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• Android Image Problem and threshold

  - by Danny_E

  Hey Guys, Long time reader never posted until now. Im having some trouble with Android, im implementing a library called JJIL its an open source imaging library. My problem is this i need to run some analysis on an image and to do so i need to have it in jjil.core.image format and once those processes are complete i need to convert the changed image from jjil.core.image to java.awt.image. I cant seem to find a method of doing this does anyone have any ideas or have any experience with this? I would be grateful of any help. Danny

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• Obtain an array of audio sample values and compare them with a threshold

  - by Dora

  I have to write an iPhone application: to start recording when I have something on the audio channel, then to stop the recording after 1.15 s, to obtain a float array with the audio levels and to compare the value to see if they are 0 or 1. Please give me some ideas...I´m a beginner.

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• Determining threshold for lock escalation

  - by Davin

  I have a table with around 2.5 millions records and will be updating around 700k of them and want to update these while still allowing other users to see the data. My update statement looks something like this: UPDATE A WITH (UPDLOCK,ROWLOCK) SET A.field = B.field FROM Table_1 A INNER JOIN Table2 B ON A.id = B.id WHERE A.field IS NULL AND B.field IS NOT NULL I was wondering if there was any way to work out at what point sql server will escalate a lock placed on an update statement (as I don't want the whole table to be locked)? I don't have permissions to run a server trace to see how the locks are being applied, so is there any other way of knowing at what point the lock will be escalated to cover the whole table? Thanks!

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• PHP array pointer craziness

  - by JMan

  I'm trying to create a "GetCurrentLevel" method that takes a point value as an input and returns what "Level" that corresponds to. I'm storing the Level = Points mapping in an array, but the array pointer is not moving logically when I use it a foreach loop. I've added echo statements for debugging. Here's my class definition: class Levels extends Model { protected $_map = array ( 'None' => 0, 'Bronze' => 50, 'Silver' => 200, 'Gold' => 500 ); public function __construct() { parent::__construct(); } public function GetCurrentLevel($points) { foreach ($this->_map as $name => $threshold) { echo "Level Name: $name<br/>"; echo "Level Threshold: $threshold<br/>"; echo "Current Level: " . key($this->_map) . "<br/>"; echo "Current Threshold: " . current($this->_map) . "<br/>"; if ($points < $threshold) /* Threshold is now above the points, so need to go back one level */ { $previousThreshold = prev($this->_map); echo "Previous Threshold: $previousThreshold<br/>"; echo "Final Level: " . key($this->_map) . "<br/>"; return key($this->_map); } echo "Go to next level <br/>"; } } And here is what I see when I call GetCurrentLevel(60): Level Name: None Level Threshold: 0 Current Level: Bronze //* Looks like foreach immediately moves the array pointer *// Current Threshold: 50 Go to next level Level Name: Bronze Level Threshold: 50 Current Level: Bronze //* WTF? Why hasn't the array pointer moved? *// Current Threshold: 50 Go to next level Level Name: Silver Level Threshold: 200 Current Level: Bronze //* WTF? Why hasn't the array pointer moved? *// Current Threshold: 50 Previous Threshold: 0 Final Level: None But the "Final Level" should be 'Bronze' since 60 points is above the 50 points needed for a Bronze medal, but below the 200 points needed for a Silver medal. Sorry for the long post. Thanks for your help!

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• How to update the following rows after the sum of the previous rows reach a threshold? MySQL

  - by Paulo Faria

  I want to update the following rows after the sum of the previous rows reach a defined threshold. I'm using MySQL, and trying to think of a way to solve this using SQL only. Here's an example. Having the threshold 100. Iterating through the rows, when the sum of the previous rows amount = 100, set the following rows to checked. Before the operation: | id | amount | checked | | 1 | 50 | false | | 2 | 50 | false | | 3 | 20 | false | | 4 | 30 | false | After the operation: | id | amount | checked | | 1 | 50 | false | | 2 | 50 | false | <- threshold reached (50 + 50 = 100) | 3 | 20 | true* | | 4 | 30 | true* | Is it possible to do it with just a SQL query? Do I need a stored procedure? How could I implement it using either solution?

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• How to make a call when iphone shakes with particular threshold?

  - by chaitanya

  Hi, I am making an app in which i need to implement calling emergency medical services when mobile shakes with a particular threshold. Is it possible to capture the threshold or the extent of phone shakes and making a call when it happens? One requirement is can this is possible when my app is not running?

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• Remove results below a certain score threshold in Solr/Lucene?

  - by snickernet

  Hi Guys, Is there a built-in functionalities in solr/lucene to filter the results if they fall below a certain score threshold? Let's say if I provide a score threshold of .2, then all documents with score less than .2 will be removed from my results. My intuition is that this is possible by updating/customizing solr or lucene. Could you point me to right direction on how to do this? Thanks in advance!

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• The Unspoken - The Why of GC Ergonomics

  - by jonthecollector

  Do you use GC ergonomics, -XX:+UseAdaptiveSizePolicy, with the UseParallelGC collector? The jist of GC ergonomics for that collector is that it tries to grow or shrink the heap to meet a specified goal. The goals that you can choose are maximum pause time and/or throughput. Don't get too excited there. I'm speaking about UseParallelGC (the throughput collector) so there are definite limits to what pause goals can be achieved. When you say out loud "I don't care about pause times, give me the best throughput I can get" and then say to yourself "Well, maybe 10 seconds really is too long", then think about a pause time goal. By default there is no pause time goal and the throughput goal is high (98% of the time doing application work and 2% of the time doing GC work). You can get more details on this in my very first blog. GC ergonomics The UseG1GC has its own version of GC ergonomics, but I'll be talking only about the UseParallelGC version. If you use this option and wanted to know what it (GC ergonomics) was thinking, try -XX:AdaptiveSizePolicyOutputInterval=1 This will print out information every i-th GC (above i is 1) about what the GC ergonomics to trying to do. For example, UseAdaptiveSizePolicy actions to meet *** throughput goal *** GC overhead (%) Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) Tenuring threshold: (attempted to decrease to balance GC costs) = 1 GC ergonomics tries to meet (in order) Pause time goal Throughput goal Minimum footprint The first line says that it's trying to meet the throughput goal. UseAdaptiveSizePolicy actions to meet *** throughput goal *** This run has the default pause time goal (i.e., no pause time goal) so it is trying to reach a 98% throughput. The lines Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) say that we're currently spending about 16% of the time doing young GC's and about 5% of the time doing full GC's. These percentages are a decaying, weighted average (earlier contributions to the average are given less weight). The source code is available as part of the OpenJDK so you can take a look at it if you want the exact definition. GC ergonomics is trying to increase the throughput by growing the heap (so says the "attempted to grow"). The last line Tenuring threshold: (attempted to decrease to balance GC costs) = 1 says that the ergonomics is trying to balance the GC times between young GC's and full GC's by decreasing the tenuring threshold. During a young collection the younger objects are copied to the survivor spaces while the older objects are copied to the tenured generation. Younger and older are defined by the tenuring threshold. If the tenuring threshold hold is 4, an object that has survived fewer than 4 young collections (and has remained in the young generation by being copied to the part of the young generation called a survivor space) it is younger and copied again to a survivor space. If it has survived 4 or more young collections, it is older and gets copied to the tenured generation. A lower tenuring threshold moves objects more eagerly to the tenured generation and, conversely a higher tenuring threshold keeps copying objects between survivor spaces longer. The tenuring threshold varies dynamically with the UseParallelGC collector. That is different than our other collectors which have a static tenuring threshold. GC ergonomics tries to balance the amount of work done by the young GC's and the full GC's by varying the tenuring threshold. Want more work done in the young GC's? Keep objects longer in the survivor spaces by increasing the tenuring threshold. This is an example of the output when GC ergonomics is trying to achieve a pause time goal UseAdaptiveSizePolicy actions to meet *** pause time goal *** GC overhead (%) Young generation: 20.74 (no change) Tenured generation: 31.70 (attempted to shrink) The pause goal was set at 50 millisecs and the last GC was 0.415: [Full GC (Ergonomics) [PSYoungGen: 2048K-0K(26624K)] [ParOldGen: 26095K-9711K(28992K)] 28143K-9711K(55616K), [Metaspace: 1719K-1719K(2473K/6528K)], 0.0758940 secs] [Times: user=0.28 sys=0.00, real=0.08 secs] The full collection took about 76 millisecs so GC ergonomics wants to shrink the tenured generation to reduce that pause time. The previous young GC was 0.346: [GC (Allocation Failure) [PSYoungGen: 26624K-2048K(26624K)] 40547K-22223K(56768K), 0.0136501 secs] [Times: user=0.06 sys=0.00, real=0.02 secs] so the pause time there was about 14 millisecs so no changes are needed. If trying to meet a pause time goal, the generations are typically shrunk. With a pause time goal in play, watch the GC overhead numbers and you will usually see the cost of setting a pause time goal (i.e., throughput goes down). If the pause goal is too low, you won't achieve your pause time goal and you will spend all your time doing GC. GC ergonomics is meant to be simple because it is meant to be used by anyone. It was not meant to be mysterious and so this output was added. If you don't like what GC ergonomics is doing, you can turn it off with -XX:-UseAdaptiveSizePolicy, but be pre-warned that you have to manage the size of the generations explicitly. If UseAdaptiveSizePolicy is turned off, the heap does not grow. The size of the heap (and the generations) at the start of execution is always the size of the heap. I don't like that and tried to fix it once (with some help from an OpenJDK contributor) but it unfortunately never made it out the door. I still have hope though. Just a side note. With the default throughput goal of 98% the heap often grows to it's maximum value and stays there. Definitely reduce the throughput goal if footprint is important. Start with -XX:GCTimeRatio=4 for a more modest throughput goal (%20 of the time spent in GC). A higher value means a smaller amount of time in GC (as the throughput goal).

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• How do I split ONE array to two separate arrays based on magnitude size and a threshold?

  - by youhaveaBigego

  I have an array which has BIG numbers and small numbers in it. I got it from after running a log from WireShark. It is the total number of Bytes of TCP traffic. But Wireshark does not discriminate(it would actually try, and hence it will tell you the traffic stats of ALL types of traffic, but since This is how the Array look like : @Array=qw(10912980 10924534 10913356 10910304 10920426 10900658 10911266 10912088 10928972 10914718 10920770 10897774 10934258 10882186 10874126 8531 8217 3876 8147 8019 68157 3432 3350 3338 3280 3280 7845 7869 3072 3002 2828 8397 1328 1280 1240 1194 1193 1192 1194 6440 1148 1218 4236 1161 1100 1102 1148 1172 6305 1010 5437 3534 4623 4669 3617 4234 959 1121 1121 1075 3122 3076 1020 3030 628 2938 2938 1611 1611 1541 1541 1541 1541 1541 1541 1541 1541 1541 1541 1541 1541 583 370 178) When you look at these this array carefully, one thing is obvious to the human eye. There are really BIG numbers and small numbers. (Basically what I am saying is, there is the 1% class and low income class, no middle class). I want to split the array to two different arrays. That would require me to set a threshold. Array 1 should be ONLY the BIG numbers (10924534-10874126), and array 2 should be the smaller numbers (68157-178). Btw, the array is not sorted. User will NOT input the threshold, and hence should be determined smartly.

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• C#: Why Decorate When You Can Intercept

  - by James Michael Hare

  We've all heard of the old Decorator Design Pattern (here) or used it at one time or another either directly or indirectly.  A decorator is a class that wraps a given abstract class or interface and presents the same (or a superset) public interface but "decorated" with additional functionality.   As a really simplistic example, consider the System.IO.BufferedStream, it itself is a descendent of System.IO.Stream and wraps the given stream with buffering logic while still presenting System.IO.Stream's public interface:   1: Stream buffStream = new BufferedStream(rawStream); Now, let's take a look at a custom-code example.  Let's say that we have a class in our data access layer that retrieves a list of products from a database:  1: // a class that handles our CRUD operations for products 2: public class ProductDao 3: { 4: ... 5:  6: // a method that would retrieve all available products 7: public IEnumerable<Product> GetAvailableProducts() 8: { 9: var results = new List<Product>(); 10:  11: // must create the connection 12: using (var con = _factory.CreateConnection()) 13: { 14: con.ConnectionString = _productsConnectionString; 15: con.Open(); 16:  17: // create the command 18: using (var cmd = _factory.CreateCommand()) 19: { 20: cmd.Connection = con; 21: cmd.CommandText = _getAllProductsStoredProc; 22: cmd.CommandType = CommandType.StoredProcedure; 23:  24: // get a reader and pass back all results 25: using (var reader = cmd.ExecuteReader()) 26: { 27: while(reader.Read()) 28: { 29: results.Add(new Product 30: { 31: Name = reader["product_name"].ToString(), 32: ... 33: }); 34: } 35: } 36: } 37: }            38:  39: return results; 40: } 41: } Yes, you could use EF or any myriad other choices for this sort of thing, but the germaine point is that you have some operation that takes a non-trivial amount of time.  What if, during the production day I notice that my application is performing slowly and I want to see how much of that slowness is in the query versus my code.  Well, I could easily wrap the logic block in a System.Diagnostics.Stopwatch and log the results to log4net or other logging flavor of choice: 1:     // a class that handles our CRUD operations for products 2:     public class ProductDao 3:     { 4:         private static readonly ILog _log = LogManager.GetLogger(typeof(ProductDao)); 5:         ... 6:         7:         // a method that would retrieve all available products 8:         public IEnumerable<Product> GetAvailableProducts() 9:         { 10:             var results = new List<Product>(); 11:             var timer = Stopwatch.StartNew(); 12:             13:             // must create the connection 14:             using (var con = _factory.CreateConnection()) 15:             { 16:                 con.ConnectionString = _productsConnectionString; 17:                 18:                 // and all that other DB code... 19:                 ... 20:             } 21:             22:             timer.Stop(); 23:             24:             if (timer.ElapsedMilliseconds > 5000) 25:             { 26:                 _log.WarnFormat("Long query in GetAvailableProducts() took {0} ms", 27:                     timer.ElapsedMillseconds); 28:             } 29:             30:             return results; 31:         } 32:     } In my eye, this is very ugly.  It violates Single Responsibility Principle (SRP), which says that a class should only ever have one responsibility, where responsibility is often defined as a reason to change.  This class (and in particular this method) has two reasons to change: If the method of retrieving products changes. If the method of logging changes. Well, we could “simplify” this using the Decorator Design Pattern (here).  If we followed the pattern to the letter, we'd need to create a base decorator that implements the DAOs public interface and forwards to the wrapped instance.  So let's assume we break out the ProductDAO interface into IProductDAO using your refactoring tool of choice (Resharper is great for this). Now, ProductDao will implement IProductDao and get rid of all logging logic: 1:     public class ProductDao : IProductDao 2:     { 3:         // this reverts back to original version except for the interface added 4:     } 5:  And we create the base Decorator that also implements the interface and forwards all calls: 1:     public class ProductDaoDecorator : IProductDao 2:     { 3:         private readonly IProductDao _wrappedDao; 4:         5:         // constructor takes the dao to wrap 6:         public ProductDaoDecorator(IProductDao wrappedDao) 7:         { 8:             _wrappedDao = wrappedDao; 9:         } 10:         11:         ... 12:         13:         // and then all methods just forward their calls 14:         public IEnumerable<Product> GetAvailableProducts() 15:         { 16:             return _wrappedDao.GetAvailableProducts(); 17:         } 18:     } This defines our base decorator, then we can create decorators that add items of interest, and for any methods we don't decorate, we'll get the default behavior which just forwards the call to the wrapper in the base decorator: 1:     public class TimedThresholdProductDaoDecorator : ProductDaoDecorator 2:     { 3:         private static readonly ILog _log = LogManager.GetLogger(typeof(TimedThresholdProductDaoDecorator)); 4:         5:         public TimedThresholdProductDaoDecorator(IProductDao wrappedDao) : 6:             base(wrappedDao) 7:         { 8:         } 9:         10:         ... 11:         12:         public IEnumerable<Product> GetAvailableProducts() 13:         { 14:             var timer = Stopwatch.StartNew(); 15:             16:             var results = _wrapped.GetAvailableProducts(); 17:             18:             timer.Stop(); 19:             20:             if (timer.ElapsedMilliseconds > 5000) 21:             { 22:                 _log.WarnFormat("Long query in GetAvailableProducts() took {0} ms", 23:                     timer.ElapsedMillseconds); 24:             } 25:             26:             return results; 27:         } 28:     } Well, it's a bit better.  Now the logging is in its own class, and the database logic is in its own class.  But we've essentially multiplied the number of classes.  We now have 3 classes and one interface!  Now if you want to do that same logging decorating on all your DAOs, imagine the code bloat!  Sure, you can simplify and avoid creating the base decorator, or chuck it all and just inherit directly.  But regardless all of these have the problem of tying the logging logic into the code itself. Enter the Interceptors.  Things like this to me are a perfect example of when it's good to write an Interceptor using your class library of choice.  Sure, you could design your own perfectly generic decorator with delegates and all that, but personally I'm a big fan of Castle's Dynamic Proxy (here) which is actually used by many projects including Moq. What DynamicProxy allows you to do is intercept calls into any object by wrapping it with a proxy on the fly that intercepts the method and allows you to add functionality.  Essentially, the code would now look like this using DynamicProxy: 1: // Note: I like hiding DynamicProxy behind the scenes so users 2: // don't have to explicitly add reference to Castle's libraries. 3: public static class TimeThresholdInterceptor 4: { 5: // Our logging handle 6: private static readonly ILog _log = LogManager.GetLogger(typeof(TimeThresholdInterceptor)); 7:  8: // Handle to Castle's proxy generator 9: private static readonly ProxyGenerator _generator = new ProxyGenerator(); 10:  11: // generic form for those who prefer it 12: public static object Create<TInterface>(object target, TimeSpan threshold) 13: { 14: return Create(typeof(TInterface), target, threshold); 15: } 16:  17: // Form that uses type instead 18: public static object Create(Type interfaceType, object target, TimeSpan threshold) 19: { 20: return _generator.CreateInterfaceProxyWithTarget(interfaceType, target, 21: new TimedThreshold(threshold, level)); 22: } 23:  24: // The interceptor that is created to intercept the interface calls. 25: // Hidden as a private inner class so not exposing Castle libraries. 26: private class TimedThreshold : IInterceptor 27: { 28: // The threshold as a positive timespan that triggers a log message. 29: private readonly TimeSpan _threshold; 30:  31: // interceptor constructor 32: public TimedThreshold(TimeSpan threshold) 33: { 34: _threshold = threshold; 35: } 36:  37: // Intercept functor for each method invokation 38: public void Intercept(IInvocation invocation) 39: { 40: // time the method invocation 41: var timer = Stopwatch.StartNew(); 42:  43: // the Castle magic that tells the method to go ahead 44: invocation.Proceed(); 45:  46: timer.Stop(); 47:  48: // check if threshold is exceeded 49: if (timer.Elapsed > _threshold) 50: { 51: _log.WarnFormat("Long execution in {0} took {1} ms", 52: invocation.Method.Name, 53: timer.ElapsedMillseconds); 54: } 55: } 56: } 57: } Yes, it's a bit longer, but notice that: This class ONLY deals with logging long method calls, no DAO interface leftovers. This class can be used to time ANY class that has an interface or virtual methods. Personally, I like to wrap and hide the usage of DynamicProxy and IInterceptor so that anyone who uses this class doesn't need to know to add a Castle library reference.  As far as they are concerned, they're using my interceptor.  If I change to a new library if a better one comes along, they're insulated. Now, all we have to do to use this is to tell it to wrap our ProductDao and it does the rest: 1: // wraps a new ProductDao with a timing interceptor with a threshold of 5 seconds 2: IProductDao dao = TimeThresholdInterceptor.Create<IProductDao>(new ProductDao(), 5000); Automatic decoration of all methods!  You can even refine the proxy so that it only intercepts certain methods. This is ideal for so many things.  These are just some of the interceptors we've dreamed up and use: Log parameters and returns of methods to XML for auditing. Block invocations to methods and return default value (stubbing). Throw exception if certain methods are called (good for blocking access to deprecated methods). Log entrance and exit of a method and the duration. Log a message if a method takes more than a given time threshold to execute. Whether you use DynamicProxy or some other technology, I hope you see the benefits this adds.  Does it completely eliminate all need for the Decorator pattern?  No, there may still be cases where you want to decorate a particular class with functionality that doesn't apply to the world at large. But for all those cases where you are using Decorator to add functionality that's truly generic.  I strongly suggest you give this a try!

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• Algorithm to split an article without breaking the reading flow or HTML code

  - by Victor Stanciu

  Hello, I have a very large database of articles, of varying lengths. The articles have HTML elements in them. I have to insert some ads (simple <script> elements) in the body of each article when it is displayed (I know, I hate ads that interrupt my reading too). Now, the problem is that each ad must be inserted at about the same position in each article. The simplest solution is to simply split the article on a fixed number of characters (without breaking words), and insert the ad code. This, however, runs the risk of inserting the ad in the middle of a HTML tag. I could go the regex way, but I was thinking about the following solution, using JS: Establish a character count threshold. For example, "the add should be inserted at about 200 words" Set accepted deviations in each direction, say -20, +20 characters. Loop through each text node inside the article, and while doing so, keep count of the total number of characters so far Once the count exceeds the threshold, make the following decision: 4.1. If count exceeds the threshold by a value lower that the positive accepted deviation (for example, 17 characters), insert the ad code just after the current text node. 4.2. If the count is greater than the sum of the threshold and the deviation, roll back to the previous text node, and make the same decision, only this time use the previous count and check if it's lower than the difference between the threshold and the deviation, and if not, insert the ad between the current node and the previous one. 4.3. If the 4.1 and 4.2 fail (which means that the previous node reached a too low character count and the current node a too high one), insert the ad after whatever character count is needed inside the current element. I know it's convoluted, but it's the first thing out of my mind and it has the advantage that, by trying to insert the ad between text nodes, perhaps it will not break the flow of the article as bad as it would if I would just stick it in (like the final 4.3 case) Here is some pseudo-code I put together, I don't trust my english-explaining skills: threshold = 200 deviation = 20 current_count = 0 for each node in article_nodes { previous_count = current_count current_count = current_count + node.length if current_count < threshold { continue // next interation } if current_count > threshold + deviation { if previous_count < threshdold - deviation { // insert ad in current node } else { // insert ad between the current and previous nodes } } else { // insert ad after the current node } break; } Am I over-complicating stuff, or am I missing a simpler, more elegant solution?

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• Unable to delete a file using bash script

  - by user3719091

  I'm having problems removing a file in a bash script. I saw the other post with the same problem but none of those solutions solved my problem. The bash script is an OP5 surveillance check and it calls an Expect process that saves a temporary file to the local drive which the bash script reads from. Once it has read the file and checked its status I would like to remove the temporary file. I'm pretty new to scripting so my script may not be as optimal as it can be. Either way it does the job except removing the file once it's done. I will post the entire code below: #!/bin/bash #GET FLAGS while getopts H:c:w: option do case "${option}" in H) HOSTADDRESS=${OPTARG};; c) CRITICAL=${OPTARG};; w) WARNING=${OPTARG};; esac done ./expect.vpn.check.sh $HOSTADDRESS #VARIABLES VPNCount=$(grep -o '[0-9]\+' $HOSTADDRESS.op5.vpn.results) # Check if the temporary results file exists if [ -f $HOSTADDRESS.op5.vpn.results ] then # If the file exist, Print "File Found" message echo Temporary results file exist. Analyze results. else # If the file does NOT exist, print "File NOT Found" message and send message to OP5 echo Temporary results file does NOT exist. Unable to analyze. # Exit with status Critical (exit code 2) exit 2 fi if [[ "$VPNCount" > $CRITICAL ]] then # If the amount of tunnels exceeds the critical threshold, echo out a warning message and current threshold and send warning to OP5 echo "The amount of VPN tunnels exceeds the critical threshold - ($VPNCount)" # Exit with status Critical (exit code 2) exit 2 elif [[ "$VPNCount" > $WARNING ]] then # If the amount of tunnels exceeds the warning threshold, echo out a warning message and current threshold and send warning to OP5 echo "The amount of VPN tunnels exceeds the warning threshold - ($VPNCount)" # Exit with status Warning (exit code 1) exit 1 else # The amount of tunnels do not exceed the warning threshold. # Print an OK message echo OK - $VPNCount # Exit with status OK exit 0 fi #Clean up temporary files. rm -f $HOSTADDRESS.op5.vpn.results I have tried the following solutions: Create a separate variable called TempFile that specifies the file. And specify that in the rm command. I tried creating another if statement similar to the one I use to verify that file exist and then rm the filename. I tried adding the complete name of the file (no variables, just plain text of the file) I can: Remove the file using the full name in both a separate script and directly in the CLI. Is there something in my script that locks the file that prevents me from removing it? I'm not sure what to try next. Thanks in advance!

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• How to effectively clip the amount of entries in a dictionary?

  - by reinier

  I had a List<myClass> myList for storing a list of items. When I had to clip this (discard any amount of items above some threshold) I used: myList.RemoveRange(threshold, myList.Count - threshold); where threshold is the max amount of things the list can contain Now I've upgraded the datatype to a Dictionary<key, myClass> myDictionary How can I basically do the same: Discard all entries above some threshold. (It doesn't matter which ones are discarded) I guess I could foreach through the keys collection and manually delete all keys/value pairs. But I was hoping there was a more elegant solution.

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• Exploring TCP throughput with DTrace (2)

  - by user12820842

  Last time, I described how we can use the overlap in distributions of unacknowledged byte counts and send window to determine whether the peer's receive window may be too small, limiting throughput. Let's combine that comparison with a comparison of congestion window and slow start threshold, all on a per-port/per-client basis. This will help us Identify whether the congestion window or the receive window are limiting factors on throughput by comparing the distributions of congestion window and send window values to the distribution of outstanding (unacked) bytes. This will allow us to get a visual sense for how often we are thwarted in our attempts to fill the pipe due to congestion control versus the peer not being able to receive any more data. Identify whether slow start or congestion avoidance predominate by comparing the overlap in the congestion window and slow start distributions. If the slow start threshold distribution overlaps with the congestion window, we know that we have switched between slow start and congestion avoidance, possibly multiple times. Identify whether the peer's receive window is too small by comparing the distribution of outstanding unacked bytes with the send window distribution (i.e. the peer's receive window). I discussed this here. # dtrace -s tcp_window.d dtrace: script 'tcp_window.d' matched 10 probes ^C cwnd 80 10.175.96.92 value ------------- Distribution ------------- count 1024 | 0 2048 | 4 4096 | 6 8192 | 18 16384 | 36 32768 |@ 79 65536 |@ 155 131072 |@ 199 262144 |@@@ 400 524288 |@@@@@@ 798 1048576 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 3848 2097152 | 0 ssthresh 80 10.175.96.92 value ------------- Distribution ------------- count 268435456 | 0 536870912 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 5543 1073741824 | 0 unacked 80 10.175.96.92 value ------------- Distribution ------------- count -1 | 0 0 | 1 1 | 0 2 | 0 4 | 0 8 | 0 16 | 0 32 | 0 64 | 0 128 | 0 256 | 3 512 | 0 1024 | 0 2048 | 4 4096 | 9 8192 | 21 16384 | 36 32768 |@ 78 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 5391 131072 | 0 swnd 80 10.175.96.92 value ------------- Distribution ------------- count 32768 | 0 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 5543 131072 | 0 Here we are observing a large file transfer via http on the webserver. Comparing these distributions, we can observe: That slow start congestion control is in operation. The distribution of congestion window values lies below the range of slow start threshold values (which are in the 536870912+ range), so the connection is in slow start mode. Both the unacked byte count and the send window values peak in the 65536-131071 range, but the send window value distribution is narrower. This tells us that the peer TCP's receive window is not closing. The congestion window distribution peaks in the 1048576 - 2097152 range while the receive window distribution is confined to the 65536-131071 range. Since the cwnd distribution ranges as low as 2048-4095, we can see that for some of the time we have been observing the connection, congestion control has been a limiting factor on transfer, but for the majority of the time the receive window of the peer would more likely have been the limiting factor. However, we know the window has never closed as the distribution of swnd values stays within the 65536-131071 range. So all in all we have a connection that has been mildly constrained by congestion control, but for the bulk of the time we have been observing it neither congestion or peer receive window have limited throughput. Here's the script: #!/usr/sbin/dtrace -s tcp:::send / (args[4]-tcp_flags & (TH_SYN|TH_RST|TH_FIN)) == 0 / { @cwnd["cwnd", args[4]-tcp_sport, args[2]-ip_daddr] = quantize(args[3]-tcps_cwnd); @ssthresh["ssthresh", args[4]-tcp_sport, args[2]-ip_daddr] = quantize(args[3]-tcps_cwnd_ssthresh); @unacked["unacked", args[4]-tcp_sport, args[2]-ip_daddr] = quantize(args[3]-tcps_snxt - args[3]-tcps_suna); @swnd["swnd", args[4]-tcp_sport, args[2]-ip_daddr] = quantize((args[4]-tcp_window)*(1 tcps_snd_ws)); } One surprise here is that slow start is still in operation - one would assume that for a large file transfer, acknowledgements would push the congestion window up past the slow start threshold over time. The slow start threshold is in fact still close to it's initial (very high) value, so that would suggest we have not experienced any congestion (the slow start threshold is adjusted when congestion occurs). Also, the above measurements were taken early in the connection lifetime, so the congestion window did not get a changes to get bumped up to the level of the slow start threshold. A good strategy when examining these sorts of measurements for a given service (such as a webserver) would be start by examining the distributions above aggregated by port number only to get an overall feel for service performance, i.e. is congestion control or peer receive window size an issue, or are we unconstrained to fill the pipe? From there, the overlap of distributions will tell us whether to drill down into specific clients. For example if the send window distribution has multiple peaks, we may want to examine if particular clients show issues with their receive window.

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• Android - How to approach fall detection algorithm

  - by bobby123

  I want to be able to feature a fairly simple fall detection algorithm in my application. At the moment in onSensorChanged(), I am getting the absolute value of the current x,x,z values and subtracting SensorManager.GRAVITY_EARTH (9.8 m/s) from this. The resulting value has to be bigger than a threshold value 10 times in a row to set a flag saying a fall has been detected by the accelerometer, the threshold value is about 8m/s. Also I'm comparing the orientation of the phone as soon as the threshold has been passed and the orienation of it when the threshold is no longer being passed, this sets another flag saying the orientation sensor has detected a fall. When both flags are set, an event occurs to check is user ok, etc etc. My problem is with the threshold, when the phone is held straight up the absolute value of accelerometer is about 9.8 m/s, but when i hold it still at an angle it can be over 15m/s. This is causing other events to trigger the fall detection, and if i increase the threshold to avoid that, it won't detect falls. Can anyone give me some advice here with what possible values i should use or how to even improve my method? Many thanks.

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• Scipy Negative Distance? What?

  - by disappearedng

  I have a input file which are all floating point numbers to 4 decimal place. i.e. 13359 0.0000 0.0000 0.0001 0.0001 0.0002` 0.0003 0.0007 ... (the first is the id). My class uses the loadVectorsFromFile method which multiplies it by 10000 and then int() these numbers. On top of that, I also loop through each vector to ensure that there are no negative values inside. However, when I perform _hclustering, I am continually seeing the error, "Linkage Z contains negative values". I seriously think this is a bug because: I checked my values, the values are no where small enough or big enough to approach the limits of the floating point numbers and the formula that I used to derive the values in the file uses absolute value (my input is DEFINITELY right). Can someone enligten me as to why I am seeing this weird error? What is going on that is causing this negative distance error? ===== def loadVectorsFromFile(self, limit, loc, assertAllPositive=True, inflate=True): """Inflate to prevent "negative" distance, we use 4 decimal points, so *10000 """ vectors = {} self.winfo("Each vector is set to have %d limit in length" % limit) with open( loc ) as inf: for line in filter(None, inf.read().split('\n')): l = line.split('\t') if limit: scores = map(float, l[1:limit+1]) else: scores = map(float, l[1:]) if inflate: vectors[ l[0]] = map( lambda x: int(x*10000), scores) #int might save space else: vectors[ l[0]] = scores if assertAllPositive: #Assert that it has no negative value for dirID, l in vectors.iteritems(): if reduce(operator.or_, map( lambda x: x < 0, l)): self.werror( "Vector %s has negative values!" % dirID) return vectors def main( self, inputDir, outputDir, limit=0, inFname="data.vectors.all", mappingFname='all.id.features.group.intermediate'): """ Loads vector from a file and start clustering INPUT vectors is { featureID: tfidfVector (list), } """ IDFeatureDic = loadIdFeatureGroupDicFromIntermediate( pjoin(self.configDir, mappingFname)) if not os.path.exists(outputDir): os.makedirs(outputDir) vectors = self.loadVectorsFromFile( limit, pjoin( inputDir, inFname)) for threshold in map( lambda x:float(x)/30, range(20,30)): clusters = self._hclustering(threshold, vectors) if clusters: outputLoc = pjoin(outputDir, "threshold.%s.result" % str(threshold)) with open(outputLoc, 'w') as outf: for clusterNo, cluster in clusters.iteritems(): outf.write('%s\n' % str(clusterNo)) for featureID in cluster: feature, group = IDFeatureDic[featureID] outline = "%s\t%s\n" % (feature, group) outf.write(outline.encode('utf-8')) outf.write("\n") else: continue def _hclustering(self, threshold, vectors): """function which you should call to vary the threshold vectors: { featureID: [ tfidf scores, tfidf score, .. ] """ clusters = defaultdict(list) if len(vectors) > 1: try: results = hierarchy.fclusterdata( vectors.values(), threshold, metric='cosine') except ValueError, e: self.werror("_hclustering: %s" % str(e)) return False for i, featureID in enumerate( vectors.keys()):

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• Why is my unsafe code block slower than my safe code?

  - by jomtois

  I am attempting to write some code that will expediently process video frames. I am receiving the frames as a System.Windows.Media.Imaging.WriteableBitmap. For testing purposes, I am just applying a simple threshold filter that will process a BGRA format image and assign each pixel to either be black or white based on the average of the BGR pixels. Here is my "Safe" version: public static void ApplyFilter(WriteableBitmap Bitmap, byte Threshold) { // Let's just make this work for this format if (Bitmap.Format != PixelFormats.Bgr24 && Bitmap.Format != PixelFormats.Bgr32) { return; } // Calculate the number of bytes per pixel (should be 4 for this format). var bytesPerPixel = (Bitmap.Format.BitsPerPixel + 7) / 8; // Stride is bytes per pixel times the number of pixels. // Stride is the byte width of a single rectangle row. var stride = Bitmap.PixelWidth * bytesPerPixel; // Create a byte array for a the entire size of bitmap. var arraySize = stride * Bitmap.PixelHeight; var pixelArray = new byte[arraySize]; // Copy all pixels into the array Bitmap.CopyPixels(pixelArray, stride, 0); // Loop through array and change pixels to black or white based on threshold for (int i = 0; i < pixelArray.Length; i += bytesPerPixel) { // i=B, i+1=G, i+2=R, i+3=A var brightness = (byte)((pixelArray[i] + pixelArray[i + 1] + pixelArray[i + 2]) / 3); var toColor = byte.MinValue; // Black if (brightness >= Threshold) { toColor = byte.MaxValue; // White } pixelArray[i] = toColor; pixelArray[i + 1] = toColor; pixelArray[i + 2] = toColor; } Bitmap.WritePixels(new Int32Rect(0, 0, Bitmap.PixelWidth, Bitmap.PixelHeight), pixelArray, stride, 0); } Here is what I think is a direct translation using an unsafe code block and the WriteableBitmap Back Buffer instead of the forebuffer: public static void ApplyFilterUnsafe(WriteableBitmap Bitmap, byte Threshold) { // Let's just make this work for this format if (Bitmap.Format != PixelFormats.Bgr24 && Bitmap.Format != PixelFormats.Bgr32) { return; } var bytesPerPixel = (Bitmap.Format.BitsPerPixel + 7) / 8; Bitmap.Lock(); unsafe { // Get a pointer to the back buffer. byte* pBackBuffer = (byte*)Bitmap.BackBuffer; for (int i = 0; i < Bitmap.BackBufferStride*Bitmap.PixelHeight; i+= bytesPerPixel) { var pCopy = pBackBuffer; var brightness = (byte)((*pBackBuffer + *pBackBuffer++ + *pBackBuffer++) / 3); pBackBuffer++; var toColor = brightness >= Threshold ? byte.MaxValue : byte.MinValue; *pCopy = toColor; *++pCopy = toColor; *++pCopy = toColor; } } // Bitmap.AddDirtyRect(new Int32Rect(0,0, Bitmap.PixelWidth, Bitmap.PixelHeight)); Bitmap.Unlock(); } This is my first foray into unsafe code blocks and pointers, so maybe the logic is not optimal. I have tested both blocks of code on the same WriteableBitmaps using: var threshold = Convert.ToByte(op.Result); var copy2 = copyFrame.Clone(); Stopwatch stopWatch = new Stopwatch(); stopWatch.Start(); BinaryFilter.ApplyFilterUnsafe(copyFrame, threshold); stopWatch.Stop(); var unsafesecs = stopWatch.ElapsedMilliseconds; stopWatch.Reset(); stopWatch.Start(); BinaryFilter.ApplyFilter(copy2, threshold); stopWatch.Stop(); Debug.WriteLine(string.Format("Unsafe: {1}, Safe: {0}", stopWatch.ElapsedMilliseconds, unsafesecs)); So I am analyzing the same image. A test run of an incoming stream of video frames: Unsafe: 110, Safe: 53 Unsafe: 136, Safe: 42 Unsafe: 106, Safe: 36 Unsafe: 95, Safe: 43 Unsafe: 98, Safe: 41 Unsafe: 88, Safe: 36 Unsafe: 129, Safe: 65 Unsafe: 100, Safe: 47 Unsafe: 112, Safe: 50 Unsafe: 91, Safe: 33 Unsafe: 118, Safe: 42 Unsafe: 103, Safe: 80 Unsafe: 104, Safe: 34 Unsafe: 101, Safe: 36 Unsafe: 154, Safe: 83 Unsafe: 134, Safe: 46 Unsafe: 113, Safe: 76 Unsafe: 117, Safe: 57 Unsafe: 90, Safe: 41 Unsafe: 156, Safe: 35 Why is my unsafe version always slower? Is it due to using the back buffer? Or am I doing something wrong? Thanks

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• Why does Clojure hang after hacing performed my calculations?

  - by Thomas

  Hi all, I'm experimenting with filtering through elements in parallel. For each element, I need to perform a distance calculation to see if it is close enough to a target point. Never mind that data structures already exist for doing this, I'm just doing initial experiments for now. Anyway, I wanted to run some very basic experiments where I generate random vectors and filter them. Here's my implementation that does all of this (defn pfilter [pred coll] (map second (filter first (pmap (fn [item] [(pred item) item]) coll)))) (defn random-n-vector [n] (take n (repeatedly rand))) (defn distance [u v] (Math/sqrt (reduce + (map #(Math/pow (- %1 %2) 2) u v)))) (defn -main [& args] (let [[n-str vectors-str threshold-str] args n (Integer/parseInt n-str) vectors (Integer/parseInt vectors-str) threshold (Double/parseDouble threshold-str) random-vector (partial random-n-vector n) u (random-vector)] (time (println n vectors (count (pfilter (fn [v] (< (distance u v) threshold)) (take vectors (repeatedly random-vector)))))))) The code executes and returns what I expect, that is the parameter n (length of vectors), vectors (the number of vectors) and the number of vectors that are closer than a threshold to the target vector. What I don't understand is why the programs hangs for an additional minute before terminating. Here is the output of a run which demonstrates the error $ time lein run 10 100000 1.0 [null] 10 100000 12283 [null] "Elapsed time: 3300.856 msecs" real 1m6.336s user 0m7.204s sys 0m1.495s Any comments on how to filter in parallel in general are also more than welcome, as I haven't yet confirmed that pfilter actually works.

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• Why closed contours are guaranteed here?

  - by user198729

  Quoted from here: BW = edge(I,'zerocross',thresh,h) specifies the zero-cross method, using the filter h. thresh is the sensitivity threshold; if the argument is empty ([]), edge chooses the sensitivity threshold automatically. If you specify a threshold of 0, the output image has closed contours, because it includes all the zero crossings in the input image. I don't understand it,can someone elaborate?

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• Keeping track of File System Utilization in Ops Center 12c

  - by S Stelting

  Enterprise Manager Ops Center 12c provides significant monitoring capabilities, combined with very flexible incident management. These capabilities even extend to monitoring the file systems associated with Solaris or Linux assets. Depending on your needs you can monitor and manage incidents, or you can fine tune alert monitoring rules to specific file systems. This article will show you how to use Ops Center 12c to Track file system utilization Adjust file system monitoring rules Disable file system rules Create custom monitoring rules If you're interested in this topic, please join us for a WebEx presentation! Date: Thursday, November 8, 2012 Time: 11:00 am, Eastern Standard Time (New York, GMT-05:00) Meeting Number: 598 796 842 Meeting Password: oracle123 To join the online meeting ------------------------------------------------------- 1. Go to https://oracleconferencing.webex.com/oracleconferencing/j.php?ED=209833597&UID=1512095432&PW=NOWQ3YjJlMmYy&RT=MiMxMQ%3D%3D 2. If requested, enter your name and email address. 3. If a password is required, enter the meeting password: oracle123 4. Click "Join". To view in other time zones or languages, please click the link: https://oracleconferencing.webex.com/oracleconferencing/j.php?ED=209833597&UID=1512095432&PW=NOWQ3YjJlMmYy&ORT=MiMxMQ%3D%3D   Monitoring File Systems for OS Assets The Libraries tab provides basic, device-level information about the storage associated with an OS instance. This tab shows you the local file system associated with the instance and any shared storage libraries mounted by Ops Center. More detailed information about file system storage is available under the Analytics tab under the sub-tab named Charts. Here, you can select and display the individual mount points of an OS, and export the utilization data if desired: In this example, the OS instance has a basic root file partition and several NFS directories. Each file system mount point can be independently chosen for display in the Ops Center chart. File Systems and Incident  Reporting Every asset managed by Ops Center has a "monitoring policy", which determines what represents a reportable issue with the asset. The policy is made up of a bunch of monitoring rules, where each rule describes An attribute to monitor The conditions which represent an issue The level or levels of severity for the issue When the conditions are met, Ops Center sends a notification and creates an incident. By default, OS instances have three monitoring rules associated with file systems: File System Reachability: Triggers an incident if a file system is not reachable NAS Library Status: Triggers an incident for a value of "WARNING" or "DEGRADED" for a NAS-based file system File System Used Space Percentage: Triggers an incident when file system utilization grows beyond defined thresholds You can view these rules in the Monitoring tab for an OS: Of course, the default monitoring rules is that they apply to every file system associated with an OS instance. As a result, any issue with NAS accessibility or disk utilization will trigger an incident. This can cause incidents for file systems to be reported multiple times if the same shared storage is used by many assets, as shown in this screen shot: Depending on the level of control you'd like, there are a number of ways to fine tune incident reporting. Note that any changes to an asset's monitoring policy will detach it from the default, creating a new monitoring policy for the asset. If you'd like, you can extract a monitoring policy from an asset, which allows you to save it and apply the customized monitoring profile to other OS assets. Solution #1: Modify the Reporting Thresholds In some cases, you may want to modify the basic conditions for incident reporting in your file system. The changes you make to a default monitoring rule will apply to all of the file systems associated with your operating system. Selecting the File Systems Used Space Percentage entry and clicking the "Edit Alert Monitoring Rule Parameters" button opens a pop-up dialog which allows you to modify the rule. The first screen lets you decide when you will check for file system usage, and how long you will wait before opening an incident in Ops Center. By default, Ops Center monitors continuously and reports disk utilization issues which exist for more than 15 minutes. The second screen lets you define actual threshold values. By default, Ops Center opens a Warning level incident is utilization rises above 80%, and a Critical level incident for utilization above 95% Solution #2: Disable Incident Reporting for File System If you'd rather not report file system incidents, you can disable the monitoring rules altogether. In this case, you can select the monitoring rules and click the "Disable Alert Monitoring Rule(s)" button to open the pop-up confirmation dialog. Like the first solution, this option affects all file system monitoring. It allows you to completely disable incident reporting for NAS library status or file system space consumption. Solution #3: Create New Monitoring Rules for Specific File Systems If you'd like to have the greatest flexibility when monitoring file systems, you can create entirely new rules. Clicking the "Add Alert Monitoring Rule" (the icon with the green plus sign) opens a wizard which allows you to define a new rule.  This rule will be based on a threshold, and will be used to monitor operating system assets. We'd like to add a rule to track disk utilization for a specific file system - the /nfs-guest directory. To do this, we specify the following attribute FileSystemUsages.name=/nfs-guest.usedSpacePercentage The value of name in the attribute allows us to define a specific NFS shared directory or file system... in the case of this OS, we could have chosen any of the values shown in the File Systems Utilization chart at the beginning of this article. usedSpacePercentage lets us define a threshold based on the percentage of total disk space used. There are a number of other values that we could use for threshold-based monitoring of FileSystemUsages, including freeSpace freeSpacePercentage totalSpace usedSpace usedSpacePercentage The final sections of the screen allow us to determine when to monitor for disk usage, and how long to wait after utilization reaches a threshold before creating an incident. The next screen lets us define the threshold values and severity levels for the monitoring rule: If historical data is available, Ops Center will display it in the screen. Clicking the Apply button will create the new monitoring rule and active it in your monitoring policy. If you combine this with one of the previous solutions, you can precisely define which file systems will generate incidents and notifications. For example, this monitoring policy has the default "File System Used Space Percentage" rule disabled, but the new rule reports ONLY on utilization for the /nfs-guest directory. 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• The softer side of BPM

  - by [email protected]

  BPM and RTD are great complementary technologies that together provide a much higher benefit than each of them separately. BPM covers the need for automating processes, making sure that there is uniformity, that rules and regulations are complied with and that the process runs smoothly and quickly processes the units flowing through it. By nature, this automation and unification can lead to a stricter, less flexible process. To avoid this problem it is common to encounter process definition that include multiple conditional branches and human input to help direct processing in the direction that best applies to the current situation. This is where RTD comes into play. The selection of branches and conditions and the optimization of decisions is better left in the hands of a system that can measure the results of its decisions in a closed loop fashion and make decisions based on the empirical knowledge accumulated through observing the running of the process.When designing a business process there are key places in which it may be beneficial to introduce RTD decisions. These are:Thresholds - whenever a threshold is used to determine the processing of a unit, there may be an opportunity to make the threshold "softer" by introducing an RTD decision based on predicted results. For example an insurance company process may have a total claim threshold to initiate an investigation. Instead of having that threshold, RTD could be used to help determine what claims to investigate based on the likelihood they are fraudulent, cost of investigation and effect on processing time.Human decisions - sometimes a process will let the human participants make decisions of flow. For example, a call center process may leave the escalation decision to the agent. While this has flexibility, it may produce undesired results and asymetry in customer treatment that is not based on objective functions but subjective reasoning by the agent. Instead, an RTD decision may be introduced to recommend escalation or other kinds of treatments.Content Selection - a process may include the use of messaging with customers. The selection of the most appropriate message to the customer given the content can be optimized with RTD.A/B Testing - a process may have optional paths for which it is not clear what populations they work better for. Rather than making the arbitrary selection or selection by committee of the option deeped the best, RTD can be introduced to dynamically determine the best path for each unit.In summary, RTD can be used to make BPM based process automation more dynamic and adaptable to the different situations encountered in processing. Effectively making the automation softer, less rigid in its processing.

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• SQL Monitor’s data repository: Alerts

  - by Chris Lambrou

  In my previous post, I introduced the SQL Monitor data repository, and described how the monitored objects are stored in a hierarchy in the data schema, in a series of tables with a _Keys suffix. In this post I had planned to describe how the actual data for the monitored objects is stored in corresponding tables with _StableSamples and _UnstableSamples suffixes. However, I’m going to postpone that until my next post, as I’ve had a request from a SQL Monitor user to explain how alerts are stored. In the SQL Monitor data repository, alerts are stored in tables belonging to the alert schema, which contains the following five tables: alert.Alert alert.Alert_Cleared alert.Alert_Comment alert.Alert_Severity alert.Alert_Type In this post, I’m only going to cover the alert.Alert and alert.Alert_Type tables. I may cover the other three tables in a later post. The most important table in this schema is alert.Alert, as each row in this table corresponds to a single alert. So let’s have a look at it. SELECT TOP 100 AlertId, AlertType, TargetObject, [Read], SubType FROM alert.Alert ORDER BY AlertId DESC;  AlertIdAlertTypeTargetObjectReadSubType 165550397:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,10 265549387:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,10 365548187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 11…     So what are we seeing here, then? Well, AlertId is an auto-incrementing identity column, so ORDER BY AlertId DESC ensures that we see the most recent alerts first. AlertType indicates the type of each alert, such as Job failed (6), Backup overdue (14) or Long-running query (12). The TargetObject column indicates which monitored object the alert is associated with. The Read column acts as a flag to indicate whether or not the alert has been read. And finally the SubType column is used in the case of a Custom metric (40) alert, to indicate which custom metric the alert pertains to. Okay, now lets look at some of those columns in more detail. The AlertType column is an easy one to start with, and it brings use nicely to the next table, data.Alert_Type. Let’s have a look at what’s in this table: SELECT AlertType, Event, Monitoring, Name, Description FROM alert.Alert_Type ORDER BY AlertType;  AlertTypeEventMonitoringNameDescription 1100Processor utilizationProcessor utilization (CPU) on a host machine stays above a threshold percentage for longer than a specified duration 2210SQL Server error log entryAn error is written to the SQL Server error log with a severity level above a specified value. 3310Cluster failoverThe active cluster node fails, causing the SQL Server instance to switch nodes. 4410DeadlockSQL deadlock occurs. 5500Processor under-utilizationProcessor utilization (CPU) on a host machine remains below a threshold percentage for longer than a specified duration 6610Job failedA job does not complete successfully (the job returns an error code). 7700Machine unreachableHost machine (Windows server) cannot be contacted on the network. 8800SQL Server instance unreachableThe SQL Server instance is not running or cannot be contacted on the network. 9900Disk spaceDisk space used on a logical disk drive is above a defined threshold for longer than a specified duration. 101000Physical memoryPhysical memory (RAM) used on the host machine stays above a threshold percentage for longer than a specified duration. 111100Blocked processSQL process is blocked for longer than a specified duration. 121200Long-running queryA SQL query runs for longer than a specified duration. 131400Backup overdueNo full backup exists, or the last full backup is older than a specified time. 141500Log backup overdueNo log backup exists, or the last log backup is older than a specified time. 151600Database unavailableDatabase changes from Online to any other state. 161700Page verificationTorn Page Detection or Page Checksum is not enabled for a database. 171800Integrity check overdueNo entry for an integrity check (DBCC DBINFO returns no date for dbi_dbccLastKnownGood field), or the last check is older than a specified time. 181900Fragmented indexesFragmentation level of one or more indexes is above a threshold percentage. 192400Job duration unusualThe duration of a SQL job duration deviates from its baseline duration by more than a threshold percentage. 202501Clock skewSystem clock time on the Base Monitor computer differs from the system clock time on a monitored SQL Server host machine by a specified number of seconds. 212700SQL Server Agent Service statusThe SQL Server Agent Service status matches the status specified. 222800SQL Server Reporting Service statusThe SQL Server Reporting Service status matches the status specified. 232900SQL Server Full Text Search Service statusThe SQL Server Full Text Search Service status matches the status specified. 243000SQL Server Analysis Service statusThe SQL Server Analysis Service status matches the status specified. 253100SQL Server Integration Service statusThe SQL Server Integration Service status matches the status specified. 263300SQL Server Browser Service statusThe SQL Server Browser Service status matches the status specified. 273400SQL Server VSS Writer Service statusThe SQL Server VSS Writer status matches the status specified. 283501Deadlock trace flag disabledThe monitored SQL Server’s trace flag cannot be enabled. 293600Monitoring stopped (host machine credentials)SQL Monitor cannot contact the host machine because authentication failed. 303700Monitoring stopped (SQL Server credentials)SQL Monitor cannot contact the SQL Server instance because authentication failed. 313800Monitoring error (host machine data collection)SQL Monitor cannot collect data from the host machine. 323900Monitoring error (SQL Server data collection)SQL Monitor cannot collect data from the SQL Server instance. 334000Custom metricThe custom metric value has passed an alert threshold. 344100Custom metric collection errorSQL Monitor cannot collect custom metric data from the target object. Basically, alert.Alert_Type is just a big reference table containing information about the 34 different alert types supported by SQL Monitor (note that the largest id is 41, not 34 – some alert types have been retired since SQL Monitor was first developed). The Name and Description columns are self evident, and I’m going to skip over the Event and Monitoring columns as they’re not very interesting. The AlertId column is the primary key, and is referenced by AlertId in the alert.Alert table. As such, we can rewrite our earlier query to join these two tables, in order to provide a more readable view of the alerts: SELECT TOP 100 AlertId, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType ORDER BY AlertId DESC;  AlertIdNameTargetObjectReadSubType 165550Monitoring error (SQL Server data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,00 265549Monitoring error (host machine data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,00 365548Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 Okay, the next column to discuss in the alert.Alert table is TargetObject. Oh boy, this one’s a bit tricky! The TargetObject of an alert is a serialized string representation of the position in the monitored object hierarchy of the object to which the alert pertains. The serialization format is somewhat convenient for parsing in the C# source code of SQL Monitor, and has some helpful characteristics, but it’s probably very awkward to manipulate in T-SQL. I could document the serialization format here, but it would be very dry reading, so perhaps it’s best to consider an example from the table above. Have a look at the alert with an AlertID of 65543. It’s a Backup overdue alert for the SqlMonitorData database running on the default instance of granger, my laptop. Each different alert type is associated with a specific type of monitored object in the object hierarchy (I described the hierarchy in my previous post). The Backup overdue alert is associated with databases, whose position in the object hierarchy is root → Cluster → SqlServer → Database. The TargetObject value identifies the target object by specifying the key properties at each level in the hierarchy, thus: Cluster: Name = "granger" SqlServer: Name = "" (an empty string, denoting the default instance) Database: Name = "SqlMonitorData" Well, look at the actual TargetObject value for this alert: "7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,". It is indeed composed of three parts, one for each level in the hierarchy: Cluster: "7:Cluster,1,4:Name,s7:granger," SqlServer: "9:SqlServer,1,4:Name,s0:," Database: "8:Database,1,4:Name,s14:SqlMonitorData," Each part is handled in exactly the same way, so let’s concentrate on the first part, "7:Cluster,1,4:Name,s7:granger,". It comprises the following: "7:Cluster," – This identifies the level in the hierarchy. "1," – This indicates how many different key properties there are to uniquely identify a cluster (we saw in my last post that each cluster is identified by a single property, its Name). "4:Name,s14:SqlMonitorData," – This represents the Name property, and its corresponding value, SqlMonitorData. It’s split up like this: "4:Name," – Indicates the name of the key property. "s" – Indicates the type of the key property, in this case, it’s a string. "14:SqlMonitorData," – Indicates the value of the property. At this point, you might be wondering about the format of some of these strings. Why is the string "Cluster" stored as "7:Cluster,"? Well an encoding scheme is used, which consists of the following: "7" – This is the length of the string "Cluster" ":" – This is a delimiter between the length of the string and the actual string’s contents. "Cluster" – This is the string itself. 7 characters. "," – This is a final terminating character that indicates the end of the encoded string. You can see that "4:Name,", "8:Database," and "14:SqlMonitorData," also conform to the same encoding scheme. In the example above, the "s" character is used to indicate that the value of the Name property is a string. If you explore the TargetObject property of alerts in your own SQL Monitor data repository, you might find other characters used for other non-string key property values. The different value types you might possibly encounter are as follows: "I" – Denotes a bigint value. For example, "I65432,". "g" – Denotes a GUID value. For example, "g32116732-63ae-4ab5-bd34-7dfdfb084c18,". "d" – Denotes a datetime value. For example, "d634815384796832438,". The value is stored as a bigint, rather than a native SQL datetime value. I’ll describe how datetime values are handled in the SQL Monitor data repostory in a future post. I suggest you have a look at the alerts in your own SQL Monitor data repository for further examples, so you can see how the TargetObject values are composed for each of the different types of alert. Let me give one further example, though, that represents a Custom metric alert, as this will help in describing the final column of interest in the alert.Alert table, SubType. Let me show you the alert I’m interested in: SELECT AlertId, a.AlertType, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType WHERE AlertId = 65769;  AlertIdAlertTypeNameTargetObjectReadSubType 16576940Custom metric7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 An AlertType value of 40 corresponds to the Custom metric alert type. The Name taken from the alert.Alert_Type table is simply Custom metric, but this doesn’t tell us anything about the specific custom metric that this alert pertains to. That’s where the SubType value comes in. For custom metric alerts, this provides us with the Id of the specific custom alert definition that can be found in the settings.CustomAlertDefinitions table. I don’t really want to delve into custom alert definitions yet (maybe in a later post), but an extra join in the previous query shows us that this alert pertains to the CPU pressure (avg runnable task count) custom metric alert. SELECT AlertId, a.AlertType, at.Name, cad.Name AS CustomAlertName, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType JOIN settings.CustomAlertDefinitions cad ON a.SubType = cad.Id WHERE AlertId = 65769;  AlertIdAlertTypeNameCustomAlertNameTargetObjectReadSubType 16576940Custom metricCPU pressure (avg runnable task count)7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 The TargetObject value in this case breaks down like this: "7:Cluster,1,4:Name,s7:granger," – Cluster named "granger". "9:SqlServer,1,4:Name,s0:," – SqlServer named "" (the default instance). "8:Database,1,4:Name,s6:master," – Database named "master". "12:CustomMetric,1,8:MetricId,I2," – Custom metric with an Id of 2. Note that the hierarchy for a custom metric is slightly different compared to the earlier Backup overdue alert. It’s root → Cluster → SqlServer → Database → CustomMetric. Also notice that, unlike Cluster, SqlServer and Database, the key property for CustomMetric is called MetricId (not Name), and the value is a bigint (not a string). Finally, delving into the custom metric tables is beyond the scope of this post, but for the sake of avoiding any future confusion, I’d like to point out that whilst the SubType references a custom alert definition, the MetricID value embedded in the TargetObject value references a custom metric definition. Although in this case both the custom metric definition and custom alert definition share the same Id value of 2, this is not generally the case. Okay, that’s enough for now, not least because as I’m typing this, it’s almost 2am, I have to go to work tomorrow, and my alarm is set for 6am – eek! In my next post, I’ll either cover the remaining three tables in the alert schema, or I’ll delve into the way SQL Monitor stores its monitoring data, as I’d originally planned to cover in this post.

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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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• Login failed for user 'sa' because the account is currently locked out. The system administrator can

  - by cabhilash

  Login failed for user 'sa' because the account is currently locked out. The system administrator can unlock it. (Microsoft SQL Server, Error: 18486) SQL server has local password policies. If policy is enabled which locks down the account after X number of failed attempts then the account is automatically locked down.This error with 'sa' account is very common. sa is default administartor login available with SQL server. So there are chances that an ousider has tried to bruteforce your system. (This can cause even if a legitimate tries to access the account with wrong password.Sometimes a user would have changed the password without informing others. So the other users would try to lo) You can unlock the account with the following options (use another admin account or connect via windows authentication) Alter account & unlock ALTER LOGIN sa WITH PASSWORD='password' UNLOCK Use another account Almost everyone is aware of the sa account. This can be the potential security risk. Even if you provide strong password hackers can lock the account by providing the wrong password. ( You can provide extra security by installing firewall or changing the default port but these measures are not always practical). As a best practice you can disable the sa account and use another account with same privileges.ALTER LOGIN sa DISABLE You can edit the lock-ot options using gpedit.msc( in command prompt type gpedit.msc and press enter). Navigate to Account Lokout policy as shown in the figure The Following options are available Account lockout threshold This security setting determines the number of failed logon attempts that causes a user account to be locked out. A locked-out account cannot be used until it is reset by an administrator or until the lockout duration for the account has expired. You can set a value between 0 and 999 failed logon attempts. If you set the value to 0, the account will never be locked out. Failed password attempts against workstations or member servers that have been locked using either CTRL+ALT+DELETE or password-protected screen savers count as failed logon attempts. Account lockout duration This security setting determines the number of minutes a locked-out account remains locked out before automatically becoming unlocked. The available range is from 0 minutes through 99,999 minutes. If you set the account lockout duration to 0, the account will be locked out until an administrator explicitly unlocks it. If an account lockout threshold is defined, the account lockout duration must be greater than or equal to the reset time. Default: None, because this policy setting only has meaning when an Account lockout threshold is specified. Reset account lockout counter after This security setting determines the number of minutes that must elapse after a failed logon attempt before the failed logon attempt counter is reset to 0 bad logon attempts. The available range is 1 minute to 99,999 minutes. If an account lockout threshold is defined, this reset time must be less than or equal to the Account lockout duration. Default: None, because this policy setting only has meaning when an Account lockout threshold is specified.When creating SQL user you can set CHECK_POLICY=on which will enforce the windows password policy on the account. The following policies will be applied Define the Enforce password history policy setting so that several previous passwords are remembered. With this policy setting, users cannot use the same password when their password expires.  Define the Maximum password age policy setting so that passwords expire as often as necessary for your environment, typically, every 30 to 90 days. With this policy setting, if an attacker cracks a password, the attacker only has access to the network until the password expires.  Define the Minimum password age policy setting so that passwords cannot be changed until they are more than a certain number of days old. This policy setting works in combination with the Enforce password historypolicy setting. If a minimum password age is defined, users cannot repeatedly change their passwords to get around the Enforce password history policy setting and then use their original password. Users must wait the specified number of days to change their passwords.  Define a Minimum password length policy setting so that passwords must consist of at least a specified number of characters. Long passwords--seven or more characters--are usually stronger than short ones. With this policy setting, users cannot use blank passwords, and they have to create passwords that are a certain number of characters long.  Enable the Password must meet complexity requirements policy setting. This policy setting checks all new passwords to ensure that they meet basic strong password requirements.  Password must meet the following complexity requirement, when they are changed or created: Not contain the user's entire Account Name or entire Full Name. The Account Name and Full Name are parsed for delimiters: commas, periods, dashes or hyphens, underscores, spaces, pound signs, and tabs. If any of these delimiters are found, the Account Name or Full Name are split and all sections are verified not to be included in the password. There is no check for any character or any three characters in succession. Contain characters from three of the following five categories:  English uppercase characters (A through Z) English lowercase characters (a through z) Base 10 digits (0 through 9) Non-alphabetic characters (for example, !, $, #, %) A catch-all category of any Unicode character that does not fall under the previous four categories. This fifth category can be regionally specific.

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• VLOOKUP in Excel, part 2: Using VLOOKUP without a database

  - by Mark Virtue

  In a recent article, we introduced the Excel function called VLOOKUP and explained how it could be used to retrieve information from a database into a cell in a local worksheet.  In that article we mentioned that there were two uses for VLOOKUP, and only one of them dealt with querying databases.  In this article, the second and final in the VLOOKUP series, we examine this other, lesser known use for the VLOOKUP function. If you haven’t already done so, please read the first VLOOKUP article – this article will assume that many of the concepts explained in that article are already known to the reader. When working with databases, VLOOKUP is passed a “unique identifier” that serves to identify which data record we wish to find in the database (e.g. a product code or customer ID).  This unique identifier must exist in the database, otherwise VLOOKUP returns us an error.  In this article, we will examine a way of using VLOOKUP where the identifier doesn’t need to exist in the database at all.  It’s almost as if VLOOKUP can adopt a “near enough is good enough” approach to returning the data we’re looking for.  In certain circumstances, this is exactly what we need. We will illustrate this article with a real-world example – that of calculating the commissions that are generated on a set of sales figures.  We will start with a very simple scenario, and then progressively make it more complex, until the only rational solution to the problem is to use VLOOKUP.  The initial scenario in our fictitious company works like this:  If a salesperson creates more than $30,000 worth of sales in a given year, the commission they earn on those sales is 30%.  Otherwise their commission is only 20%.  So far this is a pretty simple worksheet: To use this worksheet, the salesperson enters their sales figures in cell B1, and the formula in cell B2 calculates the correct commission rate they are entitled to receive, which is used in cell B3 to calculate the total commission that the salesperson is owed (which is a simple multiplication of B1 and B2). The cell B2 contains the only interesting part of this worksheet – the formula for deciding which commission rate to use: the one below the threshold of $30,000, or the one above the threshold.  This formula makes use of the Excel function called IF.  For those readers that are not familiar with IF, it works like this: IF(condition,value if true,value if false) Where the condition is an expression that evaluates to either true or false.  In the example above, the condition is the expression B1<B5, which can be read as “Is B1 less than B5?”, or, put another way, “Are the total sales less than the threshold”.  If the answer to this question is “yes” (true), then we use the value if true parameter of the function, namely B6 in this case – the commission rate if the sales total was below the threshold.  If the answer to the question is “no” (false), then we use the value if false parameter of the function, namely B7 in this case – the commission rate if the sales total was above the threshold. As you can see, using a sales total of $20,000 gives us a commission rate of 20% in cell B2.  If we enter a value of $40,000, we get a different commission rate: So our spreadsheet is working. Let’s make it more complex.  Let’s introduce a second threshold:  If the salesperson earns more than $40,000, then their commission rate increases to 40%: Easy enough to understand in the real world, but in cell B2 our formula is getting more complex.  If you look closely at the formula, you’ll see that the third parameter of the original IF function (the value if false) is now an entire IF function in its own right.  This is called a nested function (a function within a function).  It’s perfectly valid in Excel (it even works!), but it’s harder to read and understand. We’re not going to go into the nuts and bolts of how and why this works, nor will we examine the nuances of nested functions.  This is a tutorial on VLOOKUP, not on Excel in general. Anyway, it gets worse!  What about when we decide that if they earn more than $50,000 then they’re entitled to 50% commission, and if they earn more than $60,000 then they’re entitled to 60% commission? Now the formula in cell B2, while correct, has become virtually unreadable.  No-one should have to write formulae where the functions are nested four levels deep!  Surely there must be a simpler way? There certainly is.  VLOOKUP to the rescue! Let’s redesign the worksheet a bit.  We’ll keep all the same figures, but organize it in a new way, a more tabular way: Take a moment and verify for yourself that the new Rate Table works exactly the same as the series of thresholds above. Conceptually, what we’re about to do is use VLOOKUP to look up the salesperson’s sales total (from B1) in the rate table and return to us the corresponding commission rate.  Note that the salesperson may have indeed created sales that are not one of the five values in the rate table ($0, $30,000, $40,000, $50,000 or $60,000).  They may have created sales of $34,988.  It’s important to note that $34,988 does not appear in the rate table.  Let’s see if VLOOKUP can solve our problem anyway… We select cell B2 (the location we want to put our formula), and then insert the VLOOKUP function from the Formulas tab: The Function Arguments box for VLOOKUP appears.  We fill in the arguments (parameters) one by one, starting with the Lookup_value, which is, in this case, the sales total from cell B1.  We place the cursor in the Lookup_value field and then click once on cell B1: Next we need to specify to VLOOKUP what table to lookup this data in.  In this example, it’s the rate table, of course.  We place the cursor in the Table_array field, and then highlight the entire rate table – excluding the headings: Next we must specify which column in the table contains the information we want our formula to return to us.  In this case we want the commission rate, which is found in the second column in the table, so we therefore enter a 2 into the Col_index_num field: Finally we enter a value in the Range_lookup field. Important:  It is the use of this field that differentiates the two ways of using VLOOKUP.  To use VLOOKUP with a database, this final parameter, Range_lookup, must always be set to FALSE, but with this other use of VLOOKUP, we must either leave it blank or enter a value of TRUE.  When using VLOOKUP, it is vital that you make the correct choice for this final parameter. To be explicit, we will enter a value of true in the Range_lookup field.  It would also be fine to leave it blank, as this is the default value: We have completed all the parameters.  We now click the OK button, and Excel builds our VLOOKUP formula for us: If we experiment with a few different sales total amounts, we can satisfy ourselves that the formula is working. Conclusion In the “database” version of VLOOKUP, where the Range_lookup parameter is FALSE, the value passed in the first parameter (Lookup_value) must be present in the database.  In other words, we’re looking for an exact match. But in this other use of VLOOKUP, we are not necessarily looking for an exact match.  In this case, “near enough is good enough”.  But what do we mean by “near enough”?  Let’s use an example:  When searching for a commission rate on a sales total of $34,988, our VLOOKUP formula will return us a value of 30%, which is the correct answer.  Why did it choose the row in the table containing 30% ?  What, in fact, does “near enough” mean in this case?  Let’s be precise: When Range_lookup is set to TRUE (or omitted), VLOOKUP will look in column 1 and match the highest value that is not greater than the Lookup_value parameter. It’s also important to note that for this system to work, the table must be sorted in ascending order on column 1! If you would like to practice with VLOOKUP, the sample file illustrated in this article can be downloaded from here. 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