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  • Indexing data from multiple tables with Oracle Text

    - by Roger Ford
    It's well known that Oracle Text indexes perform best when all the data to be indexed is combined into a single index. The query select * from mytable where contains (title, 'dog') 0 or contains (body, 'cat') 0 will tend to perform much worse than select * from mytable where contains (text, 'dog WITHIN title OR cat WITHIN body') 0 For this reason, Oracle Text provides the MULTI_COLUMN_DATASTORE which will combine data from multiple columns into a single index. Effectively, it constructs a "virtual document" at indexing time, which might look something like: <title>the big dog</title> <body>the ginger cat smiles</body> This virtual document can be indexed using either AUTO_SECTION_GROUP, or by explicitly defining sections for title and body, allowing the query as expressed above. Note that we've used a column called "text" - this might have been a dummy column added to the table simply to allow us to create an index on it - or we could created the index on either of the "real" columns - title or body. It should be noted that MULTI_COLUMN_DATASTORE doesn't automatically handle updates to columns used by it - if you create the index on the column text, but specify that columns title and body are to be indexed, you will need to arrange triggers such that the text column is updated whenever title or body are altered. That works fine for single tables. But what if we actually want to combine data from multiple tables? In that case there are two approaches which work well: Create a real table which contains a summary of the information, and create the index on that using the MULTI_COLUMN_DATASTORE. This is simple, and effective, but it does use a lot of disk space as the information to be indexed has to be duplicated. Create our own "virtual" documents using the USER_DATASTORE. The user datastore allows us to specify a PL/SQL procedure which will be used to fetch the data to be indexed, returned in a CLOB, or occasionally in a BLOB or VARCHAR2. This PL/SQL procedure is called once for each row in the table to be indexed, and is passed the ROWID value of the current row being indexed. The actual contents of the procedure is entirely up to the owner, but it is normal to fetch data from one or more columns from database tables. In both cases, we still need to take care of updates - making sure that we have all the triggers necessary to update the indexed column (and, in case 1, the summary table) whenever any of the data to be indexed gets changed. I've written full examples of both these techniques, as SQL scripts to be run in the SQL*Plus tool. You will need to run them as a user who has CTXAPP role and CREATE DIRECTORY privilege. Part of the data to be indexed is a Microsoft Word file called "1.doc". You should create this file in Word, preferably containing the single line of text: "test document". This file can be saved anywhere, but the SQL scripts need to be changed so that the "create or replace directory" command refers to the right location. In the example, I've used C:\doc. multi_table_indexing_1.sql : creates a summary table containing all the data, and uses multi_column_datastore Download link / View in browser multi_table_indexing_2.sql : creates "virtual" documents using a procedure as a user_datastore Download link / View in browser

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  • Is DQS-in-the-cloud on its way?

    - by jamiet
    LinkedIn profiles are always a useful place to find out what's really going on in Microsoft. Today I stumbled upon this little nugget from former SSIS product team member Matt Carroll: March 2012 – December 2012 (10 months)Redmond, WA Took ownership of the SQL 2012 Data Quality Services box product and re-architected and extended it to become a cloud service. Led team and managed product to add dynamic scale, security, multi-tenancy, deployment, logging, monitoring, and telemetry as well as creating new Excel add-in and new ecosystem experience around easily sharing and finding cleansing agents. Personally designed, coded, and unit tested in-memory trigram matching algorithm core to better performance, scale and maintainability. Delivered and supported successful private preview of the new service prior to SQL wide reorganization.  http://www.linkedin.com/profile/view?id=9657184  Sounds as though a Data-Quality-Services-in-the-cloud (which I spoke of as being a useful addition to Microsoft's BI portfolio in my previous blog post Thoughts on Power BI for Office 365 ) might be on its way some time in the future. And what's this SQL wide reorganization? Interesting stuff. @Jamiet  

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  • Solving Big Problems with Oracle R Enterprise, Part II

    - by dbayard
    Part II – Solving Big Problems with Oracle R Enterprise In the first post in this series (see https://blogs.oracle.com/R/entry/solving_big_problems_with_oracle), we showed how you can use R to perform historical rate of return calculations against investment data sourced from a spreadsheet.  We demonstrated the calculations against sample data for a small set of accounts.  While this worked fine, in the real-world the problem is much bigger because the amount of data is much bigger.  So much bigger that our approach in the previous post won’t scale to meet the real-world needs. From our previous post, here are the challenges we need to conquer: The actual data that needs to be used lives in a database, not in a spreadsheet The actual data is much, much bigger- too big to fit into the normal R memory space and too big to want to move across the network The overall process needs to run fast- much faster than a single processor The actual data needs to be kept secured- another reason to not want to move it from the database and across the network And the process of calculating the IRR needs to be integrated together with other database ETL activities, so that IRR’s can be calculated as part of the data warehouse refresh processes In this post, we will show how we moved from sample data environment to working with full-scale data.  This post is based on actual work we did for a financial services customer during a recent proof-of-concept. Getting started with the Database At this point, we have some sample data and our IRR function.  We were at a similar point in our customer proof-of-concept exercise- we had sample data but we did not have the full customer data yet.  So our database was empty.  But, this was easily rectified by leveraging the transparency features of Oracle R Enterprise (see https://blogs.oracle.com/R/entry/analyzing_big_data_using_the).  The following code shows how we took our sample data SimpleMWRRData and easily turned it into a new Oracle database table called IRR_DATA via ore.create().  The code also shows how we can access the database table IRR_DATA as if it was a normal R data.frame named IRR_DATA. If we go to sql*plus, we can also check out our new IRR_DATA table: At this point, we now have our sample data loaded in the database as a normal Oracle table called IRR_DATA.  So, we now proceeded to test our R function working with database data. As our first test, we retrieved the data from a single account from the IRR_DATA table, pull it into local R memory, then call our IRR function.  This worked.  No SQL coding required! Going from Crawling to Walking Now that we have shown using our R code with database-resident data for a single account, we wanted to experiment with doing this for multiple accounts.  In other words, we wanted to implement the split-apply-combine technique we discussed in our first post in this series.  Fortunately, Oracle R Enterprise provides a very scalable way to do this with a function called ore.groupApply().  You can read more about ore.groupApply() here: https://blogs.oracle.com/R/entry/analyzing_big_data_using_the1 Here is an example of how we ask ORE to take our IRR_DATA table in the database, split it by the ACCOUNT column, apply a function that calls our SimpleMWRR() calculation, and then combine the results. (If you are following along at home, be sure to have installed our myIRR package on your database server via  “R CMD INSTALL myIRR”). The interesting thing about ore.groupApply is that the calculation is not actually performed in my desktop R environment from which I am running.  What actually happens is that ore.groupApply uses the Oracle database to perform the work.  And the Oracle database is what actually splits the IRR_DATA table by ACCOUNT.  Then the Oracle database takes the data for each account and sends it to an embedded R engine running on the database server to apply our R function.  Then the Oracle database combines all the individual results from the calls to the R function. This is significant because now the embedded R engine only needs to deal with the data for a single account at a time.  Regardless of whether we have 20 accounts or 1 million accounts or more, the R engine that performs the calculation does not care.  Given that normal R has a finite amount of memory to hold data, the ore.groupApply approach overcomes the R memory scalability problem since we only need to fit the data from a single account in R memory (not all of the data for all of the accounts). Additionally, the IRR_DATA does not need to be sent from the database to my desktop R program.  Even though I am invoking ore.groupApply from my desktop R program, because the actual SimpleMWRR calculation is run by the embedded R engine on the database server, the IRR_DATA does not need to leave the database server- this is both a performance benefit because network transmission of large amounts of data take time and a security benefit because it is harder to protect private data once you start shipping around your intranet. Another benefit, which we will discuss in a few paragraphs, is the ability to leverage Oracle database parallelism to run these calculations for dozens of accounts at once. From Walking to Running ore.groupApply is rather nice, but it still has the drawback that I run this from a desktop R instance.  This is not ideal for integrating into typical operational processes like nightly data warehouse refreshes or monthly statement generation.  But, this is not an issue for ORE.  Oracle R Enterprise lets us run this from the database using regular SQL, which is easily integrated into standard operations.  That is extremely exciting and the way we actually did these calculations in the customer proof. As part of Oracle R Enterprise, it provides a SQL equivalent to ore.groupApply which it refers to as “rqGroupEval”.  To use rqGroupEval via SQL, there is a bit of simple setup needed.  Basically, the Oracle Database needs to know the structure of the input table and the grouping column, which we are able to define using the database’s pipeline table function mechanisms. Here is the setup script: At this point, our initial setup of rqGroupEval is done for the IRR_DATA table.  The next step is to define our R function to the database.  We do that via a call to ORE’s rqScriptCreate. Now we can test it.  The SQL you use to run rqGroupEval uses the Oracle database pipeline table function syntax.  The first argument to irr_dataGroupEval is a cursor defining our input.  You can add additional where clauses and subqueries to this cursor as appropriate.  The second argument is any additional inputs to the R function.  The third argument is the text of a dummy select statement.  The dummy select statement is used by the database to identify the columns and datatypes to expect the R function to return.  The fourth argument is the column of the input table to split/group by.  The final argument is the name of the R function as you defined it when you called rqScriptCreate(). The Real-World Results In our real customer proof-of-concept, we had more sophisticated calculation requirements than shown in this simplified blog example.  For instance, we had to perform the rate of return calculations for 5 separate time periods, so the R code was enhanced to do so.  In addition, some accounts needed a time-weighted rate of return to be calculated, so we extended our approach and added an R function to do that.  And finally, there were also a few more real-world data irregularities that we needed to account for, so we added logic to our R functions to deal with those exceptions.  For the full-scale customer test, we loaded the customer data onto a Half-Rack Exadata X2-2 Database Machine.  As our half-rack had 48 physical cores (and 96 threads if you consider hyperthreading), we wanted to take advantage of that CPU horsepower to speed up our calculations.  To do so with ORE, it is as simple as leveraging the Oracle Database Parallel Query features.  Let’s look at the SQL used in the customer proof: Notice that we use a parallel hint on the cursor that is the input to our rqGroupEval function.  That is all we need to do to enable Oracle to use parallel R engines. Here are a few screenshots of what this SQL looked like in the Real-Time SQL Monitor when we ran this during the proof of concept (hint: you might need to right-click on these images to be able to view the images full-screen to see the entire image): From the above, you can notice a few things (numbers 1 thru 5 below correspond with highlighted numbers on the images above.  You may need to right click on the above images and view the images full-screen to see the entire image): The SQL completed in 110 seconds (1.8minutes) We calculated rate of returns for 5 time periods for each of 911k accounts (the number of actual rows returned by the IRRSTAGEGROUPEVAL operation) We accessed 103m rows of detailed cash flow/market value data (the number of actual rows returned by the IRR_STAGE2 operation) We ran with 72 degrees of parallelism spread across 4 database servers Most of our 110seconds was spent in the “External Procedure call” event On average, we performed 8,200 executions of our R function per second (110s/911k accounts) On average, each execution was passed 110 rows of data (103m detail rows/911k accounts) On average, we did 41,000 single time period rate of return calculations per second (each of the 8,200 executions of our R function did rate of return calculations for 5 time periods) On average, we processed over 900,000 rows of database data in R per second (103m detail rows/110s) R + Oracle R Enterprise: Best of R + Best of Oracle Database This blog post series started by describing a real customer problem: how to perform a lot of calculations on a lot of data in a short period of time.  While standard R proved to be a very good fit for writing the necessary calculations, the challenge of working with a lot of data in a short period of time remained. This blog post series showed how Oracle R Enterprise enables R to be used in conjunction with the Oracle Database to overcome the data volume and performance issues (as well as simplifying the operations and security issues).  It also showed that we could calculate 5 time periods of rate of returns for almost a million individual accounts in less than 2 minutes. In a future post, we will take the same R function and show how Oracle R Connector for Hadoop can be used in the Hadoop world.  In that next post, instead of having our data in an Oracle database, our data will live in Hadoop and we will how to use the Oracle R Connector for Hadoop and other Oracle Big Data Connectors to move data between Hadoop, R, and the Oracle Database easily.

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  • PASS Business Intelligence Virtual Chapter Upcoming Sessions (November 2013)

    - by Sergio Govoni
    Let me point out the upcoming live events, dedicated to Business Intelligence with SQL Server, that PASS Business Intelligence Virtual Chapter has scheduled for November 2013. The "Accidental Business Intelligence Project Manager"Date: Thursday 7th November - 8:00 PM GMT / 3:00 PM EST / Noon PSTSpeaker: Jen StirrupURL: https://attendee.gotowebinar.com/register/5018337449405969666 You've watched the Apprentice with Donald Trump and Lord Alan Sugar. You know that the Project Manager is usually the one gets firedYou've heard that Business Intelligence projects are prone to failureYou know that a quick Bing search for "why do Business Intelligence projects fail?" produces a search result of 25 million hits!Despite all this… you're now Business Intelligence Project Manager – now what do you do?In this session, Jen will provide a "sparks from the anvil" series of steps and working practices in Business Intelligence Project Management. What about waterfall vs agile? What is a Gantt chart anyway? Is Microsoft Project your friend or a problematic aspect of being a BI PM? Jen will give you some ideas and insights that will help you set your BI project right: assess priorities, avoid conflict, empower the BI team and generally deliver the Business Intelligence project successfully! Dimensional Modelling Design Patterns: Beyond BasicsDate: Tuesday 12th November - Noon AEDT / 1:00 AM GMT / Monday 11th November 5:00 PM PSTSpeaker: Jason Horner, Josh Fennessy and friendsURL: https://attendee.gotowebinar.com/register/852881628115426561 This session will provide a deeper dive into the art of dimensional modeling. We will look at the different types of fact tables and dimension tables, how and when to use them. We will also some approaches to creating rich hierarchies that make reporting a snap. This session promises to be very interactive and engaging, bring your toughest Dimensional Modeling quandaries. Data Vault Data Warehouse ArchitectureDate: Tuesday 19th November - 4:00 PM PST / 7 PM EST / Wednesday 20th November 11:00 PM AEDTSpeaker: Jeff Renz and Leslie WeedURL: https://attendee.gotowebinar.com/register/1571569707028142849 Data vault is a compelling architecture for an enterprise data warehouse using SQL Server 2012. A well designed data vault data warehouse facilitates fast, efficient and maintainable data integration across business systems. In this session Leslie and I will review the basics about enterprise data warehouse design, introduce you to the data vault architecture and discuss how you can leverage new features of SQL Server 2012 help make your data warehouse solution provide maximum value to your users. 

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  • Microsoft and innovation: IIF() method

    This Saturday I was watching a couple of eLearning videos from TrainSignal (thanks to the subscription I have with Pluralsight) on Querying Microsoft SQL Server 2012 (exam 70-461). 'Innovation' by Microsoft I kept myself busy learning 'new' things about Microsoft SQL Server 2012 and some best practices. It was incredible 'innovative' to see that there is an additional logic function called IIF() available now: Returns one of two values depending on the value of a logical expression. IIF(lExpression, eExpression1, eExpression2) Ups, my bad... That's actually taken from the syntax page of Visual FoxPro 9.0 SP 2. And tada, at least seven (7+) years later, there's the recent IIF() Transact-SQL version of that function: Returns one of two values, depending on whether the Boolean expression evaluates to true or false in SQL Server 2012. IIF ( boolean_expression, true_value, false_value ) Now, that's what I call innovation! But we all know what happened to Visual FoxPro... It has been reincarnated in form of Visual Studio LightSwitch (and SQL Server). Enough ranting... Happy coding!

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  • SSIS Denali as part of “Enterprise Information Management”

    - by jorg
    When watching the SQL PASS session “What’s Coming Next in SSIS?” of Steve Swartz, the Group Program Manager for the SSIS team, an interesting question came up: Why is SSIS thought of to be BI, when we use it so frequently for other sorts of data problems? The answer of Steve was that he breaks the world of data work into three parts: Process of inputs BI   Enterprise Information Management All the work you have to do when you have a lot of data to make it useful and clean and get it to the right place. This covers master data management, data quality work, data integration and lineage analysis to keep track of where the data came from. All of these are part of Enterprise Information Management. Next, Steve told Microsoft is developing SSIS as part of a large push in all of these areas in the next release of SQL. So SSIS will be, next to a BI tool, part of Enterprise Information Management in the next release of SQL Server. I'm interested in the different ways people use SSIS, I've basically used it for ETL, data migrations and processing inputs. In which ways did you use SSIS?

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  • Oracle????????????FAQ

    - by Yusuke.Yamamoto
    ??? 1.??????????? ????????????????????????????????????????????? ????? Oracle Database ?SQL????????????????????? 2.?????????????????????? ??????????????????????????????????????? ????????????????????????????? ???:????????????? ???:??????NULL???????? ????:???·????????????????? ??????:CPU????????????I/O??????????? 3.???????????????????????? ????? Oracle Database ???????????????????????????????? ????????????????????????????????????????????????? ??? 4.????????????????????????????????????????? SYS????????????????·????????????????? ????????????????????????? ?????? ? DBA_TABLES ??????? ? DBA_INDEXES ?????? ? DBA_TAB_COLUMNS 5.?????????????????????????????????? ??????????????????????? ??????:Oracle Database ???????????????????????????????????????????????????????? ??????:?????????????????????????????????????? ????????:SQL????????????????????????????????????????????????????????????????? 6.?????????????????????????????????????????????????????? SYS????????????????·????????????????? ???????????????·?????"LAST_ANALYZED"??????????????????????????????????????????? 7.?????SQL?????????????????????SQL?????????????????????????????????? DBMS_STATS ????????????????????????????????????????? ?)??????????????? EXECUTE DBMS_STATS.GATHER_TABLE_STATS('SCOTT','EMP'); ?)?????????????????? EXECUTE DBMS_STATS.GATHER_SCHEMA_STATS('SCOTT'); 8.???????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????? ??????:??????????????????????????????????? ??????:?????????????????????????????????? ????????:????????SQL???????????CPU??????? ???? ?????????????????????? ????????? Part1 ???????????????????·??????|??????????? "

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  • Finally! Entity Framework working in fully disconnected N-tier web app

    - by oazabir
    Entity Framework was supposed to solve the problem of Linq to SQL, which requires endless hacks to make it work in n-tier world. Not only did Entity Framework solve none of the L2S problems, but also it made it even more difficult to use and hack it for n-tier scenarios. It’s somehow half way between a fully disconnected ORM and a fully connected ORM like Linq to SQL. Some useful features of Linq to SQL are gone – like automatic deferred loading. If you try to do simple select with join, insert, update, delete in a disconnected architecture, you will realize not only you need to make fundamental changes from the top layer to the very bottom layer, but also endless hacks in basic CRUD operations. I will show you in this article how I have  added custom CRUD functions on top of EF’s ObjectContext to make it finally work well in a fully disconnected N-tier web application (my open source Web 2.0 AJAX portal – Dropthings) and how I have produced a 100% unit testable fully n-tier compliant data access layerfollowing the repository pattern. http://www.codeproject.com/KB/linq/ef.aspx In .NET 4.0, most of the problems are solved, but not all. So, you should read this article even if you are coding in .NET 4.0. Moreover, there’s enough insight here to help you troubleshoot EF related problems. You might think “Why bother using EF when Linq to SQL is doing good enough for me.” Linq to SQL is not going to get any innovation from Microsoft anymore. Entity Framework is the future of persistence layer in .NET framework. All the innovations are happening in EF world only, which is frustrating. There’s a big jump on EF 4.0. So, you should plan to migrate your L2S projects to EF soon.

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  • Function Folding in #PowerQuery

    - by Darren Gosbell
    Originally posted on: http://geekswithblogs.net/darrengosbell/archive/2014/05/16/function-folding-in-powerquery.aspxLooking at a typical Power Query query you will noticed that it's made up of a number of small steps. As an example take a look at the query I did in my previous post about joining a fact table to a slowly changing dimension. It was roughly built up of the following steps: Get all records from the fact table Get all records from the dimension table do an outer join between these two tables on the business key (resulting in an increase in the row count as there are multiple records in the dimension table for each business key) Filter out the excess rows introduced in step 3 remove extra columns that are not required in the final result set. If Power Query was to execute a query like this literally, following the same steps in the same order it would not be overly efficient. Particularly if your two source tables were quite large. However Power Query has a feature called function folding where it can take a number of these small steps and push them down to the data source. The degree of function folding that can be performed depends on the data source, As you might expect, relational data sources like SQL Server, Oracle and Teradata support folding, but so do some of the other sources like OData, Exchange and Active Directory. To explore how this works I took the data from my previous post and loaded it into a SQL database. Then I converted my Power Query expression to source it's data from that database. Below is the resulting Power Query which I edited by hand so that the whole thing can be shown in a single expression: let     SqlSource = Sql.Database("localhost", "PowerQueryTest"),     BU = SqlSource{[Schema="dbo",Item="BU"]}[Data],     Fact = SqlSource{[Schema="dbo",Item="fact"]}[Data],     Source = Table.NestedJoin(Fact,{"BU_Code"},BU,{"BU_Code"},"NewColumn"),     LeftJoin = Table.ExpandTableColumn(Source, "NewColumn"                                   , {"BU_Key", "StartDate", "EndDate"}                                   , {"BU_Key", "StartDate", "EndDate"}),     BetweenFilter = Table.SelectRows(LeftJoin, each (([Date] >= [StartDate]) and ([Date] <= [EndDate])) ),     RemovedColumns = Table.RemoveColumns(BetweenFilter,{"StartDate", "EndDate"}) in     RemovedColumns If the above query was run step by step in a literal fashion you would expect it to run two queries against the SQL database doing "SELECT * …" from both tables. However a profiler trace shows just the following single SQL query: select [_].[BU_Code],     [_].[Date],     [_].[Amount],     [_].[BU_Key] from (     select [$Outer].[BU_Code],         [$Outer].[Date],         [$Outer].[Amount],         [$Inner].[BU_Key],         [$Inner].[StartDate],         [$Inner].[EndDate]     from [dbo].[fact] as [$Outer]     left outer join     (         select [_].[BU_Key] as [BU_Key],             [_].[BU_Code] as [BU_Code2],             [_].[BU_Name] as [BU_Name],             [_].[StartDate] as [StartDate],             [_].[EndDate] as [EndDate]         from [dbo].[BU] as [_]     ) as [$Inner] on ([$Outer].[BU_Code] = [$Inner].[BU_Code2] or [$Outer].[BU_Code] is null and [$Inner].[BU_Code2] is null) ) as [_] where [_].[Date] >= [_].[StartDate] and [_].[Date] <= [_].[EndDate] The resulting query is a little strange, you can probably tell that it was generated programmatically. But if you look closely you'll notice that every single part of the Power Query formula has been pushed down to SQL Server. Power Query itself ends up just constructing the query and passing the results back to Excel, it does not do any of the data transformation steps itself. So now you can feel a bit more comfortable showing Power Query to your less technical Colleagues knowing that the tool will do it's best fold all the  small steps in Power Query down the most efficient query that it can against the source systems.

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  • Non use of persisted data

    - by Dave Ballantyne
    Working at a client site, that in itself is good to say, I ran into a set of circumstances that made me ponder, and appreciate, the optimizer engine a bit more. Working on optimizing a stored procedure, I found a piece of code similar to : select BillToAddressID, Rowguid, dbo.udfCleanGuid(rowguid) from sales.salesorderheaderwhere BillToAddressID = 985 A lovely scalar UDF was being used,  in actuality it was used as part of the WHERE clause but simplified here.  Normally I would use an inline table valued function here, but in this case it wasn't a good option. So this seemed like a pretty good case to use a persisted column to improve performance. The supporting index was already defined as create index idxBill on sales.salesorderheader(BillToAddressID) include (rowguid) and the function code is Create Function udfCleanGuid(@GUID uniqueidentifier)returns varchar(255)with schemabindingasbegin Declare @RetStr varchar(255) Select @RetStr=CAST(@Guid as varchar(255)) Select @RetStr=REPLACE(@Retstr,'-','') return @RetStrend Executing the Select statement produced a plan of : Nothing surprising, a seek to find the data and compute scalar to execute the UDF. Lets get optimizing and remove the UDF with a persisted column Alter table sales.salesorderheaderadd CleanedGuid as dbo.udfCleanGuid(rowguid)PERSISTED A subtle change to the SELECT statement… select BillToAddressID,CleanedGuid from sales.salesorderheaderwhere BillToAddressID = 985 and our new optimized plan looks like… Not a lot different from before!  We are using persisted data on our table, where is the lookup to fetch it ?  It didnt happen,  it was recalculated.  Looking at the properties of the relevant Compute Scalar would confirm this ,  but a more graphic example would be shown in the profiler SP:StatementCompleted event. Why did the lookup happen ? Remember the index definition,  it has included the original guid to avoid the lookup.  The optimizer knows this column will be passed into the UDF, run through its logic and decided that to recalculate is cheaper than the lookup.  That may or may not be the case in actuality,  the optimizer has no idea of the real cost of a scalar udf.  IMO the default cost of a scalar UDF should be seen as a lot higher than it is, since they are invariably higher. Knowing this, how do we avoid the function call?  Dropping the guid from the index is not an option, there may be other code reliant on it.   We are left with only one real option,  add the persisted column into the index. drop index Sales.SalesOrderHeader.idxBillgocreate index idxBill on sales.salesorderheader(BillToAddressID) include (rowguid,cleanedguid) Now if we repeat the statement select BillToAddressID,CleanedGuid from sales.salesorderheaderwhere BillToAddressID = 985 We still have a compute scalar operator, but this time it wasnt used to recalculate the persisted data.  This can be confirmed with profiler again. The takeaway here is,  just because you have persisted data dont automatically assumed that it is being used.

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  • How to SET TIMING ON for parallel upgrades to 12c?

    - by Mike Dietrich
    Have you asked yourself how to get timings in an Oracle Database 12c upgrade for all statements? When you run the parallel upgrade via catctl.pl, the parallel upgrade Perl driving script in Oracle Database 12c, you may also want to get timings written in your logfile during execution. As catctl.pl does not offer an option yet the best way to achieve this is to edit the catupses.sql script in $ORACLE/rdbms/admin as this script will get called all time over and over again throughout all steps of theupgrade run. Just add these lines marked in RED to catupses.sql and start your upgrade: Rem =============================================Rem Call Common session settingsRem =============================================@@catpses.sql Rem =============================================Rem  Set Timing On during the UpgradeRem =============================================SET TIMING ON; Rem =============================================Rem Turn off PL/SQL event used by APPSRem =============================================ALTER SESSION SET EVENTS='10933 trace name context off'; -Mike PS: This may become the default in a future patch set

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  • Improving Comparison Operators and Window Functions

    It is dangerous to assume that your data is sound. SQL already has intrinsic ways to cope with missing, or unknown data in its comparison predicate operators, or Theta operators. Can SQL be more effective in the way it deals with data quality? Joe Celko describes how the SQL Standard could soon evolve to deal with data in ways that allow aggregation and windowing in cases where the data quality is less than perfect

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  • What is SMO?

    SQL Server Management Objects (SMO) are objects designed for programmatic management of Microsoft SQL Server.

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  • Download LINQPad to learn LINQ

    - by Editor
    LINQPad lets you interactively query SQL databases in a modern query language: LINQ. Say goodbye to SQL Management Studio.LINQPad supports everything in C# 3.0 and Framework 3.5: LINQ to SQL LINQ to Objects LINQ to XML LINQPad is also a great way to learn LINQ: it comes preloaded with 200 examples from the book, C# [...]

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  • Use Expressions with LINQ to Entities

    - by EltonStoneman
    [Source: http://geekswithblogs.net/EltonStoneman] Recently I've been putting together a generic approach for paging the response from a WCF service. Paging changes the service signature, so it's not as simple as adding a behavior to an existing service in config, but the complexity of the paging is isolated in a generic base class. We're using the Entity Framework talking to SQL Server, so when we ask for a page using LINQ's .Take() method we get a nice efficient SQL query for just the rows we want, with minimal impact on SQL Server and network traffic. We use the maximum ID of the record returned as a high-water mark (rather than using .Skip() to go to the next record), so the approach caters for records being deleted between page requests. In the paged response we include a HasMorePages indicator, computed by comparing the max ID in the page of results to the max ID for the whole resultset - if the latter is bigger, then there are more pages. In some quick performance testing, the paged version of the service performed much more slowly than the unpaged version, which was unexpected. We narrowed it down to the code which gets the max ID for the full resultset - instead of building an efficient MAX() SQL query, EF was returning the whole resultset and then computing the max ID in the service layer. It's easy to reproduce - take this AdventureWorks query:             var context = new AdventureWorksEntities();             var query = from od in context.SalesOrderDetail                         where od.ModifiedDate >= modified                          && od.SalesOrderDetailID.CompareTo(id) > 0                         orderby od.SalesOrderDetailID                         select od;   We can find the maximum SalesOrderDetailID like this:             var maxIdEfficiently = query.Max(od => od.SalesOrderDetailID);   which produces our efficient MAX() SQL query. If we're doing this generically and we already have the ID function in a Func:             Func<SalesOrderDetail, int> idFunc = od => od.SalesOrderDetailID;             var maxIdInefficiently = query.Max(idFunc);   This fetches all the results from the query and then runs the Max() function in code. If you look at the difference in Reflector, the first call passes an Expression to the Max(), while the second call passes a Func. So it's an easy fix - wrap the Func in an Expression:             Expression<Func<SalesOrderDetail, int>> idExpression = od => od.SalesOrderDetailID;             var maxIdEfficientlyAgain = query.Max(idExpression);   - and we're back to running an efficient MAX() statement. Evidently the EF provider can dissect an Expression and build its equivalent in SQL, but it can't do that with Funcs.

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  • In hindsight, is basing XAML on XML a mistake or a good approach?

    - by romkyns
    XAML is essentially a subset of XML. One of the main benefits of basing XAML on XML is said to be that it can be parsed with existing tools. And it can, to a large degree, although the (syntactically non-trivial) attribute values will stay in text form and require further parsing. There are two major alternatives to describing a GUI in an XML-derived language. One is to do what WinForms did, and describe it in real code. There are numerous problems with this, though it’s not completely advantage-free (a question to compare XAML to this approach). The other major alternative is to design a completely new syntax specifically tailored for the task at hand. This is generally known as a domain-specific language. So, in hindsight, and as a lesson for the future generations, was it a good idea to base XAML on XML, or would it have been better as a custom-designed domain-specific language? If we were designing an even better UI framework, should we pick XML or a custom DSL? Since it’s much easier to think positively about the status quo, especially one that is quite liked by the community, I’ll give some example reasons for why building on top of XML might be considered a mistake. Basing a language off XML has one thing going for it: it’s much easier to parse (the core parser is already available), requires much, much less design work, and alternative parsers are also much easier to write for 3rd party developers. But the resulting language can be unsatisfying in various ways. It is rather verbose. If you change the type of something, you need to change it in the closing tag. It has very poor support for comments; it’s impossible to comment out an attribute. There are limitations placed on the content of attributes by XML. The markup extensions have to be built "on top" of the XML syntax, not integrated deeply and nicely into it. And, my personal favourite, if you set something via an attribute, you use completely different syntax than if you set the exact same thing as a content property. It’s also said that since everyone knows XML, XAML requires less learning. Strictly speaking this is true, but learning the syntax is a tiny fraction of the time spent learning a new UI framework; it’s the framework’s concepts that make the curve steep. Besides, the idiosyncracies of an XML-based language might actually add to the "needs learning" basket. Are these disadvantages outweighted by the ease of parsing? Should the next cool framework continue the tradition, or invest the time to design an awesome DSL that can’t be parsed by existing tools and whose syntax needs to be learned by everyone? P.S. Not everyone confuses XAML and WPF, but some do. XAML is the XML-like thing. WPF is the framework with support for bindings, theming, hardware acceleration and a whole lot of other cool stuff.

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  • Migrating R Scripts from Development to Production

    - by Mark Hornick
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 “How do I move my R scripts stored in one database instance to another? I have my development/test system and want to migrate to production.” Users of Oracle R Enterprise Embedded R Execution will often store their R scripts in the R Script Repository in Oracle Database, especially when using the ORE SQL API. From previous blog posts, you may recall that Embedded R Execution enables running R scripts managed by Oracle Database using both R and SQL interfaces. In ORE 1.3.1., the SQL API requires scripts to be stored in the database and referenced by name in SQL queries. The SQL API enables seamless integration with database-based applications and ease of production deployment. Loading R scripts in the repository Before talking about migration, we’ll first introduce how users store R scripts in Oracle Database. Users can add R scripts to the repository in R using the function ore.scriptCreate, or SQL using the function sys.rqScriptCreate. For the sample R script     id <- 1:10     plot(1:100,rnorm(100),pch=21,bg="red",cex =2)     data.frame(id=id, val=id / 100) users wrap this in a function and store it in the R Script Repository with a name. In R, this looks like ore.scriptCreate("RandomRedDots", function () { line-height: 115%; font-family: "Courier New";">     id <- 1:10     plot(1:100,rnorm(100),pch=21,bg="red",cex =2)     data.frame(id=id, val=id / 100)) }) In SQL, this looks like begin sys.rqScriptCreate('RandomRedDots',  'function(){     id <- 1:10     plot(1:100,rnorm(100),pch=21,bg="red",cex =2)     data.frame(id=id, val=id / 100)   }'); end; / The R function ore.scriptDrop and SQL function sys.rqScriptDrop can be used to drop these scripts as well. Note that the system will give an error if the script name already exists. Accessing R scripts once they’ve been loaded If you’re not using a source code control system, it is possible that your R scripts can be misplaced or files modified, making what is stored in Oracle Database to only or best copy of your R code. If you’ve loaded your R scripts to the database, it is straightforward to access these scripts from the database table SYS.RQ_SCRIPTS. For example, select * from sys.rq_scripts where name='myScriptName'; From R, scripts in the repository can be loaded into the R client engine using a function similar to the following: ore.scriptLoad <- function(name) { query <- paste("select script from sys.rq_scripts where name='",name,"'",sep="") str.f <- OREbase:::.ore.dbGetQuery(query) assign(name,eval(parse(text = str.f)),pos=1) } ore.scriptLoad("myFunctionName") This function is also useful if you want to load an existing R script from the repository into another R script in the repository – think modular coding style. Just include this function in the body of the other function and load the named script. Migrating R scripts from one database instance to another To move a set of functions from one system to another, the following script loads the functions from one R script repository into the client R engine, then connects to the target database and creates the scripts there with the same names. scriptNames <- OREbase:::.ore.dbGetQuery("select name from sys.rq_scripts where name not like 'RQG$%' and name not like 'RQ$%'")$NAME for(s in scriptNames) { cat(s,"\n") ore.scriptLoad(s) } ore.disconnect() ore.connect("rquser","orcl","localhost","rquser") for(s in scriptNames) { cat(s,"\n") ore.scriptDrop(s) ore.scriptCreate(s,get(s)) } Best Practice When naming R scripts, keep in mind that the name can be up to 128 characters. As such, consider organizing scripts in a directory structure manner. For example, if an organization has multiple groups or applications sharing the same database and there are multiple components, use “/” to facilitate the function organization: line-height: 115%;">ore.scriptCreate("/org1/app1/component1/myFuntion1", myFunction1) ore.scriptCreate("/org1/app1/component1/myFuntion2", myFunction2) ore.scriptCreate("/org1/app2/component2/myFuntion2", myFunction2) ore.scriptCreate("/org2/app2/component1/myFuntion3", myFunction3) ore.scriptCreate("/org3/app2/component1/myFuntion4", myFunction4) Users can then query for all functions using the path prefix when looking up functions. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • Tracking "To Do" Items

    - by Bill Graziano
    One of the challenges I struggle with is keeping a good "to do" list of things I need to do on the various SQL Servers I support. I have servers that I don't visit on a regular basis so my situation may be different than many of you. Though I'm sure you all have servers that you only touch every few months. (And it's usually the accounting server!) It's difficult for me to remember what changes I made and what changes I need to make. I've tried Outlook, OneNote and various other to do list managers and haven't been happy with any of them. Many are close but just don't give me what I need. As a result I've started writing my own. It's web-based so you can use it from anywhere -- including on a server. It also knows just enough about SQL Server to help structure your to do items and your notes. It isn't agent based and doesn't do any monitoring. Think OneNote or Evernote but with some "SQL Servery" stuff built in. If you'd like to try this or take a survey I'm putting together, add your email address to my mailing list.  I should be ready in a week or so.  I'm only going to use this list for notifications about this service. I'd like to find a small group of people that feel the same pain I do and maybe we can build something interesting.

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  • WSS V3 and connections to it’s internal database

    - by ptahiliani
    Have you ever wanted to connect to the “Windows Internal” database that WSS V3 uses? While “Windows Internal Database” is Microsoft SQL Server 2005 in a limited edition (just like MSDE, WMSDE before it), the familiar access tools to the DB went missing, and connecting using standard ways doesn’t work either. It doesn’t work right out of the box. First, you need SQL Management Studio Express. Install and start it. Specify the following connection string: \\.\pipe\mssql$microsoft##ssee\sql\query Please note that, as implied by the connection string, this connection only works locally. If you are looking for the connection string than here it is: “Provider=Sqloledb;Data Source=\\.\pipe\MSSQL$MICROSOFT##SSEE\sql\query;Database=SUSDB;Trusted_Connection=yes”

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  • How to fix “Cannot connect to the configuration database.”

    - by ybbest
    The problem: When I browse to a SharePoint site, I got the Server Error in ‘/’ Application, Cannot connect to the configuration database. The Analysis: The reason you get the message is that SharePoint WFE cannot connect to the SQL database, you need to check the weather SQL server service is started as shown below. Solution: When checking the SQL Server service, I see it is not started. After starting the service, it works like a charm.

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  • Broken Views

    - by Ajarn Mark Caldwell
    “SELECT *” isn’t just hazardous to performance, it can actually return blatantly wrong information. There are a number of blog posts and articles out there that actively discourage the use of the SELECT * FROM …syntax.  The two most common explanations that I have seen are: Performance:  The SELECT * syntax will return every column in the table, but frequently you really only need a few of the columns, and so by using SELECT * your are retrieving large volumes of data that you don’t need, but the system has to process, marshal across tiers, and so on.  It would be much more efficient to only select the specific columns that you need. Future-proof:  If you are taking other shortcuts in your code, along with using SELECT *, you are setting yourself up for trouble down the road when enhancements are made to the system.  For example, if you use SELECT * to return results from a table into a DataTable in .NET, and then reference columns positionally (e.g. myDataRow[5]) you could end up with bad data if someone happens to add a column into position 3 and skewing all the remaining columns’ ordinal position.  Or if you use INSERT…SELECT * then you will likely run into errors when a new column is added to the source table in any position. And if you use SELECT * in the definition of a view, you will run into a variation of the future-proof problem mentioned above.  One of the guys on my team, Mike Byther, ran across this in a project we were doing, but fortunately he caught it while we were still in development.  I asked him to put together a test to prove that this was related to the use of SELECT * and not some other anomaly.  I’ll walk you through the test script so you can see for yourself what happens. We are going to create a table and two views that are based on that table, one of them uses SELECT * and the other explicitly lists the column names.  The script to create these objects is listed below. IF OBJECT_ID('testtab') IS NOT NULL DROP TABLE testtabgoIF OBJECT_ID('testtab_vw') IS NOT NULL DROP VIEW testtab_vwgo IF OBJECT_ID('testtab_vw_named') IS NOT NULL DROP VIEW testtab_vw_namedgo CREATE TABLE testtab (col1 NVARCHAR(5) null, col2 NVARCHAR(5) null)INSERT INTO testtab(col1, col2)VALUES ('A','B'), ('A','B')GOCREATE VIEW testtab_vw AS SELECT * FROM testtabGOCREATE VIEW testtab_vw_named AS SELECT col1, col2 FROM testtabgo Now, to prove that the two views currently return equivalent results, select from them. SELECT 'star', col1, col2 FROM testtab_vwSELECT 'named', col1, col2 FROM testtab_vw_named OK, so far, so good.  Now, what happens if someone makes a change to the definition of the underlying table, and that change results in a new column being inserted between the two existing columns?  (Side note, I normally prefer to append new columns to the end of the table definition, but some people like to keep their columns alphabetized, and for clarity for later people reviewing the schema, it may make sense to group certain columns together.  Whatever the reason, it sometimes happens, and you need to protect yourself and your code from the repercussions.) DROP TABLE testtabgoCREATE TABLE testtab (col1 NVARCHAR(5) null, col3 NVARCHAR(5) NULL, col2 NVARCHAR(5) null)INSERT INTO testtab(col1, col3, col2)VALUES ('A','C','B'), ('A','C','B')goSELECT 'star', col1, col2 FROM testtab_vwSELECT 'named', col1, col2 FROM testtab_vw_named I would have expected that the view using SELECT * in its definition would essentially pass-through the column name and still retrieve the correct data, but that is not what happens.  When you run our two select statements again, you see that the View that is based on SELECT * actually retrieves the data based on the ordinal position of the columns at the time that the view was created.  Sure, one work-around is to recreate the View, but you can’t really count on other developers to know the dependencies you have built-in, and they won’t necessarily recreate the view when they refactor the table. I am sure that there are reasons and justifications for why Views behave this way, but I find it particularly disturbing that you can have code asking for col2, but actually be receiving data from col3.  By the way, for the record, this entire scenario and accompanying test script apply to SQL Server 2008 R2 with Service Pack 1. So, let the developer beware…know what assumptions are in effect around your code, and keep on discouraging people from using SELECT * syntax in anything but the simplest of ad-hoc queries. And of course, let’s clean up after ourselves.  To eliminate the database objects created during this test, run the following commands. DROP TABLE testtabDROP VIEW testtab_vwDROP VIEW testtab_vw_named

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  • Extracting a line section of mysql backup using sed

    - by carpii
    I occasionally need to extract a single record from a mysqlbackup To do this, I first extract the single table I want from the backup... sed -n -e '/CREATE TABLE.*usertext/,/CREATE TABLE/p' 20120930_backup.sql > table.sql In table.sql, the records are batched using extended inserts (with maybe 100 records per insert before it creates a new line starting with INSERT INTO), so they look like... INSERT INTO usertext VALUES (1, field2 etc), (2, field2 etc), INSERT INTO usertext VALUES (101, field2 etc), (102, field2 etc), ... Im trying to extract record 239560 from this, using... sed -n -e '/(239560.*/,/)/p' table.sql > record.sql Ie.. start streaming when it finds 239560, and stop when it hits the closing bracket But this isnt working as I hoped, it just results in the full insert batch being output. Please can someone give me some pointers as to where Im going wrong? Would I be better off using awk for extracting segments of lines, and use sed for extracting lines within a file?

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  • Watch out for a trailing slash on $ORACLE_HOME

    - by user12620111
    Watch out for a trailing slash on $ORACLE_HOME oracle$ export ORACLE_HOME=/u01/app/11.2.0.3/grid/ oracle$ ORACLE_SID=+ASM1 oracle$ sqlplus / as sysasm SQL*Plus: Release 11.2.0.3.0 Production on Thu Mar 29 13:04:01 2012 Copyright (c) 1982, 2011, Oracle.  All rights reserved. Connected to an idle instance. SQL> oracle$ export ORACLE_HOME=/u01/app/11.2.0.3/grid oracle$ ORACLE_SID=+ASM1 oracle$ sqlplus / as sysasm SQL*Plus: Release 11.2.0.3.0 Production on Thu Mar 29 13:04:44 2012 Copyright (c) 1982, 2011, Oracle.  All rights reserved. Connected to: Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - 64bit Production With the Real Application Clusters and Automatic Storage Management options SQL>

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  • SSIS 2008 Import and Export Wizard and Excel-based Data

    Even though the Import and Export Wizard, incorporated into the SQL Server 2008 platform, greatly simplifies the creation of SQL Server Integration Services packages, it has its limitations. This article points out the primary challenges associated with using it to copy data between SQL Server 2008 and Excel and presents methods of addressing these challenges.

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  • Suggestion: ALLFILES option for RESTORE

    - by Greg Low
    The default action when performing a backup is to append to the backup file yet the default action when restoring a backup is to restore just the first file.I constantly come across customer situations where they are puzzled that they seem to have lost data after they have completed a restore. Invariably, it's just that they haven't restored all the backups contained within a single OS file. This happens most commonly with log backups but also happens when they have not restored the most recent database backup file.It is not trivial to achieve this within simple T-SQL scripts, when the number of backup files within the OS file is unknown. It really should be.I'd like to see a FILES=ALLFILES option on the RESTORE command. For RESTORE DATABASE, it should restore the most recent database backup plus any subsequent log files. For RESTORE LOG (which is the most important missing option), it should just restore all relevant log backups that are contained.If you agree, you know what to do: please vote:  https://connect.microsoft.com/SQLServer/feedback/details/769204/option-to-restore-all-backups-files-within-a-media-setAlternately, how would you write a T-SQL command to restore all log backups within a single OS file where the number of files is unknown? Would love to hear creative solutions because all the ones that I think of are pretty messy and need dynamic SQL

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