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• Announcing Key Functional White Papers for SIM and ReIM

  - by Oracle Retail Documentation Team

  Oracle Retail has published two new documents on My Oracle Support (https://support.oracle.com)  that provide partners and retailers with deeper functional information about two products: Oracle Retail Store Inventory Management (SIM) and Oracle Retail Invoice Matching. Oracle Retail Store Inventory Management Item Configuration White Paper (Doc ID 1507221.1) There is functionality within the Store Inventory Management system related to item configuration that spans across multiple concepts that apply to the application as a whole rather than to a specific area. This white paper covers numerous topics around item configuration including: Item Transaction Levels Item Long Description Pack Size Standard Unit of Measure Standard Unit of Measure Conversion Pack Items Simple Pack Conversion Items (Notional Packs) Ranging Items Item Status Non-Sellable Items Type-2 Item Recognition UPC-E Barcodes Non-Inventory Items Consignment and Concession Items Quick Response Codes Oracle Retail Invoice Matching Financial Transactions (Doc ID 1500209.1) This document explains the financial transactions that are posted by Oracle Retail Invoice Matching (ReIM). The scope of the document is limited to ReIM transactions only, and does not explain Retail Merchandising System (RMS), Finance, or Account Receivable transactions. ReIM follows the double-entry accounting standard, which works by recording the debit and credit of each financial transaction belonging to each party involved. Each transaction means a profit to one account (debit) and a loss to another account (credit). Full invoice match processing is completed in ReIM with payment recommendations communicated to Oracle Accounts Payable. ReIM matches merchandise orders and receipts against merchandise invoices, performing automated and manual matching, as well as discrepancy-resolution processing. Matched invoices are posted to interface staging tables specifying the amount and date to pay, vendor, site ID, General Ledger Chart of Accounts (GL CoA) information, and payment terms. Other payables documents, including debit memos, credit memos and credit notes are also interfaced to Accounts Payable through the ReIM staging tables (IM_AP_STAGE_HEAD and IM_AP_STAGE_DETAIL). For information about how ReIM engages in this processing, see the latest Oracle Retail Invoice Matching Operations Guide. Certain ReIM transactions are not interfaced to Oracle Payables, but instead are interfaced to Oracle General Ledger through the IM_FINANCIAL_STAGE table. When analyzing transactions posted through the staging tables, retailers should note the transaction type, Standard/Credit, as well as the sign in the amount field. Technically, a negative sign on a credit transaction changes the transaction to a debit entry, and vice versa. This document is concerned about the financial meaning of the transactions, and will avoid a discussion of negative numbers in T-charts.

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• When using spring and hibernate, how are sessions/transactions handled?

  - by Blankman

  when using hibernate with spring, can someone explain how the session unit of work and transactions are handled? is the transaction started at the beginning of the page request, and committed at the end? can I have multiple db calls per request, that each have different transaction levels? e.g. some are left as default, while others are read-uncommitted?

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• SQL SERVER – How to Roll Back SQL Server Database Changes

  - by Pinal Dave

  In a perfect scenario, no unexpected and unplanned changes occur. There are no unpleasant surprises, no inadvertent changes. However, even with all precautions and testing, there is sometimes a need to revert a structure or data change. One of the methods that can be used in this situation is to use an older database backup that has the records or database object structure you want to revert to. For this method, you have to have the adequate full database backup and a tool that will help you with comparison and synchronization is preferred. In this article, we will focus on another method: rolling back the changes. This can be done by using: An option in SQL Server Management Studio T-SQL, or ApexSQL Log The first two solutions have been described in this article The disadvantages of these methods are that you have to know when exactly the change you want to revert happened and that all transactions on the database executed in a specific time range are rolled back – the ones you want to undo and the ones you don’t. How to easily roll back SQL Server database changes using ApexSQL Log? The biggest challenge is to roll back just specific changes, not all changes that happened in a specific time range. While SQL Server Management Studio option and T-SQL read and roll forward all transactions in the transaction log files, I will show you a solution that finds and scripts only the specific changes that match your criteria. Therefore, you don’t need to worry about all other database changes that you don’t want to roll back. ApexSQL Log is a SQL Server disaster recovery tool that reads transaction logs and provides a wide range of filters that enable you to easily rollback only specific data changes. First, connect to the online database where you want to roll back the changes. Once you select the database, ApexSQL Log will show its recovery model. Note that changes can be rolled back even for a database in the Simple recovery model, when no database and transaction log backups are available. However, ApexSQL Log achieves best results when the database is in the Full recovery model and you have a chain of subsequent transaction log backups, back to the moment when the change occurred. In this example, we will use only the online transaction log. In the next step, use filters to read only the transactions that happened in a specific time range. To remove noise, it’s recommended to use as many filters as possible. Besides filtering by the time of the transaction, ApexSQL Log can filter by the operation type: Table name: As well as transaction state (committed, aborted, running, and unknown), name of the user who committed the change, specific field values, server process IDs, and transaction description. You can select only the tables affected by the changes you want to roll back. However, if you’re not certain which tables were affected, you can leave them all selected and once the results are shown in the main grid, analyze them to find the ones you to roll back. When you set the filters, you can select how to present the results. ApexSQL Log can automatically create undo or redo scripts, export the transactions into an XML, HTML, CSV, SQL, or SQL Bulk file, and create a batch file that you can use for unattended transaction log reading. In this example, I will open the results in the grid, as I want to analyze them before rolling back the transactions. The results contain information about the transaction, as well as who and when made it. For UPDATEs, ApexSQL Log shows both old and new values, so you can easily see what has happened. To create an UNDO script that rolls back the changes, select the transactions you want to roll back and click Create undo script in the menu. For the DELETE statement selected in the screenshot above, the undo script is: INSERT INTO [Sales].[PersonCreditCard] ([BusinessEntityID], [CreditCardID], [ModifiedDate]) VALUES (297, 8010, '20050901 00:00:00.000') When it comes to rolling back database changes, ApexSQL Log has a big advantage, as it rolls back only specific transactions, while leaving all other transactions that occurred at the same time range intact. That makes ApexSQL Log a good solution for rolling back inadvertent data and schema changes on your SQL Server databases. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: ApexSQL

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• Ruby on Rails - Primary and Foreign key

  - by Eef

  Hey, I am creating a site in Ruby on Rails, I have two models a User model and a Transaction model. These models both belong to an account so they both have a field called account_id I am trying to setup a association between them like so: class User < ActiveRecord::Base belongs_to :account has_many :transactions end class Transaction < ActiveRecord::Base belongs_to :account belongs_to :user end I am using these associations like so: user = User.find(1) transactions = user.transactions At the moment the application is trying to find the transactions with the user_id, here is the SQL it generates: Mysql::Error: Unknown column 'transactions.user_id' in 'where clause': SELECT * FROM `transactions` WHERE (`transactions`.user_id = 1) This is incorrect as I would like the find the transactions via the account_id, I have tried setting the associations like so: class User < ActiveRecord::Base belongs_to :account has_many :transactions, :primary_key => :account_id, :class_name => "Transaction" end class Transaction < ActiveRecord::Base belongs_to :account belongs_to :user, :foreign_key => :account_id, :class_name => "User" end This almost achieves what I am looking to do and generates the following SQL: Mysql::Error: Unknown column 'transactions.user_id' in 'where clause': SELECT * FROM `transactions` WHERE (`transactions`.user_id = 104) The number 104 is the correct account_id but it is still trying to query the transaction table for a user_id field. Could someone give me some advice on how I setup the associations to query the transaction table for the account_id instead of the user_id resulting in a SQL query like so: SELECT * FROM `transactions` WHERE (`transactions`.account_id = 104) Cheers Eef

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• Combining two-part SQL query into one query

  - by user332523

  Hello, I have a SQL query that I'm currently solving by doing two queries. I am wondering if there is a way to do it in a single query that makes it more efficient. Consider two tables: Transaction_Entries table and Transactions, each one defined below: Transactions - id - reference_number (varchar) Transaction_Entries - id - account_id - transaction_id (references Transactions table) Notes: There are multiple transaction entries per transaction. Some transactions are related, and will have the same reference_number string. To get all transaction entries for Account X, then I would do SELECT E.*, T.reference_number FROM Transaction_Entries E JOIN Transactions T ON (E.transaction_id=T.id) where E.account_id = X The next part is the hard part. I want to find all related transactions, regardless of the account id. First I make a list of all the unique reference numbers I found in the previous result set. Then for each one, I can query all the transactions that have that reference number. Assume that I hold all the rows from the previous query in PreviousResultSet UniqueReferenceNumbers = GetUniqueReferenceNumbers(PreviousResultSet) // in Java foreach R in UniqueReferenceNumbers // in Java SELECT * FROM Transaction_Entries where transaction_id IN (SELECT * FROM Transactions WHERE reference_number=R Any suggestions how I can put this into a single efficient query?

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• BackgroundWorker Help needed

  - by ChrisMuench

  I have code that does a web-service request. While doing this request I need a progress-bar to be moving independently. My problem is that I just need to say run a progress update every 1 or 2 seconds and check to see if progress of the request has been completed. NetBasisServicesSoapClient client = new NetBasisServicesSoapClient(); TransactionDetails[] transactions = new TransactionDetails[dataGridView1.Rows.Count - 1]; for (int i = 0; i < dataGridView1.Rows.Count - 1; i++) { transactions[i] = new TransactionDetails(); transactions[i].TransactionDate = (string)dataGridView1.Rows[i].Cells[2].Value; transactions[i].TransactionType = (string)dataGridView1.Rows[i].Cells[3].Value; transactions[i].Shares = (string)dataGridView1.Rows[i].Cells[4].Value; transactions[i].Pershare = (string)dataGridView1.Rows[i].Cells[5].Value; transactions[i].TotalAmount = (string)dataGridView1.Rows[i].Cells[6].Value; } CostbasisResult result = client.Costbasis(dataGridView1.Rows[0].Cells[0].Value.ToString(), dataGridView1.Rows[0].Cells[1].Value.ToString(), transactions, false, "", "", "FIFO", true); string result1 = ConvertStringArrayToString(result.Details);

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• Can I create support multiple database transactions on a single connection?

  - by draezal

  I have created a HyperSQL Database. I was just wondering whether I could run multiple transactions on a single connection. I didn't want to spawn a new connection for each transaction due to the overhead associated with this. Looking at some similar questions the suggestion appeared to be to create a pool of database connections and then block waiting for one to become available. This is a workable, but not desirable solution. Background Info (if this is relevant to the answer). My application will create a new thread when some request comes in. This request will require a database transaction. Then some not insignificant time later this transaction will be committed. Any advice appreciated :)

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• How to implement nested SQL transactions with ADO.NET?

  - by manza_jurjur

  I need to implement nested transactions in .NET using ADO.NET. The situation is as follows: --> Start Process (Begin Transaction) --> Begin Transaction for step 1 --> Step 1 --> Commit transaction for step 1 --> Begin transaction for step 2 --> Step 2 --> Rollback transaction for step 2 --> etc ... --> End Process (Commit or Rollback ALL commited steps) Can that be done with transaction scopes? Could anyone post an example? In addition I'd need the prcoess to work for SQL Server 2005 AND Oracle 10g databases... will transaction scopes work with both database engines?

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• Is there a way to use the transactions in TCPDF when extending it with FPDI?

  - by Darryl Hein

  I am using TCPDF with FPDI's bridge. The issue I'm having is that as soon as I use the startTransaction() I get the following error: TCPDF ERROR: Cannot access protected property FPDI:$numpages / Undefined property: FPDI::$numpages and the script ends (because of the die in the TCPDF::Error() method). Here is the code I'm using: $pdf = new FPDI(); // add a page $pdf->AddPage(); $pdf->startTransaction(); $pdf->Cell(0, 0, 'blah blah blah'); $pdf->rollbackTransaction(); $pdf->Output( . time() . '.pdf', 'D'); If I change it to: $pdf = new FPDI(); // add a page $pdf->AddPage(); $pdf->Cell(0, 0, 'blah blah blah'); $pdf->Output( . time() . '.pdf', 'D'); it works fine. Is there anyway to make them work together and use TCPDF's transactions?

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• How to avoid geometric slowdown with large Linq transactions?

  - by Shaul

  I've written some really nice, funky libraries for use in LinqToSql. (Some day when I have time to think about it I might make it open source... :) ) Anyway, I'm not sure if this is related to my libraries or not, but I've discovered that when I have a large number of changed objects in one transaction, and then call DataContext.GetChangeSet(), things start getting reaalllly slooowwwww. When I break into the code, I find that my program is spinning its wheels doing an awful lot of Equals() comparisons between the objects in the change set. I can't guarantee this is true, but I suspect that if there are n objects in the change set, then the call to GetChangeSet() is causing every object to be compared to every other object for equivalence, i.e. at best (n^2-n)/2 calls to Equals()... Yes, of course I could commit each object separately, but that kinda defeats the purpose of transactions. And in the program I'm writing, I could have a batch job containing 100,000 separate items, that all need to be committed together. Around 5 billion comparisons there. So the question is: (1) is my assessment of the situation correct? Do you get this behavior in pure, textbook LinqToSql, or is this something my libraries are doing? And (2) is there a standard/reasonable workaround so that I can create my batch without making the program geometrically slower with every extra object in the change set?

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• Why would my router have different MAC addresses for IP and IPv6 transactions?

  - by user329161

  Today I was using tcpdump and I noticed my computer was having IPv6 traffic with a particular MAC address that I could not match with an IP using nmap or arping. After looking at the tcpdump logs a little more closely, I figured out it was another MAC address my router was using but exclusively for IPv6 traffic. 22:49:01.936830 90:0d:cb:ff:31:91 (oui Unknown) > 33:33:00:00:00:01 (oui Unknown), ethertype IPv6 (0x86dd), length 158: fe80::920d:cbff:feff:3191 > ip6-allnodes: ICMP6, router advertisement, length 104 Why would a router offer a different MAC address for IPv6?

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• SQL: many-to-many relationship, IN condition

  - by Maarten

  I have a table called transactions with a many-to-many relationship to items through the items_transactions table. I want to do something like this: SELECT "transactions".* FROM "transactions" INNER JOIN "items_transactions" ON "items_transactions".transaction_id = "transactions".id INNER JOIN "items" ON "items".id = "items_transactions".item_id WHERE (items.id IN (<list of items>)) But this gives me all transactions that have one or more of the items in the list associated with it and I only want it to give me the transactions that are associated with all of those items. Any help would be appreciated.

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• How to nest transactions nicely - &quot;begin transaction&quot; vs &quot;save transaction&quot; and SQL Server

  - by Brian Biales

  Do you write stored procedures that might be used by others?  And those others may or may not have already started a transaction?  And your SP does several things, but if any of them fail, you have to undo them all and return with a code indicating it failed? Well, I have written such code, and it wasn’t working right until I finally figured out how to handle the case when we are already in a transaction, as well as the case where the caller did not start a transaction.  When a problem occurred, my “ROLLBACK TRANSACTION” would roll back not just my nested transaction, but the caller’s transaction as well.  So when I tested the procedure stand-alone, it seemed to work fine, but when others used it, it would cause a problem if it had to rollback.  When something went wrong in my procedure, their entire transaction was rolled back.  This was not appreciated. Now, I knew one could "nest" transactions, but the technical documentation was very confusing.  And I still have not found the approach below documented anywhere.  So here is a very brief description of how I got it to work, I hope you find this helpful. My example is a stored procedure that must figure out on its own if the caller has started a transaction or not.  This can be done in SQL Server by checking the @@TRANCOUNT value.  If no BEGIN TRANSACTION has occurred yet, this will have a value of 0.  Any number greater than zero means that a transaction is in progress.  If there is no current transaction, my SP begins a transaction. But if a transaction is already in progress, my SP uses SAVE TRANSACTION and gives it a name.  SAVE TRANSACTION creates a “save point”.  Note that creating a save point has no effect on @@TRANCOUNT.  So my SP starts with something like this: DECLARE @startingTranCount int SET @startingTranCount = @@TRANCOUNT IF @startingTranCount > 0 SAVE TRANSACTION mySavePointName ELSE BEGIN TRANSACTION -- … Then, when ready to commit the changes, you only need to commit if we started the transaction ourselves: IF @startingTranCount = 0 COMMIT TRANSACTION And finally, to roll back just your changes so far: -- Roll back changes... IF @startingTranCount > 0 ROLLBACK TRANSACTION MySavePointName ELSE ROLLBACK TRANSACTION Here is some code that you can try that will demonstrate how the save points work inside a transaction. This sample code creates a temporary table, then executes selects and updates, documenting what is going on, then deletes the temporary table. if running in SQL Management Studio, set Query Results to: Text for best readability of the results. -- Create a temporary table to test with, we'll drop it at the end. CREATE TABLE #ATable( [Column_A] [varchar](5) NULL ) ON [PRIMARY] GO SET NOCOUNT ON -- Ensure just one row - delete all rows, add one DELETE #ATable -- Insert just one row INSERT INTO #ATable VALUES('000') SELECT 'Before TRANSACTION starts, value in table is: ' AS Note, * FROM #ATable SELECT @@trancount AS CurrentTrancount --insert into a values ('abc') UPDATE #ATable SET Column_A = 'abc' SELECT 'UPDATED without a TRANSACTION, value in table is: ' AS Note, * FROM #ATable BEGIN TRANSACTION SELECT 'BEGIN TRANSACTION, trancount is now ' AS Note, @@TRANCOUNT AS TranCount UPDATE #ATable SET Column_A = '123' SELECT 'Row updated inside TRANSACTION, value in table is: ' AS Note, * FROM #ATable SAVE TRANSACTION MySavepoint SELECT 'Save point MySavepoint created, transaction count now:' as Note, @@TRANCOUNT AS TranCount UPDATE #ATable SET Column_A = '456' SELECT 'Updated after MySavepoint created, value in table is: ' AS Note, * FROM #ATable SAVE TRANSACTION point2 SELECT 'Save point point2 created, transaction count now:' as Note, @@TRANCOUNT AS TranCount UPDATE #ATable SET Column_A = '789' SELECT 'Updated after point2 savepoint created, value in table is: ' AS Note, * FROM #ATable ROLLBACK TRANSACTION point2 SELECT 'Just rolled back savepoint "point2", value in table is: ' AS Note, * FROM #ATable ROLLBACK TRANSACTION MySavepoint SELECT 'Just rolled back savepoint "MySavepoint", value in table is: ' AS Note, * FROM #ATable SELECT 'Both save points were rolled back, transaction count still:' as Note, @@TRANCOUNT AS TranCount ROLLBACK TRANSACTION SELECT 'Just rolled back the entire transaction..., value in table is: ' AS Note, * FROM #ATable DROP TABLE #ATable The output should look like this: Note                                           Column_A ---------------------------------------------- -------- Before TRANSACTION starts, value in table is:  000 CurrentTrancount ---------------- 0 Note                                               Column_A -------------------------------------------------- -------- UPDATED without a TRANSACTION, value in table is:  abc Note                                 TranCount ------------------------------------ ----------- BEGIN TRANSACTION, trancount is now  1 Note                                                Column_A --------------------------------------------------- -------- Row updated inside TRANSACTION, value in table is:  123 Note                                                   TranCount ------------------------------------------------------ ----------- Save point MySavepoint created, transaction count now: 1 Note                                                   Column_A ------------------------------------------------------ -------- Updated after MySavepoint created, value in table is:  456 Note                                              TranCount ------------------------------------------------- ----------- Save point point2 created, transaction count now: 1 Note                                                        Column_A ----------------------------------------------------------- -------- Updated after point2 savepoint created, value in table is:  789 Note                                                     Column_A -------------------------------------------------------- -------- Just rolled back savepoint "point2", value in table is:  456 Note                                                          Column_A ------------------------------------------------------------- -------- Just rolled back savepoint "MySavepoint", value in table is:  123 Note                                                        TranCount ----------------------------------------------------------- ----------- Both save points were rolled back, transaction count still: 1 Note                                                            Column_A --------------------------------------------------------------- -------- Just rolled back the entire transaction..., value in table is:  abc

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• SQL SERVER – Out of the Box – Activty and Performance Reports from SSSMS

  - by pinaldave

  SQL Server management Studio 2008 is wonderful tool and has many different features. Many times, an average user does not use them as they are not aware about these features. Today, we will learn one such feature. SSMS comes with many inbuilt performance and activity reports, but we do not use it to the full potential. Let us see how we can access these standard reports. Connect to SQL Server Node >> Right Click on it >> Go to Reports >> Click on Standard Reports >> Pick Any Report. Click to Enlarge You can see there are many reports, which an average users needs right away, are available there. Let me list all the reports available. Server Dashboard Configuration Changes History Schema Changes History Scheduler Health Memory Consumption Activity – All Blocking Transactions Activity – All Cursors Activity – All Sessions Activity – Top Sessions Activity – Dormant Sessions Activity -  Top Connections Top Transactions by Age Top Transactions by Blocked Transactions Count Top Transactions by Locks Count Performance – Batch Execution Statistics Performance – Object Execution Statistics Performance – Top Queries by Average CPU Time Performance – Top Queries by Average IO Performance – Top Queries by Total CPU Time Performance – Top Queries by Total IO Service Broker Statistics Transactions Log Shipping Status In fact, when you look at the above list, it is fairly clear that they are very thought out and commonly needed reports that are available in SQL Server 2008. Let us run a couple of reports and observe their result. Performance – Top Queries by Total CPU Time Click to Enlarge Memory Consumption Click to Enlarge There are options for custom reports as well, which we can configure. We will learn about them in some other post. Additionally, you can right click on the reports and export in Excel or PDF. I think this tool can really help those who are just looking for some quick details. Does any of you use this feature, or this feature has some limitations and You would like to see more features? Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology

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• Fraud Detection with the SQL Server Suite Part 1

  - by Dejan Sarka

  While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of blog posts which will at the end make the whole whitepaper. Abstract With the massive usage of credit cards and web applications for banking and payment processing, the number of fraudulent transactions is growing rapidly and on a global scale. Several fraud detection algorithms are available within a variety of different products. In this paper, we focus on using the Microsoft SQL Server suite for this purpose. In addition, we will explain our original approach to solving the problem by introducing a continuous learning procedure. Our preferred type of service is mentoring; it allows us to perform the work and consulting together with transferring the knowledge onto the customer, thus making it possible for a customer to continue to learn independently. This paper is based on practical experience with different projects covering online banking and credit card usage. Introduction A fraud is a criminal or deceptive activity with the intention of achieving financial or some other gain. Fraud can appear in multiple business areas. You can find a detailed overview of the business domains where fraud can take place in Sahin Y., & Duman E. (2011), Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 Vol 1. Hong Kong: IMECS. Dealing with frauds includes fraud prevention and fraud detection. Fraud prevention is a proactive mechanism, which tries to disable frauds by using previous knowledge. Fraud detection is a reactive mechanism with the goal of detecting suspicious behavior when a fraudster surpasses the fraud prevention mechanism. A fraud detection mechanism checks every transaction and assigns a weight in terms of probability between 0 and 1 that represents a score for evaluating whether a transaction is fraudulent or not. A fraud detection mechanism cannot detect frauds with a probability of 100%; therefore, manual transaction checking must also be available. With fraud detection, this manual part can focus on the most suspicious transactions. This way, an unchanged number of supervisors can detect significantly more frauds than could be achieved with traditional methods of selecting which transactions to check, for example with random sampling. There are two principal data mining techniques available both in general data mining as well as in specific fraud detection techniques: supervised or directed and unsupervised or undirected. Supervised techniques or data mining models use previous knowledge. Typically, existing transactions are marked with a flag denoting whether a particular transaction is fraudulent or not. Customers at some point in time do report frauds, and the transactional system should be capable of accepting such a flag. Supervised data mining algorithms try to explain the value of this flag by using different input variables. When the patterns and rules that lead to frauds are learned through the model training process, they can be used for prediction of the fraud flag on new incoming transactions. Unsupervised techniques analyze data without prior knowledge, without the fraud flag; they try to find transactions which do not resemble other transactions, i.e. outliers. In both cases, there should be more frauds in the data set selected for checking by using the data mining knowledge compared to selecting the data set with simpler methods; this is known as the lift of a model. Typically, we compare the lift with random sampling. The supervised methods typically give a much better lift than the unsupervised ones. However, we must use the unsupervised ones when we do not have any previous knowledge. Furthermore, unsupervised methods are useful for controlling whether the supervised models are still efficient. Accuracy of the predictions drops over time. Patterns of credit card usage, for example, change over time. In addition, fraudsters continuously learn as well. Therefore, it is important to check the efficiency of the predictive models with the undirected ones. When the difference between the lift of the supervised models and the lift of the unsupervised models drops, it is time to refine the supervised models. However, the unsupervised models can become obsolete as well. It is also important to measure the overall efficiency of both, supervised and unsupervised models, over time. We can compare the number of predicted frauds with the total number of frauds that include predicted and reported occurrences. For measuring behavior across time, specific analytical databases called data warehouses (DW) and on-line analytical processing (OLAP) systems can be employed. By controlling the supervised models with unsupervised ones and by using an OLAP system or DW reports to control both, a continuous learning infrastructure can be established. There are many difficulties in developing a fraud detection system. As has already been mentioned, fraudsters continuously learn, and the patterns change. The exchange of experiences and ideas can be very limited due to privacy concerns. In addition, both data sets and results might be censored, as the companies generally do not want to publically expose actual fraudulent behaviors. Therefore it can be quite difficult if not impossible to cross-evaluate the models using data from different companies and different business areas. This fact stresses the importance of continuous learning even more. Finally, the number of frauds in the total number of transactions is small, typically much less than 1% of transactions is fraudulent. Some predictive data mining algorithms do not give good results when the target state is represented with a very low frequency. Data preparation techniques like oversampling and undersampling can help overcome the shortcomings of many algorithms. SQL Server suite includes all of the software required to create, deploy any maintain a fraud detection infrastructure. The Database Engine is the relational database management system (RDBMS), which supports all activity needed for data preparation and for data warehouses. SQL Server Analysis Services (SSAS) supports OLAP and data mining (in version 2012, you need to install SSAS in multidimensional and data mining mode; this was the only mode in previous versions of SSAS, while SSAS 2012 also supports the tabular mode, which does not include data mining). Additional products from the suite can be useful as well. SQL Server Integration Services (SSIS) is a tool for developing extract transform–load (ETL) applications. SSIS is typically used for loading a DW, and in addition, it can use SSAS data mining models for building intelligent data flows. SQL Server Reporting Services (SSRS) is useful for presenting the results in a variety of reports. Data Quality Services (DQS) mitigate the occasional data cleansing process by maintaining a knowledge base. Master Data Services is an application that helps companies maintaining a central, authoritative source of their master data, i.e. the most important data to any organization. For an overview of the SQL Server business intelligence (BI) part of the suite that includes Database Engine, SSAS and SSRS, please refer to Veerman E., Lachev T., & Sarka D. (2009). MCTS Self-Paced Training Kit (Exam 70-448): Microsoft® SQL Server® 2008 Business Intelligence Development and Maintenance. MS Press. For an overview of the enterprise information management (EIM) part that includes SSIS, DQS and MDS, please refer to Sarka D., Lah M., & Jerkic G. (2012). Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft® SQL Server® 2012. O'Reilly. For details about SSAS data mining, please refer to MacLennan J., Tang Z., & Crivat B. (2009). Data Mining with Microsoft SQL Server 2008. Wiley. SQL Server Data Mining Add-ins for Office, a free download for Office versions 2007, 2010 and 2013, bring the power of data mining to Excel, enabling advanced analytics in Excel. Together with PowerPivot for Excel, which is also freely downloadable and can be used in Excel 2010, is already included in Excel 2013. It brings OLAP functionalities directly into Excel, making it possible for an advanced analyst to build a complete learning infrastructure using a familiar tool. This way, many more people, including employees in subsidiaries, can contribute to the learning process by examining local transactions and quickly identifying new patterns.

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• Frameworks: "A Ride at the OK (or Cancel) Corral"

  Configure nested transactions using Oracle ADF task flows.

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• SQL query mixing aggregated results and single values

  - by Paul Flowerdew

  I have a table with transactions. Each transaction has a transaction ID, and accounting period (AP), and a posting value (PV), as well as other fields. Some of the IDs are duplicated, usually because the transaction was done in error. To give an example, part of the table might look like: ID PV AP 123 100 2 123 -100 5 In this case the transaction was added in AP2 then removed in AP5. Another example would be: ID PV AP 456 100 2 456 -100 5 456 100 8 In the first example, the problem is that if I am analyzing what was spent in AP2, there is a transaction in there which actually shouldn't be taken into account because it was taken out again in AP5. In the second example, the second two transactions shouldn't be taken into account because they cancel each other out. I want to label as many transactions as possible which shouldn't be taken into account as erroneous. To identify these transactions, I want to find the ones with duplicate IDs whose PVs sum to zero (like ID 123 above) or transactions where the PV of the earliest one is equal to sum(PV), as in the second example. This second condition is what is causing me grief. So far I have SELECT * FROM table WHERE table.ID IN (SELECT table.ID FROM table GROUP BY table.ID HAVING COUNT(*) > 1 AND (SUM(table.PV) = 0 OR SUM(table.PV) = <PV of first transaction in each group>)) ORDER BY table.ID; The bit in chevrons is what I'm trying to do and I'm stuck. Can I do it like this or is there some other method I can use in SQL to do this? Edit 1: Btw I forgot to say that I'm using SQL Compact 3.5, in case it matters. Edit 2: I think the code snippet above is a bit misleading. I still want to mark out transactions with duplicate IDs where sum(PV) = 0, as in the first example. But where the PV of the earliest transaction = sum(PV), as in the second example, what I actually want is to keep the earliest transaction and mark out all the others with the same ID. Sorry if that caused confusion. Edit 3: I've been playing with Clodoaldo's solution and have made some progress, but still can't get quite what I want. I'm trying to get the transactions I know for certain to be erroneous. Suppose the following transactions are also in the table: ID PV AP 789 100 2 789 200 5 789 -100 8 In this example sum(PV) < 0 and the earliest PV < sum(PV) so I don't want to mark any of these out. If I modify Clodoaldo's query as follows: select t.* from t left join ( select id, min(ap) as ap, sum(pv) as sum_pv from t group by id having sum(pv) <> 0 ) s on t.id = s.id and t.ap = s.ap and t.pv = s.sum_pv where s.id is null This gives the result ID PV AP 123 100 2 123 -100 5 456 -100 5 456 100 8 789 100 3 789 200 5 789 -100 8 Whilst the first 4 transactions are ok (they would be marked out), the 789 transactions are also there, and I don't want them. But I can't figure out how to modify the query so that they're not included. Any ideas?

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• Saving child collections with NHibernate

  - by Ben

  Hi, I am in the process or learning NHibernate so bare with me. I have an Order class and a Transaction class. Order has a one to many association with transaction. The transaction table in my database has a not null constraint on the OrderId foreign key. Order class: public class Order { public virtual Guid Id { get; set; } public virtual DateTime CreatedOn { get; set; } public virtual decimal Total { get; set; } public virtual ICollection<Transaction> Transactions { get; set; } public Order() { Transactions = new HashSet<Transaction>(); } } Order Mapping: <class name="Order" table="Orders"> <cache usage="read-write"/> <id name="Id"> <generator class="guid"/> </id> <property name="CreatedOn" type="datetime"/> <property name="Total" type="decimal"/> <set name="Transactions" table="Transactions" lazy="false" inverse="true"> <key column="OrderId"/> <one-to-many class="Transaction"/> </set> Transaction Class: public class Transaction { public virtual Guid Id { get; set; } public virtual DateTime ExecutedOn { get; set; } public virtual bool Success { get; set; } public virtual Order Order { get; set; } } Transaction Mapping: <class name="Transaction" table="Transactions"> <cache usage="read-write"/> <id name="Id" column="Id" type="Guid"> <generator class="guid"/> </id> <property name="ExecutedOn" type="datetime"/> <property name="Success" type="bool"/> <many-to-one name="Order" class="Order" column="OrderId" not-null="true"/> Really I don't want a bidirectional association. There is no need for my transaction objects to reference their order object directly (I just need to access the transactions of an order). However, I had to add this so that Order.Transactions is persisted to the database: Repository: public void Update(Order entity) { using (ISession session = NHibernateHelper.OpenSession()) { using (ITransaction transaction = session.BeginTransaction()) { session.Update(entity); foreach (var tx in entity.Transactions) { tx.Order = entity; session.SaveOrUpdate(tx); } transaction.Commit(); } } } My problem is that this will then issue an update for every transaction on the order collection (regardless of whether it has changed or not). What I was trying to get around was having to explicitly save the transaction before saving the order and instead just add the transactions to the order and then save the order: public void Can_add_transaction_to_existing_order() { var orderRepo = new OrderRepository(); var order = orderRepo.GetById(new Guid("aa3b5d04-c5c8-4ad9-9b3e-9ce73e488a9f")); Transaction tx = new Transaction(); tx.ExecutedOn = DateTime.Now; tx.Success = true; order.Transactions.Add(tx); orderRepo.Update(order); } Although I have found quite a few articles covering the set up of a one-to-many association, most of these discuss retrieving of data and not persisting back. Many thanks, Ben

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• Defines JEE 5 the handling of commit error using bean managed transactions?

  - by marabol

  I'm using glassfish 2.1 and 2.1.1. If I've a bean method annotated by @TransactionAttribute(value = TransactionAttributeType.REQUIRES_NEW). After doing some JPA stuff the commit fails in the afterCompletion-Phase of JTS. GlassFish logs this failure only. And the caller of this bean method has no chance to know something goes wrong. So I wonder, if there is any definition how a jee 5 server has to handle exceptions while commiting. I would expect any runtime exception. I'm using stateless beans. With SessionSynchronisation I could get the commit failue, if I use statefull beans. Is it possible to intercept, so I can throw an exception, that I've declared in my interface? This is the whole exception stacktrace: [#|2010-05-06T12:15:54.840+0000|WARNING|sun-appserver2.1|oracle.toplink.essentials.session.file:/C:/glassfish/domains/domain1/applications/j2ee-apps/my-ear-1.0.0-SNAPSHOT/my-jar-1.1.8_jar/-myPu.transaction|_ThreadID=25;_ThreadName=p: thread-pool-1; w: 15;_RequestID=67a475a1-25c3-4416-abea-0d159f715373;| java.lang.RuntimeException: Got exception during XAResource.end: oracle.jdbc.xa.OracleXAException at com.sun.enterprise.distributedtx.J2EETransactionManagerOpt.delistResource(J2EETransactionManagerOpt.java:224) at com.sun.enterprise.resource.ResourceManagerImpl.unregisterResource(ResourceManagerImpl.java:265) at com.sun.enterprise.resource.ResourceManagerImpl.delistResource(ResourceManagerImpl.java:223) at com.sun.enterprise.resource.PoolManagerImpl.resourceClosed(PoolManagerImpl.java:400) at com.sun.enterprise.resource.ConnectorAllocator$ConnectionListenerImpl.connectionClosed(ConnectorAllocator.java:72) at com.sun.gjc.spi.ManagedConnection.connectionClosed(ManagedConnection.java:639) at com.sun.gjc.spi.base.ConnectionHolder.close(ConnectionHolder.java:201) at com.sun.gjc.spi.jdbc40.ConnectionHolder40.close(ConnectionHolder40.java:519) at oracle.toplink.essentials.internal.databaseaccess.DatabaseAccessor.closeDatasourceConnection(DatabaseAccessor.java:394) at oracle.toplink.essentials.internal.databaseaccess.DatasourceAccessor.closeConnection(DatasourceAccessor.java:382) at oracle.toplink.essentials.internal.databaseaccess.DatabaseAccessor.closeConnection(DatabaseAccessor.java:417) at oracle.toplink.essentials.internal.databaseaccess.DatasourceAccessor.afterJTSTransaction(DatasourceAccessor.java:115) at oracle.toplink.essentials.threetier.ClientSession.afterTransaction(ClientSession.java:119) at oracle.toplink.essentials.internal.sessions.UnitOfWorkImpl.afterTransaction(UnitOfWorkImpl.java:1841) at oracle.toplink.essentials.transaction.AbstractSynchronizationListener.afterCompletion(AbstractSynchronizationListener.java:170) at oracle.toplink.essentials.transaction.JTASynchronizationListener.afterCompletion(JTASynchronizationListener.java:102) at com.sun.jts.jta.SynchronizationImpl.after_completion(SynchronizationImpl.java:154) at com.sun.jts.CosTransactions.RegisteredSyncs.distributeAfter(RegisteredSyncs.java:210) at com.sun.jts.CosTransactions.TopCoordinator.afterCompletion(TopCoordinator.java:2585) at com.sun.jts.CosTransactions.CoordinatorTerm.commit(CoordinatorTerm.java:433) at com.sun.jts.CosTransactions.TerminatorImpl.commit(TerminatorImpl.java:250) at com.sun.jts.CosTransactions.CurrentImpl.commit(CurrentImpl.java:623) at com.sun.jts.jta.TransactionManagerImpl.commit(TransactionManagerImpl.java:309) at com.sun.enterprise.distributedtx.J2EETransactionManagerImpl.commit(J2EETransactionManagerImpl.java:1029) at com.sun.enterprise.distributedtx.J2EETransactionManagerOpt.commit(J2EETransactionManagerOpt.java:398) at com.sun.ejb.containers.BaseContainer.completeNewTx(BaseContainer.java:3817) at com.sun.ejb.containers.BaseContainer.postInvokeTx(BaseContainer.java:3610) at com.sun.ejb.containers.BaseContainer.postInvoke(BaseContainer.java:1379) at com.sun.ejb.containers.BaseContainer.postInvoke(BaseContainer.java:1316) at com.sun.ejb.containers.EJBLocalObjectInvocationHandler.invoke(EJBLocalObjectInvocationHandler.java:205) at com.sun.ejb.containers.EJBLocalObjectInvocationHandlerDelegate.invoke(EJBLocalObjectInvocationHandlerDelegate.java:127) at $Proxy127.myNewTxMethod(Unknown Source) at mypackage.MyBean2.myMethod(MyBean2.java:197) at mypackage.MyBean2.myMethod2(MyBean2.java:166) at mypackage.MyBean2.myMethod3(MyBean2.java:105) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at com.sun.enterprise.security.application.EJBSecurityManager.runMethod(EJBSecurityManager.java:1011) at com.sun.enterprise.security.SecurityUtil.invoke(SecurityUtil.java:175) at com.sun.ejb.containers.BaseContainer.invokeTargetBeanMethod(BaseContainer.java:2920) at com.sun.ejb.containers.BaseContainer.intercept(BaseContainer.java:4011) at com.sun.ejb.containers.EJBLocalObjectInvocationHandler.invoke(EJBLocalObjectInvocationHandler.java:197) at com.sun.ejb.containers.EJBLocalObjectInvocationHandlerDelegate.invoke(EJBLocalObjectInvocationHandlerDelegate.java:127) at $Proxy158.myMethod3(Unknown Source) at mypackage.MyBean3.myMethod4(MyBean3.java:94) at mypackage.MyBean3.onMessage(MyBean3.java:85) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at com.sun.enterprise.security.SecurityUtil$2.run(SecurityUtil.java:181) at java.security.AccessController.doPrivileged(Native Method) at com.sun.enterprise.security.application.EJBSecurityManager.doAsPrivileged(EJBSecurityManager.java:985) at com.sun.enterprise.security.SecurityUtil.invoke(SecurityUtil.java:186) at com.sun.ejb.containers.BaseContainer.invokeTargetBeanMethod(BaseContainer.java:2920) at com.sun.ejb.containers.BaseContainer.intercept(BaseContainer.java:4011) at com.sun.ejb.containers.MessageBeanContainer.deliverMessage(MessageBeanContainer.java:1111) at com.sun.ejb.containers.MessageBeanListenerImpl.deliverMessage(MessageBeanListenerImpl.java:74) at com.sun.enterprise.connectors.inflow.MessageEndpointInvocationHandler.invoke(MessageEndpointInvocationHandler.java:179) at $Proxy192.onMessage(Unknown Source) at com.sun.messaging.jms.ra.OnMessageRunner.run(OnMessageRunner.java:258) at com.sun.enterprise.connectors.work.OneWork.doWork(OneWork.java:76) at com.sun.corba.ee.impl.orbutil.threadpool.ThreadPoolImpl$WorkerThread.run(ThreadPoolImpl.java:555) |#]

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• What is a simple way to get ACID transactions with persistence on the local file system (in Java)?

  - by T.R.

  I'm working on a small (java) project where a website needs to maintain a (preferably comma-separated) list of registered e-mail addresses, nothing else, and be able to check if an address is in the list. I have no control over the hosting or the server's lack of database support. Prevayler seemed a good solution, but the website is a ghost town, with example code missing from just about everywhere it's supposed to be, so I'm a little wary. What other options are recommended for such a task?

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• Is it worth using https if you are not doing financial transactions?

  - by Scarface

  Hey just a quick question for any experts out there. I have a site that lets users interact through messages and to sign up you just make a username and password, verify your age, and optionally, add an email. There isn't really any sensitive information I suppose. Is it worth using https. Will it prevent session hi jacking and will it hinder performance?

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• How to handle request-wise DB transactions in ASP.NET MVC?

  - by Dario Solera

  I'm using SubSonic 3.0 (SimpleRepository) to handle database access in my ASP.NET MVC 1.0 application. It would be nice to handle a transaction for every web request, committing if everything went smooth and rolling back in case of exception. Is this possible? If so, how? I know this topic has been discussed many times, but I just couldn't find a satisfactory answer. I have built my own solution (create a TransactionScope in the controller, then commit/rollback in OnActionExecuted), but it turns out to be very unreliable.

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• Paypal development. encrypt transactions. php p12

  - by ninchen

  when i take a look at the paypal documentation, they say "Note that the PayPal SDK for PHP does not require SSL encryption". https://developer.paypal.com/docs/classic/api/apiCredentials/#encrypting-your-certificate Is the statement of this phrase, that i don't have to create a p12 certificate when working with php, but use the public_key.pem and paypal_public_key.pem? If yes: Is it secure enough to create the encrypted form input elements without p12 certificate? If no: What do they mean? :-) Before this question came up, i've tested this little programm. http://www.softarea51.com/blog/how-to-integrate-your-custom-shopping-cart-with-paypal-website-payments-standard-using-php/ There is a config file paypal-wps-config.inc.php where i can define the paths to my certificates. // tryed to use // 'paypal_cert.p12 '; $config['private_key_path'] = '/home/folder/.cert/pp/prvkey.pem'; // must match the one you set when you created the private key $config['private_key_password'] = ''; //'my_password'; When i try to use the p12 certificate, openssl_error_string() returns "Could not sign data: error:0906D06C:PEM routines:PEM_read_bio:no start line openssl_pkcs7_sign When i instead use the prvkey.pem without password all works fine. Here is the function, which signs and encrypt the data. function signAndEncrypt($dataStr_, $ewpCertPath_, $ewpPrivateKeyPath_, $ewpPrivateKeyPwd_, $paypalCertPath_) { $dataStrFile = realpath(tempnam('/tmp', 'pp_')); $fd = fopen($dataStrFile, 'w'); if(!$fd) { $error = "Could not open temporary file $dataStrFile."; return array("status" => false, "error_msg" => $error, "error_no" => 0); } fwrite($fd, $dataStr_); fclose($fd); $signedDataFile = realpath(tempnam('/tmp', 'pp_')); **// here the error came from** if(!@openssl_pkcs7_sign( $dataStrFile, $signedDataFile, "file://$ewpCertPath_", array("file://$ewpPrivateKeyPath_", $ewpPrivateKeyPwd_), array(), PKCS7_BINARY)) { unlink($dataStrFile); unlink($signedDataFile); $error = "Could not sign data: ".openssl_error_string(); return array("status" => false, "error_msg" => $error, "error_no" => 0); } unlink($dataStrFile); $signedData = file_get_contents($signedDataFile); $signedDataArray = explode("\n\n", $signedData); $signedData = $signedDataArray[1]; $signedData = base64_decode($signedData); unlink($signedDataFile); $decodedSignedDataFile = realpath(tempnam('/tmp', 'pp_')); $fd = fopen($decodedSignedDataFile, 'w'); if(!$fd) { $error = "Could not open temporary file $decodedSignedDataFile."; return array("status" => false, "error_msg" => $error, "error_no" => 0); } fwrite($fd, $signedData); fclose($fd); $encryptedDataFile = realpath(tempnam('/tmp', 'pp_')); if(!@openssl_pkcs7_encrypt( $decodedSignedDataFile, $encryptedDataFile, file_get_contents($paypalCertPath_), array(), PKCS7_BINARY)) { unlink($decodedSignedDataFile); unlink($encryptedDataFile); $error = "Could not encrypt data: ".openssl_error_string(); return array("status" => false, "error_msg" => $error, "error_no" => 0); } unlink($decodedSignedDataFile); $encryptedData = file_get_contents($encryptedDataFile); if(!$encryptedData) { $error = "Encryption and signature of data failed."; return array("status" => false, "error_msg" => $error, "error_no" => 0); } unlink($encryptedDataFile); $encryptedDataArray = explode("\n\n", $encryptedData); $encryptedData = trim(str_replace("\n", '', $encryptedDataArray[1])); return array("status" => true, "encryptedData" => $encryptedData); } // signAndEncrypt } // PPCrypto The main questions: 1. Is it possible to use p12 cert with php, or is it secure enough to work without it? 2. Why i become an error when using openssl_pkcs7_sign Please help. Greetings ninchen

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• Commit in SQL

  - by PRajkumar

  SQL Transaction Control Language Commands (TCL)                                           (COMMIT) Commit Transaction As a SQL language we use transaction control language very frequently. Committing a transaction means making permanent the changes performed by the SQL statements within the transaction. A transaction is a sequence of SQL statements that Oracle Database treats as a single unit. This statement also erases all save points in the transaction and releases transaction locks. Oracle Database issues an implicit COMMIT before and after any data definition language (DDL) statement. Oracle recommends that you explicitly end every transaction in your application programs with a COMMIT or ROLLBACK statement, including the last transaction, before disconnecting from Oracle Database. If you do not explicitly commit the transaction and the program terminates abnormally, then the last uncommitted transaction is automatically rolled back.   Until you commit a transaction: ·         You can see any changes you have made during the transaction by querying the modified tables, but other users cannot see the changes. After you commit the transaction, the changes are visible to other users' statements that execute after the commit ·         You can roll back (undo) any changes made during the transaction with the ROLLBACK statement   Note: Most of the people think that when we type commit data or changes of what you have made has been written to data files, but this is wrong when you type commit it means that you are saying that your job has been completed and respective verification will be done by oracle engine that means it checks whether your transaction achieved consistency when it finds ok it sends a commit message to the user from log buffer but not from data buffer, so after writing data in log buffer it insists data buffer to write data in to data files, this is how it works.   Before a transaction that modifies data is committed, the following has occurred: ·         Oracle has generated undo information. The undo information contains the old data values changed by the SQL statements of the transaction ·         Oracle has generated redo log entries in the redo log buffer of the System Global Area (SGA). The redo log record contains the change to the data block and the change to the rollback block. These changes may go to disk before a transaction is committed ·         The changes have been made to the database buffers of the SGA. These changes may go to disk before a transaction is committed   Note:   The data changes for a committed transaction, stored in the database buffers of the SGA, are not necessarily written immediately to the data files by the database writer (DBWn) background process. This writing takes place when it is most efficient for the database to do so. It can happen before the transaction commits or, alternatively, it can happen some times after the transaction commits.   When a transaction is committed, the following occurs: 1.      The internal transaction table for the associated undo table space records that the transaction has committed, and the corresponding unique system change number (SCN) of the transaction is assigned and recorded in the table 2.      The log writer process (LGWR) writes redo log entries in the SGA's redo log buffers to the redo log file. It also writes the transaction's SCN to the redo log file. This atomic event constitutes the commit of the transaction 3.      Oracle releases locks held on rows and tables 4.      Oracle marks the transaction complete   Note:   The default behavior is for LGWR to write redo to the online redo log files synchronously and for transactions to wait for the redo to go to disk before returning a commit to the user. However, for lower transaction commit latency application developers can specify that redo be written asynchronously and that transaction do not need to wait for the redo to be on disk.   The syntax of Commit Statement is   COMMIT [WORK] [COMMENT ‘your comment’]; ·         WORK is optional. The WORK keyword is supported for compliance with standard SQL. The statements COMMIT and COMMIT WORK are equivalent. Examples Committing an Insert INSERT INTO table_name VALUES (val1, val2); COMMIT WORK; ·         COMMENT Comment is also optional. This clause is supported for backward compatibility. Oracle recommends that you used named transactions instead of commit comments. Specify a comment to be associated with the current transaction. The 'text' is a quoted literal of up to 255 bytes that Oracle Database stores in the data dictionary view DBA_2PC_PENDING along with the transaction ID if a distributed transaction becomes in doubt. This comment can help you diagnose the failure of a distributed transaction. Examples The following statement commits the current transaction and associates a comment with it: COMMIT     COMMENT 'In-doubt transaction Code 36, Call (415) 555-2637'; ·         WRITE Clause Use this clause to specify the priority with which the redo information generated by the commit operation is written to the redo log. This clause can improve performance by reducing latency, thus eliminating the wait for an I/O to the redo log. Use this clause to improve response time in environments with stringent response time requirements where the following conditions apply: The volume of update transactions is large, requiring that the redo log be written to disk frequently. The application can tolerate the loss of an asynchronously committed transaction. The latency contributed by waiting for the redo log write to occur contributes significantly to overall response time. You can specify the WAIT | NOWAIT and IMMEDIATE | BATCH clauses in any order. Examples To commit the same insert operation and instruct the database to buffer the change to the redo log, without initiating disk I/O, use the following COMMIT statement: COMMIT WRITE BATCH; Note: If you omit this clause, then the behavior of the commit operation is controlled by the COMMIT_WRITE initialization parameter, if it has been set. The default value of the parameter is the same as the default for this clause. Therefore, if the parameter has not been set and you omit this clause, then commit records are written to disk before control is returned to the user. WAIT | NOWAIT Use these clauses to specify when control returns to the user. The WAIT parameter ensures that the commit will return only after the corresponding redo is persistent in the online redo log. Whether in BATCH or IMMEDIATE mode, when the client receives a successful return from this COMMIT statement, the transaction has been committed to durable media. A crash occurring after a successful write to the log can prevent the success message from returning to the client. In this case the client cannot tell whether or not the transaction committed. The NOWAIT parameter causes the commit to return to the client whether or not the write to the redo log has completed. This behavior can increase transaction throughput. With the WAIT parameter, if the commit message is received, then you can be sure that no data has been lost. Caution: With NOWAIT, a crash occurring after the commit message is received, but before the redo log record(s) are written, can falsely indicate to a transaction that its changes are persistent. If you omit this clause, then the transaction commits with the WAIT behavior. IMMEDIATE | BATCH Use these clauses to specify when the redo is written to the log. The IMMEDIATE parameter causes the log writer process (LGWR) to write the transaction's redo information to the log. This operation option forces a disk I/O, so it can reduce transaction throughput. The BATCH parameter causes the redo to be buffered to the redo log, along with other concurrently executing transactions. When sufficient redo information is collected, a disk write of the redo log is initiated. This behavior is called "group commit", as redo for multiple transactions is written to the log in a single I/O operation. If you omit this clause, then the transaction commits with the IMMEDIATE behavior. ·         FORCE Clause Use this clause to manually commit an in-doubt distributed transaction or a corrupt transaction. ·         In a distributed database system, the FORCE string [, integer] clause lets you manually commit an in-doubt distributed transaction. The transaction is identified by the 'string' containing its local or global transaction ID. To find the IDs of such transactions, query the data dictionary view DBA_2PC_PENDING. You can use integer to specifically assign the transaction a system change number (SCN). If you omit integer, then the transaction is committed using the current SCN. ·         The FORCE CORRUPT_XID 'string' clause lets you manually commit a single corrupt transaction, where string is the ID of the corrupt transaction. Query the V$CORRUPT_XID_LIST data dictionary view to find the transaction IDs of corrupt transactions. You must have DBA privileges to view the V$CORRUPT_XID_LIST and to specify this clause. ·         Specify FORCE CORRUPT_XID_ALL to manually commit all corrupt transactions. You must have DBA privileges to specify this clause. Examples Forcing an in doubt transaction. Example The following statement manually commits a hypothetical in-doubt distributed transaction. Query the V$CORRUPT_XID_LIST data dictionary view to find the transaction IDs of corrupt transactions. You must have DBA privileges to view the V$CORRUPT_XID_LIST and to issue this statement. COMMIT FORCE '22.57.53';

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• World Record Siebel PSPP Benchmark on SPARC T4 Servers

  - by Brian

  Oracle's SPARC T4 servers set a new World Record for Oracle's Siebel Platform Sizing and Performance Program (PSPP) benchmark suite. The result used Oracle's Siebel Customer Relationship Management (CRM) Industry Applications Release 8.1.1.4 and Oracle Database 11g Release 2 running Oracle Solaris on three SPARC T4-2 and two SPARC T4-1 servers. The SPARC T4 servers running the Siebel PSPP 8.1.1.4 workload which includes Siebel Call Center and Order Management System demonstrates impressive throughput performance of the SPARC T4 processor by achieving 29,000 users. This is the first Siebel PSPP 8.1.1.4 benchmark supporting 29,000 concurrent users with a rate of 239,748 Business Transactions/hour. The benchmark demonstrates vertical and horizontal scalability of Siebel CRM Release 8.1.1.4 on SPARC T4 servers. Performance Landscape Systems Txn/hr Users Call Center Order Management Response Times (sec) 1 x SPARC T4-1 (1 x SPARC T4 2.85 GHz) – Web 3 x SPARC T4-2 (2 x SPARC T4 2.85 GHz) – App/Gateway 1 x SPARC T4-1 (1 x SPARC T4 2.85 GHz) – DB 239,748 29,000 0.165 0.925 Oracle: Call Center + Order Management Transactions: 197,128 + 42,620 Users: 20300 + 8700 Configuration Summary Web Server Configuration: 1 x SPARC T4-1 server 1 x SPARC T4 processor, 2.85 GHz 128 GB memory Oracle Solaris 10 8/11 iPlanet Web Server 7 Application Server Configuration: 3 x SPARC T4-2 servers, each with 2 x SPARC T4 processor, 2.85 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 10 8/11 Siebel CRM 8.1.1.5 SIA Database Server Configuration: 1 x SPARC T4-1 server 1 x SPARC T4 processor, 2.85 GHz 128 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 (11.2.0.2) Storage Configuration: 1 x Sun Storage F5100 Flash Array 80 x 24 GB flash modules Benchmark Description Siebel 8.1 PSPP benchmark includes Call Center and Order Management: Siebel Financial Services Call Center – Provides the most complete solution for sales and service, allowing customer service and telesales representatives to provide superior customer support, improve customer loyalty, and increase revenues through cross-selling and up-selling. High-level description of the use cases tested: Incoming Call Creates Opportunity, Quote and Order and Incoming Call Creates Service Request . Three complex business transactions are executed simultaneously for specific number of concurrent users. The ratios of these 3 scenarios were 30%, 40%, 30% respectively, which together were totaling 70% of all transactions simulated in this benchmark. Between each user operation and the next one, the think time averaged approximately 10, 13, and 35 seconds respectively. Siebel Order Management – Oracle's Siebel Order Management allows employees such as salespeople and call center agents to create and manage quotes and orders through their entire life cycle. Siebel Order Management can be tightly integrated with back-office applications allowing users to perform tasks such as checking credit, confirming availability, and monitoring the fulfillment process. High-level description of the use cases tested: Order & Order Items Creation and Order Updates. Two complex Order Management transactions were executed simultaneously for specific number of concurrent users concurrently with aforementioned three Call Center scenarios above. The ratio of these 2 scenarios was 50% each, which together were totaling 30% of all transactions simulated in this benchmark. Between each user operation and the next one, the think time averaged approximately 20 and 67 seconds respectively. Key Points and Best Practices No processor cores or cache were activated or deactivated on the SPARC T-Series systems to achieve special benchmark effects. See Also Siebel White Papers SPARC T4-1 Server oracle.com OTN SPARC T4-2 Server oracle.com OTN Siebel CRM oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition 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 30 September 2012.

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