execution counter - Page 6

ClearTrace Shows Execution History

- by Bill Graziano

The latest release of ClearTrace (Build 38) now shows the execution history of a particular statement. You’ll need to save the trace files to a trace group instead of just using the default. That’s as easy as typing something into the trace group name when you upload the trace. I usually put the server name in this field. Build 38 also re-enables support for statement level events. If your trace includes RPC:StmtCompleted or SQL:StmtCompleted events those will be processed and save. In the results tab you can choose to view statement level or batch level events. Please note that saving statement level events in a trace can generate HUGE trace files very quickly.

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How can I schedule execution of a program?

- by Bakhtiyor

Let's say I have a small "Hello World" Java program compiled in my home directory. I can run it with java helloWorld from my home directory and it executes without any problem. Now I need to schedule to execute this program let's say after 10mins from now. So, I am executing following commands on console: at now+10min warning: commands will be executed using /bin/sh at> java helloWorld Press CTRL+D to finish So it is scheduled properly as I can see it with at -l command. But at this time nothing happens. Why? What is wrong with it? Because, if instead of scheduling the execution my own program I schedule executing of gedit command it opens it at a specified time. But with my own program it doesn't perform anything. How can I change the situation?

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Entity Framework: Connecting to a mdf user database file via localDB during script execution

- by Marko Apfel

Problem If you run the “Generate database from model” wizard and execute the generated script the destination database could be the wrong one (for instance master of the SQL Server). Solution To use an own mdf attachable user database some connection information must specified during script execution. Execute your script opens the dialog “Connect to Server”. Press “Options” and go to the second tab “Connection Properties”. Select “Browse server” in the “Connect to database” dropdown box: Confirm the information dialog with “Yes”. In the following dialog you could choose your user database. Now the schema is created in the user database.

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Chrome 10 rend possible l'exécution d'applications Web en arrière plan, Google publie un exemple

Chrome 10 rend possible l'exécution d'applications Web en arrière plan Même quand le navigateur est fermé, Google publie un exemple Mise à jour du 24/02/11 par Gordon Fowler Google vient de dévoiler une nouvelle fonctionnalité disponible dans la version 10 (en beta) de son navigateur Chrome. La fonctionnalité, baptisée « Background Pages », bien que n'ayant pas été mise en avant lors de la sortie Chrome 10, est bel et bien là. Elle permet d'exécuter des pages Web en arrière-plan de façon totalement transparente pour l'utilisateur. Certaines applications (qualifiées « d'applications d'arrière plan ») peuvent ainsi continuer à tourn...

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Web Page Execution Internals

- by octopusgrabbus

My question is what is the subject area that covers web page execution/loading. I am looking to purchase a book by subject area that covers when things execute or load in a web page, whether it's straight html, html and Javascript, or a PHP page. Is that topic covered by a detailed html book, or should I expect to find information like that in a JavaScript of PHP book? I understand that PHP and Perl execute on the server and that Javascript is client side, and I know there is a lot of on-line documentation describing <html>, <head>, <body>, and so on. I'm just wondering what subject area a book would be in to cover all that, not a discussion of the best book or someone's favorite book, but the subject area.

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Transparent PHP script execution using mod_rewrite

- by tori3852

I am looking for a solution for this a problem: I need that every HTTP request (method is irrelevant) in Apache http server would be served only after execution of specific PHP script. This is needed because I need to gather some information about requests, etc. As far as I understand - this could be achieved using mod_rewrite module. So far I have done this (in .htaccess file): RewriteEngine on RewriteRule ^(.*)$ script.php [C] script.php is executed, but I need that after this original request would be executed. Thanks - any help is appreciated.

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Free eBook: SQL Server Execution Plans, Second Edition

Every day, out in the various online forums devoted to SQL Server, and on Twitter, the same types of questions come up repeatedly: Why is this query running slowly? Why is SQL Server ignoring my index? Why does this query run quickly sometimes and slowly at others? My response is the same in each case: have you looked at the execution plan? "A real time saver" Andy Doyle, Head of IT ServicesAndy and his team saved time by automating backup and restores with SQL Backup Pro. Find out how much time you could save. Download a free trial now.

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Perfmon: which counter identifies that threads are waiting?

- by frankadelic

While load testing an ASP.NET app, we find that the pages are taking 20-30 sec under heavy load. We suspect this is because the pages are waiting for database calls or web services. Is there a particular perfmon counter that can identify this sort of bottleneck on the web servers? CPU, Memory, and Disk are normal. Or must we use a tool other than perfmon to track down this bottleneck?

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asp.net ajax progress counter

- by xt_20

Hi all, I have a small C# ASP.NET app, and want to include an Ajax progress counter. The architecture is currently like this: Web Application -- calls a class that does the upload For example, in default.aspx, I call : FileHelper fh = new FileHelper() fh.MoveFiles(file) I have an Ajax control that fires when the above is called. This is a control that resides on the website class/project. How do I update the progress counter from the Filehelper class? (I don't think calling the control directly would work as it would be a circular reference) Also how do I continuously update the counter? Thanks all

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Cassandra performance slow down with counter column

- by tubcvt

I have a cluster (4 node ) and a node have 16 core and 24 gb ram: 192.168.23.114 datacenter1 rack1 Up Normal 44.48 GB 25.00% 192.168.23.115 datacenter1 rack1 Up Normal 44.51 GB 25.00% 192.168.23.116 datacenter1 rack1 Up Normal 44.51 GB 25.00% 192.168.23.117 datacenter1 rack1 Up Normal 44.51 GB 25.00% We use about 10 column family (counter column) to make some system statistic report. Problem on here is that When i set replication_factor of this keyspace from 1 to 2 (contain 10 counter column family ), all cpu of node increase from 10% ( when use replication factor=1) to --- 90%. :( :( who can help me work around that :( . why counter column consume too much cpu time :(. thanks all

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global counter in application: bad practice?

- by Martin

In my C++ application I sometimes create different output files for troubleshooting purposes. Each file is created at a different step of our pipelined operation and it's hard to know file came before which other one (file timestamps all show the same date). I'm thinking of adding a global counter in my application, and a function (with multithreading protection) which increments and returns that global counter. Then I can use that counter as part of the filenames I create. Is this considered bad practice? Many different modules need to create files and they're not necessarily connected to each other.

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ParallelWork: Feature rich multithreaded fluent task execution library for WPF

- by oazabir

ParallelWork is an open source free helper class that lets you run multiple work in parallel threads, get success, failure and progress update on the WPF UI thread, wait for work to complete, abort all work (in case of shutdown), queue work to run after certain time, chain parallel work one after another. It’s more convenient than using .NET’s BackgroundWorker because you don’t have to declare one component per work, nor do you need to declare event handlers to receive notification and carry additional data through private variables. You can safely pass objects produced from different thread to the success callback. Moreover, you can wait for work to complete before you do certain operation and you can abort all parallel work while they are in-flight. If you are building highly responsive WPF UI where you have to carry out multiple job in parallel yet want full control over those parallel jobs completion and cancellation, then the ParallelWork library is the right solution for you. I am using the ParallelWork library in my PlantUmlEditor project, which is a free open source UML editor built on WPF. You can see some realistic use of the ParallelWork library there. Moreover, the test project comes with 400 lines of Behavior Driven Development flavored tests, that confirms it really does what it says it does. The source code of the library is part of the “Utilities” project in PlantUmlEditor source code hosted at Google Code. The library comes in two flavors, one is the ParallelWork static class, which has a collection of static methods that you can call. Another is the Start class, which is a fluent wrapper over the ParallelWork class to make it more readable and aesthetically pleasing code. ParallelWork allows you to start work immediately on separate thread or you can queue a work to start after some duration. You can start an immediate work in a new thread using the following methods: void StartNow(Action doWork, Action onComplete) void StartNow(Action doWork, Action onComplete, Action<Exception> failed) For example, ParallelWork.StartNow(() => { workStartedAt = DateTime.Now; Thread.Sleep(howLongWorkTakes); }, () => { workEndedAt = DateTime.Now; }); Or you can use the fluent way Start.Work: Start.Work(() => { workStartedAt = DateTime.Now; Thread.Sleep(howLongWorkTakes); }) .OnComplete(() => { workCompletedAt = DateTime.Now; }) .Run(); Besides simple execution of work on a parallel thread, you can have the parallel thread produce some object and then pass it to the success callback by using these overloads: void StartNow<T>(Func<T> doWork, Action<T> onComplete) void StartNow<T>(Func<T> doWork, Action<T> onComplete, Action<Exception> fail) For example, ParallelWork.StartNow<Dictionary<string, string>>( () => { test = new Dictionary<string,string>(); test.Add("test", "test"); return test; }, (result) => { Assert.True(result.ContainsKey("test")); }); Or, the fluent way: Start<Dictionary<string, string>>.Work(() => { test = new Dictionary<string, string>(); test.Add("test", "test"); return test; }) .OnComplete((result) => { Assert.True(result.ContainsKey("test")); }) .Run(); You can also start a work to happen after some time using these methods: DispatcherTimer StartAfter(Action onComplete, TimeSpan duration) DispatcherTimer StartAfter(Action doWork,Action onComplete,TimeSpan duration) You can use this to perform some timed operation on the UI thread, as well as perform some operation in separate thread after some time. ParallelWork.StartAfter( () => { workStartedAt = DateTime.Now; Thread.Sleep(howLongWorkTakes); }, () => { workCompletedAt = DateTime.Now; }, waitDuration); Or, the fluent way: Start.Work(() => { workStartedAt = DateTime.Now; Thread.Sleep(howLongWorkTakes); }) .OnComplete(() => { workCompletedAt = DateTime.Now; }) .RunAfter(waitDuration); There are several overloads of these functions to have a exception callback for handling exceptions or get progress update from background thread while work is in progress. For example, I use it in my PlantUmlEditor to perform background update of the application. // Check if there's a newer version of the app Start<bool>.Work(() => { return UpdateChecker.HasUpdate(Settings.Default.DownloadUrl); }) .OnComplete((hasUpdate) => { if (hasUpdate) { if (MessageBox.Show(Window.GetWindow(me), "There's a newer version available. Do you want to download and install?", "New version available", MessageBoxButton.YesNo, MessageBoxImage.Information) == MessageBoxResult.Yes) { ParallelWork.StartNow(() => { var tempPath = System.IO.Path.Combine( Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData), Settings.Default.SetupExeName); UpdateChecker.DownloadLatestUpdate(Settings.Default.DownloadUrl, tempPath); }, () => { }, (x) => { MessageBox.Show(Window.GetWindow(me), "Download failed. When you run next time, it will try downloading again.", "Download failed", MessageBoxButton.OK, MessageBoxImage.Warning); }); } } }) .OnException((x) => { MessageBox.Show(Window.GetWindow(me), x.Message, "Download failed", MessageBoxButton.OK, MessageBoxImage.Exclamation); }); The above code shows you how to get exception callbacks on the UI thread so that you can take necessary actions on the UI. Moreover, it shows how you can chain two parallel works to happen one after another. Sometimes you want to do some parallel work when user does some activity on the UI. For example, you might want to save file in an editor while user is typing every 10 second. In such case, you need to make sure you don’t start another parallel work every 10 seconds while a work is already queued. You need to make sure you start a new work only when there’s no other background work going on. Here’s how you can do it: private void ContentEditor_TextChanged(object sender, EventArgs e) { if (!ParallelWork.IsAnyWorkRunning()) { ParallelWork.StartAfter(SaveAndRefreshDiagram, TimeSpan.FromSeconds(10)); } } If you want to shutdown your application and want to make sure no parallel work is going on, then you can call the StopAll() method. ParallelWork.StopAll(); If you want to wait for parallel works to complete without a timeout, then you can call the WaitForAllWork(TimeSpan timeout). It will block the current thread until the all parallel work completes or the timeout period elapses. result = ParallelWork.WaitForAllWork(TimeSpan.FromSeconds(1)); The result is true, if all parallel work completed. If it’s false, then the timeout period elapsed and all parallel work did not complete. For details how this library is built and how it works, please read the following codeproject article: ParallelWork: Feature rich multithreaded fluent task execution library for WPF http://www.codeproject.com/KB/WPF/parallelwork.aspx If you like the article, please vote for me.

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Using SQL Execution Plans to discover the Swedish alphabet

- by Rob Farley

SQL Server is quite remarkable in a bunch of ways. In this post, I’m using the way that the Query Optimizer handles LIKE to keep it SARGable, the Execution Plans that result, Collations, and PowerShell to come up with the Swedish alphabet. SARGability is the ability to seek for items in an index according to a particular set of criteria. If you don’t have SARGability in play, you need to scan the whole index (or table if you don’t have an index). For example, I can find myself in the phonebook easily, because it’s sorted by LastName and I can find Farley in there by moving to the Fs, and so on. I can’t find everyone in my suburb easily, because the phonebook isn’t sorted that way. I can’t even find people who have six letters in their last name, because also the book is sorted by LastName, it’s not sorted by LEN(LastName). This is all stuff I’ve looked at before, including in the talk I gave at SQLBits in October 2010. If I try to find everyone who’s names start with F, I can do that using a query a bit like: SELECT LastName FROM dbo.PhoneBook WHERE LEFT(LastName,1) = 'F'; Unfortunately, the Query Optimizer doesn’t realise that all the entries that satisfy LEFT(LastName,1) = 'F' will be together, and it has to scan the whole table to find them. But if I write: SELECT LastName FROM dbo.PhoneBook WHERE LastName LIKE 'F%'; then SQL is smart enough to understand this, and performs an Index Seek instead. To see why, I look further into the plan, in particular, the properties of the Index Seek operator. The ToolTip shows me what I’m after: You’ll see that it does a Seek to find any entries that are at least F, but not yet G. There’s an extra Predicate in there (a Residual Predicate if you like), which checks that each LastName is really LIKE F% – I suppose it doesn’t consider that the Seek Predicate is quite enough – but most of the benefit is seen by its working out the Seek Predicate, filtering to just the “at least F but not yet G” section of the data. This got me curious though, particularly about where the G comes from, and whether I could leverage it to create the Swedish alphabet. I know that in the Swedish language, there are three extra letters that appear at the end of the alphabet. One of them is ä that appears in the word Västerås. It turns out that Västerås is quite hard to find in an index when you’re looking it up in a Swedish map. I talked about this briefly in my five-minute talk on Collation from SQLPASS (the one which was slightly less than serious). So by looking at the plan, I can work out what the next letter is in the alphabet of the collation used by the column. In other words, if my alphabet were Swedish, I’d be able to tell what the next letter after F is – just in case it’s not G. It turns out it is… Yes, the Swedish letter after F is G. But I worked this out by using a copy of my PhoneBook table that used the Finnish_Swedish_CI_AI collation. I couldn’t find how the Query Optimizer calculates the G, and my friend Paul White (@SQL_Kiwi) tells me that it’s frustratingly internal to the QO. He’s particularly smart, even if he is from New Zealand. To investigate further, I decided to do some PowerShell, leveraging the Get-SqlPlan function that I blogged about recently (make sure you also have the SqlServerCmdletSnapin100 snap-in added). I started by indicating that I was going to use Finnish_Swedish_CI_AI as my collation of choice, and that I’d start whichever letter cam straight after the number 9. I figure that this is a cheat’s way of guessing the first letter of the alphabet (but it doesn’t actually work in Unicode – luckily I’m using varchar not nvarchar. Actually, there are a few aspects of this code that only work using ASCII, so apologies if you were wanting to apply it to Greek, Japanese, etc). I also initialised my $alphabet variable. $collation = 'Finnish_Swedish_CI_AI'; $firstletter = '9'; $alphabet = ''; Now I created the table for my test. A single field would do, and putting a Clustered Index on it would suffice for the Seeks. Invoke-Sqlcmd -server . -data tempdb -query "create table dbo.collation_test (col varchar(10) collate $collation primary key);" Now I get into the looping. $c = $firstletter; $stillgoing = $true; while ($stillgoing) { I construct the query I want, seeking for entries which start with whatever $c has reached, and get the plan for it: $query = "select col from dbo.collation_test where col like '$($c)%';"; [xml] $pl = get-sqlplan $query "." "tempdb"; At this point, my $pl variable is a scary piece of XML, representing the execution plan. A bit of hunting through it showed me that the EndRange element contained what I was after, and that if it contained NULL, then I was done. $stillgoing = ($pl.ShowPlanXML.BatchSequence.Batch.Statements.StmtSimple.QueryPlan.RelOp.IndexScan.SeekPredicates.SeekPredicateNew.SeekKeys.EndRange -ne $null); Now I could grab the value out of it (which came with apostrophes that needed stripping), and append that to my $alphabet variable. if ($stillgoing) { $c=$pl.ShowPlanXML.BatchSequence.Batch.Statements.StmtSimple.QueryPlan.RelOp.IndexScan.SeekPredicates.SeekPredicateNew.SeekKeys.EndRange.RangeExpressions.ScalarOperator.ScalarString.Replace("'",""); $alphabet += $c; } Finally, finishing the loop, dropping the table, and showing my alphabet! } Invoke-Sqlcmd -server . -data tempdb -query "drop table dbo.collation_test;"; $alphabet; When I run all this, I see that the Swedish alphabet is ABCDEFGHIJKLMNOPQRSTUVXYZÅÄÖ, which matches what I see at Wikipedia. Interesting to see that the letters on the end are still there, even with Case Insensitivity. Turns out they’re not just “letters with accents”, they’re letters in their own right. I’m sure you gave up reading long ago, and really aren’t that fazed about the idea of doing this using PowerShell. I chose PowerShell because I’d already come up with an easy way of grabbing the estimated plan for a query, and PowerShell does allow for easy navigation of XML. I find the most interesting aspect of this as the fact that the Query Optimizer uses the next letter of the alphabet to maintain the SARGability of LIKE. I’m hoping they do something similar for a whole bunch of operations. Oh, and the fact that you know how to find stuff in the IKEA catalogue. Footnote: If you are interested in whether this works in other languages, you might want to consider the following screenshot, which shows that in principle, it should work with Japanese. It might be a bit harder to run this in PowerShell though, as I’m not sure how it translates. In Hiragana, the Japanese alphabet starts ?, ?, ?, ?, ?, ...

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Same SELECT used in an INSERT has different execution plan

- by amacias

A customer complained that a query and its INSERT counterpart had different execution plans, and of course, the INSERT was slower. First lets look at the SELECT : SELECT ua_tr_rundatetime, ua_ch_treatmentcode, ua_tr_treatmentcode, ua_ch_cellid, ua_tr_cellid FROM (SELECT DISTINCT CH.treatmentcode AS UA_CH_TREATMENTCODE, CH.cellid AS UA_CH_CELLID FROM CH, DL WHERE CH.contactdatetime > SYSDATE - 5 AND CH.treatmentcode = DL.treatmentcode) CH_CELLS, (SELECT DISTINCT T.treatmentcode AS UA_TR_TREATMENTCODE, T.cellid AS UA_TR_CELLID, T.rundatetime AS UA_TR_RUNDATETIME FROM T, DL WHERE T.treatmentcode = DL.treatmentcode) TRT_CELLS WHERE CH_CELLS.ua_ch_treatmentcode(+) = TRT_CELLS.ua_tr_treatmentcode; The query has 2 DISTINCT subqueries. The execution plan shows one with DISTICT Placement transformation applied and not the other. The view in Step 5 has the prefix VW_DTP which means DISTINCT Placement. -------------------------------------------------------------------- | Id | Operation | Name | Cost (%CPU) -------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 272K(100) |* 1 | HASH JOIN OUTER | | 272K (1) | 2 | VIEW | | 4408 (1) | 3 | HASH UNIQUE | | 4408 (1) |* 4 | HASH JOIN | | 4407 (1) | 5 | VIEW | VW_DTP_48BAF62C | 1660 (2) | 6 | HASH UNIQUE | | 1660 (2) | 7 | TABLE ACCESS FULL | DL | 1644 (1) | 8 | TABLE ACCESS FULL | T | 2744 (1) | 9 | VIEW | | 267K (1) | 10 | HASH UNIQUE | | 267K (1) |* 11 | HASH JOIN | | 267K (1) | 12 | PARTITION RANGE ITERATOR| | 266K (1) |* 13 | TABLE ACCESS FULL | CH | 266K (1) | 14 | TABLE ACCESS FULL | DL | 1644 (1) -------------------------------------------------------------------- Query Block Name / Object Alias (identified by operation id): ------------------------------------------------------------- 1 - SEL$1 2 - SEL$AF418D5F / TRT_CELLS@SEL$1 3 - SEL$AF418D5F 5 - SEL$F6AECEDE / VW_DTP_48BAF62C@SEL$48BAF62C 6 - SEL$F6AECEDE 7 - SEL$F6AECEDE / DL@SEL$3 8 - SEL$AF418D5F / T@SEL$3 9 - SEL$2 / CH_CELLS@SEL$1 10 - SEL$2 13 - SEL$2 / CH@SEL$2 14 - SEL$2 / DL@SEL$2 Predicate Information (identified by operation id): --------------------------------------------------- 1 - access("CH_CELLS"."UA_CH_TREATMENTCODE"="TRT_CELLS"."UA_TR_TREATMENTCODE") 4 - access("T"."TREATMENTCODE"="ITEM_1") 11 - access("CH"."TREATMENTCODE"="DL"."TREATMENTCODE") 13 - filter("CH"."CONTACTDATETIME">SYSDATE@!-5) The outline shows PLACE_DISTINCT(@"SEL$3" "DL"@"SEL$3") indicating that the QB3 is the one that got the transformation. Outline Data ------------- /*+ BEGIN_OUTLINE_DATA IGNORE_OPTIM_EMBEDDED_HINTS OPTIMIZER_FEATURES_ENABLE('11.2.0.3') DB_VERSION('11.2.0.3') ALL_ROWS OUTLINE_LEAF(@"SEL$2") OUTLINE_LEAF(@"SEL$F6AECEDE") OUTLINE_LEAF(@"SEL$AF418D5F") PLACE_DISTINCT(@"SEL$3" "DL"@"SEL$3") OUTLINE_LEAF(@"SEL$1") OUTLINE(@"SEL$48BAF62C") OUTLINE(@"SEL$3") NO_ACCESS(@"SEL$1" "TRT_CELLS"@"SEL$1") NO_ACCESS(@"SEL$1" "CH_CELLS"@"SEL$1") LEADING(@"SEL$1" "TRT_CELLS"@"SEL$1" "CH_CELLS"@"SEL$1") USE_HASH(@"SEL$1" "CH_CELLS"@"SEL$1") FULL(@"SEL$2" "CH"@"SEL$2") FULL(@"SEL$2" "DL"@"SEL$2") LEADING(@"SEL$2" "CH"@"SEL$2" "DL"@"SEL$2") USE_HASH(@"SEL$2" "DL"@"SEL$2") USE_HASH_AGGREGATION(@"SEL$2") NO_ACCESS(@"SEL$AF418D5F" "VW_DTP_48BAF62C"@"SEL$48BAF62C") FULL(@"SEL$AF418D5F" "T"@"SEL$3") LEADING(@"SEL$AF418D5F" "VW_DTP_48BAF62C"@"SEL$48BAF62C" "T"@"SEL$3") USE_HASH(@"SEL$AF418D5F" "T"@"SEL$3") USE_HASH_AGGREGATION(@"SEL$AF418D5F") FULL(@"SEL$F6AECEDE" "DL"@"SEL$3") USE_HASH_AGGREGATION(@"SEL$F6AECEDE") END_OUTLINE_DATA */ The 10053 shows there is a comparative of cost with and without the transformation. This means the transformation belongs to Cost-Based Query Transformations (CBQT). In SEL$3 the optimization of the query block without the transformation is 6659.73 and with the transformation is 4408.41 so the transformation is kept. GBP/DP: Checking validity of GBP/DP for query block SEL$3 (#3) DP: Checking validity of distinct placement for query block SEL$3 (#3) DP: Using search type: linear DP: Considering distinct placement on query block SEL$3 (#3) DP: Starting iteration 1, state space = (5) : (0) DP: Original query DP: Costing query block. DP: Updated best state, Cost = 6659.73 DP: Starting iteration 2, state space = (5) : (1) DP: Using DP transformation in this iteration. DP: Transformed query DP: Costing query block. DP: Updated best state, Cost = 4408.41 DP: Doing DP on the original QB. DP: Doing DP on the preserved QB. In SEL$2 the cost without the transformation is less than with it so it is not kept. GBP/DP: Checking validity of GBP/DP for query block SEL$2 (#2) DP: Checking validity of distinct placement for query block SEL$2 (#2) DP: Using search type: linear DP: Considering distinct placement on query block SEL$2 (#2) DP: Starting iteration 1, state space = (3) : (0) DP: Original query DP: Costing query block. DP: Updated best state, Cost = 267936.93 DP: Starting iteration 2, state space = (3) : (1) DP: Using DP transformation in this iteration. DP: Transformed query DP: Costing query block. DP: Not update best state, Cost = 267951.66 To the same query an INSERT INTO is added and the result is a very different execution plan. INSERT INTO cc (ua_tr_rundatetime, ua_ch_treatmentcode, ua_tr_treatmentcode, ua_ch_cellid, ua_tr_cellid)SELECT ua_tr_rundatetime, ua_ch_treatmentcode, ua_tr_treatmentcode, ua_ch_cellid, ua_tr_cellidFROM (SELECT DISTINCT CH.treatmentcode AS UA_CH_TREATMENTCODE, CH.cellid AS UA_CH_CELLID FROM CH, DL WHERE CH.contactdatetime > SYSDATE - 5 AND CH.treatmentcode = DL.treatmentcode) CH_CELLS, (SELECT DISTINCT T.treatmentcode AS UA_TR_TREATMENTCODE, T.cellid AS UA_TR_CELLID, T.rundatetime AS UA_TR_RUNDATETIME FROM T, DL WHERE T.treatmentcode = DL.treatmentcode) TRT_CELLSWHERE CH_CELLS.ua_ch_treatmentcode(+) = TRT_CELLS.ua_tr_treatmentcode;----------------------------------------------------------| Id | Operation | Name | Cost (%CPU)----------------------------------------------------------| 0 | INSERT STATEMENT | | 274K(100)| 1 | LOAD TABLE CONVENTIONAL | | |* 2 | HASH JOIN OUTER | | 274K (1)| 3 | VIEW | | 6660 (1)| 4 | SORT UNIQUE | | 6660 (1)|* 5 | HASH JOIN | | 6659 (1)| 6 | TABLE ACCESS FULL | DL | 1644 (1)| 7 | TABLE ACCESS FULL | T | 2744 (1)| 8 | VIEW | | 267K (1)| 9 | SORT UNIQUE | | 267K (1)|* 10 | HASH JOIN | | 267K (1)| 11 | PARTITION RANGE ITERATOR| | 266K (1)|* 12 | TABLE ACCESS FULL | CH | 266K (1)| 13 | TABLE ACCESS FULL | DL | 1644 (1)----------------------------------------------------------Query Block Name / Object Alias (identified by operation id):------------------------------------------------------------- 1 - SEL$1 3 - SEL$3 / TRT_CELLS@SEL$1 4 - SEL$3 6 - SEL$3 / DL@SEL$3 7 - SEL$3 / T@SEL$3 8 - SEL$2 / CH_CELLS@SEL$1 9 - SEL$2 12 - SEL$2 / CH@SEL$2 13 - SEL$2 / DL@SEL$2Predicate Information (identified by operation id):--------------------------------------------------- 2 - access("CH_CELLS"."UA_CH_TREATMENTCODE"="TRT_CELLS"."UA_TR_TREATMENTCODE") 5 - access("T"."TREATMENTCODE"="DL"."TREATMENTCODE") 10 - access("CH"."TREATMENTCODE"="DL"."TREATMENTCODE") 12 - filter("CH"."CONTACTDATETIME">SYSDATE@!-5)Outline Data------------- /*+ BEGIN_OUTLINE_DATA IGNORE_OPTIM_EMBEDDED_HINTS OPTIMIZER_FEATURES_ENABLE('11.2.0.3') DB_VERSION('11.2.0.3') ALL_ROWS OUTLINE_LEAF(@"SEL$2") OUTLINE_LEAF(@"SEL$3") OUTLINE_LEAF(@"SEL$1") OUTLINE_LEAF(@"INS$1") FULL(@"INS$1" "CC"@"INS$1") NO_ACCESS(@"SEL$1" "TRT_CELLS"@"SEL$1") NO_ACCESS(@"SEL$1" "CH_CELLS"@"SEL$1") LEADING(@"SEL$1" "TRT_CELLS"@"SEL$1" "CH_CELLS"@"SEL$1") USE_HASH(@"SEL$1" "CH_CELLS"@"SEL$1") FULL(@"SEL$2" "CH"@"SEL$2") FULL(@"SEL$2" "DL"@"SEL$2") LEADING(@"SEL$2" "CH"@"SEL$2" "DL"@"SEL$2") USE_HASH(@"SEL$2" "DL"@"SEL$2") USE_HASH_AGGREGATION(@"SEL$2") FULL(@"SEL$3" "DL"@"SEL$3") FULL(@"SEL$3" "T"@"SEL$3") LEADING(@"SEL$3" "DL"@"SEL$3" "T"@"SEL$3") USE_HASH(@"SEL$3" "T"@"SEL$3") USE_HASH_AGGREGATION(@"SEL$3") END_OUTLINE_DATA */ There is no DISTINCT Placement view and no hint.The 10053 trace shows a new legend "DP: Bypassed: Not SELECT"implying that this is a transformation that it is possible only for SELECTs. GBP/DP: Checking validity of GBP/DP for query block SEL$3 (#4) DP: Checking validity of distinct placement for query block SEL$3 (#4) DP: Bypassed: Not SELECT. GBP/DP: Checking validity of GBP/DP for query block SEL$2 (#3) DP: Checking validity of distinct placement for query block SEL$2 (#3) DP: Bypassed: Not SELECT. In 12.1 (and hopefully in 11.2.0.4 when released) the restriction on applying CBQT to some DMLs and DDLs (like CTAS) is lifted.This is documented in BugTag Note:10013899.8 Allow CBQT for some DML / DDLAnd interestingly enough, it is possible to have a one-off patch in 11.2.0.3. SQL> select DESCRIPTION,OPTIMIZER_FEATURE_ENABLE,IS_DEFAULT 2 from v$system_fix_control where BUGNO='10013899'; DESCRIPTION ---------------------------------------------------------------- OPTIMIZER_FEATURE_ENABLE IS_DEFAULT ------------------------- ---------- enable some transformations for DDL and DML statements 11.2.0.4 1

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?12c database ????Adaptive Execution Plans ????????

- by Liu Maclean(???)

12c R1 ????SQL??????- Adaptive Execution Plans ????????,???????optimizer ??????(runtime)???????????????, ????????????????????? SQL???????? ????????????, ?????????????????????????????????????????????????????????????adaptive plan ????????????????????????????????????,?????subplan???????????????????? ??????, ???????? ???????????????,?????????, ?????? ???????????????”???”????, ???????????????????buffer ??????? ????????????,?????,??????????????????? ???optimizer ?????????????????????????,?????????????????????????????????????????plan???? ??12C?????????????, ???????????????????,?????? ???????????? ????????????2???: Dynamic Plans????: ???????????????????????;??????,???optimizer??????????subplans??????????????, ???????????????????,?????????????? Reoptimization????: ?Dynamic Plans????,Reoptimization??????????????????????Reoptimization??,?????????????????????????,??reoptimization????? OPTIMIZER_ADAPTIVE_REPORTING_ONLY ???? report-only????????????????TRUE,?????????report-only????,???????????????,??????????????? Dynamic Plans ??????????????,????????????????????????, ?????????????,???????????,????????????????????????????????????????? ?????????????final plan??????????????default plan, ??final plan?default plan???????,????????????? subplan ???????????????,???????????????????????? ??????,???????statistics collector ?buffer???????????statistics collector?????????????????,???????????????????????????? ?????????????????????????????????????????,??????????,?????????????? ???????????,???????buffer???? ???????????????,?????????????????????????????,??????buffer,??????final plan? ????????,???????????????????????,????????????????? ?V$SQL??????IS_RESOLVED_DYNAMIC_PLAN??????????final plan???default plan? ??????dynamic plan ???????SQL PLAN directives?????? declare cursor PLAN_DIRECTIVE_IDS is select directive_id from DBA_SQL_PLAN_DIRECTIVES; begin for z in PLAN_DIRECTIVE_IDS loop DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE(z.directive_id); end loop; end; / explain plan for select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; select * from table(dbms_xplan.display()); Plan hash value: 1255158658 www.askmaclean.com ------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 4 | 128 | 7 (0)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 4 | 128 | 7 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | |* 4 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK | 1 | | 0 (0)| 00:00:01 | | 5 | TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") alter session set events '10053 trace name context forever,level 1'; OR alter session set events 'trace[SQL_Plan_Directive] disk highest'; select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; ---------------------------------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | ---------------------------------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 7 | | | 1 | HASH JOIN | | 4 | 128 | 7 | 00:00:01 | | 2 | NESTED LOOPS | | | | | | | 3 | NESTED LOOPS | | 4 | 128 | 7 | 00:00:01 | | 4 | STATISTICS COLLECTOR | | | | | | | 5 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 | 00:00:01 | | 6 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK| 1 | | 0 | | | 7 | TABLE ACCESS BY INDEX ROWID | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | | 8 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | ---------------------------------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") ===================================== SPD: BEGIN context at statement level ===================================== Stmt: ******* UNPARSED QUERY IS ******* SELECT /*+ OPT_ESTIMATE (@"SEL$1" JOIN ("P"@"SEL$1" "O"@"SEL$1") ROWS=13.000000 ) OPT_ESTIMATE (@"SEL$1" TABLE "O"@"SEL$1" ROWS=13.000000 ) */ "P"."PRODUCT_NAME" "PRODUCT_NAME" FROM "OE"."ORDER_ITEMS" "O","OE"."PRODUCT_INFORMATION" "P" WHERE "O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1 AND "P"."PRODUCT_ID"="O"."PRODUCT_ID" Objects referenced in the statement PRODUCT_INFORMATION[P] 92194, type = 1 ORDER_ITEMS[O] 92197, type = 1 Objects in the hash table Hash table Object 92197, type = 1, ownerid = 6573730143572393221: No Dynamic Sampling Directives for the object Hash table Object 92194, type = 1, ownerid = 17822962561575639002: No Dynamic Sampling Directives for the object Return code in qosdInitDirCtx: ENBLD =================================== SPD: END context at statement level =================================== ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Inserted felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: qosdCreateFindingSingTab retCode = CREATED, fid = 2896834833840853267 SPD: qosdCreateDirCmp retCode = CREATED, fid = 2896834833840853267 SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SKIP_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Modified felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 5618517328604016300 SPD: Modified felem, fid=5618517328604016300, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 1142802697078608149 SPD: Modified felem, fid=1142802697078608149, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 2, objcnt = 2, obItr = 0, objid = 92194, objtyp = 1, vecsize = 0, obItr = 1, objid = 92197, objtyp = 1, vecsize = 0, fid = 1437680122701058051 SPD: Modified felem, fid=1437680122701058051, ftype = 1, freason = 2, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO select * from table(dbms_xplan.display_cursor(format=>'report')) ; ????report????adaptive plan Adaptive plan: ------------- This cursor has an adaptive plan, but adaptive plans are enabled for reporting mode only. The plan that would be executed if adaptive plans were enabled is displayed below. ------------------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | | | 7 (100)| | |* 1 | HASH JOIN | | 4 | 128 | 7 (0)| 00:00:01 | |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------ SQL> select SQL_ID,IS_RESOLVED_DYNAMIC_PLAN,sql_text from v$SQL WHERE SQL_TEXT like '%MALCEAN%' and sql_text not like '%like%'; SQL_ID IS -------------------------- -- SQL_TEXT -------------------------------------------------------------------------------- 6ydj1bn1bng17 Y select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id ???? explain plan for ????default plan, ??????optimizer???final plan,??V$SQL.IS_RESOLVED_DYNAMIC_PLAN???Y,????????????? DBA_SQL_PLAN_DIRECTIVES?????????????SQL PLAN DIRECTIVES, ???12c? ???MMON?????DML ???column usage??????????,????SMON??? MMON????SGA??PLAN DIRECTIVES??? ?????DBMS_SPD.flush_sql_plan_directive???? select directive_id,type,reason from DBA_SQL_PLAN_DIRECTIVES / DIRECTIVE_ID TYPE REASON ----------------------------------- -------------------------------- ----------------------------- 10321283028317893030 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 4757086536465754886 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 16085268038103121260 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE SQL> set pages 9999 SQL> set lines 300 SQL> col state format a5 SQL> col subobject_name format a11 SQL> col col_name format a11 SQL> col object_name format a13 SQL> select d.directive_id, o.object_type, o.object_name, o.subobject_name col_name, d.type, d.state, d.reason 2 from dba_sql_plan_directives d, dba_sql_plan_dir_objects o 3 where d.DIRECTIVE_ID=o.DIRECTIVE_ID 4 and o.object_name in ('ORDER_ITEMS') 5 order by d.directive_id; DIRECTIVE_ID OBJECT_TYPE OBJECT_NAME COL_NAME TYPE STATE REASON ------------ ------------ ------------- ----------- -------------------------------- ----- ------------------------------------- --- 1.8156E+19 COLUMN ORDER_ITEMS UNIT_PRICE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 TABLE ORDER_ITEMS DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 COLUMN ORDER_ITEMS QUANTITY DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE DBA_SQL_PLAN_DIRECTIVES????? _BASE_OPT_DIRECTIVE ? _BASE_OPT_FINDING SELECT d.dir_own#, d.dir_id, d.f_id, decode(type, 1, 'DYNAMIC_SAMPLING', 'UNKNOWN'), decode(state, 1, 'NEW', 2, 'MISSING_STATS', 3, 'HAS_STATS', 4, 'CANDIDATE', 5, 'PERMANENT', 6, 'DISABLED', 'UNKNOWN'), decode(bitand(flags, 1), 1, 'YES', 'NO'), cast(d.created as timestamp), cast(d.last_modified as timestamp), -- Please see QOSD_DAYS_TO_UPDATE and QOSD_PLUS_SECONDS for more details -- about 6.5 cast(d.last_used as timestamp) - NUMTODSINTERVAL(6.5, 'day') FROM sys.opt_directive$ d ??dbms_spd??? SQL PLAN DIRECTIVES, SQL PLAN DIRECTIVES???retention ???53?: Package: DBMS_SPD This package provides subprograms for managing Sql Plan Directives(SPD). SPD are objects generated automatically by Oracle server. For example, if server detects that the single table cardinality estimated by optimizer is off from the actual number of rows returned when accessing the table, it will automatically create a directive to do dynamic sampling for the table. When any Sql statement referencing the table is compiled, optimizer will perform dynamic sampling for the table to get more accurate estimate. Notes: DBMSL_SPD is a invoker-rights package. The invoker requires ADMINISTER SQL MANAGEMENT OBJECT privilege for executing most of the subprograms of this package. Also the subprograms commit the current transaction (if any), perform the operation and commit it again. DBA view dba_sql_plan_directives shows all the directives created in the system and the view dba_sql_plan_dir_objects displays the objects that are included in the directives. -- Default value for SPD_RETENTION_WEEKS SPD_RETENTION_WEEKS_DEFAULT CONSTANT varchar2(4) := '53'; | STATE : NEW : Newly created directive. | : MISSING_STATS : The directive objects do not | have relevant stats. | : HAS_STATS : The objects have stats. | : PERMANENT : A permanent directive. Server | evaluated effectiveness and these | directives are useful. | | AUTO_DROP : YES : Directive will be dropped | automatically if not | used for SPD_RETENTION_WEEKS. | This is the default behavior. | NO : Directive will not be dropped | automatically. Procedure: flush_sql_plan_directive This procedure allows manually flushing the Sql Plan directives that are automatically recorded in SGA memory while executing sql statements. The information recorded in SGA are periodically flushed by oracle background processes. This procedure just provides a way to flush the information manually. ????”_optimizer_dynamic_plans”(enable dynamic plans)????????,???TRUE??DYNAMIC PLAN? ???FALSE???????????? ????,Dynamic Plan????????????Nested Loop?Hash Join???case ,????????Nested loop???????????HASH JOIN,?HASH JOIN????????????????? ????????subplan?????,???? pass?? ?join method???,?????STATISTICS COLLECTOR???cardinality?,???????HASH JOIN?????Nested Loop,????????????subplan?????access path; ???????Sales??????????????????,????HASH JOIN,??SUBPLAN??customers?????????;?????Nested Loop,???????cust_id?????Range Scan+Access by Rowid? Cardinality feedback Cardinality feedback????????11.2????,????????re-optimization???; ???????????,Cardinality feedback?????????????????????????? ???????????????????,?????????????????,??????????Cardinality feedback????????????? ????????????????????????? ??????????????Cardinality feedback ??: ????????,???????????,??????????,????????????????selectivity ??? ????????????: ??????,?????????????????????????????????,??????????????????? ????????????????????????????????????????,?????????????????????????? ?????????,???????????????,?????????? ??????????Cardinality ????,??????join Cardinality ????????? Cardinality feedback???????cursor?,?Cursor???aged out????? SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ---------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ---------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | 20 | | | | |* 1 | HASH JOIN | | 1 | 4 | 13 |00:00:00.01 | 24 | 20 | 2061K| 2061K| 429K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 4 | 13 |00:00:00.01 | 7 | 6 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.01 | 17 | 14 | | | | ---------------------------------------------------------------------------------------------------------------------------------------- SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | | | | |* 1 | HASH JOIN | | 1 | 13 | 13 |00:00:00.01 | 24 | 2061K| 2061K| 413K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 13 | 13 |00:00:00.01 | 7 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 288 | 288 |00:00:00.01 | 17 | | | | ------------------------------------------------------------------------------------------------------------------------------- Note ----- - statistics feedback used for this statement SQL> select count(*) from v$SQL where SQL_ID='cz0hg2zkvd10y'; COUNT(*) ---------- 2 SQL>select sql_ID,USE_FEEDBACK_STATS FROM V$SQL_SHARED_CURSOR where USE_FEEDBACK_STATS ='Y'; SQL_ID U ------------- - cz0hg2zkvd10y Y ????????Cardinality feedback????,???????????????????????????,????????????order_items???????? ????2??????plan hash value??(??????????),?????2????child cursor??????gather_plan_statistics???actual : A-ROWS estimate :E-ROWS????????? Automatic Re-optimization ???dynamic plan, Re-optimization??????????????? ? ??????????????? ???????????????????????????????? ???????????,??????????????, ???????????????????? ??????????? Re-optimization??, ????????????????????? Re-optimization????dynamic plan?????????? dynamic plan????????????????????, ???????????????????? ????,??????????join order ??????????????,?????????????join order????? ??????,????????Re-optimization, ??Re-optimization ??????????????????? ?Oracle database 12c?,join statistics?????????????????????,??????????????????????Re-optimization???????????adaptive cursor sharing????? ????????????????,???????????? ????? ???????statistics collectors ????????????????????Re-optimization??????2?????????????,???????????????? ??????????????Re-optimization?????,?????????????????????? ???v$SQL??????IS_REOPTIMIZABLE?????????????????????Re-optimization,??????????Re-optimization???,?????Re-optimization ,???????reporting????? IS_REOPTIMIZABLE VARCHAR2(1) This columns shows whether the next execution matching this child cursor will trigger a reoptimization. The values are: Y: If the next execution will trigger a reoptimization R: If the child cursor contains reoptimization information, but will not trigger reoptimization because the cursor was compiled in reporting mode N: If the child cursor has no reoptimization information ??1: select plan_table_output from table (dbms_xplan.display_cursor('gwf99gfnm0t7g',NULL,'ALLSTATS LAST')); SQL_ID gwf99gfnm0t7g, child number 0 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id Plan hash value: 1906736282 ------------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 269 |00:00:00.02 | 1336 | 18 | | | | | 1 | NESTED LOOPS | | 1 | 1 | 269 |00:00:00.02 | 1336 | 18 | | | | | 2 | MERGE JOIN CARTESIAN| | 1 | 4 | 9135 |00:00:00.02 | 34 | 15 | | | | |* 3 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 1 | 87 |00:00:00.01 | 33 | 14 | | | | | 4 | BUFFER SORT | | 87 | 105 | 9135 |00:00:00.01 | 1 | 1 | 4096 | 4096 | 4096 (0)| | 5 | INDEX FULL SCAN | ORDER_PK | 1 | 105 | 105 |00:00:00.01 | 1 | 1 | | | | |* 6 | INDEX UNIQUE SCAN | ORDER_ITEMS_UK | 9135 | 1 | 269 |00:00:00.01 | 1302 | 3 | | | | ------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") SQL_ID gwf99gfnm0t7g, child number 1 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id Plan hash value: 35479787 -------------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | -------------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 269 |00:00:00.01 | 63 | 3 | | | | | 1 | NESTED LOOPS | | 1 | 269 | 269 |00:00:00.01 | 63 | 3 | | | | |* 2 | HASH JOIN | | 1 | 313 | 269 |00:00:00.01 | 42 | 3 | 1321K| 1321K| 1234K (0)| |* 3 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 87 | 87 |00:00:00.01 | 16 | 0 | | | | | 4 | INDEX FAST FULL SCAN| ORDER_ITEMS_UK | 1 | 665 | 665 |00:00:00.01 | 26 | 3 | | | | |* 5 | INDEX UNIQUE SCAN | ORDER_PK | 269 | 1 | 269 |00:00:00.01 | 21 | 0 | | | | -------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 5 - access("O"."ORDER_ID"="ORDER_ID") Note ----- - statistics feedback used for this statement SQL> select IS_REOPTIMIZABLE,child_number FROM V$SQL A where A.SQL_ID='gwf99gfnm0t7g'; IS CHILD_NUMBER -- ------------ Y 0 N 1 1* select child_number,other_xml From v$SQL_PLAN where SQL_ID='gwf99gfnm0t7g' and other_xml is not nul SQL> / CHILD_NUMBER OTHER_XML ------------ -------------------------------------------------------------------------------- 1 <other_xml><info type="cardinality_feedback">yes</info><info type="db_version">1 2.1.0.1</info><info type="parse_schema"><![CDATA["OE"]]></info><info type="plan_ hash">35479787</info><info type="plan_hash_2">3382491761</info><outline_data><hi nt><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]></hint><hint><![CDATA[OPTIMIZER_FEATUR ES_ENABLE('12.1.0.1')]]></hint><hint><![CDATA[DB_VERSION('12.1.0.1')]]></hint><h int><![CDATA[ALL_ROWS]]></hint><hint><![CDATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></ hint><hint><![CDATA[MERGE(@"SEL$2")]]></hint><hint><![CDATA[OUTLINE(@"SEL$1")]]> </hint><hint><![CDATA[OUTLINE(@"SEL$2")]]></hint><hint><![CDATA[FULL(@"SEL$F5BB7 4E1" "P"@"SEL$2")]]></hint><hint><![CDATA[INDEX_FFS(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PRODUCT_ID"))]]></hint><hint><![CDATA[I NDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA[ LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$2" "O"@"SEL$1")]]></hint><hint><![C DATA[USE_HASH(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint><hint><![CDATA[USE_NL(@"SEL$ F5BB74E1" "O"@"SEL$1")]]></hint></outline_data></other_xml> 0 <other_xml><info type="db_version">12.1.0.1</info><info type="parse_schema"><![C DATA["OE"]]></info><info type="plan_hash">1906736282</info><info type="plan_hash _2">2579473118</info><outline_data><hint><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]> </hint><hint><![CDATA[OPTIMIZER_FEATURES_ENABLE('12.1.0.1')]]></hint><hint><![CD ATA[DB_VERSION('12.1.0.1')]]></hint><hint><![CDATA[ALL_ROWS]]></hint><hint><![CD ATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></hint><hint><![CDATA[MERGE(@"SEL$2")]]></hi nt><hint><![CDATA[OUTLINE(@"SEL$1")]]></hint><hint><![CDATA[OUTLINE(@"SEL$2")]]> </hint><hint><![CDATA[FULL(@"SEL$F5BB74E1" "P"@"SEL$2")]]></hint><hint><![CDATA[ INDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA [INDEX(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PROD UCT_ID"))]]></hint><hint><![CDATA[LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$1 " "O"@"SEL$2")]]></hint><hint><![CDATA[USE_MERGE_CARTESIAN(@"SEL$F5BB74E1" "O"@" SEL$1")]]></hint><hint><![CDATA[USE_NL(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint></o utline_data></other_xml> ??2: SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 0 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | -------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWNER OBJECT_NAME COL_NAME OBJECT TYPE STATE REASON ----------------------- ----- ------------- ----------- ------ ---------------- ----- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; ELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 1 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- - cardinality feedback used for this statement SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' SELECT /*+gather_plan_statistics*/ CUST_EMAIL FROM CUSTOMERS WHERE CUST_STATE_PROVINCE='MA' AND COUNTRY_ID='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------- Select /*+gather_plan_statistics*/ cust_email From customers Where cust_state_province='MA' And country_id='US' Plan hash value: 1683234692 ------------------------------------------------------------------------------- |Id | Operation | Name | Starts|E-Rows|A-Rows| A-Time |Buffers| ------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2| 2 |00:00:00.01| 16 | ----------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- - dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OW OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- -- --------- ---------- ------- --------------- ------------- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE

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Count the prime numbers from 2 to 100 with simpler code than this

- by RufioLJ

It has to be with just functions, variables, loops, etc (Basic stuff). I'm having trouble coming up with the code from scratch from what I've I learned so far(Should be able to do it). Makes me really mad :/. If you could give me step by step to make sure I understand I'd really really appreciated. Thanks a bunch in advanced. How could I get the same result with a simpler code than this one: var primes=4; for (var counter = 2; counter <= 100; counter = counter + 1) { var isPrime = 0; if(isPrime === 0){ if(counter === 2){console.log(counter);} else if(counter === 3){console.log(counter);} else if(counter === 5){console.log(counter);} else if(counter === 7){console.log(counter);} else if(counter % 2 === 0){isPrime=0;} else if(counter % 3 === 0){isPrime=0;} else if(counter % 5 === 0){isPrime=0;} else if(counter % 7 === 0){isPrime=0;} else { console.log(counter); primes = primes + 1; } } } console.log("Counted: "+primes+" primes");

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Convert ddply {plyr} to Oracle R Enterprise, or use with Embedded R Execution

- by Mark Hornick

The plyr package contains a set of tools for partitioning a problem into smaller sub-problems that can be more easily processed. One function within {plyr} is ddply, which allows you to specify subsets of a data.frame and then apply a function to each subset. The result is gathered into a single data.frame. Such a capability is very convenient. The function ddply also has a parallel option that if TRUE, will apply the function in parallel, using the backend provided by foreach. This type of functionality is available through Oracle R Enterprise using the ore.groupApply function. In this blog post, we show a few examples from Sean Anderson's "A quick introduction to plyr" to illustrate the correpsonding functionality using ore.groupApply. To get started, we'll create a demo data set and load the plyr package. set.seed(1) d <- data.frame(year = rep(2000:2014, each = 3), count = round(runif(45, 0, 20))) dim(d) library(plyr) This first example takes the data frame, partitions it by year, and calculates the coefficient of variation of the count, returning a data frame. # Example 1 res <- ddply(d, "year", function(x) { mean.count <- mean(x$count) sd.count <- sd(x$count) cv <- sd.count/mean.count data.frame(cv.count = cv) }) To illustrate the equivalent functionality in Oracle R Enterprise, using embedded R execution, we use the ore.groupApply function on the same data, but pushed to the database, creating an ore.frame. The function ore.push creates a temporary table in the database, returning a proxy object, the ore.frame. D <- ore.push(d) res <- ore.groupApply (D, D$year, function(x) { mean.count <- mean(x$count) sd.count <- sd(x$count) cv <- sd.count/mean.count data.frame(year=x$year[1], cv.count = cv) }, FUN.VALUE=data.frame(year=1, cv.count=1)) You'll notice the similarities in the first three arguments. With ore.groupApply, we augment the function to return the specific data.frame we want. We also specify the argument FUN.VALUE, which describes the resulting data.frame. From our previous blog posts, you may recall that by default, ore.groupApply returns an ore.list containing the results of each function invocation. To get a data.frame, we specify the structure of the result. The results in both cases are the same, however the ore.groupApply result is an ore.frame. In this case the data stays in the database until it's actually required. This can result in significant memory and time savings whe data is large. R> class(res) [1] "ore.frame" attr(,"package") [1] "OREbase" R> head(res) year cv.count 1 2000 0.3984848 2 2001 0.6062178 3 2002 0.2309401 4 2003 0.5773503 5 2004 0.3069680 6 2005 0.3431743 To make the ore.groupApply execute in parallel, you can specify the argument parallel with either TRUE, to use default database parallelism, or to a specific number, which serves as a hint to the database as to how many parallel R engines should be used. The next ddply example uses the summarise function, which creates a new data.frame. In ore.groupApply, the year column is passed in with the data. Since no automatic creation of columns takes place, we explicitly set the year column in the data.frame result to the value of the first row, since all rows received by the function have the same year. # Example 2 ddply(d, "year", summarise, mean.count = mean(count)) res <- ore.groupApply (D, D$year, function(x) { mean.count <- mean(x$count) data.frame(year=x$year[1], mean.count = mean.count) }, FUN.VALUE=data.frame(year=1, mean.count=1)) R> head(res) year mean.count 1 2000 7.666667 2 2001 13.333333 3 2002 15.000000 4 2003 3.000000 5 2004 12.333333 6 2005 14.666667 Example 3 uses the transform function with ddply, which modifies the existing data.frame. With ore.groupApply, we again construct the data.frame explicilty, which is returned as an ore.frame. # Example 3 ddply(d, "year", transform, total.count = sum(count)) res <- ore.groupApply (D, D$year, function(x) { total.count <- sum(x$count) data.frame(year=x$year[1], count=x$count, total.count = total.count) }, FUN.VALUE=data.frame(year=1, count=1, total.count=1)) > head(res) year count total.count 1 2000 5 23 2 2000 7 23 3 2000 11 23 4 2001 18 40 5 2001 4 40 6 2001 18 40 In Example 4, the mutate function with ddply enables you to define new columns that build on columns just defined. Since the construction of the data.frame using ore.groupApply is explicit, you always have complete control over when and how to use columns. # Example 4 ddply(d, "year", mutate, mu = mean(count), sigma = sd(count), cv = sigma/mu) res <- ore.groupApply (D, D$year, function(x) { mu <- mean(x$count) sigma <- sd(x$count) cv <- sigma/mu data.frame(year=x$year[1], count=x$count, mu=mu, sigma=sigma, cv=cv) }, FUN.VALUE=data.frame(year=1, count=1, mu=1,sigma=1,cv=1)) R> head(res) year count mu sigma cv 1 2000 5 7.666667 3.055050 0.3984848 2 2000 7 7.666667 3.055050 0.3984848 3 2000 11 7.666667 3.055050 0.3984848 4 2001 18 13.333333 8.082904 0.6062178 5 2001 4 13.333333 8.082904 0.6062178 6 2001 18 13.333333 8.082904 0.6062178 In Example 5, ddply is used to partition data on multiple columns before constructing the result. Realizing this with ore.groupApply involves creating an index column out of the concatenation of the columns used for partitioning. This example also allows us to illustrate using the ORE transparency layer to subset the data. # Example 5 baseball.dat <- subset(baseball, year > 2000) # data from the plyr package x <- ddply(baseball.dat, c("year", "team"), summarize, homeruns = sum(hr)) We first push the data set to the database to get an ore.frame. We then add the composite column and perform the subset, using the transparency layer. Since the results from database execution are unordered, we will explicitly sort these results and view the first 6 rows. BB.DAT <- ore.push(baseball) BB.DAT$index <- with(BB.DAT, paste(year, team, sep="+")) BB.DAT2 <- subset(BB.DAT, year > 2000) X <- ore.groupApply (BB.DAT2, BB.DAT2$index, function(x) { data.frame(year=x$year[1], team=x$team[1], homeruns=sum(x$hr)) }, FUN.VALUE=data.frame(year=1, team="A", homeruns=1), parallel=FALSE) res <- ore.sort(X, by=c("year","team")) R> head(res) year team homeruns 1 2001 ANA 4 2 2001 ARI 155 3 2001 ATL 63 4 2001 BAL 58 5 2001 BOS 77 6 2001 CHA 63 Our next example is derived from the ggplot function documentation. This illustrates the use of ddply within using the ggplot2 package. We first create a data.frame with demo data and use ddply to create some statistics for each group (gp). We then use ggplot to produce the graph. We can take this same code, push the data.frame df to the database and invoke this on the database server. The graph will be returned to the client window, as depicted below. # Example 6 with ggplot2 library(ggplot2) df <- data.frame(gp = factor(rep(letters[1:3], each = 10)), y = rnorm(30)) # Compute sample mean and standard deviation in each group library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) # Set up a skeleton ggplot object and add layers: ggplot() + geom_point(data = df, aes(x = gp, y = y)) + geom_point(data = ds, aes(x = gp, y = mean), colour = 'red', size = 3) + geom_errorbar(data = ds, aes(x = gp, y = mean, ymin = mean - sd, ymax = mean + sd), colour = 'red', width = 0.4) DF <- ore.push(df) ore.tableApply(DF, function(df) { library(ggplot2) library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) ggplot() + geom_point(data = df, aes(x = gp, y = y)) + geom_point(data = ds, aes(x = gp, y = mean), colour = 'red', size = 3) + geom_errorbar(data = ds, aes(x = gp, y = mean, ymin = mean - sd, ymax = mean + sd), colour = 'red', width = 0.4) }) But let's take this one step further. Suppose we wanted to produce multiple graphs, partitioned on some index column. We replicate the data three times and add some noise to the y values, just to make the graphs a little different. We also create an index column to form our three partitions. Note that we've also specified that this should be executed in parallel, allowing Oracle Database to control and manage the server-side R engines. The result of ore.groupApply is an ore.list that contains the three graphs. Each graph can be viewed by printing the list element. df2 <- rbind(df,df,df) df2$y <- df2$y + rnorm(nrow(df2)) df2$index <- c(rep(1,300), rep(2,300), rep(3,300)) DF2 <- ore.push(df2) res <- ore.groupApply(DF2, DF2$index, function(df) { df <- df[,1:2] library(ggplot2) library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) ggplot() + geom_point(data = df, aes(x = gp, y = y)) + geom_point(data = ds, aes(x = gp, y = mean), colour = 'red', size = 3) + geom_errorbar(data = ds, aes(x = gp, y = mean, ymin = mean - sd, ymax = mean + sd), colour = 'red', width = 0.4) }, parallel=TRUE) res[[1]] res[[2]] res[[3]] To recap, we've illustrated how various uses of ddply from the plyr package can be realized in ore.groupApply, which affords the user explicit control over the contents of the data.frame result in a straightforward manner. We've also highlighted how ddply can be used within an ore.groupApply call.

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Launching php script through comman line - keeping terminal window open after execution

- by somethis

Oh, my girlfriend really likes it when I launch php scripts! There's something special about them, she says ... Thus, I coded this script to run throught the CLI (Command Line Interface) - so it's running locally, not on a web server. It launches just fine through right click open run in terminal but closes right after execution. **Is there a way to keep the terminal window open? Of course I can launch it through a terminal window - which would stay open - but I'm looking for a one click action. With bash scripts I use $SHELL but that didn't work (see code below). So far, the only thing I came up with is sleep(10); which gives me 10 seconds for my girl to check the output. I'd rather close the terminal window manually, though. #!/usr/bin/php -q <?php echo "Hello World \n"; # wait before closing terminal window sleep(10); # the following line doesn't work $SHELL; ?> (PHP 5.4.6-1ubuntu1.2 (cli) (built: Mar 11 2013 14:57:54) Copyright (c) 1997-2012 The PHP Group Zend Engine v2.4.0, Copyright (c) 1998-2012 Zend Technologies )

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Execution plan warnings–All that glitters is not gold

- by Dave Ballantyne

In a previous post, I showed you the new execution plan warnings related to implicit and explicit warnings. Pretty much as soon as i hit ’post’, I noticed something rather odd happening. This statement : select top(10) SalesOrderHeader.SalesOrderID, SalesOrderNumberfrom Sales.SalesOrderHeaderjoin Sales.SalesOrderDetail on SalesOrderHeader.SalesOrderID = SalesOrderDetail.SalesOrderID Throws the “Type conversion may affect cardinality estimation” warning. Ive done no such conversion in my statement why would that be ? Well, SalesOrderNumber is a computed column , “(isnull(N'SO'+CONVERT([nvarchar](23),[SalesOrderID],0),N'*** ERROR ***'))”, so thats where the conversion is. Wait!!! Am i saying that every type conversion will throw the warning ? Thankfully, no. It only appears for columns that are used in predicates ,even if the predicate / join condition is fine , and the column is indexed ( and/or , presumably has statistics). Hopefully , this wont lead to to many wild goose chases, but is definitely something to bear in mind. If you want to see this fixed then upvote my connect item here.

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SSIS Catalog: How to use environment in every type of package execution

- by Kevin Shyr

Here is a good blog on how to create a SSIS Catalog and setting up environments. http://sqlblog.com/blogs/jamie_thomson/archive/2010/11/13/ssis-server-catalogs-environments-environment-variables-in-ssis-in-denali.aspx Here I will summarize 3 ways I know so far to execute a package while using variables set up in SSIS Catalog environment. First way, we have SSIS project having reference to environment, and having one of the project parameter using a value set up in the environment called "Development". With this set up, you are limited to calling the packages by right-clicking on the packages in the SSIS catalog list and select Execute, but you are free to choose absolute or relative path of the environment. The following screenshot shows the 2 available paths to your SSIS environments. Personally, I use absolute path because of Option 3, just to keep everything simple for myself. The second option is to call through SQL Job. This does require you to configure your project to already reference an environment and use its variable. When a job step is set up, the configuration part will require you to select that reference again. This is more useful when you want to automate the same package that needs to be run in different environments. The third option is the most important to me as I have a SSIS framework that calls hundreds of packages. The main part of the stored procedure is in this post (http://geekswithblogs.net/LifeLongTechie/archive/2012/11/14/time-to-stop-using-ldquoexecute-package-taskrdquondash-a-way-to.aspx). But the top part had to be modified to include the logic to use environment reference. CREATE PROCEDURE [AUDIT].[LaunchPackageExecutionInSSISCatalog] @PackageName NVARCHAR(255) , @ProjectFolder NVARCHAR(255) , @ProjectName NVARCHAR(255) , @AuditKey INT , @DisableNotification BIT , @PackageExecutionLogID INT , @EnvironmentName NVARCHAR(128) = NULL , @Use32BitRunTime BIT = FALSE AS BEGIN TRY DECLARE @execution_id BIGINT = 0; -- Create a package execution IF @EnvironmentName IS NULL BEGIN EXEC [SSISDB].[catalog].[create_execution] @package_name=@PackageName, @execution_id=@execution_id OUTPUT, @folder_name=@ProjectFolder, @project_name=@ProjectName, @use32bitruntime=@Use32BitRunTime; END ELSE BEGIN DECLARE @EnvironmentID AS INT SELECT @EnvironmentID = [reference_id] FROM SSISDB.[internal].[environment_references] WITH(NOLOCK) WHERE [environment_name] = @EnvironmentName AND [environment_folder_name] = @ProjectFolder EXEC [SSISDB].[catalog].[create_execution] @package_name=@PackageName, @execution_id=@execution_id OUTPUT, @folder_name=@ProjectFolder, @project_name=@ProjectName, @reference_id=@EnvironmentID, @use32bitruntime=@Use32BitRunTime; END

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Work Execution in EAM

- by Annemarie Provisero

ADVISOR WEBCAST: Work Execution in EAM PRODUCT FAMILY: Manufacturing Enterprise Asset Management July 5, 2011 at 8 am PT, 9 am MT, 11 am ET The purpose of this webcast is to discuss EAM Work Order Management. This one-hour session is ideal for Functional Users, System Administrators, Database Administrators, and Customers with a basic knowledge of EAM and who raise or manage work orders and related processes. During this webcast, Zar will cover the various types of work orders and look at all the related activities associated with work orders including: setup, operations, tasks, work order transactions, relationship and planning. TOPICS WILL INCLUDE: Work Order Types (Routine, Planned Maintenance, Rebuild, Easy) Work Order statuses and other important setups Operations and Tasks Relationships Work Order Transactions Work Order Planning A short, live demonstration (only if applicable) and question and answer period will be included. Oracle Advisor Webcasts are dedicated to building your awareness around our products and services. This session does not replace offerings from Oracle Global Support Services. Click here to register for this session ------------------------------------------------------------------------------------------------------------- The above webcast is a service of the E-Business Suite Communities in My Oracle Support. For more information on other webcasts, please reference the Oracle Advisor Webcast Schedule.Click here to visit the E-Business Communities in My Oracle Support Note that all links require access to My Oracle Support.

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How to access CSS generated content with JavaScript

- by Boldewyn

I generate the numbering of my headers and figures with CSS's counter and content properties: img.figure:after { counter-increment: figure; content: "Fig. " counter(section) "." counter(figure); } This (appropriate browser assumed) gives a nice labelling "Fig. 1.1", "Fig. 1.2" and so on following any image. Question: How can I access this from Javascript? The question is twofold in that I'd like to access either the current value of a certain counter (at a certain DOM node) or the value of the CSS generated content (at a certain DOM node) or, obviously, both information. Background: I'd like to append to links back-referencing to figures the appropriate number, like this: <a href="#fig1">see here</h> ------------------------^ " (Fig 1.1)" inserted via JS As far as I can see, it boils down to this problem: I could access content or counter via getComputedStyle: var fig_content = window.getComputedStyle( document.getElementById('fig-a'), ':after').content; However, this is not the live value, but the one declared in the stylesheet. I cannot find any interface to access the real live value.

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Delay command execution over sockets

- by David

I've been trying to fix the game loop in a real time (tick delay) MUD. I realized using Thread.Sleep would seem clunky when the user spammed commands through their choice of client (Zmud, etc) e.g. east;south;southwest would wait three move ticks and then output everything from the past couple rooms. The game loop basically calls a Flush and Fill method for each socket during each tick (50ms) private void DoLoop() { Stopwatch stopWatch = new Stopwatch(); stopWatch.Start(); while (running) { // for each socket, flush and fill ConnectionMonitor.Update(); stopWatch.Stop(); WaitIfNeeded(stopWatch.ElapsedMilliseconds); stopWatch.Reset(); } } The Fill method fires the command events, but as mentioned before, they currently block using Thread.Sleep. I tried adding a "ready" flag to the state object that attempts to execute the command along with a queue of spammed commands, but it ends up executing one command and queuing up the rest i.e. each subsequent command executes something that got queued up that should've been executed before. I must be missing something about the timer. private readonly Queue<SpammedCommand> queuedCommands = new Queue<SpammedCommand>(); private bool ready = true; private void TryExecuteCommand(string input) { var commandContext = CommandContext.Create(input); var player = Server.Current.Database.Get<Player>(Session.Player.Key); var commandInfo = Server.Current.CommandLookup .FindCommand(commandContext.CommandName, player.IsAdmin); if (commandInfo != null) { if (!ready) { // queue command queuedCommands.Enqueue(new SpammedCommand() { Context = commandContext, Info = commandInfo }); return; } if (queuedCommands.Count > 0) { // queue the incoming command queuedCommands.Enqueue(new SpammedCommand() { Context = commandContext, Info = commandInfo, }); // dequeue and execute var command = queuedCommands.Dequeue(); command.Info.Command.Execute(Session, command.Context); setTimeout(command.Info.TickLength); return; } commandInfo.Command.Execute(Session, commandContext); setTimeout(commandInfo.TickLength); } else { Session.WriteLine("Command not recognized"); } } Finally, setTimeout was supposed to set the execution delay (TickLength) for that command, and makeReady just sets the ready flag on the state object to true. private void setTimeout(TickDelay tickDelay) { ready = false; var t = new System.Timers.Timer() { Interval = (long) tickDelay, AutoReset = false, }; t.Elapsed += makeReady; t.Start(); // fire this in tickDelay ms } // MAKE READYYYYY!!!! private void makeReady(object sender, System.Timers.ElapsedEventArgs e) { ready = true; } Am I missing something about the System.Timers.Timer created in setTimeout? How can I execute (and output) spammed commands per TickLength without using Thread.Sleep?

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Using the StopWatch class to calculate the execution time of a block of code

- by vik20000in

Many of the times while doing the performance tuning of some, class, webpage, component, control etc. we first measure the current time taken in the execution of that code. This helps in understanding the location in code which is actually causing the performance issue and also help in measuring the amount of improvement by making the changes. This measurement is very important as it helps us understand the problem in code, Helps us to write better code next time (as we have already learnt what kind of improvement can be made with different code) . Normally developers create 2 objects of the DateTime class. The exact time is collected before and after the code where the performance needs to be measured. Next the difference between the two objects is used to know about the time spent in the code that is measured. Below is an example of the sample code. DateTime dt1, dt2; dt1 = DateTime.Now; for (int i = 0; i < 1000000; i++) { string str = "string"; } dt2 = DateTime.Now; TimeSpan ts = dt2.Subtract(dt1); Console.WriteLine("Time Spent : " + ts.TotalMilliseconds.ToString()); The above code works great. But the dot net framework also provides for another way to capture the time spent on the code without doing much effort (creating 2 datetime object, timespan object etc..). We can use the inbuilt StopWatch class to get the exact time spent. Below is an example of the same work with the help of the StopWatch class. Stopwatch sw = Stopwatch.StartNew(); for (int i = 0; i < 1000000; i++) { string str = "string"; } sw.Stop(); Console.WriteLine("Time Spent : " +sw.Elapsed.TotalMilliseconds.ToString()); [Note the StopWatch class resides in the System.Diagnostics namespace] If you use the StopWatch class the time taken for measuring the performance is much better, with very little effort. Vikram

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General monitoring for SQL Server Analysis Services using Performance Monitor

- by Testas

A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs. File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor, a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used…… Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above 25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file

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