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• Spikes in Socket Performance

  - by Harun Prasad

  We are facing random spikes in high throughput transaction processing system using sockets for IPC. Below is the setup used for the run: The client opens and closes new connection for every transaction, and there are 4 exchanges between the server and the client. We have disabled the TIME_WAIT, by setting the socket linger (SO_LINGER) option via getsockopt as we thought that the spikes were caused due to the sockets waiting in TIME_WAIT. There is no processing done for the transaction. Only messages are passed. OS used Centos 5.4 The average round trip time is around 3 milli seconds, but some times the round trip time ranges from 100 milli seconds to couple of seconds. Steps used for Execution and Measurement and output Starting the server $ python sockServerLinger.py /dev/null & Starting the client to post 1 million transactions to the server. And logs the time for a transaction in the client.log file. $ python sockClient.py 1000000 client.log Once the execution finishes the following command will show the execution time greater than 100 milliseconds in the format <line_number>:<execution_time>. $ grep -n "0.[1-9]" client.log | less Below is the example code for Server and Client. Server # File: sockServerLinger.py import socket, traceback,time import struct host = '' port = 9999 l_onoff = 1 l_linger = 0 lingeropt = struct.pack('ii', l_onoff, l_linger) s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER, lingeropt) s.bind((host, port)) s.listen(1) while 1: try: clientsock, clientaddr = s.accept() print "Got connection from", clientsock.getpeername() data = clientsock.recv(1024*1024*10) #print "asdasd",data numsent=clientsock.send(data) data1 = clientsock.recv(1024*1024*10) numsent=clientsock.send(data) ret = 1 while(ret>0): data1 = clientsock.recv(1024*1024*10) ret = len(data) clientsock.close() except KeyboardInterrupt: raise except: print traceback.print_exc() continue Client # File: sockClient.py import socket, traceback,sys import time i = 0 while 1: try: st = time.time() s = socket.socket(socket.AF_INET,socket.SOCK_STREAM) while (s.connect_ex(('127.0.0.1',9999)) != 0): continue numsent=s.send("asd"*1000) response = s.recv(6000) numsent=s.send("asd"*1000) response = s.recv(6000) i+=1 if i == int(sys.argv[1]): break except KeyboardInterrupt: raise except: print "in exec:::::::::::::",traceback.print_exc() continue print time.time() -st

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• mnesia primary key

  - by maryjanne

  Hi I have two tables one notes and one tag and I want to make the id from notes primary key to use it in the tag table, but I don't know where I do wrong. My notes id is generate from another table counter, with the function dirty_update_counter. My function for the id_notes from tag looks like this: Fun = fun() -> mnesia:write(#tag{ id_note =0}) end, mnesia:transaction(Fun). generate_Oid(TableName) when is_atom(TableName) -> F = fun() -> [Oid] = mnesia:read(tag, TableName, write), NewId = Oid#tag.id_note+1, New = Oid#tag{id_note = NewId}, mnesia:write(New), NewId end, mnesia:transaction(F). insert_n(N) when is_record(N, note) -> F = fun() -> {atomic, Id} = generate_Oid(note), New = N#note{id = Id}, mnesia:write(New), New end, mnesia:transaction(F). find_n(Id) when is_integer(Id) -> {atomic, [N]} = mnesia:transaction(fun() -> mnesia:read({note, Id}) end), N. But this function don't increment my field id_note from the table tag, despite the fact that in my note table, my id field is incremented from counter table. Thanks in advance for any help.

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• How to explain to users the advantages of dumb primary key?

  - by Hao

  Primary key attractiveness I have a boss(and also users) that wants primary key to be sophisticated/smart/attractive control number(sort of like Social Security number, or credit card number format) I just padded the primary key(in Views) with zeroes to appease their desire to make the control number sophisticated,smart and attractive. But they wanted it as: first 2 digits as client code, then 4 digits as year year, then last 4 digits as transaction number on that client on a given year, then reset the transaction number of client to 1 when next year flows. Each client's transaction starts with 1. e.g. WM20090001, WM20090002, BB2009001, WM20100001, BB20100001 But as I wanted to make things as simple as possible, I forgo embedding their suggested smartness in primary key, I just keep the primary key auto increments regardless of client and year. But to make it not dull-looking(they really are adamant to make the primary key as smart control number), I made the primary key appears to them smart, on view query, I put the client code and four digit year code on front of the eight-zero padded autoincrement key, i.e. WM200900000001. Sort of slug-like information on autoincremented primary key. Keeping primary key autoincrement regardless of any other information, we are able keep other potential side effects problem when they edit a record, for example, if they made a mistake of entering the transaction on WM, then they edit the client code to BB, if we use smart primary key, the primary keys of WM customer will have gaps in their control number. Or worse yet, instead of letting the control numbers have gaps/holes, the user will request that subsequent records of that gap should shift up to that gap and have their subsequent primary keys re-adjust(decremented). How do you deal with these user requests(reasonable or otherwise)? Do you yield to their request? Or just continue using dumb primary key and explain them the repercussions of having a very smart/sophisticated primary key and educate them the significant advantages of having a dumb primary key? P.S. quotable quote(http://articles.techrepublic.com.com/5100-10878_11-1044961.html): "If you hold your tongue the first time users ask what is for them a reasonable request, things will work a lot better in the end."

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• Nature of Lock is child table while deletion(sql server)

  - by Mubashar Ahmad

  Dear Devs From couple of days i am thinking of a following scenario Consider I have 2 tables with parent child relationship of kind one-to-many. On removal of parent row i have to delete the rows in child those are related to parents. simple right? i have to make a transaction scope to do above operation i can do this as following; (its psuedo code but i am doing this in c# code using odbc connection and database is sql server) begin transaction(read committed) Read all child where child.fk = p1 foreach(child) delete child where child.pk = cx delete parent where parent.pk = p1 commit trans OR begin transaction(read committed) delete all child where child.fk = p1 delete parent where parent.pk = p1 commit trans Now there are couple of questions in my mind Which one of above is better to use specially considering a scenario of real time system where thousands of other operations(select/update/delete/insert) are being performed within a span of seconds. does it ensure that no new child with child.fk = p1 will be added until transaction completes? If yes for 2nd question then how it ensures? do it take the table level locks or what. Is there any kind of Index locking supported by sql server if yes what it does and how it can be used. Regards Mubashar

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• Guice, JDBC and managing database connections

  - by pledge

  I'm looking to create a sample project while learning Guice which uses JDBC to read/write to a SQL database. However, after years of using Spring and letting it abstract away connection handling and transactions I'm struggling to work it our conceptually. I'd like to have a service which starts and stops a transaction and calls numerous repositories which reuse the same connection and participate in the same transaction. My questions are: Where do I create my Datasource? How do I give the repositories access to the connection? (ThreadLocal?) Best way to manage the transaction (Creating an Interceptor for an annotation?) The code below shows how I would do this in Spring. The JdbcOperations injected into each repository would have access to the connection associated with the active transaction. I haven't been able to find many tutorials which cover this, beyond ones which show creating interceptors for transactions. I am happy with continuing to use Spring as it is working very well in my projects, but I'd like to know how to do this in pure Guice and JBBC (No JPA/Hibernate/Warp/Reusing Spring) @Service public class MyService implements MyInterface { @Autowired private RepositoryA repositoryA; @Autowired private RepositoryB repositoryB; @Autowired private RepositoryC repositoryC; @Override @Transactional public void doSomeWork() { this.repositoryA.someInsert(); this.repositoryB.someUpdate(); this.repositoryC.someSelect(); } } @Repository public class MyRepositoryA implements RepositoryA { @Autowired private JdbcOperations jdbcOperations; @Override public void someInsert() { //use jdbcOperations to perform an insert } } @Repository public class MyRepositoryB implements RepositoryB { @Autowired private JdbcOperations jdbcOperations; @Override public void someUpdate() { //use jdbcOperations to perform an update } } @Repository public class MyRepositoryC implements RepositoryC { @Autowired private JdbcOperations jdbcOperations; @Override public String someSelect() { //use jdbcOperations to perform a select and use a RowMapper to produce results return "select result"; } }

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• Lost Update Anomaly in Sql Server Update Command

  - by Javed

  Hi, I am very much confused. I have a transaction in ReadCommitted Isolation level. Among other things I am also updating a counter value in it, something similar to below: Update tblCount set counter = counter + 1 My application is a desktop application and this transaction happens to occur quite frequently and concurrently. We recently noticed an error that sometimes the counter value doesn't get updated or is missed. We also insert one record on each counter update so we are sure that records have been inserted but somehow counter fails to update. This happens once in 2000 simulaneous transactions. I seriously doubt it is a lost update anomaly I am facing but if you look at the command above, it's just update the counter from its own value: if I have started a transaction and the transaction has reached this statement, it should have locked the row. This should not cause lost update, but it's happening somehow. Is the thing that this update command works in two parts? Like first it reads the counter value (during which it doesn't get the exclusive lock) and then writes the new calculated value (when it does get an exclusive lock)? Please help, I have got really confused.

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• SqlBulkCopy causes Deadlock on SQL Server 2000.

  - by megatoast

  I have a customized data import executable in .NET 3.5 which the SqlBulkCopy to basically do faster inserts on large amounts of data. The app basically takes an input file, massages the data and bulk uploads it into a SQL Server 2000. It was written by a consultant who was building it with a SQL 2008 database environment. Would that env difference be causing this? SQL 2000 does have the bcp utility which is what BulkCopy is based on. So, When we ran this, it triggered a Deadlock error. Error details: Transaction (Process ID 58) was deadlocked on lock resources with another process and has been chosen as the deadlock victim. Rerun the transaction. I've tried numerous ways to try to resolve it. like temporarily setting the connection string variable MultipleActiveResultSets=true, which wasn't ideal, but it still gives a Deadlock error. I also made sure it wasn't a connection time out problem. here's the function. Any advice? /// <summary> /// Bulks the insert. /// </summary> public void BulkInsert(string destinationTableName, DataTable dataTable) { SqlBulkCopy bulkCopy; if (this.Transaction != null) { bulkCopy = new SqlBulkCopy ( this.Connection, SqlBulkCopyOptions.TableLock, this.Transaction ); } else { bulkCopy = new SqlBulkCopy ( this.Connection.ConnectionString, SqlBulkCopyOptions.TableLock | SqlBulkCopyOptions.UseInternalTransaction ); } bulkCopy.ColumnMappings.Add("FeeScheduleID", "FeeScheduleID"); bulkCopy.ColumnMappings.Add("ProcedureID", "ProcedureID"); bulkCopy.ColumnMappings.Add("AltCode", "AltCode"); bulkCopy.ColumnMappings.Add("AltDescription", "AltDescription"); bulkCopy.ColumnMappings.Add("Fee", "Fee"); bulkCopy.ColumnMappings.Add("Discount", "Discount"); bulkCopy.ColumnMappings.Add("Comment", "Comment"); bulkCopy.ColumnMappings.Add("Description", "Description"); bulkCopy.BatchSize = dataTable.Rows.Count; bulkCopy.DestinationTableName = destinationTableName; bulkCopy.WriteToServer(dataTable); bulkCopy = null; }

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• MySQL return Deadlock with insert row and FK is locked 'for update'

  - by constantin-slednev

  Hello developers! I get deadlock error in my mysql transaction. The simple example of my situation: Thread1 > set autocommit=0; Query OK, 0 rows affected (0.00 sec) Thread1 > SET TRANSACTION ISOLATION LEVEL READ COMMITTED; Query OK, 0 rows affected (0.00 sec) Thread1 > SELECT * FROM A WHERE ID=1000 FOR UPDATE; 1 row in set (0.00 sec) Thread2 > set autocommit=0; Query OK, 0 rows affected (0.00 sec) Thread2 > SET TRANSACTION ISOLATION LEVEL READ COMMITTED; Query OK, 0 rows affected (0.00 sec) Thread2 > INSERT INTO B (AID, NAME) VALUES (1000, 'Hello world'); SLEEP Query OK, 1 row affected (4.99 sec) Thread1 > INSERT INTO B (AID, NAME) VALUES (1000, 'Hello world2'); ERROR 1213 (40001): Deadlock found when trying to get lock; try restarting transaction B.AID -> FK -> A.ID I see three solutions: catch deadlock error in code and retry query. use innodb_locks_unsafe_for_binlog in my.cnf lock (for update) table A in Thread2 before insert Can you give me more solutions ? Current solutions do not fit me.

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• Delphi Application using COMMIT and ROLLBACK for Multiple SQL Updates

  - by Matt

  Is it possible to use the SQL BEGIN TRANSACTION, COMMIT TRANSACTION, ROLLBACK TRANSACTION when embedding SQL Queries into an application with mutiple calls to the SQL for Table Updates. For example I have the following code: Q.SQL.ADD(<UPDATE A RECORD>); Q.ExecSQL; Q.Close; Q.SQL.Clear; Q.SQL.ADD(<Select Some Data>); Q.Open; Set Some Variables Q.Close; Q.SQL.Clear; Q.SQL.ADD(<UPDATE A RECORD>); Q.ExecSQL; What I would like to do is if the second update fails I want to roll back the first transaction. If I set a unique notation for the BEGIN, COMMIT, ROLLBACK so as to specify what is being committed or rolled back, is it feasible. i.e. before the first Update specify BEGIN TRANSACTION_A then after the last update specify COMMIT TRANSACTION_A I hope that makes sense. If I was doing this in a SQL Stored Procedure then I would be able to specify this at the start and end of the procedure, but I have had to break the code down into manageable chunks due to process blocks and deadlocks on a heavy loaded SQL Server.

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• Microsoft Detours - DetourUpdateThread?

  - by pault543

  Hi, I have a few quick questions about the Microsoft Detours Library. I have used it before (successfully), but I just had a thought about this function: LONG DetourUpdateThread(HANDLE hThread); I read elsewhere that this function will actually suspend the thread until the transaction completes. This seems odd since most sample code calls: DetourUpdateThread(GetCurrentThread()); Anyway, apparently this function "enlists" threads so that, when the transaction commits (and the detours are made), their instruction pointers are modified if they lie "within the rewritten code in either the target function or the trampoline function." My questions are: When the transaction commits, is the current thread's instruction pointer going to be within the DetourTransactionCommit function? If so, why should we bother enlisting it to be updated? Also, if the enlisted threads are suspended, how can the current thread continue executing (given that most sample code calls DetourUpdateThread(GetCurrentThread());)? Finally, could you suspend all threads for the current process, avoiding race conditions (considering that threads could be getting created and destroyed at any time)? Perhaps this is done when the transaction begins? This would allow us to enumerate threads more safely (as it seems less likely that new threads could be created), although what about CreateRemoteThread()? Thanks, Paul For reference, here is an extract from the simple sample: // DllMain function attaches and detaches the TimedSleep detour to the // Sleep target function. The Sleep target function is referred to // through the TrueSleep target pointer. BOOL WINAPI DllMain(HINSTANCE hinst, DWORD dwReason, LPVOID reserved) { if (dwReason == DLL_PROCESS_ATTACH) { DetourTransactionBegin(); DetourUpdateThread(GetCurrentThread()); DetourAttach(&(PVOID&)TrueSleep, TimedSleep); DetourTransactionCommit(); } else if (dwReason == DLL_PROCESS_DETACH) { DetourTransactionBegin(); DetourUpdateThread(GetCurrentThread()); DetourDetach(&(PVOID&)TrueSleep, TimedSleep); DetourTransactionCommit(); } return TRUE; }

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• Filling combobox from database by using hibernate in Java

  - by denny

  Heyy; I am developing a small swing based application with hibernate in java. And I want fill combobox from database coloumn.How i can do that ? And I don't know in where(under initComponents, buttonActionPerformd) i need to do. For saving i'am using jbutton and it's code is here : private void jButton1ActionPerformed(java.awt.event.ActionEvent evt) { int idd=Integer.parseInt(jTextField1.getText()); String name=jTextField2.getText(); String description=jTextField3.getText(); Session session = null; SessionFactory sessionFactory = new Configuration().configure() .buildSessionFactory(); session = sessionFactory.openSession(); Transaction transaction = session.getTransaction(); try { ContactGroup con = new ContactGroup(); con.setId(idd); con.setGroupName(name); con.setGroupDescription(description); transaction.begin(); session.save(con); transaction.commit(); } catch (Exception e) { e.printStackTrace(); } finally{ session.close(); } }

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• SQL: Need help with query construction.

  - by Geeknidas

  Hi Guys, I am relatively new with sql and I need some help with some basic query construction. Problem: To retrieve the number of orders and the customer id from a table based on a set of parameters. I want to write a query to figure out the number of orders under each customer (Column: Customerid) along with the CustomerID where the number of orders should be greater or equal to 10 and the status of the order should be Active. Moreover, I also want to know the first transaction date of an order belonging to each customerid. Table Description: product_orders Orderid CustomerId Transaction_date Status ------- ---------- ---------------- ------- 1 23 2-2-10 Active 2 22 2-3-10 Active 3 23 2-3-10 Deleted 4 23 2-3-10 Active Query that I have written: select count(*), customerid from product_orders where status = 'Active' GROUP BY customerid ORDER BY customerid; The above statement gives me the sum of all order under a customer id but does not fulfil the condition of atleast 10 orders. I donot know how to display the first transaction date along with the order under a customerid (status: could be active or delelted doesn't matter) Ideal solutions should look like: Total Orders CustomerID Transaction Date (the first transaction date) ------------ ---------- ---------------- 11 23 1-2-10 Thanks in advance. I hope you guys would be kind enough to stop by and help me out. Cheers, Leonidas

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• HTML5 Database Transactions

  - by jiewmeng

  i am wondering abt the example W3C Offline Web Apps the example function renderNotes() { db.transaction(function(tx) { tx.executeSql('CREATE TABLE IF NOT EXISTS Notes(title TEXT, body TEXT)', []); tx.executeSql(‘SELECT * FROM Notes’, [], function(tx, rs) { for(var i = 0; i < rs.rows.length; i++) { renderNote(rs.rows[i]); } }); }); } has the create table before the 'main' executeSql(). will it be better if i do something like $(function() { // create table 1st db.transaction(function(tx) { tx.executeSql('CREATE TABLE IF NOT EXISTS Notes(title TEXT, body TEXT)', []); }); // when i execute say to select/modify data, i just do the actual action db.transaction(function(tx) { tx.executeSql(‘SELECT * FROM Notes’, [], function(tx, rs) { ... } }); db.transaction(function(tx) { tx.executeSql(‘INSERT ...’, [], function(tx, rs) { ... } }); }) i was thinking i don't need to keep repeating the CREATE IF NOT EXISTS right?

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• Return if remote stored procedure fails

  - by njk

  I am in the process of creating a stored procedure. This stored procedure runs local as well as external stored procedures. For simplicity, I'll call the local server [LOCAL] and the remote server [REMOTE]. USE [LOCAL] GO SET ANSI_NULLS ON GO SET QUOTED_IDENTIFIER ON GO ALTER PROCEDURE [dbo].[monthlyRollUp] AS SET NOCOUNT, XACT_ABORT ON BEGIN TRY EXEC [REOMTE].[DB].[table].[sp] --This transaction should only begin if the remote procedure does not fail BEGIN TRAN EXEC [LOCAL].[DB].[table].[sp1] COMMIT BEGIN TRAN EXEC [LOCAL].[DB].[table].[sp2] COMMIT BEGIN TRAN EXEC [LOCAL].[DB].[table].[sp3] COMMIT BEGIN TRAN EXEC [LOCAL].[DB].[table].[sp4] COMMIT END TRY BEGIN CATCH -- Insert error into log table INSERT INTO [dbo].[log_table] (stamp, errorNumber, errorSeverity, errorState, errorProcedure, errorLine, errorMessage) SELECT GETDATE(), ERROR_NUMBER(), ERROR_SEVERITY(), ERROR_STATE(), ERROR_PROCEDURE(), ERROR_LINE(), ERROR_MESSAGE() END CATCH GO When using a transaction on the remote procedure, it throws this error: OLE DB provider ... returned message "The partner transaction manager has disabled its support for remote/network transactions.". I get that I'm unable to run a transaction locally for a remote procedure. How can I ensure that the this procedure will exit and rollback if any part of the procedure fails?

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• mysql row locking via php

  - by deezee

  I am helping a friend with a web based form that is for their business. I am trying to get it ready to handle multiple users. I have set it up so that just before the record is displayed for editing I am locking the record with the following code. $query = "START TRANSACTION;"; mysql_query($query); $query = "SELECT field FROM table WHERE ID = \"$value\" FOR UPDATE;"; mysql_query($query); (okay that is greatly simplified but that is the essence of the mysql) It does not appear to be working. However, when I go directly to mysql from the command line, logging in with the same user and execute START TRANSACTION; SELECT field FROM table WHERE ID = "40" FOR UPDATE; I can effectively block the web form from accessing record "40" and get the timeout warning. I have tried using BEGIN instead of START TRANSACTION. I have tried doing SET AUTOCOMMIT=0 first and starting the transaction after locking but I cannot seem to lock the row from the PHP code. Since I can lock the row from the command line I do not think there is a problem with how the database is set up. I am really hoping that there is some simple something that I have missed in my reading. FYI, I am developing on XAMPP version 1.7.3 which has Apache 2.2.14, MySQL 5.1.41 and PHP 5.3.1. Thanks in advance. This is my first time posting but I have gleaned alot of knowledge from this site in the past.

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• DO NOT BOTHER. THE PROBLEM IS NOT TRANSACTIONS. DUNNO HOW TO CLOSE / DELETE

  - by matti

  THE PROBLEM IS SOLVED. I DUNNO HOW TO CLOSE THIS QUESTION. ANOTHER SERVICE IS PROPABLY RUNNING AT ALMOST SAME SYNC THAT DOES THE THING. MY WORK "MATE" WILL HEAR A THING OR TWO IN THE MORNING. I have following code in windows service: data is in the dataset and it has been populated earlier and then rows are updated and inserted to dataset tables before calling code below. using (dataSetTxn = _cnctn.BeginTransaction()) { try { _somePlanAdptr.UpdateCommand.Transaction = dataSetTxn; _someLogAdptr.InsertCommand.Transaction = dataSetTxn; _someLinkAdptr.InsertCommand.Transaction = dataSetTxn; _someDataAdptr.InsertCommand.Transaction = dataSetTxn; _log.WriteDebug("Updating SomePlanAction"); _somePlanAdptr.Update(_ofDataSet, "SomePlanAction"); _log.WriteDebug(string.Format("Inserting {0} rows to SomeLog")); //error _someLogAdptr.Update(_ofDataSet, "SomeLog"); _log.WriteDebug(string.Format("Updating SomeLink with {0} rows", _ofDataSet.Tables["SomeLink"].Rows.Count)); _someLinkAdptr.Update(_ofDataSet, "SomeLink"); _log.WriteDebug(string.Format("Updating SomeData with {0} rows", _ofDataSet.Tables["SomeData"].Rows.Count)); _someDataAdptr.Update(_ofDataSet, "SomeData"); _log.WriteDebug("Commiting all changes to database."); dataSetTxn.Commit(); } catch (Exception e) { _log.WriteError("Updating database failed -> rollback", e); if (dataSetTxn != null && _cnctn.State == ConnectionState.Open) dataSetTxn.Rollback(); throw e; } } so one of the lines causes an exception log.WriteDebug(string.Format("Inserting {0} rows to SomeLog")); //error still the first adapter's data is updated to database. when debugged the data is not yet updated until the method that calls all (including this bit) exits. The method that does all is timed with System.Threading.Timer. Thanks & BR -Matti

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• How to get around LazyInitializationException in scheduled jobs?

  - by Shreerang

  I am working on a J2EE server application which is deployed on Tomcat. I use Spring source as MVC framework and Hibernate as ORM provider. My object model has lot of Lazy relationships (dependent objects are fetched on request). The high level design is like Service level methods call a few DAO methods to perform database operation. The service method is called either from the Flex UI or as a scheduled job. When it is called from Flex UI, the service method works fine i.e. it fetches some objects using DAO methods and even Lazy loading works. This is possible by the use of OpenSessionInViewFilter configured with the UI servlet. But when the same service method is called as scheduled Job, it gives LazyInitializationException. I can not configure OpenSessionInViewFilter because there is no servlet or UI request associated with that. I tried configuring Transaction around the scheduled job method so that service method starts a transaction and all the DAO methods participate in that same transaction, hoping that the transaction will remain active and hibernate session will be available. But it does not work. Please suggest if anyone has ever been able to get such a configuration working. If needed, I can post the Hibernate configuration and log messages. Thanks a lot for help! Shreerang

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• How to subscribe to the free Oracle Linux errata yum repositories

  - by Lenz Grimmer

  Now that updates and errata for Oracle Linux are available for free (both as in beer and freedom), here's a quick HOWTO on how to subscribe your Oracle Linux system to the newly added yum repositories on our public yum server, assuming that you just installed Oracle Linux from scratch, e.g. by using the installation media (ISO images) available from the Oracle Software Delivery Cloud You need to download the appropriate yum repository configuration file from the public yum server and install it in the yum repository directory. For Oracle Linux 6, the process would look as follows: as the root user, run the following command: [root@oraclelinux62 ~]# wget http://public-yum.oracle.com/public-yum-ol6.repo \ -P /etc/yum.repos.d/ --2012-03-23 00:18:25-- http://public-yum.oracle.com/public-yum-ol6.repo Resolving public-yum.oracle.com... 141.146.44.34 Connecting to public-yum.oracle.com|141.146.44.34|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 1461 (1.4K) [text/plain] Saving to: “/etc/yum.repos.d/public-yum-ol6.repo” 100%[=================================================>] 1,461 --.-K/s in 0s 2012-03-23 00:18:26 (37.1 MB/s) - “/etc/yum.repos.d/public-yum-ol6.repo” saved [1461/1461] For Oracle Linux 5, the file name would be public-yum-ol5.repo in the URL above instead. The "_latest" repositories that contain the errata packages are already enabled by default — you can simply pull in all available updates by running "yum update" next: [root@oraclelinux62 ~]# yum update Loaded plugins: refresh-packagekit, security ol6_latest | 1.1 kB 00:00 ol6_latest/primary | 15 MB 00:42 ol6_latest 14643/14643 Setting up Update Process Resolving Dependencies --> Running transaction check ---> Package at.x86_64 0:3.1.10-43.el6 will be updated ---> Package at.x86_64 0:3.1.10-43.el6_2.1 will be an update ---> Package autofs.x86_64 1:5.0.5-39.el6 will be updated ---> Package autofs.x86_64 1:5.0.5-39.el6_2.1 will be an update ---> Package bind-libs.x86_64 32:9.7.3-8.P3.el6 will be updated ---> Package bind-libs.x86_64 32:9.7.3-8.P3.el6_2.2 will be an update ---> Package bind-utils.x86_64 32:9.7.3-8.P3.el6 will be updated ---> Package bind-utils.x86_64 32:9.7.3-8.P3.el6_2.2 will be an update ---> Package cvs.x86_64 0:1.11.23-11.el6_0.1 will be updated ---> Package cvs.x86_64 0:1.11.23-11.el6_2.1 will be an update [...] ---> Package yum.noarch 0:3.2.29-22.0.1.el6 will be updated ---> Package yum.noarch 0:3.2.29-22.0.2.el6_2.2 will be an update ---> Package yum-plugin-security.noarch 0:1.1.30-10.el6 will be updated ---> Package yum-plugin-security.noarch 0:1.1.30-10.0.1.el6 will be an update ---> Package yum-utils.noarch 0:1.1.30-10.el6 will be updated ---> Package yum-utils.noarch 0:1.1.30-10.0.1.el6 will be an update --> Finished Dependency Resolution Dependencies Resolved ===================================================================================== Package Arch Version Repository Size ===================================================================================== Installing: kernel x86_64 2.6.32-220.7.1.el6 ol6_latest 24 M kernel-uek x86_64 2.6.32-300.11.1.el6uek ol6_latest 21 M kernel-uek-devel x86_64 2.6.32-300.11.1.el6uek ol6_latest 6.3 M Updating: at x86_64 3.1.10-43.el6_2.1 ol6_latest 60 k autofs x86_64 1:5.0.5-39.el6_2.1 ol6_latest 470 k bind-libs x86_64 32:9.7.3-8.P3.el6_2.2 ol6_latest 839 k bind-utils x86_64 32:9.7.3-8.P3.el6_2.2 ol6_latest 178 k cvs x86_64 1.11.23-11.el6_2.1 ol6_latest 711 k [...] xulrunner x86_64 10.0.3-1.0.1.el6_2 ol6_latest 12 M yelp x86_64 2.28.1-13.el6_2 ol6_latest 778 k yum noarch 3.2.29-22.0.2.el6_2.2 ol6_latest 987 k yum-plugin-security noarch 1.1.30-10.0.1.el6 ol6_latest 36 k yum-utils noarch 1.1.30-10.0.1.el6 ol6_latest 94 k Transaction Summary ===================================================================================== Install 3 Package(s) Upgrade 96 Package(s) Total download size: 173 M Is this ok [y/N]: y Downloading Packages: (1/99): at-3.1.10-43.el6_2.1.x86_64.rpm | 60 kB 00:00 (2/99): autofs-5.0.5-39.el6_2.1.x86_64.rpm | 470 kB 00:01 (3/99): bind-libs-9.7.3-8.P3.el6_2.2.x86_64.rpm | 839 kB 00:02 (4/99): bind-utils-9.7.3-8.P3.el6_2.2.x86_64.rpm | 178 kB 00:00 [...] (96/99): yelp-2.28.1-13.el6_2.x86_64.rpm | 778 kB 00:02 (97/99): yum-3.2.29-22.0.2.el6_2.2.noarch.rpm | 987 kB 00:03 (98/99): yum-plugin-security-1.1.30-10.0.1.el6.noarch.rpm | 36 kB 00:00 (99/99): yum-utils-1.1.30-10.0.1.el6.noarch.rpm | 94 kB 00:00 ------------------------------------------------------------------------------------- Total 306 kB/s | 173 MB 09:38 warning: rpmts_HdrFromFdno: Header V3 RSA/SHA256 Signature, key ID ec551f03: NOKEY Retrieving key from http://public-yum.oracle.com/RPM-GPG-KEY-oracle-ol6 Importing GPG key 0xEC551F03: Userid: "Oracle OSS group (Open Source Software group) " From : http://public-yum.oracle.com/RPM-GPG-KEY-oracle-ol6 Is this ok [y/N]: y Running rpm_check_debug Running Transaction Test Transaction Test Succeeded Running Transaction Updating : yum-3.2.29-22.0.2.el6_2.2.noarch 1/195 Updating : xorg-x11-server-common-1.10.4-6.el6_2.3.x86_64 2/195 Updating : kernel-uek-headers-2.6.32-300.11.1.el6uek.x86_64 3/195 Updating : 12:dhcp-common-4.1.1-25.P1.el6_2.1.x86_64 4/195 Updating : tzdata-java-2011n-2.el6.noarch 5/195 Updating : tzdata-2011n-2.el6.noarch 6/195 Updating : glibc-common-2.12-1.47.el6_2.9.x86_64 7/195 Updating : glibc-2.12-1.47.el6_2.9.x86_64 8/195 [...] Cleanup : kernel-firmware-2.6.32-220.el6.noarch 191/195 Cleanup : kernel-uek-firmware-2.6.32-300.3.1.el6uek.noarch 192/195 Cleanup : glibc-common-2.12-1.47.el6.x86_64 193/195 Cleanup : glibc-2.12-1.47.el6.x86_64 194/195 Cleanup : tzdata-2011l-4.el6.noarch 195/195 Installed: kernel.x86_64 0:2.6.32-220.7.1.el6 kernel-uek.x86_64 0:2.6.32-300.11.1.el6uek kernel-uek-devel.x86_64 0:2.6.32-300.11.1.el6uek Updated: at.x86_64 0:3.1.10-43.el6_2.1 autofs.x86_64 1:5.0.5-39.el6_2.1 bind-libs.x86_64 32:9.7.3-8.P3.el6_2.2 bind-utils.x86_64 32:9.7.3-8.P3.el6_2.2 cvs.x86_64 0:1.11.23-11.el6_2.1 dhclient.x86_64 12:4.1.1-25.P1.el6_2.1 [...] xorg-x11-server-common.x86_64 0:1.10.4-6.el6_2.3 xulrunner.x86_64 0:10.0.3-1.0.1.el6_2 yelp.x86_64 0:2.28.1-13.el6_2 yum.noarch 0:3.2.29-22.0.2.el6_2.2 yum-plugin-security.noarch 0:1.1.30-10.0.1.el6 yum-utils.noarch 0:1.1.30-10.0.1.el6 Complete! At this point, your system is fully up to date. As the kernel was updated as well, a reboot is the recommended next action. If you want to install the latest release of the Unbreakable Enterprise Kernel Release 2 as well, you need to edit the .repo file and enable the respective yum repository (e.g. "ol6_UEK_latest" for Oracle Linux 6 and "ol5_UEK_latest" for Oracle Linux 5) manually, by setting enabled to "1". The next yum update run will download and install the second release of the Unbreakable Enterprise Kernel, which will be enabled after the next reboot. -Lenz

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• SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

  - by Brian

  Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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• How to archive data from a table to a local or remote database in SQL 2005 and SQL 2008

  - by simonsabin

  Often you have the need to archive data from a table. This leads to a number of challenges 1. How can you do it without impacting users 2. How can I make it transactionally consistent, i.e. the data I put in the archive is the data I remove from the main table 3. How can I get it to perform well Points 1 is very much tied to point 3. If it doesn't perform well then the delete of data is going to cause lots of locks and thus potentially blocking. For points 1 and 3 refer to my previous posts DELETE-TOP-x-rows-avoiding-a-table-scan and UPDATE-and-DELETE-TOP-and-ORDER-BY---Part2. In essence you need to be removing small chunks of data from your table and you want to do that avoiding a table scan. So that deals with the delete approach but archiving is about inserting that data somewhere else. Well in SQL 2008 they introduced a new feature INSERT over DML (Data Manipulation Language, i.e. SQL statements that change data), or composable DML. The ability to nest DML statements within themselves, so you can past the results of an insert to an update to a merge. I've mentioned this before here SQL-Server-2008---MERGE-and-optimistic-concurrency. This feature is currently limited to being able to consume the results of a DML statement in an INSERT statement. There are many restrictions which you can find here http://msdn.microsoft.com/en-us/library/ms177564.aspx look for the section "Inserting Data Returned From an OUTPUT Clause Into a Table" Even with the restrictions what we can do is consume the OUTPUT from a DELETE and INSERT the results into a table in another database. Note that in BOL it refers to not being able to use a remote table, remote means a table on another SQL instance. To show this working use this SQL to setup two databases foo and fooArchive create database foo go --create the source table fred in database foo select * into foo..fred from sys.objects go create database fooArchive go if object_id('fredarchive',DB_ID('fooArchive')) is null begin     select getdate() ArchiveDate,* into fooArchive..FredArchive from sys.objects where 1=2       end go And then we can use this simple statement to archive the data insert into fooArchive..FredArchive select getdate(),d.* from (delete top (1)         from foo..Fred         output deleted.*) d         go In this statement the delete can be any delete statement you wish so if you are deleting by ids or a range of values then you can do that. Refer to the DELETE-TOP-x-rows-avoiding-a-table-scan post to ensure that your delete is going to perform. The last thing you want to do is to perform 100 deletes each with 5000 records for each of those deletes to do a table scan. For a solution that works for SQL2005 or if you want to archive to a different server then you can use linked servers or SSIS. This example shows how to do it with linked servers. [ONARC-LAP03] is the source server. begin transaction insert into fooArchive..FredArchive select getdate(),d.* from openquery ([ONARC-LAP03],'delete top (1)                     from foo..Fred                     output deleted.*') d commit transaction and to prove the transactions work try, you should get the same number of records before and after. select (select count(1) from foo..Fred) fred        ,(select COUNT(1) from fooArchive..FredArchive ) fredarchive   begin transaction insert into fooArchive..FredArchive select getdate(),d.* from openquery ([ONARC-LAP03],'delete top (1)                     from foo..Fred                     output deleted.*') d rollback transaction   select (select count(1) from foo..Fred) fred        ,(select COUNT(1) from fooArchive..FredArchive ) fredarchive The transactions are very important with this solution. Look what happens when you don't have transactions and an error occurs   select (select count(1) from foo..Fred) fred        ,(select COUNT(1) from fooArchive..FredArchive ) fredarchive   insert into fooArchive..FredArchive select getdate(),d.* from openquery ([ONARC-LAP03],'delete top (1)                     from foo..Fred                     output deleted.*                     raiserror (''Oh doo doo'',15,15)') d                     select (select count(1) from foo..Fred) fred        ,(select COUNT(1) from fooArchive..FredArchive ) fredarchive Before running this think what the result would be. I got it wrong. What seems to happen is that the remote query is executed as a transaction, the error causes that to rollback. However the results have already been sent to the client and so get inserted into the

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• Integrating Coherence & Java EE 6 Applications using ActiveCache

  - by Ricardo Ferreira

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

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• Master-slave vs. peer-to-peer archictecture: benefits and problems

  - by Ashok_Ora

  Normal 0 false false false EN-US X-NONE X-NONE Almost two decades ago, I was a member of a database development team that introduced adaptive locking. Locking, the most popular concurrency control technique in database systems, is pessimistic. Locking ensures that two or more conflicting operations on the same data item don’t “trample” on each other’s toes, resulting in data corruption. In a nutshell, here’s the issue we were trying to address. In everyday life, traffic lights serve the same purpose. They ensure that traffic flows smoothly and when everyone follows the rules, there are no accidents at intersections. As I mentioned earlier, the problem with typical locking protocols is that they are pessimistic. Regardless of whether there is another conflicting operation in the system or not, you have to hold a lock! Acquiring and releasing locks can be quite expensive, depending on how many objects the transaction touches. Every transaction has to pay this penalty. To use the earlier traffic light analogy, if you have ever waited at a red light in the middle of nowhere with no one on the road, wondering why you need to wait when there’s clearly no danger of a collision, you know what I mean. The adaptive locking scheme that we invented was able to minimize the number of locks that a transaction held, by detecting whether there were one or more transactions that needed conflicting eyou could get by without holding any lock at all. In many “well-behaved” workloads, there are few conflicts, so this optimization is a huge win. If, on the other hand, there are many concurrent, conflicting requests, the algorithm gracefully degrades to the “normal” behavior with minimal cost. We were able to reduce the number of lock requests per TPC-B transaction from 178 requests down to 2! Wow! This is a dramatic improvement in concurrency as well as transaction latency. The lesson from this exercise was that if you can identify the common scenario and optimize for that case so that only the uncommon scenarios are more expensive, you can make dramatic improvements in performance without sacrificing correctness. So how does this relate to the architecture and design of some of the modern NoSQL systems? NoSQL systems can be broadly classified as master-slave sharded, or peer-to-peer sharded systems. NoSQL systems with a peer-to-peer architecture have an interesting way of handling changes. Whenever an item is changed, the client (or an intermediary) propagates the changes synchronously or asynchronously to multiple copies (for availability) of the data. Since the change can be propagated asynchronously, during some interval in time, it will be the case that some copies have received the update, and others haven’t. What happens if someone tries to read the item during this interval? The client in a peer-to-peer system will fetch the same item from multiple copies and compare them to each other. If they’re all the same, then every copy that was queried has the same (and up-to-date) value of the data item, so all’s good. If not, then the system provides a mechanism to reconcile the discrepancy and to update stale copies. So what’s the problem with this? There are two major issues: First, IT’S HORRIBLY PESSIMISTIC because, in the common case, it is unlikely that the same data item will be updated and read from different locations at around the same time! For every read operation, you have to read from multiple copies. That’s a pretty expensive, especially if the data are stored in multiple geographically separate locations and network latencies are high. Second, if the copies are not all the same, the application has to reconcile the differences and propagate the correct value to the out-dated copies. This means that the application program has to handle discrepancies in the different versions of the data item and resolve the issue (which can further add to cost and operation latency). Resolving discrepancies is only one part of the problem. What if the same data item was updated independently on two different nodes (copies)? In that case, due to the asynchronous nature of change propagation, you might land up with different versions of the data item in different copies. In this case, the application program also has to resolve conflicts and then propagate the correct value to the copies that are out-dated or have incorrect versions. This can get really complicated. My hunch is that there are many peer-to-peer-based applications that don’t handle this correctly, and worse, don’t even know it. Imagine have 100s of millions of records in your database – how can you tell whether a particular data item is incorrect or out of date? And what price are you willing to pay for ensuring that the data can be trusted? Multiple network messages per read request? Discrepancy and conflict resolution logic in the application, and potentially, additional messages? All this overhead, when all you were trying to do was to read a data item. Wouldn’t it be simpler to avoid this problem in the first place? Master-slave architectures like the Oracle NoSQL Database handles this very elegantly. A change to a data item is always sent to the master copy. Consequently, the master copy always has the most current and authoritative version of the data item. The master is also responsible for propagating the change to the other copies (for availability and read scalability). Client drivers are aware of master copies and replicas, and client drivers are also aware of the “currency” of a replica. In other words, each NoSQL Database client knows how stale a replica is. This vastly simplifies the job of the application developer. If the application needs the most current version of the data item, the client driver will automatically route the request to the master copy. If the application is willing to tolerate some staleness of data (e.g. a version that is no more than 1 second out of date), the client can easily determine which replica (or set of replicas) can satisfy the request, and route the request to the most efficient copy. This results in a dramatic simplification in application logic and also minimizes network requests (the driver will only send the request to exactl the right replica, not many). So, back to my original point. A well designed and well architected system minimizes or eliminates unnecessary overhead and avoids pessimistic algorithms wherever possible in order to deliver a highly efficient and high performance system. If you’ve every programmed an Oracle NoSQL Database application, you’ll know the difference! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

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• Can't get IIS6 to setup properly.

  - by AngryHacker

  I am trying to setup a website on a Windows 2003 R2 box. So I cranked up IIS, clicked on Web Sites, created a new website and setup the host headers for my domain. No matter what I did, I kept getting Service Unavailable errors. Finally, I followed the instructions in this link, and it worked. Basically, the page recommends switching to IIS5 isolation mode. I don't really understand why it works and I don't understand why it didn't work before. If someone could enlighten me, that would be great. Bonus for explaining how to do with the IIS6 isolation mode.

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• Continuous Integration using Docker

  - by Leon Mergen

  One of the main advantages of Docker is the isolated environment it brings, and I want to leverage that advantage in my continuous integration workflow. A "normal" CI workflow goes something like this: Poll repository for changes Pull from repository Install dependencies Run tests In a Dockerized workflow, it would be something like this: Poll repository for changes Pull from repository Build docker image Run docker image as container Run tests Kill docker container My problem is with the "run tests" step: since Docker is an isolated environment, intuitively I would like to treat it as one; this means the preferred method of communication are sockets. However, this only works well in certain situations (a webapp, for example). When testing different kind of services (for example, a background service that only communicated with a database), a different approach would be required. What is the best way to approach this problem? Is it a problem with my application's design, and should I design it in a more TDD, service-oriented way that always listens on some socket? Or should I just give up on isolation, and do something like this: Poll repository for changes Pull from repository Build docker image Run docker image as container Open SSH session into container Run tests Kill docker container SSH'ing into the container seems like an ugly solution to me, since it requires deep knowledge of the contents of the container, and thus break the isolation. I would love to hear SO's different approaches to this problem.

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• Different methods of ammo resupply

  - by Chris Mantle

  I'm writing a small game at the moment. Presently, I have one or two design elements that aren't locked down yet, and I wanted to ask for input on one of these. For dramatic effect, the player's character in my game is immobilised, alone and has a supposedly limited amount of ammo for their weapons. However, I would like to periodically resupply the player with ammo (for the purpose of balancing the level of difficulty and to allow the player to continue if they're doing well). I'm trying to think of a method of resupply that's different to the more familiar strategies of making ammo magically appear or having the antagonists drop some when they die. I'd like to emphasise the notion of the player's isolation as much as possible, and finding a way of 'sneaking' ammo to the player without removing too much of that emphasis is basically what I'm trying to think of (it's definitely a valid argument that resupplying the player removes it anyway) I have considered a sort of simple in-game 'store', where kills get you points that you can spend on ammo for your favourite weapon. This might work well, and may also be good for supporting a simple micro-transaction business model within the game. However, you'd have to pause the game often to make purchases, which would interrupt the action, and it works against the notion of isolation. Any thoughts?

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