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• Using Table-Valued Parameters in SQL Server

  - by Jesse

  I work with stored procedures in SQL Server pretty frequently and have often found myself with a need to pass in a list of values at run-time. Quite often this list contains a set of ids on which the stored procedure needs to operate the size and contents of which are not known at design time. In the past I’ve taken the collection of ids (which are usually integers), converted them to a string representation where each value is separated by a comma and passed that string into a VARCHAR parameter of a stored procedure. The body of the stored procedure would then need to parse that string into a table variable which could be easily consumed with set-based logic within the rest of the stored procedure. This approach works pretty well but the VARCHAR variable has always felt like an un-wanted “middle man” in this scenario. Of course, I could use a BULK INSERT operation to load the list of ids into a temporary table that the stored procedure could use, but that approach seems heavy-handed in situations where the list of values is usually going to contain only a few dozen values. Fortunately SQL Server 2008 introduced the concept of table-valued parameters which effectively eliminates the need for the clumsy middle man VARCHAR parameter. Example: Customer Transaction Summary Report Let’s say we have a report that can summarize the the transactions that we’ve conducted with customers over a period of time. The report returns a pretty simple dataset containing one row per customer with some key metrics about how much business that customer has conducted over the date range for which the report is being run. Sometimes the report is run for a single customer, sometimes it’s run for all customers, and sometimes it’s run for a handful of customers (i.e. a salesman runs it for the customers that fall into his sales territory). This report can be invoked from a website on-demand, or it can be scheduled for periodic delivery to certain users via SQL Server Reporting Services. Because the report can be created from different places and the query to generate the report is complex it’s been packed into a stored procedure that accepts three parameters: @startDate – The beginning of the date range for which the report should be run. @endDate – The end of the date range for which the report should be run. @customerIds – The customer Ids for which the report should be run. Obviously, the @startDate and @endDate parameters are DATETIME variables. The @customerIds parameter, however, needs to contain a list of the identity values (primary key) from the Customers table representing the customers that were selected for this particular run of the report. In prior versions of SQL Server we might have made this parameter a VARCHAR variable, but with SQL Server 2008 we can make it into a table-valued parameter. Defining And Using The Table Type In order to use a table-valued parameter, we first need to tell SQL Server about what the table will look like. We do this by creating a user defined type. For the purposes of this stored procedure we need a very simple type to model a table variable with a single integer column. We can create a generic type called ‘IntegerListTableType’ like this: CREATE TYPE IntegerListTableType AS TABLE (Value INT NOT NULL) Once defined, we can use this new type to define the @customerIds parameter in the signature of our stored procedure. The parameter list for the stored procedure definition might look like: 1: CREATE PROCEDURE dbo.rpt_CustomerTransactionSummary 2: @starDate datetime, 3: @endDate datetime, 4: @customerIds IntegerListTableTableType READONLY   Note the ‘READONLY’ statement following the declaration of the @customerIds parameter. SQL Server requires any table-valued parameter be marked as ‘READONLY’ and no DML (INSERT/UPDATE/DELETE) statements can be performed on a table-valued parameter within the routine in which it’s used. Aside from the DML restriction, however, you can do pretty much anything with a table-valued parameter as you could with a normal TABLE variable. With the user defined type and stored procedure defined as above, we could invoke like this: 1: DECLARE @cusomterIdList IntegerListTableType 2: INSERT @customerIdList VALUES (1) 3: INSERT @customerIdList VALUES (2) 4: INSERT @customerIdList VALUES (3) 5:  6: EXEC dbo.rpt_CustomerTransationSummary 7: @startDate = '2012-05-01', 8: @endDate = '2012-06-01' 9: @customerIds = @customerIdList   Note that we can simply declare a variable of type ‘IntegerListTableType’ just like any other normal variable and insert values into it just like a TABLE variable. We could also populate the variable with a SELECT … INTO or INSERT … SELECT statement if desired. Using The Table-Valued Parameter With ADO .NET Invoking a stored procedure with a table-valued parameter from ADO .NET is as simple as building a DataTable and passing it in as the Value of a SqlParameter. Here’s some example code for how we would construct the SqlParameter for the @customerIds parameter in our stored procedure: 1: var customerIdsParameter = new SqlParameter(); 2: customerIdParameter.Direction = ParameterDirection.Input; 3: customerIdParameter.TypeName = "IntegerListTableType"; 4: customerIdParameter.Value = selectedCustomerIds.ToIntegerListDataTable("Value");   All we’re doing here is new’ing up an instance of SqlParameter, setting the pamameters direction, specifying the name of the User Defined Type that this parameter uses, and setting its value. We’re assuming here that we have an IEnumerable<int> variable called ‘selectedCustomerIds’ containing all of the customer Ids for which the report should be run. The ‘ToIntegerListDataTable’ method is an extension method of the IEnumerable<int> type that looks like this: 1: public static DataTable ToIntegerListDataTable(this IEnumerable<int> intValues, string columnName) 2: { 3: var intergerListDataTable = new DataTable(); 4: intergerListDataTable.Columns.Add(columnName); 5: foreach(var intValue in intValues) 6: { 7: var nextRow = intergerListDataTable.NewRow(); 8: nextRow[columnName] = intValue; 9: intergerListDataTable.Rows.Add(nextRow); 10: } 11:  12: return intergerListDataTable; 13: }   Since the ‘IntegerListTableType’ has a single int column called ‘Value’, we pass that in for the ‘columnName’ parameter to the extension method. The method creates a new single-columned DataTable using the provided column name then iterates over the items in the IEnumerable<int> instance adding one row for each value. We can then use this SqlParameter instance when invoking the stored procedure just like we would use any other parameter. Advanced Functionality Using passing a list of integers into a stored procedure is a very simple usage scenario for the table-valued parameters feature, but I’ve found that it covers the majority of situations where I’ve needed to pass a collection of data for use in a query at run-time. I should note that BULK INSERT feature still makes sense for passing large amounts of data to SQL Server for processing. MSDN seems to suggest that 1000 rows of data is the tipping point where the overhead of a BULK INSERT operation can pay dividends. I should also note here that table-valued parameters can be used to deal with more complex data structures than single-columned tables of integers. A User Defined Type that backs a table-valued parameter can use things like identities and computed columns. That said, using some of these more advanced features might require the use the SqlDataRecord and SqlMetaData classes instead of a simple DataTable. Erland Sommarskog has a great article on his website that describes when and how to use these classes for table-valued parameters. What About Reporting Services? Earlier in the post I referenced the fact that our example stored procedure would be called from both a web application and a SQL Server Reporting Services report. Unfortunately, using table-valued parameters from SSRS reports can be a bit tricky and warrants its own blog post which I’ll be putting together and posting sometime in the near future.

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• SQL SERVER – 3 Online SQL Courses at Pluralsight and Free Learning Resources

  - by pinaldave

  Usain Bolt is an inspiration for all. He broke his own record multiple times because he wanted to do better! Read more about him on wikipedia. He is great and indeed fastest man on the planet. Usain Bolt – World’s Fastest Man “Can you teach me SQL Server Performance Tuning?” This is one of the most popular questions which I receive all the time. The answer is YES. I would love to do performance tuning training for anyone, anywhere.  It is my favorite thing to do, and it is my favorite thing to train others in.  If possible, I would love to do training 24 hours a day, 7 days a week, 365 days a year.  To me, it doesn’t feel like a job. Of course, as much as I would love to do performance tuning 24/7/365, obviously I am just one human being and can only be in one place t one time.  It is also very difficult to train more than one person at a time, and it is difficult to train two or more people at a time, especially when the two people are at different levels.  I am also limited by geography.  I live in India, and adjust to my own time zone.  Trying to teach a live course from India to someone whose time zone is 12 or more hours off of mine is very difficult.  If I am trying to teach at 2 am, I am sure I am not at my best! There was only one solution to scale – Online Trainings. I have built 3 different courses on SQL Server Performance Tuning with Pluralsight. Now I have no problem – I am 100% scalable and available 24/7 and 365. You can make me say the same things again and again till you find it right. I am in your mobile, PC as well as on XBOX. This is why I am such a big fan of online courses.  I have recorded many performance tuning classes and you can easily access them online, at your own time.  And don’t think that just because these aren’t live classes you won’t be able to get any feedback from me.  I encourage all my viewers to go ahead and ask me questions by e-mail, Twitter, Facebook, or whatever way you can get a hold of me. Here are details of three of my courses with Pluralsight. I suggest you go over the description of the course. As an author of the course, I have few FREE codes for watching the free courses. Please leave a comment with your valid email address, I will send a few of them to random winners. SQL Server Performance: Introduction to Query Tuning  SQL Server performance tuning is an art to master – for developers and DBAs alike. This course takes a systematic approach to planning, analyzing, debugging and troubleshooting common query-related performance problems. This includes an introduction to understanding execution plans inside SQL Server. In this almost four hour course we cover following important concepts. Introduction 10:22 Execution Plan Basics 45:59 Essential Indexing Techniques 20:19 Query Design for Performance 50:16 Performance Tuning Tools 01:15:14 Tips and Tricks 25:53 Checklist: Performance Tuning 07:13 The duration of each module is mentioned besides the name of the module. SQL Server Performance: Indexing Basics This course teaches you how to master the art of performance tuning SQL Server by better understanding indexes. In this almost two hour course we cover following important concepts. Introduction 02:03 Fundamentals of Indexing 22:21 Practical Indexing Implementation Techniques 37:25 Index Maintenance 16:33 Introduction to ColumnstoreIndex 08:06 Indexing Practical Performance Tips and Tricks 24:56 Checklist : Index and Performance 07:29 The duration of each module is mentioned besides the name of the module. SQL Server Questions and Answers This course is designed to help you better understand how to use SQL Server effectively. The course presents many of the common misconceptions about SQL Server, and then carefully debunks those misconceptions with clear explanations and short but compelling demos, showing you how SQL Server really works. In this almost 2 hours and 15 minutes course we cover following important concepts. Introduction 00:54 Retrieving IDENTITY value using @@IDENTITY 08:38 Concepts Related to Identity Values 04:15 Difference between WHERE and HAVING 05:52 Order in WHERE clause 07:29 Concepts Around Temporary Tables and Table Variables 09:03 Are stored procedures pre-compiled? 05:09 UNIQUE INDEX and NULLs problem 06:40 DELETE VS TRUNCATE 06:07 Locks and Duration of Transactions 15:11 Nested Transaction and Rollback 09:16 Understanding Date/Time Datatypes 07:40 Differences between VARCHAR and NVARCHAR datatypes 06:38 Precedence of DENY and GRANT security permissions 05:29 Identify Blocking Process 06:37 NULLS usage with Dynamic SQL 08:03 Appendix Tips and Tricks with Tools 20:44 The duration of each module is mentioned besides the name of the module. SQL in Sixty Seconds You will have to login and to get subscribed to the courses to view them. Here are my free video learning resources SQL in Sixty Seconds. These are 60 second video which I have built on various subjects related to SQL Server. Do let me know what you think about them? Here are three of my latest videos: Identify Most Resource Intensive Queries – SQL in Sixty Seconds #028 Copy Column Headers from Resultset – SQL in Sixty Seconds #027 Effect of Collation on Resultset – SQL in Sixty Seconds #026 You can watch and learn at your own pace.  Then you can easily ask me any questions you have.  E-mail is easiest, but for really tough questions I’m willing to talk on Skype, Gtalk, or even Facebook chat.  Please do watch and then talk with me, I am always available on the internet! Here is the video of the world’s fastest man.Usain St. Leo Bolt inspires us that we all do better than best. We can go the next level of our own record. We all can improve if we have a will and dedication.  Watch the video from 5:00 mark. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, SQLServer, T SQL, Technology, Video

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• Fun with Aggregates

  - by Paul White

  There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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

  - by Brian

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

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• Oracle's Global Single Schema

  - by david.butler(at)oracle.com

  Maximizing business process efficiencies in a heterogeneous environment is very difficult. The difficulty stems from the fact that the various applications across the Information Technology (IT) landscape employ different integration standards, different message passing strategies, and different workflow engines. Vendors such as Oracle and others are delivering tools to help IT organizations manage the complexities introduced by these differences. But the one remaining intractable problem impacting efficient operations is the fact that these applications have different definitions for the same business data. Business data is your business information codified for computer programs to use. A good data model will represent the way your organization does business. The computer applications your organization deploys to improve operational efficiency are built to operate on the business data organized into this schema.  If the schema does not represent how you do business, the applications on that schema cannot provide the features you need to achieve the desired efficiencies. Business processes span these applications. Data problems break these processes rendering them far less efficient than they need to be to achieve organization goals. Thus, the expected return on the investment in these applications is never realized. The success of all business processes depends on the availability of accurate master data.  Clearly, the solution to this problem is to consolidate all the master data an organization uses to run its business. Then clean it up, augment it, govern it, and connect it back to the applications that need it. Until now, this obvious solution has been difficult to achieve because no one had defined a data model sufficiently broad, deep and flexible enough to support transaction processing on all key business entities and serve as a master superset to all other operational data models deployed in heterogeneous IT environments. Today, the situation has changed. Oracle has created an operational data model (aka schema) that can support accurate and consistent master data across heterogeneous IT systems. This is foundational for providing a way to consolidate and integrate master data without having to replace investments in existing applications. This Global Single Schema (GSS) represents a revolutionary breakthrough that allows for true master data consolidation. Oracle has deep knowledge of applications dating back to the early 1990s.  It developed applications in the areas of Supply Chain Management (SCM), Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Human Capital Management (HCM), Financials and Manufacturing. In addition, Oracle applications were delivered for key industries such as Communications, Financial Services, Retail, Public Sector, High Tech Manufacturing (HTM) and more. Expertise in all these areas drove requirements for GSS. The following figure illustrates Oracle's unique position that enabled the creation of the Global Single Schema. GSS Requirements Gathering GSS defines all the key business entities and attributes including Customers, Contacts, Suppliers, Accounts, Products, Services, Materials, Employees, Installed Base, Sites, Assets, and Inventory to name just a few. In addition, Oracle delivers GSS pre-integrated with a wide variety of operational applications.  Business Process Automation EBusiness is about maximizing operational efficiency. At the highest level, these 'operations' span all that you do as an organization.  The following figure illustrates some of these high-level business processes. Enterprise Business Processes Supplies are procured. Assets are maintained. Materials are stored. Inventory is accumulated. Products and Services are engineered, produced and sold. Customers are serviced. And across this entire spectrum, Employees do the procuring, supporting, engineering, producing, selling and servicing. Not shown, but not to be overlooked, are the accounting and the financial processes associated with all this procuring, manufacturing, and selling activity. Supporting all these applications is the master data. When this data is fragmented and inconsistent, the business processes fail and inefficiencies multiply. But imagine having all the data under these operational business processes in one place. ·            The same accurate and timely customer data will be provided to all your operational applications from the call center to the point of sale. ·            The same accurate and timely supplier data will be provided to all your operational applications from supply chain planning to procurement. ·            The same accurate and timely product information will be available to all your operational applications from demand chain planning to marketing. You would have a single version of the truth about your assets, financial information, customers, suppliers, employees, products and services to support your business automation processes as they flow across your business applications. All company and partner personnel will access the same exact data entity across all your channels and across all your lines of business. Oracle's Global Single Schema enables this vision of a single version of the truth across the heterogeneous operational applications supporting the entire enterprise. Global Single Schema Oracle's Global Single Schema organizes hundreds of thousands of attributes into 165 major schema objects supporting over 180 business application modules. It is designed for international operations, and extensibility.  The schema is delivered with a full set of public Application Programming Interfaces (APIs) and an Integration Repository with modern Service Oriented Architecture interfaces to make data available as a services (DaaS) to business processes and enable operations in heterogeneous IT environments. ·         Key tables can be extended with unlimited numbers of additional attributes and attribute groups for maximum flexibility.  o    This enables model extensions that reflect business entities unique to your organization's operations. ·         The schema is multi-organization enabled so data manipulation can be controlled along organizational boundaries. ·         It uses variable byte Unicode to support over 31 languages. ·         The schema encodes flexible date and flexible address formats for easy localizations. No matter how complex your business is, Oracle's Global Single Schema can hold your business objects and support your global operations. Oracle's Global Single Schema identifies and defines the business objects an enterprise needs within the context of its business operations. The interrelationships between the business objects are also contained within the GSS data model. Their presence expresses fundamental business rules for the interaction between business entities. The following figure illustrates some of these connections.   Interconnected Business Entities Interconnecte business processes require interconnected business data. No other MDM vendor has this capability. Everyone else has either one entity they can master or separate disconnected models for various business entities. Higher level integrations are made available, but that is a weak architectural alternative to data level integration in this critically important aspect of Master Data Management.    

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• PTLQueue : a scalable bounded-capacity MPMC queue

  - by Dave

  Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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• SSIS: Deploying OLAP cubes using C# script tasks and AMO

  - by DrJohn

  As part of the continuing series on Building dynamic OLAP data marts on-the-fly, this blog entry will focus on how to automate the deployment of OLAP cubes using SQL Server Integration Services (SSIS) and Analysis Services Management Objects (AMO). OLAP cube deployment is usually done using the Analysis Services Deployment Wizard. However, this option was dismissed for a variety of reasons. Firstly, invoking external processes from SSIS is fraught with problems as (a) it is not always possible to ensure SSIS waits for the external program to terminate; (b) we cannot log the outcome properly and (c) it is not always possible to control the server's configuration to ensure the executable works correctly. Another reason for rejecting the Deployment Wizard is that it requires the 'answers' to be written into four XML files. These XML files record the three things we need to change: the name of the server, the name of the OLAP database and the connection string to the data mart. Although it would be reasonably straight forward to change the content of the XML files programmatically, this adds another set of complication and level of obscurity to the overall process. When I first investigated the possibility of using C# to deploy a cube, I was surprised to find that there are no other blog entries about the topic. I can only assume everyone else is happy with the Deployment Wizard! SSIS "forgets" assembly references If you build your script task from scratch, you will have to remember how to overcome one of the major annoyances of working with SSIS script tasks: the forgetful nature of SSIS when it comes to assembly references. Basically, you can go through the process of adding an assembly reference using the Add Reference dialog, but when you close the script window, SSIS "forgets" the assembly reference so the script will not compile. After repeating the operation several times, you will find that SSIS only remembers the assembly reference when you specifically press the Save All icon in the script window. This problem is not unique to the AMO assembly and has certainly been a "feature" since SQL Server 2005, so I am not amazed it is still present in SQL Server 2008 R2! Sample Package So let's take a look at the sample SSIS package I have provided which can be downloaded from here: DeployOlapCubeExample.zip  Below is a screenshot after a successful run. Connection Managers The package has three connection managers: AsDatabaseDefinitionFile is a file connection manager pointing to the .asdatabase file you wish to deploy. Note that this can be found in the bin directory of you OLAP database project once you have clicked the "Build" button in Visual Studio TargetOlapServerCS is an Analysis Services connection manager which identifies both the deployment server and the target database name. SourceDataMart is an OLEDB connection manager pointing to the data mart which is to act as the source of data for your cube. This will be used to replace the connection string found in your .asdatabase file Once you have configured the connection managers, the sample should run and deploy your OLAP database in a few seconds. Of course, in a production environment, these connection managers would be associated with package configurations or set at runtime. When you run the sample, you should see that the script logs its activity to the output screen (see screenshot above). If you configure logging for the package, then these messages will also appear in your SSIS logging. Sample Code Walkthrough Next let's walk through the code. The first step is to parse the connection string provided by the TargetOlapServerCS connection manager and obtain the name of both the target OLAP server and also the name of the OLAP database. Note that the target database does not have to exist to be referenced in an AS connection manager, so I am using this as a convenient way to define both properties. We now connect to the server and check for the existence of the OLAP database. If it exists, we drop the database so we can re-deploy. svr.Connect(olapServerName); if (svr.Connected) { // Drop the OLAP database if it already exists Database db = svr.Databases.FindByName(olapDatabaseName); if (db != null) { db.Drop(); } // rest of script } Next we start building the XMLA command that will actually perform the deployment. Basically this is a small chuck of XML which we need to wrap around the large .asdatabase file generated by the Visual Studio build process. // Start generating the main part of the XMLA command XmlDocument xmlaCommand = new XmlDocument(); xmlaCommand.LoadXml(string.Format("<Batch Transaction='false' xmlns='http://schemas.microsoft.com/analysisservices/2003/engine'><Alter AllowCreate='true' ObjectExpansion='ExpandFull'><Object><DatabaseID>{0}</DatabaseID></Object><ObjectDefinition/></Alter></Batch>", olapDatabaseName));  Next we need to merge two XML files which we can do by simply using setting the InnerXml property of the ObjectDefinition node as follows: // load OLAP Database definition from .asdatabase file identified by connection manager XmlDocument olapCubeDef = new XmlDocument(); olapCubeDef.Load(Dts.Connections["AsDatabaseDefinitionFile"].ConnectionString); // merge the two XML files by obtain a reference to the ObjectDefinition node oaRootNode.InnerXml = olapCubeDef.InnerXml;   One hurdle I had to overcome was removing detritus from the .asdabase file left by the Visual Studio build. Through an iterative process, I found I needed to remove several nodes as they caused the deployment to fail. The XMLA error message read "Cannot set read-only node: CreatedTimestamp" or similar. In comparing the XMLA generated with by the Deployment Wizard with that generated by my code, these read-only nodes were missing, so clearly I just needed to strip them out. This was easily achieved using XPath to find the relevant XML nodes, of which I show one example below: foreach (XmlNode node in rootNode.SelectNodes("//ns1:CreatedTimestamp", nsManager)) { node.ParentNode.RemoveChild(node); } Now we need to change the database name in both the ID and Name nodes using code such as: XmlNode databaseID = xmlaCommand.SelectSingleNode("//ns1:Database/ns1:ID", nsManager); if (databaseID != null) databaseID.InnerText = olapDatabaseName; Finally we need to change the connection string to point at the relevant data mart. Again this is easily achieved using XPath to search for the relevant nodes and then replace the content of the node with the new name or connection string. XmlNode connectionStringNode = xmlaCommand.SelectSingleNode("//ns1:DataSources/ns1:DataSource/ns1:ConnectionString", nsManager); if (connectionStringNode != null) { connectionStringNode.InnerText = Dts.Connections["SourceDataMart"].ConnectionString; } Finally we need to perform the deployment using the Execute XMLA command and check the returned XmlaResultCollection for errors before setting the Dts.TaskResult. XmlaResultCollection oResults = svr.Execute(xmlaCommand.InnerXml);  // check for errors during deployment foreach (Microsoft.AnalysisServices.XmlaResult oResult in oResults) { foreach (Microsoft.AnalysisServices.XmlaMessage oMessage in oResult.Messages) { if ((oMessage.GetType().Name == "XmlaError")) { FireError(oMessage.Description); HadError = true; } } } If you are not familiar with XML programming, all this may all seem a bit daunting, but perceiver as the sample code is pretty short. If you would like the script to process the OLAP database, simply uncomment the lines in the vicinity of Process method. Of course, you can extend the script to perform your own custom processing and to even synchronize the database to a front-end server. Personally, I like to keep the deployment and processing separate as the code can become overly complex for support staff.If you want to know more, come see my session at the forthcoming SQLBits conference.

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• SQL SERVER – Weekly Series – Memory Lane – #034

  - by Pinal Dave

  Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 UDF – User Defined Function to Strip HTML – Parse HTML – No Regular Expression The UDF used in the blog does fantastic task – it scans entire HTML text and removes all the HTML tags. It keeps only valid text data without HTML task. This is one of the quite commonly requested tasks many developers have to face everyday. De-fragmentation of Database at Operating System to Improve Performance Operating system skips MDF file while defragging the entire filesystem of the operating system. It is absolutely fine and there is no impact of the same on performance. Read the entire blog post for my conversation with our network engineers. Delay Function – WAITFOR clause – Delay Execution of Commands How do you delay execution of the commands in SQL Server – ofcourse by using WAITFOR keyword. In this blog post, I explain the same with the help of T-SQL script. Find Length of Text Field To measure the length of TEXT fields the function is DATALENGTH(textfield). Len will not work for text field. As of SQL Server 2005, developers should migrate all the text fields to VARCHAR(MAX) as that is the way forward. Retrieve Current Date Time in SQL Server CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} There are three ways to retrieve the current datetime in SQL SERVER. CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} Explanation and Comparison of NULLIF and ISNULL An interesting observation is NULLIF returns null if it comparison is successful, whereas ISNULL returns not null if its comparison is successful. In one way they are opposite to each other. Here is my question to you - How to create infinite loop using NULLIF and ISNULL? If this is even possible? 2008 Introduction to SERVERPROPERTY and example SERVERPROPERTY is a very interesting system function. It returns many of the system values. I use it very frequently to get different server values like Server Collation, Server Name etc. SQL Server Start Time We can use DMV to find out what is the start time of SQL Server in 2008 and later version. In this blog you can see how you can do the same. Find Current Identity of Table Many times we need to know what is the current identity of the column. I have found one of my developers using aggregated function MAX () to find the current identity. However, I prefer following DBCC command to figure out current identity. Create Check Constraint on Column Some time we just need to create a simple constraint over the table but I have noticed that developers do many different things to make table column follow rules than just creating constraint. I suggest constraint is a very useful concept and every SQL Developer should pay good attention to this subject. 2009 List Schema Name and Table Name for Database This is one of the blog post where I straight forward display script. One of the kind of blog posts, which I still love to read and write. Clustered Index on Separate Drive From Table Location A table devoid of primary key index is called heap, and here data is not arranged in a particular order, which gives rise to issues that adversely affect performance. Data must be stored in some kind of order. If we put clustered index on it then the order will be forced by that index and the data will be stored in that particular order. Understanding Table Hints with Examples Hints are options and strong suggestions specified for enforcement by the SQL Server query processor on DML statements. The hints override any execution plan the query optimizer might select for a query. 2010 Data Pages in Buffer Pool – Data Stored in Memory Cache One of my earlier year article, which I still read it many times and point developers to read it again. It is clear from the Resultset that when more than one index is used, datapages related to both or all of the indexes are stored in Memory Cache separately. TRANSACTION, DML and Schema Locks Can you create a situation where you can see Schema Lock? Well, this is a very simple question, however during the interview I notice over 50 candidates failed to come up with the scenario. In this blog post, I have demonstrated the situation where we can see the schema lock in database. 2011 Solution – Puzzle – Statistics are not updated but are Created Once In this example I have created following situation: Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Auto Update Statistics and Auto Create Statistics for database is TRUE Now I have requested two things in the example 1) Why this is happening? 2) How to fix this issue? Selecting Domain from Email Address This is a straight to script blog post where I explain how to select only domain name from entire email address. Solution – Generating Zero Without using Any Numbers in T-SQL How to get zero digit without using any digit? This is indeed a very interesting question and the answer is even interesting. Try to come up with answer in next 10 minutes and if you can’t come up with the answer the blog post read this post for solution. 2012 Simple Explanation and Puzzle with SOUNDEX Function and DIFFERENCE Function In simple words - SOUNDEX converts an alphanumeric string to a four-character code to find similar-sounding words or names. DIFFERENCE function returns an integer value. The  integer returned is the number of characters in the SOUNDEX values that are the same. Read Only Files and SQL Server Management Studio (SSMS) I have come across a very interesting feature in SSMS related to “Read Only” files. I believe it is a little unknown feature as well so decided to write a blog about the same. Identifying Column Data Type of uniqueidentifier without Querying System Tables How do I know if any table has a uniqueidentifier column and what is its value without using any DMV or System Catalogues? Only information you know is the table name and you are allowed to return any kind of error if the table does not have uniqueidentifier column. Read the blog post to find the answer. Solution – User Not Able to See Any User Created Object in Tables – Security and Permissions Issue Interesting question – “When I try to connect to SQL Server, it lets me connect just fine as well let me open and explore the database. I noticed that I do not see any user created instances but when my colleague attempts to connect to the server, he is able to explore the database as well see all the user created tables and other objects. Can you help me fix it?” Importing CSV File Into Database – SQL in Sixty Seconds #018 – Video Here is interesting small 60 second video on how to import CSV file into Database. ColumnStore Index – Batch Mode vs Row Mode Here is the logic behind when Columnstore Index uses Batch Mode and when it uses Row Mode. A batch typically represents about 1000 rows of data. Batch mode processing also uses algorithms that are optimized for the multicore CPUs and increased memory throughput. Follow up – Usage of $rowguid and $IDENTITY This is an excellent follow up blog post of my earlier blog post where I explain where to use $rowguid and $identity.  If you do not know the difference between them, this is a blog with a script example. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• Is Financial Inclusion an Obligation or an Opportunity for Banks?

  - by tushar.chitra

  Why should banks care about financial inclusion? First, the statistics, I think this will set the tone for this blog post. There are close to 2.5 billion people who are excluded from the banking stream and out of this, 2.2 billion people are from the continents of Africa, Latin America and Asia (McKinsey on Society: Global Financial Inclusion). However, this is not just a third-world phenomenon. According to Federal Deposit Insurance Corp (FDIC), in the US, post 2008 financial crisis, one family out of five has either opted out of the banking system or has been moved out (American Banker). Moving this huge unbanked population into mainstream banking is both an opportunity and a challenge for banks. An obvious opportunity is the significant untapped customer base that banks can target, so is the positive brand equity a bank can build by fulfilling its social responsibilities. Also, as banks target the cost-conscious unbanked customer, they will be forced to look at ways to offer cost-effective products and services, necessitating technology upgrades and innovations. However, cost is not the only hurdle in increasing the adoption of banking services. The potential users need to be convinced of the benefits of banking and banks will also face stiff competition from unorganized players. Finally, the banks will have to believe in the viability of this business opportunity, and not treat financial inclusion as an obligation. In what ways can banks target the unbanked For financial inclusion to be a success, banks should adopt innovative business models to develop products that address the stated and unstated needs of the unbanked population and also design delivery channels that are cost effective and viable in the long run. Through business correspondents and facilitators In rural and remote areas, one of the major hurdles in increasing banking penetration is connectivity and accessibility to banking services, which makes last mile inclusion a daunting challenge. To address this, banks can avail the services of business correspondents or facilitators. This model allows banks to establish greater connectivity through a trusted and reliable intermediary. In India, for instance, banks can leverage the local Kirana stores (the mom & pop stores) to service rural and remote areas. With a supportive nudge from the central bank, the commercial banks can enlist these shop owners as business correspondents to increase their reach. Since these neighborhood stores are acquainted with the local population, they can help banks manage the KYC norms, besides serving as a conduit for remittance. Banks also have an opportunity over a period of time to cross-sell other financial products such as micro insurance, mutual funds and pension products through these correspondents. To exercise greater operational control over the business correspondents, banks can also adopt a combination of branch and business correspondent models to deliver financial inclusion. Through mobile devices According to a 2012 world bank report on financial inclusion, out of a world population of 7 billion, over 5 billion or 70% have mobile phones and only 2 billion or 30% have a bank account. What this means for banks is that there is scope for them to leverage this phenomenal growth in mobile usage to serve the unbanked population. Banks can use mobile technology to service the basic banking requirements of their customers with no frills accounts, effectively bringing down the cost per transaction. As I had discussed in my earlier post on mobile payments, though non-traditional players have taken the lead in P2P mobile payments, banks still hold an edge in terms of infrastructure and reliability. Through crowd-funding According to the Crowdfunding Industry Report by Massolution, the global crowdfunding industry raised $2.7 billion in 2012, and is projected to grow to $5.1 billion in 2013. With credit policies becoming tighter and banks becoming more circumspect in terms of loan disbursals, crowdfunding has emerged as an alternative channel for lending. Typically, these initiatives target the unbanked population by offering small loans that are unviable for larger banks. Though a significant proportion of crowdfunding initiatives globally are run by non-banking institutions, banks are also venturing into this space. The next step towards inclusive finance Banks by themselves cannot make financial inclusion a success. There is a need for a whole ecosystem that is supportive of this mission. The policy makers, that include the regulators and government bodies, must be in sync, the IT solution providers must put on their thinking caps to come out with innovative products and solutions, communication channels such as internet and mobile need to expand their reach, and the media and the public need to play an active part. The other challenge for financial inclusion is from the banks themselves. While it is true that financial inclusion will unleash a hitherto hugely untapped market, the normal banking model may be found wanting because of issues such as flexibility, convenience and reliability. The business will be viable only when there is a focus on increasing the usage of existing infrastructure and that is possible when the banks can offer the entire range of products and services to the large number of users of essential banking services. Apart from these challenges, banks will also have to quickly master and replicate the business model to extend their reach to the remotest regions in their respective geographies. They will need to ensure that the transactions deliver a viable business benefit to the bank. For tapping cross-sell opportunities, banks will have to quickly roll-out customized and segment-specific products. The bank staff should be brought in sync with the business plan by convincing them of the viability of the business model and the need for a business correspondent delivery model. Banks, in collaboration with the government and NGOs, will have to run an extensive financial literacy program to educate the unbanked about the benefits of banking. Finally, with the growing importance of retail banking and with many unconventional players eyeing the opportunity in payments and other lucrative areas of banking, banks need to understand the importance of micro and small branches. These micro and small branches can help banks increase their presence without a huge cost burden, provide bankers an opportunity to cross sell micro products and offer a window of opportunity for the large non-banked population to transact without any interference from intermediaries. These branches can also help diminish the role of the unorganized financial sector, such as local moneylenders and unregistered credit societies. This will also help banks build a brand awareness and loyalty among the users, which by itself has a cascading effect on the business operations, especially among the rural and un-banked centers. In conclusion, with the increasingly competitive banking sector facing frequent slowdowns and downturns, the unbanked population presents a huge opportunity for banks to enhance their customer base and fulfill their social responsibility.

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• How accurate is "Business logic should be in a service, not in a model"?

  - by Jeroen Vannevel

  Situation Earlier this evening I gave an answer to a question on StackOverflow. The question: Editing of an existing object should be done in repository layer or in service? For example if I have a User that has debt. I want to change his debt. Should I do it in UserRepository or in service for example BuyingService by getting an object, editing it and saving it ? My answer: You should leave the responsibility of mutating an object to that same object and use the repository to retrieve this object. Example situation: class User { private int debt; // debt in cents private string name; // getters public void makePayment(int cents){ debt -= cents; } } class UserRepository { public User GetUserByName(string name){ // Get appropriate user from database } } A comment I received: Business logic should really be in a service. Not in a model. What does the internet say? So, this got me searching since I've never really (consciously) used a service layer. I started reading up on the Service Layer pattern and the Unit Of Work pattern but so far I can't say I'm convinced a service layer has to be used. Take for example this article by Martin Fowler on the anti-pattern of an Anemic Domain Model: There are objects, many named after the nouns in the domain space, and these objects are connected with the rich relationships and structure that true domain models have. The catch comes when you look at the behavior, and you realize that there is hardly any behavior on these objects, making them little more than bags of getters and setters. Indeed often these models come with design rules that say that you are not to put any domain logic in the the domain objects. Instead there are a set of service objects which capture all the domain logic. These services live on top of the domain model and use the domain model for data. (...) The logic that should be in a domain object is domain logic - validations, calculations, business rules - whatever you like to call it. To me, this seemed exactly what the situation was about: I advocated the manipulation of an object's data by introducing methods inside that class that do just that. However I realize that this should be a given either way, and it probably has more to do with how these methods are invoked (using a repository). I also had the feeling that in that article (see below), a Service Layer is more considered as a façade that delegates work to the underlying model, than an actual work-intensive layer. Application Layer [his name for Service Layer]: Defines the jobs the software is supposed to do and directs the expressive domain objects to work out problems. The tasks this layer is responsible for are meaningful to the business or necessary for interaction with the application layers of other systems. This layer is kept thin. It does not contain business rules or knowledge, but only coordinates tasks and delegates work to collaborations of domain objects in the next layer down. It does not have state reflecting the business situation, but it can have state that reflects the progress of a task for the user or the program. Which is reinforced here: Service interfaces. Services expose a service interface to which all inbound messages are sent. You can think of a service interface as a façade that exposes the business logic implemented in the application (typically, logic in the business layer) to potential consumers. And here: The service layer should be devoid of any application or business logic and should focus primarily on a few concerns. It should wrap Business Layer calls, translate your Domain in a common language that your clients can understand, and handle the communication medium between server and requesting client. This is a serious contrast to other resources that talk about the Service Layer: The service layer should consist of classes with methods that are units of work with actions that belong in the same transaction. Or the second answer to a question I've already linked: At some point, your application will want some business logic. Also, you might want to validate the input to make sure that there isn't something evil or nonperforming being requested. This logic belongs in your service layer. "Solution"? Following the guidelines in this answer, I came up with the following approach that uses a Service Layer: class UserController : Controller { private UserService _userService; public UserController(UserService userService){ _userService = userService; } public ActionResult MakeHimPay(string username, int amount) { _userService.MakeHimPay(username, amount); return RedirectToAction("ShowUserOverview"); } public ActionResult ShowUserOverview() { return View(); } } class UserService { private IUserRepository _userRepository; public UserService(IUserRepository userRepository) { _userRepository = userRepository; } public void MakeHimPay(username, amount) { _userRepository.GetUserByName(username).makePayment(amount); } } class UserRepository { public User GetUserByName(string name){ // Get appropriate user from database } } class User { private int debt; // debt in cents private string name; // getters public void makePayment(int cents){ debt -= cents; } } Conclusion All together not much has changed here: code from the controller has moved to the service layer (which is a good thing, so there is an upside to this approach). However this doesn't look like it had anything to do with my original answer. I realize design patterns are guidelines, not rules set in stone to be implemented whenever possible. Yet I have not found a definitive explanation of the service layer and how it should be regarded. Is it a means to simply extract logic from the controller and put it inside a service instead? Is it supposed to form a contract between the controller and the domain? Should there be a layer between the domain and the service layer? And, last but not least: following the original comment Business logic should really be in a service. Not in a model. Is this correct? How would I introduce my business logic in a service instead of the model?

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• Das T5-4 TPC-H Ergebnis naeher betrachtet

  - by Stefan Hinker

  Inzwischen haben vermutlich viele das neue TPC-H Ergebnis der SPARC T5-4 gesehen, das am 7. Juni bei der TPC eingereicht wurde.  Die wesentlichen Punkte dieses Benchmarks wurden wie gewohnt bereits von unserer Benchmark-Truppe auf  "BestPerf" zusammengefasst.  Es gibt aber noch einiges mehr, das eine naehere Betrachtung lohnt. Skalierbarkeit Das TPC raet von einem Vergleich von TPC-H Ergebnissen in unterschiedlichen Groessenklassen ab.  Aber auch innerhalb der 3000GB-Klasse ist es interessant: SPARC T4-4 mit 4 CPUs (32 Cores mit 3.0 GHz) liefert 205,792 QphH. SPARC T5-4 mit 4 CPUs (64 Cores mit 3.6 GHz) liefert 409,721 QphH. Das heisst, es fehlen lediglich 1863 QphH oder 0.45% zu 100% Skalierbarkeit, wenn man davon ausgeht, dass die doppelte Anzahl Kerne das doppelte Ergebnis liefern sollte.  Etwas anspruchsvoller, koennte man natuerlich auch einen Faktor von 2.4 erwarten, wenn man die hoehere Taktrate mit beruecksichtigt.  Das wuerde die Latte auf 493901 QphH legen.  Dann waere die SPARC T5-4 bei 83%.  Damit stellt sich die Frage: Was hat hier nicht skaliert?  Vermutlich der Plattenspeicher!  Auch hier lohnt sich eine naehere Betrachtung: Plattenspeicher Im Bericht auf BestPerf und auch im Full Disclosure Report der TPC stehen einige interessante Details zum Plattenspeicher und der Konfiguration.   In der Konfiguration der SPARC T4-4 wurden 12 2540-M2 Arrays verwendet, die jeweils ca. 1.5 GB/s Durchsatz liefert, insgesamt also eta 18 GB/s.  Dabei waren die Arrays offensichtlich mit jeweils 2 Kabeln pro Array direkt an die 24 8GBit FC-Ports des Servers angeschlossen.  Mit den 2x 8GBit Ports pro Array koennte man so ein theoretisches Maximum von 2GB/s erreichen.  Tatsaechlich wurden 1.5GB/s geliefert, was so ziemlich dem realistischen Maximum entsprechen duerfte. Fuer den Lauf mit der SPARC T5-4 wurden doppelt so viele Platten verwendet.  Dafuer wurden die 2540-M2 Arrays mit je einem zusaetzlichen Plattentray erweitert.  Mit dieser Konfiguration wurde dann (laut BestPerf) ein Maximaldurchsatz von 33 GB/s erreicht - nicht ganz das doppelte des SPARC T4-4 Laufs.  Um tatsaechlich den doppelten Durchsatz (36 GB/s) zu liefern, haette jedes der 12 Arrays 3 GB/s ueber seine 4 8GBit Ports liefern muessen.  Im FDR stehen nur 12 dual-port FC HBAs, was die Verwendung der Brocade FC Switches erklaert: Es wurden alle 4 8GBit ports jedes Arrays an die Switches angeschlossen, die die Datenstroeme dann in die 24 16GBit HBA ports des Servers buendelten.  Das theoretische Maximum jedes Storage-Arrays waere nun 4 GB/s.  Wenn man jedoch den Protokoll- und "Realitaets"-Overhead mit einrechnet, sind die tatsaechlich gelieferten 2.75 GB/s gar nicht schlecht.  Mit diesen Zahlen im Hinterkopf ist die Verdopplung des SPARC T4-4 Ergebnisses eine gute Leistung - und gleichzeitig eine gute Erklaerung, warum nicht bis zum 2.4-fachen skaliert wurde. Nebenbei bemerkt: Weder die SPARC T4-4 noch die SPARC T5-4 hatten in der gemessenen Konfiguration irgendwelche Flash-Devices. Mitbewerb Seit die T4 Systeme auf dem Markt sind, bemuehen sich unsere Mitbewerber redlich darum, ueberall den Eindruck zu hinterlassen, die Leistung des SPARC CPU-Kerns waere weiterhin mangelhaft.  Auch scheinen sie ueberzeugt zu sein, dass (ueber)grosse Caches und hohe Taktraten die einzigen Schluessel zu echter Server Performance seien.  Wenn ich mir nun jedoch die oeffentlichen TPC-H Ergebnisse ansehe, sehe ich dies: TPC-H @3000GB, Non-Clustered Systems System QphH SPARC T5-4 3.6 GHz SPARC T5 4/64 – 2048 GB 409,721.8 SPARC T4-4 3.0 GHz SPARC T4 4/32 – 1024 GB 205,792.0 IBM Power 780 4.1 GHz POWER7 8/32 – 1024 GB 192,001.1 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64 – 512 GB 162,601.7 Kurz zusammengefasst: Mit 32 Kernen (mit 3 GHz und 4MB L3 Cache), liefert die SPARC T4-4 mehr QphH@3000GB ab als IBM mit ihrer 32 Kern Power7 (bei 4.1 GHz und 32MB L3 Cache) und auch mehr als HP mit einem 64 Kern Intel Xeon System (2.27 GHz und 24MB L3 Cache).  Ich frage mich, wo genau SPARC hier mangelhaft ist? Nun koennte man natuerlich argumentieren, dass beide Ergebnisse nicht gerade neu sind.  Nun, in Ermangelung neuerer Ergebnisse kann man ja mal ein wenig spekulieren: IBMs aktueller Performance Report listet die o.g. IBM Power 780 mit einem rPerf Wert von 425.5.  Ein passendes Nachfolgesystem mit Power7+ CPUs waere die Power 780+ mit 64 Kernen, verfuegbar mit 3.72 GHz.  Sie wird mit einem rPerf Wert von  690.1 angegeben, also 1.62x mehr.  Wenn man also annimmt, dass Plattenspeicher nicht der limitierende Faktor ist (IBM hat mit 177 SSDs getestet, sie duerfen das gerne auf 400 erhoehen) und IBMs eigene Leistungsabschaetzung zugrunde legt, darf man ein theoretisches Ergebnis von 311398 QphH@3000GB erwarten.  Das waere dann allerdings immer noch weit von dem Ergebnis der SPARC T5-4 entfernt, und gerade in der von IBM so geschaetzen "per core" Metric noch weniger vorteilhaft. In der x86-Welt sieht es nicht besser aus.  Leider gibt es von Intel keine so praktischen rPerf-Tabellen.  Daher muss ich hier fuer eine Schaetzung auf SPECint_rate2006 zurueckgreifen.  (Ich bin kein grosser Fan von solchen Kreuz- und Querschaetzungen.  Insb. SPECcpu ist nicht besonders geeignet, um Datenbank-Leistung abzuschaetzen, da fast kein IO im Spiel ist.)  Das o.g. HP System wird bei SPEC mit 1580 CINT2006_rate gelistet.  Das bis einschl. 2013-06-14 beste Resultat fuer den neuen Intel Xeon E7-4870 mit 8 CPUs ist 2180 CINT2006_rate.  Das ist immerhin 1.38x besser.  (Wenn man nur die Taktrate beruecksichtigen wuerde, waere man bei 1.32x.)  Hier weiter zu rechnen, ist muessig, aber fuer die ungeduldigen Leser hier eine kleine tabellarische Zusammenfassung: TPC-H @3000GB Performance Spekulationen System QphH* Verbesserung gegenueber der frueheren Generation SPARC T4-4 32 cores SPARC T4 205,792 2x SPARC T5-464 cores SPARC T5 409,721 IBM Power 780 32 cores Power7 192,001 1.62x IBM Power 780+ 64 cores Power7+  311,398* HP ProLiant DL980 G764 cores Intel Xeon X7560 162,601 1.38x HP ProLiant DL980 G780 cores Intel Xeon E7-4870    224,348* * Keine echten Resultate  - spekulative Werte auf der Grundlage von rPerf (Power7+) oder SPECint_rate2006 (HP) Natuerlich sind IBM oder HP herzlich eingeladen, diese Werte zu widerlegen.  Aber stand heute warte ich noch auf aktuelle Benchmark Veroffentlichungen in diesem Datensegment. Was koennen wir also zusammenfassen? Es gibt einige Hinweise, dass der Plattenspeicher der begrenzende Faktor war, der die SPARC T5-4 daran hinderte, auf jenseits von 2x zu skalieren Der Mythos, dass SPARC Kerne keine Leistung bringen, ist genau das - ein Mythos.  Wie sieht es umgekehrt eigentlich mit einem TPC-H Ergebnis fuer die Power7+ aus? Cache ist nicht der magische Performance-Schalter, fuer den ihn manche Leute offenbar halten. Ein System, eine CPU-Architektur und ein Betriebsystem jenseits einer gewissen Grenze zu skalieren ist schwer.  In der x86-Welt scheint es noch ein wenig schwerer zu sein. Was fehlt?  Nun, das Thema Preis/Leistung ueberlasse ich gerne den Verkaeufern ;-) Und zu guter Letzt: Nein, ich habe mich nicht ins Marketing versetzen lassen.  Aber manchmal kann ich mich einfach nicht zurueckhalten... Disclosure Statements The views expressed on this blog are my own and do not necessarily reflect the views of Oracle. TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org, results as of 6/7/13. Prices are in USD. SPARC T5-4 409,721.8 QphH@3000GB, $3.94/QphH@3000GB, available 9/24/13, 4 processors, 64 cores, 512 threads; 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. SPEC and the benchmark names SPECfp and SPECint are registered trademarks of the Standard Performance Evaluation Corporation. Results as of June 18, 2013 from www.spec.org. HP ProLiant DL980 G7 (2.27 GHz, Intel Xeon X7560): 1580 SPECint_rate2006; HP ProLiant DL980 G7 (2.4 GHz, Intel Xeon E7-4870): 2180 SPECint_rate2006,

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• Personal Technology – Laptop Screen Blank – No Post – No BIOS – No Boot

  - by Pinal Dave

  If your laptop Screen is Blank and there is no POST, BIOS or boot, you can follow the steps mentioned here and there are chances that it will work if there is no hardware failure inside. Step 1: Remove the power cord from the laptop Step 2: Remove the battery from the laptop Step 3: Hold power button (keep it pressed) for almost 60 seconds Step 4: Plug power back in laptop Step 5: Start computer and it should just start normally. Step 6: Now shut down Step 7: Insert the battery back in the laptop Step 8: Start laptop again and it should work Note 1: If your laptop does not work after inserting back the memory. Remove the memory and repeat above process. Do not insert the battery back as it is malfunctioning. Note 2: If your screen is faulty or have issues with your hardware (motherboard, screen or anything else) this method will not fix your computer. Those, who care about how I come up with this not SQL related blog post, here is the very funny true story. If you are a married man, you will know what I am going to describe next. May be you have faced the same situation or at least you feel and understand my situation. My wife’s computer suddenly stops working when she was searching for my daughter’s mathematics worksheets online. While the fatal accident happened with my wife’s computer (which was my loyal computer for over 4 years before she got it), I was working in my home office, fixing a high priority issue (live order’s database was corrupted) with one of the largest eCommerce websites.  While I was working on production server where I was fixing database corruption, my wife ran to my home office. Here is how the conversation went: Wife: This computer does not work. I: Restart it. Wife: It does not start. I: What did you do with it? Wife: Nothing, it just stopped working. I: Okey, I will look into it later, working on the very urgent issue. Wife: I was printing my daughter’s worksheet. I: Hm.. Okey. Wife: It was the mathematics worksheet, which you promised you will teach but you never get around to do it, so I am doing it myself. I: Thanks. I appreciate it. I am very busy with this issue as million dollar transaction are not happening as the database got corrupted and … Wife: So what … umm… You mean to say that you care about this customer more than your daughter. You know she got A+ in every other class but in mathematics she got only A. She missed that extra credit question. I: She is only 4, it is okay. Wife: She is 4.5 years old not 4. So you are not going to fix this computer which does not start at all. I think our daughter next time will even get lower grades as her dad is busy fixing something. I: Alright, I give up bring me that computer. Our daughter who was listening everything so far she finally decided to speak up. Daughter: Dad, it is a laptop not computer. I: Yes, sweety get that laptop here and your dad is going to fix the this small issue of million dollar issue later on. I decided to pay attention to my wife’s computer. She was right. No matter what I do, it will not boot up, it will not start, no BIOS, no POST screen. The computer starts for a second but nothing comes up on the screen. The light indicating hard drive comes up for a second and goes off. Nothing happens. I removed every single USB drive from the laptop but it still would not start. It was indeed no fun for me. Finally I remember my days when I was not married and used to study in University of Southern California, Los Angeles. I remembered that I used to have very old second (or maybe third or fourth) hand computer with me. In polite words, I had pre-owned computer and it used to face very similar issues again and again. I had small routine I used to follow to fix my old computer and I had decided to follow the same steps again with this computer. Step 1: Remove the power cord from the laptop Step 2: Remove the battery from the laptop Step 3: Hold power button (keep it pressed) for almost 60 seconds Step 4: Plug power back in laptop Step 5: Start computer and it should just start normally. Step 6: Now shut down Step 7: Insert the battery back in the laptop Step 8: Start laptop again and it should work Note 1: If your laptop does not work after inserting back the memory. Remove the memory and repeat above process. Do not insert the battery back as it is malfunctioning. Note 2: If your screen is faulty or have issues with your hardware (motherboard, screen or anything else) this method will not fix your computer. Once I followed above process, her computer worked. I was very delighted, that now I can go back to solving the problem where millions of transactions were waiting as I was fixing corrupted database and it the current state of the database was in emergency mode. Once I fixed the computer, I looked at my wife and asked. I: Well, now this laptop is back online, can I get guaranteed that she will get A+ in mathematics in this week’s quiz? Wife: Sure, I promise. I: Fantastic. After saying that I started to look at my database corruption and my wife interrupted me again. Wife: Btw, I forgot to tell you. Our daughter had got A in mathematics last week but she had another quiz today and she already have received A+ there. I kept my promise. I looked at her and she started to walk outside room, before I say anything my phone rang. DBA from eCommerce company had called me, as he was wondering why there is no activity from my side in last 10 minutes. DBA: Hey bud, are you still connected. I see um… no activity in last 10 minutes. I: Oh, well, I was just saving the world. I am back now. After two hours I had fixed the database corruption and everything was normal. I was outsmarted by my wife but honestly I still respect and love her the same as she is the one who spends countless hours with our daughter so she does not miss me and I can continue writing blogs and keep on doing technology evangelism. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Humor, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• Developing a Cost Model for Cloud Applications

  - by BuckWoody

  Note - please pay attention to the date of this post. As much as I attempt to make the information below accurate, the nature of distributed computing means that components, units and pricing will change over time. The definitive costs for Microsoft Windows Azure and SQL Azure are located here, and are more accurate than anything you will see in this post: http://www.microsoft.com/windowsazure/offers/  When writing software that is run on a Platform-as-a-Service (PaaS) offering like Windows Azure / SQL Azure, one of the questions you must answer is how much the system will cost. I will not discuss the comparisons between on-premise costs (which are nigh impossible to calculate accurately) versus cloud costs, but instead focus on creating a general model for estimating costs for a given application. You should be aware that there are (at this writing) two billing mechanisms for Windows and SQL Azure: “Pay-as-you-go” or consumption, and “Subscription” or commitment. Conceptually, you can consider the former a pay-as-you-go cell phone plan, where you pay by the unit used (at a slightly higher rate) and the latter as a standard cell phone plan where you commit to a contract and thus pay lower rates. In this post I’ll stick with the pay-as-you-go mechanism for simplicity, which should be the maximum cost you would pay. From there you may be able to get a lower cost if you use the other mechanism. In any case, the model you create should hold. Developing a good cost model is essential. As a developer or architect, you’ll most certainly be asked how much something will cost, and you need to have a reliable way to estimate that. Businesses and Organizations have been used to paying for servers, software licenses, and other infrastructure as an up-front cost, and power, people to the systems and so on as an ongoing (and sometimes not factored) cost. When presented with a new paradigm like distributed computing, they may not understand the true cost/value proposition, and that’s where the architect and developer can guide the conversation to make a choice based on features of the application versus the true costs. The two big buckets of use-types for these applications are customer-based and steady-state. In the customer-based use type, each successful use of the program results in a sale or income for your organization. Perhaps you’ve written an application that provides the spot-price of foo, and your customer pays for the use of that application. In that case, once you’ve estimated your cost for a successful traversal of the application, you can build that into the price you charge the user. It’s a standard restaurant model, where the price of the meal is determined by the cost of making it, plus any profit you can make. In the second use-type, the application will be used by a more-or-less constant number of processes or users and no direct revenue is attached to the system. A typical example is a customer-tracking system used by the employees within your company. In this case, the cost model is often created “in reverse” - meaning that you pilot the application, monitor the use (and costs) and that cost is held steady. This is where the comparison with an on-premise system becomes necessary, even though it is more difficult to estimate those on-premise true costs. For instance, do you know exactly how much cost the air conditioning is because you have a team of system administrators? This may sound trivial, but that, along with the insurance for the building, the wiring, and every other part of the system is in fact a cost to the business. There are three primary methods that I’ve been successful with in estimating the cost. None are perfect, all are demand-driven. The general process is to lay out a matrix of: components units cost per unit and then multiply that times the usage of the system, based on which components you use in the program. That sounds a bit simplistic, but using those metrics in a calculation becomes more detailed. In all of the methods that follow, you need to know your application. The components for a PaaS include computing instances, storage, transactions, bandwidth and in the case of SQL Azure, database size. In most cases, architects start with the first model and progress through the other methods to gain accuracy. Simple Estimation The simplest way to calculate costs is to architect the application (even UML or on-paper, no coding involved) and then estimate which of the components you’ll use, and how much of each will be used. Microsoft provides two tools to do this - one is a simple slider-application located here: http://www.microsoft.com/windowsazure/pricing-calculator/  The other is a tool you download to create an “Return on Investment” (ROI) spreadsheet, which has the advantage of leading you through various questions to estimate what you plan to use, located here: https://roianalyst.alinean.com/msft/AutoLogin.do?d=176318219048082115  You can also just create a spreadsheet yourself with a structure like this: Program Element Azure Component Unit of Measure Cost Per Unit Estimated Use of Component Total Cost Per Component Cumulative Cost               Of course, the consideration with this model is that it is difficult to predict a system that is not running or hasn’t even been developed. Which brings us to the next model type. Measure and Project A more accurate model is to actually write the code for the application, using the Software Development Kit (SDK) which can run entirely disconnected from Azure. The code should be instrumented to estimate the use of the application components, logging to a local file on the development system. A series of unit and integration tests should be run, which will create load on the test system. You can use standard development concepts to track this usage, and even use Windows Performance Monitor counters. The best place to start with this method is to use the Windows Azure Diagnostics subsystem in your code, which you can read more about here: http://blogs.msdn.com/b/sumitm/archive/2009/11/18/introducing-windows-azure-diagnostics.aspx This set of API’s greatly simplifies tracking the application, and in fact you can use this information for more than just a cost model. After you have the tracking logs, you can plug the numbers into ay of the tools above, which should give a representative cost or in some cases a unit cost. The consideration with this model is that the SDK fabric is not a one-to-one comparison with performance on the actual Windows Azure fabric. Those differences are usually smaller, but they do need to be considered. Also, you may not be able to accurately predict the load on the system, which might lead to an architectural change, which changes the model. This leads us to the next, most accurate method for a cost model. Sample and Estimate Using standard statistical and other predictive math, once the application is deployed you will get a bill each month from Microsoft for your Azure usage. The bill is quite detailed, and you can export the data from it to do analysis, and using methods like regression and so on project out into the future what the costs will be. I normally advise that the architect also extrapolate a unit cost from those metrics as well. This is the information that should be reported back to the executives that pay the bills: the past cost, future projected costs, and unit cost “per click” or “per transaction”, as your case warrants. The challenge here is in the model itself - statistical methods are not foolproof, and the larger the sample (in this case I recommend the entire population, not a smaller sample) is key. References and Tools Articles: http://blogs.msdn.com/b/patrick_butler_monterde/archive/2010/02/10/windows-azure-billing-overview.aspx http://technet.microsoft.com/en-us/magazine/gg213848.aspx http://blog.codingoutloud.com/2011/06/05/azure-faq-how-much-will-it-cost-me-to-run-my-application-on-windows-azure/ http://blogs.msdn.com/b/johnalioto/archive/2010/08/25/10054193.aspx http://geekswithblogs.net/iupdateable/archive/2010/02/08/qampa-how-can-i-calculate-the-tco-and-roi-when.aspx   Other Tools: http://cloud-assessment.com/ http://communities.quest.com/community/cloud_tools

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• Merge replication stopping without errors in SQL 2008 R2

  - by Rob Farley

  A non-SQL MVP friend of mine, who also happens to be a client, asked me for some help again last week. I was planning on writing this up even before Rob Volk (@sql_r) listed his T-SQL Tuesday topic for this month. Earlier in the year, I (well, LobsterPot Solutions, although I’d been the person mostly involved) had helped out with a merge replication problem. The Merge Agent on the subscriber was just stopping every time, shortly after it started. With no errors anywhere – not in the Windows Event Log, the SQL Agent logs, not anywhere. We’d managed to get the system working again, but didn’t have a good reason about what had happened, and last week, the problem occurred again. I asked him about writing up the experience in a blog post, largely because of the red herrings that we encountered. It was an interesting experience for me, also because I didn’t end up touching my computer the whole time – just tapping on my phone via Twitter and Live Msgr. You see, the thing with replication is that a useful troubleshooting option is to reinitialise the thing. We’d done that last time, and it had started to work again – eventually. I say eventually, because the link being used between the sites is relatively slow, and it took a long while for the initialisation to finish. Meanwhile, we’d been doing some investigation into what the problem could be, and were suitably pleased when the problem disappeared. So I got a message saying that a replication problem had occurred again. Reinitialising wasn’t going to be an option this time either. In this scenario, the subscriber having the problem happened to be in a different domain to the publisher. The other subscribers (within the domain) were fine, just this one in a different domain had the problem. Part of the problem seemed to be a log file that wasn’t being backed up properly. They’d been trying to back up to a backup device that had a corruption, and the log file was growing. Turned out, this wasn’t related to the problem, but of course, any time you’re troubleshooting and you see something untoward, you wonder. Having got past that problem, my next thought was that perhaps there was a problem with the account being used. But the other subscribers were using the same account, without any problems. The client pointed out that that it was almost exactly six months since the last failure (later shown to be a complete red herring). It sounded like something might’ve expired. Checking through certificates and trusts showed no sign of anything, and besides, there wasn’t a problem running a command-prompt window using the account in question, from the subscriber box. ...except that when he ran the sqlcmd –E –S servername command I recommended, it failed with a Named Pipes error. I’ve seen problems with firewalls rejecting connections via Named Pipes but letting TCP/IP through, so I got him to look into SQL Configuration Manager to see what kind of connection was being preferred... Everything seemed fine. And strangely, he could connect via Management Studio. Turned out, he had a typo in the servername of the sqlcmd command. That particular red herring must’ve been reflected in his cheeks as he told me. During the time, I also pinged a friend of mine to find out who I should ask, and Ted Kruger (@onpnt) ‘s name came up. Ted (and thanks again, Ted – really) reconfirmed some of my thoughts around the idea of an account expiring, and also suggesting bumping up the logging to level 4 (2 is Verbose, 4 is undocumented ridiculousness). I’d just told the client to push the logging up to level 2, but the log file wasn’t appearing. Checking permissions showed that the user did have permission on the folder, but still no file was appearing. Then it was noticed that the user had been switched earlier as part of the troubleshooting, and switching it back to the real user caused the log file to appear. Still no errors. A lot more information being pushed out, but still no errors. Ted suggested making sure the FQDNs were okay from both ends, in case the servers were unable to talk to each other. DNS problems can lead to hassles which can stop replication from working. No luck there either – it was all working fine. Another server started to report a problem as well. These two boxes were both SQL 2008 R2 (SP1), while the others, still working, were SQL 2005. Around this time, the client tried an idea that I’d shown him a few years ago – using a Profiler trace to see what was being called on the servers. It turned out that the last call being made on the publisher was sp_MSenumschemachange. A quick interwebs search on that showed a problem that exists in SQL Server 2008 R2, when stored procedures have more than 4000 characters. Running that stored procedure (with the same parameters) manually on SQL 2005 listed three stored procedures, the first of which did indeed have more than 4000 characters. Still no error though, and the problem as listed at http://support.microsoft.com/kb/2539378 describes an error that should occur in the Event log. However, this problem is the type of thing that is fixed by a reinitialisation (because it doesn’t need to send the procedure change across as a transaction). And a look in the change history of the long stored procs (you all keep them, right?), showed that the problem from six months earlier could well have been down to this too. Applying SP2 (with sufficient paranoia about backups and how to get back out again if necessary) fixed the problem. The stored proc changes went through immediately after the service pack was applied, and it’s been running happily since. The funny thing is that I didn’t solve the problem. He had put the Profiler trace on the server, and had done the search that found a forum post pointing at this particular problem. I’d asked Ted too, and although he’d given some useful information, nothing that he’d come up with had actually been the solution either. Sometimes, asking for help is the most useful thing you can do. Often though, you don’t end up getting the help from the person you asked – the sounding board is actually what you need. @rob_farley

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• Benchmarking MySQL Replication with Multi-Threaded Slaves

  - by Mat Keep

  0 0 1 1145 6530 Homework 54 15 7660 14.0 Normal 0 false false false EN-US JA X-NONE /* 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-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The objective of this benchmark is to measure the performance improvement achieved when enabling the Multi-Threaded Slave enhancement delivered as a part MySQL 5.6. As the results demonstrate, Multi-Threaded Slaves delivers 5x higher replication performance based on a configuration with 10 databases/schemas. For real-world deployments, higher replication performance directly translates to: · Improved consistency of reads from slaves (i.e. reduced risk of reading "stale" data) · Reduced risk of data loss should the master fail before replicating all events in its binary log (binlog) The multi-threaded slave splits processing between worker threads based on schema, allowing updates to be applied in parallel, rather than sequentially. This delivers benefits to those workloads that isolate application data using databases - e.g. multi-tenant systems deployed in cloud environments. Multi-Threaded Slaves are just one of many enhancements to replication previewed as part of the MySQL 5.6 Development Release, which include: · Global Transaction Identifiers coupled with MySQL utilities for automatic failover / switchover and slave promotion · Crash Safe Slaves and Binlog · Optimized Row Based Replication · Replication Event Checksums · Time Delayed Replication These and many more are discussed in the “MySQL 5.6 Replication: Enabling the Next Generation of Web & Cloud Services” Developer Zone article  Back to the benchmark - details are as follows. Environment The test environment consisted of two Linux servers: · one running the replication master · one running the replication slave. Only the slave was involved in the actual measurements, and was based on the following configuration: - Hardware: Oracle Sun Fire X4170 M2 Server - CPU: 2 sockets, 6 cores with hyper-threading, 2930 MHz. - OS: 64-bit Oracle Enterprise Linux 6.1 - Memory: 48 GB Test Procedure Initial Setup: Two MySQL servers were started on two different hosts, configured as replication master and slave. 10 sysbench schemas were created, each with a single table: CREATE TABLE `sbtest` (    `id` int(10) unsigned NOT NULL AUTO_INCREMENT,    `k` int(10) unsigned NOT NULL DEFAULT '0',    `c` char(120) NOT NULL DEFAULT '',    `pad` char(60) NOT NULL DEFAULT '',    PRIMARY KEY (`id`),    KEY `k` (`k`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 10,000 rows were inserted in each of the 10 tables, for a total of 100,000 rows. When the inserts had replicated to the slave, the slave threads were stopped. The slave data directory was copied to a backup location and the slave threads position in the master binlog noted. 10 sysbench clients, each configured with 10 threads, were spawned at the same time to generate a random schema load against each of the 10 schemas on the master. Each sysbench client executed 10,000 "update key" statements: UPDATE sbtest set k=k+1 WHERE id = <random row> In total, this generated 100,000 update statements to later replicate during the test itself. Test Methodology: The number of slave workers to test with was configured using: SET GLOBAL slave_parallel_workers=<workers> Then the slave IO thread was started and the test waited for all the update queries to be copied over to the relay log on the slave. The benchmark clock was started and then the slave SQL thread was started. The test waited for the slave SQL thread to finish executing the 100k update queries, doing "select master_pos_wait()". When master_pos_wait() returned, the benchmark clock was stopped and the duration calculated. The calculated duration from the benchmark clock should be close to the time it took for the SQL thread to execute the 100,000 update queries. The 100k queries divided by this duration gave the benchmark metric, reported as Queries Per Second (QPS). Test Reset: The test-reset cycle was implemented as follows: · the slave was stopped · the slave data directory replaced with the previous backup · the slave restarted with the slave threads replication pointer repositioned to the point before the update queries in the binlog. The test could then be repeated with identical set of queries but a different number of slave worker threads, enabling a fair comparison. The Test-Reset cycle was repeated 3 times for 0-24 number of workers and the QPS metric calculated and averaged for each worker count. MySQL Configuration The relevant configuration settings used for MySQL are as follows: binlog-format=STATEMENT relay-log-info-repository=TABLE master-info-repository=TABLE As described in the test procedure, the slave_parallel_workers setting was modified as part of the test logic. The consequence of changing this setting is: 0 worker threads:    - current (i.e. single threaded) sequential mode    - 1 x IO thread and 1 x SQL thread    - SQL thread both reads and executes the events 1 worker thread:    - sequential mode    - 1 x IO thread, 1 x Coordinator SQL thread and 1 x Worker thread    - coordinator reads the event and hands it to the worker who executes 2+ worker threads:    - parallel execution    - 1 x IO thread, 1 x Coordinator SQL thread and 2+ Worker threads    - coordinator reads events and hands them to the workers who execute them Results Figure 1 below shows that Multi-Threaded Slaves deliver ~5x higher replication performance when configured with 10 worker threads, with the load evenly distributed across our 10 x schemas. This result is compared to the current replication implementation which is based on a single SQL thread only (i.e. zero worker threads). Figure 1: 5x Higher Performance with Multi-Threaded Slaves The following figure shows more detailed results, with QPS sampled and reported as the worker threads are incremented. The raw numbers behind this graph are reported in the Appendix section of this post. Figure 2: Detailed Results As the results above show, the configuration does not scale noticably from 5 to 9 worker threads. When configured with 10 worker threads however, scalability increases significantly. The conclusion therefore is that it is desirable to configure the same number of worker threads as schemas. Other conclusions from the results: · Running with 1 worker compared to zero workers just introduces overhead without the benefit of parallel execution. · As expected, having more workers than schemas adds no visible benefit. Aside from what is shown in the results above, testing also demonstrated that the following settings had a very positive effect on slave performance: relay-log-info-repository=TABLE master-info-repository=TABLE For 5+ workers, it was up to 2.3 times as fast to run with TABLE compared to FILE. Conclusion As the results demonstrate, Multi-Threaded Slaves deliver significant performance increases to MySQL replication when handling multiple schemas. This, and the other replication enhancements introduced in MySQL 5.6 are fully available for you to download and evaluate now from the MySQL Developer site (select Development Release tab). You can learn more about MySQL 5.6 from the documentation  Please don’t hesitate to comment on this or other replication blogs with feedback and questions. Appendix – Detailed Results

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• Fun with Aggregates

  - by Paul White

  There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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• TGIF: Engagement Wrap-up

  - by Michael Snow

  We've had a very busy week here at Oracle and as we build up to Oracle OpenWorld starting in less than 10 days - it doesn't look like things will be slowing down. Engagement is definitely in the air this week. Our friend, John Mancini published a great article entitled: "The World of Engagement" on his Digital Landfill blog yesterday and we hosted a great webcast with R "Ray" Wang from Constellation Research yesterday on the "9 C's of Engagement". 12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} I wanted to wrap-up the week with some key takeaways from our webcast yesterday with Ray Wang. If you missed the webcast yesterday, fear not - it is now available  On-Demand. We'll leave you this week with lots of questions about how to navigate these churning waters of engagement. Stay tuned to the Oracle WebCenter Social Business Thought Leaders Webcast Series as we fuel this dialogue. 12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Company Culture Does company support a culture of putting customer satisfaction ahead of profits? Does culture promote creativity and cross functional employee collaboration? Does culture accept different views of multi-generational workforce? Does culture promote employee training and skills development Does culture support upward mobility and long term retention? Does culture support work-life balance? Does the culture provide rewards for employee for outstanding customer support? Channels What are the current primary channels for customer communications? What do you think will be the primary channels in two years? Is company developing support model for emerging channels? Do all channels consistently deliver the same level of customer support? Do you know the cost per transaction across all channels? Do you engage customers proactively across multiple channels? Do all channels have access to the same customer information? Community Does company extend customer support into virtual communities of interest? Does company facilitate educating users through its virtual communities? Does company mine its customer’s experience into useful data? Does company increase the value for customers through using data to deliver new products and services? Does company support two way interactions with its customers through communities of interest? Does company actively support social CRM, online communities and social media markets? Credibility Does company market its trustworthiness through external certificates such as business licenses, BBB certificates or other validations? Does company promote trust through customer testimonials and case studies on ethical business practices? Does company promote truthful market campaigns Does company make it easy for customers to complain? Does company build its reputation for standing behind its products with guarantees for satisfaction? Does company protect its customer data with high security measures> Content What sources do you use to create customer content? Does company mine social media and blogs for customer content? How does your company sort, store and retain its customer content? How frequently does content get updated? What external sources do you use for customer content? How many responses are typically received from a knowledge management system inquiry? Does your company use customer content to design and develop new product and services? Context Does your company market to customers in clusters or individually? Does your company customize its messages and personalize them to specific needs of each individual customer? Does your company store customer data based on their past behaviors, purchases, sentiment analysis and current activities? Does your company manage customer context according to channels used? For example identify personal use channels versus business channels? What is your frequency of collecting customer activities across various touch points? How is your customer data stored and analyzed? Is contextual data used for future customer outreach? Cadence Which channels does your company measure-web site visits, phone calls, IVR, store visits, face to face, social media? Does company make effective use of cross channel marketing to promote more frequent customer engagement? Does your company rate the patterns relevant for your product or service and monitor usage against this pattern? Does your company measure the frequency of both online and offline channels? Does your company apply metrics to the frequency of customer engagements with product or services revenues? Does your company consolidate data for customer engagement across various channels for a complete view of its customer? Catalyst Does company offer coupon discounts? Does company have a customer loyalty program or a VIP membership program? Does company mine customer data to target specific groups of buyers? Do internal employees serve as ambassadors for customer programs? Does company drive loyalty through social media loyalty programs? Does company build rewards based on using loyalty data? Does company offer an employee incentive program to drive customer loyalty?

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• Disaster, or Migration?

  - by Rob Farley

  This post is in two parts – technical and personal. And I should point out that it’s prompted in part by this month’s T-SQL Tuesday, hosted by Allen Kinsel. First, the technical: I’ve had a few conversations with people recently about migration – moving a SQL Server database from one box to another (sometimes, but not primarily, involving an upgrade). One question that tends to come up is that of downtime. Obviously there will be some period of time between the old server being available and the new one. The way that most people seem to think of migration is this: Build a new server. Stop people from using the old server. Take a backup of the old server Restore it on the new server. Reconfigure the client applications (or alternatively, configure the new server to use the same address as the old) Make the new server online. There are other things involved, such as testing, of course. But this is essentially the process that people tell me they’re planning to follow. The bit that I want to look at today (as you’ve probably guessed from my title) is the “backup and restore” section. If a SQL database is using the Simple Recovery Model, then the only restore option is the last database backup. This backup could be full or differential. The transaction log never gets backed up in the Simple Recovery Model. Instead, it truncates regularly to stay small. One that’s using the Full Recovery Model (or Bulk-Logged) won’t truncate its log – the log must be backed up regularly. This provides the benefit of having a lot more option available for restores. It’s a requirement for most systems of High Availability, because if you’re making sure that a spare box is up-and-running, ready to take over, then you have to be interested in the logs that are happening on the current box, rather than truncating them all the time. A High Availability system such as Mirroring, Replication or Log Shipping will initialise the spare machine by restoring a full database backup (and maybe a differential backup if available), and then any subsequent log backups. Once the secondary copy is close, transactions can be applied to keep the two in sync. The main aspect of any High Availability system is to have a redundant system that is ready to take over. So the similarity for migration should be obvious. If you need to move a database from one box to another, then introducing a High Availability mechanism can help. By turning on the Full Recovery Model and then taking a backup (so that the now-interesting logs have some context), logs start being kept, and are therefore available for getting the new box ready (even if it’s an upgraded version). When the migration is ready to occur, a failover can be done, letting the new server take over the responsibility of the old, just as if a disaster had happened. Except that this is a planned failover, not a disaster at all. There’s a fine line between a disaster and a migration. Failovers can be useful in patching, upgrading, maintenance, and more. Hopefully, even an unexpected disaster can be seen as just another failover, and there can be an opportunity there – perhaps to get some work done on the principal server to increase robustness. And if I’ve just set up a High Availability system for even the simplest of databases, it’s not necessarily a bad thing. :) So now the personal: It’s been an interesting time recently... June has been somewhat odd. A court case with which I was involved got resolved (through mediation). I can’t go into details, but my lawyers tell me that I’m allowed to say how I feel about it. The answer is ‘lousy’. I don’t regret pursuing it as long as I did – but in the end I had to make a decision regarding the commerciality of letting it continue, and I’m going to look forward to the days when the kind of money I spent on my lawyers is small change. Mind you, if I had a similar situation with an employer, I’d do the same again, but that doesn’t really stop me feeling frustrated about it. The following day I had to fly to country Victoria to see my grandmother, who wasn’t expected to last the weekend. She’s still around a week later as I write this, but her 92-year-old body has basically given up on her. She’s been a Christian all her life, and is looking forward to eternity. We’ll all miss her though, and it’s hard to see my family grieving. Then on Tuesday, I was driving back to the airport with my family to come home, when something really bizarre happened. We were travelling down the freeway, just pulled out to go past a truck (farm-truck sized, not a semi-trailer), when a car-sized mass of metal fell off it. It was something like an industrial air-conditioner, but from where I was sitting, it was just a mass of spinning metal, like something out of a movie (one friend described it as “holidays by Michael Bay”). Somehow, and I’m really don’t know how, the part of it nearest us bounced high enough to clear the car, and there wasn’t even a scratch. We pulled over the check, and I was just thanking God that we’d changed lanes when we had, and that we remained unharmed. I had all kinds of thoughts about what could’ve happened if we’d had something that size land on the windscreen... All this has drilled home that while I feel that I haven’t provided as well for the family as I could’ve done (like by pursuing an expensive legal case), I shouldn’t even consider that I have proper control over things. I get to live life, and make decisions based on what I feel is right at the time. But I’m not going to get everything right, and there will be things that feel like disasters, some which could’ve been in my control and some which are very much beyond my control. The case feels like something I could’ve pursued differently, a disaster that could’ve been avoided in some way. Gran dying is lousy of course. An accident on the freeway would have been awful. I need to recognise that the worst disasters are ones that I can’t affect, and that I need to look at things in context – perhaps seeing everything that happens as a migration instead. Life is never the same from one day to the next. Every event has a before and an after – sometimes it’s clearly positive, sometimes it’s not. I remember good events in my life (such as my wedding), and bad (such as the loss of my father when I was ten, or the back injury I had eight years ago). I’m not suggesting that I know how to view everything from the “God works all things for good” perspective, but I am trying to look at last week as a migration of sorts. Those things are behind me now, and the future is in God’s hands. Hopefully I’ve learned things, and will be able to live accordingly. I’ve come through this time now, and even though I’ll miss Gran, I’ll see her again one day, and the future is bright.

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• Webcast Q&A: Qualcomm Provides a Seamless Experience for Customers with Oracle WebCenter

  - by kellsey.ruppel

  Last Thursday we had the second webcast in our WebCenter in Action webcast series, "Qualcomm Provides a Seamless Experience for Customers with Oracle WebCenter, where customer Michael Chander from Qualcomm and Vince Casarez & Gourav Goyal from Oracle Partner Keste shared how Oracle WebCenter is powering Qualcomm’s externally facing website and providing a seamless experience for their customers. In case you missed it, here's a recap of the Q&A.   Mike Chandler, Qualcomm Q: Did you run into any issues when integrating all of the different applications together?A: Definitely, our main challenges were in the area of user provisioning and security propagation, all the standard stuff you might expect when hooking up SSO for authentication and authorization. In addition, we spent several iterations getting the UI’s in sync. While everyone was given the same digital material to build too, each team interpreted and implemented it their own way. Initially as a user navigated, if you were looking for it, you could slight variations in color or font or width , stuff like that. So we had to pull all the developers responsible for the UI together and get pixel level agreement on a lot of things so we could ensure seamless transitions across applications. Q: What has been the biggest benefit your end users have seen?A: Wow, there have been several. An SSO enabled environment was huge a win for our users. The portal application that this replaced had not really been invested in by the business. With this project, we had full business participation and backing, and it really showed in some key areas like the shopping experience. For example, while ordering in the previous site, the items did not have any pictures or really usable descriptions. A tremendous amount of work was done to try and make the site more intuitive and user friendly. Site performance has also drastically improved thanks to new hardware, improved database design, and of course the fact that ADF has made great strides in runtime performance. Q: Was there any resistance internally when implementing the solution? If so, how did you overcome that?A: Within a large company, I’m sure there is always going to be competition for large projects, as there was here. Once we got through the technical analysis and settled on the technology choices, it was actually no resistance to implementing the solution. This project was fully driven by the business with the aim of long term growth. I can confidently say that the fact that this project was given the utmost importance by both the business and IT really help put down any resistance that you would typically see while implementing a new solution. Q: Given the performance, what do you estimate to be the top end capacity of the system? A:I think our top end capacity is really only limited by our hardware. I’m comfortable saying we could grow 10x on our current hardware, both in terms of transactions and users. We can easily spin up new JVM instances if needed. We already use less JVM’s than we had planned. In addition, ADF is doing a very good job with his connection pooling and application module pooling, so we see a very good ratio of users connected to the systems vs db connections, without impacting performace. Q: What's the overview or summary of feedback from the users interacting with the site?A: Feedback has been overwhelmingly positive from both the business and our customers. They’re very happy with the new SSO environment , the new LAF, and the performance of the site. Of course, it’s not all roses. No matter what, there are always going to be people that don’t like the layout or the color scheme, etc. By and large though, customers are happy and the business is happy. Q: Can you describe the impressions about the site before and after the project within Qualcomm?A: Before the project, the site worked and people were using it, but most people were not happy with it. It was slow and tended to be a bit tempermental, for example a user would perform a transaction and the system would throw and unexpected error. The user could back up and retry the steps and things would work fine, so why didn’t work the first time?. From a UI perspective, we’d hear comments like it looked like it was built by a high school student.  Vince Casarez & Gourav Goyal, Keste Q: Did you run into any obstacles when implementing the solution?A: It's interesting some people call them "obstacles" on this project we just called them "dependencies".  There were both technical and business related dependencies that we had to work out. Mike points out the SSO dependencies and the coordination and synchronization between the teams to have a seamless login experience and a seamless end user experience.  There was also a set of dependencies on the User Acceptance testing to make sure that everyone understood the use cases for how the system would be used.  With a branching into a new market and trying to match a simple user experience as many consumer sites have today, there was always a tendency for the team members to provide their suggestions on how things could be simpler.  But with all the work up front on the user design and getting the business driving this set of experiences, this minimized the downstream suggestions that tend to distract a team.  In this case, all the work up front allowed us to enumerate the "dependencies" and keep the distractions to a minimum. Q: Was there a lot of custom work that needed to be done for this particular solution?A: The focus for this particular solution was really on the custom processes. The interesting thing is that with the data flows and the integration with applications, there are some pre-built integrations, but realistically for the process flow, we had to build those. The framework and tooling we used made things easier so we didn’t have to implement core functionality, like transitioning from screen to screen or from flow to flow. The design feature of Task Flows really helped speed the development and keep the component infrastructure in line with the dynamic processes.  Task flows and other elements like Skins are core to the infrastructure or technology stack of Oracle. This then allowed the team to center the project focus around the business flows and use cases to meet the core requirements and keep the project on time. Q: What do you think were the keys to success for rolling out WebCenter?A:  The 5 main keys to success were: 1) Sponsorship from the whole organization around this project from senior executive agreement, business owners driving functionality, and IT development alignment; 2) Upfront design planning and use case definition to clearly define the project scope and requirements; 3) Focussed development and project management aligned with the top level goals and drivers; 4) User acceptance and usability testing along the way to identify potential issues and direct resolution of the issues;  and 5) Constant prioritization of the issues for development to fix by the business.  It also helps to have great team chemistry and really smart people working on the project. If you missed the webcast, be sure to catch the replay to see a live demonstration of WebCenter in action!  Qualcomm Provides a Seamless Experience for Customers with Oracle WebCenter from Oracle WebCenter

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• BI Applications overview

  - by sv744

  Welcome to Oracle BI applications blog! This blog will talk about various features, general roadmap, description of functionality and implementation steps related to Oracle BI applications. In the first post we start with an overview of the BI apps and will delve deeper into some of the topics below in the upcoming weeks and months. If there are other topics you would like us to talk about, pl feel free to provide feedback on that. The Oracle BI applications are a set of pre-built applications that enable pervasive BI by providing role-based insight for each functional area, including sales, service, marketing, contact center, finance, supplier/supply chain, HR/workforce, and executive management. For example, Sales Analytics includes role-based applications for sales executives, sales management, as well as front-line sales reps, each of whom have different needs. The applications integrate and transform data from a range of enterprise sources—including Siebel, Oracle, PeopleSoft, SAP, and others—into actionable intelligence for each business function and user role. This blog  starts with the key benefits and characteristics of Oracle BI applications. In a series of subsequent blogs, each of these points will be explained in detail. Why BI apps? Demonstrate the value of BI to a business user, show reports / dashboards / model that can answer their business questions as part of the sales cycle. Demonstrate technical feasibility of BI project and significantly lower risk and improve success Build Vs Buy benefit Don’t have to start with a blank sheet of paper. Help consolidate disparate systems Data integration in M&A situations Insulate BI consumers from changes in the OLTP Present OLTP data and highlight issues of poor data / missing data – and improve data quality and accuracy Prebuilt Integrations BI apps support prebuilt integrations against leading ERP sources: Fusion Applications, E- Business Suite, Peoplesoft, JD Edwards, Siebel, SAP Co-developed with inputs from functional experts in BI and Applications teams. Out of the box dimensional model to source model mappings Multi source and Multi Instance support Rich Data Model    BI apps have a very rich dimensionsal data model built over 10 years that incorporates best practises from BI modeling perspective as well as reflect the source system complexities  Thanks for reading a long post, and be on the lookout for future posts.  We will look forward to your valuable feedback on these topics as well as suggestions on what other topics would you like us to cover. I Conformed dimensional model across all business subject areas allows cross functional reporting, e.g. customer / supplier 360 Over 360 fact tables across 7 product areas CRM – 145, SCM – 47, Financials – 28, Procurement – 20, HCM – 27, Projects – 18, Campus Solutions – 21, PLM - 56 Supported by 300 physical dimensions Support for extensive calendars; Gregorian, enterprise and ledger based Conformed data model and metrics for real time vs warehouse based reporting  Multi-tenant enabled Extensive BI related transformations BI apps ETL and data integration support various transformations required for dimensional models and reporting requirements. All these have been distilled into common patterns and abstracted logic which can be readily reused across different modules Slowly Changing Dimension support Hierarchy flattening support Row / Column Hybrid Hierarchy Flattening As Is vs. As Was hierarchy support Currency Conversion :-  Support for 3 corporate, CRM, ledger and transaction currencies UOM conversion Internationalization / Localization Dynamic Data translations Code standardization (Domains) Historical Snapshots Cycle and process lifecycle computations Balance Facts Equalization of GL accounting chartfields/segments Standardized values for categorizing GL accounts Reconciliation between GL and subledgers to track accounted/transferred/posted transactions to GL Materialization of data only available through costly and complex APIs e.g. Fusion Payroll, EBS / Fusion Accruals Complex event Interpretation of source data – E.g. o    What constitutes a transfer o    Deriving supervisors via position hierarchy o    Deriving primary assignment in PSFT o    Categorizing and transposition to measures of Payroll Balances to specific metrics to support side by side comparison of measures of for example Fixed Salary, Variable Salary, Tax, Bonus, Overtime Payments. o    Counting of Events – E.g. converting events to fact counters so that for example the number of hires can easily be added up and compared alongside the total transfers and terminations. Multi pass processing of multiple sources e.g. headcount, salary, promotion, performance to allow side to side comparison. Adding value to data to aid analysis through banding, additional domain classifications and groupings to allow higher level analytical reporting and data discovery Calculation of complex measures examples: o    COGs, DSO, DPO, Inventory turns  etc o    Transfers within a Hierarchy or out of / into a hierarchy relative to view point in hierarchy. Configurability and Extensibility support  BI apps offer support for extensibility for various entities as automated extensibility or part of extension methodology Key Flex fields and Descriptive Flex support  Extensible attribute support (JDE)  Conformed Domains ETL Architecture BI apps offer a modular adapter architecture which allows support of multiple product lines into a single conformed model Multi Source Multi Technology Orchestration – creates load plan taking into account task dependencies and customers deployment to generate a plan based on a customers of multiple complex etl tasks Plan optimization allowing parallel ETL tasks Oracle: Bit map indexes and partition management High availability support    Follow the sun support. TCO BI apps support several utilities / capabilities that help with overall total cost of ownership and ensure a rapid implementation Improved cost of ownership – lower cost to deploy On-going support for new versions of the source application Task based setups flows Data Lineage Functional setup performed in Web UI by Functional person Configuration Test to Production support Security BI apps support both data and object security enabling implementations to quickly configure the application as per the reporting security needs Fine grain object security at report / dashboard and presentation catalog level Data Security integration with source systems  Extensible to support external data security rules Extensive Set of KPIs Over 7000 base and derived metrics across all modules Time series calculations (YoY, % growth etc) Common Currency and UOM reporting Cross subject area KPIs (analyzing HR vs GL data, drill from GL to AP/AR, etc) Prebuilt reports and dashboards 3000+ prebuilt reports supporting a large number of industries Hundreds of role based dashboards Dynamic currency conversion at dashboard level Highly tuned Performance The BI apps have been tuned over the years for both a very performant ETL and dashboard performance. The applications use best practises and advanced database features to enable the best possible performance. Optimized data model for BI and analytic queries Prebuilt aggregates& the ability for customers to create their own aggregates easily on warehouse facts allows for scalable end user performance Incremental extracts and loads Incremental Aggregate build Automatic table index and statistics management Parallel ETL loads Source system deletes handling Low latency extract with Golden Gate Micro ETL support Bitmap Indexes Partitioning support Modularized deployment, start small and add other subject areas seamlessly Source Specfic Staging and Real Time Schema Support for source specific operational reporting schema for EBS, PSFT, Siebel and JDE Application Integrations The BI apps also allow for integration with source systems as well as other applications that provide value add through BI and enable BI consumption during operational decision making Embedded dashboards for Fusion, EBS and Siebel applications Action Link support Marketing Segmentation Sales Predictor Dashboard Territory Management External Integrations The BI apps data integration choices include support for loading extenral data External data enrichment choices : UNSPSC, Item class etc. Extensible Spend Classification Broad Deployment Choices Exalytics support Databases :  Oracle, Exadata, Teradata, DB2, MSSQL ETL tool of choice : ODI (coming), Informatica Extensible and Customizable Extensible architecture and Methodology to add custom and external content Upgradable across releases

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• Oracle B2B - Synchronous Request Reply

  - by cdwright

  Introduction So first off, let me say I didn't create this demo (although I did modify it some). I got it from a member of the B2B development technical staff. Since it came with only a simple readme file, I thought I would take some time and write a more detailed explanation about how it works. Beginning with Oracle SOA Suite PS5 (11.1.1.6), B2B supports synchronous request reply over http using the b2b/syncreceiver servlet. I’m attaching the demo to this blog which includes a SOA composite archive that needs to be deployed using JDeveloper, a B2B repository with two agreements that need to be deployed using the B2B console, and a test xml file that gets sent to the b2b/syncreceiver servlet using your favorite SOAP test tool (I'm using Firefox Poster here). You can download the zip file containing the demo here. The demo works by sending the sample xml request file (req.xml) to http://<b2bhost>:8001/b2b/syncreceiver using the SOAP test tool.  The syncreceiver servlet keeps the socket connection open between itself and the test tool so that it can synchronously send the reply message back. When B2B receives the inbound request message, it is passed to the SOA composite through the default B2B Fabric binding. A simple reply is created in BPEL and returned to B2B which then sends the message back to the test tool using that same socket connection. I’ll show you the B2B configuration first, then we’ll look at the soa composite. Configuring B2B No additional configuration necessary in order to use the syncreceiver servlet. It is already running when you start SOA. After importing the GC_SyncReqRep.zip repository file into B2B, you’ll have the typical GlobalChips host trading partner and the Acme remote trading partner. Document Management The repository contains two very simple custom XML document definitions called Orders and OrdersResponse. In order to determine the trading partner agreement needed to process the inbound Orders document, you need to know two things about it; what is it and where it came from. So let’s look at how B2B identifies the appropriate document definition for the message. The XSD’s for these two document definitions themselves are not particularly interesting. Whenever you're dealing with custom XML documents, B2B identifies the appropriate document definition for each XML message using an XPath Identification Expression. The expression is entered for each of these document definitions under the document administration tab in the B2B console. The full XPATH expression for the Orders document is  //*[local-name()='shiporder']/*[local-name()='shipto']/*[local-name()='name']/text(). You can see this path in the XSD diagram below and how it uniquely identifies this message. The OrdersReponse document is identified in the same way. The XPath expression for it is //*[local-name()='Response']/*[local-name()='Status']/text(). You can see how it’s path differs uniquely identifying the reply from the request. Trading Partner Profile The trading partner profiles are very simple too. For GlobalChips, a generic identifier is being used to identify the sender of the response document using the host trading partner name. For Acme, a generic identifier is also being used to identify the sender of the inbound request using the remote trading partner name. The document types are added for the remote trading partner as usual. So the remote trading partner Acme is the sender of the Orders document, and it is the receiver of the OrdersResponse document. For the remote trading partner only, there needs to be a dummy channel which gets used in the outbound response agreement. The channel is not actually used. It is just a necessary place holder that needs to be there when creating the agreement. Trading Partner Agreement The agreements are equally simple. There is no validation and translation is not an option for a custom XML document type. For the InboundAgreement (request) the document definition is set to OrdersDef. In the Agreement Parameters section the generic identifiers have been added for the host and remote trading partners. That’s all that is needed for the inbound transaction. For the OutboundAgreement (response), the document definition is set to OrdersResponseDef and the generic identifiers for the two trading partners are added. The remote trading partner dummy delivery channel is also added to the agreement. SOA Composite Import the SOA composite archive into JDeveloper as an EJB JAR file. Open the composite and you should have a project that looks like this. In the composite, open the b2bInboundSyncSvc exposed service and advance through the setup wizard. Select your Application Server Connection and advance to the Operations window. Notice here that the B2B binding is set to Receive. It is not set for Synchronous Request Reply. Continue advancing through the wizard as you normally would and select finish at the end. Now open BPELProcess1 in the composite. The BPEL process is set as a Synchronous Request Reply as you can see below. The while loop is there just to give the process something to do. The actual reply message is prepared in the assignResponseValues assignment followed by an Invoke of the B2B binding. Open the replyResponse Invoke and go to the properties tab. You’ll see that the fromTradingPartnerId, toTradingPartner, documentTypeName, and documentProtocolRevision properties have been set. Testing the Configuration To test the configuration, I used Firefox Poster. Enter the URL for the b2b/syncreceiver servlet and browse for the req.xml file that contains the test request message. In the Headers tab, add the property ‘from’ and give it the value ‘Acme’. This is how B2B will know where the message is coming from and it will use that information along with the document type name to find the right trading partner agreement. Now post the message. You should get back a response with a status of ‘200 OK’. That’s all there is to it.

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• How to Plug a Small Hole in NetBeans JSF (Join Table) Code Generation

  - by MarkH

  I was asked recently to provide an assist with designing and building a small-but-vital application that had at its heart some basic CRUD (Create, Read, Update, & Delete) functionality, built upon an Oracle database, to be accessible from various locations. Working from the stated requirements, I fleshed out the basic application and database designs and, once validated, set out to complete the first iteration for review. Using SQL Developer, I created the requisite tables, indices, and sequences for our first run. One of the tables was a many-to-many join table with three fields: one a primary key for that table, the other two being primary keys for the other tables, represented as foreign keys in the join table. Here is a simplified example of the trio of tables: Once the database was in decent shape, I fired up NetBeans to let it have first shot at the code. NetBeans does a great job of generating a mountain of essential code, saving developers what must be millions of hours of effort each year by building a basic foundation with a few clicks and keystrokes. Lest you think it (or any tool) can do everything for you, however, occasionally something tosses a paper clip into the delicate machinery and makes you open things up to fix them. Join tables apparently qualify.  :-) In the case above, the entity class generated for the join table (New Entity Classes from Database) included an embedded object consisting solely of the two foreign key fields as attributes, in addition to an object referencing each one of the "component" tables. The Create page generated (New JSF Pages from Entity Classes) worked well to a point, but when trying to save, we were greeted with an error: Transaction aborted. Hmm. A quick debugger session later and I'd identified the issue: when trying to persist the new join-table object, the embedded "foreign-keys-only" object still had null values for its two (required value) attributes...even though the embedded table objects had populated key attributes. Here's the simple fix: In the join-table controller class, find the public String create() method. It will look something like this:     public String create() {        try {            getFacade().create(current);            JsfUtil.addSuccessMessage(ResourceBundle.getBundle("/Bundle").getString("JoinEntityCreated"));            return prepareCreate();        } catch (Exception e) {            JsfUtil.addErrorMessage(e, ResourceBundle.getBundle("/Bundle").getString("PersistenceErrorOccured"));            return null;        }    } To restore balance to the force, modify the create() method as follows (changes in red):     public String create() {         try {            // Add the next two lines to resolve:            current.getJoinEntityPK().setTbl1id(current.getTbl1().getId().toBigInteger());            current.getJoinEntityPK().setTbl2id(current.getTbl2().getId().toBigInteger());            getFacade().create(current);            JsfUtil.addSuccessMessage(ResourceBundle.getBundle("/Bundle").getString("JoinEntityCreated"));            return prepareCreate();        } catch (Exception e) {            JsfUtil.addErrorMessage(e, ResourceBundle.getBundle("/Bundle").getString("PersistenceErrorOccured"));            return null;        }    } I'll be refactoring this code shortly, but for now, it works. Iteration one is complete and being reviewed, and we've met the milestone. Here's to happy endings (and customers)! All the best,Mark

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• Wireless access point -> Powerline -> Router -> Internet, should this work?

  - by Anthony

  My network at home used to be a laptop and desktop connected wirelessly to a single Wireless ADSL router, a Cisco 877W. Wireless reception around the house with this setup was quite unreliable, so I've gone about looking to improve it. I purchased some Belkin Gigabit powerline adapters and I've got these working fine. I can hook a computer up to one of the powerline adapters, and with the other one plugged into the ADSL router the computer has internet access. Additionally I can hook a Netgear DG834G Wireless ADSL router into it with the adsl not plugged in, and after turning off DHCP can RJ45 a computer up to the network. Everything works fine. However, if I setup a wireless network on the Netgear then any computer that connects wirelessly to it cannot access the internet. It gets an IP address very slowly via DHCP which is a good one, but it cannot access the internet. It can however communicate with the RJ45'd computer also connected to the Netgear. I wondered whether this could be a problem with the Netgear so I've borrowed a Cisco Aironet 1200 and got this working fine when it's attached directly to the primary ADSL router. I can connect to it wireless and get onto the internet. However, if I then plug it into the Netgear I can communicate with other devices attached to the Netgear, but can't get any further than the Netgear. All the while though the other devices RJ45'd to the Netgear are communicating with the internet just fine. I'm starting to suspect it's one of two things causing the problem: 1) For some reason the belkin powerline adapters don't like carrying wireless-originating signals. Could this be possible? 2) The primary Cisco ADSL router doesn't want to communicate with other devices on my network more than one hop away from it. I'm making an assumption here that within the Netgear box the wireless and wired sides are handled differently. Could this be true? Has anyone successfully setup something similar to what I'm trying, with a wireless device on the otherside of a pair of powerline connectors? Update 06/07/2010 - Response to irrational John 28 June Thanks for the answer John - and for clearing up some of my questions. The model number of the belkin powerline adapters are F5D4076. Security was apparently enabled by default on them, and I didn't change them from their default setting. The network diagram in your answer shows exactly what I'm trying to setup: I've followed that guide and I'm still not able to get things working properly. The thing that perplexes me is that wired network traffic works just fine - it's only the wireless traffic that doesn't. This is with the same laptop, and the same DHCP or static IPs. "1. What IP addresses did you assign to each router? What subnet masks are you using?" - subnet is 255.255.255.0, the router connected to the adsl is 192.168.153.1 and that has the DHCP server. The access point on the other side of the powerline adapters I've tried both a static IP of 192.168.153.110, same subnet, and a DHCP-assigned IP. The other devices are DHCP, although I also tried manually entering IP settings. "2. Have you correctly enabled DHCP on only one of the routers and disabled it on all the others?" Yes I have - only the internet-connected router has DHCP enabled. The IP range for the DHCP is from 192.168.153.11 - 192.168.153.200. The strange thing is that wired connections work fine on the LAN, plugged into any router, work fine - it's only the wireless connections that aren't working when they're plugged into the non-primary AP. "Since the routers you are using appear to integrate an ADSL modem I'm assuming there is no WAN port on them." There's no NAT within the LAN, and all wired connections are connected to LAN ports. It's something wrong with the wireless - wired works fine throughout the whole LAN. Update 06/07/2010 - Response to irrational John 29 June The diagram you've drawn in your answer shows pretty much exactly what I'm trying to do. I've spent another evening trying different things and made some progress but I'm still scratching my head. I've borrowed a Netgear access point and been trying with this, and the strange thing is that my PC is working now - this is a Windows 7 PC connected to the access point in the position of where the DG834G is in the diagram. Meanwhile, however, I have an old Powerbook G4 12" I use for music, and while that has a DHCP-assigned IP address, it's not getting any network throughput to either LAN or internet addresses. To make matters more strange, my phone appears to be intermittently working when it's on the wifi. The access point is a Netgear WPN802v1, DHCP, NAT both switched off, running firmware 2.0.9.0. Last night I set it up with exactly the same settings, and similar to tonight I could get a couple of devices to work, and a couple not to. By the morning, however, everything had stopped working - nothing could get a DHCP IP address. I rebooted the 877W earlier this evening and I'm wondering whether this is why a few things are working now. "Could it be possible that the issue could be with the 877W?" I didn't configure this - is it possible that the DHCP server only likes assigning devices that are immediately attached to it? Or similar, could a firewall be stopping too many addresses that are coming through one device? (ie. the Access Point) This could explain why devices are working at the start but then not by the end. In reply to your questions, "1. I looked at the Netgear DG834G support page. There are five versions of this router. Which version do you have? Netgear usually lists this on the label on the bottom of the router. What version of the firmware does it have?" It's a DG834Gv3, and the firmware is the last on the netgear site version 4.01.40. "3. Not knowing which version you have, I glanced at the reference manual for the DG834G v3. In the section for Wireless Settings under the subsection Wireless Access Point there is a check box for a Wireless Isolation setting. If you have this setting it should be off/unchecked. If it is checked then any device connected via wireless would not be able to talk to any other device on the LAN. This sounds like your problem so maybe this is the cause?" I've checked this and it's switched off. I've made a change to the IP of the access point to something outside the DHCP range - it's now 192.158.153.5, with DHCP starting at 11 and going up to 254. Thanks for the tip about this - I only have a few devices so wouldn't anticipate the DHCP server assigning up to 110, but better safe than sorry. Finally one more thing I thought I should add, is with the Powerbook G4 that's not working - it's getting a DHCP IP address and it can communicate with the WPN802 as I can visit the administration page. Anything further than this, however, it can't reach; I can't administrate the 192.168.153.1 (877W router). Strangely, however, when I open Finder on the same powerbook it's detecting my NAS which is attached directly via wire to the 877W. If I try to browse it, it says connection failed. RE: "Perhaps the problem with your Powerbook is with DNS?.." The IP settings on the powerbook are identical to that of the PC with the exception of the IP address; the PC is 192.168.153.17 and the powerbook is 192.168.153.12. Subnets are the same, 255.255.255.0 and default gateway is the same, .1, and the DNS servers are the same. I administrate the 877W by going to 192.168.153.1 in the browser. This is what isn't working from the Powerbook, despite the PC working fine when I do the same. Meanwhile, however, I can administrate the AP on 192.168.153.5 from both PC and Powerbook Update 06/07/2010 - FINAL RESOLUTION of sorts: First off, sorry for the length of this question. I need start to practice a more concise writing style, so I'm going to try to keep this bit brief. After much fiddling, and with the hugely-appreciated help of irrational John, I have come to the conclusion that it's something wrong with the powerbook. I believe that this was perhaps the reason I doubted things worked at the very beginning. I now have the original DG834Gv3 running both wirelessly and wired, and both wired devices and wireless devices get internet connectivity. The only anomaly is the powerbook which I've had to keep wired, as no matter what I do it refuses to work wirelessly. I still have suspicions that the 877W isn't quite right; I'm fairly sure that if I RJ45 the powerline adapter into a different LAN port on it then everything will break. I've just about run out of patience to test this further, and I think I need to go into the 877W's config to match the 877w's lan port's settings. I'm accepting irrational John's answer as he's been enormously helpful, way above the call of duty, and for this line he wrote: Beats the heck out of me. which in the midst of great frustration made me chuckle, and for a sentence in one of his comments to the same answer: If it is specific to the Powerbook I would put that issue aside until after you feel you have the rest of your LAN and the additional WAP all working together correctlyt It was this second sentence that made me put the powerbook aside and concentrate on the other devices that ultimately led me to getting things working.

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• Using R to Analyze G1GC Log Files

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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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• Plesk 11: install Apache with SNI support

  - by Ueli

  If I try to update from standard Apache to Apache with SNI support with the Plesk installation program (example.com:8447), I get an error, that I have to remove apr-util-ldap-1.4.1-1.el5.x86_64 It's in german: Informationen über installierte Pakete abrufen... Installation started in background Datei wird heruntergeladen PSA_11.0.9/dist-rpm-CentOS-5-x86_64/build-11.0.9-cos5-x86_64.hdr.gz: 11%..20%..30%..40%..50%..60%..70%..81%..91%..100% fertig. Datei wird heruntergeladen PSA_11.0.9/update-rpm-CentOS-5-x86_64/update-11.0.9-cos5-x86_64.hdr.gz: 10%..20%..30%..40%..50%..60%..70%..80%..90%..100% fertig. Datei wird heruntergeladen PSA_11.0.9/thirdparty-rpm-CentOS-5-x86_64/thirdparty-11.0.9-cos5-x86_64.hdr.gz: 10%..26%..43%..77%..100% fertig. Datei wird heruntergeladen BILLING_11.0.9/thirdparty-rpm-RedHat-all-all/thirdparty-11.0.9-rhall-all.hdr.gz: 100% fertig. Datei wird heruntergeladen BILLING_11.0.9/update-rpm-RedHat-all-all/update-11.0.9-rhall-all.hdr.gz: 100% fertig. Datei wird heruntergeladen SITEBUILDER_11.0.10/thirdparty-rpm-RedHat-all-all/thirdparty-11.0.10-rhall-all.hdr.gz: 100% fertig. Datei wird heruntergeladen SITEBUILDER_11.0.10/dist-rpm-RedHat-all-all/build-11.0.10-rhall-all.hdr.gz: 10%..22%..31%..41%..51%..65%..70%..80%..90%..100% fertig. Datei wird heruntergeladen SITEBUILDER_11.0.10/update-rpm-RedHat-all-all/update-11.0.10-rhall-all.hdr.gz: 100% fertig. Datei wird heruntergeladen APACHE_2.2.22/thirdparty-rpm-CentOS-5-x86_64/thirdparty-2.2.22-rh5-x86_64.hdr.gz: 19%..25%..35%..83%..93%..100% fertig. Datei wird heruntergeladen APACHE_2.2.22/update-rpm-CentOS-5-x86_64/update-2.2.22-rh5-x86_64.hdr.gz: 100% fertig. Datei wird heruntergeladen BILLING_11.0.9/dist-rpm-RedHat-all-all/build-11.0.9-rhall-all.hdr.gz: 11%..23%..31%..41%..52%..62%..73%..83%..91%..100% fertig. Datei wird heruntergeladen APACHE_2.2.22/dist-rpm-CentOS-5-x86_64/build-2.2.22-rh5-x86_64.hdr.gz: 36%..50%..100% fertig. Pakete, die installiert werden müssen, werden ermittelt. -> Error: Mit der Installation kann erst fortgefahren werden, wenn das Paket apr-util-ldap-1.4.1-1.el5.x86_64 vom System entfernt wird. Es wurden nicht alle Pakete installiert. Bitte beheben Sie dieses Problem und versuchen Sie, die Pakete erneut zu installieren. Wenn Sie das Problem nicht selbst beheben können, wenden Sie sich bitte an den technischen Support. - «Error: The installation can be continued only if the package apr-util-ldap-1.4.1-1.el5.x86_64 is removed from the system» But I can't uninstall apr-util-ldap-1.4.1-1.el5.x86_64 without removing a lot of important packages: Dependencies Resolved ========================================================================================================================================= Package Arch Version Repository Size ========================================================================================================================================= Removing: apr-util-ldap x86_64 1.4.1-1.el5 installed 9.0 k Removing for dependencies: SSHTerm noarch 0.2.2-10.12012310 installed 4.9 M awstats noarch 7.0-11122114.swsoft installed 3.5 M httpd x86_64 2.2.23-3.el5 installed 3.4 M mailman x86_64 3:2.1.9-6.el5_6.1 installed 34 M mod-spdy-beta x86_64 0.9.3.3-386 installed 2.4 M mod_perl x86_64 2.0.4-6.el5 installed 6.8 M mod_python x86_64 3.2.8-3.1 installed 1.2 M mod_ssl x86_64 1:2.2.23-3.el5 installed 179 k perl-Apache-ASP x86_64 2.59-0.93298 installed 543 k php53 x86_64 5.3.3-13.el5_8 installed 3.4 M php53-sqlite2 x86_64 5.3.2-11041315 installed 366 k plesk-core x86_64 11.0.9-cos5.build110120608.16 installed 79 M plesk-l10n noarch 11.0.9-cos5.build110120827.16 installed 21 M pp-sitebuilder noarch 11.0.10-38572.12072100 installed 181 M psa x86_64 11.0.9-cos5.build110120608.16 installed 473 k psa-awstats-configurator noarch 11.0.9-cos5.build110120606.19 installed 0.0 psa-backup-manager x86_64 11.0.9-cos5.build110120608.16 installed 8.6 M psa-backup-manager-vz x86_64 11.0.0-cos5.build110120123.10 installed 1.6 k psa-fileserver x86_64 11.0.9-cos5.build110120608.16 installed 364 k psa-firewall x86_64 11.0.9-cos5.build110120608.16 installed 550 k psa-health-monitor noarch 11.0.9-cos5.build110120606.19 installed 2.3 k psa-horde noarch 3.3.13-cos5.build110120606.19 installed 20 M psa-hotfix1-9.3.0 x86_64 9.3.0-cos5.build93100518.16 installed 23 k psa-imp noarch 4.3.11-cos5.build110120606.19 installed 12 M psa-ingo noarch 1.2.6-cos5.build110120606.19 installed 5.1 M psa-kronolith noarch 2.3.6-cos5.build110120606.19 installed 6.3 M psa-libxml-proxy x86_64 2.7.8-0.301910 installed 1.2 M psa-mailman-configurator x86_64 11.0.9-cos5.build110120608.16 installed 5.5 k psa-migration-agents x86_64 11.0.9-cos5.build110120608.16 installed 169 k psa-migration-manager x86_64 11.0.9-cos5.build110120608.16 installed 1.1 M psa-mimp noarch 1.1.4-cos5.build110120418.19 installed 2.9 M psa-miva x86_64 1:5.06-cos5.build1013111101.14 installed 4.5 M psa-mnemo noarch 2.2.5-cos5.build110120606.19 installed 4.1 M psa-mod-fcgid-configurator x86_64 2.0.0-cos5.build1013111101.14 installed 0.0 psa-mod_aclr2 x86_64 12021319-9e86c2f installed 8.1 k psa-mod_fcgid x86_64 2.3.6-12050315 installed 222 k psa-mod_rpaf x86_64 0.6-12021310 installed 7.7 k psa-passwd noarch 3.1.3-cos5.build1013111101.14 installed 3.7 M psa-php53-configurator x86_64 1.6.2-cos5.build110120608.16 installed 6.4 k psa-rubyrails-configurator x86_64 1.1.6-cos5.build1013111101.14 installed 0.0 psa-spamassassin x86_64 11.0.9-cos5.build110120608.16 installed 167 k psa-turba noarch 2.3.6-cos5.build110120606.19 installed 6.1 M psa-updates noarch 11.0.9-cos5.build110120704.10 installed 0.0 psa-vhost noarch 11.0.9-cos5.build110120606.19 installed 160 k psa-vpn x86_64 11.0.9-cos5.build110120608.16 installed 1.9 M psa-watchdog x86_64 11.0.9-cos5.build110120608.16 installed 2.9 M webalizer x86_64 2.01_10-30.1 installed 259 k Transaction Summary ========================================================================================================================================= Remove 48 Package(s) Reinstall 0 Package(s) Downgrade 0 Package(s) What should I do?

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