkrementelle Backups werden hier nicht unterstützt.   Keine dieser Restriktionen existiert unter Oracle Data Guard.

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• Trace File Source Adapter

  The Trace File Source adapter is a useful addition to your SSIS toolbox.  It allows you to read 2005 and 2008 profiler traces stored as .trc files and read them into the Data Flow.  From there you can perform filtering and analysis using the power of SSIS. There is no need for a SQL Server connection this just uses the trace file. Example Usages Cache warming for SQL Server Analysis Services Reading the flight recorder Find out the longest running queries on a server Analyze statements for CPU, memory by user or some other criteria you choose Properties The Trace File Source adapter has two properties, both of which combine to control the source trace file that is read at runtime. SQL Server 2005 and SQL Server 2008 trace files are supported for both the Database Engine (SQL Server) and Analysis Services. The properties are managed by the Editor form or can be set directly from the Properties Grid in Visual Studio. Property Type Description AccessMode Enumeration This property determines how the Filename property is interpreted. The values available are: DirectInput Variable Filename String This property holds the path for trace file to load (*.trc). The value is either a full path, or the name of a variable which contains the full path to the trace file, depending on the AccessMode property. Trace Column Definition Hopefully the majority of you can skip this section entirely, but if you encounter some problems processing a trace file this may explain it and allow you to fix the problem. The component is built upon the trace management API provided by Microsoft. Unfortunately API methods that expose the schema of a trace file have known issues and are unreliable, put simply the data often differs from what was specified. To overcome these limitations the component uses  some simple XML files. These files enable the trace column data types and sizing attributes to be overridden. For example SQL Server Profiler or TMO generated structures define EventClass as an integer, but the real value is a string. TraceDataColumnsSQL.xml  - SQL Server Database Engine Trace Columns TraceDataColumnsAS.xml    - SQL Server Analysis Services Trace Columns The files can be found in the %ProgramFiles%\Microsoft SQL Server\100\DTS\PipelineComponents folder, e.g. "C:\Program Files\Microsoft SQL Server\100\DTS\PipelineComponents\TraceDataColumnsSQL.xml" "C:\Program Files\Microsoft SQL Server\100\DTS\PipelineComponents\TraceDataColumnsAS.xml" If at runtime the component encounters a type conversion or sizing error it is most likely due to a discrepancy between the column definition as reported by the API and the actual value encountered. Whilst most common issues have already been fixed through these files we have implemented specific exception traps to direct you to the files to enable you to fix any further issues due to different usage or data scenarios that we have not tested. An example error that you can fix through these files is shown below. Buffer exception writing value to column 'Column Name'. The string value is 999 characters in length, the column is only 111. Columns can be overridden by the TraceDataColumns XML files in "C:\Program Files\Microsoft SQL Server\100\DTS\PipelineComponents\TraceDataColumnsAS.xml". Installation The component is provided as an MSI file which you can download and run to install it. This simply places the files on disk in the correct locations and also installs the assemblies in the Global Assembly Cache as per Microsoft’s recommendations. You may need to restart the SQL Server Integration Services service, as this caches information about what components are installed, as well as restarting any open instances of Business Intelligence Development Studio (BIDS) / Visual Studio that you may be using to build your SSIS packages. Finally you will have to add the transformation to the Visual Studio toolbox manually. Right-click the toolbox, and select Choose Items.... Select the SSIS Data Flow Items tab, and then check the Trace File Source transformation in the Choose Toolbox Items window. This process has been described in detail in the related FAQ entry for How do I install a task or transform component? We recommend you follow best practice and apply the current Microsoft SQL Server Service pack to your SQL Server servers and workstations. Please note that the Microsoft Trace classes used in the component are not supported on 64-bit platforms. To use the Trace File Source on a 64-bit host you need to ensure you have the 32-bit (x86) tools available, and the way you execute your package is setup to use them, please see the help topic 64-bit Considerations for Integration Services for more details. Downloads Trace Sources for SQL Server 2005 -- Trace Sources for SQL Server 2008 Version History SQL Server 2008 Version 2.0.0.382 - SQL Sever 2008 public release. (9 Apr 2009) SQL Server 2005 Version 1.0.0.321 - SQL Server 2005 public release. (18 Nov 2008) -- Screenshots

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• Why Software Sucks...and What You Can Do About It – book review

  - by DigiMortal

      How do our users see the products we are writing for them and how happy they are with our work? Are they able to get their work done without fighting with cool features and crashes or are they just switching off resistance part of their brain to survive our software? Yeah, the overall picture of software usability landscape is not very nice. Okay, it is not even nice. But, fortunately, Why Software Sucks...and What You Can Do About It by David S. Platt explains everything. Why Software Sucks… is book for software users but I consider it as a-must reading also for developers and specially for their managers whose politics often kills all usability topics as soon as they may appear. For managers usability is soft topic that can be manipulated the way it is best in current state of project. Although developers are not UI designers and usability experts they are still very often forced to deal with these topics and this is how usability problems start (of course, also designers are able to produce designs that are stupid and too hard to use for users, but this blog here is about development). I found this book to be very interesting and funny reading. It is not humor book but it explains you all so you remember later very well what you just read. It took me about three evenings to go through this book and I am still enjoying what I found and how author explains our weird young working field to end users. I suggest this book to all developers – while you are demanding your management to hire or outsource usability expert you are at least causing less pain to end users. So, go and buy this book, just like I did. And… they thanks to mr. Platt :) There is one book more I suggest you to read if you are interested in usability - Don't Make Me Think: A Common Sense Approach to Web Usability, 2nd Edition by Steve Krug. Editorial review from Amazon Today’s software sucks. There’s no other good way to say it. It’s unsafe, allowing criminal programs to creep through the Internet wires into our very bedrooms. It’s unreliable, crashing when we need it most, wiping out hours or days of work with no way to get it back. And it’s hard to use, requiring large amounts of head-banging to figure out the simplest operations. It’s no secret that software sucks. You know that from personal experience, whether you use computers for work or personal tasks. In this book, programming insider David Platt explains why that’s the case and, more importantly, why it doesn’t have to be that way. And he explains it in plain, jargon-free English that’s a joy to read, using real-world examples with which you’re already familiar. In the end, he suggests what you, as a typical user, without a technical background, can do about this sad state of our software—how you, as an informed consumer, don’t have to take the abuse that bad software dishes out. As you might expect from the book’s title, Dave’s expose is laced with humor—sometimes outrageous, but always dead on. You’ll laugh out loud as you recall incidents with your own software that made you cry. You’ll slap your thigh with the same hand that so often pounded your computer desk and wished it was a bad programmer’s face. But Dave hasn’t written this book just for laughs. He’s written it to give long-overdue voice to your own discovery—that software does, indeed, suck, but it shouldn’t. Table of contents Acknowledgments xiii Introduction Chapter 1: Who’re You Calling a Dummy? Where We Came From Why It Still Sucks Today Control versus Ease of Use I Don’t Care How Your Program Works A Bad Feature and a Good One Stopping the Proceedings with Idiocy Testing on Live Animals Where We Are and What You Can Do Chapter 2: Tangled in the Web Where We Came From How It Works Why It Still Sucks Today Client-Centered Design versus Server-Centered Design Where’s My Eye Opener? It’s Obvious—Not! Splash, Flash, and Animation Testing on Live Animals What You Can Do about It Chapter 3: Keep Me Safe The Way It Was Why It Sucks Today What Programmers Need to Know, but Don’t A Human Operation Budgeting for Hassles Users Are Lazy Social Engineering Last Word on Security What You Can Do Chapter 4: Who the Heck Are You? Where We Came From Why It Still Sucks Today Incompatible Requirements OK, So Now What? Chapter 5: Who’re You Looking At? Yes, They Know You Why It Sucks More Than Ever Today Users Don’t Know Where the Risks Are What They Know First Milk You with Cookies? Privacy Policy Nonsense Covering Your Tracks The Google Conundrum Solution Chapter 6: Ten Thousand Geeks, Crazed on Jolt Cola See Them in Their Native Habitat All These Geeks Who Speaks, and When, and about What Selling It The Next Generation of Geeks—Passing It On Chapter 7: Who Are These Crazy Bastards Anyway? Homo Logicus Testosterone Poisoning Control and Contentment Making Models Geeks and Jocks Jargon Brains and Constraints Seven Habits of Geeks Chapter 8: Microsoft: Can’t Live With ’Em and Can’t Live Without ’Em They Run the World Me and Them Where We Came From Why It Sucks Today Damned if You Do, Damned if You Don’t We Love to Hate Them Plus ça Change Growing-Up Pains What You Can Do about It The Last Word Chapter 9: Doing Something About It 1. Buy 2. Tell 3. Ridicule 4. Trust 5. Organize Epilogue About the Author

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• SQL SERVER – PAGELATCH_DT, PAGELATCH_EX, PAGELATCH_KP, PAGELATCH_SH, PAGELATCH_UP – Wait Type – Day 12 of 28

  - by pinaldave

  This is another common wait type. However, I still frequently see people getting confused with PAGEIOLATCH_X and PAGELATCH_X wait types. Actually, there is a big difference between the two. PAGEIOLATCH is related to IO issues, while PAGELATCH is not related to IO issues but is oftentimes linked to a buffer issue. Before we delve deeper in this interesting topic, first let us understand what Latch is. Latches are internal SQL Server locks which can be described as very lightweight and short-term synchronization objects. Latches are not primarily to protect pages being read from disk into memory. It’s a synchronization object for any in-memory access to any portion of a log or data file.[Updated based on comment of Paul Randal] The difference between locks and latches is that locks seal all the involved resources throughout the duration of the transactions (and other processes will have no access to the object), whereas latches locks the resources during the time when the data is changed. This way, a latch is able to maintain the integrity of the data between storage engine and data cache. A latch is a short-living lock that is put on resources on buffer cache and in the physical disk when data is moved in either directions. As soon as the data is moved, the latch is released. Now, let us understand the wait stat type  related to latches. From Book On-Line: PAGELATCH_DT Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Destroy mode. PAGELATCH_EX Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Exclusive mode. PAGELATCH_KP Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Keep mode. PAGELATCH_SH Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Shared mode. PAGELATCH_UP Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Update mode. PAGELATCH_X Explanation: When there is a contention of access of the in-memory pages, this wait type shows up. It is quite possible that some of the pages in the memory are of very high demand. For the SQL Server to access them and put a latch on the pages, it will have to wait. This wait type is usually created at the same time. Additionally, it is commonly visible when the TempDB has higher contention as well. If there are indexes that are heavily used, contention can be created as well, leading to this wait type. Reducing PAGELATCH_X wait: The following counters are useful to understand the status of the PAGELATCH: Average Latch Wait Time (ms): The wait time for latch requests that have to wait. Latch Waits/sec: This is the number of latch requests that could not be granted immediately. Total Latch Wait Time (ms): This is the total latch wait time for latch requests in the last second. If there is TempDB contention, I suggest that you read the blog post of Robert Davis right away. He has written an excellent blog post regarding how to find out TempDB contention. The same blog post explains the terms in the allocation of GAM, SGAM and PFS. If there was a TempDB contention, Paul Randal explains the optimal settings for the TempDB in his misconceptions series. Trace Flag 1118 can be useful but use it very carefully. I totally understand that this blog post is not as clear as my other blog posts. I suggest if this wait stats is on one of your higher wait type. Do leave a comment or send me an email and I will get back to you with my solution for your situation. May the looking at all other wait stats and types together become effective as this wait type can help suggest proper bottleneck in your system. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com)   Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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• SQL SERVER – Example of Performance Tuning for Advanced Users with DB Optimizer

  - by Pinal Dave

  Performance tuning is such a subject that everyone wants to master it. In beginning everybody is at a novice level and spend lots of time learning how to master the art of performance tuning. However, as we progress further the tuning of the system keeps on getting very difficult. I have understood in my early career there should be no need of ego in the technology field. There are always better solutions and better ideas out there and we should not resist them. Instead of resisting the change and new wave I personally adopt it. Here is a similar example, as I personally progress to the master level of performance tuning, I face that it is getting harder to come up with optimal solutions. In such scenarios I rely on various tools to teach me how I can do things better. Once I learn about tools, I am often able to come up with better solutions when I face the similar situation next time. A few days ago I had received a query where the user wanted to tune it further to get the maximum out of the performance. I have re-written the similar query with the help of AdventureWorks sample database. SELECT * FROM HumanResources.Employee e INNER JOIN HumanResources.EmployeeDepartmentHistory edh ON e.BusinessEntityID = edh.BusinessEntityID INNER JOIN HumanResources.Shift s ON edh.ShiftID = s.ShiftID; User had similar query to above query was used in very critical report and wanted to get best out of the query. When I looked at the query – here were my initial thoughts Use only column in the select statements as much as you want in the application Let us look at the query pattern and data workload and find out the optimal index for it Before I give further solutions I was told by the user that they need all the columns from all the tables and creating index was not allowed in their system. He can only re-write queries or use hints to further tune this query. Now I was in the constraint box – I believe * was not a great idea but if they wanted all the columns, I believe we can’t do much besides using *. Additionally, if I cannot create a further index, I must come up with some creative way to write this query. I personally do not like to use hints in my application but there are cases when hints work out magically and gives optimal solutions. Finally, I decided to use Embarcadero’s DB Optimizer. It is a fantastic tool and very helpful when it is about performance tuning. I have previously explained how it works over here. First open DBOptimizer and open Tuning Job from File >> New >> Tuning Job. Once you open DBOptimizer Tuning Job follow the various steps indicates in the following diagram. Essentially we will take our original script and will paste that into Step 1: New SQL Text and right after that we will enable Step 2 for Generating Various cases, Step 3 for Detailed Analysis and Step 4 for Executing each generated case. Finally we will click on Analysis in Step 5 which will generate the report detailed analysis in the result pan. The detailed pan looks like. It generates various cases of T-SQL based on the original query. It applies various hints and available hints to the query and generate various execution plans of the query and displays them in the resultant. You can clearly notice that original query had a cost of 0.0841 and logical reads about 607 pages. Whereas various options which are just following it has different execution cost as well logical read. There are few cases where we have higher logical read and there are few cases where as we have very low logical read. If we pay attention the very next row to original query have Merge_Join_Query in description and have lowest execution cost value of 0.044 and have lowest Logical Reads of 29. This row contains the query which is the most optimal re-write of the original query. Let us double click over it. Here is the query: SELECT * FROM HumanResources.Employee e INNER JOIN HumanResources.EmployeeDepartmentHistory edh ON e.BusinessEntityID = edh.BusinessEntityID INNER JOIN HumanResources.Shift s ON edh.ShiftID = s.ShiftID OPTION (MERGE JOIN) If you notice above query have additional hint of Merge Join. With the help of this Merge Join query hint this query is now performing much better than before. The entire process takes less than 60 seconds. Please note that it the join hint Merge Join was optimal for this query but it is not necessary that the same hint will be helpful in all the queries. Additionally, if the workload or data pattern changes the query hint of merge join may be no more optimal join. In that case, we will have to redo the entire exercise once again. This is the reason I do not like to use hints in my queries and I discourage all of my users to use the same. However, if you look at this example, this is a great case where hints are optimizing the performance of the query. It is humanly not possible to test out various query hints and index options with the query to figure out which is the most optimal solution. Sometimes, we need to depend on the efficiency tools like DB Optimizer to guide us the way and select the best option from the suggestion provided. Let me know what you think of this article as well your experience with DB Optimizer. Please leave a comment. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Joins, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• SQL SERVER – CXPACKET – Parallelism – Usual Solution – Wait Type – Day 6 of 28

  - by pinaldave

  CXPACKET has to be most popular one of all wait stats. I have commonly seen this wait stat as one of the top 5 wait stats in most of the systems with more than one CPU. Books On-Line: Occurs when trying to synchronize the query processor exchange iterator. You may consider lowering the degree of parallelism if contention on this wait type becomes a problem. CXPACKET Explanation: When a parallel operation is created for SQL Query, there are multiple threads for a single query. Each query deals with a different set of the data (or rows). Due to some reasons, one or more of the threads lag behind, creating the CXPACKET Wait Stat. There is an organizer/coordinator thread (thread 0), which takes waits for all the threads to complete and gathers result together to present on the client’s side. The organizer thread has to wait for the all the threads to finish before it can move ahead. The Wait by this organizer thread for slow threads to complete is called CXPACKET wait. Note that not all the CXPACKET wait types are bad. You might experience a case when it totally makes sense. There might also be cases when this is unavoidable. If you remove this particular wait type for any query, then that query may run slower because the parallel operations are disabled for the query. Reducing CXPACKET wait: We cannot discuss about reducing the CXPACKET wait without talking about the server workload type. OLTP: On Pure OLTP system, where the transactions are smaller and queries are not long but very quick usually, set the “Maximum Degree of Parallelism” to 1 (one). This way it makes sure that the query never goes for parallelism and does not incur more engine overhead. EXEC sys.sp_configure N'cost threshold for parallelism', N'1' GO RECONFIGURE WITH OVERRIDE GO Data-warehousing / Reporting server: As queries will be running for long time, it is advised to set the “Maximum Degree of Parallelism” to 0 (zero). This way most of the queries will utilize the parallel processor, and long running queries get a boost in their performance due to multiple processors. EXEC sys.sp_configure N'cost threshold for parallelism', N'0' GO RECONFIGURE WITH OVERRIDE GO Mixed System (OLTP & OLAP): Here is the challenge. The right balance has to be found. I have taken a very simple approach. I set the “Maximum Degree of Parallelism” to 2, which means the query still uses parallelism but only on 2 CPUs. However, I keep the “Cost Threshold for Parallelism” very high. This way, not all the queries will qualify for parallelism but only the query with higher cost will go for parallelism. I have found this to work best for a system that has OLTP queries and also where the reporting server is set up. Here, I am setting ‘Cost Threshold for Parallelism’ to 25 values (which is just for illustration); you can choose any value, and you can find it out by experimenting with the system only. In the following script, I am setting the ‘Max Degree of Parallelism’ to 2, which indicates that the query that will have a higher cost (here, more than 25) will qualify for parallel query to run on 2 CPUs. This implies that regardless of the number of CPUs, the query will select any two CPUs to execute itself. EXEC sys.sp_configure N'cost threshold for parallelism', N'25' GO EXEC sys.sp_configure N'max degree of parallelism', N'2' GO RECONFIGURE WITH OVERRIDE GO Read all the post in the Wait Types and Queue series. Additionally a must read comment of Jonathan Kehayias. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest you all to read the online book for further clarification. All the discussion of Wait Stats over here is generic and it varies from system to system. It is recommended that you test this on the development server before implementing on the production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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• Migrating SQL Server Databases – The DBA’s Checklist (Part 2)

  - by Sadequl Hussain

  Continuing from Part 1  , our Migration Checklist continues: Step 5: Update statistics It is always a good idea to update the statistics of the database that you have just installed or migrated. To do this, run the following command against the target database: sp_updatestats The sp_updatestats system stored procedure runs the UPDATE STATISTICS command against every user and system table in the database.  However, a word of caution: running the sp_updatestats against a database with a compatibility level below 90 (SQL Server 2005) will reset the automatic UPDATE STATISTICS settings for every index and statistics of every table in the database. You may therefore want to change the compatibility mode before you run the command. Another thing you should remember to do is to ensure the new database has its AUTO_CREATE_STATISTICS and AUTO_UPDATE_STATISTICS properties set to ON. You can do so using the ALTER DATABASE command or from the SSMS. Step 6: Set database options You may have to change the state of a database after it has been restored. If the database was changed to single-user or read-only mode before backup, the restored copy will also retain these settings. This may not be an issue when you are manually restoring from Enterprise Manager or the Management Studio since you can change the properties. However, this is something to be mindful of if the restore process is invoked by an automated job or script and the database needs to be written to immediately after restore. You may want to check the database’s status programmatically in such cases. Another important option you may want to set for the newly restored / attached database is PAGE_VERIFY. This option specifies how you want SQL Server to ensure the physical integrity of the data. It is a new option from SQL Server 2005 and can have three values: CHECKSUM (default for SQL Server 2005 and latter databases), TORN_PAGE_DETECTION (default when restoring a pre-SQL Server 2005 database) or NONE. Torn page detection was itself an option for SQL Server 2000 databases. From SQL Server 2005, when PAGE_VERIFY is set to CHECKSUM, the database engine calculates the checksum for a page’s contents and writes it to the page header before storing it in disk. When the page is read from the disk, the checksum is computed again and compared with the checksum stored in the header.  Torn page detection works much like the same way in that it stores a bit in the page header for every 512 byte sector. When data is read from the page, the torn page bits stored in the header is compared with the respective sector contents. When PAGE_VERIFY is set to NONE, SQL Server does not perform any checking, even if torn page data or checksums are present in the page header.  This may not be something you would want to set unless there is a very specific reason.  Microsoft suggests using the CHECKSUM page verify option as this offers more protection. Step 7: Map database users to logins A common database migration issue is related to user access. Windows and SQL Server native logins that existed in the source instance and had access to the database may not be present in the destination. Even if the logins exist in the destination, the mapping between the user accounts and the logins will not be automatic. You can use a special system stored procedure called sp_change_users_login to address these situations. The procedure needs to be run against the newly attached or restored database and can accept four parameters. Depending on what you want to do, you may be using less than four though. The first parameter, @Action, can take three values. When you specify @Action = ‘Report’, the system will provide you with a list of database users which are not mapped to any login. If you want to map a database user to an existing SQL Server login, the value for @Action will be ‘Update_One’. In this case, you will only need to provide the database user name and the login it will map to. So if your newly restored database has a user account called “bob” and there is already a SQL Server login with the same name and you want to map the user to the login, you will execute a query like the following: sp_change_users_login         @Action = ‘Update_One’,         @UserNamePattern = ‘bob’,         @LoginName = ‘bob’ If the login does not exist, you can instruct SQL Server to create the login with the same name. In this case you will need to provide a password for the login and the value of the @Action parameter will be ‘Auto_Fix’. If the login already exists, it will be automatically mapped to the user account. Unfortunately sp_change_users_login system stored procedure cannot be used to map database users to trusted logins (Windows accounts) in SQL Server. You will need to follow a manual process to re-map the database user accounts.  Continues…

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