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  • Transactional Messaging in the Windows Azure Service Bus

    - by Alan Smith
    Introduction I’m currently working on broadening the content in the Windows Azure Service Bus Developer Guide. One of the features I have been looking at over the past week is the support for transactional messaging. When using the direct programming model and the WCF interface some, but not all, messaging operations can participate in transactions. This allows developers to improve the reliability of messaging systems. There are some limitations in the transactional model, transactions can only include one top level messaging entity (such as a queue or topic, subscriptions are no top level entities), and transactions cannot include other systems, such as databases. As the transaction model is currently not well documented I have had to figure out how things work through experimentation, with some help from the development team to confirm any questions I had. Hopefully I’ve got the content mostly correct, I will update the content in the e-book if I find any errors or improvements that can be made (any feedback would be very welcome). I’ve not had a chance to look into the code for transactions and asynchronous operations, maybe that would make a nice challenge lab for my Windows Azure Service Bus course. Transactional Messaging Messaging entities in the Windows Azure Service Bus provide support for participation in transactions. This allows developers to perform several messaging operations within a transactional scope, and ensure that all the actions are committed or, if there is a failure, none of the actions are committed. There are a number of scenarios where the use of transactions can increase the reliability of messaging systems. Using TransactionScope In .NET the TransactionScope class can be used to perform a series of actions in a transaction. The using declaration is typically used de define the scope of the transaction. Any transactional operations that are contained within the scope can be committed by calling the Complete method. If the Complete method is not called, any transactional methods in the scope will not commit.   // Create a transactional scope. using (TransactionScope scope = new TransactionScope()) {     // Do something.       // Do something else.       // Commit the transaction.     scope.Complete(); }     In order for methods to participate in the transaction, they must provide support for transactional operations. Database and message queue operations typically provide support for transactions. Transactions in Brokered Messaging Transaction support in Service Bus Brokered Messaging allows message operations to be performed within a transactional scope; however there are some limitations around what operations can be performed within the transaction. In the current release, only one top level messaging entity, such as a queue or topic can participate in a transaction, and the transaction cannot include any other transaction resource managers, making transactions spanning a messaging entity and a database not possible. When sending messages, the send operations can participate in a transaction allowing multiple messages to be sent within a transactional scope. This allows for “all or nothing” delivery of a series of messages to a single queue or topic. When receiving messages, messages that are received in the peek-lock receive mode can be completed, deadlettered or deferred within a transactional scope. In the current release the Abandon method will not participate in a transaction. The same restrictions of only one top level messaging entity applies here, so the Complete method can be called transitionally on messages received from the same queue, or messages received from one or more subscriptions in the same topic. Sending Multiple Messages in a Transaction A transactional scope can be used to send multiple messages to a queue or topic. This will ensure that all the messages will be enqueued or, if the transaction fails to commit, no messages will be enqueued.     An example of the code used to send 10 messages to a queue as a single transaction from a console application is shown below.   QueueClient queueClient = messagingFactory.CreateQueueClient(Queue1);   Console.Write("Sending");   // Create a transaction scope. using (TransactionScope scope = new TransactionScope()) {     for (int i = 0; i < 10; i++)     {         // Send a message         BrokeredMessage msg = new BrokeredMessage("Message: " + i);         queueClient.Send(msg);         Console.Write(".");     }     Console.WriteLine("Done!");     Console.WriteLine();       // Should we commit the transaction?     Console.WriteLine("Commit send 10 messages? (yes or no)");     string reply = Console.ReadLine();     if (reply.ToLower().Equals("yes"))     {         // Commit the transaction.         scope.Complete();     } } Console.WriteLine(); messagingFactory.Close();     The transaction scope is used to wrap the sending of 10 messages. Once the messages have been sent the user has the option to either commit the transaction or abandon the transaction. If the user enters “yes”, the Complete method is called on the scope, which will commit the transaction and result in the messages being enqueued. If the user enters anything other than “yes”, the transaction will not commit, and the messages will not be enqueued. Receiving Multiple Messages in a Transaction The receiving of multiple messages is another scenario where the use of transactions can improve reliability. When receiving a group of messages that are related together, maybe in the same message session, it is possible to receive the messages in the peek-lock receive mode, and then complete, defer, or deadletter the messages in one transaction. (In the current version of Service Bus, abandon is not transactional.)   The following code shows how this can be achieved. using (TransactionScope scope = new TransactionScope()) {       while (true)     {         // Receive a message.         BrokeredMessage msg = q1Client.Receive(TimeSpan.FromSeconds(1));         if (msg != null)         {             // Wrote message body and complete message.             string text = msg.GetBody<string>();             Console.WriteLine("Received: " + text);             msg.Complete();         }         else         {             break;         }     }     Console.WriteLine();       // Should we commit?     Console.WriteLine("Commit receive? (yes or no)");     string reply = Console.ReadLine();     if (reply.ToLower().Equals("yes"))     {         // Commit the transaction.         scope.Complete();     }     Console.WriteLine(); }     Note that if there are a large number of messages to be received, there will be a chance that the transaction may time out before it can be committed. It is possible to specify a longer timeout when the transaction is created, but It may be better to receive and commit smaller amounts of messages within the transaction. It is also possible to complete, defer, or deadletter messages received from more than one subscription, as long as all the subscriptions are contained in the same topic. As subscriptions are not top level messaging entities this scenarios will work. The following code shows how this can be achieved. try {     using (TransactionScope scope = new TransactionScope())     {         // Receive one message from each subscription.         BrokeredMessage msg1 = subscriptionClient1.Receive();         BrokeredMessage msg2 = subscriptionClient2.Receive();           // Complete the message receives.         msg1.Complete();         msg2.Complete();           Console.WriteLine("Msg1: " + msg1.GetBody<string>());         Console.WriteLine("Msg2: " + msg2.GetBody<string>());           // Commit the transaction.         scope.Complete();     } } catch (Exception ex) {     Console.WriteLine(ex.Message); }     Unsupported Scenarios The restriction of only one top level messaging entity being able to participate in a transaction makes some useful scenarios unsupported. As the Windows Azure Service Bus is under continuous development and new releases are expected to be frequent it is possible that this restriction may not be present in future releases. The first is the scenario where messages are to be routed to two different systems. The following code attempts to do this.   try {     // Create a transaction scope.     using (TransactionScope scope = new TransactionScope())     {         BrokeredMessage msg1 = new BrokeredMessage("Message1");         BrokeredMessage msg2 = new BrokeredMessage("Message2");           // Send a message to Queue1         Console.WriteLine("Sending Message1");         queue1Client.Send(msg1);           // Send a message to Queue2         Console.WriteLine("Sending Message2");         queue2Client.Send(msg2);           // Commit the transaction.         Console.WriteLine("Committing transaction...");         scope.Complete();     } } catch (Exception ex) {     Console.WriteLine(ex.Message); }     The results of running the code are shown below. When attempting to send a message to the second queue the following exception is thrown: No active Transaction was found for ID '35ad2495-ee8a-4956-bbad-eb4fedf4a96e:1'. The Transaction may have timed out or attempted to span multiple top-level entities such as Queue or Topic. The server Transaction timeout is: 00:01:00..TrackingId:947b8c4b-7754-4044-b91b-4a959c3f9192_3_3,TimeStamp:3/29/2012 7:47:32 AM.   Another scenario where transactional support could be useful is when forwarding messages from one queue to another queue. This would also involve more than one top level messaging entity, and is therefore not supported.   Another scenario that developers may wish to implement is performing transactions across messaging entities and other transactional systems, such as an on-premise database. In the current release this is not supported.   Workarounds for Unsupported Scenarios There are some techniques that developers can use to work around the one top level entity limitation of transactions. When sending two messages to two systems, topics and subscriptions can be used. If the same message is to be sent to two destinations then the subscriptions would have the default subscriptions, and the client would only send one message. If two different messages are to be sent, then filters on the subscriptions can route the messages to the appropriate destination. The client can then send the two messages to the topic in the same transaction.   In scenarios where a message needs to be received and then forwarded to another system within the same transaction topics and subscriptions can also be used. A message can be received from a subscription, and then sent to a topic within the same transaction. As a topic is a top level messaging entity, and a subscription is not, this scenario will work.

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  • So…is it a Seek or a Scan?

    - by Paul White
    You’re probably most familiar with the terms ‘Seek’ and ‘Scan’ from the graphical plans produced by SQL Server Management Studio (SSMS).  The image to the left shows the most common ones, with the three types of scan at the top, followed by four types of seek.  You might look to the SSMS tool-tip descriptions to explain the differences between them: Not hugely helpful are they?  Both mention scans and ranges (nothing about seeks) and the Index Seek description implies that it will not scan the index entirely (which isn’t necessarily true). Recall also yesterday’s post where we saw two Clustered Index Seek operations doing very different things.  The first Seek performed 63 single-row seeking operations; and the second performed a ‘Range Scan’ (more on those later in this post).  I hope you agree that those were two very different operations, and perhaps you are wondering why there aren’t different graphical plan icons for Range Scans and Seeks?  I have often wondered about that, and the first person to mention it after yesterday’s post was Erin Stellato (twitter | blog): Before we go on to make sense of all this, let’s look at another example of how SQL Server confusingly mixes the terms ‘Scan’ and ‘Seek’ in different contexts.  The diagram below shows a very simple heap table with two columns, one of which is the non-clustered Primary Key, and the other has a non-unique non-clustered index defined on it.  The right hand side of the diagram shows a simple query, it’s associated query plan, and a couple of extracts from the SSMS tool-tip and Properties windows. Notice the ‘scan direction’ entry in the Properties window snippet.  Is this a seek or a scan?  The different references to Scans and Seeks are even more pronounced in the XML plan output that the graphical plan is based on.  This fragment is what lies behind the single Index Seek icon shown above: You’ll find the same confusing references to Seeks and Scans throughout the product and its documentation. Making Sense of Seeks Let’s forget all about scans for a moment, and think purely about seeks.  Loosely speaking, a seek is the process of navigating an index B-tree to find a particular index record, most often at the leaf level.  A seek starts at the root and navigates down through the levels of the index to find the point of interest: Singleton Lookups The simplest sort of seek predicate performs this traversal to find (at most) a single record.  This is the case when we search for a single value using a unique index and an equality predicate.  It should be readily apparent that this type of search will either find one record, or none at all.  This operation is known as a singleton lookup.  Given the example table from before, the following query is an example of a singleton lookup seek: Sadly, there’s nothing in the graphical plan or XML output to show that this is a singleton lookup – you have to infer it from the fact that this is a single-value equality seek on a unique index.  The other common examples of a singleton lookup are bookmark lookups – both the RID and Key Lookup forms are singleton lookups (an RID lookup finds a single record in a heap from the unique row locator, and a Key Lookup does much the same thing on a clustered table).  If you happen to run your query with STATISTICS IO ON, you will notice that ‘Scan Count’ is always zero for a singleton lookup. Range Scans The other type of seek predicate is a ‘seek plus range scan’, which I will refer to simply as a range scan.  The seek operation makes an initial descent into the index structure to find the first leaf row that qualifies, and then performs a range scan (either backwards or forwards in the index) until it reaches the end of the scan range. The ability of a range scan to proceed in either direction comes about because index pages at the same level are connected by a doubly-linked list – each page has a pointer to the previous page (in logical key order) as well as a pointer to the following page.  The doubly-linked list is represented by the green and red dotted arrows in the index diagram presented earlier.  One subtle (but important) point is that the notion of a ‘forward’ or ‘backward’ scan applies to the logical key order defined when the index was built.  In the present case, the non-clustered primary key index was created as follows: CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col ASC) ) ; Notice that the primary key index specifies an ascending sort order for the single key column.  This means that a forward scan of the index will retrieve keys in ascending order, while a backward scan would retrieve keys in descending key order.  If the index had been created instead on key_col DESC, a forward scan would retrieve keys in descending order, and a backward scan would return keys in ascending order. A range scan seek predicate may have a Start condition, an End condition, or both.  Where one is missing, the scan starts (or ends) at one extreme end of the index, depending on the scan direction.  Some examples might help clarify that: the following diagram shows four queries, each of which performs a single seek against a column holding every integer from 1 to 100 inclusive.  The results from each query are shown in the blue columns, and relevant attributes from the Properties window appear on the right: Query 1 specifies that all key_col values less than 5 should be returned in ascending order.  The query plan achieves this by seeking to the start of the index leaf (there is no explicit starting value) and scanning forward until the End condition (key_col < 5) is no longer satisfied (SQL Server knows it can stop looking as soon as it finds a key_col value that isn’t less than 5 because all later index entries are guaranteed to sort higher). Query 2 asks for key_col values greater than 95, in descending order.  SQL Server returns these results by seeking to the end of the index, and scanning backwards (in descending key order) until it comes across a row that isn’t greater than 95.  Sharp-eyed readers may notice that the end-of-scan condition is shown as a Start range value.  This is a bug in the XML show plan which bubbles up to the Properties window – when a backward scan is performed, the roles of the Start and End values are reversed, but the plan does not reflect that.  Oh well. Query 3 looks for key_col values that are greater than or equal to 10, and less than 15, in ascending order.  This time, SQL Server seeks to the first index record that matches the Start condition (key_col >= 10) and then scans forward through the leaf pages until the End condition (key_col < 15) is no longer met. Query 4 performs much the same sort of operation as Query 3, but requests the output in descending order.  Again, we have to mentally reverse the Start and End conditions because of the bug, but otherwise the process is the same as always: SQL Server finds the highest-sorting record that meets the condition ‘key_col < 25’ and scans backward until ‘key_col >= 20’ is no longer true. One final point to note: seek operations always have the Ordered: True attribute.  This means that the operator always produces rows in a sorted order, either ascending or descending depending on how the index was defined, and whether the scan part of the operation is forward or backward.  You cannot rely on this sort order in your queries of course (you must always specify an ORDER BY clause if order is important) but SQL Server can make use of the sort order internally.  In the four queries above, the query optimizer was able to avoid an explicit Sort operator to honour the ORDER BY clause, for example. Multiple Seek Predicates As we saw yesterday, a single index seek plan operator can contain one or more seek predicates.  These seek predicates can either be all singleton seeks or all range scans – SQL Server does not mix them.  For example, you might expect the following query to contain two seek predicates, a singleton seek to find the single record in the unique index where key_col = 10, and a range scan to find the key_col values between 15 and 20: SELECT key_col FROM dbo.Example WHERE key_col = 10 OR key_col BETWEEN 15 AND 20 ORDER BY key_col ASC ; In fact, SQL Server transforms the singleton seek (key_col = 10) to the equivalent range scan, Start:[key_col >= 10], End:[key_col <= 10].  This allows both range scans to be evaluated by a single seek operator.  To be clear, this query results in two range scans: one from 10 to 10, and one from 15 to 20. Final Thoughts That’s it for today – tomorrow we’ll look at monitoring singleton lookups and range scans, and I’ll show you a seek on a heap table. Yes, a seek.  On a heap.  Not an index! If you would like to run the queries in this post for yourself, there’s a script below.  Thanks for reading! IF OBJECT_ID(N'dbo.Example', N'U') IS NOT NULL BEGIN DROP TABLE dbo.Example; END ; -- Test table is a heap -- Non-clustered primary key on 'key_col' CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ; -- Non-unique non-clustered index on the 'data' column CREATE NONCLUSTERED INDEX [IX dbo.Example data] ON dbo.Example (data) ; -- Add 100 rows INSERT dbo.Example WITH (TABLOCKX) ( key_col, data ) SELECT key_col = V.number, data = V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 100 ; -- ================ -- Singleton lookup -- ================ ; -- Single value equality seek in a unique index -- Scan count = 0 when STATISTIS IO is ON -- Check the XML SHOWPLAN SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col = 32 ; -- =========== -- Range Scans -- =========== ; -- Query 1 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col <= 5 ORDER BY E.key_col ASC ; -- Query 2 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col > 95 ORDER BY E.key_col DESC ; -- Query 3 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col >= 10 AND E.key_col < 15 ORDER BY E.key_col ASC ; -- Query 4 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col >= 20 AND E.key_col < 25 ORDER BY E.key_col DESC ; -- Final query (singleton + range = 2 range scans) SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col = 10 OR E.key_col BETWEEN 15 AND 20 ORDER BY E.key_col ASC ; -- === TIDY UP === DROP TABLE dbo.Example; © 2011 Paul White email: [email protected] twitter: @SQL_Kiwi

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  • Basics of Join Predicate Pushdown in Oracle

    - by Maria Colgan
    Happy New Year to all of our readers! We hope you all had a great holiday season. We start the new year by continuing our series on Optimizer transformations. This time it is the turn of Predicate Pushdown. I would like to thank Rafi Ahmed for the content of this blog.Normally, a view cannot be joined with an index-based nested loop (i.e., index access) join, since a view, in contrast with a base table, does not have an index defined on it. A view can only be joined with other tables using three methods: hash, nested loop, and sort-merge joins. Introduction The join predicate pushdown (JPPD) transformation allows a view to be joined with index-based nested-loop join method, which may provide a more optimal alternative. In the join predicate pushdown transformation, the view remains a separate query block, but it contains the join predicate, which is pushed down from its containing query block into the view. The view thus becomes correlated and must be evaluated for each row of the outer query block. These pushed-down join predicates, once inside the view, open up new index access paths on the base tables inside the view; this allows the view to be joined with index-based nested-loop join method, thereby enabling the optimizer to select an efficient execution plan. The join predicate pushdown transformation is not always optimal. The join predicate pushed-down view becomes correlated and it must be evaluated for each outer row; if there is a large number of outer rows, the cost of evaluating the view multiple times may make the nested-loop join suboptimal, and therefore joining the view with hash or sort-merge join method may be more efficient. The decision whether to push down join predicates into a view is determined by evaluating the costs of the outer query with and without the join predicate pushdown transformation under Oracle's cost-based query transformation framework. The join predicate pushdown transformation applies to both non-mergeable views and mergeable views and to pre-defined and inline views as well as to views generated internally by the optimizer during various transformations. The following shows the types of views on which join predicate pushdown is currently supported. UNION ALL/UNION view Outer-joined view Anti-joined view Semi-joined view DISTINCT view GROUP-BY view Examples Consider query A, which has an outer-joined view V. The view cannot be merged, as it contains two tables, and the join between these two tables must be performed before the join between the view and the outer table T4. A: SELECT T4.unique1, V.unique3 FROM T_4K T4,            (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3) VWHERE T4.unique3 = V.hundred(+) AND       T4.ten = V.ten(+) AND       T4.thousand = 5; The following shows the non-default plan for query A generated by disabling join predicate pushdown. When query A undergoes join predicate pushdown, it yields query B. Note that query B is expressed in a non-standard SQL and shows an internal representation of the query. B: SELECT T4.unique1, V.unique3 FROM T_4K T4,           (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3             AND T4.unique3 = V.hundred(+)             AND T4.ten = V.ten(+)) V WHERE T4.thousand = 5; The execution plan for query B is shown below. In the execution plan BX, note the keyword 'VIEW PUSHED PREDICATE' indicates that the view has undergone the join predicate pushdown transformation. The join predicates (shown here in red) have been moved into the view V; these join predicates open up index access paths thereby enabling index-based nested-loop join of the view. With join predicate pushdown, the cost of query A has come down from 62 to 32.  As mentioned earlier, the join predicate pushdown transformation is cost-based, and a join predicate pushed-down plan is selected only when it reduces the overall cost. Consider another example of a query C, which contains a view with the UNION ALL set operator.C: SELECT R.unique1, V.unique3 FROM T_5K R,            (SELECT T1.unique3, T2.unique1+T1.unique1             FROM T_5K T1, T_10K T2             WHERE T1.unique1 = T2.unique1             UNION ALL             SELECT T1.unique3, T2.unique2             FROM G_4K T1, T_10K T2             WHERE T1.unique1 = T2.unique1) V WHERE R.unique3 = V.unique3 and R.thousand < 1; The execution plan of query C is shown below. In the above, 'VIEW UNION ALL PUSHED PREDICATE' indicates that the UNION ALL view has undergone the join predicate pushdown transformation. As can be seen, here the join predicate has been replicated and pushed inside every branch of the UNION ALL view. The join predicates (shown here in red) open up index access paths thereby enabling index-based nested loop join of the view. Consider query D as an example of join predicate pushdown into a distinct view. We have the following cardinalities of the tables involved in query D: Sales (1,016,271), Customers (50,000), and Costs (787,766).  D: SELECT C.cust_last_name, C.cust_city FROM customers C,            (SELECT DISTINCT S.cust_id             FROM sales S, costs CT             WHERE S.prod_id = CT.prod_id and CT.unit_price > 70) V WHERE C.cust_state_province = 'CA' and C.cust_id = V.cust_id; The execution plan of query D is shown below. As shown in XD, when query D undergoes join predicate pushdown transformation, the expensive DISTINCT operator is removed and the join is converted into a semi-join; this is possible, since all the SELECT list items of the view participate in an equi-join with the outer tables. Under similar conditions, when a group-by view undergoes join predicate pushdown transformation, the expensive group-by operator can also be removed. With the join predicate pushdown transformation, the elapsed time of query D came down from 63 seconds to 5 seconds. Since distinct and group-by views are mergeable views, the cost-based transformation framework also compares the cost of merging the view with that of join predicate pushdown in selecting the most optimal execution plan. Summary We have tried to illustrate the basic ideas behind join predicate pushdown on different types of views by showing example queries that are quite simple. Oracle can handle far more complex queries and other types of views not shown here in the examples. Again many thanks to Rafi Ahmed for the content of this blog post.

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  • Are Chromebooks the New Netbooks, and What Does That Mean?

    - by Chris Hoffman
    Netbooks — small, cheap, slow laptops — were once very popular. They fell out of favor — people bought them because they seemed cheap and portable, but the actual experience was lackluster. Most netbooks now sit unused. Windows netbooks have vanished from stores today, but there’s a new super-cheap laptop — the Chromebook. Chromebook sales numbers are impressive, but their usage statistics tell a different story. Are Chromebooks just the new netbook? The Problem With Netbooks Netbooks seemed appealing, especially in an age before tablets and lightweight ultrabooks. You could buy a netbook for $200 or so and have a portable device that let you get on the Internet. The name “netbook” spelled that out — it was a portable device for getting on the ‘net. They weren’t really that great. The original netbook was a lightweight Asus Eee PC that ran Linux alone and had a small amount of fast flash storage. Netbooks eventually ran heavier Windows XP operating systems — Windows Vista was out, but it was just too bloated to run on netbooks. Manufacturers added slow magnetic hard drives, bloatware, and even DVD drives! They couldn’t run most Windows software very well. The build quality was poor and their keyboards were tiny and cramped. People liked the idea of a lightweight device that let them get on the Internet and loved the cheap price, but the actual experience wasn’t great. Chromebook Sales Chromebook sales numbers seem surprisingly high. NPD reported that Chromebooks were 21% of all notebooks sold in the US in 2013. If you combine laptop and tablet sales into a single statistic, Chromebooks were 9.6% of all those devices sold. That’s 2/3 as many Chromebooks sold as iPads in the US! Of Amazon’s best-selling laptop computers, two of the top three are Chromebooks. These definitely look like successful products. Unlike netbooks, Chromebooks are taking off in a big way in the education market. Many schools are buying Chromebooks for their students instead of more expensive Windows laptops. They’re easier to manage and lock down than Windows laptops, but — more importantly for cash-strapped schools — they’re very cheap. Netbooks never had this sort of momentum in schools. Chromebook Usage Statistics Here’s where the rosy picture of Chromebooks starts to become more realistic. StatCounter’s browser usage statistics show how widely used different operating systems are. For example, Windows 7 has the highest share with 35.71% of web activity in April, 2014. The chart doesn’t even show Chrome OS at all, although there is an “Other” number near the bottom. Click the Download Data link to download a CSV file and we can view more detailed information. Chrome OS only accounted for 0.38% of web usage in April, 2014. Desktop Linux, which people often shrug at, accounted for 1.52% in the same month. To its credit, Chrome OS usage has increased. Chromebooks were widely mocked back in November, 2013 when the sales numbers came out. After all, they only accounted for 0.11% of web usage globally in November, 2013! But Chrome OS numbers have been improving: Nov, 2013: 0.11% Dec, 2013: 0.22% Jan, 2014: 0.31% Feb, 2014: 0.35% Mar, 2014: 0.36% Apr, 2014: 0.38% Chrome OS is climbing, but it’s definitely still in the “Other” category. It isn’t as high as we’d expect to see it with those types of sales numbers. Chromebooks vs. Netbooks Chromebooks are more limited devices than traditional PCs. You can do quite a few things, but you have to do it all using Chrome or Chrome apps. Most people won’t be enabling developer mode and installing a Linux desktop. You don’t have access to the powerful desktop software available for Windows and even Mac OS X. On the other hand, these Chromebooks are less compromised than netbooks in many ways. They come with a lightweight operating system designed for portable, mobile devices. They don’t come packed with any bloatware, like the bloatware you’ll find on competing Windows PCs and the original netbooks. They’re cheaper because the manufacturer doesn’t have to pay for a Windows license. There’s no need for antivirus software weighing the operating system down. They’re larger than the original netbooks, with many of them being 11.6-inches instead of the original 8-inch bodies many older netbooks came with. They have larger, more comfortable keyboards and fast solid-state storage. Really, Chromebooks are what netbooks wanted to be. People didn’t buy netbooks to use typical Windows software — they just wanted a lightweight PC. Of course, for many people, the real successor to netbooks is tablets. If all you want is a portable device to throw in a bag so you can get online, maybe a tablet is better. Where Does This Leave Chromebooks? So, are Chromebooks the new netbooks? It’s a bit early to answer that question. Chromebooks are definitely not out of the competition — their sales look good and their usage share is increasing. On the other hand, Chrome OS is still pretty far behind. They’re not catching fire like tablets did. Maybe netbooks were just before their time and Chromebooks were what they were always meant to be. Just as Microsoft’s Windows XP tablets failed, Windows XP netbooks also failed. Tablets took off with a more refined operating system on better hardware years later. “Netbooks” — or Chromebooks — are now taking off with a more purpose-built operating system on better hardware, too. It’s hard to count Chromebooks out because they provide a much better experience than netbooks ever did. If you’re one of the people who wants to use old Windows desktop apps on your portable laptop, you may think netbooks were better — but most people don’t want that. But maybe people either want a full desktop PC experience or a full mobile tablet experience. Is there a place for a laptop with a keyboard that can only view websites? We’ll have to wait and see. Image Credit: Kevin Jarret on Flickr, Clive Darra on Flickr, Sean Freese on Flickr

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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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  • Performance considerations for common SQL queries

    - by Jim Giercyk
    Originally posted on: http://geekswithblogs.net/NibblesAndBits/archive/2013/10/16/performance-considerations-for-common-sql-queries.aspxSQL offers many different methods to produce the same results.  There is a never-ending debate between SQL developers as to the “best way” or the “most efficient way” to render a result set.  Sometimes these disputes even come to blows….well, I am a lover, not a fighter, so I decided to collect some data that will prove which way is the best and most efficient.  For the queries below, I downloaded the test database from SQLSkills:  http://www.sqlskills.com/sql-server-resources/sql-server-demos/.  There isn’t a lot of data, but enough to prove my point: dbo.member has 10,000 records, and dbo.payment has 15,554.  Our result set contains 6,706 records. The following queries produce an identical result set; the result set contains aggregate payment information for each member who has made more than 1 payment from the dbo.payment table and the first and last name of the member from the dbo.member table.   /*************/ /* Sub Query  */ /*************/ SELECT  a.[Member Number] ,         m.lastname ,         m.firstname ,         a.[Number Of Payments] ,         a.[Average Payment] ,         a.[Total Paid] FROM    ( SELECT    member_no 'Member Number' ,                     AVG(payment_amt) 'Average Payment' ,                     SUM(payment_amt) 'Total Paid' ,                     COUNT(Payment_No) 'Number Of Payments'           FROM      dbo.payment           GROUP BY  member_no           HAVING    COUNT(Payment_No) > 1         ) a         JOIN dbo.member m ON a.[Member Number] = m.member_no         /***************/ /* Cross Apply  */ /***************/ SELECT  ca.[Member Number] ,         m.lastname ,         m.firstname ,         ca.[Number Of Payments] ,         ca.[Average Payment] ,         ca.[Total Paid] FROM    dbo.member m         CROSS APPLY ( SELECT    member_no 'Member Number' ,                                 AVG(payment_amt) 'Average Payment' ,                                 SUM(payment_amt) 'Total Paid' ,                                 COUNT(Payment_No) 'Number Of Payments'                       FROM      dbo.payment                       WHERE     member_no = m.member_no                       GROUP BY  member_no                       HAVING    COUNT(Payment_No) > 1                     ) ca /********/                    /* CTEs  */ /********/ ; WITH    Payments           AS ( SELECT   member_no 'Member Number' ,                         AVG(payment_amt) 'Average Payment' ,                         SUM(payment_amt) 'Total Paid' ,                         COUNT(Payment_No) 'Number Of Payments'                FROM     dbo.payment                GROUP BY member_no                HAVING   COUNT(Payment_No) > 1              ),         MemberInfo           AS ( SELECT   p.[Member Number] ,                         m.lastname ,                         m.firstname ,                         p.[Number Of Payments] ,                         p.[Average Payment] ,                         p.[Total Paid]                FROM     dbo.member m                         JOIN Payments p ON m.member_no = p.[Member Number]              )     SELECT  *     FROM    MemberInfo /************************/ /* SELECT with Grouping   */ /************************/ SELECT  p.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         COUNT(Payment_No) 'Number Of Payments' ,         AVG(payment_amt) 'Average Payment' ,         SUM(payment_amt) 'Total Paid' FROM    dbo.payment p         JOIN dbo.member m ON m.member_no = p.member_no GROUP BY p.member_no ,         m.lastname ,         m.firstname HAVING  COUNT(Payment_No) > 1   We can see what is going on in SQL’s brain by looking at the execution plan.  The Execution Plan will demonstrate which steps and in what order SQL executes those steps, and what percentage of batch time each query takes.  SO….if I execute all 4 of these queries in a single batch, I will get an idea of the relative time SQL takes to execute them, and how it renders the Execution Plan.  We can settle this once and for all.  Here is what SQL did with these queries:   Not only did the queries take the same amount of time to execute, SQL generated the same Execution Plan for each of them.  Everybody is right…..I guess we can all finally go to lunch together!  But wait a second, I may not be a fighter, but I AM an instigator.     Let’s see how a table variable stacks up.  Here is the code I executed: /********************/ /*  Table Variable  */ /********************/ DECLARE @AggregateTable TABLE     (       member_no INT ,       AveragePayment MONEY ,       TotalPaid MONEY ,       NumberOfPayments MONEY     ) INSERT  @AggregateTable         SELECT  member_no 'Member Number' ,                 AVG(payment_amt) 'Average Payment' ,                 SUM(payment_amt) 'Total Paid' ,                 COUNT(Payment_No) 'Number Of Payments'         FROM    dbo.payment         GROUP BY member_no         HAVING  COUNT(Payment_No) > 1   SELECT  at.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         at.NumberOfPayments 'Number Of Payments' ,         at.AveragePayment 'Average Payment' ,         at.TotalPaid 'Total Paid' FROM    @AggregateTable at         JOIN dbo.member m ON m.member_no = at.member_no In the interest of keeping things in groupings of 4, I removed the last query from the previous batch and added the table variable query.  Here’s what I got:     Since we first insert into the table variable, then we read from it, the Execution Plan renders 2 steps.  BUT, the combination of the 2 steps is only 22% of the batch.  It is actually faster than the other methods even though it is treated as 2 separate queries in the Execution Plan.  The argument I often hear against Table Variables is that SQL only estimates 1 row for the table size in the Execution Plan.  While this is true, the estimate does not come in to play until you read from the table variable.  In this case, the table variable had 6,706 rows, but it still outperformed the other queries.  People argue that table variables should only be used for hash or lookup tables.  The fact is, you have control of what you put IN to the variable, so as long as you keep it within reason, these results suggest that a table variable is a viable alternative to sub-queries. If anyone does volume testing on this theory, I would be interested in the results.  My suspicion is that there is a breaking point where efficiency goes down the tubes immediately, and it would be interesting to see where the threshold is. Coding SQL is a matter of style.  If you’ve been around since they introduced DB2, you were probably taught a little differently than a recent computer science graduate.  If you have a company standard, I strongly recommend you follow it.    If you do not have a standard, generally speaking, there is no right or wrong answer when talking about the efficiency of these types of queries, and certainly no hard-and-fast rule.  Volume and infrastructure will dictate a lot when it comes to performance, so your results may vary in your environment.  Download the database and try it!

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  • SQL SERVER – Solution – Puzzle – Statistics are not Updated but are Created Once

    - by pinaldave
    Earlier I asked puzzle why statistics are not updated. Read the complete details over here: Statistics are not Updated but are Created Once In the question I have demonstrated even though statistics should have been updated after lots of insert in the table are not updated.(Read the details SQL SERVER – When are Statistics Updated – What triggers Statistics to Update) 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? I have many answers – here is the how I fixed it which has resolved the issue for me. NOTE: There are multiple answers to this problem and I will do my best to list all. Solution: Create nonclustered Index on column City Here is the working example for the same. Let us understand this script and there is added explanation at the end. -- Execution Plans Difference -- Estimated Execution Plan Vs Actual Execution Plan -- Create Sample Database CREATE DATABASE SampleDB GO USE SampleDB GO -- Create Table CREATE TABLE ExecTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO CREATE NONCLUSTERED INDEX IX_ExecTable1 ON ExecTable (City); GO -- Insert One Thousand Records -- INSERT 1 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -------------------------------------------------------------- -- Round 2 -- Insert One Thousand Records -- INSERT 2 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -- Clean up Database DROP TABLE ExecTable GO When I created non clustered index on the column city, it also created statistics on the same column with same name as index. When we populate the data in the column the index is update – resulting execution plan to be invalided – this leads to the statistics to be updated in next execution of SELECT. This behavior does not happen on Heap or column where index is auto created. If you explicitly update the index, often you can see the statistics are updated as well. You can see this is for sure happening if you follow the tell of John Sansom. John Sansom‘s suggestion: That was fun! Although the column statistics are invalidated by the time the second select statement is executed, the query is not compiled/recompiled but instead the existing query plan is reused. It is the “next” compiled query against the column statistics that will see that they are out of date and will then in turn instantiate the action of updating statistics. You can see this in action by forcing the second statement to recompile. SELECT FirstName, LastName, City FROM ExecTable WHERE City = ‘New York’ option(RECOMPILE) GO Kevin Cross also have another suggestion: I agree with John. It is reusing the Execution Plan. Aside from OPTION(RECOMPILE), clearing the Execution Plan Cache before the subsequent tests will also work. i.e., run this before round 2: ————————————————————– – Clear execution plan cache before next test DBCC FREEPROCCACHE WITH NO_INFOMSGS; ————————————————————– Nice puzzle! Kevin As this was puzzle John and Kevin both got the correct answer, there was no condition for answer to be part of best practices. I know John and he is finest DBA around – his tremendous knowledge has always impressed me. John and Kevin both will agree that clearing cache either using DBCC FREEPROCCACHE and recompiling each query every time is for sure not good advice on production server. It is correct answer but not best practice. By the way, if you have better solution or have better suggestion please advise. I am open to change my answer and publish further improvement to this solution. On very separate note, I like to have clustered index on my Primary Key, which I have not mentioned here as it is out of the scope of this puzzle. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Index, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Statistics

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  • Agile Development Requires Agile Support

    - by Matt Watson
    Agile developmentAgile development has become the standard methodology for application development. The days of long term planning with giant Gantt waterfall charts and detailed requirements is fading away. For years the product planning process frustrated product owners and businesses because no matter the plan, nothing ever went to plan. Agile development throws the detailed planning out the window and instead focuses on giving developers some basic requirements and pointing them in the right direction. Constant collaboration via quick iterations with the end users, product owners, and the development team helps ensure the project is done correctly.  The various agile development methodologies have helped greatly with creating products faster, but not without causing new problems. Complicated application deployments now occur weekly or monthly. Most of the products are web-based and deployed as a software service model. System performance and availability of these apps becomes mission critical. This is all much different from the old process of mailing new releases of client-server apps on CD once per quarter or year.The steady stream of new products and product enhancements puts a lot of pressure on IT operations to keep up with the software deployments and adding infrastructure capacity. The problem is most operations teams still move slowly thanks to change orders, documentation, procedures, testing and other processes. Operations can slow the process down and push back on the development team in some organizations. The DevOps movement is trying to solve some of these problems by integrating the development and operations teams more together. Rapid change introduces new problemsThe rapid product change ultimately creates some application problems along the way. Higher rates of change increase the likelihood of new application defects. Delivering applications as a software service also means that scalability of applications is critical. Development teams struggle to keep up with application defects and scalability concerns in their applications. Fixing application problems is a never ending job for agile development teams. Fixing problems before your customers do and fixing them quickly is critical. Most companies really struggle with this due to the divide between the development and operations groups. Fixing application problems typically requires querying databases, looking at log files, reviewing config files, reviewing error logs and other similar tasks. It becomes difficult to work on new features when your lead developers are working on defects from the last product version. Developers need more visibilityThe problem is most developers are not given access to see server and application information in the production environments. The operations team doesn’t trust giving all the developers the keys to the kingdom to log in to production and poke around the servers. The challenge is either give them no access, or potentially too much access. Those with access can still waste time figuring out the location of the application and how to connect to it over VPN. In addition, reproducing problems in test environments takes too much time and isn't always possible. System administrators spend a lot of time helping developers track down server information. Most companies give key developers access to all of the production resources so they can help resolve application defects. The problem is only those key people have access and they become a bottleneck. They end up spending 25-50% of their time on a daily basis trying to solve application issues because they are the only ones with access. These key employees’ time is best spent on strategic new projects, not addressing application defects. This job should fall to entry level developers, provided they have access to all the information they need to troubleshoot the problems.The solution to agile application support is giving all the developers limited access to the production environment and all the server information they need to see. Some companies create their own solutions internally to collect log files, centralize errors or other things to address the problem. Some developers even have access to server monitoring or other tools. But they key is giving them access to everything they need so they can see the full picture and giving access to the whole team. Giving access to everyone scales up the application support team and creates collaboration around providing improved application support.Stackify enables agile application supportStackify has created a solution that can give all developers a secure and read only view of the entire production server environment without console or remote desktop access.They provide a web application that provides real time visibility to the important information that developers need to see. An application centric view enables them to see all of their apps across multiple datacenters and environments. They don’t need to know where the application is deployed, just the name of the application to find it and dig in to see more. All your developers can see server health, application health, log files, config files, windows event viewer, deployment history, application notes, and much more. They can receive email and text alerts when problems arise and even safely query your production databases.Stackify enables companies that do agile development to scale up their application support team by getting more team members involved. The lead developers can spend more time on new projects. Application issues can be fixed quicker than ever. Operations can spend less time helping developers collect server information. Agile application support starts with Stackify. Visit Stackify.com to learn more.

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  • Interesting interview question. .Net

    - by rahul
    Coding Problem NumTrans There is an integer K. You are allowed to add to K any of its divisors not equal to 1and K. The same operation can be applied to the resulting number and so on. Notice that starting from the number 4, we can reach any composite number by applying several such operations. For example, the number 24 can be reached starting from 4 using 5 operations: 468121824 You will solve a more general problem. Given integers n and m, return the minimal number of the described operations necessary to transform n into m. Return -1 if m can't be obtained from n. Definition Method signature: int GetLeastCount (int n, int m) Constraints N will be between 4 and 100000, inclusive. M will be between N and 100000, inclusive. Examples 1) 4 576 Returns: 14 The shortest order of operations is: 468121827365481108162243324432576 2) 8748 83462 Returns: 10 The shortest order of operations is: 874813122196832624439366590497873283106834488346083462 3) 4 99991 Returns: -1 The number 99991 can't be obtained because it’s prime!

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  • How to achieve this site structure?

    - by Sushant
    Hi, I need to develop a website that looks like this. In central administration however, in the operations tab, It shows Central Administration-- Operations. But I checked, operations is not a subsite. Then what is it. In my application, I always get Home-- Operations. To add to trouble,it changes the name at the top as Operations. I need to keep it central administration only. Please help me sort this out. Thanks.

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  • NHibernate and Composite Key References

    - by Rich
    I have a weird situation. I have three entities, Company, Employee, Plan and Participation (in retirement plan). Company PK: Company ID Plan PK: Company ID, Plan ID Employee PK: Company ID, SSN, Employee ID Participation PK: Company ID, SSN, Plan ID The problem is in linking the employee to the participation. From a DB perspective, participation should have Employee ID in the PK (it's not even in table). But it doesn't. NHibernate won't let me map the "has many" because the link expects 3 columns (since Employee PK has 3 columns), but I'd only provide 2. Any ideas on how to do this?

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  • CRM@Oracle Series: Complete Customer Intelligence

    - by tony.berk
    How do you to get sales folks to use a CRM system? Ah, the age-old question (well, at least the one that has been around for the past 10-15 years). Of course, some combination of "Carrot and Stick" always come up as possible answers. You hear talk about, "WIFM" or "What's in it For Me?" Oracle's internal implementation of Customer 360 consolidates all customer information in one place in Siebel CRM and enables the sales rep to present one-face to the customer. Additionally, Customer 360 provides customer intelligence and prospecting tools making it a darn nice "Carrot" for our sales teams to chew on. Check out today's slidecast for an overview. CRM@Oracle - Customer 360 Click here to learn more about Siebel CRM and other Oracle CRM products and here to learn about customers using Oracle CRM. Are you enjoying the CRM@Oracle Series? If you have a particular CRM area or function which you'd like to hear how Oracle implemented it internally, let us know and we'll get it on our list.

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  • Not so long ago in a city not so far away by Carlos Martin

    - by Maria Sandu
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 This is the story of how the EMEA Presales Center turned an Oracle intern into a trusted technology advisor for both Oracle’s Sales and customers. It was the summer of 2011 when I was finishing my Computer Engineering studies as well as my internship at Oracle when I was offered what could possibly be THE dream job for any young European Computer Engineer. Apart from that, it also seemed like the role was particularly tailored to me as I could leverage almost everything I learned at University and during the internship. And all of it in one of the best cities to live in, not only from my home country but arguably from Europe: Malaga! A day at EPC As part of the EPC Technology pillar, and later on completely focused on WebCenter, there was no way to describe a normal day on the job as each day had something unique. Some days I was researching documentation in order to elaborate accurate answers for a customer’s question within a Request for Information or Proposal (RFI/RFP), other days I was doing heavy programming in order to bring a Proof of Concept (PoC) for a customer to life and last not but least, some days I presented to the customer via webconference the demo I built for them the past weeks. So as you can see, the role has research, development and presentation, could you ask for more? Well, don’t worry because there IS more! Internationality As the organization’s name suggests, EMEA Presales Center, it is the Center of Presales within Europe, Middle East and Africa so I got the chance to work with great professionals from all this regions, expanding my network and learning things from one country to apply them to others. In addition to that, the teams based in the Malaga office are comprised of many young professionals hailing mainly from Western and Central European countries (although there are a couple of exceptions!) with very different backgrounds and personalities which guaranteed many laughs and stories during lunch or coffee breaks (or even while working on projects!). Furthermore, having EPC offices in Bucharest and Bangalore and thanks to today’s tele-presence technologies, I was working every day with people from India or Romania as if they were sitting right next to me and the bonding with them got stronger day by day. Career development Apart from the research and self-study I’ve earlier mentioned, one of the EPC’s Key Performance Indicators (KPI) is that 15% of your time is spent on training so you get lots and lots of trainings in order to develop both your technical product knowledge and your presentation, negotiation and other soft skills. Sometimes the training is via webcast, sometimes the trainer comes to the office and sometimes, the best times, you get to travel abroad in order to attend a training, which also helps you to further develop your network by meeting face to face with many people you only know from some email or instant messaging interaction. And as the months go by, your skills improving at a very fast pace, your relevance increasing with each new project you successfully deliver, it’s only a matter of time (and a bit of self-promoting!) that you get the attention of the manager of a more senior team and are offered the opportunity to take a new step in your professional career. For me it took 2 years to move to my current position, Technology Sales Consultant at the Oracle Direct organization. During those 2 years I had built a good relationship with the Oracle Direct Spanish sales and sales managers, who are also based in the Malaga office. I supported their former Sales Consultant in a couple of presentations and demos and were very happy with my overall performance and attitude so even before the position got eventually vacant, I got a heads-up from then in advance that their current Sales Consultant was going to move to a different position. To me it felt like a natural step, same as when I joined EPC, I had at least a 50% of the “homework” already done but wanted to experience that extra 50% to add new product and soft skills to my arsenal. The rest is history, I’ve been in the role for more than half a year as I’m writing this, achieved already some important wins, gained a lot of trust and confidence in front of customers and broadened my view of Oracle’s Fusion Middleware portfolio. I look back at the 2 years I spent in EPC and think: “boy, I’d recommend that experience to absolutely anyone with the slightest interest in IT, there are so many different things you can do as there are different kind of roles you can end up taking thanks to the experience gained at EPC” /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • Learn More about Fusion CRM at the Oracle Applications Virtual Tradeshow

    - by ruth.donohue
    Sales reps spend just 22% of their time selling. The remainder is spent on administrative activities. How can you improve this ratio so that you sales reps can focus on what really matters? Join Mark Woollen, VP of CRM Product Management, at the Oracle Applications Virtual Tradeshow this Thursday, February 3rd at 10:30 AM PST / 1:30 PM EST to learn how Fusion CRM can improve sales productivity. Register now and be sure to check out Brian Dayton's blog post "What's In It For You? The Oracle Applications Virtual Tradeshow" to learn more about other sessions that may be of interest in Customer Relationship Management, Master Data Management, Enterprise Performance Management, Financials, and Human Capital Management.

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  • ArvinMeritor Sees Business Improvement: Uses Oracle Demand Management, Supply Chain Planning and Tra

    - by [email protected]
    As manufacturers begin repositioning for the economic recovery, they are reevaluating their supply chain networks, extending lean into their supply chains and making logistics visibility a priority. ArvinMeritor leveraged Oracle's Demantra, ASCP and Transportation Management applications to: Optimize operations execution by building consensus-driven demand, sales and operations plans Slash transportation costs by rationalizing shippers, optimizing routes and improving delivery performance Demantra for demand management, forecasting, sales and operations planning and global trade management Advanced Supply Chain Planning for material and capacity planning across global distribution and manufacturing facilities based on consensus forecasts, sales orders, production status, purchase orders, and inventory policy recommendations Transportation Management for transportation planning, execution, freight payment, and business process automation on a single application across all modes of transportation, from full truckload to complex multileg air, ocean, and rail shipments Oracle hosted an 'open-house/showcase" on March 30th, 2010 atArvinMeritor Global Headquarters 2135 West Maple RoadTroy, MI 48084 

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  • ODI 12c - Aggregating Data

    - by David Allan
    This posting will look at the aggregation component that was introduced in ODI 12c. For many ETL tool users this shouldn't be a big surprise, its a little different than ODI 11g but for good reason. You can use this component for composing data with relational like operations such as sum, average and so forth. Also, Oracle SQL supports special functions called Analytic SQL functions, you can use a specially configured aggregation component or the expression component for these now in ODI 12c. In database systems an aggregate transformation is a transformation where the values of multiple rows are grouped together as input on certain criteria to form a single value of more significant meaning - that's exactly the purpose of the aggregate component. In the image below you can see the aggregate component in action within a mapping, for how this and a few other examples are built look at the ODI 12c Aggregation Viewlet here - the viewlet illustrates a simple aggregation being built and then some Oracle analytic SQL such as AVG(EMP.SAL) OVER (PARTITION BY EMP.DEPTNO) built using both the aggregate component and the expression component. In 11g you used to just write the aggregate expression directly on the target, this made life easy for some cases, but it wan't a very obvious gesture plus had other drawbacks with ordering of transformations (agg before join/lookup. after set and so forth) and supporting analytic SQL for example - there are a lot of postings from creative folks working around this in 11g - anything from customizing KMs, to bypassing aggregation analysis in the ODI code generator. The aggregate component has a few interesting aspects. 1. Firstly and foremost it defines the attributes projected from it - ODI automatically will perform the grouping all you do is define the aggregation expressions for those columns aggregated. In 12c you can control this automatic grouping behavior so that you get the code you desire, so you can indicate that an attribute should not be included in the group by, that's what I did in the analytic SQL example using the aggregate component. 2. The component has a few other properties of interest; it has a HAVING clause and a manual group by clause. The HAVING clause includes a predicate used to filter rows resulting from the GROUP BY clause. Because it acts on the results of the GROUP BY clause, aggregation functions can be used in the HAVING clause predicate, in 11g the filter was overloaded and used for both having clause and filter clause, this is no longer the case. If a filter is after an aggregate, it is after the aggregate (not sometimes after, sometimes having).  3. The manual group by clause let's you use special database grouping grammar if you need to. For example Oracle has a wealth of highly specialized grouping capabilities for data warehousing such as the CUBE function. If you want to use specialized functions like that you can manually define the code here. The example below shows the use of a manual group from an example in the Oracle database data warehousing guide where the SUM aggregate function is used along with the CUBE function in the group by clause. The SQL I am trying to generate looks like the following from the data warehousing guide; SELECT channel_desc, calendar_month_desc, countries.country_iso_code,       TO_CHAR(SUM(amount_sold), '9,999,999,999') SALES$ FROM sales, customers, times, channels, countries WHERE sales.time_id=times.time_id AND sales.cust_id=customers.cust_id AND   sales.channel_id= channels.channel_id  AND customers.country_id = countries.country_id  AND channels.channel_desc IN   ('Direct Sales', 'Internet') AND times.calendar_month_desc IN   ('2000-09', '2000-10') AND countries.country_iso_code IN ('GB', 'US') GROUP BY CUBE(channel_desc, calendar_month_desc, countries.country_iso_code); I can capture the source datastores, the filters and joins using ODI's dataset (or as a traditional flow) which enables us to incrementally design the mapping and the aggregate component for the sum and group by as follows; In the above mapping you can see the joins and filters declared in ODI's dataset, allowing you to capture the relationships of the datastores required in an entity-relationship style just like ODI 11g. The mix of ODI's declarative design and the common flow design provides for a familiar design experience. The example below illustrates flow design (basic arbitrary ordering) - a table load where only the employees who have maximum commission are loaded into a target. The maximum commission is retrieved from the bonus datastore and there is a look using employees as the driving table and only those with maximum commission projected. Hopefully this has given you a taster for some of the new capabilities provided by the aggregate component in ODI 12c. In summary, the actions should be much more consistent in behavior and more easily discoverable for users, the use of the components in a flow graph also supports arbitrary designs and the tool (rather than the interface designer) takes care of the realization using ODI's knowledge modules. Interested to know if a deep dive into each component is interesting for folks. Any thoughts? 

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  • 11/15 Webinar: How Top High Tech Companies Grow Channel Revenue and ROMI

    - by Charles Knapp
    See the results of recent Aberdeen research on best practices in sales and marketing effectiveness. Discover how top performing high tech companies manage and use enterprise customer data, measure marketing spend effectiveness, and support internal and channel sales throughout their customer lifecycle -- messaging to leads, selling to prospects, and serving customers. Our speakers will be: Peter Ostrow, Research Director - Sales Effectiveness, Aberdeen Group David Lasher, Global Business Services Partner, IBM Jonathan Oomrigar, Vice President, Global High Technology Business Unit, Oracle Reserve your place now! This global webinar is on Tuesday, November 15, 10-11 am PST / 1-2 pm EST / 6-7 GMT / 7-8 CET

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  • Asynchrony in C# 5 (Part I)

    - by javarg
    I’ve been playing around with the new Async CTP preview available for download from Microsoft. It’s amazing how language trends are influencing the evolution of Microsoft’s developing platform. Much effort is being done at language level today than previous versions of .NET. In these post series I’ll review some major features contained in this release: Asynchronous functions TPL Dataflow Task based asynchronous Pattern Part I: Asynchronous Functions This is a mean of expressing asynchronous operations. This kind of functions must return void or Task/Task<> (functions returning void let us implement Fire & Forget asynchronous operations). The two new keywords introduced are async and await. async: marks a function as asynchronous, indicating that some part of its execution may take place some time later (after the method call has returned). Thus, all async functions must include some kind of asynchronous operations. This keyword on its own does not make a function asynchronous thought, its nature depends on its implementation. await: allows us to define operations inside a function that will be awaited for continuation (more on this later). Async function sample: Async/Await Sample async void ShowDateTimeAsync() {     while (true)     {         var client = new ServiceReference1.Service1Client();         var dt = await client.GetDateTimeTaskAsync();         Console.WriteLine("Current DateTime is: {0}", dt);         await TaskEx.Delay(1000);     } } The previous sample is a typical usage scenario for these new features. Suppose we query some external Web Service to get data (in this case the current DateTime) and we do so at regular intervals in order to refresh user’s UI. Note the async and await functions working together. The ShowDateTimeAsync method indicate its asynchronous nature to the caller using the keyword async (that it may complete after returning control to its caller). The await keyword indicates the flow control of the method will continue executing asynchronously after client.GetDateTimeTaskAsync returns. The latter is the most important thing to understand about the behavior of this method and how this actually works. The flow control of the method will be reconstructed after any asynchronous operation completes (specified with the keyword await). This reconstruction of flow control is the real magic behind the scene and it is done by C#/VB compilers. Note how we didn’t use any of the regular existing async patterns and we’ve defined the method very much like a synchronous one. Now, compare the following code snippet  in contrast to the previuous async/await: Traditional UI Async void ComplicatedShowDateTime() {     var client = new ServiceReference1.Service1Client();     client.GetDateTimeCompleted += (s, e) =>     {         Console.WriteLine("Current DateTime is: {0}", e.Result);         client.GetDateTimeAsync();     };     client.GetDateTimeAsync(); } The previous implementation is somehow similar to the first shown, but more complicated. Note how the while loop is implemented as a chained callback to the same method (client.GetDateTimeAsync) inside the event handler (please, do not do this in your own application, this is just an example).  How it works? Using an state workflow (or jump table actually), the compiler expands our code and create the necessary steps to execute it, resuming pending operations after any asynchronous one. The intention of the new Async/Await pattern is to let us think and code as we normally do when designing and algorithm. It also allows us to preserve the logical flow control of the program (without using any tricky coding patterns to accomplish this). The compiler will then create the necessary workflow to execute operations as the happen in time.

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  • What's New in OIC Analytics 11g?

    - by LuciaC
    Oracle Incentive Compensation (OIC) Analytics for Oracle Data Integrator (ODI) breaks down traditional front and back office silos bringing together sales performance data with those responsible for the sale and selling costs. It is a framework for Sales Performance Management  based on a data mart of key performance metrics regardless of whether or not these metrics are incentivized.Commissionable metrics are brought into OIC for commission calculation and brought back to enrich the performance data mart.  Executives and Product Marketing/Product Line Managers are provided with actionable sales performance analytics.  Incentivized salesreps and partners are provided with commission dashboards on a frequent basis to inform them how they are doing and how far they are from their goals.OIC Analytics is now certified with 11g and has additional features.  Oracle continues to invest in OIC Analytics but the baseline for the investments will be the 11gR1 certification version of OIC Analytics.  Read about what's new and the certification details in Doc ID 1590729.1.

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  • Introducing the EMEA Oracle Partner Days: Maximize your Potential

    - by Julien Haye
    The EMEA Oracle PartnerNetwork Days - which used to incorporate Partner Executive Forum (local/regional live events delivering sales strategy to a partner executive audience) and Satellite Events (local/regional live events targeting sales and consultants delivering Oracle strategy, engagement around specializations, executive keynotes and deep dive content-related breakout sessions) is now made of two distinct Partner events in EMEA: Oracle Partner Days. They are similar to the Satellite events from last year: local/Regional live events targeting the key contacts in sales and consultancy delivering Oracle strategy, engaging around the several perspectives of the Oracle portfolio, executive keynotes and deep dive Business content-related breakout sessions. Learn more about the EMEA Oracle Partner Days on www.oracle.com/partners/goto/partnerdays-emea Oracle Partner Executive Forums that are on invitation only. Please contact your local Alliances manager for any questions.

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  • S&OP best practices that can help your organization be more responsive and effective

    - by user717691
    If you want to increase revenue by quickly responding to market changes or want to ensure that your operating plans drive towards corporate financial goals, you need real-time sales and operations planning.Watch the replay of our recent Webcast to hear Christopher Neff from NCR Corporation discuss how NCR Corporation is leveraging Oracle's Real-Time Sales and Operations Planning solutions. Learn best practices that can help your organization be more responsive and effective. Discover how Oracle's comprehensive suite of best-in-class capabilities can: Synchronize plans and actions across the extended enterprise Maximize profits with the ability to sense, influence, and fulfill demand with industry leading demand management and real-time sales & operations Drive tactical decisions into operational planning and execution, while monitoring performance Profitably balance supply, demand, and budgets Move planning processes from periodic and reactive to real-time, iterative and proactive Register now for the on demand Webcast! http://www.oracle.com/webapps/dialogue/ns/dlgwelcome.jsp?p_ext=Y&p_dlg_id=8664804&src=6811174&Act=99NCR Corporation is a leader in Self Service Solution such as POS Solutions, Payment and Imaging Systems.

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  • CRM Webcast: Territory Setup and Manage Matching Attributes

    - by LuciaC
    Subject:  Territory Setup and Manage Matching Attributes Date: July 9, 2013 at 1pm ET, 12pm CT, 11am MT, 10am PT, 6pm, BST (London, GMT+01:00), 10:30 pm IST (Mumbai, GMT+05:30)Territories are used in a number of different EBS CRM applications, including Sales, Field Service and Service Contracts.  If you want to know more about how territories work and how to set them up, join our experts in this webcast.  The webcast will a demonstrate a high level setup for one of the Sales products and examples of how other applications use the Territory Manager. Topics will include: Enabling Matching Attributes Custom Matching Attributes Examples for Account, Leads, Quote, Proposals, Opportunities in the Sales product. Running Concurrent Requests Details & Registration: Doc ID 1544622.1

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Add data to Django form class using modelformset_factory

    - by dean
    I have a problem where I need to display a lot of forms for detail data for a hierarchical data set. I want to display some relational fields as labels for the forms and I'm struggling with a way to do this in a more robust way. Here is the code... class Category(models.Model): name = models.CharField(max_length=160) class Item(models.Model): category = models.ForeignKey('Category') name = models.CharField(max_length=160) weight = models.IntegerField(default=0) class Meta: ordering = ('category','weight','name') class BudgetValue(models.Model): value = models.IntegerField() plan = models.ForeignKey('Plan') item = models.ForeignKey('Item') I use the modelformset_factory to create a formset of budgetvalue forms for a particular plan. What I'd like is item name and category name for each BudgetValue. When I iterate through the forms each one will be labeled properly. class BudgetValueForm(forms.ModelForm): item = forms.ModelChoiceField(queryset=Item.objects.all(),widget=forms.HiddenInput()) plan = forms.ModelChoiceField(queryset=Plan.objects.all(),widget=forms.HiddenInput()) category = "" < assign dynamically on form creation > item = "" < assign dynamically on form creation > class Meta: model = BudgetValue fields = ('item','plan','value') What I started out with is just creating a dictionary of budgetvalue.item.category.name, budgetvalue.item.name, and the form for each budget value. This gets passed to the template and I render it as I intended. I'm assuming that the ordering of the forms in the formset and the querset used to genererate the formset keep the budgetvalues in the same order and the dictionary is created correctly. That is the budgetvalue.item.name is associated with the correct form. This scares me and I'm thinking there has to be a better way. Any help would be greatly appreciated.

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  • Working with PivotTables in Excel

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