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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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• c# Truncate HTML safely for article summary

  - by WickedW

  Hi All, Does anyone have a c# variation of this? This is so I can take some html and display it without breaking as a summary lead in to an article? http://stackoverflow.com/questions/1193500/php-truncate-html-ignoring-tags Save me from reinventing the wheel! Thank you very much ---------- edit ------------------ Sorry, new here, and your right, should have phrased the question better, heres a bit more info I wish to take a html string and truncate it to a set number of words (or even char length) so I can then show the start of it as a summary (which then leads to the main article). I wish to preserve the html so I can show the links etc in preview. The main issue I have to solve is the fact that we may well end up with unclosed html tags if we truncate in the middle of 1 or more tags! The idea I have for solution is to a) truncate the html to N words (words better but chars ok) first (be sure not to stop in the middle of a tag and truncate a require attribute) b) work through the opened html tags in this truncated string (maybe stick them on stack as I go?) c) then work through the closing tags and ensure they match the ones on stack as I pop them off? d) if any open tags left on stack after this, then write them to end of truncated string and html should be good to go!!!! -- edit 12112009 Here is what I have bumbled together so far as a unittest file in VS2008, this 'may' help someone in future My hack attempts based on Jan code are at top for char version + word version (DISCLAIMER: this is dirty rough code!! on my part) I assume working with 'well-formed' HTML in all cases (but not necessarily a full document with a root node as per XML version) Abels XML version is at bottom, but not yet got round to fully getting tests to run on this yet (plus need to understand the code) ... I will update when I get chance to refine having trouble with posting code? is there no upload facility on stack? Thanks for all comments :) using System; using System.Collections.Generic; using System.Text.RegularExpressions; using System.Xml; using System.Xml.XPath; using Microsoft.VisualStudio.TestTools.UnitTesting; namespace PINET40TestProject { [TestClass] public class UtilityUnitTest { public static string TruncateHTMLSafeishChar(string text, int charCount) { bool inTag = false; int cntr = 0; int cntrContent = 0; // loop through html, counting only viewable content foreach (Char c in text) { if (cntrContent == charCount) break; cntr++; if (c == '<') { inTag = true; continue; } if (c == '>') { inTag = false; continue; } if (!inTag) cntrContent++; } string substr = text.Substring(0, cntr); //search for nonclosed tags MatchCollection openedTags = new Regex("<[^/](.|\n)*?>").Matches(substr); MatchCollection closedTags = new Regex("<[/](.|\n)*?>").Matches(substr); // create stack Stack<string> opentagsStack = new Stack<string>(); Stack<string> closedtagsStack = new Stack<string>(); // to be honest, this seemed like a good idea then I got lost along the way // so logic is probably hanging by a thread!! foreach (Match tag in openedTags) { string openedtag = tag.Value.Substring(1, tag.Value.Length - 2); // strip any attributes, sure we can use regex for this! if (openedtag.IndexOf(" ") >= 0) { openedtag = openedtag.Substring(0, openedtag.IndexOf(" ")); } // ignore brs as self-closed if (openedtag.Trim() != "br") { opentagsStack.Push(openedtag); } } foreach (Match tag in closedTags) { string closedtag = tag.Value.Substring(2, tag.Value.Length - 3); closedtagsStack.Push(closedtag); } if (closedtagsStack.Count < opentagsStack.Count) { while (opentagsStack.Count > 0) { string tagstr = opentagsStack.Pop(); if (closedtagsStack.Count == 0 || tagstr != closedtagsStack.Peek()) { substr += "</" + tagstr + ">"; } else { closedtagsStack.Pop(); } } } return substr; } public static string TruncateHTMLSafeishWord(string text, int wordCount) { bool inTag = false; int cntr = 0; int cntrWords = 0; Char lastc = ' '; // loop through html, counting only viewable content foreach (Char c in text) { if (cntrWords == wordCount) break; cntr++; if (c == '<') { inTag = true; continue; } if (c == '>') { inTag = false; continue; } if (!inTag) { // do not count double spaces, and a space not in a tag counts as a word if (c == 32 && lastc != 32) cntrWords++; } } string substr = text.Substring(0, cntr) + " ..."; //search for nonclosed tags MatchCollection openedTags = new Regex("<[^/](.|\n)*?>").Matches(substr); MatchCollection closedTags = new Regex("<[/](.|\n)*?>").Matches(substr); // create stack Stack<string> opentagsStack = new Stack<string>(); Stack<string> closedtagsStack = new Stack<string>(); foreach (Match tag in openedTags) { string openedtag = tag.Value.Substring(1, tag.Value.Length - 2); // strip any attributes, sure we can use regex for this! if (openedtag.IndexOf(" ") >= 0) { openedtag = openedtag.Substring(0, openedtag.IndexOf(" ")); } // ignore brs as self-closed if (openedtag.Trim() != "br") { opentagsStack.Push(openedtag); } } foreach (Match tag in closedTags) { string closedtag = tag.Value.Substring(2, tag.Value.Length - 3); closedtagsStack.Push(closedtag); } if (closedtagsStack.Count < opentagsStack.Count) { while (opentagsStack.Count > 0) { string tagstr = opentagsStack.Pop(); if (closedtagsStack.Count == 0 || tagstr != closedtagsStack.Peek()) { substr += "</" + tagstr + ">"; } else { closedtagsStack.Pop(); } } } return substr; } public static string TruncateHTMLSafeishCharXML(string text, int charCount) { // your data, probably comes from somewhere, or as params to a methodint XmlDocument xml = new XmlDocument(); xml.LoadXml(text); // create a navigator, this is our primary tool XPathNavigator navigator = xml.CreateNavigator(); XPathNavigator breakPoint = null; // find the text node we need: while (navigator.MoveToFollowing(XPathNodeType.Text)) { string lastText = navigator.Value.Substring(0, Math.Min(charCount, navigator.Value.Length)); charCount -= navigator.Value.Length; if (charCount <= 0) { // truncate the last text. Here goes your "search word boundary" code: navigator.SetValue(lastText); breakPoint = navigator.Clone(); break; } } // first remove text nodes, because Microsoft unfortunately merges them without asking while (navigator.MoveToFollowing(XPathNodeType.Text)) { if (navigator.ComparePosition(breakPoint) == XmlNodeOrder.After) { navigator.DeleteSelf(); } } // moves to parent, then move the rest navigator.MoveTo(breakPoint); while (navigator.MoveToFollowing(XPathNodeType.Element)) { if (navigator.ComparePosition(breakPoint) == XmlNodeOrder.After) { navigator.DeleteSelf(); } } // moves to parent // then remove *all* empty nodes to clean up (not necessary): // TODO, add empty elements like <br />, <img /> as exclusion navigator.MoveToRoot(); while (navigator.MoveToFollowing(XPathNodeType.Element)) { while (!navigator.HasChildren && (navigator.Value ?? "").Trim() == "") { navigator.DeleteSelf(); } } // moves to parent navigator.MoveToRoot(); return navigator.InnerXml; } [TestMethod] public void TestTruncateHTMLSafeish() { // Case where we just make it to start of HREF (so effectively an empty link) // 'simple' nested none attributed tags Assert.AreEqual(@"<h1>1234</h1><b><i>56789</i>012</b>", TruncateHTMLSafeishChar( @"<h1>1234</h1><b><i>56789</i>012345</b>", 12)); // In middle of a! Assert.AreEqual(@"<h1>1234</h1><a href=""testurl""><b>567</b></a>", TruncateHTMLSafeishChar( @"<h1>1234</h1><a href=""testurl""><b>5678</b></a><i><strong>some italic nested in string</strong></i>", 7)); // more Assert.AreEqual(@"<div><b><i><strong>1</strong></i></b></div>", TruncateHTMLSafeishChar( @"<div><b><i><strong>12</strong></i></b></div>", 1)); // br Assert.AreEqual(@"<h1>1 3 5</h1><br />6", TruncateHTMLSafeishChar( @"<h1>1 3 5</h1><br />678<br />", 6)); } [TestMethod] public void TestTruncateHTMLSafeishWord() { // zero case Assert.AreEqual(@" ...", TruncateHTMLSafeishWord( @"", 5)); // 'simple' nested none attributed tags Assert.AreEqual(@"<h1>one two <br /></h1><b><i>three ...</i></b>", TruncateHTMLSafeishWord( @"<h1>one two <br /></h1><b><i>three </i>four</b>", 3), "we have added ' ...' to end of summary"); // In middle of a! Assert.AreEqual(@"<h1>one two three </h1><a href=""testurl""><b class=""mrclass"">four ...</b></a>", TruncateHTMLSafeishWord( @"<h1>one two three </h1><a href=""testurl""><b class=""mrclass"">four five </b></a><i><strong>some italic nested in string</strong></i>", 4)); // start of h1 Assert.AreEqual(@"<h1>one two three ...</h1>", TruncateHTMLSafeishWord( @"<h1>one two three </h1><a href=""testurl""><b>four five </b></a><i><strong>some italic nested in string</strong></i>", 3)); // more than words available Assert.AreEqual(@"<h1>one two three </h1><a href=""testurl""><b>four five </b></a><i><strong>some italic nested in string</strong></i> ...", TruncateHTMLSafeishWord( @"<h1>one two three </h1><a href=""testurl""><b>four five </b></a><i><strong>some italic nested in string</strong></i>", 99)); } [TestMethod] public void TestTruncateHTMLSafeishWordXML() { // zero case Assert.AreEqual(@" ...", TruncateHTMLSafeishWord( @"", 5)); // 'simple' nested none attributed tags string output = TruncateHTMLSafeishCharXML( @"<body><h1>one two </h1><b><i>three </i>four</b></body>", 13); Assert.AreEqual(@"<body>\r\n <h1>one two </h1>\r\n <b>\r\n <i>three</i>\r\n </b>\r\n</body>", output, "XML version, no ... yet and addeds '\r\n + spaces?' to format document"); // In middle of a! Assert.AreEqual(@"<h1>one two three </h1><a href=""testurl""><b class=""mrclass"">four ...</b></a>", TruncateHTMLSafeishCharXML( @"<body><h1>one two three </h1><a href=""testurl""><b class=""mrclass"">four five </b></a><i><strong>some italic nested in string</strong></i></body>", 4)); // start of h1 Assert.AreEqual(@"<h1>one two three ...</h1>", TruncateHTMLSafeishCharXML( @"<h1>one two three </h1><a href=""testurl""><b>four five </b></a><i><strong>some italic nested in string</strong></i>", 3)); // more than words available Assert.AreEqual(@"<h1>one two three </h1><a href=""testurl""><b>four five </b></a><i><strong>some italic nested in string</strong></i> ...", TruncateHTMLSafeishCharXML( @"<h1>one two three </h1><a href=""testurl""><b>four five </b></a><i><strong>some italic nested in string</strong></i>", 99)); } } }

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• what is the use of inner classes in java ? is nested classes and inner classes are same?

  - by subran

  what is the use of inner classes in java ? is nested classes and inner classes are same?

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• Benchmark MySQL Cluster using flexAsynch: No free node id found for mysqld(API)?

  - by quanta

  I am going to benchmark MySQL Cluster using flexAsynch follow this guide, details as below: mkdir /usr/local/mysqlc732/ cd /usr/local/src/mysql-cluster-gpl-7.3.2 cmake . -DCMAKE_INSTALL_PREFIX=/usr/local/mysqlc732/ -DWITH_NDB_TEST=ON make make install Everything works fine until this step: # /usr/local/mysqlc732/bin/flexAsynch -t 1 -p 80 -l 2 -o 100 -c 100 -n FLEXASYNCH - Starting normal mode Perform benchmark of insert, update and delete transactions 1 number of concurrent threads 80 number of parallel operation per thread 100 transaction(s) per round 2 iterations Load Factor is 80% 25 attributes per table 1 is the number of 32 bit words per attribute Tables are with logging Transactions are executed with hint provided No force send is used, adaptive algorithm used Key Errors are disallowed Temporary Resource Errors are allowed Insufficient Space Errors are disallowed Node Recovery Errors are allowed Overload Errors are allowed Timeout Errors are allowed Internal NDB Errors are allowed User logic reported Errors are allowed Application Errors are disallowed Using table name TAB0 NDBT_ProgramExit: 1 - Failed ndb_cluster.log: WARNING -- Failed to allocate nodeid for API at 127.0.0.1. Returned eror: 'No free node id found for mysqld(API).' I also have recompiled with -DWITH_DEBUG=1 -DWITH_NDB_DEBUG=1. How can I run flexAsynch in the debug mode? # /usr/local/mysqlc732/bin/flexAsynch -h FLEXASYNCH Perform benchmark of insert, update and delete transactions Arguments: -t Number of threads to start, default 1 -p Number of parallel transactions per thread, default 32 -o Number of transactions per loop, default 500 -l Number of loops to run, default 1, 0=infinite -load_factor Number Load factor in index in percent (40 -> 99) -a Number of attributes, default 25 -c Number of operations per transaction -s Size of each attribute, default 1 (PK is always of size 1, independent of this value) -simple Use simple read to read from database -dirty Use dirty read to read from database -write Use writeTuple in insert and update -n Use standard table names -no_table_create Don't create tables in db -temp Create table(s) without logging -no_hint Don't give hint on where to execute transaction coordinator -adaptive Use adaptive send algorithm (default) -force Force send when communicating -non_adaptive Send at a 10 millisecond interval -local 1 = each thread its own node, 2 = round robin on node per parallel trans 3 = random node per parallel trans -ndbrecord Use NDB Record -r Number of extra loops -insert Only run inserts on standard table -read Only run reads on standard table -update Only run updates on standard table -delete Only run deletes on standard table -create_table Only run Create Table of standard table -drop_table Only run Drop Table on standard table -warmup_time Warmup Time before measurement starts -execution_time Execution Time where measurement is done -cooldown_time Cooldown time after measurement completed -table Number of standard table, default 0

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• What is Granularity?

  - by tonyrogerson

  Granularity defines “the lowest level of detail”; but what is meant by “the lowest level of detail”? Consider the Transactions table below: create table Transactions ( TransactionID int not null primary key clustered, TransactionDate date not null, ClientID int not null, StockID int not null, TransactionAmount decimal ( 28 , 2 ) not null, CommissionAmount decimal ( 28 , 5 ) not null ) A Client can Trade in one or many Stocks on any date – there is no uniqueness to ClientID, Stock and TransactionDate...(read more)

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• T-SQL in SQL Azure

  - by kaleidoscope

  The following table summarizes the Transact-SQL support provided by SQL Azure Database at PDC 2009: Transact-SQL Features Supported Transact-SQL Features Unsupported Constants Constraints Cursors Index management and rebuilding indexes Local temporary tables Reserved keywords Stored procedures Statistics management Transactions Triggers Tables, joins, and table variables Transact-SQL language elements such as Create/drop databases Create/alter/drop tables Create/alter/drop users and logins User-defined functions Views, including sys.synonyms view Common Language Runtime (CLR) Database file placement Database mirroring Distributed queries Distributed transactions Filegroup management Global temporary tables Spatial data and indexes SQL Server configuration options SQL Server Service Broker System tables Trace Flags   Amit, S

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• System Requirements of a write-heavy applications serving hundreds of requests per second

  - by Rolando Cruz

  NOTE: I am a self-taught PHP developer who has little to none experience managing web and database servers. I am about to write a web-based attendance system for a very large userbase. I expect around 1000 to 1500 users logged-in at the same time making at least 1 request every 10 seconds or so for a span of 30 minutes a day, 3 times a week. So it's more or less 100 requests per second, or at the very worst 1000 requests in a second (average of 16 concurrent requests? But it could be higher given the short timeframe that users will make these requests. crosses fingers to avoid 100 concurrent requests). I expect two types of transactions, a local (not referring to a local network) and a foreign transaction. local transactions basically download userdata in their locality and cache it for 1 - 2 weeks. Attendance equests will probably be two numeric strings only: userid and eventid. foreign transactions are for attendance of those do not belong in the current locality. This will pass in the following data instead: (numeric) locality_id, (string) full_name. Both requests are done in Ajax so no HTML data included, only JSON. Both type of requests expect at the very least a single numeric response from the server. I think there will be a 50-50 split on the frequency of local and foreign transactions, but there's only a few bytes of difference anyways in the sizes of these transactions. As of this moment the userid may only reach 6 digits and eventid are 4 to 5-digit integers too. I expect my users table to have at least 400k rows, and the event table to have as many as 10k rows, a locality table with at least 1500 rows, and my main attendance table to increase by 400k rows (based on the number of users in the users table) a day for 3 days a week (1.2M rows a week). For me, this sounds big. But is this really that big? Or can this be handled by a single server (not sure about the server specs yet since I'll probably avail of a VPS from ServInt or others)? I tried to read on multiple server setups Heatbeat, DRBD, master-slave setups. But I wonder if they're really necessary. the users table will add around 500 1k rows a week. If this can't be handled by a single server, then if I am to choose a MySQL replication topology, what would be the best setup for this case? Sorry, if I sound vague or the question is too wide. I just don't know what to ask or what do you want to know at this point.

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• Beyond Syntax Highlighting - What other code representations are possible today?

  - by Mathieu Hélie

  Despite GUI applications having been around for 30ish years, software is still written as lines of text instructions, for various valid reasons. But we've also found that manipulating these text instructions is mind-blowingly difficult unless we apply a layer of coloring on different words to represent their syntax, thus allowing us to quickly parse through these text files without having to read the whole words. But besides the Sublime Text minimap feature, I've yet to see any innovation in visual representation of code since colors came around on CRT monitors. I can think of one obviously essential representation that modern graphics technology allows: visual hierarchies for nested structures. If we make nested text slightly smaller than its outer context, and zoom on it when the cursor is focused on the line, then we will be able to browse huge files of nested statements very quickly. This becomes even more essential as languages based on closures and anonymous functions become filled with deep statements. Has anyone attempted to implement this in a text editor? Do you know of any otherwise useful improvements in representing code text graphically?

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• Database Snapshot in Sql Server 2005

  A database snapshot is a read-only, static view of a database (called the source database). Each database snapshot is transactionally consistent with the source database at the moment of the snapshot's creation. When you create a database snapshot, the source database will typically have open transactions. Before the snapshot becomes available, the open transactions are rolled back to make the database snapshot transactionally consistent.

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• What is lightweight lock in distributed shared memory systems?

  - by Kutluhan Metin

  I started reading Tanenbaum's Distributed Systems book a while ago. I read about two phase locking and timestamp reordering in transactions chapter. While having a deeper look from google I heard of lightweight transactions/lightweight transactional memory. But I couldn't find any good explanation and implementation. So what is lightweight memory? What are the benefits of lightweight locks? And how can I implement them?

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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. Download Our Practice Excel File Similar Articles Productive Geek Tips Magnify Selected Cells In Excel 2007Share Access Data with Excel in Office 2010Make Excel 2007 Print Gridlines In Workbook FileMake Excel 2007 Always Save in Excel 2003 FormatConvert Older Excel Documents to Excel 2007 Format TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser Scan for Viruses in Ubuntu using ClamAV Replace Your Windows Task Manager With System Explorer

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• ?12c database ????Adaptive Execution Plans ????????

  - by Liu Maclean(???)

  12c R1 ????SQL??????- Adaptive Execution Plans ????????,???????optimizer ??????(runtime)???????????????, ????????????????????? SQL???????? ????????????, ?????????????????????????????????????????????????????????????adaptive plan ????????????????????????????????????,?????subplan???????????????????? ??????, ???????? ???????????????,?????????, ?????? ???????????????”???”????, ???????????????????buffer ???????  ????????????,?????,??????????????????? ???optimizer ?????????????????????????,?????????????????????????????????????????plan???? ??12C?????????????, ???????????????????,?????? ???????????? ????????????2???: Dynamic Plans????: ???????????????????????;??????,???optimizer??????????subplans??????????????, ???????????????????,?????????????? Reoptimization????: ?Dynamic Plans????,Reoptimization??????????????????????Reoptimization??,?????????????????????????,??reoptimization????? OPTIMIZER_ADAPTIVE_REPORTING_ONLY ???? report-only????????????????TRUE,?????????report-only????,???????????????,??????????????? Dynamic Plans ??????????????,????????????????????????, ?????????????,???????????,????????????????????????????????????????? ?????????????final plan??????????????default plan, ??final plan?default plan???????,????????????? subplan ???????????????,???????????????????????? ??????,???????statistics collector ?buffer???????????statistics collector?????????????????,???????????????????????????? ?????????????????????????????????????????,??????????,?????????????? ???????????,???????buffer???? ???????????????,?????????????????????????????,??????buffer,??????final plan? ????????,???????????????????????,????????????????? ?V$SQL??????IS_RESOLVED_DYNAMIC_PLAN??????????final plan???default plan? ??????dynamic plan ???????SQL PLAN directives?????? declare cursor PLAN_DIRECTIVE_IDS is select directive_id from DBA_SQL_PLAN_DIRECTIVES; begin for z in PLAN_DIRECTIVE_IDS loop DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE(z.directive_id); end loop; end; / explain plan for select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; select * from table(dbms_xplan.display()); Plan hash value: 1255158658 www.askmaclean.com ------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 4 | 128 | 7 (0)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 4 | 128 | 7 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | |* 4 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK | 1 | | 0 (0)| 00:00:01 | | 5 | TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") alter session set events '10053 trace name context forever,level 1'; OR alter session set events 'trace[SQL_Plan_Directive] disk highest'; select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; ---------------------------------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | ---------------------------------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 7 | | | 1 | HASH JOIN | | 4 | 128 | 7 | 00:00:01 | | 2 | NESTED LOOPS | | | | | | | 3 | NESTED LOOPS | | 4 | 128 | 7 | 00:00:01 | | 4 | STATISTICS COLLECTOR | | | | | | | 5 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 | 00:00:01 | | 6 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK| 1 | | 0 | | | 7 | TABLE ACCESS BY INDEX ROWID | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | | 8 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | ---------------------------------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") ===================================== SPD: BEGIN context at statement level ===================================== Stmt: ******* UNPARSED QUERY IS ******* SELECT /*+ OPT_ESTIMATE (@"SEL$1" JOIN ("P"@"SEL$1" "O"@"SEL$1") ROWS=13.000000 ) OPT_ESTIMATE (@"SEL$1" TABLE "O"@"SEL$1" ROWS=13.000000 ) */ "P"."PRODUCT_NAME" "PRODUCT_NAME" FROM "OE"."ORDER_ITEMS" "O","OE"."PRODUCT_INFORMATION" "P" WHERE "O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1 AND "P"."PRODUCT_ID"="O"."PRODUCT_ID" Objects referenced in the statement PRODUCT_INFORMATION[P] 92194, type = 1 ORDER_ITEMS[O] 92197, type = 1 Objects in the hash table Hash table Object 92197, type = 1, ownerid = 6573730143572393221: No Dynamic Sampling Directives for the object Hash table Object 92194, type = 1, ownerid = 17822962561575639002: No Dynamic Sampling Directives for the object Return code in qosdInitDirCtx: ENBLD =================================== SPD: END context at statement level =================================== ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Inserted felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: qosdCreateFindingSingTab retCode = CREATED, fid = 2896834833840853267 SPD: qosdCreateDirCmp retCode = CREATED, fid = 2896834833840853267 SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SKIP_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Modified felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 5618517328604016300 SPD: Modified felem, fid=5618517328604016300, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 1142802697078608149 SPD: Modified felem, fid=1142802697078608149, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 2, objcnt = 2, obItr = 0, objid = 92194, objtyp = 1, vecsize = 0, obItr = 1, objid = 92197, objtyp = 1, vecsize = 0, fid = 1437680122701058051 SPD: Modified felem, fid=1437680122701058051, ftype = 1, freason = 2, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO select * from table(dbms_xplan.display_cursor(format=>'report')) ; ????report????adaptive plan Adaptive plan: ------------- This cursor has an adaptive plan, but adaptive plans are enabled for reporting mode only.  The plan that would be executed if adaptive plans were enabled is displayed below. ------------------------------------------------------------------------------------------ | Id  | Operation          | Name                | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------ |   0 | SELECT STATEMENT   |                     |       |       |     7 (100)|          | |*  1 |  HASH JOIN         |                     |     4 |   128 |     7   (0)| 00:00:01 | |*  2 |   TABLE ACCESS FULL| ORDER_ITEMS         |     4 |    48 |     3   (0)| 00:00:01 | |   3 |   TABLE ACCESS FULL| PRODUCT_INFORMATION |     1 |    20 |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------ SQL> select SQL_ID,IS_RESOLVED_DYNAMIC_PLAN,sql_text from v$SQL WHERE SQL_TEXT like '%MALCEAN%' and sql_text not like '%like%'; SQL_ID IS -------------------------- -- SQL_TEXT -------------------------------------------------------------------------------- 6ydj1bn1bng17 Y select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id ???? explain plan for ????default plan, ??????optimizer???final plan,??V$SQL.IS_RESOLVED_DYNAMIC_PLAN???Y,????????????? DBA_SQL_PLAN_DIRECTIVES?????????????SQL PLAN DIRECTIVES, ???12c? ???MMON?????DML ???column usage??????????,????SMON??? MMON????SGA??PLAN DIRECTIVES??? ?????DBMS_SPD.flush_sql_plan_directive???? select directive_id,type,reason from DBA_SQL_PLAN_DIRECTIVES / DIRECTIVE_ID TYPE REASON ----------------------------------- -------------------------------- ----------------------------- 10321283028317893030 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 4757086536465754886 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 16085268038103121260 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE SQL> set pages 9999 SQL> set lines 300 SQL> col state format a5 SQL> col subobject_name format a11 SQL> col col_name format a11 SQL> col object_name format a13 SQL> select d.directive_id, o.object_type, o.object_name, o.subobject_name col_name, d.type, d.state, d.reason 2 from dba_sql_plan_directives d, dba_sql_plan_dir_objects o 3 where d.DIRECTIVE_ID=o.DIRECTIVE_ID 4 and o.object_name in ('ORDER_ITEMS') 5 order by d.directive_id; DIRECTIVE_ID OBJECT_TYPE OBJECT_NAME COL_NAME TYPE STATE REASON ------------ ------------ ------------- ----------- -------------------------------- ----- ------------------------------------- --- 1.8156E+19 COLUMN ORDER_ITEMS UNIT_PRICE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 TABLE ORDER_ITEMS DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 COLUMN ORDER_ITEMS QUANTITY DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE DBA_SQL_PLAN_DIRECTIVES????? _BASE_OPT_DIRECTIVE ? _BASE_OPT_FINDING SELECT d.dir_own#, d.dir_id, d.f_id, decode(type, 1, 'DYNAMIC_SAMPLING', 'UNKNOWN'), decode(state, 1, 'NEW', 2, 'MISSING_STATS', 3, 'HAS_STATS', 4, 'CANDIDATE', 5, 'PERMANENT', 6, 'DISABLED', 'UNKNOWN'), decode(bitand(flags, 1), 1, 'YES', 'NO'), cast(d.created as timestamp), cast(d.last_modified as timestamp), -- Please see QOSD_DAYS_TO_UPDATE and QOSD_PLUS_SECONDS for more details -- about 6.5 cast(d.last_used as timestamp) - NUMTODSINTERVAL(6.5, 'day') FROM sys.opt_directive$ d ??dbms_spd??? SQL PLAN DIRECTIVES, SQL PLAN DIRECTIVES???retention ???53?: Package: DBMS_SPD This package provides subprograms for managing Sql Plan Directives(SPD). SPD are objects generated automatically by Oracle server. For example, if server detects that the single table cardinality estimated by optimizer is off from the actual number of rows returned when accessing the table, it will automatically create a directive to do dynamic sampling for the table. When any Sql statement referencing the table is compiled, optimizer will perform dynamic sampling for the table to get more accurate estimate. Notes: DBMSL_SPD is a invoker-rights package. The invoker requires ADMINISTER SQL MANAGEMENT OBJECT privilege for executing most of the subprograms of this package. Also the subprograms commit the current transaction (if any), perform the operation and commit it again. DBA view dba_sql_plan_directives shows all the directives created in the system and the view dba_sql_plan_dir_objects displays the objects that are included in the directives. -- Default value for SPD_RETENTION_WEEKS SPD_RETENTION_WEEKS_DEFAULT CONSTANT varchar2(4) := '53'; | STATE : NEW : Newly created directive. | : MISSING_STATS : The directive objects do not | have relevant stats. | : HAS_STATS : The objects have stats. | : PERMANENT : A permanent directive. Server | evaluated effectiveness and these | directives are useful. | | AUTO_DROP : YES : Directive will be dropped | automatically if not | used for SPD_RETENTION_WEEKS. | This is the default behavior. | NO : Directive will not be dropped | automatically. Procedure: flush_sql_plan_directive This procedure allows manually flushing the Sql Plan directives that are automatically recorded in SGA memory while executing sql statements. The information recorded in SGA are periodically flushed by oracle background processes. This procedure just provides a way to flush the information manually. ????”_optimizer_dynamic_plans”(enable dynamic plans)????????,???TRUE??DYNAMIC PLAN? ???FALSE???????????? ????,Dynamic Plan????????????Nested Loop?Hash Join???case ,????????Nested loop???????????HASH JOIN,?HASH JOIN????????????????? ????????subplan?????,???? pass?? ?join method???,?????STATISTICS COLLECTOR???cardinality?,???????HASH JOIN?????Nested Loop,????????????subplan?????access path; ???????Sales??????????????????,????HASH JOIN,??SUBPLAN??customers?????????;?????Nested Loop,???????cust_id?????Range Scan+Access by Rowid? Cardinality feedback Cardinality feedback????????11.2????,????????re-optimization???;  ???????????,Cardinality feedback?????????????????????????? ???????????????????,?????????????????,??????????Cardinality feedback????????????? ????????????????????????? ??????????????Cardinality feedback ??: ????????,???????????,??????????,????????????????selectivity ??? ????????????: ??????,?????????????????????????????????,??????????????????? ????????????????????????????????????????,?????????????????????????? ?????????,???????????????,?????????? ??????????Cardinality ????,??????join Cardinality ????????? Cardinality feedback???????cursor?,?Cursor???aged out????? SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ---------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ---------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | 20 | | | | |* 1 | HASH JOIN | | 1 | 4 | 13 |00:00:00.01 | 24 | 20 | 2061K| 2061K| 429K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 4 | 13 |00:00:00.01 | 7 | 6 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.01 | 17 | 14 | | | | ---------------------------------------------------------------------------------------------------------------------------------------- SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | | | | |* 1 | HASH JOIN | | 1 | 13 | 13 |00:00:00.01 | 24 | 2061K| 2061K| 413K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 13 | 13 |00:00:00.01 | 7 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 288 | 288 |00:00:00.01 | 17 | | | | ------------------------------------------------------------------------------------------------------------------------------- Note ----- - statistics feedback used for this statement SQL> select count(*) from v$SQL where SQL_ID='cz0hg2zkvd10y'; COUNT(*) ---------- 2 SQL>select sql_ID,USE_FEEDBACK_STATS FROM V$SQL_SHARED_CURSOR where USE_FEEDBACK_STATS ='Y'; SQL_ID U ------------- - cz0hg2zkvd10y Y ????????Cardinality feedback????,???????????????????????????,????????????order_items???????? ????2??????plan hash value??(??????????),?????2????child cursor??????gather_plan_statistics???actual : A-ROWS  estimate :E-ROWS????????? Automatic Re-optimization ???dynamic plan, Re-optimization???????????????  ?  ??????????????? ????????????????????????????????  ???????????,??????????????, ???????????????????? ???????????  Re-optimization??, ????????????????????? Re-optimization????dynamic plan??????????  dynamic plan????????????????????, ???????????????????? ????,??????????join order ??????????????,?????????????join order????? ??????,????????Re-optimization, ??Re-optimization ??????????????????? ?Oracle database 12c?,join statistics?????????????????????,??????????????????????Re-optimization???????????adaptive cursor sharing????? ????????????????,???????????? ????? ???????statistics collectors ????????????????????Re-optimization??????2?????????????,???????????????? ??????????????Re-optimization?????,?????????????????????? ???v$SQL??????IS_REOPTIMIZABLE?????????????????????Re-optimization,??????????Re-optimization???,?????Re-optimization ,???????reporting????? IS_REOPTIMIZABLE VARCHAR2(1) This columns shows whether the next execution matching this child cursor will trigger a reoptimization. The values are:   Y: If the next execution will trigger a reoptimization R: If the child cursor contains reoptimization information, but will not trigger reoptimization because the cursor was compiled in reporting mode N: If the child cursor has no reoptimization information ??1: select plan_table_output from table (dbms_xplan.display_cursor('gwf99gfnm0t7g',NULL,'ALLSTATS LAST')); SQL_ID  gwf99gfnm0t7g, child number 0 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 1906736282 ------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation             | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT      |                     |      1 |        |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   1 |  NESTED LOOPS         |                     |      1 |      1 |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   2 |   MERGE JOIN CARTESIAN|                     |      1 |      4 |   9135 |00:00:00.02 |      34 |     15 |       |       |          | |*  3 |    TABLE ACCESS FULL  | PRODUCT_INFORMATION |      1 |      1 |     87 |00:00:00.01 |      33 |     14 |       |       |          | |   4 |    BUFFER SORT        |                     |     87 |    105 |   9135 |00:00:00.01 |       1 |      1 |  4096 |  4096 | 4096  (0)| |   5 |     INDEX FULL SCAN   | ORDER_PK            |      1 |    105 |    105 |00:00:00.01 |       1 |      1 |       |       |          | |*  6 |   INDEX UNIQUE SCAN   | ORDER_ITEMS_UK      |   9135 |      1 |    269 |00:00:00.01 |    1302 |      3 |       |       |          | ------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") SQL_ID  gwf99gfnm0t7g, child number 1 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 35479787 -------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation              | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | -------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT       |                     |      1 |        |    269 |00:00:00.01 |      63 |      3 |       |       |          | |   1 |  NESTED LOOPS          |                     |      1 |    269 |    269 |00:00:00.01 |      63 |      3 |       |       |          | |*  2 |   HASH JOIN            |                     |      1 |    313 |    269 |00:00:00.01 |      42 |      3 |  1321K|  1321K| 1234K (0)| |*  3 |    TABLE ACCESS FULL   | PRODUCT_INFORMATION |      1 |     87 |     87 |00:00:00.01 |      16 |      0 |       |       |          | |   4 |    INDEX FAST FULL SCAN| ORDER_ITEMS_UK      |      1 |    665 |    665 |00:00:00.01 |      26 |      3 |       |       |          | |*  5 |   INDEX UNIQUE SCAN    | ORDER_PK            |    269 |      1 |    269 |00:00:00.01 |      21 |      0 |       |       |          | -------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    5 - access("O"."ORDER_ID"="ORDER_ID") Note -----    - statistics feedback used for this statement    SQL> select IS_REOPTIMIZABLE,child_number FROM V$SQL  A where A.SQL_ID='gwf99gfnm0t7g'; IS CHILD_NUMBER -- ------------ Y             0 N             1    1* select child_number,other_xml From v$SQL_PLAN  where SQL_ID='gwf99gfnm0t7g' and other_xml is not nul SQL> / CHILD_NUMBER OTHER_XML ------------ --------------------------------------------------------------------------------            1 <other_xml><info type="cardinality_feedback">yes</info><info type="db_version">1              2.1.0.1</info><info type="parse_schema"><![CDATA["OE"]]></info><info type="plan_              hash">35479787</info><info type="plan_hash_2">3382491761</info><outline_data><hi              nt><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]></hint><hint><![CDATA[OPTIMIZER_FEATUR              ES_ENABLE('12.1.0.1')]]></hint><hint><![CDATA[DB_VERSION('12.1.0.1')]]></hint><h              int><![CDATA[ALL_ROWS]]></hint><hint><![CDATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></              hint><hint><![CDATA[MERGE(@"SEL$2")]]></hint><hint><![CDATA[OUTLINE(@"SEL$1")]]>              </hint><hint><![CDATA[OUTLINE(@"SEL$2")]]></hint><hint><![CDATA[FULL(@"SEL$F5BB7              4E1" "P"@"SEL$2")]]></hint><hint><![CDATA[INDEX_FFS(@"SEL$F5BB74E1" "O"@"SEL$2"              ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PRODUCT_ID"))]]></hint><hint><![CDATA[I              NDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA[              LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$2" "O"@"SEL$1")]]></hint><hint><![C              DATA[USE_HASH(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint><hint><![CDATA[USE_NL(@"SEL$              F5BB74E1" "O"@"SEL$1")]]></hint></outline_data></other_xml>            0 <other_xml><info type="db_version">12.1.0.1</info><info type="parse_schema"><![C              DATA["OE"]]></info><info type="plan_hash">1906736282</info><info type="plan_hash              _2">2579473118</info><outline_data><hint><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]>              </hint><hint><![CDATA[OPTIMIZER_FEATURES_ENABLE('12.1.0.1')]]></hint><hint><![CD              ATA[DB_VERSION('12.1.0.1')]]></hint><hint><![CDATA[ALL_ROWS]]></hint><hint><![CD              ATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></hint><hint><![CDATA[MERGE(@"SEL$2")]]></hi              nt><hint><![CDATA[OUTLINE(@"SEL$1")]]></hint><hint><![CDATA[OUTLINE(@"SEL$2")]]>              </hint><hint><![CDATA[FULL(@"SEL$F5BB74E1" "P"@"SEL$2")]]></hint><hint><![CDATA[              INDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA              [INDEX(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PROD              UCT_ID"))]]></hint><hint><![CDATA[LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$1              " "O"@"SEL$2")]]></hint><hint><![CDATA[USE_MERGE_CARTESIAN(@"SEL$F5BB74E1" "O"@"              SEL$1")]]></hint><hint><![CDATA[USE_NL(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint></o              utline_data></other_xml> ??2: SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 0 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | -------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWNER OBJECT_NAME COL_NAME OBJECT TYPE STATE REASON ----------------------- ----- ------------- ----------- ------ ---------------- ----- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; ELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 1 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- - cardinality feedback used for this statement SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' SELECT /*+gather_plan_statistics*/ CUST_EMAIL FROM CUSTOMERS WHERE CUST_STATE_PROVINCE='MA' AND COUNTRY_ID='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------- Select /*+gather_plan_statistics*/ cust_email From customers Where cust_state_province='MA' And country_id='US' Plan hash value: 1683234692 ------------------------------------------------------------------------------- |Id | Operation | Name | Starts|E-Rows|A-Rows| A-Time |Buffers| ------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2| 2 |00:00:00.01| 16 | ----------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- - dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OW OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- -- --------- ---------- ------- --------------- ------------- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE

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• SQL SERVER – LCK_M_XXX – Wait Type – Day 15 of 28

  - by pinaldave

  Locking is a mechanism used by the SQL Server Database Engine to synchronize access by multiple users to the same piece of data, at the same time. In simpler words, it maintains the integrity of data by protecting (or preventing) access to the database object. From Book On-Line: LCK_M_BU Occurs when a task is waiting to acquire a Bulk Update (BU) lock. LCK_M_IS Occurs when a task is waiting to acquire an Intent Shared (IS) lock. LCK_M_IU Occurs when a task is waiting to acquire an Intent Update (IU) lock. LCK_M_IX Occurs when a task is waiting to acquire an Intent Exclusive (IX) lock. LCK_M_S Occurs when a task is waiting to acquire a Shared lock. LCK_M_SCH_M Occurs when a task is waiting to acquire a Schema Modify lock. LCK_M_SCH_S Occurs when a task is waiting to acquire a Schema Share lock. LCK_M_SIU Occurs when a task is waiting to acquire a Shared With Intent Update lock. LCK_M_SIX Occurs when a task is waiting to acquire a Shared With Intent Exclusive lock. LCK_M_U Occurs when a task is waiting to acquire an Update lock. LCK_M_UIX Occurs when a task is waiting to acquire an Update With Intent Exclusive lock. LCK_M_X Occurs when a task is waiting to acquire an Exclusive lock. LCK_M_XXX Explanation: I think the explanation of this wait type is the simplest. When any task is waiting to acquire lock on any resource, this particular wait type occurs. The common reason for the task to be waiting to put lock on the resource is that the resource is already locked and some other operations may be going on within it. This wait also indicates that resources are not available or are occupied at the moment due to some reasons. There is a good chance that the waiting queries start to time out if this wait type is very high. Client application may degrade the performance as well. You can use various methods to find blocking queries: EXEC sp_who2 SQL SERVER – Quickest Way to Identify Blocking Query and Resolution – Dirty Solution DMV – sys.dm_tran_locks DMV – sys.dm_os_waiting_tasks Reducing LCK_M_XXX wait: Check the Explicit Transactions. If transactions are very long, this wait type can start building up because of other waiting transactions. Keep the transactions small. Serialization Isolation can build up this wait type. If that is an acceptable isolation for your business, this wait type may be natural. The default isolation of SQL Server is ‘Read Committed’. One of my clients has changed their isolation to “Read Uncommitted”. I strongly discourage the use of this because this will probably lead to having lots of dirty data in the database. Identify blocking queries mentioned using various methods described above, and then optimize them. Partition can be one of the options to consider because this will allow transactions to execute concurrently on different partitions. If there are runaway queries, use timeout. (Please discuss this solution with your database architect first as timeout can work against you). Check if there is no memory and IO-related issue using the following counters: Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussion of Wait Stats in this blog is generic and varies from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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• Replication Services in a BI environment

  - by jorg

  In this blog post I will explain the principles of SQL Server Replication Services without too much detail and I will take a look on the BI capabilities that Replication Services could offer in my opinion. SQL Server Replication Services provides tools to copy and distribute database objects from one database system to another and maintain consistency afterwards. These tools basically copy or synchronize data with little or no transformations, they do not offer capabilities to transform data or apply business rules, like ETL tools do. The only “transformations” Replication Services offers is to filter records or columns out of your data set. You can achieve this by selecting the desired columns of a table and/or by using WHERE statements like this: SELECT <published_columns> FROM [Table] WHERE [DateTime] >= getdate() - 60 There are three types of replication: Transactional Replication This type replicates data on a transactional level. The Log Reader Agent reads directly on the transaction log of the source database (Publisher) and clones the transactions to the Distribution Database (Distributor), this database acts as a queue for the destination database (Subscriber). Next, the Distribution Agent moves the cloned transactions that are stored in the Distribution Database to the Subscriber. The Distribution Agent can either run at scheduled intervals or continuously which offers near real-time replication of data! So for example when a user executes an UPDATE statement on one or multiple records in the publisher database, this transaction (not the data itself) is copied to the distribution database and is then also executed on the subscriber. When the Distribution Agent is set to run continuously this process runs all the time and transactions on the publisher are replicated in small batches (near real-time), when it runs on scheduled intervals it executes larger batches of transactions, but the idea is the same. Snapshot Replication This type of replication makes an initial copy of database objects that need to be replicated, this includes the schemas and the data itself. All types of replication must start with a snapshot of the database objects from the Publisher to initialize the Subscriber. Transactional replication need an initial snapshot of the replicated publisher tables/objects to run its cloned transactions on and maintain consistency. The Snapshot Agent copies the schemas of the tables that will be replicated to files that will be stored in the Snapshot Folder which is a normal folder on the file system. When all the schemas are ready, the data itself will be copied from the Publisher to the snapshot folder. The snapshot is generated as a set of bulk copy program (BCP) files. Next, the Distribution Agent moves the snapshot to the Subscriber, if necessary it applies schema changes first and copies the data itself afterwards. The application of schema changes to the Subscriber is a nice feature, when you change the schema of the Publisher with, for example, an ALTER TABLE statement, that change is propagated by default to the Subscriber(s). Merge Replication Merge replication is typically used in server-to-client environments, for example when subscribers need to receive data, make changes offline, and later synchronize changes with the Publisher and other Subscribers, like with mobile devices that need to synchronize one in a while. Because I don’t really see BI capabilities here, I will not explain this type of replication any further. Replication Services in a BI environment Transactional Replication can be very useful in BI environments. In my opinion you never want to see users to run custom (SSRS) reports or PowerPivot solutions directly on your production database, it can slow down the system and can cause deadlocks in the database which can cause errors. Transactional Replication can offer a read-only, near real-time database for reporting purposes with minimal overhead on the source system. Snapshot Replication can also be useful in BI environments, if you don’t need a near real-time copy of the database, you can choose to use this form of replication. Next to an alternative for Transactional Replication it can be used to stage data so it can be transformed and moved into the data warehousing environment afterwards. In many solutions I have seen developers create multiple SSIS packages that simply copies data from one or more source systems to a staging database that figures as source for the ETL process. The creation of these packages takes a lot of (boring) time, while Replication Services can do the same in minutes. It is possible to filter out columns and/or records and it can even apply schema changes automatically so I think it offers enough features here. I don’t know how the performance will be and if it really works as good for this purpose as I expect, but I want to try this out soon!

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• Aggregating cache data from OCEP in CQL

  - by Manju James

  There are several use cases where OCEP applications need to join stream data with external data, such as data available in a Coherence cache. OCEP’s streaming language, CQL, supports simple cache-key based joins of stream data with data in Coherence (more complex queries will be supported in a future release). However, there are instances where you may need to aggregate the data in Coherence based on input data from a stream. This blog describes a sample that does just that. For our sample, we will use a simplified credit card fraud detection use case. The input to this sample application is a stream of credit card transaction data. The input stream contains information like the credit card ID, transaction time and transaction amount. The purpose of this application is to detect suspicious transactions and send out a warning event. For the sake of simplicity, we will assume that all transactions with amounts greater than $1000 are suspicious. The transaction history is available in a Coherence distributed cache. For every suspicious transaction detected, a warning event must be sent with maximum amount, total amount and total number of transactions over the past 30 days, as shown in the diagram below. Application Input Stream input to the EPN contains events of type CCTransactionEvent. This input has to be joined with the cache with all credit card transactions. The cache is configured in the EPN as shown below: <wlevs:caching-system id="CohCacheSystem" provider="coherence"/> <wlevs:cache id="CCTransactionsCache" value-type="CCTransactionEvent" key-properties="cardID, transactionTime" caching-system="CohCacheSystem"> </wlevs:cache> Application Output The output that must be produced by the application is a fraud warning event. This event is configured in the spring file as shown below. Source for cardHistory property can be seen here. <wlevs:event-type type-name="FraudWarningEvent"> <wlevs:properties type="tuple"> <wlevs:property name="cardID" type="CHAR"/> <wlevs:property name="transactionTime" type="BIGINT"/> <wlevs:property name="transactionAmount" type="DOUBLE"/> <wlevs:property name="cardHistory" type="OBJECT"/> </wlevs:properties </wlevs:event-type> Cache Data Aggregation using Java Cartridge In the output warning event, cardHistory property contains data from the cache aggregated over the past 30 days. To get this information, we use a java cartridge method. This method uses Coherence’s query API on credit card transactions cache to get the required information. Therefore, the java cartridge method requires a reference to the cache. This may be set up by configuring it in the spring context file as shown below: <bean class="com.oracle.cep.ccfraud.CCTransactionsAggregator"> <property name="cache" ref="CCTransactionsCache"/> </bean> This is used by the java class to set a static property: public void setCache(Map cache) { s_cache = (NamedCache) cache; } The code snippet below shows how the total of all the transaction amounts in the past 30 days is computed. Rest of the information required by CardHistory object is calculated in a similar manner. Complete source of this class can be found here. To find out more information about using Coherence's API to query a cache, please refer Coherence Developer’s Guide. public static CreditHistoryData(String cardID) { … Filter filter = QueryHelper.createFilter("cardID = :cardID and transactionTime :transactionTime", map); CardHistoryData history = new CardHistoryData(); Double sum = (Double) s_cache.aggregate(filter, new DoubleSum("getTransactionAmount")); history.setTotalAmount(sum); … return history; } The java cartridge method is used from CQL as seen below: select cardID, transactionTime, transactionAmount, CCTransactionsAggregator.execute(cardID) as cardHistory from inputChannel where transactionAmount1000 This produces a warning event, with history data, for every credit card transaction over $1000. That is all there is to it. The complete source for the sample application, along with the configuration files, is available here. In the sample, I use a simple java bean to load the cache with initial transaction history data. An input adapter is used to create and send transaction events for the input stream.

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• SPARC T4-4 Delivers World Record First Result on PeopleSoft Combined Benchmark

  - by Brian

  Oracle's SPARC T4-4 servers running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved World Record 18,000 concurrent users while executing a PeopleSoft Payroll batch job of 500,000 employees in 43.32 minutes and maintaining online users response time at < 2 seconds. This world record is the first to run online and batch workloads concurrently. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 35% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. This is the first three tier mixed workload (online and batch) PeopleSoft benchmark also processing PeopleSoft payroll batch workload. Performance Landscape PeopleSoft HR Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-2 (db) 18,000 0.944 0.503 43.32 64 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory 5 x 300 GB SAS internal disks 1 x 100 GB and 2 x 300 GB internal SSDs 2 x 10 Gbe HBA Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 2 x 300 GB SAS internal disks 1 x 100 GB internal SSD Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two Oracle PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Management oracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Oracle's PeopleSoft HR and Payroll combined benchmark, www.oracle.com/us/solutions/benchmark/apps-benchmark/peoplesoft-167486.html, results 09/30/2012.

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• Detecting Duplicates Using Oracle Business Rules

  - by joeywong-Oracle

  Recently I was involved with a Business Process Management Proof of Concept (BPM PoC) where we wanted to show how customers could use Oracle Business Rules (OBR) to easily define some rules to detect certain conditions, such as duplicate account numbers, duplicate names, high transaction amounts, etc, in a set of transactions. Traditionally you would have to loop through the transactions and compare each transaction with each other to find matching conditions. This is not particularly nice as it relies on more traditional approaches (coding) and is not the most efficient way. OBR is a great place to house these types’ of rules as it allows users/developers to externalise the rules, in a simpler manner, externalising the rules from the message flows and allows users to change them when required. So I went ahead looking for some examples. After quite a bit of time spent Googling, I did not find much out in the blogosphere. In fact the best example was actually from...... wait for it...... Oracle Documentation! (http://docs.oracle.com/cd/E28271_01/user.1111/e10228/rules_start.htm#ASRUG228) However, if you followed the link there was not much explanation provided with the example. So the aim of this article is to provide a little more explanation to the example so that it can be better understood. Note: I won’t be covering the BPM parts in great detail. Use case: Payment instruction file is required to be processed. Before instruction file can be processed it needs to be approved by a business user. Before the approval process, it would be useful to run the payment instruction file through OBR to look for transactions of interest. The output of the OBR can then be used to flag the transactions for the approvers to investigate. Example BPM Process So let’s start defining the Business Rules Dictionary. For the input into our rules, we will be passing in an array of payments which contain some basic information for our demo purposes. Input to Business Rules And for our output we want to have an array of rule output messages. Note that the element I am using for the output is only for one rule message element and not an array. We will configure the Business Rules component later to return an array instead. Output from Business Rules Business Rule – Create Dictionary Fill in all the details and click OK. Open the Business Rules component and select Decision Functions from the side. Modify the Decision Function Configuration Select the decision function and click on the edit button (the pencil), don’t worry that JDeveloper indicates that there is an error with the decision function. Then click the Ouputs tab and make sure the checkbox under the List column is checked, this is to tell the Business Rules component that it should return an array of rule message elements. Updating the Decision Service Next we will define the actual rules. Click on Ruleset1 on the side and then the Create Rule in the IF/THEN Rule section. Creating new rule in ruleset Ok, this is where some detailed explanation is required. Remember that the input to this Business Rules dictionary is a list of payments, each of those payments were of the complex type PaymentType. Each of those payments in the Oracle Business Rules engine is treated as a fact in its working memory. Implemented rule So in the IF/THEN rule, the first task is to grab two PaymentType facts from the working memory and assign them to temporary variable names (payment1 and payment2 in our example). Matching facts Once we have them in the temporary variables, we can then start comparing them to each other. For our demonstration we want to find payments where the account numbers were the same but the account name was different. Suspicious payment instruction And to stop the rule from comparing the same facts to each other, over and over again, we have to include the last test. Stop rule from comparing endlessly And that’s it! No for loops, no need to keep track of what you have or have not compared, OBR handles all that for you because everything is done in its working memory. And once all the tests have been satisfied we need to assert a new fact for the output. Assert the output fact Save your Business Rules. Next step is to complete the data association in the BPM process. Pay extra care to use Copy List instead of the default Copy when doing data association at an array level. Input and output data association Deploy and test. Test data Rule matched Parting words: Ideally you would then use the output of the Business Rules component to then display/flag the transactions which triggered the rule so that the approver can investigate. Link: SOA Project Archive [Download]

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• Singleton by Jon Skeet clarification

  - by amutha

  public sealed class Singleton { Singleton() { } public static Singleton Instance { get { return Nested.instance; } } class Nested { // Explicit static constructor to tell C# compiler // not to mark type as beforefieldinit static Nested() { } internal static readonly Singleton instance = new Singleton(); } } I wish to implement Jon Skeet's Singleton pattern in my current application in C#. I have two doubts on the code 1) How is it possible to access the outer class inside nested class? I mean internal static readonly Singleton instance = new Singleton(); Is something called closure? 2) I did not get this comment // Explicit static constructor to tell C# compiler // not to mark type as beforefieldinit what does this comment suggest us?

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• Can not parse table information from html document.

  - by Harikrishna

  I am parsing many html documents.I am using html agility pack And I want to parse the tabular information from each document. And there may be any number of tables in each document.But I want to extract only one table from each document which has column header name NAME,PHONE NO,ADDRESS.And this table can be anywhere in the document,like in the document there is ten tables and from ten table there is one table which has many nested tables and from nested table there may be a table what I want to extract means table can be anywhere in the document and I want to find that table from the document by column header name.If I got that table then I want to then extract the information from that table. Now I can find the table which has column header NAME,PHONE NO,ADDRESS and also can extract the information from that.I am doing for that is, first I find the all tables in a document by foreach (var table in doc.DocumentNode.Descendants("table")) then for each table got I find the row for each table like, var rows = table.Descendants("tr"); and then for each row I am checking that row has that header name NAME,ADDRESS,PHONENO and if it is then I skip that row and extract all information after that row foreach (var row in rows.Skip(rowNo)) { var data = new List<string>(); foreach (var column in row.Descendants("td")) { data.Add(properText); } } Such that I am extracting all information from almost many document. But now problem is sometimes what happened that in some document I can not parse the information.Like a document in which there are like 10 tables and from these 10 tables 1 table is like there are many nested tables in that table. And from these nested tables I want to find the table which tabel has column header like NAME,ADDRESS,PHONE NO.So if table may be anywhere in the document even in the nested tables or anywhere it can be find through column header name.So I can parse the information from that table and skip the outer tabular information from that table.

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• Can not parse tabular information from html document.

  - by Harikrishna

  I am parsing many html documents.I am using html agility pack And I want to parse the tabular information from each document. And there may be any number of tables in each document.But I want to extract only one table from each document which has column header name NAME,PHONE NO,ADDRESS.And this table can be anywhere in the document,like in the document there is ten tables and from ten table there is one table which has many nested tables and from nested table there may be a table what I want to extract means table can be anywhere in the document and I want to find that table from the document by column header name.If I got that table then I want to then extract the information from that table. Now I can find the table which has column header NAME,PHONE NO,ADDRESS and also can extract the information from that.I am doing for that is, first I find the all tables in a document by foreach (var table in doc.DocumentNode.Descendants("table")) then for each table got I find the row for each table like, var rows = table.Descendants("tr"); and then for each row I am checking that row has that header name NAME,ADDRESS,PHONENO and if it is then I skip that row and extract all information after that row foreach (var row in rows.Skip(rowNo)) { var data = new List<string>(); foreach (var column in row.Descendants("td")) { data.Add(properText); } } Such that I am extracting all information from almost many document. But now problem is sometimes what happened that in some document I can not parse the information.Like a document in which there are like 10 tables and from these 10 tables 1 table is like there are many nested tables in that table. And from these nested tables I want to find the table which tabel has column header like NAME,ADDRESS,PHONE NO.So if table may be anywhere in the document even in the nested tables or anywhere it can be find through column header name.So I can parse the information from that table and skip the outer tabular information of that table.

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• Rails fields_for :child_index option explanation

  - by Timothy

  I have been trying to create a complex form with many nested models, and make it dynamic. Now I found that making a nested model isn't difficult with accepts_nested_attributes_for, but making it nested and dynamic was seemingly impossible if there were multiple nested models. I came across http://github.com/ryanb/complex-form-examples/blob/master/app/helpers/application_helper.rb which does it very elegantly. Could anyone shed some light on lines 13 and 16? 13 form_builder.object.class.reflect_on_association(method).klass.new and 16 form_builder.fields_for(method, options[:object], :child_index => "new_#{method}") do |f| From intuition, line 13 instantiates a new object, but why must it do so many method calls? I couldn't find any documentation for the :child_index option on line 16. When the form is created, a very large number is used as an index for new models, whereas existing models are indexed by their id. How does this work?

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• Asp.Net MVC 2: How exactly does a view model bind back to the model upon post back?

  - by Dr. Zim

  Sorry for the length, but a picture is worth 1000 words: In ASP.NET MVC 2, the input form field "name" attribute must contain exactly the syntax below that you would use to reference the object in C# in order to bind it back to the object upon post back. That said, if you have an object like the following where it contains multiple Orders having multiple OrderLines, the names would look and work well like this (case sensitive): This works: Order[0].id Order[0].orderDate Order[0].Customer.name Order[0].Customer.Address Order[0].OrderLine[0].itemID // first order line Order[0].OrderLine[0].description Order[0].OrderLine[0].qty Order[0].OrderLine[0].price Order[0].OrderLine[1].itemID // second order line, same names Order[0].OrderLine[1].description Order[0].OrderLine[1].qty Order[0].OrderLine[1].price However we want to add order lines and remove order lines at the client browser. Apparently, the indexes must start at zero and contain every consecutive index number to N. The black belt ninja Phil Haack's blog entry here explains how to remove the [0] index, have duplicate names, and let MVC auto-enumerate duplicate names with the [0] notation. However, I have failed to get this to bind back using a nested object: This fails: Order.id // Duplicate names should enumerate at 0 .. N Order.orderDate Order.Customer.name Order.Customer.Address Order.OrderLine.itemID // And likewise for nested properties? Order.OrderLine.description Order.OrderLine.qty Order.OrderLine.price Order.OrderLine.itemID Order.OrderLine.description Order.OrderLine.qty Order.OrderLine.price I haven't found any advice out there yet that describes how this works for binding back nested ViewModels on post. Any links to existing code examples or strict examples on the exact names necessary to do nested binding with ILists? Steve Sanderson has code that does this sort of thing here, but we cannot seem to get this to bind back to nested objects. Anything not having the [0]..[n] AND being consecutive in numbering simply drops off of the return object. Any ideas?

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• Strange error in SpringMVC Application Startup

  - by Euzel Villanueva

  I'm getting a very strange stack trace when trying to load a SpringMVC application and at a lost to why this is occurring. org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'org.springframework.web.servlet.mvc.annotation.AnnotationMethodHandlerAdapter#0': Cannot create inner bean '(inner bean)' of type [org.springframework.http.converter.xml.XmlAwareFormHttpMessageConverter] while setting bean property 'messageConverters' with key [4]; nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name '(inner bean)#6': Instantiation of bean failed; nested exception is org.springframework.beans.BeanInstantiationException: Could not instantiate bean class [org.springframework.http.converter.xml.XmlAwareFormHttpMessageConverter]: Constructor threw exception; nested exception is java.lang.OutOfMemoryError: Java heap space at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveInnerBean(BeanDefinitionValueResolver.java:281) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveValueIfNecessary(BeanDefinitionValueResolver.java:125) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveManagedList(BeanDefinitionValueResolver.java:353) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveValueIfNecessary(BeanDefinitionValueResolver.java:153) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.applyPropertyValues(AbstractAutowireCapableBeanFactory.java:1325) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.populateBean(AbstractAutowireCapableBeanFactory.java:1086) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:517) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:456) at org.springframework.beans.factory.support.AbstractBeanFactory$1.getObject(AbstractBeanFactory.java:291) at org.springframework.beans.factory.support.DefaultSingletonBeanRegistry.getSingleton(DefaultSingletonBeanRegistry.java:222) at org.springframework.beans.factory.support.AbstractBeanFactory.doGetBean(AbstractBeanFactory.java:288) at org.springframework.beans.factory.support.AbstractBeanFactory.getBean(AbstractBeanFactory.java:190) at org.springframework.beans.factory.support.DefaultListableBeanFactory.preInstantiateSingletons(DefaultListableBeanFactory.java:580) at org.springframework.context.support.AbstractApplicationContext.finishBeanFactoryInitialization(AbstractApplicationContext.java:895) at org.springframework.context.support.AbstractApplicationContext.refresh(AbstractApplicationContext.java:425) at org.springframework.web.servlet.FrameworkServlet.createWebApplicationContext(FrameworkServlet.java:442) at org.springframework.web.servlet.FrameworkServlet.createWebApplicationContext(FrameworkServlet.java:458) at org.springframework.web.servlet.FrameworkServlet.initWebApplicationContext(FrameworkServlet.java:339) at org.springframework.web.servlet.FrameworkServlet.initServletBean(FrameworkServlet.java:306) at org.springframework.web.servlet.HttpServletBean.init(HttpServletBean.java:127) at javax.servlet.GenericServlet.init(GenericServlet.java:160) at org.apache.catalina.core.StandardWrapper.initServlet(StandardWrapper.java:1133) at org.apache.catalina.core.StandardWrapper.loadServlet(StandardWrapper.java:1087) at org.apache.catalina.core.StandardWrapper.load(StandardWrapper.java:996) at org.apache.catalina.core.StandardContext.loadOnStartup(StandardContext.java:4834) at org.apache.catalina.core.StandardContext$3.call(StandardContext.java:5155) at org.apache.catalina.core.StandardContext$3.call(StandardContext.java:5150) at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:303) at java.util.concurrent.FutureTask.run(FutureTask.java:138) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:619) Caused by: org.springframework.beans.factory.BeanCreationException: Error creating bean with name '(inner bean)#6': Instantiation of bean failed; nested exception is org.springframework.beans.BeanInstantiationException: Could not instantiate bean class [org.springframework.http.converter.xml.XmlAwareFormHttpMessageConverter]: Constructor threw exception; nested exception is java.lang.OutOfMemoryError: Java heap space at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.instantiateBean(AbstractAutowireCapableBeanFactory.java:965) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBeanInstance(AbstractAutowireCapableBeanFactory.java:911) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:485) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:456) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveInnerBean(BeanDefinitionValueResolver.java:270) ... 31 more Caused by: org.springframework.beans.BeanInstantiationException: Could not instantiate bean class [org.springframework.http.converter.xml.XmlAwareFormHttpMessageConverter]: Constructor threw exception; nested exception is java.lang.OutOfMemoryError: Java heap space at org.springframework.beans.BeanUtils.instantiateClass(BeanUtils.java:141) at org.springframework.beans.factory.support.SimpleInstantiationStrategy.instantiate(SimpleInstantiationStrategy.java:74) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.instantiateBean(AbstractAutowireCapableBeanFactory.java:958) ... 35 more

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• How can i get HWND of external application's listview? In Windows Api using c++

  - by Marko29

  So i am trying to make app to get content of my explorer listviews and get item text etc.. from it but here are the problems... If i inspect windows explorer folder(using spy++) with listview, just for testing purposes i will use random folder. It shows me that caption of the window is "FolderView" with class "SysListView32" and the top level window where this listview is nested is called "reference", this is also the title of windows explorer folder where all the files are. So what i do is.. HWND hWndLV = FindWindow(NULL, TEXT("reference")); // first i get hwnd of the main window, this is where listview window is also nested according to spy++, thats why i do this first. HWND child = FindWindowEx(hWndLV, NULL,NULL,TEXT("FolderView")); // trying to get hwnd of the listview here but it fails, same happens if i also put the class name along as HWND child = FindWindowEx(hWndLV, NULL,TEXT("SysListView32"),TEXT("FolderView")); I am using bool test = IsWindow(child); to test for fail, also VS debugger shows 0x0000000000 each time so i am sure i am reading results well. So i am stuck on this probably simple thing for most of people:( p.s. i am on vista64(if that matters anyhow) edit: It appears that this function works only if i search the first nested level of a parent window i am searching. So i assume what i need is a way to get handle with some sort of deep nested level search. I also tried to go step by step by defining hwnd of every parent then i use findwindowex on it but oh boy then i get to the point where there are 5 nested windows all with the same name and only one of them contains my listview, so nice uh?

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• anti-if campaign

  - by Andrew Siemer

  I recently ran against a very interesting site that expresses a very interesting idea - the anti-if campaign. You can see this here at www.antiifcampaign.com. I have to agree that complex nested IF statements are an absolute pain in the rear. I am currently on a project that up until very recently had some crazy nested IFs that scrolled to the right for quite a ways. We cured our issues in two ways - we used Windows Workflow Foundation to address routing (or workflow) concerns. And we are in the process of implementing all of our business rules utilizing ILOG Rules for .NET (recently purchased by IBM!!). This for the most part has cured our nested IF pains...but I find myself wondering how many people cure their pains in the manner that the good folks at the AntiIfCampaign suggest (see an example here) by creating numerous amounts of abstract classes to represent a given scenario that was originally covered by the nested IF. I wonder if another way to address the removal of this complexity might also be in using an IoC container such as StructureMap to move in and out of different bits of functionality. Either way... Question: Given a scenario where I have a nested complex IF or SWITCH statement that is used to evaluate a given type of thing (say evaluating an Enum) to determine how I want to handle the processing of that thing by enum type - what are some ways to do the same form of processing without using the IF or SWITCH hierarchical structure? public enum WidgetTypes { Type1, Type2, Type3, Type4 } ... WidgetTypes _myType = WidgetTypes.Type1; ... switch(_myType) { case WidgetTypes.Type1: //do something break; case WidgetTypes.Type2: //do something break; //etc... }

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