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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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  • virtualenv on Windows: not over-riding installed package

    - by Tom
    My current setup is Python 2.5/ Django 1.1.1 on Windows. I want to start using Django 1.2 on some projects, but can't use it for everything. Which is just the sort of thing I've got virtualenv for. However, I'm running into a problem I've never encountered and it's hard to Google for: installing Django 1.2 into a virtualenv has no effect for me. If I then activate the environment and do python import django django.VERSION I get "1.1.1 Final". Django is installed in the site-packages directory of my environment and the init file in the root shows that it is 1.2. But the environment falls back to 1.1.1, even if I create the environment with the --no-site-packages flag. What am I screwing up?

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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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  • Play or Lift: which one is more explicit?

    - by Andrea
    I am going to investigate web development with Scala, and the choice is between learning Lift or Play: probably I will not have enough time to try both, at least at first. Now, many comparisons between the two are available on the internet, but I would like to know how do they compare with respect to being explicit and involving less magic. Let me explain what I mean by example. I have used, to various degrees, CakePHP, symfony2, Django and Grails. I feel a very clear distinction between Django and symfony2, which are very explicit about what you are doing, and Grails and CakePHP, which try to do their best to guess what you are trying to achieve and often feel "magical". Let me give some examples comparing Django and Grails. In Django, views are functions that take a request as input and return a response. You can instantiate explicitly an instance of HttpResponse and populate its body with a string, or you can use shortcut functions to leverage the template system. In any case the return value from your view always has the same type. In contrast, the render method from Grails is highly polymorphic. You can throw a context at it and it will try to render a template which is found by convention using that context. Or you can pass it a pair of a template path and a context and that will work too. Or a string. Or XML. Grails tries hard to make sense of whatever you return from your controller. In the Django ORM, each model class has a static attribute representing the manager for that class. That manager exposes a fluent interface to build querysets. In Grails, you can have a similar functionality by composing detached criteria. Still, the most common way to query objects seems to be the use of runtime-generated methods like FindUserByEmailNotNull or FindPostByDateGreaterThan. I will not go further, but my point is that in Django-like frameworks you have control over the whole flow of the request/response process, while in Grails-like ones I feel I only have to feel the blanks and the framework will manage the rest of the flow for me. This is not to criticize Grails or CakePHP; which type you prefer is mainly a matter of preference. In fact, I happen to like some aspects of Grails, but I feel more comfortable with a framework which does less for me. Back to the point of the question: which one among Play and Lift is more explicit about what you do and which one tries to simplify more what you have to do with a layer of "magic"?

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  • apt-get install problem: Errors were encountered while processing: sun-j2sdk1.6

    - by pyeleven
    I have the following problem every time i run apt-get install: for example : installing python-django-south ... Unpacking python-django-south (from .../python-django-south_0.5-2_all.deb) ... Setting up sun-j2sdk1.6 (1.6.0+update22-linux-i586.) ... update-alternatives: error: alternative path /usr/lib/j2sdk1.6-sun/jre/plugin/amd64/ns7/libjavaplugin_oji.so doesn't exist. dpkg: error processing sun-j2sdk1.6 (--configure): subprocess installed post-installation script returned error exit status 2 Setting up python-django-south (0.5-2) ... Processing triggers for python-support ... Errors were encountered while processing: sun-j2sdk1.6 E: Sub-process /usr/bin/dpkg returned an error code (1) What could be the problem? I have 9.10 Ubuntu

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  • Komodo Edit - How to disable the 'Linter' for a language?

    - by TM.
    I've been using Komodo Edit to work on a Django project. It works great except for one little annoyance: When I am editing Django template files, Komodo likes to put red squiggly lines underneath the first HTML tag that follows a Django tag, because it thinks it is an invalid HTML doc (although it isn't, it just has Django template tags/filters in it). Note that this red squiggly line is called a "Linter error" in the docs that I can find. Is there some way to turn off this red squiggly for only a specific type of language? It's nice to have when working on Python code but it's annoying to have a red squiggly on every single one of my Django templates.

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  • Python virtualenv questions

    - by orokusaki
    I'm using VirtualEnv on Windows XP. I'm wondering if I have my brain wrapped around it correctly. I ran virtualenv ENV and it created C:\WINDOWS\system32\ENV. I then changed my PATH variable to include C:\WINDOWS\system32\ENV\Scripts instead of C:\Python27\Scripts. Then, I checked out Django into C:\WINDOWS\system32\ENV\Lib\site-packages\django-trunk, updated my PYTHON_PATH variable to point the new Django directory, and continued to easy_install other things (which of course go into my new C:\WINDOWS\system32\ENV\Lib\site-packages directory). I understand why I should use VirtualEnv so I can run multiple versions of Django, and other libraries on the same machine, but does this mean that to switch between environments I have to basically change my PATH and PYTHON_PATH variable? So, I go from developing one Django project which uses Django 1.2 in an environment called ENV and then change my PATH and such so that I can use an environment called ENV2 which has the dev version of Django? Is that basically it, or is there some better way to automatically do all this (I could update my path in Python code, but that would require me to write machine-specific code in my application)? Also, how does this process compare to using VirtualEnv on Linux (I'm quite the beginner at Linux).

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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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  • 400 error with nginx subdomains over https

    - by aquavitae
    Not sure what I'm doing wrong, but I'm trying to get gunicorn/django through nginx using only https. Here is my nginx configuration: upstream app_server { server unix:/srv/django/app/run/gunicorn.sock fail_timeout=0; } server { listen 80; return 301 https://$host$request_uri; } server { listen 443; server_name app.mydomain.com; ssl on; ssl_certificate /etc/nginx/ssl/nginx.crt; ssl_certificate_key /etc/nginx/ssl/nginx.key; client_max_body_size 4G; access_log /srv/django/app/logs/nginx-access.log; error_log /srv/django/app/logs/nginx-error.log; location /static/ { alias /srv/django/app/data/static/; } location /media/ { alias /wrv/django/app/data/media/; } location / { proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto https; proxy_set_header Host $http_host; proxy_pass http://app_server; } } I get a 400 error on app.mydomain.com, but the app is published on mydomain.com. Is there an error in my configuration?

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  • Get the last checked checkboxes...

    - by Sara
    Hi everyone, I'm not sure how to accomplish this issue which has been confusing me for a few days. I have a form that updates a user record in MySQL when a checkbox is checked. Now, this is how my form does this: if (isset($_POST['Update'])) { $paymentr = $_POST['paymentr']; //put checkboxes array into variable $paymentr2 = implode(', ', $paymentr); //implode array for mysql $query = "UPDATE transactions SET paymentreceived=NULL"; $result = mysql_query($query); $query = "UPDATE transactions SET paymentdate='0000-00-00'"; $result = mysql_query($query); $query = "UPDATE transactions SET paymentreceived='Yes' WHERE id IN ($paymentr2)"; $result = mysql_query($query); $query = "UPDATE transactions SET paymentdate=NOW() WHERE id IN ($paymentr2)"; $result = mysql_query($query); foreach ($paymentr as $v) { //should collect last updated records and put them into variable for emailing. $query = "SELECT id, refid, affid FROM transactions WHERE id = '$v'"; $result = mysql_query($query) or die("Query Failed: ".mysql_errno()." - ".mysql_error()."<BR>\n$query<BR>\n"); $trans = mysql_fetch_array($result, MYSQL_ASSOC); $transactions .= '<br>User ID:'.$trans['id'].' -- '.$trans['refid'].' -- '.$trans['affid'].'<br>'; } } Unfortunately, it then updates ALL the user records with the latest date which is not what I want it to do. The alternative I thought of was, via Javascript, giving the checkbox a value that would be dynamically updated when the user selected it. Then, only THOSE checkboxes would be put into the array. Is this possible? Is there a better solution? I'm not even sure I could wrap my brain around how to do that WITH Javascript. Does the answer perhaps lie in how my mysql code is written? Thanks - I sincerely appreciate it!!!

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  • Move Data into the grid for scalable, predictable response times

    - by JuergenKress
    CloudTran is pleased to introduce the availability of the CloudTran Transaction and Persistence Manager for creating scalable, reliable data services on the Oracle Coherence In-Memory Data Grid (IMDG). Use of IMDG architectures has been key to handling today’s web-scale loads because it eliminates database latency by storing important and frequently access data in memory instead of on disk. The CloudTran product lets developers easily use an IMDG for full ACID-compliant transactions without having to be concerned about the location or spread of data. The system has its own implementation of fast, scalable distributed transactions that does NOT depend on XA protocols but still guarantees all ACID properties. Plus, CloudTran asynchronously replicates data going into the IMDG to back-end datastores and back-up data centers, again ensuring ACID properties. CloudTran can be accessed through Java Persistence API (JPA via TopLink Grid) and now, through a new Low-Level API, or LLAPI. This is ideal for use in SOA applications that need data reliability, high availability, performance, and scalability. It is still in its limited beta release, the LLAPI gives developers the ability to use standard put/remove logic available in Coherence and then wrap logic with simple Spring annotations or XML+AspectJ to start transactions. An important feature of LLAPI is the ability to join transactions. This is a common outcome for SOA applications that need to reduce network traffic by aggregating data into single cache entries and then doing SOA service processing in the node holding the data. This results in the need to orchestrate transaction processing across multiple service calls. CloudTran has the capability to handle these “multi-client” transactions at speed with no loss in ACID properties. Developing software around an IMDG like Oracle Coherence is an important choice for today’s web-scale applications and services. But this introduces new architectural considerations to maintain scalability in light of increased network loads and data movement. Without using CloudTran, developers are faced with an incredibly difficult task to ensure data reliability, availability, performance, and scalability when working with an IMDG. Working with highly distributed data that is entirely volatile while stored in memory presents numerous edge cases where failures can result in data loss. The CloudTran product takes care of all of this, leaving developers with the confidence and peace of mind that all data is processed correctly. For those interested in evaluating the CloudTran product and IMDGs, take a look at this link for more information: http://www.CloudTran.com/downloadAPI.ph , or send your questions to [email protected]. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: CloudTran,data grid,M,SOA Community,Oracle SOA,Oracle BPM,BPM,Community,OPN,Jürgen Kress

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  • Move Data into the Grid for Scalable, Predictable Response Times

    - by JuergenKress
    CloudTran is pleased to introduce the availability of the CloudTran Transaction and Persistence Manager for creating scalable, reliable data services on the Oracle Coherence In-Memory Data Grid (IMDG). Use of IMDG architectures has been key to handling today’s web-scale loads because it eliminates database latency by storing important and frequently access data in memory instead of on disk. The CloudTran product lets developers easily use an IMDG for full ACID-compliant transactions without having to be concerned about the location or spread of data. The system has its own implementation of fast, scalable distributed transactions that does NOT depend on XA protocols but still guarantees all ACID properties. Plus, CloudTran asynchronously replicates data going into the IMDG to back-end datastores and back-up data centers, again ensuring ACID properties. CloudTran can be accessed through Java Persistence API (JPA via TopLink Grid) and now, through a new Low-Level API, or LLAPI. This is ideal for use in SOA applications that need data reliability, high availability, performance, and scalability. Still in limited beta release, the LLAPI gives developers the ability to use standard put/remove logic available in Coherence and then wrap logic with simple Spring annotations or XML+AspectJ to start transactions. An important feature of LLAPI is the ability to join transactions. This is a common outcome for SOA applications that need to reduce network traffic by aggregating data into single cache entries and then doing SOA service processing in the node holding the data. This results in the need to orchestrate transaction processing across multiple service calls. CloudTran has the capability to handle these “multi-client” transactions at speed with no loss in ACID properties. Developing software around an IMDG like Oracle Coherence is an important choice for today’s web-scale applications and services. But this introduces new architectural considerations to maintain scalability in light of increased network loads and data movement. Without using CloudTran, developers are faced with an incredibly difficult task to ensure data reliability, availability, performance, and scalability when working with an IMDG. Working with highly distributed data that is entirely volatile while stored in memory presents numerous edge cases where failures can result in data loss. The CloudTran product takes care of all of this, leaving developers with the confidence and peace of mind that all data is processed correctly. For those interested in evaluating the CloudTran product and IMDGs, take a look at this link for more information: http://www.CloudTran.com/downloadAPI.php, or, send your questions to [email protected]. WebLogic Partner Community For regular information become a member in the WebLogic Partner Community please visit: http://www.oracle.com/partners/goto/wls-emea ( OPN account required). If you need support with your account please contact the Oracle Partner Business Center. BlogTwitterLinkedInMixForumWiki Technorati Tags: Coherence,cloudtran,cache,WebLogic Community,Oracle,OPN,Jürgen Kress

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  • How to implement Template Inheritance (like Django?) in PHP5

    - by anonymous coward
    Is there an existing good example, or how should one approach creating a basic Template system (thinking MVC) that supports "Template Inheritance" in PHP5? For an example of what I define as Template Inheritance, refer to the Django (a Python framework for web development) Templates documentation: http://docs.djangoproject.com/en/dev/topics/templates/#id1 I especially like the idea of PHP itself being the "template language", though it's not necessarily a requirement. If listing existing solutions that implement "Template Inheritance", please try to form answers as individual systems, for the benefit of 'popular vote'.

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  • Grails benchmarks compared to other web MVC platform (Rails, Django, ASP MVC)?

    - by fabien7474
    I have been searching the web for recent benchmarks measuring Grails overall performance compared to its competitors (Rails, Django, ASP.NET MVC...), but I didn't find anything more recent than a 3 years-old article with obsolete grails version (0.5). See here and here. So, starting from grails 1.2, are there any more recent grails benchmarks you are aware of ? Or do you have your own performance tests for grails (compared to others if possible) ?

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  • has any tools easy to download or uploaed data from gae ..

    - by zjm1126
    i find this: http://aralbalkan.com/1784 but it is : Gaebar is an easy-to-use, standalone Django application that you can plug in to your existing Google App Engine Django or app-engine-patch-based Django applications on Google App Engine to give them datastore backup and restore functionality. my app is not based on django,so did you know any tools esay to do this . thanks

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  • Paperclip: delete attachment and "can't convert nil into String" error

    - by snitko
    I'm using Paperclip and here's what I do in the model to delete attachments: def before_save self.avatar = nil if @delete_avatar == 1.to_s end Works fine unless @delete_avatar flag is set when the user is actually uploading the image (so the model receives both params[:user][:avatar] and params[:user][:delete_avatar]. This results in the following error: TypeError: can't convert nil into String from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:40:in `dirname' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:40:in `flush_writes' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:38:in `each' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:38:in `flush_writes' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/attachment.rb:144:in `save' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/attachment.rb:162:in `destroy' from /Work/project/src/app/models/user.rb:72:in `before_save' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/callbacks.rb:347:in `send' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/callbacks.rb:347:in `callback' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/callbacks.rb:249:in `create_or_update' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/base.rb:2538:in `save_without_validation' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/validations.rb:1078:in `save_without_dirty' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/dirty.rb:79:in `save_without_transactions' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:229:in `send' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:229:in `with_transaction_returning_status' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/connection_adapters/abstract/database_statements.rb:136:in `transaction' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:182:in `transaction' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:228:in `with_transaction_returning_status' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:196:in `save' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:208:in `rollback_active_record_state!' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:196:in `save' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/base.rb:723:in `create' I assume it has something to do with the avatar.dirty? value because when it certainly is true when this happens. The question is, how do I totally reset the thing if there are changes to be saved and abort avatar upload when the flag is set?

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  • Paperclip: delete attachments and "can't convert nil into String" error

    - by snitko
    I'm using Paperclip and here's what I do in the model to delete attachments: def before_save self.avatar = nil if @delete_avatar == 1.to_s end Works fine unless @delete_avatar flag is set when the user is actually uploading the image (so the model receives both params[:user][:avatar] and params[:user][:delete_avatar]. This results in the following error: TypeError: can't convert nil into String from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:40:in `dirname' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:40:in `flush_writes' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:38:in `each' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/storage.rb:38:in `flush_writes' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/attachment.rb:144:in `save' from /Work/project/src/vendor/plugins/paperclip/lib/paperclip/attachment.rb:162:in `destroy' from /Work/project/src/app/models/user.rb:72:in `before_save' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/callbacks.rb:347:in `send' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/callbacks.rb:347:in `callback' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/callbacks.rb:249:in `create_or_update' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/base.rb:2538:in `save_without_validation' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/validations.rb:1078:in `save_without_dirty' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/dirty.rb:79:in `save_without_transactions' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:229:in `send' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:229:in `with_transaction_returning_status' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/connection_adapters/abstract/database_statements.rb:136:in `transaction' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:182:in `transaction' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:228:in `with_transaction_returning_status' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:196:in `save' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:208:in `rollback_active_record_state!' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/transactions.rb:196:in `save' from /usr/lib/ruby/gems/1.8/gems/activerecord-2.3.5/lib/active_record/base.rb:723:in `create' I assume it has something to do with the avatar.dirty? value because when it certainly is true when this happens. The question is, how do I totally reset the thing if there are changes to be saved and abort avatar upload when the flag is set?

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  • Optimizing tasks to reduce CPU in a trading application

    - by Joel
    Hello, I have designed a trading application that handles customers stocks investment portfolio. I am using two datastore kinds: Stocks - Contains unique stock name and its daily percent change. UserTransactions - Contains information regarding a specific purchase of a stock made by a user : the value of the purchase along with a reference to Stock for the current purchase. db.Model python modules: class Stocks (db.Model): stockname = db.StringProperty(multiline=True) dailyPercentChange=db.FloatProperty(default=1.0) class UserTransactions (db.Model): buyer = db.UserProperty() value=db.FloatProperty() stockref = db.ReferenceProperty(Stocks) Once an hour I need to update the database: update the daily percent change in Stocks and then update the value of all entities in UserTransactions that refer to that stock. The following python module iterates over all the stocks, update the dailyPercentChange property, and invoke a task to go over all UserTransactions entities which refer to the stock and update their value: Stocks.py # Iterate over all stocks in datastore for stock in Stocks.all(): # update daily percent change in datastore db.run_in_transaction(updateStockTxn, stock.key()) # create a task to update all user transactions entities referring to this stock taskqueue.add(url='/task', params={'stock_key': str(stock.key(), 'value' : self.request.get ('some_val_for_stock') }) def updateStockTxn(stock_key): #fetch the stock again - necessary to avoid concurrency updates stock = db.get(stock_key) stock.dailyPercentChange= data.get('some_val_for_stock') # I get this value from outside ... some more calculations here ... stock.put() Task.py (/task) # Amount of transaction per task amountPerCall=10 stock=db.get(self.request.get("stock_key")) # Get all user transactions which point to current stock user_transaction_query=stock.usertransactions_set cursor=self.request.get("cursor") if cursor: user_transaction_query.with_cursor(cursor) # Spawn another task if more than 10 transactions are in datastore transactions = user_transaction_query.fetch(amountPerCall) if len(transactions)==amountPerCall: taskqueue.add(url='/task', params={'stock_key': str(stock.key(), 'value' : self.request.get ('some_val_for_stock'), 'cursor': user_transaction_query.cursor() }) # Iterate over all transaction pointing to stock and update their value for transaction in transactions: db.run_in_transaction(updateUserTransactionTxn, transaction.key()) def updateUserTransactionTxn(transaction_key): #fetch the transaction again - necessary to avoid concurrency updates transaction = db.get(transaction_key) transaction.value= transaction.value* self.request.get ('some_val_for_stock') db.put(transaction) The problem: Currently the system works great, but the problem is that it is not scaling well… I have around 100 Stocks with 300 User Transactions, and I run the update every hour. In the dashboard, I see that the task.py takes around 65% of the CPU (Stock.py takes around 20%-30%) and I am using almost all of the 6.5 free CPU hours given to me by app engine. I have no problem to enable billing and pay for additional CPU, but the problem is the scaling of the system… Using 6.5 CPU hours for 100 stocks is very poor. I was wondering, given the requirements of the system as mentioned above, if there is a better and more efficient implementation (or just a small change that can help with the current implemntation) than the one presented here. Thanks!! Joel

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  • SQL SERVER – Concurrency Basics – Guest Post by Vinod Kumar

    - by pinaldave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions from SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. Learning is always fun when it comes to SQL Server and learning the basics again can be more fun. I did write about Transaction Logs and recovery over my blogs and the concept of simplifying the basics is a challenge. In the real world we always see checks and queues for a process – say railway reservation, banks, customer supports etc there is a process of line and queue to facilitate everyone. Shorter the queue higher is the efficiency of system (a.k.a higher is the concurrency). Every database does implement this using checks like locking, blocking mechanisms and they implement the standards in a way to facilitate higher concurrency. In this post, let us talk about the topic of Concurrency and what are the various aspects that one needs to know about concurrency inside SQL Server. Let us learn the concepts as one-liners: Concurrency can be defined as the ability of multiple processes to access or change shared data at the same time. The greater the number of concurrent user processes that can be active without interfering with each other, the greater the concurrency of the database system. Concurrency is reduced when a process that is changing data prevents other processes from reading that data or when a process that is reading data prevents other processes from changing that data. Concurrency is also affected when multiple processes are attempting to change the same data simultaneously. Two approaches to managing concurrent data access: Optimistic Concurrency Model Pessimistic Concurrency Model Concurrency Models Pessimistic Concurrency Default behavior: acquire locks to block access to data that another process is using. Assumes that enough data modification operations are in the system that any given read operation is likely affected by a data modification made by another user (assumes conflicts will occur). Avoids conflicts by acquiring a lock on data being read so no other processes can modify that data. Also acquires locks on data being modified so no other processes can access the data for either reading or modifying. Readers block writer, writers block readers and writers. Optimistic Concurrency Assumes that there are sufficiently few conflicting data modification operations in the system that any single transaction is unlikely to modify data that another transaction is modifying. Default behavior of optimistic concurrency is to use row versioning to allow data readers to see the state of the data before the modification occurs. Older versions of the data are saved so a process reading data can see the data as it was when the process started reading and not affected by any changes being made to that data. Processes modifying the data is unaffected by processes reading the data because the reader is accessing a saved version of the data rows. Readers do not block writers and writers do not block readers, but, writers can and will block writers. Transaction Processing A transaction is the basic unit of work in SQL Server. Transaction consists of SQL commands that read and update the database but the update is not considered final until a COMMIT command is issued (at least for an explicit transaction: marked with a BEGIN TRAN and the end is marked by a COMMIT TRAN or ROLLBACK TRAN). Transactions must exhibit all the ACID properties of a transaction. ACID Properties Transaction processing must guarantee the consistency and recoverability of SQL Server databases. Ensures all transactions are performed as a single unit of work regardless of hardware or system failure. A – Atomicity C – Consistency I – Isolation D- Durability Atomicity: Each transaction is treated as all or nothing – it either commits or aborts. Consistency: ensures that a transaction won’t allow the system to arrive at an incorrect logical state – the data must always be logically correct.  Consistency is honored even in the event of a system failure. Isolation: separates concurrent transactions from the updates of other incomplete transactions. SQL Server accomplishes isolation among transactions by locking data or creating row versions. Durability: After a transaction commits, the durability property ensures that the effects of the transaction persist even if a system failure occurs. If a system failure occurs while a transaction is in progress, the transaction is completely undone, leaving no partial effects on data. Transaction Dependencies In addition to supporting all four ACID properties, a transaction might exhibit few other behaviors (known as dependency problems or consistency problems). Lost Updates: Occur when two processes read the same data and both manipulate the data, changing its value and then both try to update the original data to the new value. The second process might overwrite the first update completely. Dirty Reads: Occurs when a process reads uncommitted data. If one process has changed data but not yet committed the change, another process reading the data will read it in an inconsistent state. Non-repeatable Reads: A read is non-repeatable if a process might get different values when reading the same data in two reads within the same transaction. This can happen when another process changes the data in between the reads that the first process is doing. Phantoms: Occurs when membership in a set changes. It occurs if two SELECT operations using the same predicate in the same transaction return a different number of rows. Isolation Levels SQL Server supports 5 isolation levels that control the behavior of read operations. Read Uncommitted All behaviors except for lost updates are possible. Implemented by allowing the read operations to not take any locks, and because of this, it won’t be blocked by conflicting locks acquired by other processes. The process can read data that another process has modified but not yet committed. When using the read uncommitted isolation level and scanning an entire table, SQL Server can decide to do an allocation order scan (in page-number order) instead of a logical order scan (following page pointers). If another process doing concurrent operations changes data and move rows to a new location in the table, the allocation order scan can end up reading the same row twice. Also can happen if you have read a row before it is updated and then an update moves the row to a higher page number than your scan encounters later. Performing an allocation order scan under Read Uncommitted can cause you to miss a row completely – can happen when a row on a high page number that hasn’t been read yet is updated and moved to a lower page number that has already been read. Read Committed Two varieties of read committed isolation: optimistic and pessimistic (default). Ensures that a read never reads data that another application hasn’t committed. If another transaction is updating data and has exclusive locks on data, your transaction will have to wait for the locks to be released. Your transaction must put share locks on data that are visited, which means that data might be unavailable for others to use. A share lock doesn’t prevent others from reading but prevents them from updating. Read committed (snapshot) ensures that an operation never reads uncommitted data, but not by forcing other processes to wait. SQL Server generates a version of the changed row with its previous committed values. Data being changed is still locked but other processes can see the previous versions of the data as it was before the update operation began. Repeatable Read This is a Pessimistic isolation level. Ensures that if a transaction revisits data or a query is reissued the data doesn’t change. That is, issuing the same query twice within a transaction cannot pickup any changes to data values made by another user’s transaction because no changes can be made by other transactions. However, this does allow phantom rows to appear. Preventing non-repeatable read is a desirable safeguard but cost is that all shared locks in a transaction must be held until the completion of the transaction. Snapshot Snapshot Isolation (SI) is an optimistic isolation level. Allows for processes to read older versions of committed data if the current version is locked. Difference between snapshot and read committed has to do with how old the older versions have to be. It’s possible to have two transactions executing simultaneously that give us a result that is not possible in any serial execution. Serializable This is the strongest of the pessimistic isolation level. Adds to repeatable read isolation level by ensuring that if a query is reissued rows were not added in the interim, i.e, phantoms do not appear. Preventing phantoms is another desirable safeguard, but cost of this extra safeguard is similar to that of repeatable read – all shared locks in a transaction must be held until the transaction completes. In addition serializable isolation level requires that you lock data that has been read but also data that doesn’t exist. Ex: if a SELECT returned no rows, you want it to return no. rows when the query is reissued. This is implemented in SQL Server by a special kind of lock called the key-range lock. Key-range locks require that there be an index on the column that defines the range of values. If there is no index on the column, serializable isolation requires a table lock. Gets its name from the fact that running multiple serializable transactions at the same time is equivalent of running them one at a time. Now that we understand the basics of what concurrency is, the subsequent blog posts will try to bring out the basics around locking, blocking, deadlocks because they are the fundamental blocks that make concurrency possible. Now if you are with me – let us continue learning for SQL Server Locking Basics. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Concurrency

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  • What's the difference between General Ledger Transfer Program, Create Accounting and Submit Accounting?

    - by Oracle_EBS
    In Release 12, the General Ledger Transfer Program is no longer used. Use Create Accounting or Submit Accounting instead. Submit Accounting spawns the Revenue Recognition Process. The Create Accounting program does not. So if you create transactions with rules, then you would want to run Submit Accounting Process to spawn Revenue Recognition to create the distribution rows, which Create Accounting is then spawned to process to the GL. Create Accounting Submit Accounting Short Name for Concurrent Program XLAACCPB ARACCPB Specific to Receivables No Yes Runs Revenue Recognition automatically No Yes Can be run real-time for one Transaction/Receipt at a time Yes No Spawns the following Programs 1) XLAACCPB module: Create Accounting 2) XLAACCUP module: Accounting Program 3) GLLEZL module: Journal Import 1) ARTERRPM module: Revenue Recognition Master Program 2) ARTERRPW module: Revenue Recognition with parallel workers - could be numerous 3) ARREVSWP - Revenue Contingency Analyzer 4) XLAACCPB module: Create Accounting 5) XLAACCUP module: Accounting Program 5) GLLEZL module: Journal Import Keep in mind, Reports owned by application 'Subledger Accounting' cannot be seen when running the report from Receivables responsibility. You may want to request your sysadmin to attach the following SLA reports/programs to your AR responsibility as you will need these for your AR closing process: XLAPEXRPT : Subledger Period Close Exception Report - shows transactions in status final, incomplete and unprocessed. XLAGLTRN : Transfer Journal Entries to GL - transfers transactions in final status and manually created transactions to GL To add reports/programs owned by application 'Subledger Accounting' (Subledger Period Close Exception Report and Transfer Journal Entries to GL_ Add to the request group as follows: Let's use Subledger Accounting Report XLATBRPT: Open Account Balances Listing Report as an example. Responsibility: System Administrator Navigation: Security > Responsibility > Define Query the name of your Receivables Responsibility and note the Request Group (ie. Receivables All) Navigation: Security > Responsibility > Request Query the Request Group Go to Request Zone and Click on Add Record Enter the following: Type: Program Name: Open Account Balances Listing Save Responsibility: Receivables Manager Navigation: Control > Requests > Run In the list of values you should now see 'Open Account Balances Listing' report References: Note: 748999.1 How to add reports for application subledger accounting to receivables responsibiilty Note: 759534.1 R12 ARGLTP General Ledger Transfer Program Errors Out Note: 1121944.1 Understanding and Troubleshooting Revenue Recognition in Oracle Receivables

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