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• How to: Avoid Inserting Related Entities?

  - by niaher

  I have this schema: I want to insert an Order with OrderItems into database, so I wrote this method: public void SaveOrder(Order order) { using (var repository = new StoreEntities()) { // Add order. repository.Orders.AddObject(order); // Add order items. foreach (OrderItem orderItem in order.OrderItems) { repository.OrderItems.AddObject(orderItem); } repository.SaveChanges(); } } Everything is inserted just fine, except that new Product records are inserted too, which is not what I want. What I want is to insert Order and its OrderItems, without going any further down the object graph. How can that be achieved? Any help is really appreciated, thank you.

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• For which substring of the string1 matches occurred with the string2.

  - by Harikrishna

  I want to know that in particular string1 for which substring of the string1 the string2 matches.Like String str1="Order Number Order Time Trade Number"; String str2="Order Tm"; string regex = Regex.Escape(str2.Replace(@"\ ", @"\s*"); bool isColumnNameMatched = Regex.IsMatch(str1, regex, RegexOptions.IgnoreCase); I am using regex because "Order Tm" will also matches "Order Time".It gives bool value that matches occurred or not. But if it str2 is in str1 then I want to know at which position in the str1 the str2 matches. Like str2="Order Tm" then something like that returns the string which is matched with str2 in the str1.Here str2="Order Tm" then it should return like in the str1,Order Time is the substring where matches is occurred.

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• Given a 2d array sorted in increasing order from left to right and top to bottom, what is the best w

  - by Phukab

  I was recently given this interview question and I'm curious what a good solution to it would be. Say I'm given a 2d array where all the numbers in the array are in increasing order from left to right and top to bottom. What is the best way to search and determine if a target number is in the array? Now, my first inclination is to utilize a binary search since my data is sorted. I can determine if a number is in a single row in O(log N) time. However, it is the 2 directions that throw me off. Another solution I could use, if I could be sure the matrix is n x n, is to start at the middle. If the middle value is less than my target, then I can be sure it is in the left square portion of the matrix from the middle. I then move diagnally and check again, reducing the size of the square that the target could potentially be in until I have honed in on the target number. Does anyone have any good ideas on solving this problem? Example array: Sorted left to right, top to bottom. 1 2 4 5 6 2 3 5 7 8 4 6 8 9 10 5 8 9 10 11

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• Are "strings.xml" string arrays always parsed/deserialized in the same order?

  - by PhilaPhan80

  Can I count on string arrays within the "strings.xml" resource file to be parsed/deserialized in the same order every time? If anyone can cite any documentation that clearly spells out this guarantee, I'd appreciate it. Or, at the very least, offer a significant amount of experience with this topic. Also, is this a best practice or am I missing a simpler solution? Note: This will be a small list, so I'm not looking to implement a more complicated database or custom XML solution unless I absolutely have to. <!--KEYS (ALWAYS CORRESPONDS TO LIST BELOW ??)--> <string-array name="keys"> <item>1</item> <item>2</item> <item>3</item> </string-array> <!--VALUES (ALWAYS CORRESPONDS TO LIST ABOVE ??)--> <string-array name="values"> <item>one</item> <item>two</item> <item>three</item> </string-array>

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• Node.js Adventure - When Node Flying in Wind

  - by Shaun

  In the first post of this series I mentioned some popular modules in the community, such as underscore, async, etc.. I also listed a module named “Wind (zh-CN)”, which is created by one of my friend, Jeff Zhao (zh-CN). Now I would like to use a separated post to introduce this module since I feel it brings a new async programming style in not only Node.js but JavaScript world. If you know or heard about the new feature in C# 5.0 called “async and await”, or you learnt F#, you will find the “Wind” brings the similar async programming experience in JavaScript. By using “Wind”, we can write async code that looks like the sync code. The callbacks, async stats and exceptions will be handled by “Wind” automatically and transparently.   What’s the Problem: Dense “Callback” Phobia Let’s firstly back to my second post in this series. As I mentioned in that post, when we wanted to read some records from SQL Server we need to open the database connection, and then execute the query. In Node.js all IO operation are designed as async callback pattern which means when the operation was done, it will invoke a function which was taken from the last parameter. For example the database connection opening code would be like this. 1: sql.open(connectionString, function(error, conn) { 2: if(error) { 3: // some error handling code 4: } 5: else { 6: // connection opened successfully 7: } 8: }); And then if we need to query the database the code would be like this. It nested in the previous function. 1: sql.open(connectionString, function(error, conn) { 2: if(error) { 3: // some error handling code 4: } 5: else { 6: // connection opened successfully 7: conn.queryRaw(command, function(error, results) { 8: if(error) { 9: // failed to execute this command 10: } 11: else { 12: // records retrieved successfully 13: } 14: }; 15: } 16: }); Assuming if we need to copy some data from this database to another then we need to open another connection and execute the command within the function under the query function. 1: sql.open(connectionString, function(error, conn) { 2: if(error) { 3: // some error handling code 4: } 5: else { 6: // connection opened successfully 7: conn.queryRaw(command, function(error, results) { 8: if(error) { 9: // failed to execute this command 10: } 11: else { 12: // records retrieved successfully 13: target.open(targetConnectionString, function(error, t_conn) { 14: if(error) { 15: // connect failed 16: } 17: else { 18: t_conn.queryRaw(copy_command, function(error, results) { 19: if(error) { 20: // copy failed 21: } 22: else { 23: // and then, what do you want to do now... 24: } 25: }; 26: } 27: }; 28: } 29: }; 30: } 31: }); This is just an example. In the real project the logic would be more complicated. This means our application might be messed up and the business process will be fragged by many callback functions. I would like call this “Dense Callback Phobia”. This might be a challenge how to make code straightforward and easy to read, something like below. 1: try 2: { 3: // open source connection 4: var s_conn = sqlConnect(s_connectionString); 5: // retrieve data 6: var results = sqlExecuteCommand(s_conn, s_command); 7: 8: // open target connection 9: var t_conn = sqlConnect(t_connectionString); 10: // prepare the copy command 11: var t_command = getCopyCommand(results); 12: // execute the copy command 13: sqlExecuteCommand(s_conn, t_command); 14: } 15: catch (ex) 16: { 17: // error handling 18: }   What’s the Problem: Sync-styled Async Programming Similar as the previous problem, the callback-styled async programming model makes the upcoming operation as a part of the current operation, and mixed with the error handling code. So it’s very hard to understand what on earth this code will do. And since Node.js utilizes non-blocking IO mode, we cannot invoke those operations one by one, as they will be executed concurrently. For example, in this post when I tried to copy the records from Windows Azure SQL Database (a.k.a. WASD) to Windows Azure Table Storage, if I just insert the data into table storage one by one and then print the “Finished” message, I will see the message shown before the data had been copied. This is because all operations were executed at the same time. In order to make the copy operation and print operation executed synchronously I introduced a module named “async” and the code was changed as below. 1: async.forEach(results.rows, 2: function (row, callback) { 3: var resource = { 4: "PartitionKey": row[1], 5: "RowKey": row[0], 6: "Value": row[2] 7: }; 8: client.insertEntity(tableName, resource, function (error) { 9: if (error) { 10: callback(error); 11: } 12: else { 13: console.log("entity inserted."); 14: callback(null); 15: } 16: }); 17: }, 18: function (error) { 19: if (error) { 20: error["target"] = "insertEntity"; 21: res.send(500, error); 22: } 23: else { 24: console.log("all done."); 25: res.send(200, "Done!"); 26: } 27: }); It ensured that the “Finished” message will be printed when all table entities had been inserted. But it cannot promise that the records will be inserted in sequence. It might be another challenge to make the code looks like in sync-style? 1: try 2: { 3: forEach(row in rows) { 4: var entity = { /* ... */ }; 5: tableClient.insert(tableName, entity); 6: } 7:  8: console.log("Finished"); 9: } 10: catch (ex) { 11: console.log(ex); 12: }   How “Wind” Helps “Wind” is a JavaScript library which provides the control flow with plain JavaScript for asynchronous programming (and more) without additional pre-compiling steps. It’s available in NPM so that we can install it through “npm install wind”. Now let’s create a very simple Node.js application as the example. This application will take some website URLs from the command arguments and tried to retrieve the body length and print them in console. Then at the end print “Finish”. I’m going to use “request” module to make the HTTP call simple so I also need to install by the command “npm install request”. The code would be like this. 1: var request = require("request"); 2:  3: // get the urls from arguments, the first two arguments are `node.exe` and `fetch.js` 4: var args = process.argv.splice(2); 5:  6: // main function 7: var main = function() { 8: for(var i = 0; i < args.length; i++) { 9: // get the url 10: var url = args[i]; 11: // send the http request and try to get the response and body 12: request(url, function(error, response, body) { 13: if(!error && response.statusCode == 200) { 14: // log the url and the body length 15: console.log( 16: "%s: %d.", 17: response.request.uri.href, 18: body.length); 19: } 20: else { 21: // log error 22: console.log(error); 23: } 24: }); 25: } 26: 27: // finished 28: console.log("Finished"); 29: }; 30:  31: // execute the main function 32: main(); Let’s execute this application. (I made them in multi-lines for better reading.) 1: node fetch.js 2: "http://www.igt.com/us-en.aspx" 3: "http://www.igt.com/us-en/games.aspx" 4: "http://www.igt.com/us-en/cabinets.aspx" 5: "http://www.igt.com/us-en/systems.aspx" 6: "http://www.igt.com/us-en/interactive.aspx" 7: "http://www.igt.com/us-en/social-gaming.aspx" 8: "http://www.igt.com/support.aspx" Below is the output. As you can see the finish message was printed at the beginning, and the pages’ length retrieved in a different order than we specified. This is because in this code the request command, console logging command are executed asynchronously and concurrently. Now let’s introduce “Wind” to make them executed in order, which means it will request the websites one by one, and print the message at the end.   First of all we need to import the “Wind” package and make sure the there’s only one global variant named “Wind”, and ensure it’s “Wind” instead of “wind”. 1: var Wind = require("wind");   Next, we need to tell “Wind” which code will be executed asynchronously so that “Wind” can control the execution process. In this case the “request” operation executed asynchronously so we will create a “Task” by using a build-in helps function in “Wind” named Wind.Async.Task.create. 1: var requestBodyLengthAsync = function(url) { 2: return Wind.Async.Task.create(function(t) { 3: request(url, function(error, response, body) { 4: if(error || response.statusCode != 200) { 5: t.complete("failure", error); 6: } 7: else { 8: var data = 9: { 10: uri: response.request.uri.href, 11: length: body.length 12: }; 13: t.complete("success", data); 14: } 15: }); 16: }); 17: }; The code above created a “Task” from the original request calling code. In “Wind” a “Task” means an operation will be finished in some time in the future. A “Task” can be started by invoke its start() method, but no one knows when it actually will be finished. The Wind.Async.Task.create helped us to create a task. The only parameter is a function where we can put the actual operation in, and then notify the task object it’s finished successfully or failed by using the complete() method. In the code above I invoked the request method. If it retrieved the response successfully I set the status of this task as “success” with the URL and body length. If it failed I set this task as “failure” and pass the error out.   Next, we will change the main() function. In “Wind” if we want a function can be controlled by Wind we need to mark it as “async”. This should be done by using the code below. 1: var main = eval(Wind.compile("async", function() { 2: })); When the application is running, Wind will detect “eval(Wind.compile(“async”, function” and generate an anonymous code from the body of this original function. Then the application will run the anonymous code instead of the original one. In our example the main function will be like this. 1: var main = eval(Wind.compile("async", function() { 2: for(var i = 0; i < args.length; i++) { 3: try 4: { 5: var result = $await(requestBodyLengthAsync(args[i])); 6: console.log( 7: "%s: %d.", 8: result.uri, 9: result.length); 10: } 11: catch (ex) { 12: console.log(ex); 13: } 14: } 15: 16: console.log("Finished"); 17: })); As you can see, when I tried to request the URL I use a new command named “$await”. It tells Wind, the operation next to $await will be executed asynchronously, and the main thread should be paused until it finished (or failed). So in this case, my application will be pause when the first response was received, and then print its body length, then try the next one. At the end, print the finish message.   Finally, execute the main function. The full code would be like this. 1: var request = require("request"); 2: var Wind = require("wind"); 3:  4: var args = process.argv.splice(2); 5:  6: var requestBodyLengthAsync = function(url) { 7: return Wind.Async.Task.create(function(t) { 8: request(url, function(error, response, body) { 9: if(error || response.statusCode != 200) { 10: t.complete("failure", error); 11: } 12: else { 13: var data = 14: { 15: uri: response.request.uri.href, 16: length: body.length 17: }; 18: t.complete("success", data); 19: } 20: }); 21: }); 22: }; 23:  24: var main = eval(Wind.compile("async", function() { 25: for(var i = 0; i < args.length; i++) { 26: try 27: { 28: var result = $await(requestBodyLengthAsync(args[i])); 29: console.log( 30: "%s: %d.", 31: result.uri, 32: result.length); 33: } 34: catch (ex) { 35: console.log(ex); 36: } 37: } 38: 39: console.log("Finished"); 40: })); 41:  42: main().start();   Run our new application. At the beginning we will see the compiled and generated code by Wind. Then we can see the pages were requested one by one, and at the end the finish message was printed. Below is the code Wind generated for us. As you can see the original code, the output code were shown. 1: // Original: 2: function () { 3: for(var i = 0; i < args.length; i++) { 4: try 5: { 6: var result = $await(requestBodyLengthAsync(args[i])); 7: console.log( 8: "%s: %d.", 9: result.uri, 10: result.length); 11: } 12: catch (ex) { 13: console.log(ex); 14: } 15: } 16: 17: console.log("Finished"); 18: } 19:  20: // Compiled: 21: /* async << function () { */ (function () { 22: var _builder_$0 = Wind.builders["async"]; 23: return _builder_$0.Start(this, 24: _builder_$0.Combine( 25: _builder_$0.Delay(function () { 26: /* var i = 0; */ var i = 0; 27: /* for ( */ return _builder_$0.For(function () { 28: /* ; i < args.length */ return i < args.length; 29: }, function () { 30: /* ; i ++) { */ i ++; 31: }, 32: /* try { */ _builder_$0.Try( 33: _builder_$0.Delay(function () { 34: /* var result = $await(requestBodyLengthAsync(args[i])); */ return _builder_$0.Bind(requestBodyLengthAsync(args[i]), function (result) { 35: /* console.log("%s: %d.", result.uri, result.length); */ console.log("%s: %d.", result.uri, result.length); 36: return _builder_$0.Normal(); 37: }); 38: }), 39: /* } catch (ex) { */ function (ex) { 40: /* console.log(ex); */ console.log(ex); 41: return _builder_$0.Normal(); 42: /* } */ }, 43: null 44: ) 45: /* } */ ); 46: }), 47: _builder_$0.Delay(function () { 48: /* console.log("Finished"); */ console.log("Finished"); 49: return _builder_$0.Normal(); 50: }) 51: ) 52: ); 53: /* } */ })   How Wind Works Someone may raise a big concern when you find I utilized “eval” in my code. Someone may assume that Wind utilizes “eval” to execute some code dynamically while “eval” is very low performance. But I would say, Wind does NOT use “eval” to run the code. It only use “eval” as a flag to know which code should be compiled at runtime. When the code was firstly been executed, Wind will check and find “eval(Wind.compile(“async”, function”. So that it knows this function should be compiled. Then it utilized parse-js to analyze the inner JavaScript and generated the anonymous code in memory. Then it rewrite the original code so that when the application was running it will use the anonymous one instead of the original one. Since the code generation was done at the beginning of the application was started, in the future no matter how long our application runs and how many times the async function was invoked, it will use the generated code, no need to generate again. So there’s no significant performance hurt when using Wind.   Wind in My Previous Demo Let’s adopt Wind into one of my previous demonstration and to see how it helps us to make our code simple, straightforward and easy to read and understand. In this post when I implemented the functionality that copied the records from my WASD to table storage, the logic would be like this. 1, Open database connection. 2, Execute a query to select all records from the table. 3, Recreate the table in Windows Azure table storage. 4, Create entities from each of the records retrieved previously, and then insert them into table storage. 5, Finally, show message as the HTTP response. But as the image below, since there are so many callbacks and async operations, it’s very hard to understand my logic from the code. Now let’s use Wind to rewrite our code. First of all, of course, we need the Wind package. Then we need to include the package files into project and mark them as “Copy always”. Add the Wind package into the source code. Pay attention to the variant name, you must use “Wind” instead of “wind”. 1: var express = require("express"); 2: var async = require("async"); 3: var sql = require("node-sqlserver"); 4: var azure = require("azure"); 5: var Wind = require("wind"); Now we need to create some async functions by using Wind. All async functions should be wrapped so that it can be controlled by Wind which are open database, retrieve records, recreate table (delete and create) and insert entity in table. Below are these new functions. All of them are created by using Wind.Async.Task.create. 1: sql.openAsync = function (connectionString) { 2: return Wind.Async.Task.create(function (t) { 3: sql.open(connectionString, function (error, conn) { 4: if (error) { 5: t.complete("failure", error); 6: } 7: else { 8: t.complete("success", conn); 9: } 10: }); 11: }); 12: }; 13:  14: sql.queryAsync = function (conn, query) { 15: return Wind.Async.Task.create(function (t) { 16: conn.queryRaw(query, function (error, results) { 17: if (error) { 18: t.complete("failure", error); 19: } 20: else { 21: t.complete("success", results); 22: } 23: }); 24: }); 25: }; 26:  27: azure.recreateTableAsync = function (tableName) { 28: return Wind.Async.Task.create(function (t) { 29: client.deleteTable(tableName, function (error, successful, response) { 30: console.log("delete table finished"); 31: client.createTableIfNotExists(tableName, function (error, successful, response) { 32: console.log("create table finished"); 33: if (error) { 34: t.complete("failure", error); 35: } 36: else { 37: t.complete("success", null); 38: } 39: }); 40: }); 41: }); 42: }; 43:  44: azure.insertEntityAsync = function (tableName, entity) { 45: return Wind.Async.Task.create(function (t) { 46: client.insertEntity(tableName, entity, function (error, entity, response) { 47: if (error) { 48: t.complete("failure", error); 49: } 50: else { 51: t.complete("success", null); 52: } 53: }); 54: }); 55: }; Then in order to use these functions we will create a new function which contains all steps for data copying. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: } 4: catch (ex) { 5: console.log(ex); 6: res.send(500, "Internal error."); 7: } 8: })); Let’s execute steps one by one with the “$await” keyword introduced by Wind so that it will be invoked in sequence. First is to open the database connection. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: } 7: catch (ex) { 8: console.log(ex); 9: res.send(500, "Internal error."); 10: } 11: })); Then retrieve all records from the database connection. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: } 10: catch (ex) { 11: console.log(ex); 12: res.send(500, "Internal error."); 13: } 14: })); After recreated the table, we need to create the entities and insert them into table storage. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: if (results.rows.length > 0) { 10: // recreate the table 11: $await(azure.recreateTableAsync(tableName)); 12: console.log("table created"); 13: // insert records in table storage one by one 14: for (var i = 0; i < results.rows.length; i++) { 15: var entity = { 16: "PartitionKey": results.rows[i][1], 17: "RowKey": results.rows[i][0], 18: "Value": results.rows[i][2] 19: }; 20: $await(azure.insertEntityAsync(tableName, entity)); 21: console.log("entity inserted"); 22: } 23: } 24: } 25: catch (ex) { 26: console.log(ex); 27: res.send(500, "Internal error."); 28: } 29: })); Finally, send response back to the browser. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: if (results.rows.length > 0) { 10: // recreate the table 11: $await(azure.recreateTableAsync(tableName)); 12: console.log("table created"); 13: // insert records in table storage one by one 14: for (var i = 0; i < results.rows.length; i++) { 15: var entity = { 16: "PartitionKey": results.rows[i][1], 17: "RowKey": results.rows[i][0], 18: "Value": results.rows[i][2] 19: }; 20: $await(azure.insertEntityAsync(tableName, entity)); 21: console.log("entity inserted"); 22: } 23: // send response 24: console.log("all done"); 25: res.send(200, "All done!"); 26: } 27: } 28: catch (ex) { 29: console.log(ex); 30: res.send(500, "Internal error."); 31: } 32: })); If we compared with the previous code we will find now it became more readable and much easy to understand. It’s very easy to know what this function does even though without any comments. When user go to URL “/was/copyRecords” we will execute the function above. The code would be like this. 1: app.get("/was/copyRecords", function (req, res) { 2: copyRecords(req, res).start(); 3: }); And below is the logs printed in local compute emulator console. As we can see the functions executed one by one and then finally the response back to me browser.   Scaffold Functions in Wind Wind provides not only the async flow control and compile functions, but many scaffold methods as well. We can build our async code more easily by using them. I’m going to introduce some basic scaffold functions here. In the code above I created some functions which wrapped from the original async function such as open database, create table, etc.. All of them are very similar, created a task by using Wind.Async.Task.create, return error or result object through Task.complete function. In fact, Wind provides some functions for us to create task object from the original async functions. If the original async function only has a callback parameter, we can use Wind.Async.Binding.fromCallback method to get the task object directly. For example the code below returned the task object which wrapped the file exist check function. 1: var Wind = require("wind"); 2: var fs = require("fs"); 3:  4: fs.existsAsync = Wind.Async.Binding.fromCallback(fs.exists); In Node.js a very popular async function pattern is that, the first parameter in the callback function represent the error object, and the other parameters is the return values. In this case we can use another build-in function in Wind named Wind.Async.Binding.fromStandard. For example, the open database function can be created from the code below. 1: sql.openAsync = Wind.Async.Binding.fromStandard(sql.open); 2:  3: /* 4: sql.openAsync = function (connectionString) { 5: return Wind.Async.Task.create(function (t) { 6: sql.open(connectionString, function (error, conn) { 7: if (error) { 8: t.complete("failure", error); 9: } 10: else { 11: t.complete("success", conn); 12: } 13: }); 14: }); 15: }; 16: */ When I was testing the scaffold functions under Wind.Async.Binding I found for some functions, such as the Azure SDK insert entity function, cannot be processed correctly. So I personally suggest writing the wrapped method manually.   Another scaffold method in Wind is the parallel tasks coordination. In this example, the steps of open database, retrieve records and recreated table should be invoked one by one, but it can be executed in parallel when copying data from database to table storage. In Wind there’s a scaffold function named Task.whenAll which can be used here. Task.whenAll accepts a list of tasks and creates a new task. It will be returned only when all tasks had been completed, or any errors occurred. For example in the code below I used the Task.whenAll to make all copy operation executed at the same time. 1: var copyRecordsInParallel = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: if (results.rows.length > 0) { 10: // recreate the table 11: $await(azure.recreateTableAsync(tableName)); 12: console.log("table created"); 13: // insert records in table storage in parallal 14: var tasks = new Array(results.rows.length); 15: for (var i = 0; i < results.rows.length; i++) { 16: var entity = { 17: "PartitionKey": results.rows[i][1], 18: "RowKey": results.rows[i][0], 19: "Value": results.rows[i][2] 20: }; 21: tasks[i] = azure.insertEntityAsync(tableName, entity); 22: } 23: $await(Wind.Async.Task.whenAll(tasks)); 24: // send response 25: console.log("all done"); 26: res.send(200, "All done!"); 27: } 28: } 29: catch (ex) { 30: console.log(ex); 31: res.send(500, "Internal error."); 32: } 33: })); 34:  35: app.get("/was/copyRecordsInParallel", function (req, res) { 36: copyRecordsInParallel(req, res).start(); 37: });   Besides the task creation and coordination, Wind supports the cancellation solution so that we can send the cancellation signal to the tasks. It also includes exception solution which means any exceptions will be reported to the caller function.   Summary In this post I introduced a Node.js module named Wind, which created by my friend Jeff Zhao. As you can see, different from other async library and framework, adopted the idea from F# and C#, Wind utilizes runtime code generation technology to make it more easily to write async, callback-based functions in a sync-style way. By using Wind there will be almost no callback, and the code will be very easy to understand. Currently Wind is still under developed and improved. There might be some problems but the author, Jeff, should be very happy and enthusiastic to learn your problems, feedback, suggestion and comments. You can contact Jeff by - Email: [email protected] - Group: https://groups.google.com/d/forum/windjs - GitHub: https://github.com/JeffreyZhao/wind/issues   Source code can be download here.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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• How to change source order of <div> in less steps/automatically?

  - by metal-gear-solid

  How can i do this task automate. i need to change source order of div, which has same id in above 100 pages. i created example This is default condition <div class="identification"> <div class="number">Number 1</div> </div> <div class="identification"> <div class="number">Number 2</div> </div> <div class="identification"> <div class="number">Number 3</div> </div> <div class="identification"> <div class="number">Number 4</div> </div> <div class="identification"> <div class="number">Number 5</div> </div> <div class="identification"> <div class="number">Number 6</div> </div> I need lik this <div class="identification"> <div class="number">Number 1</div> </div> <div class="identification"> <div class="number">Number 3</div> </div> <div class="identification"> <div class="number">Number 2</div> </div> <div class="identification"> <div class="number">Number 6</div> </div> <div class="identification"> <div class="number">Number 4</div> </div> <div class="identification"> <div class="number">Number 5</div> </div> Is the manual editing only option? I use dreamweaver.

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• Reading a user input (character or string of letters) into ggplot command inside a switch statement or a nested ifelse (with functions in it)

  - by statisticalbeginner

  I have code like AA <- as.integer(readline("Select any number")) switch(AA, 1={ num <-as.integer(readline("Select any one of the options \n")) print('You have selected option 1') #reading user data var <- readline("enter the variable name \n") #aggregating the data based on required condition gg1 <- aggregate(cbind(get(var))~Mi+hours,a, FUN=mean) #Ploting ggplot(gg1, aes(x = hours, y = get(var), group = Mi, fill = Mi, color = Mi)) + geom_point() + geom_smooth(stat="smooth", alpha = I(0.01)) }, 2={ print('bar') }, { print('default') } ) The dataset is [dataset][1] I have loaded the dataset into object list a <- read.table(file.choose(), header=FALSE,col.names= c("Ei","Mi","hours","Nphy","Cphy","CHLphy","Nhet","Chet","Ndet","Cdet","DON","DOC","DIN","DIC","AT","dCCHO","TEPC","Ncocco","Ccocco","CHLcocco","PICcocco","par","Temp","Sal","co2atm","u10","dicfl","co2ppm","co2mol","pH")) I am getting error like source ("switch_statement_check.R") Select any one of the options 1 [1] "You have selected option 1" enter the variable name Nphy Error in eval(expr, envir, enclos) : (list) object cannot be coerced to type 'double' > gg1 is getting data that is fine. I dont know what to do to make the variable entered by user to work in that ggplot command. Please suggest any solution for this. The dput output structure(list(Ei = c(1L, 1L, 1L, 1L, 1L, 1L), Mi = c(1L, 1L, 1L, 1L, 1L, 1L), hours = 1:6, Nphy = c(0.1023488, 0.104524, 0.1064772, 0.1081702, 0.1095905, 0.110759), Cphy = c(0.6534707, 0.6448216, 0.6369597, 0.6299084, 0.6239005, 0.6191941), CHLphy = c(0.1053458, 0.110325, 0.1148174, 0.1187672, 0.122146, 0.1249877), Nhet = c(0.04994161, 0.04988347, 0.04982555, 0.04976784, 0.04971029, 0.04965285), Chet = c(0.3308593, 0.3304699, 0.3300819, 0.3296952, 0.3293089, 0.3289243), Ndet = c(0.04991916, 0.04984045, 0.04976363, 0.0496884, 0.04961446, 0.04954156), Cdet = c(0.3307085, 0.3301691, 0.3296314, 0.3290949, 0.3285598, 0.3280252), DON = c(0.05042275, 0.05085697, 0.05130091, 0.05175249, 0.05220978, 0.05267118 ), DOC = c(49.76304, 49.52745, 49.29323, 49.06034, 48.82878, 48.59851), DIN = c(14.9933, 14.98729, 14.98221, 14.9781, 14.97485, 14.97225), DIC = c(2050.132, 2050.264, 2050.396, 2050.524, 2050.641, 2050.758), AT = c(2150.007, 2150.007, 2150.007, 2150.007, 2150.007, 2150.007), dCCHO = c(0.964222, 0.930869, 0.8997098, 0.870544, 0.843196, 0.8175117), TEPC = c(0.1339044, 0.1652179, 0.1941872, 0.2210289, 0.2459341, 0.2690721), Ncocco = c(0.1040715, 0.1076058, 0.1104229, 0.1125141, 0.1140222, 0.1151228), Ccocco = c(0.6500288, 0.6386706, 0.6291149, 0.6213265, 0.6152447, 0.6108502), CHLcocco = c(0.1087667, 0.1164099, 0.1225822, 0.1273103, 0.1308843, 0.1336465), PICcocco = c(0.1000664, 0.1001396, 0.1007908, 0.101836, 0.1034179, 0.1055634), par = c(0, 0, 0.8695131, 1.551317, 2.777707, 4.814341), Temp = c(9.9, 9.9, 9.9, 9.9, 9.9, 9.9), Sal = c(31.31, 31.31, 31.31, 31.31, 31.31, 31.31), co2atm = c(370, 370, 370, 370, 370, 370), u10 = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), dicfl = c(-2.963256, -2.971632, -2.980446, -2.989259, -2.997877, -3.005702), co2ppm = c(565.1855, 565.7373, 566.3179, 566.8983, 567.466, 567.9814), co2mol = c(0.02562326, 0.02564828, 0.0256746, 0.02570091, 0.02572665, 0.02575002 ), pH = c(7.879427, 7.879042, 7.878636, 7.878231, 7.877835, 7.877475)), .Names = c("Ei", "Mi", "hours", "Nphy", "Cphy", "CHLphy", "Nhet", "Chet", "Ndet", "Cdet", "DON", "DOC", "DIN", "DIC", "AT", "dCCHO", "TEPC", "Ncocco", "Ccocco", "CHLcocco", "PICcocco", "par", "Temp", "Sal", "co2atm", "u10", "dicfl", "co2ppm", "co2mol", "pH"), row.names = c(NA, 6L), class = "data.frame") As per the below suggestions I have tried a lot but it is not working. Summarizing I will say: var <- readline("enter a variable name") I cant use get(var) inside any command but not inside ggplot, it wont work. gg1$var it also doesnt work, even after changing the column names. Does it have a solution or should I just choose to import from an excel sheet, thats better? Tried with if else and functions fun1 <- function() { print('You have selected option 1') my <- as.character((readline("enter the variable name \n"))) gg1 <- aggregate(cbind(get(my))~Mi+hours,a, FUN=mean) names(gg1)[3] <- my #print(names(gg1)) ggplot (gg1,aes_string(x="hours",y=(my),group="Mi",color="Mi")) + geom_point() } my <- as.integer(readline("enter a number")) ifelse(my == 1,fun1(),"") ifelse(my == 2,print ("its 2"),"") ifelse(my == 3,print ("its 3"),"") ifelse(my != (1 || 2|| 3) ,print("wrong number"),"") Not working either...:(

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• Filter syslog in php functions, then display contents in JS div?

  - by qx3rt

  Let's revise this question with a new approach...I have three files: logtail.php, ajax.js and index.php. My goal is to create a syslog viewer (Linux). On index.php I made a div where I want to display only the filtered contents of the syslog. I must filter the contents in logtail.php. I have to use a shell_exec and | grep the contents with multiple different regexes. Right now I | grep the entire syslog file and it displays live in the log viewer, but my filters are not working as planned. I need help figuring out how to use $_GET to grab only the contents from the syslog that the user wants to see. I have a text field and submit button prepared for that in my index.php file. Should I use functions (tried this already)? Or is there a better approach? Can you give me some examples? logtail.php //Executes a shell script to grab all file contents from syslog on the device //Explodes that content into an array by new line, sorts from most recent entry to oldest entry if (file_exists($filename = '/var/log/syslog')) { $syslogContent = shell_exec("cat $filename | grep -e '.*' $filename"); $contentArray = explode("\n", $syslogContent); rsort($contentArray); print_r($contentArray); } ajax.js (working properly) function createRequest() { var request = null; try { request = new XMLHttpRequest(); } catch (trymicrosoft) { try { request = new ActiveXObject("Msxml2.XMLHTTP"); } catch (othermicrosoft) { try { request = new ActiveXObject("Microsoft.XMLHTTP"); } catch (failed) { request = null; } } } if (request == null) { return alert("Error creating request object!"); } else { return request; } } var request = createRequest(); function getLog(timer) { var url = 'logtail.php'; request.open("GET", url, true); request.onreadystatechange = updatePage; request.send(null); startTail(timer); } function startTail(timer) { if (timer == "stop") { stopTail(); } else { t = setTimeout("getLog()",1000); } } function stopTail() { clearTimeout(t); var pause = "The log viewer has been paused. To begin viewing again, click the Start Log button.\n"; logDiv = document.getElementById("log"); var newNode = document.createTextNode(pause); logDiv.replaceChild(newNode,logDiv.childNodes[0]); } function updatePage() { if (request.readyState == 4) { if (request.status == 200) { var currentLogValue = request.responseText.split("\n"); eval(currentLogValue); logDiv = document.getElementById("log"); var logLine = ' '; for (i = 0; i < currentLogValue.length - 1; i++) { logLine += currentLogValue[i] + "<br/>\n"; } logDiv.innerHTML = logLine; } else alert("Error! Request status is " + request.status); } } index.php <script type="text/javascript" src="scripts/ajax.js"></script> <button style="margin-left:25px;" onclick="getLog('start');">Start Log</button> <button onclick="stopTail();">Stop Log</button> <form action="" method="get"> //This is where the filter options would be Date & Time (ex. Nov 03 07:24:57): <input type="text" name="dateTime" /> <input type="submit" value="submit" /> </form> <br> <div id="log" style="..."> //This is where the log contents are displayed </div>

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• How to automate org-refile for multiple todo

  - by lawlist

  I'm looking to automate org-refile so that it will find all of the matches and re-file them to a specific location (but not archive). I found a fully automated method of archiving multiple todo, and I am hopeful to find or create (with some help) something similar to this awesome function (but for a different heading / location other than archiving): https://github.com/tonyday567/jwiegley-dot-emacs/blob/master/dot-org.el (defun org-archive-done-tasks () (interactive) (save-excursion (goto-char (point-min)) (while (re-search-forward "\* \\(None\\|Someday\\) " nil t) (if (save-restriction (save-excursion (org-narrow-to-subtree) (search-forward ":LOGBOOK:" nil t))) (forward-line) (org-archive-subtree) (goto-char (line-beginning-position)))))) I also found this (written by aculich), which is a step in the right direction, but still requires repeating the function manually: http://stackoverflow.com/questions/7509463/how-to-move-a-subtree-to-another-subtree-in-org-mode-emacs ;; I also wanted a way for org-refile to refile easily to a subtree, so I wrote some code and generalized it so that it will set an arbitrary immediate target anywhere (not just in the same file). ;; Basic usage is to move somewhere in Tree B and type C-c C-x C-m to mark the target for refiling, then move to the entry in Tree A that you want to refile and type C-c C-w which will immediately refile into the target location you set in Tree B without prompting you, unless you called org-refile-immediate-target with a prefix arg C-u C-c C-x C-m. ;; Note that if you press C-c C-w in rapid succession to refile multiple entries it will preserve the order of your entries even if org-reverse-note-order is set to t, but you can turn it off to respect the setting of org-reverse-note-order with a double prefix arg C-u C-u C-c C-x C-m. (defvar org-refile-immediate nil "Refile immediately using `org-refile-immediate-target' instead of prompting.") (make-local-variable 'org-refile-immediate) (defvar org-refile-immediate-preserve-order t "If last command was also `org-refile' then preserve ordering.") (make-local-variable 'org-refile-immediate-preserve-order) (defvar org-refile-immediate-target nil) "Value uses the same format as an item in `org-refile-targets'." (make-local-variable 'org-refile-immediate-target) (defadvice org-refile (around org-immediate activate) (if (not org-refile-immediate) ad-do-it ;; if last command was `org-refile' then preserve ordering (let ((org-reverse-note-order (if (and org-refile-immediate-preserve-order (eq last-command 'org-refile)) nil org-reverse-note-order))) (ad-set-arg 2 (assoc org-refile-immediate-target (org-refile-get-targets))) (prog1 ad-do-it (setq this-command 'org-refile))))) (defadvice org-refile-cache-clear (after org-refile-history-clear activate) (setq org-refile-targets (default-value 'org-refile-targets)) (setq org-refile-immediate nil) (setq org-refile-immediate-target nil) (setq org-refile-history nil)) ;;;###autoload (defun org-refile-immediate-target (&optional arg) "Set current entry as `org-refile' target. Non-nil turns off `org-refile-immediate', otherwise `org-refile' will immediately refile without prompting for target using most recent entry in `org-refile-targets' that matches `org-refile-immediate-target' as the default." (interactive "P") (if (equal arg '(16)) (progn (setq org-refile-immediate-preserve-order (not org-refile-immediate-preserve-order)) (message "Order preserving is turned: %s" (if org-refile-immediate-preserve-order "on" "off"))) (setq org-refile-immediate (unless arg t)) (make-local-variable 'org-refile-targets) (let* ((components (org-heading-components)) (level (first components)) (heading (nth 4 components)) (string (substring-no-properties heading))) (add-to-list 'org-refile-targets (append (list (buffer-file-name)) (cons :regexp (format "^%s %s$" (make-string level ?*) string)))) (setq org-refile-immediate-target heading)))) (define-key org-mode-map "\C-c\C-x\C-m" 'org-refile-immediate-target) It sure would be helpful if aculich, or some other maven, could please create a variable similar to (setq org-archive-location "~/0.todo.org::* Archived Tasks") so users can specify the file and heading, which is already a part of the org-archive-subtree functionality. I'm doing a search and mark because I don't have the wherewithal to create something like org-archive-location for this setup. EDIT: One step closer -- almost home free . . . (defun lawlist-auto-refile () (interactive) (beginning-of-buffer) (re-search-forward "\* UNDATED") (org-refile-immediate-target) ;; cursor must be on a heading to work. (save-excursion (re-search-backward "\* UNDATED") ;; must be written in such a way so that sub-entries of * UNDATED are not searched; or else infinity loop. (while (re-search-backward "\* \\(None\\|Someday\\) " nil t) (org-refile) ) ) )

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• Designing an API with compile-time option to remove first parameter to most functions and use a glob

  - by tomlogic

  I'm trying to design a portable API in ANSI C89/ISO C90 to access a wireless networking device on a serial interface. The library will have multiple network layers, and various versions need to run on embedded devices as small as an 8-bit micro with 32K of code and 2K of data, on up to embedded devices with a megabyte or more of code and data. In most cases, the target processor will have a single network interface and I'll want to use a single global structure with all state information for that device. I don't want to pass a pointer to that structure through the network layers. In a few cases (e.g., device with more resources that needs to live on two networks) I will interface to multiple devices, each with their own global state, and will need to pass a pointer to that state (or an index to a state array) through the layers. I came up with two possible solutions, but neither one is particularly pretty. Keep in mind that the full driver will potentially be 20,000 lines or more, cover multiple files, and contain hundreds of functions. The first solution requires a macro that discards the first parameter for every function that needs to access the global state: // network.h typedef struct dev_t { int var; long othervar; char name[20]; } dev_t; #ifdef IF_MULTI #define foo_function( x, a, b, c) _foo_function( x, a, b, c) #define bar_function( x) _bar_function( x) #else extern dev_t DEV; #define IFACE (&DEV) #define foo_function( x, a, b, c) _foo_function( a, b, c) #define bar_function( x) _bar_function( ) #endif int bar_function( dev_t *IFACE); int foo_function( dev_t *IFACE, int a, long b, char *c); // network.c #ifndef IF_MULTI dev_t DEV; #endif int bar_function( dev_t *IFACE) { memset( IFACE, 0, sizeof *IFACE); return 0; } int foo_function( dev_t *IFACE, int a, long b, char *c) { bar_function( IFACE); IFACE->var = a; IFACE->othervar = b; strcpy( IFACE->name, c); return 0; } The second solution defines macros to use in the function declarations: // network.h typedef struct dev_t { int var; long othervar; char name[20]; } dev_t; #ifdef IF_MULTI #define DEV_PARAM_ONLY dev_t *IFACE #define DEV_PARAM DEV_PARAM_ONLY, #else extern dev_t DEV; #define IFACE (&DEV) #define DEV_PARAM_ONLY void #define DEV_PARAM #endif int bar_function( DEV_PARAM_ONLY); // I don't like the missing comma between DEV_PARAM and arg2... int foo_function( DEV_PARAM int a, long b, char *c); // network.c #ifndef IF_MULTI dev_t DEV; #endif int bar_function( DEV_PARAM_ONLY) { memset( IFACE, 0, sizeof *IFACE); return 0; } int foo_function( DEV_PARAM int a, long b, char *c) { bar_function( IFACE); IFACE->var = a; IFACE->othervar = b; strcpy( IFACE->name, c); return 0; } The C code to access either method remains the same: // multi.c - example of multiple interfaces #define IF_MULTI #include "network.h" dev_t if0, if1; int main() { foo_function( &if0, -1, 3.1415926, "public"); foo_function( &if1, 42, 3.1415926, "private"); return 0; } // single.c - example of a single interface #include "network.h" int main() { foo_function( 11, 1.0, "network"); return 0; } Is there a cleaner method that I haven't figured out? I lean toward the second since it should be easier to maintain, and it's clearer that there's some macro magic in the parameters to the function. Also, the first method requires prefixing the function names with "_" when I want to use them as function pointers. I really do want to remove the parameter in the "single interface" case to eliminate unnecessary code to push the parameter onto the stack, and to allow the function to access the first "real" parameter in a register instead of loading it from the stack. And, if at all possible, I don't want to have to maintain two separate codebases. Thoughts? Ideas? Examples of something similar in existing code? (Note that using C++ isn't an option, since some of the planned targets don't have a C++ compiler available.)

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• SOA Suite 11g Native Format Builder Complex Format Example

  - by bob.webster

  This rather long posting details the steps required to process a grouping of fixed length records using Format Builder.   If it’s 10 pm and you’re feeling beat you might want to leave this until tomorrow.  But if it’s 10 pm and you need to get a Format Builder Complex template done, read on… The goal is to process individual orders from a file using the 11g File Adapter and Format Builder Sample Data =========== 001Square Widget            0245.98 102Triagular Widget         1120.00 403Circular Widget           0099.45 ORD8898302/01/2011 301Hexagon Widget         1150.98 ORD6735502/01/2011 The records are fixed length records representing a number of logical Order records. Each order record consists of a number of item records starting with a 3 digit number, followed by a single Summary Record which starts with the constant ORD. How can this file be processed so that the first poll returns the first order? 001Square Widget            0245.98 102Triagular Widget         1120.00 403Circular Widget           0099.45 ORD8898302/01/2011 And the second poll returns the second order? 301Hexagon Widget           1150.98 ORD6735502/01/2011 Note: if you need more than one order per poll, that’s also possible, see the “Multiple Messages” field in the “File Adapter Step 6 of 9” snapshot further down.   To follow along with this example you will need - Studio Edition Version 11.1.1.4.0    with the   - SOA Extension for JDeveloper 11.1.1.4.0 installed Both can be downloaded from here:  http://www.oracle.com/technetwork/middleware/soasuite/downloads/index.html You will not need a running WebLogic Server domain to complete the steps and Format Builder tests in this article.     Start with a SOA Composite containing a File Adapter The Format Builder is part of the File Adapter so start by creating a new SOA Project and Composite. Here is a quick summary for those not familiar with these steps - Start JDeveloper - From the Main Menu choose File->New - In the New Gallery window that opens Expand the “General” category and Select the Applications node.   Then choose SOA Application from the Items section on the right.  Finally press the OK button. - In Step 1 of the “Create SOA Application wizard” that appears enter an Application Name and an Directory of your     choice,   then press the Next button. - In Step 2 of the “Create SOA Application wizard”, press the Next button leaving all entries as defaulted. - In Step 3 of the “Create SOA Application wizard”, Enter a composite name of your choice and Press the Finish   Button These steps result in a new Application and SOA Project. The SOA Project contains a composite.xml file which is opened and shown below. For our example we have not defined a Mediator or a BPEL process to minimize the steps, but one or the other would eventually be needed to use the File Adapter we are about to create. Drag and drop the File Adapter icon from the Component Pallette onto either the LEFT side of the diagram under “Exposed Services” or the right side under “External References”.  (See the Green Circle in the image below).  Placing the adapter on the left side would indicate the file being processed is inbound to the composite, if the adapter is placed on the right side then the data is outbound to a file.     Note that the same Format Builder definition can be used in both directions.  For example we could use the format with a File Adapter on the left side of the composite to parse fixed data into XML, modify the data in our Composite or BPEL process and then use the same Format Builder definition with a File adapter on the right side of the composite to write the data back out in the same fixed data format When the File Adapter is dropped on the Composite the File Adapter Wizard Appears. Skip Past the first page, Step 1 of 9 by pressing the Next button. In Step 2 enter a service name of your choice as shown below, then press Next   When the Native Format Builder appears, skip the welcome page by pressing next. Also press the Next button to accept the settings on Step 3 of 9 On Step 4, select Read File and press the Next button as shown below.   On Step 5 enter a directory that will contain a file with the input data, then  Press the Next button as shown below. In step 6, enter *.txt or another file format to select input files from the input directory mentioned in step 5. ALSO check the “Files contain Multiple Messages” checkbox and set the “Publish Messages in Batches of” field to 1.  The value can be set higher to increase the number of logical order group records returned on each poll of the file adapter.  In other words, it determines the number of Orders that will be sent to each instance of a Mediator or Composite processing using the File Adapter.   Skip Step 7 by pressing the Next button In Step 8 press the Gear Icon on the right side to load the Native Format Builder.       Native Format Builder  appears Before diving into the format, here is an overview of the process. Approach - Bottom up Assuming an Order is a grouping of item records and a summary record…. - Define a separate  Complex Type for each Record Type found in the group.    (One for itemRecord and one for summaryRecord) - Define a Complex Type to contain the Group of Record types defined above   (LogicalOrderRecord) - Define a top level element to represent an order.  (order)   The order element will be of type LogicalOrderRecord   Defining the Format In Step 1 select   “Create new”  and  “Complex Type” and “Next”   In Step two browse to and select a file containing the test data shown at the start of this article. A link is provided at the end of this article to download a file containing the test data. Press the Next button     In Step 3 Complex types must be define for each type of input record. Select the Root-Element and Click on the Add Complex Type icon This creates a new empty complex type definition shown below. The fastest way to create the definition is to highlight the first line of the Sample File data and drag the line onto the  <new_complex_type> Format Builder introspects the data and provides a grid to define additional fields. Change the “Complex Type Name” to  “itemRecord” Then click on the ruler to indicate the position of fixed columns.  Drag the red triangle icons to the exact columns if necessary. Double click on an existing red triangle to remove an unwanted entry. In the case below fields are define in columns 0-3, 4-28, 29-eol When the field definitions are correct, press the “Generate Fields” button. Field entries named C1, C2 and C3 will be created as shown below. Click on the field names and rename them from C1->itemNum, C2->itemDesc and C3->itemCost  When all the fields are correctly defined press OK to save the complex type.        Next, the process is repeated to define a Complex Type for the SummaryRecord. Select the Root-Element in the schema tree and press the new complex type icon Then highlight and drag the Summary Record from the sample data onto the <new_complex_type>   Change the complex type name to “summaryRecord” Mark the fixed fields for Order Number and Order Date. Press the Generate Fields button and rename C1 and C2 to itemNum and orderDate respectively.   The last complex type to be defined is a type to hold the group of items and the summary record. Select the Root-Element in the schema tree and click the new complex type icon Select the “<new_complex_type>” entry and click the pencil icon   On the Complex Type Details page change the name and type of each input field. Change line 1 to be named item and set the Type  to “itemRecord” Change line 2 to be named summary and set the Type to “summaryRecord” We also need to indicate that itemRecords repeat in the input file. Click the pencil icon at the right side of the item line. On the Edit Details page change the “Max Occurs” entry from 1 to UNBOUNDED. We also need to indicate how to identify an itemRecord.  Since each item record has “.” in column 32 we can use this fact to differentiate an item record from a summary record. Change the “Look Ahead” field to value 32 and enter a period in the “Look For” field Press the OK button to save entry.     Finally, its time to create a top level element to represent an order. Select the “Root-Element” in the schema tree and press the New element icon Click on the <new_element> and press the pencil icon.   Set the Element Name to “order” and change the Data Type to “logicalOrderRecord” Press the OK button to save the element definition.   The final definition should match the screenshot below. Press the Next Button to view the definition source.     Press the Test Button to test the definition   Press the Green Triangle Icon to run the test.   And we are presented with an unwelcome error. The error states that the processor ran out of data while working through the definition. The processor was unable to differentiate between itemRecords and summaryRecords and therefore treated the entire file as a list of itemRecords.  At end of file, the “summary” portion of the logicalOrderRecord remained unprocessed but mandatory.   This root cause of this error is the loss of our “lookAhead” definition used to identify itemRecords. This appears to be a bug in the  Native Format Builder 11.1.1.4.0 Luckily, a simple workaround exists. Press the Cancel button and return to the “Step 4 of 4” Window. Manually add    nxsd:lookAhead="32" nxsd:lookFor="."   attributes after the maxOccurs attribute of the item element. as shown in the highlighted text below.   When the lookAhead and lookFor attributes have been added Press the Test button and on the Test page press the Green Triangle. The test is now successful, the first order in the file is returned by the File Adapter.     Below is a complete listing of the Result XML from the right column of the screen above   Try running it The downloaded input test file and completed schema file can be used for testing without following all the Native Format Builder steps in this example. Use the following link to download a file containing the sample data. Download Sample Input Data This is the best approach rather than cutting and pasting the input data at the top of the article.  Since the data is fixed length it’s very important to watch out for trailing spaces in the data and to ensure an eol character at the end of every line. The download file is correctly formatted. The final schema definition can be downloaded at the following link Download Completed Schema Definition   - Save the inputData.txt file to a known location like the xsd folder in your project. - Save the inputData_6.xsd file to the xsd folder in your project. - At step 1 in the Native Format Builder wizard  (as shown above) check the “Edit existing” radio button,    then browse and select the inputData_6.xsd file - At step 2 of the Format Builder configuration Wizard (as shown above) supply the path and filename for    the inputData.txt file. - You can then proceed to the test page and run a test. - Remember the wizard bug will drop the lookAhead and lookFor attributes,  you will need to manually add   nxsd:lookAhead="32" nxsd:lookFor="."    after the maxOccurs attribute of the item element in the   LogicalOrderRecord Complex Type.  (as shown above)   Good Luck with your Format Project

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• Adding Unobtrusive Validation To MVCContrib Fluent Html

  - by srkirkland

  ASP.NET MVC 3 includes a new unobtrusive validation strategy that utilizes HTML5 data-* attributes to decorate form elements.  Using a combination of jQuery validation and an unobtrusive validation adapter script that comes with MVC 3, those attributes are then turned into client side validation rules. A Quick Introduction to Unobtrusive Validation To quickly show how this works in practice, assume you have the following Order.cs class (think Northwind) [If you are familiar with unobtrusive validation in MVC 3 you can skip to the next section]: public class Order : DomainObject { [DataType(DataType.Date)] public virtual DateTime OrderDate { get; set; }   [Required] [StringLength(12)] public virtual string ShipAddress { get; set; }   [Required] public virtual Customer OrderedBy { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Note the System.ComponentModel.DataAnnotations attributes, which provide the validation and metadata information used by ASP.NET MVC 3 to determine how to render out these properties.  Now let’s assume we have a form which can edit this Order class, specifically let’s look at the ShipAddress property: @Html.LabelFor(x => x.Order.ShipAddress) @Html.EditorFor(x => x.Order.ShipAddress) @Html.ValidationMessageFor(x => x.Order.ShipAddress) .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now the Html.EditorFor() method is smart enough to look at the ShipAddress attributes and write out the necessary unobtrusive validation html attributes.  Note we could have used Html.TextBoxFor() or even Html.TextBox() and still retained the same results. If we view source on the input box generated by the Html.EditorFor() call, we get the following: <input type="text" value="Rua do Paço, 67" name="Order.ShipAddress" id="Order_ShipAddress" data-val-required="The ShipAddress field is required." data-val-length-max="12" data-val-length="The field ShipAddress must be a string with a maximum length of 12." data-val="true" class="text-box single-line input-validation-error"> .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } As you can see, we have data-val-* attributes for both required and length, along with the proper error messages and additional data as necessary (in this case, we have the length-max=”12”). And of course, if we try to submit the form with an invalid value, we get an error on the client: Working with MvcContrib’s Fluent Html The MvcContrib project offers a fluent interface for creating Html elements which I find very expressive and useful, especially when it comes to creating select lists.  Let’s look at a few quick examples: @this.TextBox(x => x.FirstName).Class("required").Label("First Name:") @this.MultiSelect(x => x.UserId).Options(ViewModel.Users) @this.CheckBox("enabled").LabelAfter("Enabled").Title("Click to enable.").Styles(vertical_align => "middle")   @(this.Select("Order.OrderedBy").Options(Model.Customers, x => x.Id, x => x.CompanyName) .Selected(Model.Order.OrderedBy != null ? Model.Order.OrderedBy.Id : "") .FirstOption(null, "--Select A Company--") .HideFirstOptionWhen(Model.Order.OrderedBy != null) .Label("Ordered By:")) .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } These fluent html helpers create the normal html you would expect, and I think they make life a lot easier and more readable when dealing with complex markup or select list data models (look ma: no anonymous objects for creating class names!). Of course, the problem we have now is that MvcContrib’s fluent html helpers don’t know about ASP.NET MVC 3’s unobtrusive validation attributes and thus don’t take part in client validation on your page.  This is not ideal, so I wrote a quick helper method to extend fluent html with the knowledge of what unobtrusive validation attributes to include when they are rendered. Extending MvcContrib’s Fluent Html Before posting the code, there are just a few things you need to know.  The first is that all Fluent Html elements implement the IElement interface (MvcContrib.FluentHtml.Elements.IElement), and the second is that the base System.Web.Mvc.HtmlHelper has been extended with a method called GetUnobtrusiveValidationAttributes which we can use to determine the necessary attributes to include.  With this knowledge we can make quick work of extending fluent html: public static class FluentHtmlExtensions { public static T IncludeUnobtrusiveValidationAttributes<T>(this T element, HtmlHelper htmlHelper) where T : MvcContrib.FluentHtml.Elements.IElement { IDictionary<string, object> validationAttributes = htmlHelper .GetUnobtrusiveValidationAttributes(element.GetAttr("name"));   foreach (var validationAttribute in validationAttributes) { element.SetAttr(validationAttribute.Key, validationAttribute.Value); }   return element; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The code is pretty straight forward – basically we use a passed HtmlHelper to get a list of validation attributes for the current element and then add each of the returned attributes to the element to be rendered. The Extension In Action Now let’s get back to the earlier ShipAddress example and see what we’ve accomplished.  First we will use a fluent html helper to render out the ship address text input (this is the ‘before’ case): @this.TextBox("Order.ShipAddress").Label("Ship Address:").Class("class-name") .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } And the resulting HTML: <label id="Order_ShipAddress_Label" for="Order_ShipAddress">Ship Address:</label> <input type="text" value="Rua do Paço, 67" name="Order.ShipAddress" id="Order_ShipAddress" class="class-name"> Now let’s do the same thing except here we’ll use the newly written extension method: @this.TextBox("Order.ShipAddress").Label("Ship Address:") .Class("class-name").IncludeUnobtrusiveValidationAttributes(Html) .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } And the resulting HTML: <label id="Order_ShipAddress_Label" for="Order_ShipAddress">Ship Address:</label> <input type="text" value="Rua do Paço, 67" name="Order.ShipAddress" id="Order_ShipAddress" data-val-required="The ShipAddress field is required." data-val-length-max="12" data-val-length="The field ShipAddress must be a string with a maximum length of 12." data-val="true" class="class-name"> .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Excellent!  Now we can continue to use unobtrusive validation and have the flexibility to use ASP.NET MVC’s Html helpers or MvcContrib’s fluent html helpers interchangeably, and every element will participate in client side validation. Wrap Up Overall I’m happy with this solution, although in the best case scenario MvcContrib would know about unobtrusive validation attributes and include them automatically (of course if it is enabled in the web.config file).  I know that MvcContrib allows you to author global behaviors, but that requires changing the base class of your views, which I am not willing to do. Enjoy!

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

  - by James Michael Hare

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

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• Organization &amp; Architecture UNISA Studies &ndash; Chap 6

  - by MarkPearl

  Learning Outcomes Discuss the physical characteristics of magnetic disks Describe how data is organized and accessed on a magnetic disk Discuss the parameters that play a role in the performance of magnetic disks Describe different optical memory devices Magnetic Disk The way data is stored on and retried from magnetic disks Data is recorded on and later retrieved form the disk via a conducting coil named the head (in many systems there are two heads) The writ mechanism exploits the fact that electricity flowing through a coil produces a magnetic field. Electric pulses are sent to the write head, and the resulting magnetic patterns are recorded on the surface below with different patterns for positive and negative currents The physical characteristics of a magnetic disk   Summarize from book   The factors that play a role in the performance of a disk Seek time – the time it takes to position the head at the track Rotational delay / latency – the time it takes for the beginning of the sector to reach the head Access time – the sum of the seek time and rotational delay Transfer time – the time it takes to transfer data RAID The rate of improvement in secondary storage performance has been considerably less than the rate for processors and main memory. Thus secondary storage has become a bit of a bottleneck. RAID works on the concept that if one disk can be pushed so far, additional gains in performance are to be had by using multiple parallel components. Points to note about RAID… RAID is a set of physical disk drives viewed by the operating system as a single logical drive Data is distributed across the physical drives of an array in a scheme known as striping Redundant disk capacity is used to store parity information, which guarantees data recoverability in case of a disk failure (not supported by RAID 0 or RAID 1) Interesting to note that the increase in the number of drives, increases the probability of failure. To compensate for this decreased reliability RAID makes use of stored parity information that enables the recovery of data lost due to a disk failure.   The RAID scheme consists of 7 levels…   Category Level Description Disks Required Data Availability Large I/O Data Transfer Capacity Small I/O Request Rate Striping 0 Non Redundant N Lower than single disk Very high Very high for both read and write Mirroring 1 Mirrored 2N Higher than RAID 2 – 5 but lower than RAID 6 Higher than single disk Up to twice that of a signle disk for read Parallel Access 2 Redundant via Hamming Code N + m Much higher than single disk Highest of all listed alternatives Approximately twice that of a single disk Parallel Access 3 Bit interleaved parity N + 1 Much higher than single disk Highest of all listed alternatives Approximately twice that of a single disk Independent Access 4 Block interleaved parity N + 1 Much higher than single disk Similar to RAID 0 for read, significantly lower than single disk for write Similar to RAID 0 for read, significantly lower than single disk for write Independent Access 5 Block interleaved parity N + 1 Much higher than single disk Similar to RAID 0 for read, lower than single disk for write Similar to RAID 0 for read, generally  lower than single disk for write Independent Access 6 Block interleaved parity N + 2 Highest of all listed alternatives Similar to RAID 0 for read; lower than RAID 5 for write Similar to RAID 0 for read, significantly lower than RAID 5  for write   Read page 215 – 221 for detailed explanation on RAID levels Optical Memory There are a variety of optical-disk systems available. Read through the table on page 222 – 223 Some of the devices include… CD CD-ROM CD-R CD-RW DVD DVD-R DVD-RW Blue-Ray DVD Magnetic Tape Most modern systems use serial recording – data is lade out as a sequence of bits along each track. The typical recording used in serial is referred to as serpentine recording. In this technique when data is being recorded, the first set of bits is recorded along the whole length of the tape. When the end of the tape is reached the heads are repostioned to record a new track, and the tape is again recorded on its whole length, this time in the opposite direction. That process continued back and forth until the tape is full. To increase speed, the read-write head is capable of reading and writing a number of adjacent tracks simultaneously. Data is still recorded serially along individual tracks, but blocks in sequence are stored on adjacent tracks as suggested. A tape drive is a sequential access device. Magnetic tape was the first kind of secondary memory. It is still widely used as the lowest-cost, slowest speed member of the memory hierarchy.

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• How do I use Java to sort surnames in alphabetical order from file to file?

  - by user577939

  I have written this code and don't know how to sort surnames in alphabetical order from my file to another file. import java.io.*; import java.util.*; class Asmuo { String pavarde; String vardas; long buvLaikas; int atv1; int atv2; int atv3; } class Irasas { Asmuo duom; Irasas kitas; } class Sarasas { private Irasas p; Sarasas() { p = null; } Irasas itrauktiElementa(String pv, String v, long laikas, int d0, int d1, int d2) { String pvrd, vrd; int data0; int data1; int data2; long lks; lks = laikas; pvrd = pv; vrd = v; data0 = d0; data1 = d1; data2 = d2; Irasas r = new Irasas(); r.duom = new Asmuo(); uzpildymasDuomenimis(r, pvrd, vrd, lks, d0, d1, d2); r.kitas = p; p = r; return r; } void uzpildymasDuomenimis(Irasas r, String pv, String v, long laik, int d0, int d1, int d2) { r.duom.pavarde = pv; r.duom.vardas = v; r.duom.atv1 = d0; r.duom.buvLaikas = laik; r.duom.atv2 = d1; r.duom.atv3 = d2; } void spausdinti() { Irasas d = p; int i = 0; try { FileWriter fstream = new FileWriter("rez.txt"); BufferedWriter rez = new BufferedWriter(fstream); while (d != null) { System.out.println(d.duom.pavarde + " " + d.duom.vardas + " " + d.duom.buvLaikas + " " + d.duom.atv1 + " " + d.duom.atv2 + " " + d.duom.atv3); rez.write(d.duom.pavarde + " " + d.duom.vardas + " " + d.duom.buvLaikas + " " + d.duom.atv1 + " " + d.duom.atv2 + " " + d.duom.atv3 + "\n"); d = d.kitas; i++; } rez.close(); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); } } } public class Gyventojai { public static void main(String args[]) { Sarasas sar = new Sarasas(); Calendar atv = Calendar.getInstance(); Calendar isv = Calendar.getInstance(); try { FileInputStream fstream = new FileInputStream("duom.txt"); DataInputStream in = new DataInputStream(fstream); BufferedReader br = new BufferedReader(new InputStreamReader(in)); String eil; while ((eil = br.readLine()) != null) { String[] cells = eil.split(" "); String pvrd = cells[0]; String vrd = cells[1]; atv.set(Integer.parseInt(cells[2]), Integer.parseInt(cells[3]), Integer.parseInt(cells[4])); isv.set(Integer.parseInt(cells[5]), Integer.parseInt(cells[6]), Integer.parseInt(cells[7])); long laik = (isv.getTimeInMillis() - atv.getTimeInMillis()) / (24 * 60 * 60 * 1000); int d0 = Integer.parseInt(cells[2]); int d1 = Integer.parseInt(cells[3]); int d2 = Integer.parseInt(cells[4]); sar.itrauktiElementa(pvrd, vrd, laik, d0, d1, d2); } in.close(); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); } sar.spausdinti(); } }

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• NET Math Libraries

  - by JoshReuben

  NET Mathematical Libraries   .NET Builder for Matlab The MathWorks Inc. - http://www.mathworks.com/products/netbuilder/ MATLAB Builder NE generates MATLAB based .NET and COM components royalty-free deployment creates the components by encrypting MATLAB functions and generating either a .NET or COM wrapper around them. .NET/Link for Mathematica www.wolfram.com a product that 2-way integrates Mathematica and Microsoft's .NET platform call .NET from Mathematica - use arbitrary .NET types directly from the Mathematica language. use and control the Mathematica kernel from a .NET program. turns Mathematica into a scripting shell to leverage the computational services of Mathematica. write custom front ends for Mathematica or use Mathematica as a computational engine for another program comes with full source code. Leverages MathLink - a Wolfram Research's protocol for sending data and commands back and forth between Mathematica and other programs. .NET/Link abstracts the low-level details of the MathLink C API. Extreme Optimization http://www.extremeoptimization.com/ a collection of general-purpose mathematical and statistical classes built for the.NET framework. It combines a math library, a vector and matrix library, and a statistics library in one package. download the trial of version 4.0 to try it out. Multi-core ready - Full support for Task Parallel Library features including cancellation. Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical analysis (integration and differentiation), equation solvers. Mathematics leverages parallelism using .NET 4.0's Task Parallel Library. Basic math: Complex numbers, 'special functions' like Gamma and Bessel functions, numerical differentiation. Solving equations: Solve equations in one variable, or solve systems of linear or nonlinear equations. Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials. Optimization: find the minimum or maximum of a function in one or more variables, linear programming and mixed integer programming. Numerical integration: Compute integrals over finite or infinite intervals, over 2D and higher dimensional regions. Integrate systems of ordinary differential equations (ODE's). Fast Fourier Transforms: 1D and 2D FFT's using managed or fast native code (32 and 64 bit) BigInteger, BigRational, and BigFloat: Perform operations with arbitrary precision. Vector and Matrix Library Real and complex vectors and matrices. Single and double precision for elements. Structured matrix types: including triangular, symmetrical and band matrices. Sparse matrices. Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition. Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit). Statistics Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding. Statistical Models: Simple, multiple, nonlinear, logistic, Poisson regression. Generalized Linear Models. One and two-way ANOVA. Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances. Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions. Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions. Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers. New in version 4.0 (November, 2010) Support for .NET Framework Version 4.0 and Visual Studio 2010 TPL Parallellized – multicore ready sparse linear program solver - can solve problems with more than 1 million variables. Mixed integer linear programming using a branch and bound algorithm. special functions: hypergeometric, Riemann zeta, elliptic integrals, Frensel functions, Dawson's integral. Full set of window functions for FFT's. Product  Price Update subscription Single Developer License $999  $399  Team License (3 developers) $1999  $799  Department License (8 developers) $3999  $1599  Site License (Unlimited developers in one physical location) $7999  $3199    NMath http://www.centerspace.net .NET math and statistics libraries matrix and vector classes random number generators Fast Fourier Transforms (FFTs) numerical integration linear programming linear regression curve and surface fitting optimization hypothesis tests analysis of variance (ANOVA) probability distributions principal component analysis cluster analysis built on the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). Product  Price Update subscription Single Developer License $1295 $388 Team License (5 developers) $5180 $1554   DotNumerics http://www.dotnumerics.com/NumericalLibraries/Default.aspx free DotNumerics is a website dedicated to numerical computing for .NET that includes a C# Numerical Library for .NET containing algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, ports from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. Linear Algebra (CSLapack, CSBlas and CSEispack). Systems of linear equations, eigenvalue problems, least-squares solutions of linear systems and singular value problems. Differential Equations. Initial-value problem for nonstiff and stiff ordinary differential equations ODEs (explicit Runge-Kutta, implicit Runge-Kutta, Gear's BDF and Adams-Moulton). Optimization. Unconstrained and bounded constrained optimization of multivariate functions (L-BFGS-B, Truncated Newton and Simplex methods).   Math.NET Numerics http://numerics.mathdotnet.com/ free an open source numerical library - includes special functions, linear algebra, probability models, random numbers, interpolation, integral transforms. A merger of dnAnalytics with Math.NET Iridium in addition to a purely managed implementation will also support native hardware optimization. constants & special functions complex type support real and complex, dense and sparse linear algebra (with LU, QR, eigenvalues, ... decompositions) non-uniform probability distributions, multivariate distributions, sample generation alternative uniform random number generators descriptive statistics, including order statistics various interpolation methods, including barycentric approaches and splines numerical function integration (quadrature) routines integral transforms, like fourier transform (FFT) with arbitrary lengths support, and hartley spectral-space aware sequence manipulation (signal processing) combinatorics, polynomials, quaternions, basic number theory. parallelized where appropriate, to leverage multi-core and multi-processor systems fully managed or (if available) using native libraries (Intel MKL, ACMS, CUDA, FFTW) provides a native facade for F# developers

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• HTML, CSS: how can I merge these divs in order to use float:left property on their children ?

  - by Patrick

  hi, I've 2 sets of thumbnails and in each set I'm displaying them one nearby each other in 4 columns using float:left. I would like to "merge" the 2 sets (but I cannot change the html code) because I want the thumbnails of the second set floating right after the last thumbnail of the first set. In other terms, if in the last row there are only 2 thumbnails and the last 2 columns are empty, the thumbnails of the second set should fill the empty columns of the last row of the first set. This is the code... <div class="field field-type-filefield field-field-image"> <div class="field-items"> <div class="field-item odd"> <a rel="lightbox[field_image][First image&lt;br /&gt;&lt;br /&gt;&lt;a href=&quot;/lancelmaat/content/stalkshow&quot; id=&quot;node_link_text&quot; class=&quot;active&quot;&gt;View Image Details&lt;/a&gt;]" href="http://localhost/lancelmaat/sites/default/files/files/projects/Stalkshow/images/LPrisPetjong.jpeg" class="lightbox-processed"><img width="89" height="89" title="" alt="First image" src="http://localhost/lancelmaat/sites/default/files/imagecache/galleryImage/files/projects/Stalkshow/images/LPrisPetjong.jpeg"></a> </div> <div class="field-item even"> <a rel="lightbox[field_image][Second image&lt;br /&gt;&lt;br /&gt;&lt;a href=&quot;/lancelmaat/content/stalkshow&quot; id=&quot;node_link_text&quot; class=&quot;active&quot;&gt;View Image Details&lt;/a&gt;]" href="http://localhost/lancelmaat/sites/default/files/files/projects/Stalkshow/images/SeoulLEDScreen2a.jpeg" class="lightbox-processed"><img width="89" height="89" title="" alt="Second image" src="http://localhost/lancelmaat/sites/default/files/imagecache/galleryImage/files/projects/Stalkshow/images/SeoulLEDScreen2a.jpeg"></a> </div> <div class="field-item odd"> <a rel="lightbox[field_image][Third image&lt;br /&gt;&lt;br /&gt;&lt;a href=&quot;/lancelmaat/content/stalkshow&quot; id=&quot;node_link_text&quot; class=&quot;active&quot;&gt;View Image Details&lt;/a&gt;]" href="http://localhost/lancelmaat/sites/default/files/files/projects/Stalkshow/images/SeoulSKT6.jpeg" class="lightbox-processed"><img width="89" height="89" title="" alt="Third image" src="http://localhost/lancelmaat/sites/default/files/imagecache/galleryImage/files/projects/Stalkshow/images/SeoulSKT6.jpeg"></a> </div> </div> <!-- second set --> <div class="field field-type-filefield field-field-video"> <div class="field-items"> <div class="field-item odd"> <a rel="lightbox[field_video][Video Number 1&lt;br /&gt;&lt;br /&gt;&lt;a href=&quot;/lancelmaat/content/stalkshow&quot; id=&quot;node_link_text&quot; class=&quot;active&quot;&gt;View Image Details&lt;/a&gt;]" href="http://localhost/lancelmaat/sites/default/files/files/projects/Stalkshow/videos/StalkSeoul8d1Mbps.flv" class="lightbox-processed"><img title="" alt="Video Number 1" src="http://localhost/lancelmaat/sites/default/files/imagecache/galleryVideo/files/projects/Stalkshow/videos/StalkSeoul8d1Mbps.flv"></a> </div> <div class="field-item even"> <a rel="lightbox[field_video][Video Number 2&lt;br /&gt;&lt;br /&gt;&lt;a href=&quot;/lancelmaat/content/stalkshow&quot; id=&quot;node_link_text&quot; class=&quot;active&quot;&gt;View Image Details&lt;/a&gt;]" href="http://localhost/lancelmaat/sites/default/files/files/projects/Stalkshow/videos/stalkshowdvd21Mbps.flv" class="lightbox-processed"><img title="" alt="Video Number 2" src="http://localhost/lancelmaat/sites/default/files/imagecache/galleryVideo/files/projects/Stalkshow/videos/stalkshowdvd21Mbps.flv"></a> </div> <div class="field-item odd"> <a rel="lightbox[field_video][Video Number 3&lt;br /&gt;&lt;br /&gt;&lt;a href=&quot;/lancelmaat/content/stalkshow&quot; id=&quot;node_link_text&quot; class=&quot;active&quot;&gt;View Image Details&lt;/a&gt;]" href="http://localhost/lancelmaat/sites/default/files/files/projects/Stalkshow/videos/StalkShowMoscow1Mbps.flv" class="lightbox-processed"><img title="" alt="Video Number 3" src="http://localhost/lancelmaat/sites/default/files/imagecache/galleryVideo/files/projects/Stalkshow/videos/StalkShowMoscow1Mbps.flv"></a> </div> </div> </div> How can I merge these divs in order to use float:left property on their children ? thanks

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• Boost Python : How to only expose the constructor of a class with virtual (pure & impure) methods

  - by fallino

  Hello, I'm a newbie with Boost::Python but I tried to search on the web to do so I want to expose a 3rd party library to Python. One of the class of the library (.hpp) is composed of a public constructor with arguments a protected constructor and functions various regular functions various pure virtual functions various non pure virtual functions First, I did not succeed in building it without having errors about this protected constructor. I finally commented it. A first question would be : Is there a way to exclude these protected functions since I don't want to expose them ? (I know it's possible and easy with Py++, but I started without using it) Then I tried to expose all of my functions, beginning with the pure virtual ones (commenting them all except one), which wasn't a success too So I finally decided not to expose these virtual functions (which in fact seems logical...), but, here again, I didn't manage building it with a simple constructor with arguments (without no_init). So my second question is : Is there a way to exclude these virtual functions since I don't want to expose them ? Sorry if it seems trivial but I didn't find anything explicit on the web and I need something rather explicit :). Thanks in advance

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• How do I conditionally compile C code snippets to my Perl module?

  - by mobrule

  I have a module that will target several different operating systems and configurations. Sometimes, some C code can make this module's task a little easier, so I have some C functions that I would like to bind the code. I don't have to bind the C functions -- I can't guarantee that the end-user even has a C compiler, for instance, and it's generally not a problem to failover gracefully to a pure Perl way of accomplishing the same thing -- but it would be nice if I could call the C functions from the Perl script. Still with me? Here's another tricky part. Just about all of the C code is system specific -- a function written for Windows won't compile on Linux and vice-versa, and the function that does a similar thing on Solaris will look totally different. #include <some/Windows/headerfile.h> int foo_for_Windows_c(int a,double b) { do_windows_stuff(); return 42; } #include <path/to/linux/headerfile.h> int foo_for_linux_c(int a,double b) { do_linux_stuff(7); return 42; } Furthermore, even for native code that targets the same system, it's possible that only some of them can be compiled on any particular configuration. #include <some/headerfile/that/might/not/even/exist.h> int bar_for_solaris_c(int a,double b) { call_solaris_library_that_might_be_installed_here(11); return 19; } But ideally we could still use the C functions that would compile with that configuration. So my questions are: how can I compile C functions conditionally (compile only the code that is appropriate for the current value of $^O)? how can I compile C functions individually (some functions might not compile, but we still want to use the ones that can)? can I do this at build-time (while the end-user is installing the module) or at run-time (with Inline::C, for example)? Which way is better? how would I tell which functions were successfully compiled and are available for use from Perl? All thoughts appreciated!

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• byte and short data types in Java can accept the value outside the range by explicit cast. The higher data types however can not. Why?

  - by Lion

  Let's consider the following expressions in Java. byte a = 32; byte b = (byte) 250; int i = a + b; This is valid in Java even though the expression byte b = (byte) 250; is forced to assign the value 250 to b which is outside the range of the type byte. Therefore, b is assigned -6 and consequently i is assigned the value 26 through the statement int i = a + b;. The same thing is possible with short as follows. short s1=(short) 567889999; Although the specified value is outside the range of short, this statement is legal. The same thing is however wrong with higher data types such int, double, folat etc and hence, the following case is invalid and causes a compile-time error. int z=2147483648; This is illegal, since the range of int in Java is from -2,147,483,648 to 2147483647 which the above statement exceeds and issues a compile-time error. Why is such not wrong with byte and short data types in Java?

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• Advanced TSQL Tuning: Why Internals Knowledge Matters

  - by Paul White

  There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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• Python regex to parse text file, get the items in list and count the list

  - by Nemo

  I have a text file which contains some data. I m particularly interested in finding the count of the number of items in v_dims v_dims pattern in my text file looks like this : v_dims={ "Sales", "Product Family", "Sales Organization", "Region", "Sales Area", "Sales office", "Sales Division", "Sales Person", "Sales Channel", "Sales Order Type", "Sales Number", "Sales Person", "Sales Quantity", "Sales Amount" } So I m thinking of getting all the elements in v_dims and dumping them out in a Python list. Then compute the len(mylist) to get the count of the items. The challenge is in getting all the elements of v_dims from my text file and putting them in an empty list. I m particularly interested in items in v_dims in my text file. The text file has data in the form of v_dims pattern i showed in my original post. Some data has nested patterns of v_dims. Thanks. Here's what I have tried and failed. Any help is appreciated. TIA. import re fname = "C:\Users\XXXX\Test.mrk" with open(fname, "r") as fo: content_as_string = fo.read() match = re.findall(r'v_dims={\"(.+?)\"}',content_as_string) Though I have a big text file, Here's a snippet of what's the structure of my text file version "1"; // Computer generated object language file object 'MRKR' "Main" { Data_Type=2, HeaderBlock={ Version_String="6.3 (25)" }, Printer_Info={ Orientation=0, Page_Width=8.50000000, Page_Height=11.00000000, Page_Header="", Page_Footer="", Margin_type=0, Top_Margin=0.50000000, Left_Margin=0.50000000, Bottom_Margin=0.50000000, Right_Margin=0.50000000 }, Marker_Options={ Close_All="TRUE", Hide_Console="FALSE", Console_Left="FALSE", Console_Width=217, Main_Style="Maximized", MDI_Rect={ 0, 0, 892, 1063 } }, Dives={ { Dive="A", Windows={ { View_Index=0, Window_Info={ Window_Rect={ 0, -288, 400, 1008 }, Window_Style="Maximized Front", Window_Name="Theater [Previous Qtr Diveplan-Dive A]" }, Dependent_bool="FALSE", Colset={ Dive_Type="Normal", Dimension_Name="Theater", Action_List={ Actions={ { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater" }, Key_Indexes={ { "AMERICAS" } } }, { Action_Type="Focus", Focus_Rows="True" }, { Action_Type="Dimensions", v_dims={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag", "Product Item Family" }, Xtab_Bool="False", Xtab_Flip="False" }, { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag" }, Key_Indexes={ { "AMERICAS", "ATMOS", "Latin America CS Division", "37000 CS Region", "Mexico", "", "", "", "", "DIRECT", "EMC", "N", "0" } } } } }, Num_Palette_cols=0, Num_Palette_rows=0 }, Format={ Window_Type="Tabular", Tabular={ Num_row_labels=8 } } } } } }, Widget_Set={ Widget_Layout="Vertical", Go_Button=1, Picklist_Width=0, Sort_Subset_Dimensions="TRUE", Order={ } }, Views={ { Data_Type=1, dbname="Previous Qtr Diveplan", diveline_dbname="Current Qtr Diveplan", logical_name="Current Qtr Diveplan", cols={ { name="Total TSS installs", column_type="Calc[Total TSS installs]", output_type="Number", format_string="." }, { name="TSS Valid Connectivity Records", column_type="Calc[TSS Valid Connectivity Records]", output_type="Number", format_string="." }, { name="% TSS Connectivity Record", column_type="Calc[% TSS Connectivity Record]", output_type="Number" }, { name="TSS Not Applicable", column_type="Calc[TSS Not Applicable]", output_type="Number", format_string="." }, { name="TSS Customer Refusals", column_type="Calc[TSS Customer Refusals]", output_type="Number", format_string="." }, { name="% TSS Refusals", column_type="Calc[% TSS Refusals]", output_type="Number" }, { name="TSS Eligible for Physical Connectivity", column_type="Calc[TSS Eligible for Physical Connectivity]", output_type="Number", format_string="." }, { name="TSS Boxes with Physical Connectivty", column_type="Calc[TSS Boxes with Physical Connectivty]", output_type="Number", format_string="." }, { name="% TSS Physical Connectivity", column_type="Calc[% TSS Physical Connectivity]", output_type="Number" } }, dim_cols={ { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Connect In Type", column_type="Dimension[Connect In Type]", output_type="None" }, { name="Connect Home Type", column_type="Dimension[Connect Home Type]", output_type="None" }, { name="SymmConnect Enabled", column_type="Dimension[SymmConnect Enabled]", output_type="None" }, { name="Theater", column_type="Dimension[Theater]", output_type="None" }, { name="Division", column_type="Dimension[Division]", output_type="None" }, { name="Region", column_type="Dimension[Region]", output_type="None" }, { name="Sales Order Number", column_type="Dimension[Sales Order Number]", output_type="None" }, { name="Product Item Family", column_type="Dimension[Product Item Family]", output_type="None" }, { name="Item Serial Number", column_type="Dimension[Item Serial Number]", output_type="None" }, { name="Sales Order Deal Number", column_type="Dimension[Sales Order Deal Number]", output_type="None" }, { name="Item Install Date", column_type="Dimension[Item Install Date]", output_type="None" }, { name="SYR Last Dial Home Date", column_type="Dimension[SYR Last Dial Home Date]", output_type="None" }, { name="Maintained By Group", column_type="Dimension[Maintained By Group]", output_type="None" }, { name="PS Flag", column_type="Dimension[PS Flag]", output_type="None" }, { name="Connect Home Refusal Reason", column_type="Dimension[Connect Home Refusal Reason]", output_type="None", col_width=177 }, { name="Cust Name", column_type="Dimension[Cust Name]", output_type="None" }, { name="Sales Order Channel Type", column_type="Dimension[Sales Order Channel Type]", output_type="None" }, { name="Sales Order Type", column_type="Dimension[Sales Order Type]", output_type="None" }, { name="Part Model Key", column_type="Dimension[Part Model Key]", output_type="None" }, { name="Ship Date", column_type="Dimension[Ship Date]", output_type="None" }, { name="Model Number", column_type="Dimension[Model Number]", output_type="None" }, { name="Item Description", column_type="Dimension[Item Description]", output_type="None" }, { name="Customer Classification", column_type="Dimension[Customer Classification]", output_type="None" }, { name="CS Customer Name", column_type="Dimension[CS Customer Name]", output_type="None" }, { name="Install At Customer Number", column_type="Dimension[Install At Customer Number]", output_type="None" }, { name="Install at Country Name", column_type="Dimension[Install at Country Name]", output_type="None" }, { name="TLA Serial Number", column_type="Dimension[TLA Serial Number]", output_type="None" }, { name="Product Version", column_type="Dimension[Product Version]", output_type="None" }, { name="Avalanche Flag", column_type="Dimension[Avalanche Flag]", output_type="None" }, { name="Product Family", column_type="Dimension[Product Family]", output_type="None" }, { name="Project Number", column_type="Dimension[Project Number]", output_type="None" }, { name="PROJECT_STATUS", column_type="Dimension[PROJECT_STATUS]", output_type="None" } }, Available_Columns={ "Total TSS installs", "TSS Valid Connectivity Records", "% TSS Connectivity Record", "TSS Not Applicable", "TSS Customer Refusals", "% TSS Refusals", "TSS Eligible for Physical Connectivity", "TSS Boxes with Physical Connectivty", "% TSS Physical Connectivity", "Total Installs", "All Boxes with Valid Connectivty Record", "% All Connectivity Record", "Overall Refusals", "Overall Refusals %", "All Eligible for Physical Connectivty", "Boxes with Physical Connectivity", "% All with Physical Conectivity" }, Remaining_columns={ { name="Total Installs", column_type="Calc[Total Installs]", output_type="Number", format_string="." }, { name="All Boxes with Valid Connectivty Record", column_type="Calc[All Boxes with Valid Connectivty Record]", output_type="Number", format_string="." }, { name="% All Connectivity Record", column_type="Calc[% All Connectivity Record]", output_type="Number" }, { name="Overall Refusals", column_type="Calc[Overall Refusals]", output_type="Number", format_string="." }, { name="Overall Refusals %", column_type="Calc[Overall Refusals %]", output_type="Number" }, { name="All Eligible for Physical Connectivty", column_type="Calc[All Eligible for Physical Connectivty]", output_type="Number" }, { name="Boxes with Physical Connectivity", column_type="Calc[Boxes with Physical Connectivity]", output_type="Number" }, { name="% All with Physical Conectivity", column_type="Calc[% All with Physical Conectivity]", output_type="Number" } }, calcs={ { name="Total TSS installs", definition="Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Valid Connectivity Records", definition="Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% TSS Connectivity Record", definition="Total[PS Boxes w/ valid connectivity record (1=yes)] /Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Not Applicable", definition="Total[Bozes w/ valid connectivity record (1=yes)]-Total[Boxes Eligible (1=yes)]-Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="TSS Customer Refusals", definition="Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="% TSS Refusals", definition="Total[TSS Refusals]/Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="TSS Eligible for Physical Connectivity", definition="Total[TSS Eligible]-Total[Exception]", ts_flag="Not TS Calc" }, { name="TSS Boxes with Physical Connectivty", definition="Total[PS Physical Connectivity] - Total[PS Physical Connectivity, SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% TSS Physical Connectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" }, { name="Total Installs", definition="Total[Total Installs]", ts_flag="Not TS Calc" }, { name="All Boxes with Valid Connectivty Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% All Connectivity Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]/Total[Total Installs]", ts_flag="Not TS Calc" }, { name="Overall Refusals", definition="Total[Overall Refusals]", ts_flag="Not TS Calc" }, { name="Overall Refusals %", definition="Total[Overall Refusals]/Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="All Eligible for Physical Connectivty", definition="Total[Boxes Eligible (1=yes)]-Total[Exception]", ts_flag="Not TS Calc" }, { name="Boxes with Physical Connectivity", definition="Total[Boxes w/ phys conn]-Total[Boxes w/ phys conn,SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% All with Physical Conectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" } }, merge_type="consolidate", merge_dbs={ { dbname="connectivityallproducts.mdl", diveline_dbname="/DI_PSREPORTING/connectivityallproducts.mdl" } }, skip_constant_columns="FALSE", categories={ { name="Geography", dimensions={ "Theater", "Division", "Region", "Install at Country Name" } }, { name="Mappings and Flags", dimensions={ "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "Customer Installable", "PS Flag", "Top Level Flag", "Avalanche Flag" } }, { name="Product Information", dimensions={ "Product Family", "Product Item Family", "Product Version", "Item Description" } }, { name="Sales Order Info", dimensions={ "Sales Order Deal Number", "Sales Order Number", "Sales Order Type" } }, { name="Dates", dimensions={ "Item Install Date", "Ship Date", "SYR Last Dial Home Date" } }, { name="Details", dimensions={ "Item Serial Number", "TLA Serial Number", "Part Model Key", "Model Number" } }, { name="Customer Infor", dimensions={ "CS Customer Name", "Install At Customer Number", "Customer Classification", "Cust Name" } }, { name="Other Dimensions", dimensions={ "Model" } } }, Maintain_Category_Order="FALSE", popup_info="false" } } };

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• Heaps of Trouble?

  - by Paul White NZ

  If you’re not already a regular reader of Brad Schulz’s blog, you’re missing out on some great material.  In his latest entry, he is tasked with optimizing a query run against tables that have no indexes at all.  The problem is, predictably, that performance is not very good.  The catch is that we are not allowed to create any indexes (or even new statistics) as part of our optimization efforts. In this post, I’m going to look at the problem from a slightly different angle, and present an alternative solution to the one Brad found.  Inevitably, there’s going to be some overlap between our entries, and while you don’t necessarily need to read Brad’s post before this one, I do strongly recommend that you read it at some stage; he covers some important points that I won’t cover again here. The Example We’ll use data from the AdventureWorks database, copied to temporary unindexed tables.  A script to create these structures is shown below: CREATE TABLE #Custs ( CustomerID INTEGER NOT NULL, TerritoryID INTEGER NULL, CustomerType NCHAR(1) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL, ); GO CREATE TABLE #Prods ( ProductMainID INTEGER NOT NULL, ProductSubID INTEGER NOT NULL, ProductSubSubID INTEGER NOT NULL, Name NVARCHAR(50) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL, ); GO CREATE TABLE #OrdHeader ( SalesOrderID INTEGER NOT NULL, OrderDate DATETIME NOT NULL, SalesOrderNumber NVARCHAR(25) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL, CustomerID INTEGER NOT NULL, ); GO CREATE TABLE #OrdDetail ( SalesOrderID INTEGER NOT NULL, OrderQty SMALLINT NOT NULL, LineTotal NUMERIC(38,6) NOT NULL, ProductMainID INTEGER NOT NULL, ProductSubID INTEGER NOT NULL, ProductSubSubID INTEGER NOT NULL, ); GO INSERT #Custs ( CustomerID, TerritoryID, CustomerType ) SELECT C.CustomerID, C.TerritoryID, C.CustomerType FROM AdventureWorks.Sales.Customer C WITH (TABLOCK); GO INSERT #Prods ( ProductMainID, ProductSubID, ProductSubSubID, Name ) SELECT P.ProductID, P.ProductID, P.ProductID, P.Name FROM AdventureWorks.Production.Product P WITH (TABLOCK); GO INSERT #OrdHeader ( SalesOrderID, OrderDate, SalesOrderNumber, CustomerID ) SELECT H.SalesOrderID, H.OrderDate, H.SalesOrderNumber, H.CustomerID FROM AdventureWorks.Sales.SalesOrderHeader H WITH (TABLOCK); GO INSERT #OrdDetail ( SalesOrderID, OrderQty, LineTotal, ProductMainID, ProductSubID, ProductSubSubID ) SELECT D.SalesOrderID, D.OrderQty, D.LineTotal, D.ProductID, D.ProductID, D.ProductID FROM AdventureWorks.Sales.SalesOrderDetail D WITH (TABLOCK); The query itself is a simple join of the four tables: SELECT P.ProductMainID AS PID, P.Name, D.OrderQty, H.SalesOrderNumber, H.OrderDate, C.TerritoryID FROM #Prods P JOIN #OrdDetail D ON P.ProductMainID = D.ProductMainID AND P.ProductSubID = D.ProductSubID AND P.ProductSubSubID = D.ProductSubSubID JOIN #OrdHeader H ON D.SalesOrderID = H.SalesOrderID JOIN #Custs C ON H.CustomerID = C.CustomerID ORDER BY P.ProductMainID ASC OPTION (RECOMPILE, MAXDOP 1); Remember that these tables have no indexes at all, and only the single-column sampled statistics SQL Server automatically creates (assuming default settings).  The estimated query plan produced for the test query looks like this (click to enlarge): The Problem The problem here is one of cardinality estimation – the number of rows SQL Server expects to find at each step of the plan.  The lack of indexes and useful statistical information means that SQL Server does not have the information it needs to make a good estimate.  Every join in the plan shown above estimates that it will produce just a single row as output.  Brad covers the factors that lead to the low estimates in his post. In reality, the join between the #Prods and #OrdDetail tables will produce 121,317 rows.  It should not surprise you that this has rather dire consequences for the remainder of the query plan.  In particular, it makes a nonsense of the optimizer’s decision to use Nested Loops to join to the two remaining tables.  Instead of scanning the #OrdHeader and #Custs tables once (as it expected), it has to perform 121,317 full scans of each.  The query takes somewhere in the region of twenty minutes to run to completion on my development machine. A Solution At this point, you may be thinking the same thing I was: if we really are stuck with no indexes, the best we can do is to use hash joins everywhere. We can force the exclusive use of hash joins in several ways, the two most common being join and query hints.  A join hint means writing the query using the INNER HASH JOIN syntax; using a query hint involves adding OPTION (HASH JOIN) at the bottom of the query.  The difference is that using join hints also forces the order of the join, whereas the query hint gives the optimizer freedom to reorder the joins at its discretion. Adding the OPTION (HASH JOIN) hint results in this estimated plan: That produces the correct output in around seven seconds, which is quite an improvement!  As a purely practical matter, and given the rigid rules of the environment we find ourselves in, we might leave things there.  (We can improve the hashing solution a bit – I’ll come back to that later on). Faster Nested Loops It might surprise you to hear that we can beat the performance of the hash join solution shown above using nested loops joins exclusively, and without breaking the rules we have been set. The key to this part is to realize that a condition like (A = B) can be expressed as (A <= B) AND (A >= B).  Armed with this tremendous new insight, we can rewrite the join predicates like so: SELECT P.ProductMainID AS PID, P.Name, D.OrderQty, H.SalesOrderNumber, H.OrderDate, C.TerritoryID FROM #OrdDetail D JOIN #OrdHeader H ON D.SalesOrderID >= H.SalesOrderID AND D.SalesOrderID <= H.SalesOrderID JOIN #Custs C ON H.CustomerID >= C.CustomerID AND H.CustomerID <= C.CustomerID JOIN #Prods P ON P.ProductMainID >= D.ProductMainID AND P.ProductMainID <= D.ProductMainID AND P.ProductSubID = D.ProductSubID AND P.ProductSubSubID = D.ProductSubSubID ORDER BY D.ProductMainID OPTION (RECOMPILE, LOOP JOIN, MAXDOP 1, FORCE ORDER); I’ve also added LOOP JOIN and FORCE ORDER query hints to ensure that only nested loops joins are used, and that the tables are joined in the order they appear.  The new estimated execution plan is: This new query runs in under 2 seconds. Why Is It Faster? The main reason for the improvement is the appearance of the eager Index Spools, which are also known as index-on-the-fly spools.  If you read my Inside The Optimiser series you might be interested to know that the rule responsible is called JoinToIndexOnTheFly. An eager index spool consumes all rows from the table it sits above, and builds a index suitable for the join to seek on.  Taking the index spool above the #Custs table as an example, it reads all the CustomerID and TerritoryID values with a single scan of the table, and builds an index keyed on CustomerID.  The term ‘eager’ means that the spool consumes all of its input rows when it starts up.  The index is built in a work table in tempdb, has no associated statistics, and only exists until the query finishes executing. The result is that each unindexed table is only scanned once, and just for the columns necessary to build the temporary index.  From that point on, every execution of the inner side of the join is answered by a seek on the temporary index – not the base table. A second optimization is that the sort on ProductMainID (required by the ORDER BY clause) is performed early, on just the rows coming from the #OrdDetail table.  The optimizer has a good estimate for the number of rows it needs to sort at that stage – it is just the cardinality of the table itself.  The accuracy of the estimate there is important because it helps determine the memory grant given to the sort operation.  Nested loops join preserves the order of rows on its outer input, so sorting early is safe.  (Hash joins do not preserve order in this way, of course). The extra lazy spool on the #Prods branch is a further optimization that avoids executing the seek on the temporary index if the value being joined (the ‘outer reference’) hasn’t changed from the last row received on the outer input.  It takes advantage of the fact that rows are still sorted on ProductMainID, so if duplicates exist, they will arrive at the join operator one after the other. The optimizer is quite conservative about introducing index spools into a plan, because creating and dropping a temporary index is a relatively expensive operation.  It’s presence in a plan is often an indication that a useful index is missing. I want to stress that I rewrote the query in this way primarily as an educational exercise – I can’t imagine having to do something so horrible to a production system. Improving the Hash Join I promised I would return to the solution that uses hash joins.  You might be puzzled that SQL Server can create three new indexes (and perform all those nested loops iterations) faster than it can perform three hash joins.  The answer, again, is down to the poor information available to the optimizer.  Let’s look at the hash join plan again: Two of the hash joins have single-row estimates on their build inputs.  SQL Server fixes the amount of memory available for the hash table based on this cardinality estimate, so at run time the hash join very quickly runs out of memory. This results in the join spilling hash buckets to disk, and any rows from the probe input that hash to the spilled buckets also get written to disk.  The join process then continues, and may again run out of memory.  This is a recursive process, which may eventually result in SQL Server resorting to a bailout join algorithm, which is guaranteed to complete eventually, but may be very slow.  The data sizes in the example tables are not large enough to force a hash bailout, but it does result in multiple levels of hash recursion.  You can see this for yourself by tracing the Hash Warning event using the Profiler tool. The final sort in the plan also suffers from a similar problem: it receives very little memory and has to perform multiple sort passes, saving intermediate runs to disk (the Sort Warnings Profiler event can be used to confirm this).  Notice also that because hash joins don’t preserve sort order, the sort cannot be pushed down the plan toward the #OrdDetail table, as in the nested loops plan. Ok, so now we understand the problems, what can we do to fix it?  We can address the hash spilling by forcing a different order for the joins: SELECT P.ProductMainID AS PID, P.Name, D.OrderQty, H.SalesOrderNumber, H.OrderDate, C.TerritoryID FROM #Prods P JOIN #Custs C JOIN #OrdHeader H ON H.CustomerID = C.CustomerID JOIN #OrdDetail D ON D.SalesOrderID = H.SalesOrderID ON P.ProductMainID = D.ProductMainID AND P.ProductSubID = D.ProductSubID AND P.ProductSubSubID = D.ProductSubSubID ORDER BY D.ProductMainID OPTION (MAXDOP 1, HASH JOIN, FORCE ORDER); With this plan, each of the inputs to the hash joins has a good estimate, and no hash recursion occurs.  The final sort still suffers from the one-row estimate problem, and we get a single-pass sort warning as it writes rows to disk.  Even so, the query runs to completion in three or four seconds.  That’s around half the time of the previous hashing solution, but still not as fast as the nested loops trickery. Final Thoughts SQL Server’s optimizer makes cost-based decisions, so it is vital to provide it with accurate information.  We can’t really blame the performance problems highlighted here on anything other than the decision to use completely unindexed tables, and not to allow the creation of additional statistics. I should probably stress that the nested loops solution shown above is not one I would normally contemplate in the real world.  It’s there primarily for its educational and entertainment value.  I might perhaps use it to demonstrate to the sceptical that SQL Server itself is crying out for an index. Be sure to read Brad’s original post for more details.  My grateful thanks to him for granting permission to reuse some of his material. Paul White Email: [email protected] Twitter: @PaulWhiteNZ

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• Uninitialized constant Item::Types

  - by Rasmus

  Hi! First of, im a newbie ruby programmer so please bare with me if this is a very dumb question. I get this uninitialized constant error when i submit my nested forms. order.rb class Order < ActiveRecord::Base has_many :items, :dependent => :destroy has_many :types, :through => :items accepts_nested_attributes_for :items accepts_nested_attributes_for :types validates_associated :items validates_associated :types end item.rb class Item < ActiveRecord::Base has_one :types belongs_to :order accepts_nested_attributes_for :types validates_associated :types end type.rb class Type < ActiveRecord::Base belongs_to :items belongs_to :orders end new.erb.html <% form_for @order do |f| %> <%= f.error_messages %> <% f.fields_for :items do |builder| %> <table border="0"> <th>Type</th> <th>Amount</th> <th>Text</th> <th>Price</th> <tr> <% f.fields_for :type do |m| %> <td> <%= m.collection_select :type, Type.find(:all, :order => "created_at DESC"), :id, :name, {:prompt => "Select a Type" }, {:id => "selector", :onchange => "type_change(this)"} %> </td> <% end %> <td> <%= f.text_field :amount, :id => "amountField", :onchange => "change_total_price()" %> </td> <td> <%= f.text_field :text, :id => "textField" %> </td> <td> <%= f.text_field :price, :class => "priceField", :onChange => "change_total_price()" %> </td> <td> <%= link_to_remove_fields "Remove Item", f %> </td> </tr> </table> <% end %> <p><%= link_to_add_fields "Add Item", f, :items %></p> <p> <%= f.label :total_price %><br /> <%= f.text_field :total_price, :class => "priceField", :id => "totalPrice" %> </p> <p><%= f.submit "Create"%></p> <% end %> <%= link_to 'Back', orders_path %> create method in orders_controller.rb def create @order = Order.new(params[:order]) respond_to do |format| if @order.save flash[:notice] = 'Post was successfully created.' format.html { redirect_to(@order) } format.xml { render :xml => @order, :status => :created, :location => @order } else format.html { render :action => "new" } format.xml { render :xml => @order.errors, :status => :unprocessable_entity } end end end Hopefully you can see what i cant

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• VLC Caching levels

  - by Svish

  When I open the Preferences of VLC and go to Input & Codecs, I have a setting called Default Caching Level. I can choose between Cusom Lowest latency Low latency Normal High latency Higher latency I'm used to caching being set in seconds or something like that. So, more seconds/higher buffer means less chane of buffer underrun while streaming. What is latency? What does it mean to set it lower or higher? In what cases should I go in what direction? If I'm struggling with buffer underruns, should I set it to lower or higher latency?

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