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  • Check if a connection is in a transaction

    - by acidzombie24
    I am getting a SqlConnection does not support parallel transactions. exception and this answer mentions its when a connection tries to open two transactions. This is exactly what i am doing. I thought nested transactions were ok (i was using sqlite for the prototype). How do i check if the connection is already in a transaction? I am using Microsoft SQL Server Database File.

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  • BizTalk and SQL: Alternatives to the SQL receive adapter. Using Msmq to receive SQL data

    - by Leonid Ganeline
    If we have to get data from the SQL database, the standard way is to use a receive port with SQL adapter. SQL receive adapter is a solicit-response adapter. It periodically polls the SQL database with queries. That’s only way it can work. Sometimes it is undesirable. With new WCF-SQL adapter we can use the lightweight approach but still with the same principle, the WCF-SQL adapter periodically solicits the database with queries to check for the new records. Imagine the situation when the new records can appear in very broad time limits, some - in a second interval, others - in the several minutes interval. Our requirement is to process the new records ASAP. That means the polling interval should be near the shortest interval between the new records, a second interval. As a result the most of the poll queries would return nothing and would load the database without good reason. If the database is working under heavy payload, it is very undesirable. Do we have other choices? Sure. We can change the polling to the “eventing”. The good news is the SQL server could issue the event in case of new records with triggers. Got a new record –the trigger event is fired. No new records – no the trigger events – no excessive load to the database. The bad news is the SQL Server doesn’t have intrinsic methods to send the event data outside. For example, we would rather use the adapters that do listen for the data and do not solicit. There are several such adapters-listeners as File, Ftp, SOAP, WCF, and Msmq. But the SQL Server doesn’t have methods to create and save files, to consume the Web-services, to create and send messages in the queue, does it? Can we use the File, FTP, Msmq, WCF adapters to get data from SQL code? Yes, we can. The SQL Server 2005 and 2008 have the possibility to use .NET code inside SQL code. See the SQL Integration. How it works for the Msmq, for example: ·         New record is created, trigger is fired ·         Trigger calls the CLR stored procedure and passes the message parameters to it ·         The CLR stored procedure creates message and sends it to the outgoing queue in the SQL Server computer. ·         Msmq service transfers message to the queue in the BizTalk Server computer. ·         WCF-NetMsmq adapter receives the message from this queue. For the File adapter the idea is the same, the CLR stored procedure creates and stores the file with message, and then the File adapter picks up this file. Using WCF-NetMsmq adapter to get data from SQL I am describing the full set of the deployment and development steps for the case with the WCF-NetMsmq adapter. Development: 1.       Create the .NET code: project, class and method to create and send the message to the MSMQ queue. 2.       Create the SQL code in triggers to call the .NET code. Installation and Deployment: 1.       SQL Server: a.       Register the CLR assembly with .NET (CLR) code b.      Install the MSMQ Services 2.       BizTalk Server: a.       Install the MSMQ Services b.      Create the MSMQ queue c.       Create the WCF-NetMsmq receive port. The detailed description is below. Code .NET code … using System.Xml; using System.Xml.Linq; using System.Xml.Serialization;   //namespace MyCompany.MySolution.MyProject – doesn’t work. The assembly name is MyCompany.MySolution.MyProject // I gave up with the compound namespace. Seems the CLR Integration cannot work with it L. Maybe I’m wrong.     public class Event     {         static public XElement CreateMsg(int par1, int par2, int par3)         {             XNamespace ns = "http://schemas.microsoft.com/Sql/2008/05/TypedPolling/my_storedProc";             XElement xdoc =                 new XElement(ns + "TypedPolling",                     new XElement(ns + "TypedPollingResultSet0",                         new XElement(ns + "TypedPollingResultSet0",                             new XElement(ns + "par1", par1),                             new XElement(ns + "par2", par2),                             new XElement(ns + "par3", par3),                         )                     )                 );             return xdoc;         }     }   //////////////////////////////////////////////////////////////////////// … using System.ServiceModel; using System.ServiceModel.Channels; using System.Transactions; using System.Data; using System.Data.Sql; using System.Data.SqlTypes;   public class MsmqHelper {     [Microsoft.SqlServer.Server.SqlProcedure]     // msmqAddress as "net.msmq://localhost/private/myapp.myqueue";     public static void SendMsg(string msmqAddress, string action, int par1, int par2, int par3)     {         using (TransactionScope scope = new TransactionScope(TransactionScopeOption.Suppress))         {             NetMsmqBinding binding = new NetMsmqBinding(NetMsmqSecurityMode.None);             binding.ExactlyOnce = true;             EndpointAddress address = new EndpointAddress(msmqAddress);               using (ChannelFactory<IOutputChannel> factory = new ChannelFactory<IOutputChannel>(binding, address))             {                 IOutputChannel channel = factory.CreateChannel();                 try                 {                     XElement xe = Event.CreateMsg(par1, par2, par3);                     XmlReader xr = xe.CreateReader();                     Message msg = Message.CreateMessage(MessageVersion.Default, action, xr);                     channel.Send(msg);                     //SqlContext.Pipe.Send(…); // to test                 }                 catch (Exception ex)                 { …                 }             }             scope.Complete();         }     }   SQL code in triggers   -- sp_SendMsg was registered as a name of the MsmqHelper.SendMsg() EXEC sp_SendMsg'net.msmq://biztalk_server_name/private/myapp.myqueue', 'Create', @par1, @par2, @par3   Installation and Deployment On the SQL Server Registering the CLR assembly 1.       Prerequisites: .NET 3.5 SP1 Framework. It could be the issue for the production SQL Server! 2.       For more information, please, see the link http://nielsb.wordpress.com/sqlclrwcf/ 3.       Copy files: >copy “\Windows\Microsoft.net\Framework\v3.0\Windows Communication Foundation\Microsoft.Transactions.Bridge.dll” “\Program Files\Reference Assemblies\Microsoft\Framework\v3.0 \Microsoft.Transactions.Bridge.dll” If your machine is a 64-bit, run two commands: >copy “\Windows\Microsoft.net\Framework\v3.0\Windows Communication Foundation\Microsoft.Transactions.Bridge.dll” “\Program Files (x86)\Reference Assemblies\Microsoft\Framework\v3.0 \Microsoft.Transactions.Bridge.dll” >copy “\Windows\Microsoft.net\Framework64\v3.0\Windows Communication Foundation\Microsoft.Transactions.Bridge.dll” “\Program Files\Reference Assemblies\Microsoft\Framework\v3.0 \Microsoft.Transactions.Bridge.dll” 4.       Execute the SQL code to register the .NET assemblies: -- For x64 OS: CREATE ASSEMBLY SMdiagnostics AUTHORIZATION dbo FROM 'C:\Windows\Microsoft.NET\Framework\v3.0\Windows Communication Foundation\SMdiagnostics.dll' WITH permission_set = unsafe CREATE ASSEMBLY [System.Web] AUTHORIZATION dbo FROM 'C:\Windows\Microsoft.NET\Framework64\v2.0.50727\System.Web.dll' WITH permission_set = unsafe CREATE ASSEMBLY [System.Messaging] AUTHORIZATION dbo FROM 'C:\Windows\Microsoft.NET\Framework\v2.0.50727\System.Messaging.dll' WITH permission_set = unsafe CREATE ASSEMBLY [System.ServiceModel] AUTHORIZATION dbo FROM 'C:\Program Files (x86)\Reference Assemblies\Microsoft\Framework\v3.0\System.ServiceModel.dll' WITH permission_set = unsafe CREATE ASSEMBLY [System.Xml.Linq] AUTHORIZATION dbo FROM 'C:\Program Files\Reference Assemblies\Microsoft\Framework\v3.5\System.Xml.Linq.dll' WITH permission_set = unsafe   -- For x32 OS: --CREATE ASSEMBLY SMdiagnostics AUTHORIZATION dbo FROM 'C:\Windows\Microsoft.NET\Framework\v3.0\Windows Communication Foundation\SMdiagnostics.dll' WITH permission_set = unsafe --CREATE ASSEMBLY [System.Web] AUTHORIZATION dbo FROM 'C:\Windows\Microsoft.NET\Framework\v2.0.50727\System.Web.dll' WITH permission_set = unsafe --CREATE ASSEMBLY [System.Messaging] AUTHORIZATION dbo FROM 'C:\Windows\Microsoft.NET\Framework\v2.0.50727\System.Messaging.dll' WITH permission_set = unsafe --CREATE ASSEMBLY [System.ServiceModel] AUTHORIZATION dbo FROM 'C:\Program Files\Reference Assemblies\Microsoft\Framework\v3.0\System.ServiceModel.dll' WITH permission_set = unsafe 5.       Register the assembly with the external stored procedure: CREATE ASSEMBLY [HelperClass] AUTHORIZATION dbo FROM ’<FilePath>MyCompany.MySolution.MyProject.dll' WITH permission_set = unsafe where the <FilePath> - the path of the file on this machine! 6. Create the external stored procedure CREATE PROCEDURE sp_SendMsg (        @msmqAddress nvarchar(100),        @Action NVARCHAR(50),        @par1 int,        @par2 int,        @par3 int ) AS EXTERNAL NAME HelperClear.MsmqHelper.SendMsg   Installing the MSMQ Services 1.       Check if the MSMQ service is NOT installed. To check:  Start / Administrative Tools / Computer Management, on the left pane open the “Services and Applications”, search to the “Message Queuing”. If you cannot see it, follow next steps. 2.       Start / Control Panel / Programs and Features 3.       Click “Turn Windows Features on or off” 4.       Click Features, click “Add Features” 5.       Scroll down the feature list; open the “Message Queuing” / “Message Queuing Services”; and check the “Message Queuing Server” option  6.       Click Next; Click Install; wait to the successful finish of the installation Creating the MSMQ queue We don’t need to create the queue on the “sender” side. On the BizTalk Server Installing the MSMQ Services The same is as for the SQL Server. Creating the MSMQ queue 1.       Start / Administrative Tools / Computer Management, on the left pane open the “Services and Applications”, open the “Message Queuing”, and open the “Private Queues”. 2.       Right-click the “Private Queues”; choose New; choose “Private Queue”. 3.       Type the Queue name as ’myapp.myqueue'; check the “Transactional” option. Creating the WCF-NetMsmq receive port I will not go through this step in all details. It is straightforward. URI for this receive location should be 'net.msmq://localhost/private/myapp.myqueue'. Notes ·         The biggest problem is usually on the step the “Registering the CLR assembly”. It is hard to predict where are the assemblies from the assembly list, what version should be used, x86 or x64. It is pity of such “rude” integration of the SQL with .NET. ·         In couple cases the new WCF-NetMsmq port was not able to work with the queue. Try to replace the WCF- NetMsmq port with the WCF-Custom port with netMsmqBinding. It was working fine for me. ·         To test how messages go through the queue you can turn on the Journal /Enabled option for the queue. I used the QueueExplorer utility to look to the messages in Journal. The Computer Management can also show the messages but it shows only small part of the message body and in the weird format. The QueueExplorer can do the better job; it shows the whole body and Xml messages are in good color format.

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  • Benchmark MySQL Cluster using flexAsynch: No free node id found for mysqld(API)?

    - by quanta
    I am going to benchmark MySQL Cluster using flexAsynch follow this guide, details as below: mkdir /usr/local/mysqlc732/ cd /usr/local/src/mysql-cluster-gpl-7.3.2 cmake . -DCMAKE_INSTALL_PREFIX=/usr/local/mysqlc732/ -DWITH_NDB_TEST=ON make make install Everything works fine until this step: # /usr/local/mysqlc732/bin/flexAsynch -t 1 -p 80 -l 2 -o 100 -c 100 -n FLEXASYNCH - Starting normal mode Perform benchmark of insert, update and delete transactions 1 number of concurrent threads 80 number of parallel operation per thread 100 transaction(s) per round 2 iterations Load Factor is 80% 25 attributes per table 1 is the number of 32 bit words per attribute Tables are with logging Transactions are executed with hint provided No force send is used, adaptive algorithm used Key Errors are disallowed Temporary Resource Errors are allowed Insufficient Space Errors are disallowed Node Recovery Errors are allowed Overload Errors are allowed Timeout Errors are allowed Internal NDB Errors are allowed User logic reported Errors are allowed Application Errors are disallowed Using table name TAB0 NDBT_ProgramExit: 1 - Failed ndb_cluster.log: WARNING -- Failed to allocate nodeid for API at 127.0.0.1. Returned eror: 'No free node id found for mysqld(API).' I also have recompiled with -DWITH_DEBUG=1 -DWITH_NDB_DEBUG=1. How can I run flexAsynch in the debug mode? # /usr/local/mysqlc732/bin/flexAsynch -h FLEXASYNCH Perform benchmark of insert, update and delete transactions Arguments: -t Number of threads to start, default 1 -p Number of parallel transactions per thread, default 32 -o Number of transactions per loop, default 500 -l Number of loops to run, default 1, 0=infinite -load_factor Number Load factor in index in percent (40 -> 99) -a Number of attributes, default 25 -c Number of operations per transaction -s Size of each attribute, default 1 (PK is always of size 1, independent of this value) -simple Use simple read to read from database -dirty Use dirty read to read from database -write Use writeTuple in insert and update -n Use standard table names -no_table_create Don't create tables in db -temp Create table(s) without logging -no_hint Don't give hint on where to execute transaction coordinator -adaptive Use adaptive send algorithm (default) -force Force send when communicating -non_adaptive Send at a 10 millisecond interval -local 1 = each thread its own node, 2 = round robin on node per parallel trans 3 = random node per parallel trans -ndbrecord Use NDB Record -r Number of extra loops -insert Only run inserts on standard table -read Only run reads on standard table -update Only run updates on standard table -delete Only run deletes on standard table -create_table Only run Create Table of standard table -drop_table Only run Drop Table on standard table -warmup_time Warmup Time before measurement starts -execution_time Execution Time where measurement is done -cooldown_time Cooldown time after measurement completed -table Number of standard table, default 0

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  • What is Granularity?

    - by tonyrogerson
    Granularity defines “the lowest level of detail”; but what is meant by “the lowest level of detail”? Consider the Transactions table below: create table Transactions ( TransactionID int not null primary key clustered, TransactionDate date not null, ClientID int not null, StockID int not null, TransactionAmount decimal ( 28 , 2 ) not null, CommissionAmount decimal ( 28 , 5 ) not null ) A Client can Trade in one or many Stocks on any date – there is no uniqueness to ClientID, Stock and TransactionDate...(read more)

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  • T-SQL in SQL Azure

    - by kaleidoscope
    The following table summarizes the Transact-SQL support provided by SQL Azure Database at PDC 2009: Transact-SQL Features Supported Transact-SQL Features Unsupported Constants Constraints Cursors Index management and rebuilding indexes Local temporary tables Reserved keywords Stored procedures Statistics management Transactions Triggers Tables, joins, and table variables Transact-SQL language elements such as Create/drop databases Create/alter/drop tables Create/alter/drop users and logins User-defined functions Views, including sys.synonyms view Common Language Runtime (CLR) Database file placement Database mirroring Distributed queries Distributed transactions Filegroup management Global temporary tables Spatial data and indexes SQL Server configuration options SQL Server Service Broker System tables Trace Flags   Amit, S

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  • System Requirements of a write-heavy applications serving hundreds of requests per second

    - by Rolando Cruz
    NOTE: I am a self-taught PHP developer who has little to none experience managing web and database servers. I am about to write a web-based attendance system for a very large userbase. I expect around 1000 to 1500 users logged-in at the same time making at least 1 request every 10 seconds or so for a span of 30 minutes a day, 3 times a week. So it's more or less 100 requests per second, or at the very worst 1000 requests in a second (average of 16 concurrent requests? But it could be higher given the short timeframe that users will make these requests. crosses fingers to avoid 100 concurrent requests). I expect two types of transactions, a local (not referring to a local network) and a foreign transaction. local transactions basically download userdata in their locality and cache it for 1 - 2 weeks. Attendance equests will probably be two numeric strings only: userid and eventid. foreign transactions are for attendance of those do not belong in the current locality. This will pass in the following data instead: (numeric) locality_id, (string) full_name. Both requests are done in Ajax so no HTML data included, only JSON. Both type of requests expect at the very least a single numeric response from the server. I think there will be a 50-50 split on the frequency of local and foreign transactions, but there's only a few bytes of difference anyways in the sizes of these transactions. As of this moment the userid may only reach 6 digits and eventid are 4 to 5-digit integers too. I expect my users table to have at least 400k rows, and the event table to have as many as 10k rows, a locality table with at least 1500 rows, and my main attendance table to increase by 400k rows (based on the number of users in the users table) a day for 3 days a week (1.2M rows a week). For me, this sounds big. But is this really that big? Or can this be handled by a single server (not sure about the server specs yet since I'll probably avail of a VPS from ServInt or others)? I tried to read on multiple server setups Heatbeat, DRBD, master-slave setups. But I wonder if they're really necessary. the users table will add around 500 1k rows a week. If this can't be handled by a single server, then if I am to choose a MySQL replication topology, what would be the best setup for this case? Sorry, if I sound vague or the question is too wide. I just don't know what to ask or what do you want to know at this point.

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  • Database Snapshot in Sql Server 2005

    A database snapshot is a read-only, static view of a database (called the source database). Each database snapshot is transactionally consistent with the source database at the moment of the snapshot's creation. When you create a database snapshot, the source database will typically have open transactions. Before the snapshot becomes available, the open transactions are rolled back to make the database snapshot transactionally consistent.

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  • What is lightweight lock in distributed shared memory systems?

    - by Kutluhan Metin
    I started reading Tanenbaum's Distributed Systems book a while ago. I read about two phase locking and timestamp reordering in transactions chapter. While having a deeper look from google I heard of lightweight transactions/lightweight transactional memory. But I couldn't find any good explanation and implementation. So what is lightweight memory? What are the benefits of lightweight locks? And how can I implement them?

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  • Working with PivotTables in Excel

    - by Mark Virtue
    PivotTables are one of the most powerful features of Microsoft Excel.  They allow large amounts of data to be analyzed and summarized in just a few mouse clicks. In this article, we explore PivotTables, understand what they are, and learn how to create and customize them. Note:  This article is written using Excel 2010 (Beta).  The concept of a PivotTable has changed little over the years, but the method of creating one has changed in nearly every iteration of Excel.  If you are using a version of Excel that is not 2010, expect different screens from the ones you see in this article. A Little History In the early days of spreadsheet programs, Lotus 1-2-3 ruled the roost.  Its dominance was so complete that people thought it was a waste of time for Microsoft to bother developing their own spreadsheet software (Excel) to compete with Lotus.  Flash-forward to 2010, and Excel’s dominance of the spreadsheet market is greater than Lotus’s ever was, while the number of users still running Lotus 1-2-3 is approaching zero.  How did this happen?  What caused such a dramatic reversal of fortunes? Industry analysts put it down to two factors:  Firstly, Lotus decided that this fancy new GUI platform called “Windows” was a passing fad that would never take off.  They declined to create a Windows version of Lotus 1-2-3 (for a few years, anyway), predicting that their DOS version of the software was all anyone would ever need.  Microsoft, naturally, developed Excel exclusively for Windows.  Secondly, Microsoft developed a feature for Excel that Lotus didn’t provide in 1-2-3, namely PivotTables.  The PivotTables feature, exclusive to Excel, was deemed so staggeringly useful that people were willing to learn an entire new software package (Excel) rather than stick with a program (1-2-3) that didn’t have it.  This one feature, along with the misjudgment of the success of Windows, was the death-knell for Lotus 1-2-3, and the beginning of the success of Microsoft Excel. Understanding PivotTables So what is a PivotTable, exactly? Put simply, a PivotTable is a summary of some data, created to allow easy analysis of said data.  But unlike a manually created summary, Excel PivotTables are interactive.  Once you have created one, you can easily change it if it doesn’t offer the exact insights into your data that you were hoping for.  In a couple of clicks the summary can be “pivoted” – rotated in such a way that the column headings become row headings, and vice versa.  There’s a lot more that can be done, too.  Rather than try to describe all the features of PivotTables, we’ll simply demonstrate them… The data that you analyze using a PivotTable can’t be just any data – it has to be raw data, previously unprocessed (unsummarized) – typically a list of some sort.  An example of this might be the list of sales transactions in a company for the past six months. Examine the data shown below: Notice that this is not raw data.  In fact, it is already a summary of some sort.  In cell B3 we can see $30,000, which apparently is the total of James Cook’s sales for the month of January.  So where is the raw data?  How did we arrive at the figure of $30,000?  Where is the original list of sales transactions that this figure was generated from?  It’s clear that somewhere, someone must have gone to the trouble of collating all of the sales transactions for the past six months into the summary we see above.  How long do you suppose this took?  An hour?  Ten?  Probably. If we were to track down the original list of sales transactions, it might look something like this: You may be surprised to learn that, using the PivotTable feature of Excel, we can create a monthly sales summary similar to the one above in a few seconds, with only a few mouse clicks.  We can do this – and a lot more too! How to Create a PivotTable First, ensure that you have some raw data in a worksheet in Excel.  A list of financial transactions is typical, but it can be a list of just about anything:  Employee contact details, your CD collection, or fuel consumption figures for your company’s fleet of cars. So we start Excel… …and we load such a list… Once we have the list open in Excel, we’re ready to start creating the PivotTable. Click on any one single cell within the list: Then, from the Insert tab, click the PivotTable icon: The Create PivotTable box appears, asking you two questions:  What data should your new PivotTable be based on, and where should it be created?  Because we already clicked on a cell within the list (in the step above), the entire list surrounding that cell is already selected for us ($A$1:$G$88 on the Payments sheet, in this example).  Note that we could select a list in any other region of any other worksheet, or even some external data source, such as an Access database table, or even a MS-SQL Server database table.  We also need to select whether we want our new PivotTable to be created on a new worksheet, or on an existing one.  In this example we will select a new one: The new worksheet is created for us, and a blank PivotTable is created on that worksheet: Another box also appears:  The PivotTable Field List.  This field list will be shown whenever we click on any cell within the PivotTable (above): The list of fields in the top part of the box is actually the collection of column headings from the original raw data worksheet.  The four blank boxes in the lower part of the screen allow us to choose the way we would like our PivotTable to summarize the raw data.  So far, there is nothing in those boxes, so the PivotTable is blank.  All we need to do is drag fields down from the list above and drop them in the lower boxes.  A PivotTable is then automatically created to match our instructions.  If we get it wrong, we only need to drag the fields back to where they came from and/or drag new fields down to replace them. The Values box is arguably the most important of the four.  The field that is dragged into this box represents the data that needs to be summarized in some way (by summing, averaging, finding the maximum, minimum, etc).  It is almost always numerical data.  A perfect candidate for this box in our sample data is the “Amount” field/column.  Let’s drag that field into the Values box: Notice that (a) the “Amount” field in the list of fields is now ticked, and “Sum of Amount” has been added to the Values box, indicating that the amount column has been summed. If we examine the PivotTable itself, we indeed find the sum of all the “Amount” values from the raw data worksheet: We’ve created our first PivotTable!  Handy, but not particularly impressive.  It’s likely that we need a little more insight into our data than that. Referring to our sample data, we need to identify one or more column headings that we could conceivably use to split this total.  For example, we may decide that we would like to see a summary of our data where we have a row heading for each of the different salespersons in our company, and a total for each.  To achieve this, all we need to do is to drag the “Salesperson” field into the Row Labels box: Now, finally, things start to get interesting!  Our PivotTable starts to take shape….   With a couple of clicks we have created a table that would have taken a long time to do manually. So what else can we do?  Well, in one sense our PivotTable is complete.  We’ve created a useful summary of our source data.  The important stuff is already learned!  For the rest of the article, we will examine some ways that more complex PivotTables can be created, and ways that those PivotTables can be customized. First, we can create a two-dimensional table.  Let’s do that by using “Payment Method” as a column heading.  Simply drag the “Payment Method” heading to the Column Labels box: Which looks like this: Starting to get very cool! Let’s make it a three-dimensional table.  What could such a table possibly look like?  Well, let’s see… Drag the “Package” column/heading to the Report Filter box: Notice where it ends up…. This allows us to filter our report based on which “holiday package” was being purchased.  For example, we can see the breakdown of salesperson vs payment method for all packages, or, with a couple of clicks, change it to show the same breakdown for the “Sunseekers” package: And so, if you think about it the right way, our PivotTable is now three-dimensional.  Let’s keep customizing… If it turns out, say, that we only want to see cheque and credit card transactions (i.e. no cash transactions), then we can deselect the “Cash” item from the column headings.  Click the drop-down arrow next to Column Labels, and untick “Cash”: Let’s see what that looks like…As you can see, “Cash” is gone. Formatting This is obviously a very powerful system, but so far the results look very plain and boring.  For a start, the numbers that we’re summing do not look like dollar amounts – just plain old numbers.  Let’s rectify that. A temptation might be to do what we’re used to doing in such circumstances and simply select the whole table (or the whole worksheet) and use the standard number formatting buttons on the toolbar to complete the formatting.  The problem with that approach is that if you ever change the structure of the PivotTable in the future (which is 99% likely), then those number formats will be lost.  We need a way that will make them (semi-)permanent. First, we locate the “Sum of Amount” entry in the Values box, and click on it.  A menu appears.  We select Value Field Settings… from the menu: The Value Field Settings box appears. Click the Number Format button, and the standard Format Cells box appears: From the Category list, select (say) Accounting, and drop the number of decimal places to 0.  Click OK a few times to get back to the PivotTable… As you can see, the numbers have been correctly formatted as dollar amounts. While we’re on the subject of formatting, let’s format the entire PivotTable.  There are a few ways to do this.  Let’s use a simple one… Click the PivotTable Tools/Design tab: Then drop down the arrow in the bottom-right of the PivotTable Styles list to see a vast collection of built-in styles: Choose any one that appeals, and look at the result in your PivotTable:   Other Options We can work with dates as well.  Now usually, there are many, many dates in a transaction list such as the one we started with.  But Excel provides the option to group data items together by day, week, month, year, etc.  Let’s see how this is done. First, let’s remove the “Payment Method” column from the Column Labels box (simply drag it back up to the field list), and replace it with the “Date Booked” column: As you can see, this makes our PivotTable instantly useless, giving us one column for each date that a transaction occurred on – a very wide table! To fix this, right-click on any date and select Group… from the context-menu: The grouping box appears.  We select Months and click OK: Voila!  A much more useful table: (Incidentally, this table is virtually identical to the one shown at the beginning of this article – the original sales summary that was created manually.) Another cool thing to be aware of is that you can have more than one set of row headings (or column headings): …which looks like this…. You can do a similar thing with column headings (or even report filters). Keeping things simple again, let’s see how to plot averaged values, rather than summed values. First, click on “Sum of Amount”, and select Value Field Settings… from the context-menu that appears: In the Summarize value field by list in the Value Field Settings box, select Average: While we’re here, let’s change the Custom Name, from “Average of Amount” to something a little more concise.  Type in something like “Avg”: Click OK, and see what it looks like.  Notice that all the values change from summed totals to averages, and the table title (top-left cell) has changed to “Avg”: If we like, we can even have sums, averages and counts (counts = how many sales there were) all on the same PivotTable! Here are the steps to get something like that in place (starting from a blank PivotTable): Drag “Salesperson” into the Column Labels Drag “Amount” field down into the Values box three times For the first “Amount” field, change its custom name to “Total” and it’s number format to Accounting (0 decimal places) For the second “Amount” field, change its custom name to “Average”, its function to Average and it’s number format to Accounting (0 decimal places) For the third “Amount” field, change its name to “Count” and its function to Count Drag the automatically created field from Column Labels to Row Labels Here’s what we end up with: Total, average and count on the same PivotTable! Conclusion There are many, many more features and options for PivotTables created by Microsoft Excel – far too many to list in an article like this.  To fully cover the potential of PivotTables, a small book (or a large website) would be required.  Brave and/or geeky readers can explore PivotTables further quite easily:  Simply right-click on just about everything, and see what options become available to you.  There are also the two ribbon-tabs: PivotTable Tools/Options and Design.  It doesn’t matter if you make a mistake – it’s easy to delete the PivotTable and start again – a possibility old DOS users of Lotus 1-2-3 never had. We’ve included an Excel that should work with most versions of Excel, so you can download to practice your PivotTable skills. Download Our Practice Excel File Similar Articles Productive Geek Tips Magnify Selected Cells In Excel 2007Share Access Data with Excel in Office 2010Make Excel 2007 Print Gridlines In Workbook FileMake Excel 2007 Always Save in Excel 2003 FormatConvert Older Excel Documents to Excel 2007 Format TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser Scan for Viruses in Ubuntu using ClamAV Replace Your Windows Task Manager With System Explorer

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  • SQL SERVER – LCK_M_XXX – Wait Type – Day 15 of 28

    - by pinaldave
    Locking is a mechanism used by the SQL Server Database Engine to synchronize access by multiple users to the same piece of data, at the same time. In simpler words, it maintains the integrity of data by protecting (or preventing) access to the database object. From Book On-Line: LCK_M_BU Occurs when a task is waiting to acquire a Bulk Update (BU) lock. LCK_M_IS Occurs when a task is waiting to acquire an Intent Shared (IS) lock. LCK_M_IU Occurs when a task is waiting to acquire an Intent Update (IU) lock. LCK_M_IX Occurs when a task is waiting to acquire an Intent Exclusive (IX) lock. LCK_M_S Occurs when a task is waiting to acquire a Shared lock. LCK_M_SCH_M Occurs when a task is waiting to acquire a Schema Modify lock. LCK_M_SCH_S Occurs when a task is waiting to acquire a Schema Share lock. LCK_M_SIU Occurs when a task is waiting to acquire a Shared With Intent Update lock. LCK_M_SIX Occurs when a task is waiting to acquire a Shared With Intent Exclusive lock. LCK_M_U Occurs when a task is waiting to acquire an Update lock. LCK_M_UIX Occurs when a task is waiting to acquire an Update With Intent Exclusive lock. LCK_M_X Occurs when a task is waiting to acquire an Exclusive lock. LCK_M_XXX Explanation: I think the explanation of this wait type is the simplest. When any task is waiting to acquire lock on any resource, this particular wait type occurs. The common reason for the task to be waiting to put lock on the resource is that the resource is already locked and some other operations may be going on within it. This wait also indicates that resources are not available or are occupied at the moment due to some reasons. There is a good chance that the waiting queries start to time out if this wait type is very high. Client application may degrade the performance as well. You can use various methods to find blocking queries: EXEC sp_who2 SQL SERVER – Quickest Way to Identify Blocking Query and Resolution – Dirty Solution DMV – sys.dm_tran_locks DMV – sys.dm_os_waiting_tasks Reducing LCK_M_XXX wait: Check the Explicit Transactions. If transactions are very long, this wait type can start building up because of other waiting transactions. Keep the transactions small. Serialization Isolation can build up this wait type. If that is an acceptable isolation for your business, this wait type may be natural. The default isolation of SQL Server is ‘Read Committed’. One of my clients has changed their isolation to “Read Uncommitted”. I strongly discourage the use of this because this will probably lead to having lots of dirty data in the database. Identify blocking queries mentioned using various methods described above, and then optimize them. Partition can be one of the options to consider because this will allow transactions to execute concurrently on different partitions. If there are runaway queries, use timeout. (Please discuss this solution with your database architect first as timeout can work against you). Check if there is no memory and IO-related issue using the following counters: Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussion of Wait Stats in this blog is generic and varies from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Replication Services in a BI environment

    - by jorg
    In this blog post I will explain the principles of SQL Server Replication Services without too much detail and I will take a look on the BI capabilities that Replication Services could offer in my opinion. SQL Server Replication Services provides tools to copy and distribute database objects from one database system to another and maintain consistency afterwards. These tools basically copy or synchronize data with little or no transformations, they do not offer capabilities to transform data or apply business rules, like ETL tools do. The only “transformations” Replication Services offers is to filter records or columns out of your data set. You can achieve this by selecting the desired columns of a table and/or by using WHERE statements like this: SELECT <published_columns> FROM [Table] WHERE [DateTime] >= getdate() - 60 There are three types of replication: Transactional Replication This type replicates data on a transactional level. The Log Reader Agent reads directly on the transaction log of the source database (Publisher) and clones the transactions to the Distribution Database (Distributor), this database acts as a queue for the destination database (Subscriber). Next, the Distribution Agent moves the cloned transactions that are stored in the Distribution Database to the Subscriber. The Distribution Agent can either run at scheduled intervals or continuously which offers near real-time replication of data! So for example when a user executes an UPDATE statement on one or multiple records in the publisher database, this transaction (not the data itself) is copied to the distribution database and is then also executed on the subscriber. When the Distribution Agent is set to run continuously this process runs all the time and transactions on the publisher are replicated in small batches (near real-time), when it runs on scheduled intervals it executes larger batches of transactions, but the idea is the same. Snapshot Replication This type of replication makes an initial copy of database objects that need to be replicated, this includes the schemas and the data itself. All types of replication must start with a snapshot of the database objects from the Publisher to initialize the Subscriber. Transactional replication need an initial snapshot of the replicated publisher tables/objects to run its cloned transactions on and maintain consistency. The Snapshot Agent copies the schemas of the tables that will be replicated to files that will be stored in the Snapshot Folder which is a normal folder on the file system. When all the schemas are ready, the data itself will be copied from the Publisher to the snapshot folder. The snapshot is generated as a set of bulk copy program (BCP) files. Next, the Distribution Agent moves the snapshot to the Subscriber, if necessary it applies schema changes first and copies the data itself afterwards. The application of schema changes to the Subscriber is a nice feature, when you change the schema of the Publisher with, for example, an ALTER TABLE statement, that change is propagated by default to the Subscriber(s). Merge Replication Merge replication is typically used in server-to-client environments, for example when subscribers need to receive data, make changes offline, and later synchronize changes with the Publisher and other Subscribers, like with mobile devices that need to synchronize one in a while. Because I don’t really see BI capabilities here, I will not explain this type of replication any further. Replication Services in a BI environment Transactional Replication can be very useful in BI environments. In my opinion you never want to see users to run custom (SSRS) reports or PowerPivot solutions directly on your production database, it can slow down the system and can cause deadlocks in the database which can cause errors. Transactional Replication can offer a read-only, near real-time database for reporting purposes with minimal overhead on the source system. Snapshot Replication can also be useful in BI environments, if you don’t need a near real-time copy of the database, you can choose to use this form of replication. Next to an alternative for Transactional Replication it can be used to stage data so it can be transformed and moved into the data warehousing environment afterwards. In many solutions I have seen developers create multiple SSIS packages that simply copies data from one or more source systems to a staging database that figures as source for the ETL process. The creation of these packages takes a lot of (boring) time, while Replication Services can do the same in minutes. It is possible to filter out columns and/or records and it can even apply schema changes automatically so I think it offers enough features here. I don’t know how the performance will be and if it really works as good for this purpose as I expect, but I want to try this out soon!

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  • Aggregating cache data from OCEP in CQL

    - by Manju James
    There are several use cases where OCEP applications need to join stream data with external data, such as data available in a Coherence cache. OCEP’s streaming language, CQL, supports simple cache-key based joins of stream data with data in Coherence (more complex queries will be supported in a future release). However, there are instances where you may need to aggregate the data in Coherence based on input data from a stream. This blog describes a sample that does just that. For our sample, we will use a simplified credit card fraud detection use case. The input to this sample application is a stream of credit card transaction data. The input stream contains information like the credit card ID, transaction time and transaction amount. The purpose of this application is to detect suspicious transactions and send out a warning event. For the sake of simplicity, we will assume that all transactions with amounts greater than $1000 are suspicious. The transaction history is available in a Coherence distributed cache. For every suspicious transaction detected, a warning event must be sent with maximum amount, total amount and total number of transactions over the past 30 days, as shown in the diagram below. Application Input Stream input to the EPN contains events of type CCTransactionEvent. This input has to be joined with the cache with all credit card transactions. The cache is configured in the EPN as shown below: <wlevs:caching-system id="CohCacheSystem" provider="coherence"/> <wlevs:cache id="CCTransactionsCache" value-type="CCTransactionEvent" key-properties="cardID, transactionTime" caching-system="CohCacheSystem"> </wlevs:cache> Application Output The output that must be produced by the application is a fraud warning event. This event is configured in the spring file as shown below. Source for cardHistory property can be seen here. <wlevs:event-type type-name="FraudWarningEvent"> <wlevs:properties type="tuple"> <wlevs:property name="cardID" type="CHAR"/> <wlevs:property name="transactionTime" type="BIGINT"/> <wlevs:property name="transactionAmount" type="DOUBLE"/> <wlevs:property name="cardHistory" type="OBJECT"/> </wlevs:properties </wlevs:event-type> Cache Data Aggregation using Java Cartridge In the output warning event, cardHistory property contains data from the cache aggregated over the past 30 days. To get this information, we use a java cartridge method. This method uses Coherence’s query API on credit card transactions cache to get the required information. Therefore, the java cartridge method requires a reference to the cache. This may be set up by configuring it in the spring context file as shown below: <bean class="com.oracle.cep.ccfraud.CCTransactionsAggregator"> <property name="cache" ref="CCTransactionsCache"/> </bean> This is used by the java class to set a static property: public void setCache(Map cache) { s_cache = (NamedCache) cache; } The code snippet below shows how the total of all the transaction amounts in the past 30 days is computed. Rest of the information required by CardHistory object is calculated in a similar manner. Complete source of this class can be found here. To find out more information about using Coherence's API to query a cache, please refer Coherence Developer’s Guide. public static CreditHistoryData(String cardID) { … Filter filter = QueryHelper.createFilter("cardID = :cardID and transactionTime :transactionTime", map); CardHistoryData history = new CardHistoryData(); Double sum = (Double) s_cache.aggregate(filter, new DoubleSum("getTransactionAmount")); history.setTotalAmount(sum); … return history; } The java cartridge method is used from CQL as seen below: select cardID, transactionTime, transactionAmount, CCTransactionsAggregator.execute(cardID) as cardHistory from inputChannel where transactionAmount1000 This produces a warning event, with history data, for every credit card transaction over $1000. That is all there is to it. The complete source for the sample application, along with the configuration files, is available here. In the sample, I use a simple java bean to load the cache with initial transaction history data. An input adapter is used to create and send transaction events for the input stream.

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  • SPARC T4-4 Delivers World Record First Result on PeopleSoft Combined Benchmark

    - by Brian
    Oracle's SPARC T4-4 servers running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved World Record 18,000 concurrent users while executing a PeopleSoft Payroll batch job of 500,000 employees in 43.32 minutes and maintaining online users response time at < 2 seconds. This world record is the first to run online and batch workloads concurrently. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 35% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. This is the first three tier mixed workload (online and batch) PeopleSoft benchmark also processing PeopleSoft payroll batch workload. Performance Landscape PeopleSoft HR Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-2 (db) 18,000 0.944 0.503 43.32 64 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory 5 x 300 GB SAS internal disks 1 x 100 GB and 2 x 300 GB internal SSDs 2 x 10 Gbe HBA Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 2 x 300 GB SAS internal disks 1 x 100 GB internal SSD Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two Oracle PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Management oracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Oracle's PeopleSoft HR and Payroll combined benchmark, www.oracle.com/us/solutions/benchmark/apps-benchmark/peoplesoft-167486.html, results 09/30/2012.

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  • Detecting Duplicates Using Oracle Business Rules

    - by joeywong-Oracle
    Recently I was involved with a Business Process Management Proof of Concept (BPM PoC) where we wanted to show how customers could use Oracle Business Rules (OBR) to easily define some rules to detect certain conditions, such as duplicate account numbers, duplicate names, high transaction amounts, etc, in a set of transactions. Traditionally you would have to loop through the transactions and compare each transaction with each other to find matching conditions. This is not particularly nice as it relies on more traditional approaches (coding) and is not the most efficient way. OBR is a great place to house these types’ of rules as it allows users/developers to externalise the rules, in a simpler manner, externalising the rules from the message flows and allows users to change them when required. So I went ahead looking for some examples. After quite a bit of time spent Googling, I did not find much out in the blogosphere. In fact the best example was actually from...... wait for it...... Oracle Documentation! (http://docs.oracle.com/cd/E28271_01/user.1111/e10228/rules_start.htm#ASRUG228) However, if you followed the link there was not much explanation provided with the example. So the aim of this article is to provide a little more explanation to the example so that it can be better understood. Note: I won’t be covering the BPM parts in great detail. Use case: Payment instruction file is required to be processed. Before instruction file can be processed it needs to be approved by a business user. Before the approval process, it would be useful to run the payment instruction file through OBR to look for transactions of interest. The output of the OBR can then be used to flag the transactions for the approvers to investigate. Example BPM Process So let’s start defining the Business Rules Dictionary. For the input into our rules, we will be passing in an array of payments which contain some basic information for our demo purposes. Input to Business Rules And for our output we want to have an array of rule output messages. Note that the element I am using for the output is only for one rule message element and not an array. We will configure the Business Rules component later to return an array instead. Output from Business Rules Business Rule – Create Dictionary Fill in all the details and click OK. Open the Business Rules component and select Decision Functions from the side. Modify the Decision Function Configuration Select the decision function and click on the edit button (the pencil), don’t worry that JDeveloper indicates that there is an error with the decision function. Then click the Ouputs tab and make sure the checkbox under the List column is checked, this is to tell the Business Rules component that it should return an array of rule message elements. Updating the Decision Service Next we will define the actual rules. Click on Ruleset1 on the side and then the Create Rule in the IF/THEN Rule section. Creating new rule in ruleset Ok, this is where some detailed explanation is required. Remember that the input to this Business Rules dictionary is a list of payments, each of those payments were of the complex type PaymentType. Each of those payments in the Oracle Business Rules engine is treated as a fact in its working memory. Implemented rule So in the IF/THEN rule, the first task is to grab two PaymentType facts from the working memory and assign them to temporary variable names (payment1 and payment2 in our example). Matching facts Once we have them in the temporary variables, we can then start comparing them to each other. For our demonstration we want to find payments where the account numbers were the same but the account name was different. Suspicious payment instruction And to stop the rule from comparing the same facts to each other, over and over again, we have to include the last test. Stop rule from comparing endlessly And that’s it! No for loops, no need to keep track of what you have or have not compared, OBR handles all that for you because everything is done in its working memory. And once all the tests have been satisfied we need to assert a new fact for the output. Assert the output fact Save your Business Rules. Next step is to complete the data association in the BPM process. Pay extra care to use Copy List instead of the default Copy when doing data association at an array level. Input and output data association Deploy and test. Test data Rule matched Parting words: Ideally you would then use the output of the Business Rules component to then display/flag the transactions which triggered the rule so that the approver can investigate. Link: SOA Project Archive [Download]

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  • Oracle LogMiner (Unsupported Operation)

    - by Sarith
    Hi all, I'm currently using Oracle 11g on RHEL5. I'm digging to see what are in the archived log files. After I query from v$logmnr_contents, I see many transactions of UNSUPPORTED operation. What do these unsupported transactions mean? I think that it's the cause that make my database generates lots of archived logs. Moreover, I'm using global temporary table for generating reports. I discover that when I insert and delete from those temporary table, it also records in the archived log file. How to do to reduce those recorded transactions? Regards, Sarith

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  • Excel - Linking multiple source spreadsheets with variable amounts of rows to a destination spreadsh

    - by Emilio
    I have multiple source spreadsheets, each with a variable number of rows. An example might be one spreadsheet per bank account, with one row for each transaction, with a date and amount. One spreadsheet might have 30 rows, the other 50, and so on. I want to create another spreadsheet which links to the various source spreadsheets and lists an aggregate of all transactions from all sources. So if 3 source sheets with 30, 50 and 20 rows respectively, the destination sheet would have 100 rows. The number of rows (transactions) in the source sheets can grow or shrink over time. I'd like the destination sheet to show one contiguous list of transactions without gaps (spaces). How can I do this?

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  • Is it safe to Update and/or Insert records into an accounting software's database? (Pastel Evolution)

    - by user1020317
    Our CRM system can post transactions to our accounts software (Pastel Evolution), but it doesn't perform the required currency conversion. Both systems have different "base" currencies (because of our location), so the figure thats sent to Pastel evolution is right, but it is reflected in the wrong currency. The CRM uses an ODBC connection to post figures to Evolution. I can make a tool which sits between the two systems which can update and/or insert the transactions into Pastel, by mimicking what the CRM would have done if it was doing the POST. Is it safe for me to mimic and/or update these transactions directly in the Pastel database? Is it common for important validation to be performed in the ODBC layer, and if so, can I tap into an applications ODBC, or is there a custom driver built by the CRM to communicate with the DB?

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  • SOA &amp; Application Grid Specialization &ndash; 6 steps to success &ndash; part 1 OMM

    - by Jürgen Kress
    SOA Specialization – Oracle Open Market Model (OMM) Dear Application Grid SOA Partners, Or goal is to SOA Specialize you, in the next weeks we will inform you in a series how you can achieve SOA Specialization. Specialization is key the be recognized by Oracle and to be preferred by our Customers. The first step to become SOA Specialized is to proof 2 transactions. You can either resell, co-sell or referral – as a proof point we do use our Open Market Model (OMM). To create your account go to our new Partner Portal: go to login of your OPN-Homepage: http://oraclepartnernetwork.oracle.com click on: "Sales" "Create a PRM User Account" Enter your User ID: Enter Company Identifier: ((please ask your OPN IC)) Finish Wait for a Confirmation Email If you need OMM support please contact out dedicated team: Nordics  please ask: [email protected] Portugal, Spain please ask: [email protected] Austria, Belgium, Germany, Luxembourg, Netherlands, Switzerland, United Arab Emirates, United Kingdom please ask: [email protected] For more information about OMM watch our on-demand webcast “Recognising the Value of Partners: Register Oracle Deals through the Open Market Model (OMM)”. Become SOA Specialized today SOA Specialized & Application Grid Specialized Create your references, create your OMM Entry, take the SOA Sales assessment, take the SOA Pre-Sales assessment, take the Support assessment and register for the SOA Implementation assessment. For more information on Specialization please visit our OPN Specialized Webcast Series To get support on Specialization please contact the Partner Business Centers.   SOA Specialized Application Grid Specialized Proof 2 transactions with OMM Proof 2 transactions with OMM Create your 2 references Create your 2 references SOA Sales assessment 3, Oracle Application Grid Sales Specialist  SOA Pre-Sales assessment 3 Oracle Application Grid PreSales Specialist Support assessment 1 Support assessment 2 SOA Implementation assessment 4 Application Grid Implementation assessment 4

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  • Help Creating a Google Analytics Funnel for Check out process

    - by Drew
    have a funnel question. I am currently working on tracking (through GA) guest and logged in member activity once they get to my sites shopping cart. But need help with setting up funnels. Specifically to see; Total sales Logged in member total sales List item Guest member sales The urls associated to the check out proces are: Logged in members /cart (arriving to checkout) /checkout (checking out as a logged in member) /checkout/confirmation (thank you - confirmed sale) Guest members - /cart (arriving to checkout) - /checkout-guest (checking out as a guest) - /checkout/confirmation (thanks you - confirmed sale) I've tested the funnels set up for the above with 9 transactions. But the end maths doesn't seem to line up. Total sales funnel shows 9 completed transactions when only tracking these to urls: - /cart - /checkout/confirmation Which is great - cause it's working Logged in member sales show a total of 9 completed transactions based on each step of the logged in url steps (above) being tracked in a funnel. Not good because this number should be 3. Guest check out funnel (see guest steps above) shows 9 as well. What the?!?!?!? The results I am looking for should reflect the following - total sales = 9, logged in members = 3, guest members = 6 Is there any way to set these urls up so that the funnels report the correct results - or do I need to changed the urls and provide logged in members and guest stand alone purchase confirmation pages (this would mean I can not track total sales which combine results from both streams)? Any knowledge in this area is welcome. Thanks.

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  • How should we deal with multiple transaction-report requests?

    - by Mithir
    We are developing a system for the retail market which one of it's features will enable clients(actually consumer clubs) to go through all transactions made by end-clients. One of the ways to get this information will be via an API. The idea is that there will be requests for reports with a start date and an end date, and a response will have all the transactions between those dates. We are worry that some reports may be very large, and that some clients will repeatedly request for reports, in this case the DB and CPU will be very overloaded. The same server that will service those requests, also takes care the the actual retail transactions (received by proprietary devices) and a Web application. We are not sure about how to limit the report requests from the API so that it won't affect the system too much. So, how should we deal with this scenario? any thoughts? EDIT: just to make clear: When I mentioned proprietary devices I meant "On-Location" devices which are used during sales with end-clients, this means that these requests shouldn't get delayed, and this is the main concern.

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  • Error when logging in with Machinist in Shoulda test

    - by user303747
    I am having some trouble getting the right usage of Machinist and Shoulda in my testing. Here is my test: context "on POST method rating" do p = Product.make u = nil setup do u = login_as post :vote, :rating => 3, :id => p end should "set rating for product to 3" do assert_equal p.get_user_vote(u), 3 end And here's my blueprints: Sham.login { Faker::Internet.user_name } Sham.name { Faker::Lorem.words} Sham.email { Faker::Internet.email} Sham.body { Faker::Lorem.paragraphs(2)} User.blueprint do login password "testpass" password_confirmation { password } email end Product.blueprint do name {Sham.name} user {User.make} end And my authentication test helper: def login_as(u = nil) u ||= User.make() @controller.stubs(:current_user).returns(u) u end The error I get is: /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/validations.rb:1090:in `save_without_dirty!': Validation failed: Login has already been taken, Email has already been taken (ActiveRecord::RecordInvalid) from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/dirty.rb:87:in `save_without_transactions!' from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/transactions.rb:200:in `save!' from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/connection_adapters/abstract/database_statements.rb:136:in `transaction' from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/transactions.rb:182:in `transaction' from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/transactions.rb:200:in `save!' from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/transactions.rb:208:in `rollback_active_record_state!' from /home/jason/moderndarwin/vendor/rails/activerecord/lib/active_record/transactions.rb:200:in `save!' from /usr/lib/ruby/gems/1.8/gems/machinist-1.0.6/lib/machinist/active_record.rb:55:in `make' from /home/jason/moderndarwin/test/blueprints.rb:37 from /usr/lib/ruby/gems/1.8/gems/machinist-1.0.6/lib/machinist.rb:77:in `generate_attribute_value' from /usr/lib/ruby/gems/1.8/gems/machinist-1.0.6/lib/machinist.rb:46:in `method_missing' from /home/jason/moderndarwin/test/blueprints.rb:37 from /usr/lib/ruby/gems/1.8/gems/machinist-1.0.6/lib/machinist.rb:20:in `instance_eval' from /usr/lib/ruby/gems/1.8/gems/machinist-1.0.6/lib/machinist.rb:20:in `run' from /usr/lib/ruby/gems/1.8/gems/machinist-1.0.6/lib/machinist/active_record.rb:53:in `make' from ./test/functional/products_controller_test.rb:25:in `__bind_1269805681_945912' from /home/jason/moderndarwin/vendor/gems/thoughtbot-shoulda-2.10.2/lib/shoulda/context.rb:293:in `call' from /home/jason/moderndarwin/vendor/gems/thoughtbot-shoulda-2.10.2/lib/shoulda/context.rb:293:in `merge_block' from /home/jason/moderndarwin/vendor/gems/thoughtbot-shoulda-2.10.2/lib/shoulda/context.rb:288:in `initialize' from /home/jason/moderndarwin/vendor/gems/thoughtbot-shoulda-2.10.2/lib/shoulda/context.rb:169:in `new' from /home/jason/moderndarwin/vendor/gems/thoughtbot-shoulda-2.10.2/lib/shoulda/context.rb:169:in `context' from ./test/functional/products_controller_test.rb:24 I can't figure out what it is I'm doing wrong... I have tested the login_as with my auth (Authlogic) in my user_controller testing. Any pointers in the right direction would be much appreciated!

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  • Can't destroy record in many-to-many relationship

    - by Dmart
    I'm new to Rails, so I'm sure I've made a simple mistake. I've set up a many-to-many relationship between two models: User and Group. They're connected through the junction model GroupMember. Here are my models (removed irrelevant stuff): class User < ActiveRecord::Base has_many :group_members has_many :groups, :through => :group_members end class GroupMember < ActiveRecord::Base belongs_to :group belongs_to :user end class Group < ActiveRecord::Base has_many :group_members has_many :users, :through => :group_members end The table for GroupMembers contains additional information about the relationship, so I didn't use has_and_belongs_to_many (as per the Rails "Active Record Associations" guide). The problem I'm having is that I can't destroy a GroupMember. Here's the output from rails console: irb(main):006:0> m = GroupMember.new => #<GroupMember group_id: nil, user_id: nil, active: nil, created_at: nil, updated_at: nil> irb(main):007:0> m.group_id =1 => 1 irb(main):008:0> m.user_id = 16 => 16 irb(main):009:0> m.save => true irb(main):010:0> m.destroy NoMethodError: undefined method `eq' for nil:NilClass from /usr/local/lib/ruby/gems/1.8/gems/activesupport-3.0.4/lib/active_support/whiny_nil.rb:48:in `method_missing' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/persistence.rb:79:in `destroy' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/locking/optimistic.rb:110:in `destroy' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/callbacks.rb:260:in `destroy' from /usr/local/lib/ruby/gems/1.8/gems/activesupport-3.0.4/lib/active_support/callbacks.rb:413:in `_run_destroy_callbacks' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/callbacks.rb:260:in `destroy' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/transactions.rb:235:in `destroy' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/transactions.rb:292:in `with_transaction_returning_status' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/connection_adapters/abstract/database_statements.rb:139:in `transaction' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/transactions.rb:207:in `transaction' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/transactions.rb:290:in `with_transaction_returning_status' from /usr/local/lib/ruby/gems/1.8/gems/activerecord-3.0.4/lib/active_record/transactions.rb:235:in `destroy' from (irb):10 This is driving me crazy, so any help would be greatly appreciated.

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  • Integrating Coherence & Java EE 6 Applications using ActiveCache

    - by Ricardo Ferreira
    OK, so you are a developer and are starting a new Java EE 6 application using the most wonderful features of the Java EE platform like Enterprise JavaBeans, JavaServer Faces, CDI, JPA e another cool stuff technologies. And your architecture need to hold piece of data into distributed caches to improve application's performance, scalability and reliability? If this is your current facing scenario, maybe you should look closely in the solutions provided by Oracle WebLogic Server. Oracle had integrated WebLogic Server and its champion data caching technology called Oracle Coherence. This seamless integration between this two products provides a comprehensive environment to develop applications without the complexity of extra Java code to manage cache as a dependency, since Oracle provides an DI ("Dependency Injection") mechanism for Coherence, the same DI mechanism available in standard Java EE applications. This feature is called ActiveCache. In this article, I will show you how to configure ActiveCache in WebLogic and at your Java EE application. Configuring WebLogic to manage Coherence Before you start changing your application to use Coherence, you need to configure your Coherence distributed cache. The good news is, you can manage all this stuff without writing a single line of code of XML or even Java. This configuration can be done entirely in the WebLogic administration console. The first thing to do is the setup of a Coherence cluster. A Coherence cluster is a set of Coherence JVMs configured to form one single view of the cache. This means that you can insert or remove members of the cluster without the client application (the application that generates or consume data from the cache) knows about the changes. This concept allows your solution to scale-out without changing the application server JVMs. You can growth your application only in the data grid layer. To start the configuration, you need to configure an machine that points to the server in which you want to execute the Coherence JVMs. WebLogic Server allows you to do this very easily using the Administration Console. In this example, I will call the machine as "coherence-server". Remember that in order to the machine concept works, you need to ensure that the NodeManager are being executed in the target server that the machine points to. The NodeManager executable can be found in <WLS_HOME>/server/bin/startNodeManager.sh. The next thing to do is to configure a Coherence cluster. In the WebLogic administration console, go to Environment > Coherence Clusters and click in "New". Call this Coherence cluster of "my-coherence-cluster". Click in next. Specify a valid cluster address and port. The Coherence members will communicate with each other through this address and port. Our Coherence cluster are now configured. Now it is time to configure the Coherence members and add them to this cluster. In the WebLogic administration console, go to Environment > Coherence Servers and click in "New". In the field "Name" set to "coh-server-1". In the field "Machine", associate this Coherence server to the machine "coherence-server". In the field "Cluster", associate this Coherence server to the cluster named "my-coherence-cluster". Click in "Finish". Start the Coherence server using the "Control" tab of WebLogic administration console. This will instruct WebLogic to start a new JVM of Coherence in the target machine that should join the pre-defined Coherence cluster. Configuring your Java EE Application to Access Coherence Now lets pass to the funny part of the configuration. The first thing to do is to inform your Java EE application which Coherence cluster to join. Oracle had updated WebLogic server deployment descriptors so you will not have to change your code or the containers deployment descriptors like application.xml, ejb-jar.xml or web.xml. In this example, I will show you how to enable DI ("Dependency Injection") to a Coherence cache from a Servlet 3.0 component. In the WEB-INF/weblogic.xml deployment descriptor, put the following metadata information: <?xml version="1.0" encoding="UTF-8"?> <wls:weblogic-web-app xmlns:wls="http://xmlns.oracle.com/weblogic/weblogic-web-app" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://java.sun.com/xml/ns/javaee http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd http://xmlns.oracle.com/weblogic/weblogic-web-app http://xmlns.oracle.com/weblogic/weblogic-web-app/1.4/weblogic-web-app.xsd"> <wls:context-root>myWebApp</wls:context-root> <wls:coherence-cluster-ref> <wls:coherence-cluster-name>my-coherence-cluster</wls:coherence-cluster-name> </wls:coherence-cluster-ref> </wls:weblogic-web-app> As you can see, using the "coherence-cluster-name" tag, we are informing our Java EE application that it should join the "my-coherence-cluster" when it loads in the web container. Without this information, the application will not be able to access the predefined Coherence cluster. It will form its own Coherence cluster without any members. So never forget to put this information. Now put the coherence.jar and active-cache-1.0.jar dependencies at your WEB-INF/lib application classpath. You need to deploy this dependencies so ActiveCache can automatically take care of the Coherence cluster join phase. This dependencies can be found in the following locations: - <WLS_HOME>/common/deployable-libraries/active-cache-1.0.jar - <COHERENCE_HOME>/lib/coherence.jar Finally, you need to write down the access code to the Coherence cache at your Servlet. In the following example, we have a Servlet 3.0 component that access a Coherence cache named "transactions" and prints into the browser output the content (the ammount property) of one specific transaction. package com.oracle.coherence.demo.activecache; import java.io.IOException; import javax.annotation.Resource; import javax.servlet.ServletException; import javax.servlet.annotation.WebServlet; import javax.servlet.http.HttpServlet; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; import com.tangosol.net.NamedCache; @WebServlet("/demo/specificTransaction") public class TransactionServletExample extends HttpServlet { @Resource(mappedName = "transactions") NamedCache transactions; protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { int transId = Integer.parseInt(request.getParameter("transId")); Transaction transaction = (Transaction) transactions.get(transId); response.getWriter().println("<center>" + transaction.getAmmount() + "</center>"); } } Thats it! No more configuration is necessary and you have all set to start producing and getting data to/from Coherence. As you can see in the example code, the Coherence cache are treated as a normal dependency in the Java EE container. The magic happens behind the scenes when the ActiveCache allows your application to join the defined Coherence cluster. The most interesting thing about this approach is, no matter which type of Coherence cache your are using (Distributed, Partitioned, Replicated, WAN-Remote) for the client application, it is just a simple attribute member of com.tangosol.net.NamedCache type. And its all managed by the Java EE container as an dependency. This means that if you inject the same dependency (the Coherence cache named "transactions") in another Java EE component (JSF managed-bean, Stateless EJB) the cache will be the same. Cool isn't it? Thanks to the CDI technology, we can extend the same support for non-Java EE standards components like simple POJOs. This means that you are not forced to only use Servlets, EJBs or JSF in order to inject Coherence caches. You can do the same approach for regular POJOs created for you and managed by lightweight containers like Spring or Seam.

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  • Improved Performance on PeopleSoft Combined Benchmark using SPARC T4-4

    - by Brian
    Oracle's SPARC T4-4 server running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved a world record 18,000 concurrent users experiencing subsecond response time while executing a PeopleSoft Payroll batch job of 500,000 employees in 32.4 minutes. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 47% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. Performance Landscape Results are presented for the PeopleSoft HRMS Self-Service and Payroll combined benchmark. The new result with 128 streams shows significant improvement in the payroll batch processing time with little impact on the self-service component response time. PeopleSoft HRMS Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.988 0.539 32.4 128 SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.944 0.503 43.3 64 The following results are for the PeopleSoft HRMS Self-Service benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the payroll component. PeopleSoft HRMS Self-Service 9.1 Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) 2x SPARC T4-2 (db) 18,000 1.048 0.742 N/A N/A The following results are for the PeopleSoft Payroll benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the self-service component. PeopleSoft Payroll (N.A.) 9.1 - 500K Employees (7 Million SQL PayCalc, Unicode) Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-4 (db) N/A N/A N/A 30.84 96 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 PeopleTools 8.52 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Micro Focus Server Express (COBOL v 5.1.00) Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. A total of 128 PeopleSoft streams server processes where used on the database node to complete payroll batch job of 500,000 employees in 32.4 minutes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Managementoracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 8 November 2012.

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  • Oracle EBS?????(Order->AR)

    - by Pan.Tian
    ???? ??:Order Management > Orders,Returns > Sales Orders ???????,??,????,???? ???????,????,??... ??Book Order,??Book??,????????Status??????“Booked”,???????"Awaiting Shipping",?????????,??????????????? ??:??Book??,????????????,????Shipping Transactions Form,????,?????????Line Status?Ready to Release,Next Step?Pick Release Pick Release ??:Order Management > Shipping > Release Sales Orders > Release Sales Orders Pick Release????(?????????).?Order  Number?????????? Auto Pick Confirm???No Auto Allocate???N Auto Allocate?Auto Pick Confirm??????Yes,???????????,??????No,???Yes??,?????Allocate?Pick Confirm??,??????????? ??????????Pick  Release,”Concurrent“??Pick Release?????Concurrent Request???,"Execute Now"????????Pick Release,??????????????User,??????Concurrent??? Pick Release?????????Pick Release?????Pick Wave??Move Order,??Move Order????????????????????(Staging),????INV??????????? INV_MOVE_ORDER_PUB.CREATE_MOVE_ORDER_HEADER???Move Order??(??Pick Release?????????????:Pick Release Process) ????????,?Pick Release??,?????????????Reservation(??),?????????Soft Reservations,?????????????,????Org?????????? ??:????,Shipping Transaction?Line Status?"Released to Warehouse",Next Step?"Transact Move Order";????????Booked,?????”Awaiting Shipping“? Pick Confirm Pick Confirm(????)????????Transact Move Order????,?Allocate????,?Transact Move Order. ??:Inventory > Move Orders > Transact Move Orders ????,Pick Wave??Tab,????? ??TMO????,??Allocate,Allocate?????????Picking Rule?????,??????Suggestion????,Suggestion?????? MTL_MATERIAL_TRANSACTIONS_TEMP?(?Pending Transactions)? ????Allocate??,??????Allocation????Single,Multiple??None???,Single??, ??????????Suggestion?Transaction??,Multiple???????;None??????Suggestion? ?(????????????????) ????????Transact??Move Order ?Transact??,Inventory Transaction Manager ???Suggestion Transactions(MMTT),???????????????,??????Subinventory??????(Staging)??? Transction???Material Transaction?Form????? ????Reservation??,?Transact??,???????,Reservation????????,????Sub,locator???? ??:????,Shipping Transaction?Line Status?"Staged/Pick Confirmed",Next Step?"Ship Confirm/Close Trip Stop";????????Booked,??????”Picked“? Ship Confirm Deliveries ??:Order Management > Shipping > Transactions ???Delivery??,??Ship Confirm(????),????Pick Release???,????Autocreate Delivery,???????Define Shipping Parameters????????,??shipping parameters???????,?????????Ship Confirm?????Action->Auto-create Deliveries. Delivery????????????????,????????.... Delivery??,??Ship Confirm???,???????,"Defer Interface"?????,?????????Interface Trip Stop SRS,????Defer Interface,?OK? Delivery was successfully confirmed!!! Ship Confirm????????????MTL_TRANSACTIONS_INTERFACE??,??MTI??????Sales Order Issue,??????????Interface Trip Stop???,???MTI??MMT??? ??:????,Shipping Transaction?Line Status?"Shipped",Next Step?"Run Interfaces";????????Booked,??????”Shipped“? Interface Trip Stop - SRS ?????Ship Confirm??????Defer Interface,??????????????Interface Trip Stop - SRS? ??:Order Management > Shipping > Interface > Run > Request:Interface Trip Stop - SRS Interface Trip Stop????????:Inventory Interface  SRS(????????)? Order Management Interface  SRS(?????????????AR??)? Inventory Interface  SRS???Shipping Transaction??????MTI,??INV Manager????MTI????MMT??,??Sales Order Issue?transaction??????,???????????Reservation????Inventory Interface  SRS?????,???WSH_DELIVERY_DETAILS??INV_INTERFACED_FLAG???Y? Order Management Interface - SRS??Inventory Interface  SRS?????,??Request?????????????AR??,OM Interface????????WSH_DELIVERY_DETAILS??OE_INTERFACED_FLAG?Y? ??:????,Shipping Transaction?Line Status?"Interfaced",Next Step?"Not Applicable";????????Booked,??????”Shipped“? Workflow background Process ??:Inventory > Workflow Background Engine Item Type:OM Order Line Process Deferred:Yes Process Timeout:No ??program????Deffered???workflow,Workflow Background Process???,???????Order????RA Interface???(RA_INTERFACE_LINES_ALL,RA_INTERFACE_SALESCREDITS_ALL,RA_Interface_distribution) ????????SQL???RA Interface??: 1.SELECT * FROM RA_INTERFACE_LINES_ALL WHERE sales_order = '65961'; 2.SELECT * FROM RA_INTERFACE_SALESCREDITS_ALL WHERE INTERFACE_LINE_ID IN (SELECT INTERFACE_LINE_ID FROM RA_INTERFACE_LINES_ALL WHERE sales_order = '65961' ); 3.SELECT * FROM RA_INTERFACE_DISTRIBUTIONS_ALL WHERE INTERFACE_LINE_ID IN (SELECT INTERFACE_LINE_ID FROM RA_INTERFACE_LINES_ALL WHERE sales_order = '65961' ); ?????RA Interface??,??OE_ORDER_LINES_ALL?INVOICE_INTERFACE_STATUS_CODE????? Yes,INVOICED_QUANTITY?????????????????????????Closed,????????Booked? AutoInvoice ????AR?? ??:Account Receivable > Interface > AutoInvoice Name:Autoinvoice Master Program Invoice Source:Order Entry Default Day:???? ???,?request????”Autoinvoice Import Program“???? ???,????Auto Invoice Program????RA?interface?,?????????????,???????AR???? (RA_CUSTOMER_TRX_ALL,RA_CUSTOMER_TRX_LINES,AR_PAYMENT_SCHEDULES). ?????? Order > Action > Additional Information > Invoices/Credit Memos????????,???????SQL?????AR??, SELECT ooha.order_number , oola.line_number so_line_number , oola.ordered_item , oola.ordered_quantity * oola.unit_selling_price so_extended_price , rcta.trx_number invoice_number , rcta.trx_date , rctla.line_number inv_line_number , rctla.unit_selling_price inv_unit_selling_price FROM oe_order_headers_all ooha , oe_order_lines_all oola , ra_customer_trx_all rcta , ra_customer_trx_lines_all rctla WHERE ooha.header_id = oola.header_id AND rcta.customer_trx_id = rctla.customer_trx_id AND rctla.interface_line_attribute6 = TO_CHAR (oola.line_id) AND rctla.interface_line_attribute1 = TO_CHAR (ooha.order_number) AND order_number = :p_order_number; ??Autoinvoice Import Program???error???,?????RA_INTERFACE_ERRORS_ALL?Message_text??,???????? Closing the Order ?????????,?????????(Close??Cancel)?0.5?,??????Workflow Background Process??????? ????????:you can wait until month-end and the “Order Flow – Generic” workflow will close it for you. Order&Shipping Transactions Status Summary Step Order Header Status Order Line Status Order Flow Workflow Status (Order Header) Line Flow Workflow Status (Order Line) Shipping Transaction  Status(RELEASED_STATUS in WDD) 1. Enter an Order Entered Entered Book Order Manual Enter – Line                              N/A 2. Book the Order Booked Awaiting Shipping Close Order Schedule ->Create Supply ->Ship – Line                       Ready to Release(R) 3. Pick the Order Booked Picked Close Order Ship – Line 1.Released to Warehouse(S)(Pick Release but not pick confirm) 2.Staged/Pick Confirmed(Y)(After pick confirm) 4. Ship the Order Booked Shipped Close Order Fulfill – Deferred 1.Shipped(After ship confirm) 2.Interfaced(C)(After ITS) Booked Closed Close Order Fulfill ->Invoice Interface ->Close Line -> End 5. Close the Order Closed Closed End End ????,shipping txn???,??????????:http://blog.csdn.net/pan_tian/article/details/7696528 ======EOF======

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