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  • Be the surgeon

    - by Rob Farley
    It’s a phrase I use often, especially when teaching, and I wish I had realised the concept years earlier. (And of course, fits with this month’s T-SQL Tuesday topic, hosted by Argenis Fernandez) When I’m sick enough to go to the doctor, I see a GP. I used to typically see the same guy, but he’s moved on now. However, when he has been able to roughly identify the area of the problem, I get referred to a specialist, sometimes a surgeon. Being a surgeon requires a refined set of skills. It’s why they often don’t like to be called “Doctor”, and prefer the traditional “Mister” (the history is that the doctor used to make the diagnosis, and then hand the patient over to the person who didn’t have a doctorate, but rather was an expert cutter, typically from a background in butchering). But if you ask the surgeon about the pain you have in your leg sometimes, you’ll get told to ask your GP. It’s not that your surgeon isn’t interested – they just don’t know the answer. IT is the same now. That wasn’t something that I really understood when I got out of university. I knew there was a lot to know about IT – I’d just done an honours degree in it. But I also knew that I’d done well in just about all my subjects, and felt like I had a handle on everything. I got into developing, and still felt that having a good level of understanding about every aspect of IT was a good thing. This got me through for the first six or seven years of my career. But then I started to realise that I couldn’t compete. I’d moved into management, and was spending my days running projects, rather than writing code. The kids were getting older. I’d had a bad back injury (ask anyone with chronic pain how it affects  your ability to concentrate, retain information, etc). But most of all, IT was getting larger. I knew kids without lives who knew more than I did. And I felt like I could easily identify people who were better than me in whatever area I could think of. Except writing queries (this was before I discovered technical communities, and people like Paul White and Dave Ballantyne). And so I figured I’d specialise. I wish I’d done it years earlier. Now, I can tell you plenty of people who are better than me at any area you can pick. But there are also more people who might consider listing me in some of their lists too. If I’d stayed the GP, I’d be stuck in management, and finding that there were better managers than me too. If you’re reading this, SQL could well be your thing. But it might not be either. Your thing might not even be in IT. Find out, and then see if you can be a world-beater at it. But it gets even better, because you can find other people to complement the things that you’re not so good at. My company, LobsterPot Solutions, has six people in it at the moment. I’ve hand-picked those six people, along with the one who quit. The great thing about it is that I’ve been able to pick people who don’t necessarily specialise in the same way as me. I don’t write their T-SQL for them – generally they’re good enough at that themselves. But I’m on-hand if needed. Consider Roger Noble, for example. He’s doing stuff in HTML5 and jQuery that I could never dream of doing to create an amazing HTML5 version of PivotViewer. Or Ashley Sewell, a guy who does project management far better than I do. I could go on. My team is brilliant, and I love them to bits. We’re all surgeons, and when we work together, I like to think we’re pretty good! @rob_farley

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  • Restrict number of characters to be typed for af:autoSuggestBehavior

    - by Arunkumar Ramamoorthy
    When using AutoSuggestBehavior for a UI Component, the auto suggest list is displayed as soon as the user starts typing in the field. In this article, we will find how to restrict the autosuggest list to be displayed till the user types in couple of characters. This would be more useful in the low latency networks and also the autosuggest list is bigger. We could display a static message to let the user know that they need to type in more characters to get a list for picking a value from. Final output we would expect is like the below image Lets see how we can implement this. Assuming we have an input text for the users to enter the country name and an autosuggest behavior is added to it. <af:inputText label="Country" id="it1"> <af:autoSuggestBehavior /> </af:inputText> Also, assuming we have a VO (we'll name it as CountryView for this example), with a view criteria to filter out the VO based on the bind variable passed. Now, we would generate View Impl class from the java node (including bind variables) and then expose the setter method of the bind variable to client interface. In the View layer, we would create a tree binding for the VO and the method binding for the setter method of the bind variable exposed above, in the pagedef file As we've already added an input text and an autosuggestbehavior for the test, we would not need to build the suggested items for the autosuggest list.Let us add a method in the backing bean to return us List of select items to be bound to the autosuggest list. padding: 5px; background-color: #fbfbfb; min-height: 40px; width: 544px; height: 168px; overflow: auto;"> public List onSuggest(String searchTerm) { ArrayList<SelectItem> selectItems = new ArrayList<SelectItem>(); if(searchTerm.length()>1) { //get access to the binding context and binding container at runtime BindingContext bctx = BindingContext.getCurrent(); BindingContainer bindings = bctx.getCurrentBindingsEntry(); //set the bind variable value that is used to filter the View Object //query of the suggest list. The View Object instance has a View //Criteria assigned OperationBinding setVariable = (OperationBinding) bindings.get("setBind_CountryName"); setVariable.getParamsMap().put("value", searchTerm); setVariable.execute(); //the data in the suggest list is queried by a tree binding. JUCtrlHierBinding hierBinding = (JUCtrlHierBinding) bindings.get("CountryView1"); //re-query the list based on the new bind variable values hierBinding.executeQuery(); //The rangeSet, the list of queries entries, is of type //JUCtrlValueBndingRef. List<JUCtrlValueBindingRef> displayDataList = hierBinding.getRangeSet(); for (JUCtrlValueBindingRef displayData : displayDataList){ Row rw = displayData.getRow(); //populate the SelectItem list selectItems.add(new SelectItem( (String)rw.getAttribute("Name"), (String)rw.getAttribute("Name"))); } } else{ SelectItem a = new SelectItem("","Type in two or more characters..","",true); selectItems.add(a); } return selectItems; } So, what we are doing in the above method is, to check the length of the search term and if it is more than 1 (i.e 2 or more characters), the return the actual suggest list. Otherwise, create a read only select item new SelectItem("","Type in two or more characters..","",true); and add it to the list of suggested items to be displayed. The last parameter for the SelectItem (boolean) is to make it as readOnly, so that users would not be able to select this static message from the displayed list. Finally, bind this method to the input text's autosuggestbehavior's suggestedItems property. <af:inputText label="Country" id="it1"> <af:autoSuggestBehavior suggestedItems="#{AutoSuggestBean.onSuggest}"/> </af:inputText>

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  • New R Interface to Oracle Data Mining Available for Download

    - by charlie.berger
      The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining's in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies. R-ODM is especially useful for: Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application Scripting of "production" data mining methodologies Customizing graphics of ODM data mining results (examples: classification, regression, anomaly detection) The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc. R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment's Comprehensive R Archive Network (CRAN). For information about the R environment, R packages and CRAN, see www.r-project.org. R-ODM is particularly intended for data analysts and statisticians familiar with R but not necessarily familiar with the Oracle database environment or PL/SQL. It is a convenient environment to rapidly experiment and prototype Data Mining models and applications. Data Mining models prototyped in the R environment can easily be deployed in their final form in the database environment, just like any other standard Oracle Data Mining model. What is R? R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files. The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme. R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. Since mid-1997 there has been a core group (the "R Core Team") who can modify the R source code archive. Besides this core group many R users have contributed application code as represented in the near 1,500 publicly-available packages in the CRAN archive (which has shown exponential growth since 2001; R News Volume 8/2, October 2008). Today the R community is a vibrant and growing group of dozens of thousands of users worldwide. It is free software distributed under a GNU-style copyleft, and an official part of the GNU project ("GNU S"). Resources: R website / CRAN R-ODM

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  • Oracle BI Applications for Industry Sectors

    - by Mike.Hallett(at)Oracle-BI&EPM
    Normal 0 false false false EN-GB X-NONE X-NONE Oracle BI Applications already provide pre-built line-of-business analytic applications to over 4,000 customers: these expose the data otherwise locked inside ERP and CRM applications, giving the business user the analytics they need, and a greater ability to self-service ad-hoc queries. Now you can also take advantage of the pre-built Oracle BI Applications approach for industry sector specific analytics to streamline your client’s operations, offer better services, and increase profit margins. Find out more at http://www.oracle.com/us/solutions/business-analytics/analytic-applications/industry/overview/index.html. Retail Education Oracle Retail Merchandising Analytics Oracle Student Information Analytics Oracle Retail Customer Analytics Public Sector Financial Services Oracle Tax Analytics Oracle Financial Analytics Manufacturing Health Care Oracle Manufacturing Analytics Oracle Enterprise Healthcare Analytics Asset Intensive Oracle Clinical Development Analytics Oracle Enterprise Asset Management Analytics Oracle Operating Room Analytics Related Links Health Sciences Analytic Applications for Your Business Role Oracle Health Sciences Clinical Development Analytics Analytic Applications for Your Product Line Oracle Argus Analytics Oracle Business Intelligence Tools and Technology Communication Oracle Exalytics In-Memory Machine "The adoption of Oracle Financial Services Analytic Applications is of great significance to the bank's transition to more rigorous and risk-averse management practices."Yang Changxue, Project Manager Oracle Communications Data Model /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} table.MsoTableGrid {mso-style-name:"Table Grid"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-priority:59; mso-style-unhide:no; border:solid windowtext 1.0pt; mso-border-alt:solid windowtext .5pt; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-border-insideh:.5pt solid windowtext; mso-border-insidev:.5pt solid windowtext; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;}

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  • How the number of indexes built on a table can impact performances?

    - by Davide Mauri
    We all know that putting too many indexes (I’m talking of non-clustered index only, of course) on table may produce performance problems due to the overhead that each index bring to all insert/update/delete operations on that table. But how much? I mean, we all agree – I think – that, generally speaking, having many indexes on a table is “bad”. But how bad it can be? How much the performance will degrade? And on a concurrent system how much this situation can also hurts SELECT performances? If SQL Server take more time to update a row on a table due to the amount of indexes it also has to update, this also means that locks will be held for more time, slowing down the perceived performance of all queries involved. I was quite curious to measure this, also because when teaching it’s by far more impressive and effective to show to attended a chart with the measured impact, so that they can really “feel” what it means! To do the tests, I’ve create a script that creates a table (that has a clustered index on the primary key which is an identity column) , loads 1000 rows into the table (inserting 1000 row using only one insert, instead of issuing 1000 insert of one row, in order to minimize the overhead needed to handle the transaction, that would have otherwise ), and measures the time taken to do it. The process is then repeated 16 times, each time adding a new index on the table, using columns from table in a round-robin fashion. Test are done against different row sizes, so that it’s possible to check if performance changes depending on row size. The result are interesting, although expected. This is the chart showing how much time it takes to insert 1000 on a table that has from 0 to 16 non-clustered indexes. Each test has been run 20 times in order to have an average value. The value has been cleaned from outliers value due to unpredictable performance fluctuations due to machine activity. The test shows that in a  table with a row size of 80 bytes, 1000 rows can be inserted in 9,05 msec if no indexes are present on the table, and the value grows up to 88 (!!!) msec when you have 16 indexes on it This means a impact on performance of 975%. That’s *huge*! Now, what happens if we have a bigger row size? Say that we have a table with a row size of 1520 byte. Here’s the data, from 0 to 16 indexes on that table: In this case we need near 22 msec to insert 1000 in a table with no indexes, but we need more that 500msec if the table has 16 active indexes! Now we’re talking of a 2410% impact on performance! Now we can have a tangible idea of what’s the impact of having (too?) many indexes on a table and also how the size of a row also impact performances. That’s why the golden rule of OLTP databases “few indexes, but good” is so true! (And in fact last week I saw a database with tables with 1700bytes row size and 23 (!!!) indexes on them!) This also means that a too heavy denormalization is really not a good idea (we’re always talking about OLTP systems, keep it in mind), since the performance get worse with the increase of the row size. So, be careful out there, and keep in mind the “equilibrium” is the key world of a database professional: equilibrium between read and write performance, between normalization and denormalization, between to few and too may indexes. PS Tests are done on a VMWare Workstation 7 VM with 2 CPU and 4 GB of Memory. Host machine is a Dell Precsioni M6500 with i7 Extreme X920 Quad-Core HT 2.0Ghz and 16Gb of RAM. Database is stored on a SSD Intel X-25E Drive, Simple Recovery Model, running on SQL Server 2008 R2. If you also want to to tests on your own, you can download the test script here: Open TestIndexPerformance.sql

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • SSAS: Utility to export SQL code from your cube's Data Source View (DSV)

    - by DrJohn
    When you are working on a cube, particularly in a multi-person team, it is sometimes necessary to review what changes that have been done to the SQL queries in the cube's data source view (DSV). This can be a problem as the SQL editor in the DSV is not the best interface to review code. Now of course you can cut and paste the SQL into SSMS, but you have to do each query one-by-one. What is worse your DBA is unlikely to have BIDS installed, so you will have to manually export all the SQL yourself and send him the files. To make it easy to get hold of the SQL in a Data Source View, I developed a C# utility which connects to an OLAP database and uses Analysis Services Management Objects (AMO) to obtain and export all the SQL to a series of files. The added benefit of this approach is that these SQL files can be placed under source code control which means the DBA can easily compare one version with another. The Trick When I came to implement this utility, I quickly found that the AMO API does not give direct access to anything useful about the tables in the data source view. Iterating through the DSVs and tables is easy, but getting to the SQL proved to be much harder. My Google searches returned little of value, so I took a look at the idea of using the XmlDom to open the DSV’s XML and obtaining the SQL from that. This is when the breakthrough happened. Inspecting the DSV’s XML I saw the things I was interested in were called TableType DbTableName FriendlyName QueryDefinition Searching Google for FriendlyName returned this page: Programming AMO Fundamental Objects which hinted at the fact that I could use something called ExtendedProperties to obtain these XML attributes. This simplified my code tremendously to make the implementation almost trivial. So here is my code with appropriate comments. The full solution can be downloaded from here: ExportCubeDsvSQL.zip   using System;using System.Data;using System.IO;using Microsoft.AnalysisServices; ... class code removed for clarity// connect to the OLAP server Server olapServer = new Server();olapServer.Connect(config.olapServerName);if (olapServer != null){ // connected to server ok, so obtain reference to the OLAP databaseDatabase olapDatabase = olapServer.Databases.FindByName(config.olapDatabaseName);if (olapDatabase != null){ Console.WriteLine(string.Format("Succesfully connected to '{0}' on '{1}'",   config.olapDatabaseName,   config.olapServerName));// export SQL from each data source view (usually only one, but can be many!)foreach (DataSourceView dsv in olapDatabase.DataSourceViews){ Console.WriteLine(string.Format("Exporting SQL from DSV '{0}'", dsv.Name));// for each table in the DSV, export the SQL in a fileforeach (DataTable dt in dsv.Schema.Tables){ Console.WriteLine(string.Format("Exporting SQL from table '{0}'", dt.TableName)); // get name of the table in the DSV// use the FriendlyName as the user inputs this and therefore has control of itstring queryName = dt.ExtendedProperties["FriendlyName"].ToString().Replace(" ", "_");string sqlFilePath = Path.Combine(targetDir.FullName, queryName + ".sql"); // delete the sql file if it exists... file deletion code removed for clarity// write out the SQL to a fileif (dt.ExtendedProperties["TableType"].ToString() == "View"){ File.WriteAllText(sqlFilePath, dt.ExtendedProperties["QueryDefinition"].ToString());}if (dt.ExtendedProperties["TableType"].ToString() == "Table"){ File.WriteAllText(sqlFilePath, dt.ExtendedProperties["DbTableName"].ToString()); } } } Console.WriteLine(string.Format("Successfully written out SQL scripts to '{0}'", targetDir.FullName)); } }   Of course, if you are following industry best practice, you should be basing your cube on a series of views. This will mean that this utility will be of limited practical value unless of course you are inheriting a project and want to check if someone did the implementation correctly.

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  • Be the surgeon

    - by Rob Farley
    It’s a phrase I use often, especially when teaching, and I wish I had realised the concept years earlier. (And of course, fits with this month’s T-SQL Tuesday topic, hosted by Argenis Fernandez) When I’m sick enough to go to the doctor, I see a GP. I used to typically see the same guy, but he’s moved on now. However, when he has been able to roughly identify the area of the problem, I get referred to a specialist, sometimes a surgeon. Being a surgeon requires a refined set of skills. It’s why they often don’t like to be called “Doctor”, and prefer the traditional “Mister” (the history is that the doctor used to make the diagnosis, and then hand the patient over to the person who didn’t have a doctorate, but rather was an expert cutter, typically from a background in butchering). But if you ask the surgeon about the pain you have in your leg sometimes, you’ll get told to ask your GP. It’s not that your surgeon isn’t interested – they just don’t know the answer. IT is the same now. That wasn’t something that I really understood when I got out of university. I knew there was a lot to know about IT – I’d just done an honours degree in it. But I also knew that I’d done well in just about all my subjects, and felt like I had a handle on everything. I got into developing, and still felt that having a good level of understanding about every aspect of IT was a good thing. This got me through for the first six or seven years of my career. But then I started to realise that I couldn’t compete. I’d moved into management, and was spending my days running projects, rather than writing code. The kids were getting older. I’d had a bad back injury (ask anyone with chronic pain how it affects  your ability to concentrate, retain information, etc). But most of all, IT was getting larger. I knew kids without lives who knew more than I did. And I felt like I could easily identify people who were better than me in whatever area I could think of. Except writing queries (this was before I discovered technical communities, and people like Paul White and Dave Ballantyne). And so I figured I’d specialise. I wish I’d done it years earlier. Now, I can tell you plenty of people who are better than me at any area you can pick. But there are also more people who might consider listing me in some of their lists too. If I’d stayed the GP, I’d be stuck in management, and finding that there were better managers than me too. If you’re reading this, SQL could well be your thing. But it might not be either. Your thing might not even be in IT. Find out, and then see if you can be a world-beater at it. But it gets even better, because you can find other people to complement the things that you’re not so good at. My company, LobsterPot Solutions, has six people in it at the moment. I’ve hand-picked those six people, along with the one who quit. The great thing about it is that I’ve been able to pick people who don’t necessarily specialise in the same way as me. I don’t write their T-SQL for them – generally they’re good enough at that themselves. But I’m on-hand if needed. Consider Roger Noble, for example. He’s doing stuff in HTML5 and jQuery that I could never dream of doing to create an amazing HTML5 version of PivotViewer. Or Ashley Sewell, a guy who does project management far better than I do. I could go on. My team is brilliant, and I love them to bits. We’re all surgeons, and when we work together, I like to think we’re pretty good! @rob_farley

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  • Production Access Denied! Who caused this rule anyways?

    - by Matt Watson
    One of the biggest challenges for most developers is getting access to production servers. In smaller dev teams of less than about 5 people everyone usually has access. Then you hire developer #6, he messes something up in production... and now nobody has access. That is how it always starts in small dev teams. I think just about every rule of life there is gets created this way. One person messes it up for the rest of us. Rules are then put in place to try and prevent it from happening again.Breaking the rules is in our nature. In this example it is for good cause and a necessity to support our applications and troubleshoot problems as they arise. So how do developers typically break the rules? Some create their own method to collect log files off servers so they can see them. Expensive log management programs can collect log files, but log files alone are not enough. Centralizing where important errors are logged to is common. Some lucky developers are given production server access by the IT operations team out of necessity. Wait. That's not fair to all developers and knowingly breaks the company rule!  When customers complain or the system is down, the rules go out the window. Commonly lead developers get production access because they are ultimately responsible for supporting the application and may be the only person who knows how to fix it. The problem with only giving lead developers production access is it doesn't scale from a support standpoint. Those key employees become the go to people to help solve application problems, but they also become a bottleneck. They end up spending up to half of their time every day helping resolve application defects, performance problems, or whatever the fire of the day is. This actually the last thing you want your lead developers doing. They should be working on something more strategic like major enhancements to the product. Having production access can actually be a curse if you are the guy stuck hunting down log files all day. Application defects are good tasks for junior developers. They can usually handle figuring out simple application problems. But nothing is worse than being a junior developer who can't figure out those problems and the back log of them grows and grows. Some of them require production server access to verify a deployment was done correctly, verify config settings, view log files, or maybe just restart an application. Since the junior developers don't have access, they end up bugging the developers who do have access or they track down a system admin to help. It can take hours or days to see server information that would take seconds or minutes if they had access of their own. It is very frustrating to the developer trying to solve the problem, the system admin being forced to help, and most importantly your customers who are not happy about the situation. This process is terribly inefficient. Production database access is also important for solving application problems, but presents a lot of risk if developers are given access. They could see data they shouldn't.  They could write queries on accident to update data, delete data, or merely select every record from every table and bring your database to its knees. Since most of the application we create are data driven, it can be very difficult to track down application bugs without access to the production databases.Besides it being against the rule, why don't all developers have access? Most of the time it comes down to security, change of control, lack of training, and other valid reasons. Developers have been known to tinker with different settings to try and solve a problem and in the process forget what they changed and made the problem worse. So it is a double edge sword. Don't give them access and fixing bugs is more difficult, or give them access and risk having more bugs or major outages being created!Matt WatsonFounder, CEOStackifyAgile Support for Agile Developers

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  • Administer, manage, monitor, and fine tune the performance of your Oracle SOA Suite 11g Service Infrastructure and SOA composite applications.

    - by JuergenKress
    Key Features of the book If you are an Oracle SOA suite administrator, then this book is your bible. It gives you everything you need to know about all your tasks and help you to apply what you learn in your everyday life right from the first chapter. The book walks through promoting code across environments, performance tuning the service infrastructure, monitoring the environment, configuring security policies, managing the dehydration store, backing and restoring environments and so on. Packed with real-world examples from authors' own experiences, this books offers a unique insight into Oracle SOA Suite Administration. Detailed description The book begins with an introduction of SOA and quickly moves on to management of SOA composite applications. Readers will learn how to manage composite applications, their deployments and lifecycles. Equipped with this knowledge, readers will be introduced to monitoring and performance tuning SOA Suite, monitoring instances, messages, and composite applications, managing faults and exceptions, configuring audit levels of composite applications to include end-to-end monitoring through the use of extended logging as well as administering and configuring all SOA Suite components. A very important aspect of administration is tuning and optimizing the infrastructure for performance and book offers real work recommendations to monitor and performance tune service engines, the underlying WebLogic server, threads and timeouts, files systems, and composite applications. It also covers detailed administration of individual service components, configuring the infrastructure MBeans using both Oracle Enterprise Manager Fusion Middleware Control and WLST based scripts, migrating worklist preferences and BAM data across environments, setting up Email, LDAP and custom XPath. An administrator is always trusted with troubleshooting and root causing problems in the infrastructure and this book will help you through the troubleshooting approaches as how to identify faults and exception through extended logging and thread dumps and find solutions to common startup problems and deployment issues. The advanced contents of this book explains OWSM security framework and how to secure components deployed to the infrastructure along with the details of all groundwork needed to ready the environment. Last few chapters help you to understand and deal with managing the metadata services repository and dehydration store, backup and recovery and concluding with advanced topics such as silent/scripted installations, cloning, upgrading, patching and high availability installations. Packed with real-world examples, and tips straight from the trench; this book offers insights into SOA Suite administration that you will not find elsewhere. Part of our writing style in this book draws heavily on the philosophy of reuse and as such the book provide an ample of executable SQL queries and WLST scripts that administrators can reuse and extend to perform most of the administration tasks such as monitoring instances, processing times, instance states and perform automatic deployments, tuning, migration, and installation. These scripts are spread over each of the chapters in the book and can also be downloaded from here. The book is available in different formats at the following websites: Paperback and eBook versions & Kindle version. It is available for order and signed copies are available through our web site. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: SOA book,SOA Suite Adminsitration,SOA Community,Oracle SOA,Oracle BPM,Community,OPN,Jürgen Kress

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  • Detect Unicode Usage in SQL Column

    One optimization you can make to a SQL table that is overly large is to change from nvarchar (or nchar) to varchar (or char).  Doing so will cut the size used by the data in half, from 2 bytes per character (+ 2 bytes of overhead for varchar) to only 1 byte per character.  However, you will lose the ability to store Unicode characters, such as those used by many non-English alphabets.  If the tables are storing user-input, and your application is or might one day be used internationally, its likely that using Unicode for your characters is a good thing.  However, if instead the data is being generated by your application itself or your development team (such as lookup data), and you can be certain that Unicode character sets are not required, then switching such columns to varchar/char can be an easy improvement to make. Avoid Premature Optimization If you are working with a lookup table that has a small number of rows, and is only ever referenced in the application by its numeric ID column, then you wont see any benefit to using varchar vs. nvarchar.  More generally, for small tables, you wont see any significant benefit.  Thus, if you have a general policy in place to use nvarchar/nchar because it offers more flexibility, do not take this post as a recommendation to go against this policy anywhere you can.  You really only want to act on measurable evidence that suggests that using Unicode is resulting in a problem, and that you wont lose anything by switching to varchar/char. Obviously the main reason to make this change is to reduce the amount of space required by each row.  This in turn affects how many rows SQL Server can page through at a time, and can also impact index size and how much disk I/O is required to respond to queries, etc.  If for example you have a table with 100 million records in it and this table has a column of type nchar(5), this column will use 5 * 2 = 10 bytes per row, and with 100M rows that works out to 10 bytes * 100 million = 1000 MBytes or 1GB.  If it turns out that this column only ever stores ASCII characters, then changing it to char(5) would reduce this to 5*1 = 5 bytes per row, and only 500MB.  Of course, if it turns out that it only ever stores the values true and false then you could go further and replace it with a bit data type which uses only 1 byte per row (100MB  total). Detecting Whether Unicode Is In Use So by now you think that you have a problem and that it might be alleviated by switching some columns from nvarchar/nchar to varchar/char but youre not sure whether youre currently using Unicode in these columns.  By definition, you should only be thinking about this for a column that has a lot of rows in it, since the benefits just arent there for a small table, so you cant just eyeball it and look for any non-ASCII characters.  Instead, you need a query.  Its actually very simple: SELECT DISTINCT(CategoryName)FROM CategoriesWHERE CategoryName <> CONVERT(varchar, CategoryName) Summary Gregg Stark for the tip. Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Delivering SOA Governance with EAMS and Oracle Enterprise Repository by Link Consulting Team

    - by JuergenKress
    In the last 12 years Link Consulting has been making its presence in specific areas such as Governance and Architecture, both in terms of practices and methodologies, products, know-how and technological expertise. The Enterprise Architecture Management System - Oracle Enterprise Edition (EAMS - OER Edition) is the result of this experience and combines the architecture management solution with OER in order to deliver a product specialized for SOA Governance that gathers the better of two worlds in solution that enables SOA Governance projects, initiatives and programs. Enterprise Architecture Management System Enterprise Architecture Management System (EAMS), is an automation based solution that enables the efficient management of Enterprise Architectures. The solution uses configured enterprise repositories and takes advantages of its features to provide automation capabilities to the users. EAMS provides capabilities to create/customize/analyze repository data, architectural blueprints, reports and analytic charts. Oracle Enterprise Repository Oracle Enterprise Repository (OER) is one of the major and central elements of the Oracle SOA Governance solution. Oracle Enterprise Repository provides the tools to manage and govern the metadata for any type of software asset, from business processes and services to patterns, frameworks, applications, components, and models. OER maps the relationships and inter-dependencies that connect those assets to improve impact analysis, promote and optimize their reuse, and measure their impact on the bottom line. It provides the visibility, feedback, controls, and analytics to keep your SOA on track to deliver business value. The intense focus on automation helps to overcome barriers to SOA adoption and streamline governance throughout the lifecycle. Core capabilities of the OER include: Asset Management Asset Lifecycle Management Usage Tracking Service Discovery Version Management Dependency Analysis Portfolio Management EAMS - OER Edition The solution takes the advantages and features from both products and combines them in a symbiotic tool that enhances the quality of SOA Governance Initiatives and Programs. EAMS is able to produce a vast number of outputs by combining its analytical engine, SOA-specific configurations and the assets in OER and other related tools, catalogs and repositories. The configurations encompass not only the extendable parametrization of the metadata but also fully configurable blueprints, PowerPoint reports, charts and queries. The SOA blueprints The solution comes with a set of predefined architectural representations that help the organization better perceive their SOA landscape. More blueprints can be easily created in order to accommodate the organizations needs in terms of detail, audience and metadata. Charts & Dashboards The solution encompasses a set of predefined charts and dashboards that promote a more agile way to control and explore the assets. Time Based Visualization All representations are time bound, and with EAMS - OER you can truly govern SOA with a complete view of the Past, Present and Future; The solution delivers Gap Analysis, a project oriented approach while taking into consideration the As-Was, As-Is an To-Be. Time based visualization differentiating factors: Extensive automation and maintenance of architectural representations Organization wide solution. Easy access and navigation to and between all architectural artifacts and representations. Flexible meta-model, customization and extensibility capabilities. Lifecycle management and enforcement of the time dimension over all the repository content. Profile based customization. Comprehensive visibility Architectural alignment Friendly and striking user interfaces For more information on EAMS visit us here. For more information on SOA visit us here. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: Link Consulting,OER,OSR,SOA Governance,SOA Community,Oracle SOA,Oracle BPM,BPM Community,OPN,Jürgen Kress

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  • Crime Scene Investigation: SQL Server

    - by Rodney Landrum
    “The packages are running slower in Prod than they are in Dev” My week began with this simple declaration from one of our lead BI developers, quickly followed by an emailed spreadsheet demonstrating that, over 5 executions, an extensive ETL process was running average 630 seconds faster on Dev than on Prod. The situation needed some scientific investigation to determine why the same code, the same data, the same schema would yield consistently slower results on a more powerful server. Prod had yet to be officially christened with a “Go Live” date so I had the time, and having recently been binge watching CSI: New York, I also had the inclination. An inspection of the two systems, Prod and Dev, revealed the first surprise: although Prod was indeed a “bigger” system, with double the amount of RAM of Dev, the latter actually had twice as many processor cores. On neither system did I see much sign of resources being heavily taxed, while the ETL process was running. Without any real supporting evidence, I jumped to a conclusion that my years of performance tuning should have helped me avoid, and that was that the hardware differences explained the better performance on Dev. We spent time setting up a Test system, similarly scoped to Prod except with 4 times the cores, and ported everything across. The results of our careful benchmarks left us truly bemused; the ETL process on the new server was slower than on both other systems. We burned more time tweaking server configurations, monitoring IO and network latency, several times believing we’d uncovered the smoking gun, until the results of subsequent test runs pitched us back into confusion. Finally, I decided, enough was enough. Hadn’t I learned very early in my DBA career that almost all bottlenecks were caused by code and database design, not hardware? It was time to get back to basics. With over 100 SSIS packages and hundreds of queries, each handling specific tasks such as file loads, bulk inserts, transforms, logging, and so on, the task seemed formidable. And yet, after barely an hour spent with Profiler, Extended Events, and wait statistics DMVs, I had a lead in the shape of a query that joined three tables, containing millions of rows, returned 3279 results, but performed 239K logical reads. As soon as I looked at the execution plans for the query in Dev and Test I saw the culprit, an implicit conversion warning on a join predicate field that was numeric in one table and a varchar(50) in another! I turned this information over to the BI developers who quickly resolved the data type mismatches and found and fixed “several” others as well. After the schema changes the same query with the same databases ran in under 1 second on all systems and reduced the logical reads down to fewer than 300. The analysis also revealed that on Dev, the ETL task was pulling data across a LAN, whereas Prod and Test were connected across slower WAN, in large part explaining why the same process ran slower on the latter two systems. Loading the data locally on Prod delivered a further 20% gain in performance. As we progress through our DBA careers we learn valuable lessons. Sometimes, with a project deadline looming and pressure mounting, we choose to forget them. I was close to giving into the temptation to throw more hardware at the problem. I’m pleased at least that I resisted, though I still kick myself for not looking at the code on day one. It can seem a daunting prospect to return to the fundamentals of the code so close to roll out, but with the right tools, and surprisingly little time, you can collect the evidence that reveals the true problem. It is a lesson I trust I will remember for my next 20 years as a DBA, if I’m ever again tempted to bypass the evidence.

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  • Social Search: Looking for Love

    - by Mike Stiles
    For marketers and enterprise executives who have placed a higher priority on and allocated bigger budgets to search over social, it might be time to notice yet another shift that’s well underway. Social is search. Search marketing was always more of an internal slam-dunk than other digital initiatives. Even a C-suite that understood little about the new technology world knew it’s a good thing when people are able to find you. Google was the new Yellow Pages. Only with Google, you could get your listing first without naming yourself “AAAA Plumbing.” There were wizards out there who could give your business prominence in front of people who were specifically looking for what you offered. Other search giants like Bing also came along to offer such ideal matchmaking possibilities. But what if the consumer isn’t using a search engine to find what they’re looking for? And what if the search engines started altering their algorithms so that search placement manipulation was more difficult? Both of those things have started to happen. Experian Hitwise’s numbers show that visits to the major search engines in the UK dropped 100 million through August. Search engines are far from dead, or even challenged. But more and more, the public is discovering the sites and brands they need through advice they get via social, not search. You’ll find the worlds of social and search increasingly co-mingling as well. Search behemoths Google and Bing are including Facebook and Google+ into their engines. Meanwhile, Facebook and Twitter have done some integration of global web search into their platforms. So what makes social such a worthwhile search entity for brands? First and foremost, the consumer has demonstrated a behavior of acting on recommendations from social connections. A cry in the wilderness like, “Anybody know any good catering companies?” will usually yield a link (and an endorsement) from a friend such as “Yeah, check out Just-Cheese-Balls Catering.” There’s no such human-driven force/influence behind the big search engines. Facebook’s Mark Zuckerberg and others call it “Friend Mining.” It is, in essence, searching for answers from friends’ experiences as opposed to faceless code. And Facebook has all of those friends’ experiences already stored as data. eMarketer says search in an $18 billion business, and investors are really into it. So no shock Facebook’s ready to leverage their social graph into relevant search. What do you do about all this as a brand? For one thing, it’s going to lead to some interesting paid marketing opportunities around the corner, including Sponsored Stories bought against certain queries, inserting deals into search results, capitalizing on social search results on mobile, etc. Apart from that, it might be time to stop mentally separating social and search in your strategic planning and budgeting. Courting your fans on social will cumulatively add up to more valuable, personally endorsed recommendations for your company when a consumer conducts a search on social. Fail to foster those relationships, fail to engage, fail to provide knock-em-dead customer service, fail to wow them with your actual products and services…and you’ll wind up with the visibility you deserve in social search results.

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  • Customers Discuss: Real-World Operational Reporting with Oracle GoldenGate

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} As businesses leverage business intelligence and analytics for day-to-day decision making, operational reporting solutions become more and more common. While some companies can use their production OLTP system for running operational reports, for many it is too much overhead and performance impact for transaction processing systems.  Oracle GoldenGate’s real-time data integration capabilities enable companies to create a real-time replica of their OLTP systems, dedicated for operational reporting. This instance can be optimized for the reports needed as well such as containing only the tables needed from the source. Oracle GoldenGate has certified solutions for many Oracle applications such as EBusiness Suite, Peoplesoft, JD Edwards, to offload operational reporting to another reporting server that has real-time data feeding from the production system. At Oracle OpenWorld we will be hearing from a panel of Oracle GoldenGate customers how they deployed GoldenGate for operational reporting. Comcast, Turk Telekom, and Raymond James will be sharing their experiences and the benefits achieved when implementing GoldenGate’s solution. If you have performance degradation in your production systems due to reporting or ad-hoc queries, and you will be at OpenWorld, don’t miss this informative session: Real-World Operational Reporting with Oracle GoldenGate: Customer Panel-- Tuesday Oct 2nd 11:45am Mascone West 3005. For other data integration sessions at OpenWorld, please check our Focus-On document.  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} If you cannot attend OpenWorld, please check out related white paper “Using Oracle GoldenGate to Achieve Operational Reporting for Oracle Applications” to learn more.

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  • TechEd North America 2012 – Day 1 #msTechEd

    - by Marco Russo (SQLBI)
    Yesterday I and Alberto delivered the PreCon day about BISM Tabular in Analysis Services 2012. We received very good feedback and now I am looking forward to meet people that read our blogs and our books! Ping me on Twitter at @marcorus if you want to contact me during the conference. This is my schedule for the next few days: ·         Monday, June 11, 2012 o   10:30am-12:30pm I will be in the Technical Learning Center area, at the Breaktrough Insights (station #8) in the Database & Business Intelligence area (dedicated to SQL Server 2012) o   I will try to watch some sessions in the afternoon o   6:30pm-7:00pm I will be at the O’Reilly booth meeting book readers and doing some book signing ·         Tuesday, June 12, 2012 o   12:30pm-3:30pm I will be in the Technical Learning Center area, at the Breaktrough Insights (station #8) in the Database & Business Intelligence area (dedicated to SQL Server 2012) o   5:00pm-6:15pm I will attend the Alberto’s session DBI413 Many-to-Many Relationships in BISM Tabular (room S330E) o   6:15pm-9:00pm Community Night & Ask the Experts, we’ll discuss about Analysis Services, Tabular and Multidimensional! ·         Wednesday, June 13, 2012 o   11:15am-11:30am Don’t miss this special demo session at the Private Cloud, Public Cloud and Data Platform Theater in the Technical Learning Center area (next to the SQL Server 2012 zone). I and Alberto will present Querying multi-billion rows with many to many relationships in SSAS Tabular (xVelocity) and you’re invited to guess the response time of DAX queries on a 4 billion rows table with many-to-many relationships before we run them! We’ll give away some 8GB USB key if you guess the right answer! o   12:30pm-1:00pm I and Alberto will have a book signing session at the TechEd Bookstore o   3:00pm-5:00pm I will be in the Technical Learning Center area, at the Breaktrough Insights (station #8) in the Database & Business Intelligence area (dedicated to SQL Server 2012) ·         Thursday, June 14, 2012 o   2:45pm-4:00pm I will deliver my DBI319 BISM: Multidimensional vs. Tabular breakthrough session in room S320A. I expect many questions here! And if you want to learn more about Analysis Services Tabular, we announced two more online sessions of our SSAS Tabular Workshop: ·         July 2-3, 2012 - SSAS Workshop Online - America's time zone ·         September 3-4, 2012 - SSAS Workshop Online - America's time zone Register now if you are interested, the early bird for the July session expires on June 19, 2012! I will also deliver a SSAS Workshop in Oslo (Norway) on August 27-28, 2012.  

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  • Good DBAs Do Baselines

    - by Louis Davidson
    One morning, you wake up and feel funny. You can’t quite put your finger on it, but something isn’t quite right. What now? Unless you happen to be a hypochondriac, you likely drag yourself out of bed, get on with the day and gather more “evidence”. You check your symptoms over the next few days; do you feel the same, better, worse? If better, then great, it was some temporal issue, perhaps caused by an allergic reaction to some suspiciously spicy chicken. If the same or worse then you go to the doctor for some health advice, but armed with some data to share, and having ruled out certain possible causes that are fixed with a bit of rest and perhaps an antacid. Whether you realize it or not, in comparing how you feel one day to the next, you have taken baseline measurements. In much the same way, a DBA uses baselines to gauge the gauge health of their database servers. Of course, while SQL Server is very willing to share data regarding its health and activities, it has almost no idea of the difference between good and bad. Over time, experienced DBAs develop “mental” baselines with which they can gauge the health of their servers almost as easily as their own body. They accumulate knowledge of the daily, natural state of each part of their database system, and so know instinctively when one of their databases “feels funny”. Equally, they know when an “issue” is just a passing tremor. They see their SQL Server with all of its four CPU cores running close 100% and don’t panic anymore. Why? It’s 5PM and every day the same thing occurs when the end-of-day reports, which are very CPU intensive, are running. Equally, they know when they need to respond in earnest when it is the first time they have heard about an issue, even if it has been happening every day. Nevertheless, no DBA can retain mental baselines for every characteristic of their systems, so we need to collect physical baselines too. In my experience, surprisingly few DBAs do this very well. Part of the problem is that SQL Server provides a lot of instrumentation. If you look, you will find an almost overwhelming amount of data regarding user activity on your SQL Server instances, and use and abuse of the available CPU, I/O and memory. It seems like a huge task even to work out which data you need to collect, let alone start collecting it on a regular basis, managing its storage over time, and performing detailed comparative analysis. However, without baselines, though, it is very difficult to pinpoint what ails a server, just by looking at a single snapshot of the data, or to spot retrospectively what caused the problem by examining aggregated data for the server, collected over many months. It isn’t as hard as you think to get started. You’ve probably already established some troubleshooting queries of the type SELECT Value FROM SomeSystemTableOrView. Capturing a set of baseline values for such a query can be as easy as changing it as follows: INSERT into BaseLine.SomeSystemTable (value, captureTime) SELECT Value, SYSDATETIME() FROM SomeSystemTableOrView; Of course, there are monitoring tools that will collect and manage this baseline data for you, automatically, and allow you to perform comparison of metrics over different periods. However, to get yourself started and to prove to yourself (or perhaps the person who writes the checks for tools) the value of baselines, stick something similar to the above query into an agent job, running every hour or so, and you are on your way with no excuses! Then, the next time you investigate a slow server, and see x open transactions, y users logged in, and z rows added per hour in the Orders table, compare to your baselines and see immediately what, if anything, has changed!

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  • Enabling Google Webmaster Tools With Your GWB Blog

    - by ToStringTheory
    I’ll be honest and save you some time, if you don’t have your own domain for your GWB blog, this won’t help, you may just want to move on…  I don’t want to waste your time……… Still here?  Good.  How great are Google’s website tools?  I don’t just mean Analytics which rocks, but also their Webmaster Tools (https://www.google.com/webmasters/tools/) which gives you a glimpse into the queries that provide you your website traffic, search engine behavior on your site, and important keywords, just to name a few.   Pictured Above: Cool statistics. Problem Thanks to svickn over at wtfnext.com (another GeeksWithBlogs blog), we already have the knowledge on how to setup Google Analytics (wtfnext.com - How to: Set up Google Analytics on your GeeksWithBlogs blog).  However, one of the questions raised in the post, and even semi-answered in the questions, was how to setup Google Webmaster Tools with your blog as well. At first glance, it seems like it can’t be done.  Google graciously gives you several different options on how to authorize that you own a site.  The authentication options are: 1. (Recommended) – Upload an HTML file to your server 2. Add a meta tag to your site’s home page 3. Use your Google Analytics account 4. Add a DNS record to your domain’s configuration Since you don’t have access to the base path, you can’t do #1.  Same goes for #2 since you can’t edit the master/index page.  As for #3, they REQUIRE the Analytics code to be in the <head> section of your page, so even though we can use the workaround of hosting it in the news section, it won’t allow it since it isn’t in the correct place. Solution Last I checked, I didn’t see the DNS record option for Webmaster Tools.  Maybe this was recently added, or maybe I don’t remember it since I was always able to use some other method to authorize it.  In this case though, this is the option that we need.  My registrar wasn’t in their list, but they provide detailed enough instructions for the ‘Other’ option: Simply create a TXT record with your domain hoster (mine is DynDns), fill in the tag information, and then click verify.  My entry was able to be resolved immediately, but since you are working with DNS, it may take longer.  If after 24 hours you still aren’t able to verify, you can use a site such as mxtoolbox.com, and in the searchbox type “txt: {domain-name-here}”, to see if your TXT record was entered successfully. It is pretty simple to setup the TXT entry in DynDns, but if you have questions/comments, feel free to post them. Conclusion With this simple workaround (not really a workaround, but feature since they offer it..), you are now able to see loads of information regarding your standings in the world of the Google Search Engine.  No critical issues?  Did I do something wrong?! As an aside, you can do the same thing with the Bing Webmaster Tools by adding a CNAME record to bing.verify.com…  Instructions can be found on the ‘Add Site’ popup when adding your site. If you don’t have your own domain, but continued, to read to this point – thank you!

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  • Stale statistics on a newly created temporary table in a stored procedure can lead to poor performance

    - by sqlworkshops
    When you create a temporary table you expect a new table with no past history (statistics based on past existence), this is not true if you have less than 6 updates to the temporary table. This might lead to poor performance of queries which are sensitive to the content of temporary tables.I was optimizing SQL Server Performance at one of my customers who provides search functionality on their website. They use stored procedure with temporary table for the search. The performance of the search depended on who searched what in the past, option (recompile) by itself had no effect. Sometimes a simple search led to timeout because of non-optimal plan usage due to this behavior. This is not a plan caching issue rather temporary table statistics caching issue, which was part of the temporary object caching feature that was introduced in SQL Server 2005 and is also present in SQL Server 2008 and SQL Server 2012. In this customer case we implemented a workaround to avoid this issue (see below for example for workarounds).When temporary tables are cached, the statistics are not newly created rather cached from the past and updated based on automatic update statistics threshold. Caching temporary tables/objects is good for performance, but caching stale statistics from the past is not optimal.We can work around this issue by disabling temporary table caching by explicitly executing a DDL statement on the temporary table. One possibility is to execute an alter table statement, but this can lead to duplicate constraint name error on concurrent stored procedure execution. The other way to work around this is to create an index.I think there might be many customers in such a situation without knowing that stale statistics are being cached along with temporary table leading to poor performance.Ideal solution is to have more aggressive statistics update when the temporary table has less number of rows when temporary table caching is used. I will open a connect item to report this issue.Meanwhile you can mitigate the issue by creating an index on the temporary table. You can monitor active temporary tables using Windows Server Performance Monitor counter: SQL Server: General Statistics->Active Temp Tables. The script to understand the issue and the workaround is listed below:set nocount onset statistics time offset statistics io offdrop table tab7gocreate table tab7 (c1 int primary key clustered, c2 int, c3 char(200))gocreate index test on tab7(c2, c1, c3)gobegin trandeclare @i intset @i = 1while @i <= 50000begininsert into tab7 values (@i, 1, ‘a’)set @i = @i + 1endcommit trangoinsert into tab7 values (50001, 1, ‘a’)gocheckpointgodrop proc test_slowgocreate proc test_slow @i intasbegindeclare @j intcreate table #temp1 (c1 int primary key)insert into #temp1 (c1) select @iselect @j = t7.c1 from tab7 t7 inner join #temp1 t on (t7.c2 = t.c1)endgodbcc dropcleanbuffersset statistics time onset statistics io ongo–high reads as expected for parameter ’1'exec test_slow 1godbcc dropcleanbuffersgo–high reads that are not expected for parameter ’2'exec test_slow 2godrop proc test_with_recompilegocreate proc test_with_recompile @i intasbegindeclare @j intcreate table #temp1 (c1 int primary key)insert into #temp1 (c1) select @iselect @j = t7.c1 from tab7 t7 inner join #temp1 t on (t7.c2 = t.c1)option (recompile)endgodbcc dropcleanbuffersset statistics time onset statistics io ongo–high reads as expected for parameter ’1'exec test_with_recompile 1godbcc dropcleanbuffersgo–high reads that are not expected for parameter ’2'–low reads on 3rd execution as expected for parameter ’2'exec test_with_recompile 2godrop proc test_with_alter_table_recompilegocreate proc test_with_alter_table_recompile @i intasbegindeclare @j intcreate table #temp1 (c1 int primary key)–to avoid caching of temporary tables one can create a constraint–but this might lead to duplicate constraint name error on concurrent usagealter table #temp1 add constraint test123 unique(c1)insert into #temp1 (c1) select @iselect @j = t7.c1 from tab7 t7 inner join #temp1 t on (t7.c2 = t.c1)option (recompile)endgodbcc dropcleanbuffersset statistics time onset statistics io ongo–high reads as expected for parameter ’1'exec test_with_alter_table_recompile 1godbcc dropcleanbuffersgo–low reads as expected for parameter ’2'exec test_with_alter_table_recompile 2godrop proc test_with_index_recompilegocreate proc test_with_index_recompile @i intasbegindeclare @j intcreate table #temp1 (c1 int primary key)–to avoid caching of temporary tables one can create an indexcreate index test on #temp1(c1)insert into #temp1 (c1) select @iselect @j = t7.c1 from tab7 t7 inner join #temp1 t on (t7.c2 = t.c1)option (recompile)endgoset statistics time onset statistics io ondbcc dropcleanbuffersgo–high reads as expected for parameter ’1'exec test_with_index_recompile 1godbcc dropcleanbuffersgo–low reads as expected for parameter ’2'exec test_with_index_recompile 2go

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  • Fetching Partition Information

    - by Mike Femenella
    For a recent SSIS package at work I needed to determine the distinct values in a partition, the number of rows in each partition and the file group name on which each partition resided in order to come up with a grouping mechanism. Of course sys.partitions comes to mind for some of that but there are a few other tables you need to link to in order to grab the information required. The table I’m working on contains 8.8 billion rows. Finding the distinct partition keys from this table was not a fast operation. My original solution was to create  a temporary table, grab the distinct values for the partitioned column, then update via sys.partitions for the rows and the $partition function for the partitionid and finally look back to the sys.filegroups table for the filegroup names. It wasn’t pretty, it could take up to 15 minutes to return the results. The primary issue is pulling distinct values from the table. Queries for distinct against 8.8 billion rows don’t go quickly. A few beers into a conversation with a friend and we ended up talking about work which led to a conversation about the task described above. The solution was already built in SQL Server, just needed to pull it together. The first table I needed was sys.partition_range_values. This contains one row for each range boundary value for a partition function. In my case I have a partition function which uses dayid values. For example July 4th would be represented as an int, 20130704. This table lists out all of the dayid values which were defined in the function. This eliminated the need to query my source table for distinct dayid values, everything I needed was already built in here for me. The only caveat was that in my SSIS package I needed to create a bucket for any dayid values that were out of bounds for my function. For example if my function handled 20130501 through 20130704 and I had day values of 20130401 or 20130705 in my table, these would not be listed in sys.partition_range_values. I just created an “everything else” bucket in my ssis package just in case I had any dayid values unaccounted for. To get the number of rows for a partition is very easy. The sys.partitions table contains values for each partition. Easy enough to achieve by querying for the object_id and index value of 1 (the clustered index) The final piece of information was the filegroup name. There are 2 options available to get the filegroup name, sys.data_spaces or sys.filegroups. For my query I chose sys.filegroups but really it’s a matter of preference and data needs. In order to bridge between sys.partitions table and either sys.data_spaces or sys.filegroups you need to get the container_id. This can be done by joining sys.allocation_units.container_id to the sys.partitions.hobt_id. sys.allocation_units contains the field data_space_id which then lets you join in either sys.data_spaces or sys.file_groups. The end result is the query below, which typically executes for me in under 1 second. I’ve included the join to sys.filegroups and to sys.dataspaces, and I’ve  just commented out the join sys.filegroups. As I mentioned above, this shaves a good 10-15 minutes off of my original ssis package and is a really easy tweak to get a boost in my ETL time. Enjoy.

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • Consumer Oriented Search In Oracle Endeca Information Discovery – Part 1

    - by Bob Zurek
    Information Discovery, a core capability of Oracle Endeca Information Discovery, enables business users to rapidly search, discover and navigate through a wide variety of big data including structured, unstructured and semi-structured data. One of the key capabilities, among many, that differentiate our solution from others in the Information Discovery market is our deep support for search across this growing amount of varied big data. Our method and approach is very different than classic simple keyword search that is found in may information discovery solutions. In this first part of a series on the topic of search, I will walk you through many of the key capabilities that go beyond the simple search box that you might experience in products where search was clearly an afterthought or attempt to catch up to our core capabilities in this area. Lets explore. The core data management solution of Oracle Endeca Information Discovery is the Endeca Server, a hybrid search-analytical database that his highly scalable and column-oriented in nature. We will talk in more technical detail about the capabilities of the Endeca Server in future blog posts as this post is intended to give you a feel for the deep search capabilities that are an integral part of the Endeca Server. The Endeca Server provides best-of-breed search features aw well as a new class of features that are the first to be designed around the requirement to bridge structured, semi-structured and unstructured big data. Some of the key features of search include type a heads, automatic alphanumeric spell corrections, positional search, Booleans, wildcarding, natural language, and category search and query classification dialogs. This is just a subset of the advanced search capabilities found in Oracle Endeca Information Discovery. Search is an important feature that makes it possible for business users to explore on the diverse data sets the Endeca Server can hold at any one time. The search capabilities in the Endeca server differ from other Information Discovery products with simple “search boxes” in the following ways: The Endeca Server Supports Exploratory Search.  Enterprise data frequently requires the user to explore content through an ad hoc dialog, with guidance that helps them succeed. This has implications for how to design search features. Traditional search doesn’t assume a dialog, and so it uses relevance ranking to get its best guess to the top of the results list. It calculates many relevance factors for each query, like word frequency, distance, and meaning, and then reduces those many factors to a single score based on a proprietary “black box” formula. But how can a business users, searching, act on the information that the document is say only 38.1% relevant? In contrast, exploratory search gives users the opportunity to clarify what is relevant to them through refinements and summaries. This approach has received consumer endorsement through popular ecommerce sites where guided navigation across a broad range of products has helped consumers better discover choices that meet their, sometimes undetermined requirements. This same model exists in Oracle Endeca Information Discovery. In fact, the Endeca Server powers many of the most popular e-commerce sites in the world. The Endeca Server Supports Cascading Relevance. Traditional approaches of search reduce many relevance weights to a single score. This means that if a result with a good title match gets a similar score to one with an exact phrase match, they’ll appear next to each other in a list. But a user can’t deduce from their score why each got it’s ranking, even though that information could be valuable. Oracle Endeca Information Discovery takes a different approach. The Endeca Server stratifies results by a primary relevance strategy, and then breaks ties within a strata by ordering them with a secondary strategy, and so on. Application managers get the explicit means to compose these strategies based on their knowledge of their own domain. This approach gives both business users and managers a deterministic way to set and understand relevance. Now that you have an understanding of two of the core search capabilities in Oracle Endeca Information Discovery, our next blog post on this topic will discuss more advanced features including set search, second-order relevance as well as an understanding of faceted search mechanisms that include queries and filters.  

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  • Designing An ACL Based Permission System

    - by ryanzec
    I am trying to create a permissions system where everything is going to be stored in MySQL (or some database) and pulled using PHP for a project management system I am building.  I am right now trying to do it is an ACL kind of way.  There are a number key features I want to be able to support: 1.  Being able to assign permissions without being tied to a specific object. The reason for this is that I want to be able to selectively show/hide elements of the UI based on permissions at a point where I am not directly looking at a domain object instance.  For instance, a button to create a new project should only should only be shown to users that have the pm.project.create permission but obviously you can assign a create permission to an domain object instance (as it is already created). 2.  Not have to assign permissions for every single object. Obviously creating permissions entries for every single object (projects, tickets, comments, etc…) would become a nightmare to maintain so I want to have some level of permission inheritance. *3.  Be able to filter queries based on permissions. This would be a really nice to have but I am not sure if it is possible.  What I mean by this is say I have a page that list all projects.  I want the query that pulls all projects to incorporate the ACL so that it would not show projects that the current user does not have pm.project.read access to.  This would have to be incorporated into the main query as if it is a process that is done after that main query (which I know I could do) certain features like pagination become much more difficult. Right now this is my basic design for the tables: AclEntities id - the primary key key - the unique identifier for the domain object (usually the primary key of that object) parentId - the parent of the domain object (like the project object if this was a ticket object) aclDomainObjectId - metadata about the domain object AclDomainObjects id - primary key title - simple string to unique identify the domain object(ie. project, ticket, comment, etc…) fullyQualifiedClassName - the fully qualified class name for use in code (I am using namespaces) There would also be tables mapping AclEntities to Users and UserGroups. I also have this interface that all acl entity based object have to implement: IAclEntity getAclKey() - to the the unique key for this specific instance of the acl domain object (generally return the primary key or a concatenated string of a composite primary key) getAclTitle() - to get the unique title for the domain object (generally just returning a static string) getAclDisplayString() - get the string that represents this entity (generally one or more field on the object) getAclParentEntity() - get the parent acl entity object (or null if no parent) getAclEntity() - get the acl enitty object for this instance of the domain object (or null if one has not been created yet) hasPermission($permissionString, $user = null) - whether or not the user has the permission for this instance of the domain object static getFromAclEntityId($aclEntityId) - get a specific instance of the domain object from an acl entity id. Do any of these features I am looking for seems hard to support or are just way off base? Am I missing or not taking in account anything in my implementation? Is performance something I should keep in mind?

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  • Know your Data Lineage

    - by Simon Elliston Ball
    An academic paper without the footnotes isn’t an academic paper. Journalists wouldn’t base a news article on facts that they can’t verify. So why would anyone publish reports without being able to say where the data has come from and be confident of its quality, in other words, without knowing its lineage. (sometimes referred to as ‘provenance’ or ‘pedigree’) The number and variety of data sources, both traditional and new, increases inexorably. Data comes clean or dirty, processed or raw, unimpeachable or entirely fabricated. On its journey to our report, from its source, the data can travel through a network of interconnected pipes, passing through numerous distinct systems, each managed by different people. At each point along the pipeline, it can be changed, filtered, aggregated and combined. When the data finally emerges, how can we be sure that it is right? How can we be certain that no part of the data collection was based on incorrect assumptions, that key data points haven’t been left out, or that the sources are good? Even when we’re using data science to give us an approximate or probable answer, we cannot have any confidence in the results without confidence in the data from which it came. You need to know what has been done to your data, where it came from, and who is responsible for each stage of the analysis. This information represents your data lineage; it is your stack-trace. If you’re an analyst, suspicious of a number, it tells you why the number is there and how it got there. If you’re a developer, working on a pipeline, it provides the context you need to track down the bug. If you’re a manager, or an auditor, it lets you know the right things are being done. Lineage tracking is part of good data governance. Most audit and lineage systems require you to buy into their whole structure. If you are using Hadoop for your data storage and processing, then tools like Falcon allow you to track lineage, as long as you are using Falcon to write and run the pipeline. It can mean learning a new way of running your jobs (or using some sort of proxy), and even a distinct way of writing your queries. Other Hadoop tools provide a lot of operational and audit information, spread throughout the many logs produced by Hive, Sqoop, MapReduce and all the various moving parts that make up the eco-system. To get a full picture of what’s going on in your Hadoop system you need to capture both Falcon lineage and the data-exhaust of other tools that Falcon can’t orchestrate. However, the problem is bigger even that that. Often, Hadoop is just one piece in a larger processing workflow. The next step of the challenge is how you bind together the lineage metadata describing what happened before and after Hadoop, where ‘after’ could be  a data analysis environment like R, an application, or even directly into an end-user tool such as Tableau or Excel. One possibility is to push as much as you can of your key analytics into Hadoop, but would you give up the power, and familiarity of your existing tools in return for a reliable way of tracking lineage? Lineage and auditing should work consistently, automatically and quietly, allowing users to access their data with any tool they require to use. The real solution, therefore, is to create a consistent method by which to bring lineage data from these data various disparate sources into the data analysis platform that you use, rather than being forced to use the tool that manages the pipeline for the lineage and a different tool for the data analysis. The key is to keep your logs, keep your audit data, from every source, bring them together and use the data analysis tools to trace the paths from raw data to the answer that data analysis provides.

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  • Getting Wrong Answer in range maximum query [on hold]

    - by user3186829
    I've just learnt range minimum and maximum queries using segment trees.But when I implemented it on my own I'm getting wrong answer.Logically I don't find any mistake in my code but if any one can point it out then I would be really thankful. Code in C++: #include<bits/stdc++.h> using namespace std; #define LL long long #define mp make_pair #define pb push_back #define gc getchar_unlocked #define pc putchar_unlocked #define LD long double #define MAXN 19999999 #define max(a,b) ((a)>(b)?(a):(b)) LL P[MAXN+15]; LL ST[2*MAXN+25]; long N,M,i,A,B,K; void build(long id,long L,long R) { long M=(L+R)>>1L; long LCT=id<<1L; long RCT=LCT+1L; if(L==R) { ST[id]=P[L]; return; } build(LCT,L,M); build(RCT,M+1,R); } //Range Update of segment tree void updateST(long id,long L,long R,long Q1,long Q2,long val) { long M=(L+R)>>1L; long LCT=id<<1L; long RCT=LCT+1L; if(L>Q2||R<Q1) { return; } if(L==Q1&&R==Q2) { ST[id]+=val; return; } if(Q2<=M) { updateST(LCT,L,M,Q1,Q2,val); } else if(Q1>M) { updateST(RCT,M+1,R,Q1,Q2,val); } else { updateST(LCT,L,M,Q1,M,val); updateST(RCT,M+1,R,M+1,Q2,val); } } //Query for finding maximum element in a given range[Q1,Q2] and 1<=Q1,Q2<=N LL query2(long id,long L,long R,long Q1,long Q2) { long M=(L+R)>>1; long LCT=id<<1; long RCT=LCT+1; if(L>Q2||R<Q1) { return 0; } if(L==Q1&&R==Q2) { return ST[id]; } if(Q2<=M) { return query2(LCT,L,M,Q1,Q2); } else if(Q1>M) { return query2(RCT,M+1,R,Q1,Q2); } else { LL G=query2(LCT,L,M,Q1,M); LL H=query2(RCT,M+1,R,M+1,Q2); LL RES=max(G,H); return RES; } } int main() { scanf("%ld %ld",&N,&M); build(1,1,N); for(i=0;i<M;i++) { scanf("%ld %ld %ld",&A,&B,&K); updateST(1,1,N,A,B,K); } //Finding maximum element in range[1,N]] cout<<query2(1,1,N,1,N); return 0; }

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