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  • jQuery Time Entry with Time Navigation Keys

    - by Rick Strahl
    So, how do you display time values in your Web applications? Displaying date AND time values in applications is lot less standardized than date display only. While date input has become fairly universal with various date picker controls available, time entry continues to be a bit of a non-standardized. In my own applications I tend to use the jQuery UI DatePicker control for date entries and it works well for that. Here's an example: The date entry portion is well defined and it makes perfect sense to have a calendar pop up so you can pick a date from a rich UI when necessary. However, time values are much less obvious when it comes to displaying a UI or even just making time entries more useful. There are a slew of time picker controls available but other than adding some visual glitz, they are not really making time entry any easier. Part of the reason for this is that time entry is usually pretty simple. Clicking on a dropdown of any sort and selecting a value from a long scrolling list tends to take more user interaction than just typing 5 characters (7 if am/pm is used). Keystrokes can make Time Entry easier Time entry maybe pretty simple, but I find that adding a few hotkeys to handle date navigation can make it much easier. Specifically it'd be nice to have keys to: Jump to the current time (Now) Increase/decrease minutes Increase/decrease hours The timeKeys jQuery PlugIn Some time ago I created a small plugin to handle this scenario. It's non-visual other than tooltip that pops up when you press ? to display the hotkeys that are available: Try it Online The keys loosely follow the ancient Quicken convention of using the first and last letters of what you're increasing decreasing (ie. H to decrease, R to increase hours and + and - for the base unit or minutes here). All navigation happens via the keystrokes shown above, so it's all non-visual, which I think is the most efficient way to deal with dates. To hook up the plug-in, start with the textbox:<input type="text" id="txtTime" name="txtTime" value="12:05 pm" title="press ? for time options" /> Note the title which might be useful to alert people using the field that additional functionality is available. To hook up the plugin code is as simple as:$("#txtTime").timeKeys(); You essentially tie the plugin to any text box control. OptionsThe syntax for timeKeys allows for an options map parameter:$(selector).timeKeys(options); Options are passed as a parameter map object which can have the following properties: timeFormatYou can pass in a format string that allows you to format the date. The default is "hh:mm t" which is US time format that shows a 12 hour clock with am/pm. Alternately you can pass in "HH:mm" which uses 24 hour time. HH, hh, mm and t are translated in the format string - you can arrange the format as you see fit. callbackYou can also specify a callback function that is called when the date value has been set. This allows you to either re-format the date or perform post processing (such as displaying highlight if it's after a certain hour for example). Here's another example that uses both options:$("#txtTime").timeKeys({ timeFormat: "HH:mm", callback: function (time) { showStatus("new time is: " + time.toString() + " " + $(this).val() ); } }); The plugin code itself is fairly simple. It hooks the keydown event and checks for the various keys that affect time navigation which is straight forward. The bulk of the code however deals with parsing the time value and formatting the output using a Time class that implements parsing, formatting and time navigation methods. Here's the code for the timeKeys jQuery plug-in:/// <reference path="jquery.js" /> /// <reference path="ww.jquery.js" /> (function ($) { $.fn.timeKeys = function (options) { /// <summary> /// Attaches a set of hotkeys to time fields /// + Add minute - subtract minute /// H Subtract Hour R Add houR /// ? Show keys /// </summary> /// <param name="options" type="object"> /// Options: /// timeFormat: "hh:mm t" by default HH:mm alternate /// callback: callback handler after time assignment /// </param> /// <example> /// var proxy = new ServiceProxy("JsonStockService.svc/"); /// proxy.invoke("GetStockQuote",{symbol:"msft"},function(quote) { alert(result.LastPrice); },onPageError); ///</example> if (this.length < 1) return this; var opt = { timeFormat: "hh:mm t", callback: null } $.extend(opt, options); return this.keydown(function (e) { var $el = $(this); var time = new Time($el.val()); //alert($(this).val() + " " + time.toString() + " " + time.date.toString()); switch (e.keyCode) { case 78: // [N]ow time = new Time(new Date()); break; case 109: case 189: // - time.addMinutes(-1); break; case 107: case 187: // + time.addMinutes(1); break; case 72: //H time.addHours(-1); break; case 82: //R time.addHours(1); break; case 191: // ? if (e.shiftKey) $(this).tooltip("<b>N</b> Now<br/><b>+</b> add minute<br /><b>-</b> subtract minute<br /><b>H</b> Subtract Hour<br /><b>R</b> add hour", 4000, { isHtml: true }); return false; default: return true; } $el.val(time.toString(opt.timeFormat)); if (opt.callback) { // call async and set context in this element setTimeout(function () { opt.callback.call($el.get(0), time) }, 1); } return false; }); } Time = function (time, format) { /// <summary> /// Time object that can parse and format /// a time values. /// </summary> /// <param name="time" type="object"> /// A time value as a string (12:15pm or 23:01), a Date object /// or time value. /// /// </param> /// <param name="format" type="string"> /// Time format string: /// HH:mm (23:01) /// hh:mm t (11:01 pm) /// </param> /// <example> /// var time = new Time( new Date()); /// time.addHours(5); /// time.addMinutes(10); /// var s = time.toString(); /// /// var time2 = new Time(s); // parse with constructor /// var t = time2.parse("10:15 pm"); // parse with .parse() method /// alert( t.hours + " " + t.mins + " " + t.ampm + " " + t.hours25) ///</example> var _I = this; this.date = new Date(); this.timeFormat = "hh:mm t"; if (format) this.timeFormat = format; this.parse = function (time) { /// <summary> /// Parses time value from a Date object, or string in format of: /// 12:12pm or 23:01 /// </summary> /// <param name="time" type="any"> /// A time value as a string (12:15pm or 23:01), a Date object /// or time value. /// /// </param> if (!time) return null; // Date if (time.getDate) { var t = {}; var d = time; t.hours24 = d.getHours(); t.mins = d.getMinutes(); t.ampm = "am"; if (t.hours24 > 11) { t.ampm = "pm"; if (t.hours24 > 12) t.hours = t.hours24 - 12; } time = t; } if (typeof (time) == "string") { var parts = time.split(":"); if (parts < 2) return null; var time = {}; time.hours = parts[0] * 1; time.hours24 = time.hours; time.mins = parts[1].toLowerCase(); if (time.mins.indexOf("am") > -1) { time.ampm = "am"; time.mins = time.mins.replace("am", ""); if (time.hours == 12) time.hours24 = 0; } else if (time.mins.indexOf("pm") > -1) { time.ampm = "pm"; time.mins = time.mins.replace("pm", ""); if (time.hours < 12) time.hours24 = time.hours + 12; } time.mins = time.mins * 1; } _I.date.setMinutes(time.mins); _I.date.setHours(time.hours24); return time; }; this.addMinutes = function (mins) { /// <summary> /// adds minutes to the internally stored time value. /// </summary> /// <param name="mins" type="number"> /// number of minutes to add to the date /// </param> _I.date.setMinutes(_I.date.getMinutes() + mins); } this.addHours = function (hours) { /// <summary> /// adds hours the internally stored time value. /// </summary> /// <param name="hours" type="number"> /// number of hours to add to the date /// </param> _I.date.setHours(_I.date.getHours() + hours); } this.getTime = function () { /// <summary> /// returns a time structure from the currently /// stored time value. /// Properties: hours, hours24, mins, ampm /// </summary> return new Time(new Date()); h } this.toString = function (format) { /// <summary> /// returns a short time string for the internal date /// formats: 12:12 pm or 23:12 /// </summary> /// <param name="format" type="string"> /// optional format string for date /// HH:mm, hh:mm t /// </param> if (!format) format = _I.timeFormat; var hours = _I.date.getHours(); if (format.indexOf("t") > -1) { if (hours > 11) format = format.replace("t", "pm") else format = format.replace("t", "am") } if (format.indexOf("HH") > -1) format = format.replace("HH", hours.toString().padL(2, "0")); if (format.indexOf("hh") > -1) { if (hours > 12) hours -= 12; if (hours == 0) hours = 12; format = format.replace("hh", hours.toString().padL(2, "0")); } if (format.indexOf("mm") > -1) format = format.replace("mm", _I.date.getMinutes().toString().padL(2, "0")); return format; } // construction if (time) this.time = this.parse(time); } String.prototype.padL = function (width, pad) { if (!width || width < 1) return this; if (!pad) pad = " "; var length = width - this.length if (length < 1) return this.substr(0, width); return (String.repeat(pad, length) + this).substr(0, width); } String.repeat = function (chr, count) { var str = ""; for (var x = 0; x < count; x++) { str += chr }; return str; } })(jQuery); The plugin consists of the actual plugin and the Time class which handles parsing and formatting of the time value via the .parse() and .toString() methods. Code like this always ends up taking up more effort than the actual logic unfortunately. There are libraries out there that can handle this like datejs or even ww.jquery.js (which is what I use) but to keep the code self contained for this post the plugin doesn't rely on external code. There's one optional exception: The code as is has one dependency on ww.jquery.js  for the tooltip plugin that provides the small popup for all the hotkeys available. You can replace that code with some other mechanism to display hotkeys or simply remove it since that behavior is optional. While we're at it: A jQuery dateKeys plugIn Although date entry tends to be much better served with drop down calendars to pick dates from, often it's also easier to pick dates using a few simple hotkeys. Navigation that uses + - for days and M and H for MontH navigation, Y and R for YeaR navigation are a quick way to enter dates without having to resort to using a mouse and clicking around to what you want to find. Note that this plugin does have a dependency on ww.jquery.js for the date formatting functionality.$.fn.dateKeys = function (options) { /// <summary> /// Attaches a set of hotkeys to date 'fields' /// + Add day - subtract day /// M Subtract Month H Add montH /// Y Subtract Year R Add yeaR /// ? Show keys /// </summary> /// <param name="options" type="object"> /// Options: /// dateFormat: "MM/dd/yyyy" by default "MMM dd, yyyy /// callback: callback handler after date assignment /// </param> /// <example> /// var proxy = new ServiceProxy("JsonStockService.svc/"); /// proxy.invoke("GetStockQuote",{symbol:"msft"},function(quote) { alert(result.LastPrice); },onPageError); ///</example> if (this.length < 1) return this; var opt = { dateFormat: "MM/dd/yyyy", callback: null }; $.extend(opt, options); return this.keydown(function (e) { var $el = $(this); var d = new Date($el.val()); if (!d) d = new Date(1900, 0, 1, 1, 1); var month = d.getMonth(); var year = d.getFullYear(); var day = d.getDate(); switch (e.keyCode) { case 84: // [T]oday d = new Date(); break; case 109: case 189: d = new Date(year, month, day - 1); break; case 107: case 187: d = new Date(year, month, day + 1); break; case 77: //M d = new Date(year, month - 1, day); break; case 72: //H d = new Date(year, month + 1, day); break; case 191: // ? if (e.shiftKey) $el.tooltip("<b>T</b> Today<br/><b>+</b> add day<br /><b>-</b> subtract day<br /><b>M</b> subtract Month<br /><b>H</b> add montH<br/><b>Y</b> subtract Year<br/><b>R</b> add yeaR", 5000, { isHtml: true }); return false; default: return true; } $el.val(d.formatDate(opt.dateFormat)); if (opt.callback) // call async setTimeout(function () { opt.callback.call($el.get(0),d); }, 10); return false; }); } The logic for this plugin is similar to the timeKeys plugin, but it's a little simpler as it tries to directly parse the date value from a string via new Date(inputString). As mentioned it also uses a helper function from ww.jquery.js to format dates which removes the logic to perform date formatting manually which again reduces the size of the code. And the Key is… I've been using both of these plugins in combination with the jQuery UI datepicker for datetime values and I've found that I rarely actually pop up the date picker any more. It's just so much more efficient to use the hotkeys to navigate dates. It's still nice to have the picker around though - it provides the expected behavior for date entry. For time values however I can't justify the UI overhead of a picker that doesn't make it any easier to pick a time. Most people know how to type in a time value and if they want shortcuts keystrokes easily beat out any pop up UI. Hopefully you'll find this as useful as I have found it for my code. Resources Online Sample Download Sample Project © Rick Strahl, West Wind Technologies, 2005-2011Posted in jQuery  HTML   Tweet (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Fan not working on thinkpad L430, laptop overheating

    - by Dirk B.
    I'm having problems controlling the fan of my Lenovo Thinkpad L430. The fan doesn't start. Without any fan control installed the fan just doesn't run. If I run stress, it does run a little, but it's nowhere near the speed it should be. After a while, the laptop just overheats and stops. I Tried to install tp-fancontrol, and enabled thinkpad_acpi fancontrol=1, but to no avail. If I try to set the fan speed manually, it doesn't start up. In windows, there's a program called TPFanControl. It turns out that this laptop uses a different scheme to control the fan than other thinkpads. The level runs from 0 to 255, and max = 0 and min=255. Now I'm looking for a fan control program that works for linux. Does anyone know if it actually exists? Anyone with any experience on fan control on a L430? Update: sudo pwmconfig gives the following output: # pwmconfig revision 5857 (2010-08-22) This program will search your sensors for pulse width modulation (pwm) controls, and test each one to see if it controls a fan on your motherboard. Note that many motherboards do not have pwm circuitry installed, even if your sensor chip supports pwm. We will attempt to briefly stop each fan using the pwm controls. The program will attempt to restore each fan to full speed after testing. However, it is ** very important ** that you physically verify that the fans have been to full speed after the program has completed. Found the following devices: hwmon0 is acpitz hwmon1/device is coretemp hwmon2/device is thinkpad Found the following PWM controls: hwmon2/device/pwm1 hwmon2/device/pwm1 is currently setup for automatic speed control. In general, automatic mode is preferred over manual mode, as it is more efficient and it reacts faster. Are you sure that you want to setup this output for manual control? (n) y Giving the fans some time to reach full speed... Found the following fan sensors: hwmon2/device/fan1_input current speed: 0 ... skipping! There are no working fan sensors, all readings are 0. Make sure you have a 3-wire fan connected. You may also need to increase the fan divisors. See doc/fan-divisors for more information. regards, Dirk

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  • Print SSRS Report / PDF automatically from SQL Server agent or Windows Service

    - by Jeremy Ramos
    Originally posted on: http://geekswithblogs.net/JeremyRamos/archive/2013/10/22/print-ssrs-report--pdf-from-sql-server-agent-or.aspxI have turned the Web upside-down to find a solution to this considering the least components and least maintenance as possible to achieve automated printing of an SSRS report. This is for the reason that we do not have a full software development team to maintain an app and we have to minimize the support overhead for the support team.Here is my setup:SQL Server 2008 R2 in Windows Server 2008 R2PDF format reports generated by SSRS Reports subscriptions to a Windows File ShareNetwork printerColoured reports with logo and brandingI have found and tested the following solutions to no avail:ProsConsCalling Adobe Acrobat Reader exe: "C:\Program Files (x86)\Adobe\Reader 11.0\Reader\acroRd32.exe" /n /s /o /h /t "C:\temp\print.pdf" \\printserver\printername"Very simple optionAdobe Acrobat reader requires to launch the GUI to send a job to a printer. Hence, this option cannot be used when printing from a service.Calling Adobe Acrobat Reader exe as a process from a .NET console appA bit harder than above, but still a simple solutionSame as cons abovePowershell script(Start-Process -FilePath "C:\temp\print.pdf" -Verb Print)Very simple optionUses default PDF client in quiet mode to Print, but also requires an active session.    Foxit ReaderVery simple optionRequires GUI same as Adobe Acrobat Reader Using the Reporting Services Web service to run and stream the report to an image object and then passed to the printerQuite complexThis is what we're trying to avoid  After pulling my hair out for two days, testing and evaluating the above solutions, I ended up learning more about printers (more than ever in my entire life) and how printer drivers work with PostScripts. I then bumped on to a PostScript interpreter called GhostScript (http://www.ghostscript.com/) and then the solution starts to get clearer and clearer.I managed to achieve a solution (maybe not be the simplest but efficient enough to achieve the least-maintenance-least-components goal) in 3-simple steps:Install GhostScript (http://www.ghostscript.com/download/) - this is an open-source PostScript and PDF interpreter. Printing directly using GhostScript only produces grayscale prints using the laserjet generic driver unless you save as BMP image and then interpret the colours using the imageInstall GSView (http://pages.cs.wisc.edu/~ghost/gsview/)- this is a GhostScript add-on to make it easier to directly print to a Windows printer. GSPrint automates the above  PDF -> BMP -> Printer Driver.Run the GSPrint command from SQL Server agent or Windows Service:"C:\Program Files\Ghostgum\gsview\gsprint.exe" -color -landscape -all -printer "printername" "C:\temp\print.pdf"Command line options are here: http://pages.cs.wisc.edu/~ghost/gsview/gsprint.htmAnother lesson learned is, since you are calling the script from the Service Account, it will not necessarily have the Printer mapped in its Windows profile (if it even has one). The workaround to this is by adding a local printer as you normally would and then map this printer to the network printer. Note that you may need to install the Printer Driver locally in the server.So, that's it! There are many ways to achieve a solution. The key thing is how you provide the smartest solution!

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  • Don&rsquo;t Forget! In-Memory Databases are Hot

    - by andrewbrust
    If you’re left scratching your head over SAP’s intention to acquire Sybase for almost $6 million, you’re not alone.  Despite Sybase’s 1990s reign as the supreme database standard in certain sectors (including Wall Street), the company’s flagship product has certainly fallen from grace.  Why would SAP pay a greater than 50% premium over Sybase’s closing price on the day of the announcement just to acquire a relational database which is firmly stuck in maintenance mode? Well there’s more to Sybase than the relational database product.  Take, for example, its mobile application platform.  It hit Gartner’s “Leaders’ Quadrant” in January of last year, and SAP needs a good mobile play.  Beyond the platform itself, Sybase has a slew of mobile services; click this link to look them over. There’s a second major asset that Sybase has though, and I wonder if it figured prominently into SAP’s bid: Sybase IQ.  Sybase IQ is a columnar database.  Columnar databases place values from a given database column contiguously, unlike conventional relational databases, which store all of a row’s data in close proximity.  Storing column values together works well in aggregation reporting scenarios, because the figures to be aggregated can be scanned in one efficient step.  It also makes for high rates of compression because values from a single column tend to be close to each other in magnitude and may contain long sequences of repeating values.  Highly compressible databases use much less disk storage and can be largely or wholly loaded into memory, resulting in lighting fast query performance.  For an ERP company like SAP, with its own legacy BI platform (SAP BW) and the entire range of Business Objects and Crystal Reports BI products (which it acquired in 2007) query performance is extremely important. And it’s a competitive necessity too.  QlikTech has built an entire company on a columnar, in-memory BI product (QlikView).  So too has startup company Vertica.  IBM’s TM1 product has been doing in-memory OLAP for years.  And guess who else has the in-memory religion?  Microsoft does, in the form of its new PowerPivot product.  I expect the technology in PowerPivot to become strategic to the full-blown SQL Server Analysis Services product and the entire Microsoft BI stack.  I sure don’t blame SAP for jumping on the in-memory bandwagon, if indeed the Sybase acquisition is, at least in part, motivated by that. It will be interesting to watch and see what SAP does with Sybase’s product line-up (assuming the acquisition closes), including the core database, the mobile platform, IQ, and even tools like PowerBuilder.  It is also fascinating to watch columnar’s encroachment on relational.  Perhaps this acquisition will be columnar’s tipping point and people will no longer see it as a fad.  Are you listening Larry Ellison?

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  • Lenovo Thinkpad L430 overheating due to fan problems

    - by Dirk B.
    This is the same question as Fan not working on thinkpad L430, laptop overheating, but that question has been marked as a duplicate, which it is not, and I cannot reopen it. I'm having problems controlling the fan of my Lenovo Thinkpad L430. The fan doesn't start. Without any fan control installed the fan just doesn't run. If I run stress, it does run a little, but it's nowhere near the speed it should be. After a while, the laptop just overheats and stops. I Tried to install tp-fancontrol, and enabled thinkpad_acpi fancontrol=1, but to no avail. If I try to set the fan speed manually, it doesn't start up. In windows, there's a program called TPFanControl. It turns out that this laptop uses a different scheme to control the fan than other thinkpads. The level runs from 0 to 255, and max = 0 and min=255. Now I'm looking for a fan control program that works for linux. Does anyone know if it actually exists? Anyone with any experience on fan control on a L430? Update: sudo pwmconfig gives the following output: # pwmconfig revision 5857 (2010-08-22) This program will search your sensors for pulse width modulation (pwm) controls, and test each one to see if it controls a fan on your motherboard. Note that many motherboards do not have pwm circuitry installed, even if your sensor chip supports pwm. We will attempt to briefly stop each fan using the pwm controls. The program will attempt to restore each fan to full speed after testing. However, it is ** very important ** that you physically verify that the fans have been to full speed after the program has completed. Found the following devices: hwmon0 is acpitz hwmon1/device is coretemp hwmon2/device is thinkpad Found the following PWM controls: hwmon2/device/pwm1 hwmon2/device/pwm1 is currently setup for automatic speed control. In general, automatic mode is preferred over manual mode, as it is more efficient and it reacts faster. Are you sure that you want to setup this output for manual control? (n) y Giving the fans some time to reach full speed... Found the following fan sensors: hwmon2/device/fan1_input current speed: 0 ... skipping! There are no working fan sensors, all readings are 0. Make sure you have a 3-wire fan connected. You may also need to increase the fan divisors. See doc/fan-divisors for more information. update: If you need it, lspci is available here

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  • ASP.NET Frameworks and Raw Throughput Performance

    - by Rick Strahl
    A few days ago I had a curious thought: With all these different technologies that the ASP.NET stack has to offer, what's the most efficient technology overall to return data for a server request? When I started this it was mere curiosity rather than a real practical need or result. Different tools are used for different problems and so performance differences are to be expected. But still I was curious to see how the various technologies performed relative to each just for raw throughput of the request getting to the endpoint and back out to the client with as little processing in the actual endpoint logic as possible (aka Hello World!). I want to clarify that this is merely an informal test for my own curiosity and I'm sharing the results and process here because I thought it was interesting. It's been a long while since I've done any sort of perf testing on ASP.NET, mainly because I've not had extremely heavy load requirements and because overall ASP.NET performs very well even for fairly high loads so that often it's not that critical to test load performance. This post is not meant to make a point  or even come to a conclusion which tech is better, but just to act as a reference to help understand some of the differences in perf and give a starting point to play around with this yourself. I've included the code for this simple project, so you can play with it and maybe add a few additional tests for different things if you like. Source Code on GitHub I looked at this data for these technologies: ASP.NET Web API ASP.NET MVC WebForms ASP.NET WebPages ASMX AJAX Services  (couldn't get AJAX/JSON to run on IIS8 ) WCF Rest Raw ASP.NET HttpHandlers It's quite a mixed bag, of course and the technologies target different types of development. What started out as mere curiosity turned into a bit of a head scratcher as the results were sometimes surprising. What I describe here is more to satisfy my curiosity more than anything and I thought it interesting enough to discuss on the blog :-) First test: Raw Throughput The first thing I did is test raw throughput for the various technologies. This is the least practical test of course since you're unlikely to ever create the equivalent of a 'Hello World' request in a real life application. The idea here is to measure how much time a 'NOP' request takes to return data to the client. So for this request I create the simplest Hello World request that I could come up for each tech. Http Handler The first is the lowest level approach which is an HTTP handler. public class Handler : IHttpHandler { public void ProcessRequest(HttpContext context) { context.Response.ContentType = "text/plain"; context.Response.Write("Hello World. Time is: " + DateTime.Now.ToString()); } public bool IsReusable { get { return true; } } } WebForms Next I added a couple of ASPX pages - one using CodeBehind and one using only a markup page. The CodeBehind page simple does this in CodeBehind without any markup in the ASPX page: public partial class HelloWorld_CodeBehind : System.Web.UI.Page { protected void Page_Load(object sender, EventArgs e) { Response.Write("Hello World. Time is: " + DateTime.Now.ToString() ); Response.End(); } } while the Markup page only contains some static output via an expression:<%@ Page Language="C#" AutoEventWireup="false" CodeBehind="HelloWorld_Markup.aspx.cs" Inherits="AspNetFrameworksPerformance.HelloWorld_Markup" %> Hello World. Time is <%= DateTime.Now %> ASP.NET WebPages WebPages is the freestanding Razor implementation of ASP.NET. Here's the simple HelloWorld.cshtml page:Hello World @DateTime.Now WCF REST WCF REST was the token REST implementation for ASP.NET before WebAPI and the inbetween step from ASP.NET AJAX. I'd like to forget that this technology was ever considered for production use, but I'll include it here. Here's an OperationContract class: [ServiceContract(Namespace = "")] [AspNetCompatibilityRequirements(RequirementsMode = AspNetCompatibilityRequirementsMode.Allowed)] public class WcfService { [OperationContract] [WebGet] public Stream HelloWorld() { var data = Encoding.Unicode.GetBytes("Hello World" + DateTime.Now.ToString()); var ms = new MemoryStream(data); // Add your operation implementation here return ms; } } WCF REST can return arbitrary results by returning a Stream object and a content type. The code above turns the string result into a stream and returns that back to the client. ASP.NET AJAX (ASMX Services) I also wanted to test ASP.NET AJAX services because prior to WebAPI this is probably still the most widely used AJAX technology for the ASP.NET stack today. Unfortunately I was completely unable to get this running on my Windows 8 machine. Visual Studio 2012  removed adding of ASP.NET AJAX services, and when I tried to manually add the service and configure the script handler references it simply did not work - I always got a SOAP response for GET and POST operations. No matter what I tried I always ended up getting XML results even when explicitly adding the ScriptHandler. So, I didn't test this (but the code is there - you might be able to test this on a Windows 7 box). ASP.NET MVC Next up is probably the most popular ASP.NET technology at the moment: MVC. Here's the small controller: public class MvcPerformanceController : Controller { public ActionResult Index() { return View(); } public ActionResult HelloWorldCode() { return new ContentResult() { Content = "Hello World. Time is: " + DateTime.Now.ToString() }; } } ASP.NET WebAPI Next up is WebAPI which looks kind of similar to MVC. Except here I have to use a StringContent result to return the response: public class WebApiPerformanceController : ApiController { [HttpGet] public HttpResponseMessage HelloWorldCode() { return new HttpResponseMessage() { Content = new StringContent("Hello World. Time is: " + DateTime.Now.ToString(), Encoding.UTF8, "text/plain") }; } } Testing Take a minute to think about each of the technologies… and take a guess which you think is most efficient in raw throughput. The fastest should be pretty obvious, but the others - maybe not so much. The testing I did is pretty informal since it was mainly to satisfy my curiosity - here's how I did this: I used Apache Bench (ab.exe) from a full Apache HTTP installation to run and log the test results of hitting the server. ab.exe is a small executable that lets you hit a URL repeatedly and provides counter information about the number of requests, requests per second etc. ab.exe and the batch file are located in the \LoadTests folder of the project. An ab.exe command line  looks like this: ab.exe -n100000 -c20 http://localhost/aspnetperf/api/HelloWorld which hits the specified URL 100,000 times with a load factor of 20 concurrent requests. This results in output like this:   It's a great way to get a quick and dirty performance summary. Run it a few times to make sure there's not a large amount of varience. You might also want to do an IISRESET to clear the Web Server. Just make sure you do a short test run to warm up the server first - otherwise your first run is likely to be skewed downwards. ab.exe also allows you to specify headers and provide POST data and many other things if you want to get a little more fancy. Here all tests are GET requests to keep it simple. I ran each test: 100,000 iterations Load factor of 20 concurrent connections IISReset before starting A short warm up run for API and MVC to make sure startup cost is mitigated Here is the batch file I used for the test: IISRESET REM make sure you add REM C:\Program Files (x86)\Apache Software Foundation\Apache2.2\bin REM to your path so ab.exe can be found REM Warm up ab.exe -n100 -c20 http://localhost/aspnetperf/MvcPerformance/HelloWorldJsonab.exe -n100 -c20 http://localhost/aspnetperf/api/HelloWorldJson ab.exe -n100 -c20 http://localhost/AspNetPerf/WcfService.svc/HelloWorld ab.exe -n100000 -c20 http://localhost/aspnetperf/handler.ashx > handler.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/HelloWorld_CodeBehind.aspx > AspxCodeBehind.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/HelloWorld_Markup.aspx > AspxMarkup.txt ab.exe -n100000 -c20 http://localhost/AspNetPerf/WcfService.svc/HelloWorld > Wcf.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/MvcPerformance/HelloWorldCode > Mvc.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/api/HelloWorld > WebApi.txt I ran each of these tests 3 times and took the average score for Requests/second, with the machine otherwise idle. I did see a bit of variance when running many tests but the values used here are the medians. Part of this has to do with the fact I ran the tests on my local machine - result would probably more consistent running the load test on a separate machine hitting across the network. I ran these tests locally on my laptop which is a Dell XPS with quad core Sandibridge I7-2720QM @ 2.20ghz and a fast SSD drive on Windows 8. CPU load during tests ran to about 70% max across all 4 cores (IOW, it wasn't overloading the machine). Ideally you can try running these tests on a separate machine hitting the local machine. If I remember correctly IIS 7 and 8 on client OSs don't throttle so the performance here should be Results Ok, let's cut straight to the chase. Below are the results from the tests… It's not surprising that the handler was fastest. But it was a bit surprising to me that the next fastest was WebForms and especially Web Forms with markup over a CodeBehind page. WebPages also fared fairly well. MVC and WebAPI are a little slower and the slowest by far is WCF REST (which again I find surprising). As mentioned at the start the raw throughput tests are not overly practical as they don't test scripting performance for the HTML generation engines or serialization performances of the data engines. All it really does is give you an idea of the raw throughput for the technology from time of request to reaching the endpoint and returning minimal text data back to the client which indicates full round trip performance. But it's still interesting to see that Web Forms performs better in throughput than either MVC, WebAPI or WebPages. It'd be interesting to try this with a few pages that actually have some parsing logic on it, but that's beyond the scope of this throughput test. But what's also amazing about this test is the sheer amount of traffic that a laptop computer is handling. Even the slowest tech managed 5700 requests a second, which is one hell of a lot of requests if you extrapolate that out over a 24 hour period. Remember these are not static pages, but dynamic requests that are being served. Another test - JSON Data Service Results The second test I used a JSON result from several of the technologies. I didn't bother running WebForms and WebPages through this test since that doesn't make a ton of sense to return data from the them (OTOH, returning text from the APIs didn't make a ton of sense either :-) In these tests I have a small Person class that gets serialized and then returned to the client. The Person class looks like this: public class Person { public Person() { Id = 10; Name = "Rick"; Entered = DateTime.Now; } public int Id { get; set; } public string Name { get; set; } public DateTime Entered { get; set; } } Here are the updated handler classes that use Person: Handler public class Handler : IHttpHandler { public void ProcessRequest(HttpContext context) { var action = context.Request.QueryString["action"]; if (action == "json") JsonRequest(context); else TextRequest(context); } public void TextRequest(HttpContext context) { context.Response.ContentType = "text/plain"; context.Response.Write("Hello World. Time is: " + DateTime.Now.ToString()); } public void JsonRequest(HttpContext context) { var json = JsonConvert.SerializeObject(new Person(), Formatting.None); context.Response.ContentType = "application/json"; context.Response.Write(json); } public bool IsReusable { get { return true; } } } This code adds a little logic to check for a action query string and route the request to an optional JSON result method. To generate JSON, I'm using the same JSON.NET serializer (JsonConvert.SerializeObject) used in Web API to create the JSON response. WCF REST   [ServiceContract(Namespace = "")] [AspNetCompatibilityRequirements(RequirementsMode = AspNetCompatibilityRequirementsMode.Allowed)] public class WcfService { [OperationContract] [WebGet] public Stream HelloWorld() { var data = Encoding.Unicode.GetBytes("Hello World " + DateTime.Now.ToString()); var ms = new MemoryStream(data); // Add your operation implementation here return ms; } [OperationContract] [WebGet(ResponseFormat=WebMessageFormat.Json,BodyStyle=WebMessageBodyStyle.WrappedRequest)] public Person HelloWorldJson() { // Add your operation implementation here return new Person(); } } For WCF REST all I have to do is add a method with the Person result type.   ASP.NET MVC public class MvcPerformanceController : Controller { // // GET: /MvcPerformance/ public ActionResult Index() { return View(); } public ActionResult HelloWorldCode() { return new ContentResult() { Content = "Hello World. Time is: " + DateTime.Now.ToString() }; } public JsonResult HelloWorldJson() { return Json(new Person(), JsonRequestBehavior.AllowGet); } } For MVC all I have to do for a JSON response is return a JSON result. ASP.NET internally uses JavaScriptSerializer. ASP.NET WebAPI public class WebApiPerformanceController : ApiController { [HttpGet] public HttpResponseMessage HelloWorldCode() { return new HttpResponseMessage() { Content = new StringContent("Hello World. Time is: " + DateTime.Now.ToString(), Encoding.UTF8, "text/plain") }; } [HttpGet] public Person HelloWorldJson() { return new Person(); } [HttpGet] public HttpResponseMessage HelloWorldJson2() { var response = new HttpResponseMessage(HttpStatusCode.OK); response.Content = new ObjectContent<Person>(new Person(), GlobalConfiguration.Configuration.Formatters.JsonFormatter); return response; } } Testing and Results To run these data requests I used the following ab.exe commands:REM JSON RESPONSES ab.exe -n100000 -c20 http://localhost/aspnetperf/Handler.ashx?action=json > HandlerJson.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/MvcPerformance/HelloWorldJson > MvcJson.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/api/HelloWorldJson > WebApiJson.txt ab.exe -n100000 -c20 http://localhost/AspNetPerf/WcfService.svc/HelloWorldJson > WcfJson.txt The results from this test run are a bit interesting in that the WebAPI test improved performance significantly over returning plain string content. Here are the results:   The performance for each technology drops a little bit except for WebAPI which is up quite a bit! From this test it appears that WebAPI is actually significantly better performing returning a JSON response, rather than a plain string response. Snag with Apache Benchmark and 'Length Failures' I ran into a little snag with Apache Benchmark, which was reporting failures for my Web API requests when serializing. As the graph shows performance improved significantly from with JSON results from 5580 to 6530 or so which is a 15% improvement (while all others slowed down by 3-8%). However, I was skeptical at first because the WebAPI test reports showed a bunch of errors on about 10% of the requests. Check out this report: Notice the Failed Request count. What the hey? Is WebAPI failing on roughly 10% of requests when sending JSON? Turns out: No it's not! But it took some sleuthing to figure out why it reports these failures. At first I thought that Web API was failing, and so to make sure I re-ran the test with Fiddler attached and runiisning the ab.exe test by using the -X switch: ab.exe -n100 -c10 -X localhost:8888 http://localhost/aspnetperf/api/HelloWorldJson which showed that indeed all requests where returning proper HTTP 200 results with full content. However ab.exe was reporting the errors. After some closer inspection it turned out that the dates varying in size altered the response length in dynamic output. For example: these two results: {"Id":10,"Name":"Rick","Entered":"2012-09-04T10:57:24.841926-10:00"} {"Id":10,"Name":"Rick","Entered":"2012-09-04T10:57:24.8519262-10:00"} are different in length for the number which results in 68 and 69 bytes respectively. The same URL produces different result lengths which is what ab.exe reports. I didn't notice at first bit the same is happening when running the ASHX handler with JSON.NET result since it uses the same serializer that varies the milliseconds. Moral: You can typically ignore Length failures in Apache Benchmark and when in doubt check the actual output with Fiddler. Note that the other failure values are accurate though. Another interesting Side Note: Perf drops over Time As I was running these tests repeatedly I was finding that performance steadily dropped from a startup peak to a 10-15% lower stable level. IOW, with Web API I'd start out with around 6500 req/sec and in subsequent runs it keeps dropping until it would stabalize somewhere around 5900 req/sec occasionally jumping lower. For these tests this is why I did the IIS RESET and warm up for individual tests. This is a little puzzling. Looking at Process Monitor while the test are running memory very quickly levels out as do handles and threads, on the first test run. Subsequent runs everything stays stable, but the performance starts going downwards. This applies to all the technologies - Handlers, Web Forms, MVC, Web API - curious to see if others test this and see similar results. Doing an IISRESET then resets everything and performance starts off at peak again… Summary As I stated at the outset, these were informal to satiate my curiosity not to prove that any technology is better or even faster than another. While there clearly are differences in performance the differences (other than WCF REST which was by far the slowest and the raw handler which was by far the highest) are relatively minor, so there is no need to feel that any one technology is a runaway standout in raw performance. Choosing a technology is about more than pure performance but also about the adequateness for the job and the easy of implementation. The strengths of each technology will make for any minor performance difference we see in these tests. However, to me it's important to get an occasional reality check and compare where new technologies are heading. Often times old stuff that's been optimized and designed for a time of less horse power can utterly blow the doors off newer tech and simple checks like this let you compare. Luckily we're seeing that much of the new stuff performs well even in V1.0 which is great. To me it was very interesting to see Web API perform relatively badly with plain string content, which originally led me to think that Web API might not be properly optimized just yet. For those that caught my Tweets late last week regarding WebAPI's slow responses was with String content which is in fact considerably slower. Luckily where it counts with serialized JSON and XML WebAPI actually performs better. But I do wonder what would make generic string content slower than serialized code? This stresses another point: Don't take a single test as the final gospel and don't extrapolate out from a single set of tests. Certainly Twitter can make you feel like a fool when you post something immediate that hasn't been fleshed out a little more <blush>. Egg on my face. As a result I ended up screwing around with this for a few hours today to compare different scenarios. Well worth the time… I hope you found this useful, if not for the results, maybe for the process of quickly testing a few requests for performance and charting out a comparison. Now onwards with more serious stuff… Resources Source Code on GitHub Apache HTTP Server Project (ab.exe is part of the binary distribution)© Rick Strahl, West Wind Technologies, 2005-2012Posted in ASP.NET  Web Api   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Large File Upload in SharePoint 2010

    - by Sahil Malik
    Ad:: SharePoint 2007 Training in .NET 3.5 technologies (more information). Okay this is a big BIG B-I-G problem. And with SP2010 it’s going to be more prominent, because atleast at the server side, SharePoint can support large files much much better than SharePoint 2007 ever did. The issue with very large files being uploaded through any browser based API are - Reliably transferring gigabyte or bigger files without breakages over a protocol like HTTP, which is better suited for tiny transfers like images and text. Not killing your browser because it has to load all that in memory Not killing your web server because All that you upload through HTTP post, first gets streamed into IIS Memory, w3wp.exe memory before the ENTIRE FILE finishes uploading .. before it is stored. Which means, You cannot show an accurate and live progress bar of the upload, IIS gives you no such accurate metric of an upload. All the counters it gives you are approximate. Your w3wp.exe eats up all server memory – 4GB of it, for a 4GB upload. A thread is kept busy for the entire duration of the upload, thereby greatly limiting your web server’s capability to serve newer requests. Kills effective load balancing. Not killing your content database because, As you are uploading a very large file, that large file gets written sequentially into the DB, and therefore for a very large file very severely impacts the database performance. I had put together another video showing RBS usage in SharePoint 2010. I talked about many practical ramifications of using RBS in SharePoint in that video. Note that enabling large file support will never ever be a point and click job, simply because there are too many questions one needs to ask, and too many things one needs to plan for. However, one part that will remain common across all large file upload scenarios, in SharePoint or outside of SharePoint is to do it efficiently while not killing the web server. In this video, I describe using the Telerik Silverlight Upload control with SharePoint 2010 to enable efficient large file uploads in SharePoint. Presenting .. The video Comment on the article ....

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  • JRockit R28/JRockit Mission Control 4.0 is out!

    - by Marcus Hirt
    The next major release of JRockit is finally out! Here are some highlights: Includes the all new JRockit Flight Recorder – supersedes the old JRockit Runtime Analyser. The new flight recorder is inspired by the “black box” in airplanes. It uses a highly efficient recording engine and thread local buffers to capture data about the runtime and the application running in the JVM. It can be configured to always be on, so that whenever anything “interesting” happens, data can be dumped for some time back. Think of it as your own personal profiling time machine. Automatic shortest path calculation in Memleak – no longer any need for running around in circles when trying to find your way back to a thread root from an instance. Memleak can now show class loader related information and split graphs on a per class loader basis. More easily configured JMX agent – default port for both RMI Registry and RMI Server can be configured, and is by default the same, allowing easier configuration of firewalls. Up to 64 GB (was 4GB) compressed references. Per thread allocation profiling in the Management Console. Native Memory Tracking – it is now possible to track native memory allocations with very high resolution. The information can either be accessed using JRCMD, or the dedicated Native Memory Tracking experimental plug-in for the Management Console (alas only available for the upcoming 4.0.1 release). JRockit can now produce heap dumps in HPROF format. Cooperative suspension – JRockit is no longer using system signals for stopping threads, which could lead to hangs if signals were lost or blocked (for example bad NFS shares). Now threads check periodically to see if they are suspended. VPAT/Section 508 compliant JRMC – greatly improved keyboard navigation and screen reader support. See New and Noteworthy for more information. JRockit Mission Control 4.0.0 can be downloaded from here: http://www.oracle.com/technology/software/products/jrockit/index.html <shameless ad> There is even a book to go with JRMC 4.0.0/JRockit R28! http://www.packtpub.com/oracle-jrockit-the-definitive-guide/book/ </shameless ad>

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  • Survey Probes the Project Management Concerns of Financial Services Executives

    - by Melissa Centurio Lopes
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Do you wonder what are the top reasons why large projects in the financial industry fail to meet budgets, schedules, and other key performance criteria? Being able to answer this question can provide important insight and value of good project management practices for your organization. According to 400 senior executives who participated in a new survey conducted by the Economist Intelligence Unit and sponsored by Oracle, unrealistic project goals is the main reason for roadblocks to success Other common stumbling blocks are poor alignment between project and organizational goals, inadequate human resources, lack of strong leadership, and unwillingness among team members to point out problems. This survey sample also had a lot to say about the impact of regulatory compliance on the overall portfolio management process. Thirty-nine percent acknowledged that regulations enabled efficient functioning of their businesses. But a similar number said that regulations often require more financial resources than were originally allocated to bring projects in on time. Regulations were seen by 35 percent of the executives as roadblocks to their ability to invest in the organization’s growth and success. These revelations among others are discussed in depth in a new on-demand Webcast titled “Too Good to Fail: Developing Project Management Expertise in Financial Services” now available from Oracle. The Webcast features Brian Gardner, editor of the Economist Intelligence Unit, who presents these findings from this survey along with Guy Barlow, director of industry strategy for Oracle Primavera. Together, they analyze what the numbers mean for project and program managers and the financial services industry. Register today to watch the on-demand Webcast and get a full rundown and analysis of the survey results. Take the Economist Intelligence Unit benchmarking survey and see how your views compare with those of other financial services industry executives in ensuring project success.  Read more in the October Edition of the quarterly Information InDepth EPPM Newsletter

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  • Part 2: The Customization Lifecycle

    - by volker.eckardt(at)oracle.com
    To understand the challenges when working with Customizations better, please allow me to explain my understanding from the Customization Lifecycle.  The starting point is the functional GAP list. Any GAP can lead to a customization (but not have to). The decision is driven by priority, gain, costs, future functionality, accepted workarounds etc. Let's assume the customization has been accepted as such - including estimation. (Otherwise this blog would not have any value)Now the customization life-cycle starts and could look like this:-    Functional specification-    Technical specification-    Technical development-    Functional setup-    Module Test-    System Test-    Integration Test (if required)-    Acceptance Test-    Production mode-    Usage-    10 x Rework-    10 x Retest -    2 x Upgrade-    2 x Upgrade Test-    Usage-    10 x Rework-    10 x Retest -    1 x Upgrade-    1 x Upgrade Test-    Usage-    Review for Retirement-    Accepted Retirement-    De-installationWhat I like to highlight herewith is that any material and documentation you create upfront or during the first phases will usually be used multiple times, partial or complete, will be enhanced, reviewed, retested. The better the quality right from the beginning is, the better we can perform the next steps.What I see very often is the wish to remove a customization, our customers are upgrading and they like to get at least some of the customizations replaced with standard functionality. To be able to support this process best, the customization documentation should contain at least the following key information: What is/are the business process(es) where this customization is used or linked to?Who was involved in the different customization phases?What are the objects comprising the customization?What is the setup necessary for the customization?What setup comes with the customization, what has to be done via other tools or manually?What are the test steps and test results (in all test areas)?What are linked customizations? What is the customization complexity?How is this customization classified?Which technologies were used?How many days were needed to create/test/upgrade the customization?Etc.If all this is available, a replacement / retirement can be done much more efficient and precise, or an estimation and upgrade itself can be executed with much better support.In the following blog entries I will explain in more detail why we suggest tracking such information, by whom this task shall be done and how.Volker Eckardt

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  • Oracle Financial Analytics for SAP Certified with Oracle Data Integrator EE

    - by denis.gray
    Two days ago Oracle announced the release of Oracle Financial Analytics for SAP.  With the amount of press this has garnered in the past two days, there's a key detail that can't be missed.  This release is certified with Oracle Data Integrator EE - now making the combination of Data Integration and Business Intelligence a force to contend with.  Within the Oracle Press Release there were two important bullets: ·         Oracle Financial Analytics for SAP includes a pre-packaged ABAP code compliant adapter and is certified with Oracle Data Integrator Enterprise Edition to integrate SAP Financial Accounting data directly with the analytic application.  ·         Helping to integrate SAP financial data and disparate third-party data sources is Oracle Data Integrator Enterprise Edition which delivers fast, efficient loading and transformation of timely data into a data warehouse environment through its high-performance Extract Load and Transform (E-LT) technology. This is very exciting news, demonstrating Oracle's overall commitment to Oracle Data Integrator EE.   This is a great way to start off the new year and we look forward to building on this momentum throughout 2011.   The following links contain additional information and media responses about the Oracle Financial Analytics for SAP release. IDG News Service (Also appeared in PC World, Computer World, CIO: "Oracle is moving further into rival SAP's turf with Oracle Financial Analytics for SAP, a new BI (business intelligence) application that can crunch ERP (enterprise resource planning) system financial data for insights." Information Week: "Oracle talks a good game about the appeal of an optimized, all-Oracle stack. But the company also recognizes that we live in a predominantly heterogeneous IT world" CRN: "While some businesses with SAP Financial Accounting already use Oracle BI, those integrations had to be custom developed. The new offering provides pre-built integration capabilities." ECRM Guide:  "Among other features, Oracle Financial Analytics for SAP helps front-line managers improve financial performance and decision-making with what the company says is comprehensive, timely and role-based information on their departments' expenses and revenue contributions."   SAP Getting Started Guide for ODI on OTN: http://www.oracle.com/technetwork/middleware/data-integrator/learnmore/index.html For more information on the ODI and its SAP connectivity please review the Oracle® Fusion Middleware Application Adapters Guide for Oracle Data Integrator11g Release 1 (11.1.1)

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  • Gain Quick Access to the Cache in Firefox

    - by Asian Angel
    Are you looking for a quick and simple way to view the contents of the cache in Firefox? Then you will definitely want to see how easy it can be using the CacheViewer extension. Note: CacheViewer is a front-end app for easily accessing and searching the memory cache. Before Viewing the cache in Firefox using “about:cache” provides some information about the contents but may not be the most efficient method available for some people. CacheViewer in Action Once you have installed the extension there are three easy ways to access your new cache viewer. The first is using the “CacheViewer Command” available in the “Tools Menu” and the second is using the keyboard shortcut “Ctrl + Shift + C”. The third way is by adding a “Toolbar Button” to your browser’s UI. All three work equally well…choose the method that best suits your personal needs. When you access the “CacheViewer Window” this is what it will look like. You may decide to resize it and move (or hide) some of the columns for the best viewing. You can easily scroll through the cache contents and preview images if desired as shown here. If you keep the “CacheViewer Window” open you can refresh it as you browse using the “Refresh Button” in the lower right corner. This is a nice, quick, and very simple way to access the cache on demand and save items to your hard-drive if desired. Note: The “CacheViewer” can also be set to open in a new tab instead (see “Options”). Options Choose whether “CacheViewer” opens in a separate window (default) or in a new tab. Conclusion If you want a quick and simple way to view the cache in Firefox then the CacheViewer extension is just what you have been looking for. Link Download the CacheViewer extension (Mozilla Add-ons) Similar Articles Productive Geek Tips Add a Cache Clearing Button to FirefoxSearch for Install Packages from the Ubuntu Command LineQuick Tip: Empty Internet Explorer 7 Cache when Browser is ClosedView Internet Explorer Cache Files the Easy WayQuick Hits: 11 Firefox Tab How-Tos TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Out of band Security Update for Internet Explorer 7 Cool Looking Screensavers for Windows SyncToy syncs Files and Folders across Computers on a Network (or partitions on the same drive) If it were only this easy Classic Cinema Online offers 100’s of OnDemand Movies OutSync will Sync Photos of your Friends on Facebook and Outlook

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  • Add Global Hotkeys to Windows Media Player

    - by DigitalGeekery
    Do you use Windows Media Player in the background while working in other applications? The WMP Keys plug-in for Media Player adds global keyboard shortcuts that allow you to control Media Player even when it isn’t in focus. Windows Media Player has a slew of keyboard shortcuts that work only when the media player is active, but these shortcuts stop working once WMP is no longer in focus or minimized. WMP Keys add the following default global hotkeys for Windows Media Player 10, 11, and 12. Ctrl+Alt+Home – Play / Pause Ctrl+Alt+Right – Next track Ctrl+Alt+Left – Previous track Ctrl+Alt+Up Arrow Key – Volume Up Ctrl+Alt+Down Arrow Key – Volume Down Ctrl+Alt+F – Fast Forward Ctrl+Alt+B – Fast Backward Ctrl+Alt+[1-5] – Rate 1-5 stars Note: Tapping Ctrl+Alt+F and Ctrl+Alt+B will skip ahead or back in 5 second intervals. Close out of Windows Media Player and then download and install WMP Keys (link below). After you’ve installed WMP Keys, you’ll need to enable it. Select Organize and then Options… In the Options window, select the Plug-ins tab, click Background in the Category window, then check the box for Wmpkeys Plugin. Click OK to save and exit. You can also enable the plug-in by selecting Tools > Plug-ins and clicking Wmpkeys Plugin. You to view and edit the global hotkeys in the WMPKeys settings window. Select Tools > Plug-in properties and click Wmpkeys Plugin. Below you can see all the default WMP Keys shortcuts.   To change any of the shortcuts, select the text box then press the new keyboard shortcut. Click OK when finished. WMP Keys is very simple little plug-in that makes using WMP while you’re multitasking just a little bit easier and more efficient.  Looking for more plugins for Windows Media Player? Check out our previous articles on adding new features with Media Player Plus, and displaying song lyrics with Lyrics Plugin. Download WMP Keys Similar Articles Productive Geek Tips Built-in Quick Launch Hotkeys in Windows VistaFixing When Windows Media Player Library Won’t Let You Add FilesKantaris is a Unique Media Player Based on VLCInstall and Use the VLC Media Player on Ubuntu LinuxAssign Keyboard Media Keys to Work in Winamp TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips HippoRemote Pro 2.2 Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server XPS file format & XPS Viewer Explained Microsoft Office Web Apps Guide Know if Someone Accessed Your Facebook Account Shop for Music with Windows Media Player 12 Access Free Documentaries at BBC Documentaries Rent Cameras In Bulk At CameraRenter

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  • The Legend of the Filtered Index

    - by Johnm
    Once upon a time there was a big and bulky twenty-nine million row table. He tempestuously hoarded data like a maddened shopper amid a clearance sale. Despite his leviathan nature and eager appetite he loved to share his treasures. Multitudes from all around would embark upon an epiphanous journey to sample contents of his mythical purse of knowledge. After a long day of performing countless table scans the table was overcome with fatigue. After a short period of unavailability, he decided that he needed to consider a new way to share his prized possessions in a more efficient manner. Thus, a non-clustered index was born. She dutifully directed the pilgrims that sought the table's data - no longer would those despicable table scans darken the doorsteps of this quaint village. and yet, the table's veracious appetite did not wane. Any bit or byte that wondered near him was consumed with vigor. His columns and rows continued to expand beyond the expectations of even the most liberal estimation. As his rows grew grander they became more difficult to organize and maintain. The once bright and cheerful disposition of the non-clustered index began to dim. The wait time for those who sought the table's treasures began to increase. Some of those who came to nibble upon the banquet of knowledge even timed-out and never realized their aspired enlightenment. After a period of heart-wrenching introspection, the table decided to drop the index and attempt another solution. At the darkest hour of the table's desperation came a grand flash of light. As his eyes regained their vision there stood several creatures who looked very similar to his former, beloved, non-clustered index. They all spoke in unison as they introduced themselves: "Fear not, for we come to organize your data and direct those who seek to partake in it. We are the filtered index." Immediately, the filtered indexes began to scurry about. One took control of the past quarter's data. Another took control of the previous quarter's data. All of the remaining filtered indexes followed suit. As the nearly gluttonous habits of the table scaled forward more filtered indexes appeared. Regardless of the table's size, all of the eagerly awaiting data seekers were delivered data as quickly as a Jimmy John's sandwich. The table was moved to tears. All in the land of data rejoiced and all lived happily ever after, at least until the next data challenge crept from the fearsome cave of the unknown. The End.

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  • The Work Order Printing Challenge

    - by celine.beck
    One of the biggest concerns we've heard from maintenance practitioners is the ability to print and batch print work order details along with its accompanying attachments. Indeed, maintenance workers traditionally rely on work order packets to complete their job. A standard work order packet can include a variety of information like equipment documentation, operating instructions, checklists, end-of-task feedback forms and the likes. Now, the problem is that most Asset Lifecycle Management applications do not provide a simple and efficient solution for process printing with document attachments. Work order forms can be easily printed but attachments are usually left out of the printing process. This sounds like a minor problem, but when you are processing high volume of work orders on a regular basis, this inconvenience can result in important inefficiencies. In order to print work order and its related attachments, maintenance personnel need to print the work order details and then go back to the work order and open each individual attachment using the proper authoring application to view and print each document. The printed output is collated into a work order packet. The AutoVue Document Print Service products that were just released in April 2010 aim at helping organizations address the work order printing challenge. Customers and partners can leverage the AutoVue Document Print Services to build a complete printing solution that complements their existing print server solution with AutoVue's document- and platform-agnostic document print services. The idea is to leverage AutoVue's printing services to invoke printing either programmatically or manually directly from within the work order management application, and efficiently process the printing of complete work order packets, including all types of attachments, from office files to more advanced engineering documents like 2D CAD drawings. Oracle partners like MIPRO Consulting, specialists in PeopleSoft implementations, have already expressed interest in the AutoVue Document Print Service products for their ability to offer print services to the PeopleSoft ALM suite, so that customers are able to print packages of documents for maintenance personnel. For more information on the subject, please consult MIPRO Consulting's article entitled Unsung Value: Primavera and AutoVue Integration into PeopleSoft posted on their blog. The blog post entitled Introducing AutoVue Document Print Service provides additional information on how the solution works. We would also love to hear what your thoughts are on the topic, so please do not hesitate to post your comments/feedback on our blog. Related Articles: Introducing AutoVue Document Print Service Print Any Document Type with AutoVue Document Print Services

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  • Tellago announces SQL Server 2008 R2 BI quick adoption programs

    - by Vishal
    During the last year, we (Tellago) have been involved in various business intelligence initiatives that leverage some emerging BI techniques such as self-service BI or complex event processing (CEP). Specifically, in the last few months, we have partnered with Microsoft to deliver a series of events across the country where we present the different technologies of the SQL Server 2008 R2 BI stack such as PowerPivot, StreamInsight, Ad-Hoc Reporting and Master Data Services. As part of those events, we try to go beyond the traditional technology presentation and provide a series of best practices and lessons we have learned on real world BI projects that leverage these technologies. Now that SQL Server 2008 R2 has been released to manufacturing, we have launched a series of quick adoption programs that are designed to help customers understand how they can embrace the newest additions to Microsoft's BI stack as part of their IT initiatives. The programs are also designed to help customers understand how the new SQL Server features interact with established technologies such as SQL Server Analysis Services or SQL Server Integration Services. We try to keep these adoption programs very practical by doing a lot of prototyping and design sessions that will give our customers a practical glimpse of the capabilities of the technologies and how they can fit in their enterprise architecture roadmap. Here is our official announcement (you can blame my business partner, BI enthusiast, and Tellago's CEO Elizabeth Redding for the marketing pitch ;)): Tellago Marks Microsoft's SQL Server 2008 R2 Launch With Business Intelligence Quick Adoption Program Microsoft launched SQL Server 2008 R2 last week, which delivers several breakthrough business intelligence (BI) capabilities that enable organizations to:  Efficiently process, analyze and mine data Improve IT and developer efficiency Enable highly scalable and well-managed Business Intelligence on a self-service basis for business users The release offers a new feature called PowerPivot, which enables self service BI through connecting business users directly to enterprise data sources and providing improved reporting and analytics. The release also offers Master Data Management which helps enterprises centrally manage critical data assets company-wide and across diverse systems, enabling increased integrity of information over time. Finally, the release includes StreamInsight, which is a framework for implementing Complex Event Processing (CEP) applications on the Microsoft platform. With StreamInsight, IT organizations can implement the infrastructure to process a large volume of events near real time, execute continuous queries against event streams and enable real time business intelligence. As a thought leader in the Business Intelligence community, Tellago has recognized the occasion by launching a series of quick adoption programs to enable the adoption of this new BI technology stack in your enterprise. Our Quick Adoption programs are designed to help you: Brainstorm BI solution options  Architect initial infrastructure components Prototype key features of a solution As a 2-3 day program, our approach is more efficient and cost effective than a traditional Proof of Concept because it allows you to understand the new SQL Server 2008 R2 feature set  while seeing directly how you can leverage it for your business intelligence needs. If you are interested in learning more about the BI capabilities of Microsoft's Business Intelligence stack, including SQL Server 2008 R2, we can help.  As industry experts and software content advisers to Microsoft, Tellago is the place where ideas meet technology expertise.  Let us help you see for yourself the advantages that you can gain from Microsoft's  SQL Server 2008 R2. Email or call for more information - [email protected] or 847-925-2399.

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  • BAM design pointers

    - by Kavitha Srinivasan
    In working recently with a large Oracle customer on SOA and BAM, I discovered that some BAM best practices are not quite well known as I had always assumed ! There is a doc bug out to formally incorporate those learnings but here are a few notes..  EMS-DO parity When using EMS (Enterprise Message Source) as a BAM feed, the best practice is to use one EMS to write to one Data Object. There is a possibility of collisions and duplicates when multiple EMS write to the same row of a DO at the same time. This customer had 17 EMS writing to one DO at the same time. Every sensor in their BPEL process writes to one topic but the Topic was read by 1 EMS corresponding to one sensor. They then used XSL within BAM to transform the payload into the BAM DO format. And hence for a given BPEL instance, 17 sensors fired, populated 1 JMS topic, was consumed by 17 EMS which in turn wrote to 1 DataObject.(You can image what would happen for later versions of the application that needs to send more information to BAM !).  We modified their design to use one Master XSL based on sensorname for all sensors relating to a DO- say Data Object 'Orders' and were able to thus reduce the 17 EMS to 1 with a master XSL. For those of you wondering about how squeaky clean this design is, you are right ! This is indeed not squeaky clean and that brings us to yet another 'inferred' best practice. (I try very hard not to state the obvious in my blogs with the hope that everytime I blog, it is very useful but this one is an exception.) Transformations and Calculations It is optimal to do transformations within an engine like BPEL. Not only does this provide modelling ease with a nice GUI XSL mapper in JDeveloper, the XSL engine in BPEL is quite efficient at runtime as well. And so, doing XSL transformations in BAM is not quite prudent.  The same is true for any non-trivial calculations as well. It is best to do all transformations,calcuations and sanitize the data in a BPEL or like layer and then send this to BAM (via JMS, WS etc.) This then delegates simply the function of report rendering and mechanics of real-time reporting to the Oracle BAM reporting tool which it is most suited to do. All nulls are not created equal Here is yet another possibly known fact but reiterated here. For an EMS with an Upsert operation: a) If Empty tags or tags with no value are sent like <Tag1/> or <Tag1></Tag1>, the DO will be overwritten with --null-- b) If Empty tags are suppressed ie not generated at all, the corresponding DO field will NOT be overwritten. The field will have whatever value existed previously.  For an EMS with an Insert operation, both tags with an empty value and no tags result in –null-- being written to the DO. Hope this helps .. Happy 4th!

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  • A* navigational mesh path finding

    - by theguywholikeslinux
    So I've been making this top down 2D java game in this framework called Greenfoot [1] and I've been working on the AI for the guys you are gonna fight. I want them to be able to move around the world realistically so I soon realized, amongst a couple of other things, I would need some kind of pathfinding. I have made two A* prototypes. One is grid based and then I made one that works with waypoints so now I need to work out a way to get from a 2d "map" of the obstacles/buildings to a graph of nodes that I can make a path from. The actual pathfinding seems fine, just my open and closed lists could use a more efficient data structure, but I'll get to that if and when I need to. I intend to use a navigational mesh for all the reasons out lined in this post on ai-blog.net [2]. However, the problem I have faced is that what A* thinks is the shortest path from the polygon centres/edges is not necessarily the shortest path if you travel through any part of the node. To get a better idea you can see the question I asked on stackoverflow [3]. I got a good answer concerning a visibility graph. I have since purchased the book (Computational Geometry: Algorithms and Applications [4]) and read further into the topic, however I am still in favour of a navigational mesh (See "Managing Complexity" [5] from Amit’s Notes about Path-Finding [6]). (As a side note, maybe I could possibly use Theta* to convert multiple waypoints into one straight line if the first and last are not obscured. Or each time I move back check to the waypoint before last to see if I can go straight from that to this) So basically what I want is a navigational mesh where once I have put it through a funnel algorithm (e.g. this one from Digesting Duck [7]) I will get the true shortest path, rather than get one that is the shortest path following node to node only, but not the actual shortest given that you can go through some polygons and skip nodes/edges. Oh and I also want to know how you suggest storing the information concerning the polygons. For the waypoint prototype example I made I just had each node as an object and stored a list of all the other nodes you could travel to from that node, I'm guessing that won't work with polygons? and how to I tell if a polygon is open/traversable or if it is a solid object? How do I store which nodes make up the polygon? Finally, for the record: I do want to programme this by myself from scratch even though there are already other solutions available and I don't intend to be (re) using this code in anything other than this game so it does not matter that it will inevitably be poor quality. http://greenfoot.org http://www.ai-blog.net/archives/000152.html http://stackoverflow.com/q/7585515/ http://www.cs.uu.nl/geobook/ http://theory.stanford.edu/~amitp/GameProgramming/MapRepresentations.html http://theory.stanford.edu/~amitp/GameProgramming/ http://digestingduck.blogspot.com/2010/03/simple-stupid-funnel-algorithm.html

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  • Oracle Systems and Solutions at OpenWorld Tokyo 2012

    - by ferhat
    Oracle OpenWorld Tokyo and JavaOne Tokyo will start next week April 4th. We will cover Oracle systems and Oracle Optimized Solutions in several keynote talks and general sessions. Full schedule can be found here. Come by the DemoGrounds to learn more about mission critical integration and optimization of complete Oracle stack. Our Oracle Optimized Solutions experts will be at hand to discuss 1-1 several of Oracle's systems solutions and technologies. Oracle Optimized Solutions are proven blueprints that eliminate integration guesswork by combing best in class hardware and software components to deliver complete system architectures that are fully tested, and include documented best practices that reduce integration risks and deliver better application performance. And because they are highly flexible by design, Oracle Optimized Solutions can be implemented as an end-to-end solution or easily adapted into existing environments. Oracle Optimized Solutions, Servers,  Storage, and Oracle Solaris  Sessions, Keynotes, and General Session Talks DAY TIME TITLE Notes Session Wednesday  April 4 9:00 - 11:15 Keynote: ENGINEERED FOR INNOVATION - Engineered Systems Mark Hurd,  President, Oracle Takao Endo, President & CEO, Oracle Corporation Japan John Fowler, EVP of Systems, Oracle Ed Screven, Chief Corporate Architect, Oracle English Session K1-01 11:50 - 12:35 Simplifying IT: Transforming the Data Center with Oracle's Engineered Systems Robert Shimp, Group VP, Product Marketing, Oracle English Session S1-01 15:20 - 16:05 Introducing Tiered Storage Solution for low cost Big Data Archiving S1-33 16:30 - 17:15 Simplifying IT - IT System Consolidation that also Accelerates Business Agility S1-42 Thursday  April 5 9:30 - 11:15 Keynote: Extreme Innovation Larry Ellison, Chief Executive Officer, Oracle English Session K2-01 11:50 - 13:20 General Session: Server and Storage Systems Strategy John Fowler, EVP of Systems, Oracle English Session G2-01 16:30 - 17:15 Top 5 Reasons why ZFS Storage appliance is "The cloud storage" by SAKURA Internet Inc L2-04 16:30 - 17:15 The UNIX based Exa* Performance IT Integration Platform - SPARC SuperCluster S2-42 17:40 - 18:25 Full stack solutions of hardware and software with SPARC SuperCluster and Oracle E-Business Suite  to minimize the business cost while maximizing the agility, performance, and availability S2-53 Friday April 6 9:30 - 11:15 Keynote: Oracle Fusion Applications & Cloud Robert Shimp, Group VP, Product Marketing Anthony Lye, Senior VP English Session K3-01 11:50 - 12:35 IT at Oracle: The Art of IT Transformation to Enable Business Growth English Session S3-02 13:00-13:45 ZFS Storagge Appliance: Architecture of high efficient and high performance S3-13 14:10 - 14:55 Why "Niko Niko doga" chose ZFS Storage Appliance to support their growing requirements and storage infrastructure By DWANGO Co, Ltd. S3-21 15:20 - 16:05 Osaka University: Lower TCO and higher flexibility for student study by Virtual Desktop By Osaka University S3-33 Oracle Developer Sessions with Oracle Systems and Oracle Solaris DAY TIME TITLE Notes LOCATION Friday April 6 13:00 - 13:45 Oracle Solaris 11 Developers D3-03 13:00 - 14:30 Oracle Solaris Tuning Contest Hands-On Lab D3-04 14:00 - 14:35 How to build high performance and high security Oracle Database environment with Oracle SPARC/Solaris English Session D3-13 15:00 - 15:45 IT Assets preservation and constructive migration with Oracle Solaris virtualization D3-24 16:00 - 17:30 The best packaging system for cloud environment - Creating an IPS package D3-34 Follow Oracle Infrared at Twitter, Facebook, Google+, and LinkedIn  to catch the latest news, developments, announcements, and inside views from  Oracle Optimized Solutions.

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  • Manage Your Favorite Social Accounts in Chrome and Iron with Seesmic

    - by Asian Angel
    Are you looking for a way to manage your Twitter, Facebook, Google Buzz, LinkedIn, and Foursquare accounts all in one place? Using the Seesmic Web App for Chrome and Iron you can access your favorite accounts and manage them in a single, simple-to-use interface. A feature that we loved from the start was the ability to access Twitter without creating a special Seesmic account. And in these days of multiple accounts who needs another one to complicate things up? All that you need to do is to sign in with your user name/e-mail along with your password. You do have to authorize access for Seesmic to connect with your account but the whole process (login & authorization) is handled in a single window instance. Now on to a quick look at some of the UI features… The sidebar allows you to add additional columns to the main interface, set your favorite location for Trends, and tie in additional social services as desired. You can also access additional options and controls in the upper right corner. When you are ready to start tweeting click in the blank at the top and enter your text, etc. in the convenient drop-down window that appears. Another nice perk is the ability to switch to a black and grey theme if the white is too bright for your needs. The Seesmic web app provides a simple-to-use, highly efficient way to manage your Twitter account and other favorite social services in a single tab interface. Seesmic [Chrome Web Store] Latest Features How-To Geek ETC Should You Delete Windows 7 Service Pack Backup Files to Save Space? What Can Super Mario Teach Us About Graphics Technology? Windows 7 Service Pack 1 is Released: But Should You Install It? How To Make Hundreds of Complex Photo Edits in Seconds With Photoshop Actions How to Enable User-Specific Wireless Networks in Windows 7 How to Use Google Chrome as Your Default PDF Reader (the Easy Way) Manage Your Favorite Social Accounts in Chrome and Iron with Seesmic E.T. II – Extinction [Fake Movie Sequel Video] Remastered King’s Quest Games Offer Classic Gaming on Modern Machines Compare Your Internet Cost and Speed to Global Averages [Infographic] Orbital Battle for Terra Wallpaper WizMouse Enables Mouse Over Scrolling on Any Window

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  • Logic for capturing unique characteristics in an object array. C# LINQ [closed]

    - by Shawn H.
    Given the following "response" or array of objects, what would be the most efficient way to get the desired results. There must be an easier way than the exhaustive and tedious way I'm doing it now. A LINQ solution would be fantastic. Situation #1 <things> <thing id="1"> <feature>Tall</feature> </thing> <thing id="2"> <feature>Tall</feature> </thing> <thing id="3"> <feature>Tall</feature> <feature>Wide</feature> </thing> <thing id="4"> <feature>Tall</feature> </thing> </things> Result: Wide Situation #2 <things> <thing id="1"> <feature>Short</feature> </thing> <thing id="2"> <feature>Tall</feature> </thing> <thing id="3"> <feature>Tall</feature> <feature>Wide</feature> </thing> <thing id="4"> <feature>Tall</feature> </thing> </things> Result: Wide, Short, Tall Situation #3 <things> <thing id="1"> <feature>Tall</feature> <feature>Thin</feature> </thing> <thing id="2"> <feature>Tall</feature> </thing> <thing id="3"> <feature>Tall</feature> <feature>Wide</feature> </thing> <thing id="4"> <feature>Tall</feature> </thing> </things> Result: Wide, Thin Thanks.

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  • How do you structure computer science University notes?

    - by Sai Perchard
    I am completing a year of postgraduate study in CS next semester. I am finishing a law degree this year, and I will use this to briefly explain what I mean when I refer to the 'structure' of University notes. My preferred structure for authoring law notes: Word Two columns 0.5cm margins (top, right, bottom, middle, left) Body text (10pt, regular), 3 levels of headings (14/12/10pt, bold), 3 levels of bulleted lists Color A background for cases Color B background for legislation I find that it's crucial to have a good structure from the outset. My key advice to a law student would be to ensure styles allows cases and legislation to be easily identified from supporting text, and not to include too much detail regarding the facts of cases. More than 3 levels of headings is too deep. More than 3 levels of a bulleted list is too deep. In terms of CS, I am interested in similar advice; for example, any strategies that have been successfully employed regarding structure, and general advice regarding note taking. Has latex proved better than Word? Code would presumably need to be stylistically differentiated, and use a monospaced font - perhaps code could be written in TextMate so that it could be copied to retain syntax highlighting? (Are notes even that useful in a CS degree? I am tempted to simply use a textbook. They are crucial in law.) I understand that different people may employ varying techniques and that people will have personal preferences, however I am interested in what these different techniques are. Update Thank you for the responses so far. To clarify, I am not suggesting that the approach should be comparable to that I employ for law. I could have been clearer. The consensus so far seems to be - just learn it. Structure of notes/notes themselves are not generally relevant. This is what I was alluding to when I said I was just tempted to use a textbook. Re the comment that said textbooks are generally useless - I strongly disagree. Sure, perhaps the recommended textbook is useless. But if I'm going to learn a programming language, I will (1) identify what I believe to be the best textbook, and (2) read it. I was unsure if the combination of theory with code meant that lecture notes may be a more efficient way to study for an exam. I imagine that would depend on the subject. A subject specifically on a programming language, reading a textbook and coding would be my preferred approach. But I was unsure if, given a subject containing substantive theory that may not be covered in a single textbook, people may have preferences regarding note taking and structure.

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  • SQLAuthority News – A Quick Note on @Pluralsight Video – Call Me Maybe Developer Way

    - by pinaldave
    I write a lot about how important learning and training is.  Any of my readers will know that I think the key to success is staying current with your education and taking very opportunity to increase your “tool kit” of skills.  I hope that I have not made the impression that it is all in the employees hands to make sure they are happy and satisfied at their jobs. I also firmly believe that a good boss will make good employees.  A boss who is good at communicating,  and leading, who knows how to nip problem in the bud and allocate resources wisely will have a well-oiled machine.  This means happy employees and a great work environment. It is important to have a healthy work environment because you will not succeed without one.  Successful business will always have the type of environment that fosters creativity and has efficient employees.  A healthy environment doesn’t force employees to produce results, but allows them to progress and create the results themselves. The result of a healthy work environment is that employees will enjoy their work and then work harder.  This can bring the company more revenue, and hopefully the employees will see the result of their hard work in bonuses and raises.  However, money is important but it is certainly secondary – the important part is the dedication of the employees to their work and to their company.  This is the true key to success. Any employee who recognizes this description as their working environment should consider themselves fortunate.  They are allowed to grow and do better, and employees being treated fairly can be a rarity in this world.  One company that I believe adheres to this principle is Pluralsight – as evidenced by this fun video. I have blogged about it earlier. (check out my cameo at 0:37). It was great fun to work with the employees at Pluralsight while making this video.  They are a great bunch and clearly have a great work environment – we wouldn’t have had this much fun if not!  I have to tell you a little bit about making this video.  My wife shot it with her mobile phone, which was certainly a different but exciting experience!  It was hard to get the look of the video right, since I was trying to portray a body builder – this was a little outside of my own personal experience.  I have what I like to call a “healthy” body type, so trying to look extremely fit like some of the other “actors” in this video was a challenge – but I do hope that you all think I succeeded.  All in all, it was great fun to participate in this video and I hope to see my friends at Pluralsight again soon. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video

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  • Inverted schedctl usage in the JVM

    - by Dave
    The schedctl facility in Solaris allows a thread to request that the kernel defer involuntary preemption for a brief period. The mechanism is strictly advisory - the kernel can opt to ignore the request. Schedctl is typically used to bracket lock critical sections. That, in turn, can avoid convoying -- threads piling up on a critical section behind a preempted lock-holder -- and other lock-related performance pathologies. If you're interested see the man pages for schedctl_start() and schedctl_stop() and the schedctl.h include file. The implementation is very efficient. schedctl_start(), which asks that preemption be deferred, simply stores into a thread-specific structure -- the schedctl block -- that the kernel maps into user-space. Similarly, schedctl_stop() clears the flag set by schedctl_stop() and then checks a "preemption pending" flag in the block. Normally, this will be false, but if set schedctl_stop() will yield to politely grant the CPU to other threads. Note that you can't abuse this facility for long-term preemption avoidance as the deferral is brief. If your thread exceeds the grace period the kernel will preempt it and transiently degrade its effective scheduling priority. Further reading : US05937187 and various papers by Andy Tucker. We'll now switch topics to the implementation of the "synchronized" locking construct in the HotSpot JVM. If a lock is contended then on multiprocessor systems we'll spin briefly to try to avoid context switching. Context switching is wasted work and inflicts various cache and TLB penalties on the threads involved. If context switching were "free" then we'd never spin to avoid switching, but that's not the case. We use an adaptive spin-then-park strategy. One potentially undesirable outcome is that we can be preempted while spinning. When our spinning thread is finally rescheduled the lock may or may not be available. If not, we'll spin and then potentially park (block) again, thus suffering a 2nd context switch. Recall that the reason we spin is to avoid context switching. To avoid this scenario I've found it useful to enable schedctl to request deferral while spinning. But while spinning I've arranged for the code to periodically check or poll the "preemption pending" flag. If that's found set we simply abandon our spinning attempt and park immediately. This avoids the double context-switch scenario above. One annoyance is that the schedctl blocks for the threads in a given process are tightly packed on special pages mapped from kernel space into user-land. As such, writes to the schedctl blocks can cause false sharing on other adjacent blocks. Hopefully the kernel folks will make changes to avoid this by padding and aligning the blocks to ensure that one cache line underlies at most one schedctl block at any one time.

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