Query is too complex?

huitor 2000-09-05 02:42:00

我有一个表70个字段，使用ado访问，提交时提示如题，请指教。

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huitor 2000-09-06

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我记错了，一个表有120个字段，程序编译没有问题，运行也没有问题，我使用的是数据绑定控件。如果仅仅修改几个字段就提交，程序也没有问题，但如果修改多个字段再提交，程序就会提示：Query is too complex.

playpcgame 2000-09-05

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Contents Overview 1 Lesson 1: Index Concepts 3 Lesson 2: Concepts – Statistics 29 Lesson 3: Concepts – Query Optimization 37 Lesson 4: Information Collection and Analysis 61 Lesson 5: Formulating and Implementing Resolution 75 Module 6: Troubleshooting Query Performance Overview At the end of this module, you will be able to:  Describe the different types of indexes and how indexes can be used to improve performance.  Describe what statistics are used for and how they can help in optimizing query performance.  Describe how queries are optimized.  Analyze the information collected from various tools.  Formulate resolution to query performance problems. Lesson 1: Index Concepts Indexes are the most useful tool for improving query performance. Without a useful index, Microsoft® SQL Server™ must search every row on every page in table to find the rows to return. With a multitable query, SQL Server must sometimes search a table multiple times so each page is scanned much more than once. Having useful indexes speeds up finding individual rows in a table, as well as finding the matching rows needed to join two tables. What You Will Learn After completing this lesson, you will be able to:  Understand the structure of SQL Server indexes.  Describe how SQL Server uses indexes to find rows.  Describe how fillfactor can impact the performance of data retrieval and insertion.  Describe the different types of fragmentation that can occur within an index. Recommended Reading  Chapter 8: “Indexes”, Inside SQL Server 2000 by Kalen Delaney  Chapter 11: “Batches, Stored Procedures and Functions”, Inside SQL Server 2000 by Kalen Delaney Finding Rows without Indexes With No Indexes, A Table Must Be Scanned SQL Server keeps track of which pages belong to a table or index by using IAM pages. If there is no clustered index, there is a sysindexes row for the table with an indid value of 0, and that row will keep track of the address of the first IAM for the table. The IAM is a giant bitmap, and every 1 bit indicates that the corresponding extent belongs to the table. The IAM allows SQL Server to do efficient prefetching of the table’s extents, but every row still must be examined. General Index Structure All SQL Server Indexes Are Organized As B-Trees Indexes in SQL Server store their information using standard B-trees. A B-tree provides fast access to data by searching on a key value of the index. B-trees cluster records with similar keys. The B stands for balanced, and balancing the tree is a core feature of a B-tree’s usefulness. The trees are managed, and branches are grafted as necessary, so that navigating down the tree to find a value and locate a specific record takes only a few page accesses. Because the trees are balanced, finding any record requires about the same amount of resources, and retrieval speed is consistent because the index has the same depth throughout. Clustered and Nonclustered Indexes Both Index Types Have Many Common Features An index consists of a tree with a root from which the navigation begins, possible intermediate index levels, and bottom-level leaf pages. You use the index to find the correct leaf page. The number of levels in an index will vary depending on the number of rows in the table and the size of the key column or columns for the index. If you create an index using a large key, fewer entries will fit on a page, so more pages (and possibly more levels) will be needed for the index. On a qualified select, update, or delete, the correct leaf page will be the lowest page of the tree in which one or more rows with the specified key or keys reside. A qualified operation is one that affects only specific rows that satisfy the conditions of a WHERE clause, as opposed to accessing the whole table. An index can have multiple node levels An index page above the leaf is called a node page. Each index row in node pages contains an index key (or set of keys for a composite index) and a pointer to a page at the next level for which the first key value is the same as the key value in the current index row. Leaf Level contains all key values In any index, whether clustered or nonclustered, the leaf level contains every key value, in key sequence. In SQL Server 2000, the sequence can be either ascending or descending. The sysindexes table contains all sizing, location and distribution information Any information about size of indexes or tables is stored in sysindexes. The only source of any storage location information is the sysindexes table, which keeps track of the address of the root page for every index, and the first IAM page for the index or table. There is also a column for the first page of the table, but this is not guaranteed to be reliable. SQL Server can find all pages belonging to an index or table by examining the IAM pages. Sysindexes contains a pointer to the first IAM page, and each IAM page contains a pointer to the next one. The Difference between Clustered and Nonclustered Indexes The main difference between the two types of indexes is how much information is stored at the leaf. The leaf levels of both types of indexes contain all the key values in order, but they also contain other information. Clustered Indexes The Leaf Level of a Clustered Index Is the Data The leaf level of a clustered index contains the data pages, not just the index keys. Another way to say this is that the data itself is part of the clustered index. A clustered index keeps the data in a table ordered around the key. The data pages in the table are kept in a doubly linked list called the page chain. The order of pages in the page chain, and the order of rows on the data pages, is the order of the index key or keys. Deciding which key to cluster on is an important performance consideration. When the index is traversed to the leaf level, the data itself has been retrieved, not simply pointed to. Uniqueness Is Maintained In Key Values In SQL Server 2000, all clustered indexes are unique. If you build a clustered index without specifying the unique keyword, SQL Server forces uniqueness by adding a uniqueifier to the rows when necessary. This uniqueifier is a 4-byte value added as an additional sort key to only the rows that have duplicates of their primary sort key. You can see this extra value if you use DBCC PAGE to look at the actual index rows the section on indexes internal. . Finding Rows in a Clustered Index The Leaf Level of a Clustered Index Contains the Data A clustered index is like a telephone directory in which all of the rows for customers with the same last name are clustered together in the same part of the book. Just as the organization of a telephone directory makes it easy for a person to search, SQL Server quickly searches a table with a clustered index. Because a clustered index determines the sequence in which rows are stored in a table, there can only be one clustered index for a table at a time. Performance Considerations Keeping your clustered key value small increases the number of index rows that can be placed on an index page and decreases the number of levels that must be traversed. This minimizes I/O. As we’ll see, the clustered key is duplicated in every nonclustered index row, so keeping your clustered key small will allow you to have more index fit per page in all your indexes. Note The query corresponding to the slide is: SELECT lastname, firstname FROM member WHERE lastname = ‘Ota’ Nonclustered Indexes The Leaf Level of a Nonclustered Index Contains a Bookmark A nonclustered index is like the index of a textbook. The data is stored in one place and the index is stored in another. Pointers indicate the storage location of the indexed items in the underlying table. In a nonclustered index, the leaf level contains each index key, plus a bookmark that tells SQL Server where to find the data row corresponding to the key in the index. A bookmark can take one of two forms:  If the table has a clustered index, the bookmark is the clustered index key for the corresponding data row. This clustered key can be multiple column if the clustered index is composite, or is defined to be non-unique.  If the table is a heap (in other words, it has no clustered index), the bookmark is a RID, which is an actual row locator in the form File#:Page#:Slot#. Finding Rows with a NC Index on a Heap Nonclustered Indexes Are Very Efficient When Searching For A Single Row After the nonclustered key at the leaf level of the index is found, only one more page access is needed to find the data row. Searching for a single row using a nonclustered index is almost as efficient as searching for a single row in a clustered index. However, if we are searching for multiple rows, such as duplicate values, or keys in a range, anything more than a small number of rows will make the nonclustered index search very inefficient. Note The query corresponding to the slide is: SELECT lastname, firstname FROM member WHERE lastname BETWEEN ‘Master’ AND ‘Rudd’ Finding Rows with a NC Index on a Clustered Table A Clustered Key Is Used as the Bookmark for All Nonclustered Indexes If the table has a clustered index, all columns of the clustered key will be duplicated in the nonclustered index leaf rows, unless there is overlap between the clustered and nonclustered key. For example, if the clustered index is on (lastname, firstname) and a nonclustered index is on firstname, the firstname value will not be duplicated in the nonclustered index leaf rows. Note The query corresponding to the slide is: SELECT lastname, firstname, phone FROM member WHERE firstname = ‘Mike’ Covering Indexes A Covering Index Provides the Fastest Data Access A covering index contains ALL the fields accessed in the query. Normally, only the columns in the WHERE clause are helpful in determining useful indexes, but for a covering index, all columns must be included. If all columns needed for the query are in the index, SQL Server never needs to access the data pages. If even one column in the query is not part of the index, the data rows must be accessed. The leaf level of an index is the only level that contains every key value, or set of key values. For a clustered index, the leaf level is the data itself, so in reality, a clustered index ALWAYS covers any query. Nevertheless, for most of our optimization discussions, we only consider nonclustered indexes. Scanning the leaf level of a nonclustered index is almost always faster than scanning a clustered index, so covering indexes are particular valuable when we need ALL the key values of a particular nonclustered index. Example: Select an aggregate value of a column with a clustered index. Suppose we have a nonclustered index on price, this query is covered: SELECT avg(price) from titles Since the clustered key is included in every nonclustered index row, the clustered key can be included in the covering. Suppose you have a nonclustered index on price and a clustered index on title_id; then this query is covered: SELECT title_id, price FROM titles WHERE price between 10 and 20 Performance Considerations In general, you do want to keep your indexes narrow. However, if you have a critical query that just is not giving you satisfactory performance no matter what you do, you should consider creating an index to cover it, or adding one or two extra columns to an existing index, so that the query will be covered. The leaf level of a nonclustered index is like a ‘mini’ clustered index, so you can have most of the benefits of clustering, even if there already is another clustered index on the table. The tradeoff to adding more, wider indexes for covering queries are the added disk space, and more overhead for updating those columns that are now part of the index. Bug In general, SQL Server will detect when a query is covered, and detect the possible covering indexes. However, in some cases, you must force SQL Server to use a covering index by including a WHERE clause, even if the WHERE clause will return ALL the rows in the table. This is SHILOH bug #352079 Steps to reproduce 1. Make copy of orders table from Northwind: USE Northwind CREATE TABLE [NewOrders] ( [OrderID] [int] NOT NULL , [CustomerID] [nchar] (5) NULL , [EmployeeID] [int] NULL , [OrderDate] [datetime] NULL , [RequiredDate] [datetime] NULL , [ShippedDate] [datetime] NULL , [ShipVia] [int] NULL , [Freight] [money] NULL , [ShipName] [nvarchar] (40) NULL, [ShipAddress] [nvarchar] (60) , [ShipCity] [nvarchar] (15) NULL, [ShipRegion] [nvarchar] (15) NULL, [ShipPostalCode] [nvarchar] (10) NULL, [ShipCountry] [nvarchar] (15) NULL ) INSERT into NewOrders SELECT * FROM Orders 2. Build nc index on OrderDate: create index dateindex on neworders(orderdate) 3. Test Query by looking at query plan: select orderdate from NewOrders The index is being scanned, as expected. 4. Build an index on orderId: create index orderid_index on neworders(orderID) 5. Test Query by looking at query plan: select orderdate from NewOrders Now the TABLE is being scanned, instead of the original index! Index Intersection Multiple Indexes Can Be Used On A Single Table In versions prior to SQL Server 7, only one index could be used for any table to process any single query. The only exception was a query involving an OR. In current SQL Server versions, multiple nonclustered indexes can each be accessed, retrieving a set of keys with bookmarks, and then the result sets can be joined on the common bookmarks. The optimizer weighs the cost of performing the unindexed join on the intermediate result sets, with the cost of only using one index, and then scanning the entire result set from that single index. Fillfactor and Performance Creating an Index with a Low Fillfactor Delays Page Splits when Inserting DBCC SHOWCONTIG will show you a low value for “Avg. Page Density” when a low fillfactor has been specified. This is good for inserts and updates, because it will delay the need to split pages to make room for new rows. It can be bad for scans, because fewer rows will be on each page, and more pages must be read to access the same amount of data. However, this cost will be minimal if the scan density value is good. Index Reorganization DBCC SHOWCONTIG Provides Lots of Information Here’s some sample output from running a basic DBCC SHOWCONTIG on the order details table in the Northwind database: DBCC SHOWCONTIG scanning 'Order Details' table... Table: 'Order Details' (325576198); index ID: 1, database ID:6 TABLE level scan performed. - Pages Scanned................................: 9 - Extents Scanned..............................: 6 - Extent Switches..............................: 5 - Avg. Pages per Extent........................: 1.5 - Scan Density [Best Count:Actual Count].......: 33.33% [2:6] - Logical Scan Fragmentation ..................: 0.00% - Extent Scan Fragmentation ...................: 16.67% - Avg. Bytes Free per Page.....................: 673.2 - Avg. Page Density (full).....................: 91.68% By default, DBCC SHOWCONTIG scans the page chain at the leaf level of the specified index and keeps track of the following values:  Average number of bytes free on each page (Avg. Bytes Free per Page)  Number of pages accessed (Pages scanned)  Number of extents accessed (Extents scanned)  Number of times a page had a lower page number than the previous page in the scan (This value for Out of order pages is not displayed, but is used for additional computations.)  Number of times a page in the scan was on a different extent than the previous page in the scan (Extent switches) SQL Server also keeps track of all the extents that have been accessed, and then it determines how many gaps are in the used extents. An extent is identified by the page number of its first page. So, if extents 8, 16, 24, 32, and 40 make up an index, there are no gaps. If the extents are 8, 16, 24, and 40, there is one gap. The value in DBCC SHOWCONTIG’s output called Extent Scan Fragmentation is computed by dividing the number of gaps by the number of extents, so in this example the Extent Scan Fragmentation is ¼, or 25 percent. A table using extents 8, 24, 40, and 56 has three gaps, and its Extent Scan Fragmentation is ¾, or 75 percent. The maximum number of gaps is the number of extents - 1, so Extent Scan Fragmentation can never be 100 percent. The value in DBCC SHOWCONTIG’s output called Logical Scan Fragmentation is computed by dividing the number of Out of order pages by the number of pages in the table. This value is meaningless in a heap. You can use either the Extent Scan Fragmentation value or the Logical Scan Fragmentation value to determine the general level of fragmentation in a table. The lower the value, the less fragmentation there is. Alternatively, you can use the value called Scan Density, which is computed by dividing the optimum number of extent switches by the actual number of extent switches. A high value means that there is little fragmentation. Scan Density is not valid if the table spans multiple files; therefore, it is less useful than the other values. SQL Server 2000 allows online defragmentation You can choose from several methods for removing fragmentation from an index. You could rebuild the index and have SQL Server allocate all new contiguous pages for you. To rebuild the index, you can use a simple DROP INDEX and CREATE INDEX combination, but in many cases using these commands is less than optimal. In particular, if the index is supporting a constraint, you cannot use the DROP INDEX command. Alternatively, you can use DBCC DBREINDEX, which can rebuild all the indexes on a table in one operation, or you can use the drop_existing clause along with CREATE INDEX. The drawback of these methods is that the table is unavailable while SQL Server is rebuilding the index. When you are rebuilding only nonclustered indexes, SQL Server takes a shared lock on the table, which means that users cannot make modifications, but other processes can SELECT from the table. Of course, those SELECT queries cannot take advantage of the index you are rebuilding, so they might not perform as well as they would otherwise. If you are rebuilding a clustered index, SQL Server takes an exclusive lock and does not allow access to the table, so your data is temporarily unavailable. SQL Server 2000 lets you defragment an index without completely rebuilding it. DBCC INDEXDEFRAG reorders the leaf-level pages into physical order as well as logical order, but using only the pages that are already allocated to the leaf level. This command does an in-place ordering, which is similar to a sorting technique called bubble sort (you might be familiar with this technique if you've studied and compared various sorting algorithms). In-place ordering can reduce logical fragmentation to 2 percent or less, making an ordered scan through the leaf level much faster. DBCC INDEXDEFRAG also compacts the pages of an index, based on the original fillfactor. The pages will not always end up with the original fillfactor, but SQL Server uses that value as a goal. The defragmentation process attempts to leave at least enough space for one average-size row on each page. In addition, if SQL Server cannot obtain a lock on a page during the compaction phase of DBCC INDEXDEFRAG, it skips the page and does not return to it. Any empty pages created as a result of compaction are removed. The algorithm SQL Server 2000 uses for DBCC INDEXDEFRAG finds the next physical page in a file belonging to the index's leaf level and the next logical page in the leaf level to swap it with. To find the next physical page, the algorithm scans the IAM pages belonging to that index. In a database spanning multiple files, in which a table or index has pages on more than one file, SQL Server handles pages on different files separately. SQL Server finds the next logical page by scanning the index's leaf level. After each page move, SQL Server drops all locks and saves the last key on the last page it moved. The next iteration of the algorithm uses the last key to find the next logical page. This process lets other users update the table and index while DBCC INDEXDEFRAG is running. Let us look at an example in which an index's leaf level consists of the following pages in the following logical order: 47 22 83 32 12 90 64 The first key is on page 47, and the last key is on page 64. SQL Server would have to scan the pages in this order to retrieve the data in sorted order. As its first step, DBCC INDEXDEFRAG would find the first physical page, 12, and the first logical page, 47. It would then swap the pages, using a temporary buffer as a holding area. After the first swap, the leaf level would look like this: 12 22 83 32 47 90 64 The next physical page is 22, which is also the next logical page, so no work would be necessary. DBCC INDEXDEFRAG would then swap the next physical page, 32, with the next logical page, 83: 12 22 32 83 47 90 64 After the next swap of 47 with 83, the leaf level would look like this: 12 22 32 47 83 90 64 Then, the defragmentation process would swap 64 with 83: 12 22 32 47 64 90 83 and 83 with 90: 12 22 32 47 64 83 90 At the end of the DBCC INDEXDEFRAG operation, the pages in the table or index are not contiguous, but their logical order matches their physical order. Now, if the pages were accessed from disk in sorted order, the head would need to move in only one direction. Keep in mind that DBCC INDEXDEFRAG uses only pages that are already part of the index's leaf level; it allocates no new pages. In addition, defragmenting a large table can take quite a while, and you will get a report every 5 minutes about the estimated percentage completed. However, except for the locks on the pages being switched, this command needs no additional locks. All the table's other pages and indexes are fully available for your applications to use during the defragmentation process. If you must completely rebuild an index because you want a new fillfactor, or if simple defragmentation is not enough because you want to remove all fragmentation from your indexes, another SQL Server 2000 improvement makes index rebuilding less of an imposition on the rest of the system. SQL Server 2000 lets you create an index in parallel—that is, using multiple processors—which drastically reduces the time necessary to perform the rebuild. The algorithm SQL Server 2000 uses, allows near-linear scaling with the number of processors you use for the rebuild, so four processors will take only one-fourth the time that one processor requires to rebuild an index. System availability increases because the length of time that a table is unavailable decreases. Note that only the SQL Server 2000 Enterprise Edition supports parallel index creation. Indexes on Views and Computed Columns Building an Index Gives the Data Physical Existence Normally, views are only logical and the rows comprising the view’s data are not generated until the view is accessed. The values for computed columns are typically not stored anywhere in the database; only the definition for the computation is stored and the computation is redone every time a computed column is accessed. The first index on a view must be a clustered index, so that the leaf level can hold all the actual rows that make up the view. Once that clustered index has been build, and the view’s data is now physical, additional (nonclustered) indexes can be built. An index on a computed column can be nonclustered, because all we need to store is the index key values. Common Prerequisites for Indexed Views and Indexes on Computed Columns In order for SQL Server to create use these special indexes, you must have the seven SET options correctly specified: ARITHABORT, CONCAT_NULL_YIELDS_NULL, QUOTED_IDENTIFIER, ANSI_NULLS, ANSI_PADDING, ANSI_WARNING must be all ON NUMERIC_ROUNDABORT must be OFF Only deterministic expressions can be used in the definition of Indexed Views or indexes on Computed Columns. See the BOL for the list of deterministic functions and expressions. Property functions are available to check if a column or view meets the requirements and is indexable. SELECT OBJECTPROPERTY (Object_id, ‘IsIndexable’) SELECT COLUMNPROPERTY (Object_id, column_name , ‘IsIndexable’ ) Schema Binding Guarantees That Object Definition Won’t Change A view can only be indexed if it has been built with schema binding. The SQL Server Optimizer Determines If the Indexed View Can Be Used The query must request a subset of the data contained in the view. The ability of the optimizer to use the indexed view even if the view is not directly referenced is available only in SQL Server 2000 Enterprise Edition. In Standard edition, you can create indexed views, and you can select directly from them, but the optimizer will not choose to use them if they are not directly referenced. Examples of Indexed Views: The best candidates for improvement by indexed views are queries performing aggregations and joins. We will explain how the useful indexed views may be created for these two major groups of queries. The considerations are valid also for queries and indexed views using both joins and aggregations. -- Example: USE Northwind -- Identify 5 products with overall biggest discount total. -- This may be expressed for example by two different queries: -- Q1. select TOP 5 ProductID, SUM(UnitPrice*Quantity)- SUM(UnitPrice*Quantity*(1.00-Discount)) Rebate from [order details] group by ProductID order by Rebate desc --Q2. select TOP 5 ProductID, SUM(UnitPrice*Quantity*Discount) Rebate from [order details] group by ProductID order by Rebate desc --The following indexed view will be used to execute Q1. create view Vdiscount1 with schemabinding as select SUM(UnitPrice*Quantity) SumPrice, SUM(UnitPrice*Quantity*(1.00-Discount)) SumDiscountPrice, COUNT_BIG(*) Count, ProductID from dbo.[order details] group By ProductID create unique clustered index VDiscountInd on Vdiscount1 (ProductID) However, it will not be used by the Q2 because the indexed view does not contain the SUM(UnitPrice*Quantity*Discount) aggregate. We can construct another indexed view create view Vdiscount2 with schemabinding as select SUM(UnitPrice*Quantity) SumPrice, SUM(UnitPrice*Quantity*(1.00-Discount)) SumDiscountPrice, SUM(UnitPrice*Quantity*Discount) SumDiscoutPrice2, COUNT_BIG(*) Count, ProductID from dbo.[order details] group By ProductID create unique clustered index VDiscountInd on Vdiscount2 (ProductID) This view may be used by both Q1 and Q2. Observe that the indexed view Vdiscount2 will have the same number of rows and only one more column compared to Vdiscount1, and it may be used by more queries. In general, try to design indexed views that may be used by more queries. The following query asking for the order with the largest total discount -- Q3. select TOP 3 OrderID, SUM(UnitPrice*Quantity*Discount) OrderRebate from dbo.[order details] group By OrderID Q3 can use neither of the Vdiscount views because the column OrderID is not included in the view definition. To address this variation of the discount analysis query we may create a different indexed view, similar to the query itself. An attempt to generalize the previous indexed view Vdiscount2 so that all three queries Q1, Q2, and Q3 can take advantage of a single indexed view would require a view with both OrderID and ProductID as grouping columns. Because the OrderID, ProductID combination is unique in the original order details table the resulting view would have as many rows as the original table and we would see no savings in using such view compared to using the original table. Consider the size of the resulting indexed view. In the case of pure aggregation, the indexed view may provide no significant performance gains if its size is close to the size of the original table. Complex aggregates (STDEV, VARIANCE, AVG) cannot participate in the index view definition. However, SQL Server may use an indexed view to execute a query containing AVG aggregate. Query containing STDEV or VARIANCE cannot use indexed view to pre-compute these values. The next example shows a query producing the average price for a particular product -- Q4. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from [order details] od, Products p where od.ProductID=p.ProductID group by ProductName, od.ProductID This is an example of indexed view that will be considered by the SQL Server to answer the Q4 create view v3 with schemabinding as select od.ProductID, SUM(od.UnitPrice*(1.00-Discount)) Price, COUNT_BIG(*) Count, SUM(od.Quantity) Units from dbo.[order details] od group by od.ProductID go create UNIQUE CLUSTERED index iv3 on v3 (ProductID) go Observe that the view definition does not contain the table Products. The indexed view does not need to contain all tables used in the query that uses the indexed view. In addition, the following query (same as above Q4 only with one additional search condition) will use the same indexed view. Observe that the added predicate references only columns from tables not present in the v3 view definition. -- Q5. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from [order details] od, Products p where od.ProductID=p.ProductID and p.ProductName like '%tofu%' group by ProductName, od.ProductID The following query cannot use the indexed view because the added search condition od.UnitPrice>10 contains a column from the table in the view definition and the column is neither grouping column nor the predicate appears in the view definition. -- Q6. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from [order details] od, Products p where od.ProductID=p.ProductID and od.UnitPrice>10 group by ProductName, od.ProductID To contrast the Q6 case, the following query will use the indexed view v3 since the added predicate is on the grouping column of the view v3. -- Q7. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from [order details] od, Products p where od.ProductID=p.ProductID and od.ProductID in (1,2,13,41) group by ProductName, od.ProductID -- The previous query Q6 will use the following indexed view V4: create view V4 with schemabinding as select ProductName, od.ProductID, SUM(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units, COUNT_BIG(*) Count from dbo.[order details] od, dbo.Products p where od.ProductID=p.ProductID and od.UnitPrice>10 group by ProductName, od.ProductID create unique clustered index VDiscountInd on V4 (ProductName, ProductID) The same index on the view V4 will be used also for a query where a join to the table Orders is added, for example -- Q8. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from dbo.[order details] od, dbo.Products p, dbo.Orders o where od.ProductID=p.ProductID and o.OrderID=od.OrderID and od.UnitPrice>10 group by ProductName, od.ProductID We will show several modifications of the query Q8 and explain why such modifications cannot use the above view V4. -- Q8a. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from dbo.[order details] od, dbo.Products p, dbo.Orders o where od.ProductID=p.ProductID and o.OrderID=od.OrderID and od.UnitPrice>25 group by ProductName, od.ProductID 8a cannot use the indexed view because of the where clause mismatch. Observe that table Orders does not participate in the indexed view V4 definition. In spite of that, adding a predicate on this table will disallow using the indexed view because the added predicate may eliminate additional rows participating in the aggregates as it is shown in Q8b. -- Q8b. select ProductName, od.ProductID, AVG(od.UnitPrice*(1.00-Discount)) AvgPrice, SUM(od.Quantity) Units from dbo.[order details] od, dbo.Products p, dbo.Orders o where od.ProductID=p.ProductID and o.OrderID=od.OrderID and od.UnitPrice>10 and o.OrderDate>'01/01/1998' group by ProductName, od.ProductID Locking and Indexes In General, You Should Let SQL Server Control the Locking within Indexes The stored procedure sp_indexoption lets you manually control the unit of locking within an index. It also lets you disallow page locks or row locks within an index. Since these options are available only for indexes, there is no way to control the locking within the data pages of a heap. (But remember that if a table has a clustered index, the data pages are part of the index and are affected by the sp_indexoption setting.) The index options are set for each table or index individually. Two options, Allow Rowlocks and AllowPageLocks, are both set to TRUE initially for every table and index. If both of these options are set to FALSE for a table, only full table locks are allowed. As described in Module 4, SQL Server determines at runtime whether to initially lock rows, pages, or the entire table. The locking of rows (or keys) is heavily favored. The type of locking chosen is based on the number of rows and pages to be scanned, the number of rows on a page, the isolation level in effect, the update activity going on, the number of users on the system needing memory for their own purposes, and so on. SAP databases frequently use sp_indexoption to reduce deadlocks Setting vs. Querying In SQL Server 2000, the procedure sp_indexoption should only be used for setting an index option. To query an option, use the INDEXPROPERTY function. Lesson 2: Concepts – Statistics Statistics are the most important tool that the SQL Server query optimizer has to determine the ideal execution plan for a query. Statistics that are out of date or nonexistent seriously jeopardize query performance. SQL Server 2000 computes and stores statistics in a completely different format that all earlier versions of SQL Server. One of the improvements is an increased ability to determine which values are out of the normal range in terms of the number of occurrences. The new statistics maintenance routines are particularly good at determining when a key value has a very unusual skew of data. What You Will Learn After completing this lesson, you will be able to:  Define terms related to statistics collected by SQL Server.  Describe how statistics are maintained by SQL Server.  Discuss the autostats feature of SQL Server.  Describe how statistics are used in query optimization. Recommended Reading  Statistics Used by the Query Optimizer in Microsoft SQL Server 2000 http://msdn.microsoft.com/library/techart/statquery.htm Definitions Cardinality The cardinality means how many unique values exist in the data. Density For each index and set of column statistics, SQL Server keeps track of details about the uniqueness (or density) of the data values encountered, which provides a measure of how selective the index is. A unique index, of course, has the lowest density —by definition, each index entry can point to only one row. A unique index has a density value of 1/number of rows in the table. Density values range from 0 through 1. Highly selective indexes have density values of 0.10 or lower. For example, a unique index on a table with 8345 rows has a density of 0.00012 (1/8345). If a nonunique nonclustered index has a density of 0.2165 on the same table, each index key can be expected to point to about 1807 rows (0.2165 × 8345). This is probably not selective enough to be more efficient than just scanning the table, so this index is probably not useful. Because driving the query from a nonclustered index means that the pages must be retrieved in index order, an estimated 1807 data page accesses (or logical reads) are needed if there is no clustered index on the table and the leaf level of the index contains the actual RID of the desired data row. The only time a data page doesn’t need to be reaccessed is when the occasional coincidence occurs in which two adjacent index entries happen to point to the same data page. In general, you can think of density as the average number of duplicates. We can also talk about the term ‘join density’, which applies to the average number of duplicates in the foreign key column. This would answer the question: in this one-to-many relationship, how many is ‘many’? Selectivity In general selectivity applies to a particular data value referenced in a WHERE clause. High selectivity means that only a small percentage of the rows satisfy the WHERE clause filter, and a low selectivity means that many rows will satisfy the filter. For example, in an employees table, the column employee_id is probably very selective, and the column gender is probably not very selective at all. Statistics Statistics are a histogram consisting of an even sampling of values for a column or for an index key (or the first column of the key for a composite index) based on the current data. The histogram is stored in the statblob field of the sysindexes table, which is of type image. (Remember that image data is actually stored in structures separate from the data row itself. The data row merely contains a pointer to the image data. For simplicity’s sake, we’ll talk about the index statistics as being stored in the image field called statblob.) To fully estimate the usefulness of an index, the optimizer also needs to know the number of pages in the table or index; this information is stored in the dpages column of sysindexes. During the second phase of query optimization, index selection, the query optimizer determines whether an index exists for a columns in your WHERE clause, assesses the index’s usefulness by determining the selectivity of the clause (that is, how many rows will be returned), and estimates the cost of finding the qualifying rows. Statistics for a single column index consist of one histogram and one density value. The multicolumn statistics for one set of columns in a composite index consist of one histogram for the first column in the index and density values for each prefix combination of columns (including the first column alone). The fact that density information is kept for all columns helps the optimizer decide how useful the index is for joins. Suppose, for example, that an index is composed of three key fields. The density on the first column might be 0.50, which is not too useful. However, as you look at more key columns in the index, the number of rows pointed to is fewer than (or in the worst case, the same as) the first column, so the density value goes down. If you are looking at both the first and second columns, the density might be 0.25, which is somewhat better. Moreover, if you examine three columns, the density might be 0.03, which is highly selective. It does not make sense to refer to the density of only the second column. The lead column density is always needed. Statistics Maintenance Statistics Information Tracks the Distribution of Key Values SQL Server statistics is basically a histogram that contains up to 200 values of a given key column. In addition to the histogram, the statblob field contains the following information:  The time of the last statistics collection  The number of rows used to produce the histogram and density information  The average key length  Densities for other combinations of columns In the statblob column, up to 200 sample values are stored; the range of key values between each sample value is called a step. The sample value is the endpoint of the range. Three values are stored along with each step: a value called EQ_ROWS, which is the number of rows that have a value equal to that sample value; a value called RANGE_ROWS, which specifies how many other values are inside the range (between two adjacent sample values); and the number of distinct values, or RANGE_DENSITY of the range. DBCC SHOW_STATISTICS The DBCC SHOW_STATISTICS output shows us the first two of these three values, but not the range density. The RANGE_DENSITY is instead used to compute two additional values:  DISTINCT_RANGE_ROWS—the number of distinct rows inside this range (not counting the RANGE_HI_KEY value itself. This is computed as 1/RANGE_DENSITY.  AVG_RANGE_ROWS—the average number of rows per distinct value, computed as RANGE_DENSITY * RANGE_ROWS. In addition to statistics on indexes, SQL Server can also keep track of statistics on columns with no indexes. Knowing the density, or the likelihood of a particular value occurring, can help the optimizer determine an optimum processing strategy, even if SQL Server can’t use an index to actually locate the values. Statistics on Columns Column statistics can be useful for two main purposes  When the SQL Server optimizer is determining the optimal join order, it frequently is best to have the smaller input processed first. By ‘input’ we mean table after all filters in the WHERE clause have been applied. Even if there is no useful index on a column in the WHERE clause, statistics could tell us that only a few rows will quality, and those the resulting input will be very small.  The SQL Server query optimizer can use column statistics on non-initial columns in a composite nonclustered index to determine if scanning the leaf level to obtain the bookmarks will be an efficient processing strategy. For example, in the member table in the credit database, the first name column is almost unique. Suppose we have a nonclustered index on (lastname, firstname), and we issue this query: select * from member where firstname = 'MPRO' In this case, statistics on the firstname column would indicate very few rows satisfying this condition, so the optimizer will choose to scan the nonclustered index, since it is smaller than the clustered index (the table). The small number of bookmarks will then be followed to retrieve the actual data. Manually Updating Statistics You can also manually force statistics to be updated in one of two ways. You can run the UPDATE STATISTICS command on a table or on one specific index or column statistics, or you can also execute the procedure sp_updatestats, which runs UPDATE STATISTICS against all user-defined tables in the current database. You can create statistics on unindexed columns using the CREATE STATISTICS command or by executing sp_createstats, which creates single-column statistics for all eligible columns for all user tables in the current database. This includes all columns except computed columns and columns of the ntext, text, or image datatypes, and columns that already have statistics or are the first column of an index. Autostats By Default SQL Server Will Update Statistics on Any Index or Column as Needed Every database is created with the database options auto create statistics and auto update statistics set to true, but you can turn either one off. You can also turn off automatic updating of statistics for a specific table in one of two ways:  UPDATE STATISTICS In addition to updating the statistics, the option WITH NORECOMPUTE indicates that the statistics should not be automatically recomputed in the future. Running UPDATE STATISTICS again without the WITH NORECOMPUTE option enables automatic updates.  sp_autostats This procedure sets or unsets a flag for a table to indicate that statistics should or should not be updated automatically. You can also use this procedure with only the table name to find out whether the table is set to automatically have its index statistics updated. ' However, setting the database option auto update statistics to FALSE overrides any individual table settings. In other words, no automatic updating of statistics takes place. This is not a recommended practice unless thorough testing has shown you that you do not need the automatic updates or that the performance overhead is more than you can afford. Trace Flags Trace flag 205 – reports recompile due to autostats. Trace flag 8721 – writes information to the errorlog when AutoStats has been run. For more information, see the following Knowledge Base article: Q195565 “INF: How SQL Server 7.0 Autostats Work.” Statistics and Performance The Performance Penalty of NOT Having Up-To-Date Statistics Far Outweighs the Benefit of Avoiding Automatic Updating Autostats should be turned off only after thorough testing shows it to be necessary. Because autostats only forces a recompile after a certain number or percentage of rows has been changed, you do not have to make any adjustments for a read-only database. Lesson 3: Concepts – Query Optimization What You Will Learn After completing this lesson, you will be able to:  Describe the phases of query optimization.  Discuss how SQL Server estimates the selectivity of indexes and column and how this estimate is used in query optimization. Recommended Reading  Chapter 15: “The Query Processor”, Inside SQL Server 2000 by Kalen Delaney  Chapter 16: “Query Tuning”, Inside SQL Server 2000 by Kalen Delaney  Whitepaper about SQL Server Query Processor Architecture by Hal Berenson and Kalen Delaney http://msdn.microsoft.com/library/backgrnd/html/sqlquerproc.htm Phases of Query Optimization Query Optimization Involves several phases Trivial Plan Optimization Optimization itself goes through several steps. The first step is something called Trivial Plan Optimization. The whole idea of trivial plan optimization is that cost based optimization is a bit expensive to run. The optimizer can try a great many possible variations trying to find the cheapest plan. If SQL Server knows that there is only one really viable plan for a query, it could avoid a lot of work. A prime example is a query that consists of an INSERT with a VALUES clause. There is only one possible plan. Another example is a SELECT where all the columns are in a unique covering index, and that index is the only one that is useable. There is no other index that has that set of columns in it. These two examples are cases where SQL Server should just generate the plan and not try to find something better. The trivial plan optimizer finds the really obvious plans, which are typically very inexpensive. In fact, all the plans that get through the autoparameterization template result in plans that the trivial plan optimizer can find. Between those two mechanisms, the plans that are simple tend to be weeded out earlier in the process and do not pay a lot of the compilation cost. This is a good thing, because the number of potential plans in 7.0 went up astronomically as SQL Server added hash joins, merge joins and index intersections, to its list of processing techniques. Simplification and Statistics Loading If a plan is not found by the trivial plan optimizer, SQL Server can perform some simplifications, usually thought of as syntactic transformations of the query itself, looking for commutative properties and operations that can be rearranged. SQL Server can do constant folding, and other operations that do not require looking at the cost or analyzing what indexes are, but that can result in a more efficient query. SQL Server then loads up the metadata including the statistics information on the indexes, and then the optimizer goes through a series of phases of cost based optimization. Cost Based Optimization Phases The cost based optimizer is designed as a set of transformation rules that try various permutations of indexes and join strategies. Because of the number of potential plans in SQL Server 7.0 and SQL Server 2000, if the optimizer just ran through all the combinations and produced a plan, the optimization process would take a very long time to run. Therefore, optimization is broken up into phases. Each phase is a set of rules. After each phase is run, the cost of any resulting plan is examined, and if SQL Server determines that the plan is cheap enough, that plan is kept and executed. If the plan is not cheap enough, the optimizer runs the next phase, which is another set of rules. In the vast majority of cases, a good plan will be found in the preliminary phases. Typically, if the plan that a query would have had in SQL Server 6.5 is also the optimal plan in SQL Server 7.0 and SQL Server 2000, the plan will tend to be found either by the trivial plan optimizer or by the first phase of the cost based optimizer. The rules were intentionally organized to try to make that be true. The plan will probably consist of using a single index and using nested loops. However, every once in a while, because of lack of statistical information, or some other nuance, the optimizer will have to proceed with the later phases of optimization. Sometimes this is because there is a real possibility that the optimizer could find a better plan. When a plan is found, it becomes the optimizer’s output, and then SQL Server goes through all the caching mechanisms that we have already discussed in Module 5. Full Optimization At some point, the optimizer determines that it has gone through enough preliminary phases, and it reverts to a phase called full optimization. If the optimizer goes through all the preliminary phases, and still has not found a cheap plan, it examines the cost for the plan that it has so far. If the cost is above the threshold, the optimizer goes into a phase called full optimization. This threshold is configurable, as the configuration option ‘cost threshold for parallelism’. The full optimization phase assumes that this plan should be run this in parallel. If the machine is very busy, the plan will end up running it in serial, but the optimizer has a goal to produce a good parallel. If the cost is below the threshold (or a single processor machine), the full optimization phase just uses a brute force method to find a serial plan. Selectivity Estimation Selectivity Is One of The Most Important Pieces of Information One of the most import things the optimizer needs to know is the number of rows from any table that will meet all the conditions in the query. If there are no restrictions on a table, and all the rows will be needed, the optimizer can determine the number of rows from the sysindexes table. This number is not absolutely guaranteed to be accurate, but it is the number the optimizer uses. If there is a filter on the table in a WHERE clause, the optimizer needs statistics information. Indexes automatically maintain statistics, and the optimizer will use these values to determine the usefulness of the index. If there is no index on the column involved in the filter, then column statistics can be used or generated. Optimizing Search Arguments In General, the Filters in the WHERE Clause Determine Which Indexes Will Be Useful If an indexed column is referenced in a Search Argument (SARG), the optimizer will analyze the cost of using that index. A SARG has the form:  column value  value column  Operator must be one of =, >, >= <, <= The value can be a constant, an operation, or a variable. Some functions also will be treated as SARGs. These queries have SARGs, and a nonclustered index on firstname will be used in most cases: select * from member where firstname < 'AKKG' select * from member where firstname = substring('HAAKGALSFJA', 2,5) select * from member where firstname = 'AA' + 'KG' declare @name char(4) set @name = 'AKKG' select * from member where firstname < @name Not all functions can be used in SARGs. select * from charge where charge_amt < 2*2 select * from charge where charge_amt < sqrt(16) Compare these queries to ones using = instead of <. With =, the optimizer can use the density information to come up with a good row estimate, even if it’s not going to actually perform the function’s calculations. A filter with a variable is usually a SARG The issue is, can the optimizer come up with useful costing information? A filter with a variable is not a SARG if the variable is of a different datatype, and the column must be converted to the variable’s datatype For more information, see the following Knowledge Base article: Q198625 Enter Title of KB Article Here Use credit go CREATE TABLE [member2] ( [member_no] [smallint] NOT NULL , [lastname] [shortstring] NOT NULL , [firstname] [shortstring] NOT NULL , [middleinitial] [letter] NULL , [street] [shortstring] NOT NULL , [city] [shortstring] NOT NULL , [state_prov] [statecode] NOT NULL , [country] [countrycode] NOT NULL , [mail_code] [mailcode] NOT NULL ) GO insert into member2 select member_no, lastname, firstname, middleinitial, street, city, state_prov, country, mail_code from member alter table member2 add constraint pk_member2 primary key clustered (lastname, member_no, firstname, country) declare @id int set @id = 47 update member2 set city = city + ' City', state_prov = state_prov + ' State' where lastname = 'Barr' and member_no = @id and firstname = 'URQYJBFVRRPWKVW' and country = 'USA' These queries don’t have SARGs, and a table scan will be done: select * from member where substring(lastname, 1,2) = ‘BA’ Some non-SARGs can be converted select * from member where lastname like ‘ba%’ In some cases, you can rewrite your query to turn a non-SARG into a SARG; for example, you can rewrite the substring query above and the LIKE query that follows it. Join Order and Types of Joins Join Order and Strategy Is Determined By the Optimizer The execution plan output will display the join order from top to bottom; i.e. the table listed on top is the first one accessed in a join. You can override the optimizer’s join order decision in two ways:  OPTION (FORCE ORDER) applies to one query  SET FORCEPLAN ON applies to entire session, until set OFF If either of these options is used, the join order is determined by the order the tables are listed in the query’s FROM clause, and no optimizer on JOIN ORDER is done. Forcing the JOIN order may force a particular join strategy. For example, in most outer join operations, the outer table is processed first, and a nested loops join is done. However, if you force the inner table to be accessed first, a merge join will need to be done. Compare the query plan for this query with and without the FORCE ORDER hint: select * from titles right join publishers on titles.pub_id = publishers.pub_id -- OPTION (FORCE ORDER) Nested Loop Join A nested iteration is when the query optimizer constructs a set of nested loops, and the result set grows as it progresses through the rows. The query optimizer performs the following steps. 1. Finds a row from the first table. 2. Uses that row to scan the next table. 3. Uses the result of the previous table to scan the next table. Evaluating Join Combinations The query optimizer automatically evaluates at least four or more possible join combinations, even if those combinations are not specified in the join predicate. You do not have to add redundant clauses. The query optimizer balances the cost and uses statistics to determine the number of join combinations that it evaluates. Evaluating every possible join combination is inefficient and costly. Evaluating Cost of Query Performance When the query optimizer performs a nested join, you should be aware that certain costs are incurred. Nested loop joins are far superior to both merge joins and hash joins when executing small transactions, such as those affecting only a small set of rows. The query optimizer:  Uses nested loop joins if the outer input is quite small and the inner input is indexed and quite large.  Uses the smaller input as the outer table.  Requires that a useful index exist on the join predicate for the inner table.  Always uses a nested loop join strategy if the join operation uses an operator other than an equality operator. Merge Joins The columns of the join conditions are used as inputs to process a merge join. SQL Server performs the following steps when using a merge join strategy: 1. Gets the first input values from each input set. 2. Compares input values. 3. Performs a merge algorithm. • If the input values are equal, the rows are returned. • If the input values are not equal, the lower value is discarded, and the next input value from that input is used for the next comparison. 4. Repeats the process until all of the rows from one of the input sets have been processed. 5. Evaluates any remaining search conditions in the query and returns only rows that qualify. Note Only one pass per input is done. The merge join operation ends after all of the input values of one input have been evaluated. The remaining values from the other input are not processed. Requires That Joined Columns Are Sorted If you execute a query with join operations, and the joined columns are in sorted order, the query optimizer processes the query by using a merge join strategy. A merge join is very efficient because the columns are already sorted, and it requires fewer page I/O. Evaluates Sorted Values For the query optimizer to use the merge join, the inputs must be sorted. The query optimizer evaluates sorted values in the following order: 1. Uses an existing index tree (most typical). The query optimizer can use the index tree from a clustered index or a covered nonclustered index. 2. Leverages sort operations that the GROUP BY, ORDER BY, and CUBE clauses use. The sorting operation only has to be performed once. 3. Performs its own sort operation in which a SORT operator is displayed when graphically viewing the execution plan. The query optimizer does this very rarely. Performance Considerations Consider the following facts about the query optimizer's use of the merge join:  SQL Server performs a merge join for all types of join operations (except cross join or full join operations), including UNION operations.  A merge join operation may be a one-to-one, one-to-many, or many-to-many operation. If the merge join is a many-to-many operation, SQL Server uses a temporary table to store the rows. If duplicate values from each input exist, one of the inputs rewinds to the start of the duplicates as each duplicate value from the other input is processed.  Query performance for a merge join is very fast, but the cost can be high if the query optimizer must perform its own sort operation. If the data volume is large and the desired data can be obtained presorted from existing Balanced-Tree (B-Tree) indexes, merge join is often the fastest join algorithm.  A merge join is typically used if the two join inputs have a large amount of data and are sorted on their join columns (for example, if the join inputs were obtained by scanning sorted indexes).  Merge join operations can only be performed with an equality operator in the join predicate. Hashing is a strategy for dividing data into equal sets of a manageable size based on a given property or characteristic. The grouped data can then be used to determine whether a particular data item matches an existing value. Note Duplicate data or ranges of data are not useful for hash joins because the data is not organized together or in order. When a Hash Join Is Used The query optimizer uses a hash join option when it estimates that it is more efficient than processing queries by using a nested loop or merge join. It typically uses a hash join when an index does not exist or when existing indexes are not useful. Assigns a Build and Probe Input The query optimizer assigns a build and probe input. If the query optimizer incorrectly assigns the build and probe input (this may occur because of imprecise density estimates), it reverses them dynamically. The ability to change input roles dynamically is called role reversal. Build input consists of the column values from a table with the lowest number of rows. Build input creates a hash table in memory to store these values. The hash bucket is a storage place in the hash table in which each row of the build input is inserted. Rows from one of the join tables are placed into the hash bucket where the hash key value of the row matches the hash key value of the bucket. Hash buckets are stored as a linked list and only contain the columns that are needed for the query. A hash table contains hash buckets. The hash table is created from the build input. Probe input consists of the column values from the table with the most rows. Probe input is what the build input checks to find a match in the hash buckets. Note The query optimizer uses column or index statistics to help determine which input is the smaller of the two. Processing a Hash Join The following list is a simplified description of how the query optimizer processes a hash join. It is not intended to be comprehensive because the algorithm is very complex. SQL Server: 1. Reads the probe input. Each probe input is processed one row at a time. 2. Performs the hash algorithm against each probe input and generates a hash key value. 3. Finds the hash bucket that matches the hash key value. 4. Accesses the hash bucket and looks for the matching row. 5. Returns the row if a match is found. Performance Considerations Consider the following facts about the hash joins that the query optimizer uses:  Similar to merge joins, a hash join is very efficient, because it uses hash buckets, which are like a dynamic index but with less overhead for combining rows.  Hash joins can be performed for all types of join operations (except cross join operations), including UNION and DIFFERENCE operations.  A hash operator can remove duplicates and group data, such as SUM (salary) GROUP BY department. The query optimizer uses only one input for both the build and probe roles.  If join inputs are large and are of similar size, the performance of a hash join operation is similar to a merge join with prior sorting. However, if the size of the join inputs is significantly different, the performance of a hash join is often much faster.  Hash joins can process large, unsorted, non-indexed inputs efficiently. Hash joins are useful in complex queries because the intermediate results: • Are not indexed (unless explicitly saved to disk and then indexed). • Are often not sorted for the next operation in the execution plan.  The query optimizer can identify incorrect estimates and make corrections dynamically to process the query more efficiently.  A hash join reduces the need for database denormalization. Denormalization is typically used to achieve better performance by reducing join operations despite redundancy, such as inconsistent updates. Hash joins give you the option to vertically partition your data as part of your physical database design. Vertical partitioning represents groups of columns from a single table in separate files or indexes. Subquery Performance Joins Are Not Inherently Better Than Subqueries Here is an example showing three different ways to update a table, using a second table for lookup purposes. The first uses a JOIN with the update, the second uses a regular introduced with IN, and the third uses a correlated subquery. All three yield nearly identical performance. Note Note that performance comparisons cannot just be made based on I/Os. With HASHING and MERGING techniques, the number of reads may be the same for two queries, yet one may take a lot longer and use more memory resources. Also, always be sure to monitor statistics time. Suppose you want to add a 5 percent discount to order items in the Order Details table for which the supplier is Exotic Liquids, whose supplierid is 1. -- JOIN solution BEGIN TRAN UPDATE OD SET discount = discount + 0.05 FROM [Order Details] AS OD JOIN Products AS P ON OD.productid = P.productid WHERE supplierid = 1 ROLLBACK TRAN -- Regular subquery solution BEGIN TRAN UPDATE [Order Details] SET discount = discount + 0.05 WHERE productid IN (SELECT productid FROM Products WHERE supplierid = 1) ROLLBACK TRAN -- Correlated Subquery Solution BEGIN TRAN UPDATE [Order Details] SET discount = discount + 0.05 WHERE EXISTS(SELECT supplierid FROM Products WHERE [Order Details].productid = Products.productid AND supplierid = 1) ROLLBACK TRAN Internally, Your Join May Be Rewritten SQL Server’s query processor had many different ways of resolving your JOIN expressions. Subqueries may be converted to a JOIN with an implied distinct, which may result in a logical operator of SEMI JOIN. Compare the plans of the first two queries: USE credit select member_no from member where member_no in (select member_no from charge) select distinct m.member_no from member m join charge c on m.member_no = c.member_no The second query uses a HASH MATCH as the final step to remove the duplicates. The first query only had to do a semi join. For these queries, although the I/O values are the same, the first query (with the subquery) runs much faster (almost twice as fast). Another similar looking join is

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PowerBI系列之Power Query专题1. 获取数据数据源种类介绍和获取Excel数据源输入数据和拷贝数据：创建辅助表解析Json/XML数据格式获取Web网页数据和URL添加动态参数连接数据的四种模式：Import、DirectQuery、Live Connection、Dual双属于混合模式连接数据库：Sql server、 Mysql（直连但是必须先安装一个mysql插件）DirectQuery直连查询：Sql serverODBC方式获取数据表关联或多个Sql或调用存储过程获取数据SQL中动态传参和自定义函数: sql中使用参数或数据库名称使用参数连接Sharepoint和OneDrive数据源连接Dataset和Dataflow 替换本地数据源为Sharepoint数据源并保留数据处理操作终止当前数据刷新Loading：Cancel Query数据源设置-重置数据连接凭证PBIDS连接数据源创建和使用报表模块（输入或值列表）利用报表模板和参数控制线下报表数据权限DirectQuery启用自动页面刷新和更改检测管理聚合表提高DirectQuery查询性能动态M查询参数提高DirectQuery查询性能添加数据刷新时间 DateTime.LocalNow()和Getdate()2. 数据清洗和M语言M语言和官方文档介绍PowerQuery中查阅M函数：=#shared, Ctrl+Space提示数据清洗之常用技能：提升标题、更改数据类型、保留删除错误或空行，删除重复项、选择列和删除列、填充单元格、合并列、拆分、提取、替换、条件替换、添加自定义列，添加条件列、添加索引列、分组、添加年月日列、追加和合并查询透视和逆透视以及转置合并单元格的Excel文件处理导入文件夹中多Excel文件并合并解决多文件合并中列顺序不一致使用参数和函数批量导入文件文本中提取中文、英文、数字等处理双引号转义列拆分详解解决列名改变错误解决列丢失错误动态显示、排序和重命名列为所有列名添加前缀列名字母大写和分隔符调整Trim标题列中的多余空格如何处理load数据错误为什么load的Excel数据有null空行为什么load的Excel数据标题在第二行灵活添加占位符规范同类相似数据数据按多列排序为分组添加Index序号分组内值合并诊断工具分析数据处理过程PowerQuery小技巧分享新冠病例活动轨迹地图标识

这是获取硬件信息的 Delphi 第三方控件包，非常强大。官方在2013年7月24日更新的版本，试用版在软件运行时会弹出控件版权信息框。破解MSI_Common.dcu后，无信息框弹出！控件详细说明： Product: MiTeC System Information Component Suite Version: 11.2.0 Author: Michal Mutl E-Mail: michal.mutl@mitec.cz Target: Delphi 5.x, Delphi 6.x, Delphi 7.x, Delphi 2005, Delphi 2006, Delphi 2007, Delphi 2009, Delphi 2010, Delphi XE, Delphi XE2, Delphi XE3, Delphi XE4 Platform: W95, W98, NT, W2000, Windows ME, Windows XP, Windows 2003, Windows Vista, Windows 7, Windows Server 2008, Windows Server 2008 R2, Windows 8 Status: Up date Source: N/A Description: The most complex system probe in the Delphi world. It consists of many standalone components covering many system information problematics: + TMiTeC_SystemInfo: gathers all following components to one for simple use + TMiTeC_APM: provides informaton about Advanced Power Management + TMiTeC_BT: detects Bluetooth devices using Native Bluetooth Enumerator) + TMiTeC_CPU: provides detailed CPU information + TMiTeC_Devices: provides devices information like Windows Device Manager + TMiTeC_Disk: provides logical drive information + TMiTeC_Display: provides display adapter information + TMiTeC_DMA: provides direct memory acceess + TMiTeC_Engines: provides information about various installed engines + TMiTeC_Machine: provides informaton about computer or virtual machine, BIOS etc. + TMiTeC_Media: provides media devices information + TMiTeC_Memory: provides memory information + TMiTeC_Monitor: provides all connected moitors information + TMiTeC_Network: provides network card info, TCP/IP ad Winsock config, installed protocols, clients and services. + TMiTeC_OperatingSystem: provides OS detailed information, Locale, Timezone, NT specific info, hotfixes, internet settings etc. + TMiTeC_Printers: detects installed printers and their properties + TMiTeC_ProcessList: collects list of running processes, services, drivers and windows and their properties + TMiTeC_SMBIOS: reads SMBIO

1：外文原文 Struts——an open-source MVC implementation This article introduces Struts, a Model-View-Controller implementation that uses servlets and JavaServer Pages (JSP) technology. Struts can help you control change in your Web project and promote specialization. Even if you never implement a system with Struts, you may get some ideas for your future servlets and JSP page implementation. Introduction Kids in grade school put HTML pages on the Internet. However, there is a monumental difference between a grade school page and a professionally developed Web site. The page designer (or HTML developer) must understand colors, the customer, product flow, page layout, browser compatibility, image creation, JavaScript, and more. Putting a great looking site together takes a lot of work, and most Java developers are more interested in creating a great looking object interface than a user interface. JavaServer Pages (JSP) technology provides the glue between the page designer and the Java developer. If you have worked on a large-scale Web application, you understand the term change. Model-View-Controller (MVC) is a design pattern put together to help control change. MVC decouples interface from business logic and data. Struts is an MVC implementation that uses Servlets 2.2 and JSP 1.1 tags, from the J2EE specifications, as part of the implementation. You may never implement a system with Struts, but looking at Struts may give you some ideas on your future Servlets and JSP implementations. Model-View-Controller (MVC) JSP tags solved only part of our problem. We still have issues with validation, flow control, and updating the state of the application. This is where MVC comes to the rescue. MVC helps resolve some of the issues with the single module approach by dividing the problem into three categories: • Model The model contains the core of the application's functionality. The model encapsulates the state of the application. Sometimes the only functionality it contains is state. It knows nothing about the view or controller. • View The view provides the presentation of the model. It is the look of the application. The view can access the model getters, but it has no knowledge of the setters. In addition, it knows nothing about the controller. The view should be notified when changes to the model occur. • Controller The controller reacts to the user input. It creates and sets the model. MVC Model 2 The Web brought some unique challenges to software developers, most notably the stateless connection between the client and the server. This stateless behavior made it difficult for the model to notify the view of changes. On the Web, the browser has to re-query the server to discover modification to the state of the application. Another noticeable change is that the view uses different technology for implementation than the model or controller. Of course, we could use Java (or PERL, C/C++ or what ever) code to generate HTML. There are several disadvantages to that approach: • Java programmers should develop services, not HTML. • Changes to layout would require changes to code. • Customers of the service should be able to create pages to meet their specific needs. • The page designer isn't able to have direct involvement in page development. • HTML embedded into code is ugly. For the Web, the classical form of MVC needed to change. Figure 4 displays the Web adaptation of MVC, also commonly known as MVC Model 2 or MVC 2. The ActionServlet class Do you remember the days of function mappings? You would map some input event to a pointer to a function. If you where slick, you would place the configuration information into a file and load the file at run time. Function pointer arrays were the good old days of structured programming in C. Life is better now that we have Java technology, XML, J2EE, and all that. The Struts Controller is a servlet that maps events (an event generally being an HTTP post) to classes. And guess what -- the Controller uses a configuration file so you don_t have to hard-code the values. Life changes, but stays the same. ActionServlet is the Command part of the MVC implementation and is the core of the Framework. ActionServlet (Command) creates and uses Action, an ActionForm, and ActionForward. As mentioned earlier, the struts-config.xml file configures the Command. During the creation of the Web project, Action and ActionForm are extended to solve the specific problem space. The file struts-config.xml instructs ActionServlet on how to use the extended classes. There are several advantages to this approach: • The entire logical flow of the application is in a hierarchical text file. This makes it easier to view and understand, especially with large applications. • The page designer does not have to wade through Java code to understand the flow of the application. • The Java developer does not need to recompile code when making flow changes. Command functionality can be added by extending ActionServlet. The ActionForm class ActionForm maintains the session state for the Web application. ActionForm is an abstract class that is sub-classed for each input form model. When I say input form model, I am saying ActionForm represents a general concept of data that is set or updated by a HTML form. For instance, you may have a UserActionForm that is set by an HTML Form. The Struts framework will: • Check to see if a UserActionForm exists; if not, it will create an instance of the class. • Struts will set the state of the UserActionForm using corresponding fields from the HttpServletRequest. No more dreadful request.getParameter() calls. For instance, the Struts framework will take fname from request stream and call UserActionForm.setFname(). • The Struts framework updates the state of the UserActionForm before passing it to the business wrapper UserAction. • Before passing it to the Action class, Struts will also conduct form state validation by calling the validation() method on UserActionForm. Note: This is not always wise to do. There might be ways of using UserActionForm in other pages or business objects, where the validation might be different. Validation of the state might be better in the UserAction class. • The UserActionForm can be maintained at a session level. Notes: • The struts-config.xml file controls which HTML form request maps to which ActionForm. • Multiple requests can be mapped UserActionForm. • UserActionForm can be mapped over multiple pages for things such as wizards. The Action class The Action class is a wrapper around the business logic. The purpose of Action class is to translate the HttpServletRequest to the business logic. To use Action, subclass and overwrite the process() method. The ActionServlet (Command) passes the parameterized classes to ActionForm using the perform() method. Again, no more dreadful request.getParameter() calls. By the time the event gets here, the input form data (or HTML form data) has already been translated out of the request stream and into an ActionForm class. Struts, an MVC 2 implementation Struts is a set of cooperating classes, servlets, and JSP tags that make up a reusable MVC 2 design. This definition implies that Struts is a framework, rather than a library, but Struts also contains an extensive tag library and utility classes that work independently of the framework. Figure 5 displays an overview of Struts. Struts overview • Client browser An HTTP request from the client browser creates an event. The Web container will respond with an HTTP response. • Controller The Controller receives the request from the browser, and makes the decision where to send the request. With Struts, the Controller is a command design pattern implemented as a servlet. The struts-config.xml file configures the Controller. • Business logic The business logic updates the state of the model and helps control the flow of the application. With Struts this is done with an Action class as a thin wrapper to the actual business logic. • Model state The model represents the state of the application. The business objects update the application state. ActionForm bean represents the Model state at a session or request level, and not at a persistent level. The JSP file reads information from the ActionForm bean using JSP tags. • View The view is simply a JSP file. There is no flow logic, no business logic, and no model information -- just tags. Tags are one of the things that make Struts unique compared to other frameworks like Velocity. Note: "Think thin" when extending the Action class. The Action class should control the flow and not the logic of the application. By placing the business logic in a separate package or EJB, we allow flexibility and reuse. Another way of thinking about Action class is as the Adapter design pattern. The purpose of the Action is to "Convert the interface of a class into another interface the clients expect. Adapter lets classes work together that couldn_t otherwise because of incompatibility interface" (from Design Patterns - Elements of Reusable OO Software by Gof). The client in this instance is the ActionServlet that knows nothing about our specific business class interface. Therefore, Struts provides a business interface it does understand, Action. By extending the Action, we make our business interface compatible with Struts business interface. (An interesting observation is that Action is a class and not an interface. Action started as an interface and changed into a class over time. Nothing's perfect.) The Error classes The UML diagram also included ActionError and ActionErrors. ActionError encapsulates an individual error message. ActionErrors is a container of ActionError classes that the View can access using tags. ActionErrors is Struts way of keeping up with a list of errors. The ActionMapping class An incoming event is normally in the form of an HTTP request, which the servlet Container turns into an HttpServletRequest. The Controller looks at the incoming event and dispatches the request to an Action class. The struts-config.xml determines what Action class the Controller calls. The struts-config.xml configuration information is translated into a set of ActionMapping, which are put into container of ActionMappings. (If you have not noticed it, classes that end with s are containers) The ActionMapping contains the knowledge of how a specific event maps to specific Actions. The ActionServlet (Command) passes the ActionMapping to the Action class via the perform() method. This allows Action to access the information to control flow. ActionMappings ActionMappings is a collection of ActionMapping objects. Struts pros • Use of JSP tag mechanism The tag feature promotes reusable code and abstracts Java code from the JSP file. This feature allows nice integration into JSP-based development tools that allow authoring with tags. • Tag library Why re-invent the wheel, or a tag library? If you cannot find something you need in the library, contribute. In addition, Struts provides a starting point if you are learning JSP tag technology. • Open source You have all the advantages of open source, such as being able to see the code and having everyone else using the library reviewing the code. Many eyes make for great code review. • Sample MVC implementation Struts offers some insight if you want to create your own MVC implementation. • Manage the problem space Divide and conquer is a nice way of solving the problem and making the problem manageable. Of course, the sword cuts both ways. The problem is more complex and needs more management. Struts cons • Youth Struts development is still in preliminary form. They are working toward releasing a version 1.0, but as with any 1.0 version, it does not provide all the bells and whistles. • Change The framework is undergoing a rapid amount of change. A great deal of change has occurred between Struts 0.5 and 1.0. You may want to download the most current Struts nightly distributions, to avoid deprecated methods. In the last 6 months, I have seen the Struts library grow from 90K to over 270K. I had to modify my examples several times because of changes in Struts, and I am not going to guarantee my examples will work with the version of Struts you download. • Correct level of abstraction Does Struts provide the correct level of abstraction? What is the proper level of abstraction for the page designer? That is the $64K question. Should we allow a page designer access to Java code in page development? Some frameworks like Velocity say no, and provide yet another language to learn for Web development. There is some validity to limiting Java code access in UI development. Most importantly, give a page designer a little bit of Java, and he will use a lot of Java. I saw this happen all the time in Microsoft ASP development. In ASP development, you were supposed to create COM objects and then write a little ASP script to glue it all together. Instead, the ASP developers would go crazy with ASP script. I would hear "Why wait for a COM developer to create it when I can program it directly with VBScript?" Struts helps limit the amount of Java code required in a JSP file via tag libraries. One such library is the Logic Tag, which manages conditional generation of output, but this does not prevent the UI developer from going nuts with Java code. Whatever type of framework you decide to use, you should understand the environment in which you are deploying and maintaining the framework. Of course, this task is easier said than done. • Limited scope Struts is a Web-based MVC solution that is meant be implemented with HTML, JSP files, and servlets. • J2EE application support Struts requires a servlet container that supports JSP 1.1 and Servlet 2.2 specifications. This alone will not solve all your install issues, unless you are using Tomcat 3.2. I have had a great deal of problems installing the library with Netscape iPlanet 6.0, which is supposedly the first J2EE-compliant application server. I recommend visiting the Struts User Mailing List archive (see Resources) when you run into problems. • Complexity Separating the problem into parts introduces complexity. There is no question that some education will have to go on to understand Struts. With the constant changes occurring, this can be frustrating at times. Welcome to the Web. • Where is... I could point out other issues, for instance, where are the client side validations, adaptable workflow, and dynamic strategy pattern for the controller? However, at this point, it is too easy to be a critic, and some of the issues are insignificant, or are reasonable for a 1.0 release. The way the Struts team goes at it, Struts might have these features by the time you read this article, or soon after. Future of Struts Things change rapidly in this new age of software development. In less than 5 years, I have seen things go from cgi/perl, to ISAPI/NSAPI, to ASP with VB, and now Java and J2EE. Sun is working hard to adapt changes to the JSP/servlet architecture, just as they have in the past with the Java language and API. You can obtain drafts of the new JSP 1.2 and Servlet 2.3 specifications from the Sun Web site. Additionally, a standard tag library for JSP files is appearing. 2:外文资料翻译译文 Struts——MVC 的一种开放源码实现本文介绍 Struts，它是使用 servlet 和 JavaServer Pages 技术的一种 Model-View-Controller 实现。Struts 可帮助您控制 Web 项目中的变化并提高专业化水平。尽管您可能永远不会用 Struts 实现一个系统，但您可以将其中的一些思想用于您以后的 servlet 和 JSP 网页的实现中。简介小学生也可以在因特网上发布 HTML 网页。但是，小学生的网页和专业开发的网站有质的区别。网页设计人员（或者 HTML 开发人员）必须理解颜色、用户、生产流程、网页布局、浏览器兼容性、图像创建和 JavaScript 等等。设计漂亮的网站需要做大量的工作，大多数 Java 开发人员更注重创建优美的对象接口，而不是用户界面。JavaServer Pages (JSP) 技术为网页设计人员和 Java 开发人员提供了一种联系钮带。如果您开发过大型 Web 应用程序，您就理解变化这个词的含义。“模型-视图-控制器”(MVC) 就是用来帮助您控制变化的一种设计模式。MVC 减弱了业务逻辑接口和数据接口之间的耦合。Struts 是一种 MVC 实现，它将 Servlet 2.2 和 JSP 1.1 标记（属于 J2EE 规范）用作实现的一部分。尽管您可能永远不会用 Struts 实现一个系统，但了解一下 Struts 或许使您能将其中的一些思想用于您以后的 Servlet 的 JSP 实现中。模型-视图-控制器 (MVC) JSP 标记只解决了部分问题。我们还得处理验证、流程控制和更新应用程序的状态等问题。这正是 MVC 发挥作用的地方。MVC 通过将问题分为三个类别来帮助解决单一模块方法所遇到的某些问题： • Model（模型）模型包含应用程序的核心功能。模型封装了应用程序的状态。有时它包含的唯一功能就是状态。它对视图或控制器一无所知。 • View（视图）视图提供模型的表示。它是应用程序的外观。视图可以访问模型的读方法，但不能访问写方法。此外，它对控制器一无所知。当更改模型时，视图应得到通知。 • Controller（控制器）控制器对用户的输入作出反应。它创建并设置模型。 MVC Model 2 Web 向软件开发人员提出了一些特有的挑战，最明显的就是客户机和服务器的无状态连接。这种无状态行为使得模型很难将更改通知视图。在 Web 上，为了发现对应用程序状态的修改，浏览器必须重新查询服务器。另一个重大变化是实现视图所用的技术与实现模型或控制器的技术不同。当然，我们可以使用 Java（或者 PERL、C/C++ 或别的语言）代码生成 HTML。这种方法有几个缺点： • Java 程序员应该开发服务，而不是 HTML。 • 更改布局时需要更改代码。 • 服务的用户应该能够创建网页来满足它们的特定需要。 • 网页设计人员不能直接参与网页开发。 • 嵌在代码中的 HTML 很难看。对于 Web，需要修改标准的 MVC 形式。图 4 显示了 MVC 的 Web 改写版，通常也称为 MVC Model 2 或 MVC 2。 Struts，MVC 2 的一种实现 Struts 是一组相互协作的类、servlet 和 JSP 标记，它们组成一个可重用的 MVC 2 设计。这个定义表示 Struts 是一个框架，而不是一个库，但 Struts 也包含了丰富的标记库和独立于该框架工作的实用程序类。图 5 显示了 Struts 的一个概览。 Struts 概览 • Client browser（客户浏览器）来自客户浏览器的每个 HTTP 请求创建一个事件。Web 容器将用一个 HTTP 响应作出响应。 • Controller（控制器）控制器接收来自浏览器的请求，并决定将这个请求发往何处。就 Struts 而言，控制器是以 servlet 实现的一个命令设计模式。 struts-config.xml 文件配置控制器。 • 业务逻辑业务逻辑更新模型的状态，并帮助控制应用程序的流程。就 Struts 而言，这是通过作为实际业务逻辑“瘦”包装的 Action 类完成的。 • Model（模型）的状态模型表示应用程序的状态。业务对象更新应用程序的状态。ActionForm bean 在会话级或请求级表示模型的状态，而不是在持久级。JSP 文件使用 JSP 标记读取来自 ActionForm bean 的信息。 • View（视图）视图就是一个 JSP 文件。其中没有流程逻辑，没有业务逻辑，也没有模型信息 -- 只有标记。标记是使 Struts 有别于其他框架（如 Velocity）的因素之一。详细分析 Struts 图 6 显示的是 org.apache.struts.action 包的一个最简 UML 图。图 6 显示了 ActionServlet (Controller)、 ActionForm (Form State) 和 Action (Model Wrapper) 之间的最简关系。 ActionServlet 类您还记得函数映射的日子吗？在那时，您会将某些输入事件映射到一个函数指针上。如果您对此比较熟悉，您会将配置信息放入一个文件，并在运行时加载这个文件。函数指针数组曾经是用 C 语言进行结构化编程的很好方法。现在好多了，我们有了 Java 技术、XML、J2EE，等等。Struts 的控制器是将事件（事件通常是 HTTP post）映射到类的一个 servlet。正如您所料 -- 控制器使用配置文件以使您不必对这些值进行硬编码。时代变了，但方法依旧。 ActionServlet 是该 MVC 实现的 Command 部分，它是这一框架的核心。 ActionServlet (Command) 创建并使用 Action 、 ActionForm 和 ActionForward 。如前所述， struts-config.xml 文件配置该 Command。在创建 Web 项目时，您将扩展 Action 和 ActionForm 来解决特定的问题。文件 struts-config.xml 指示 ActionServlet 如何使用这些扩展的类。这种方法有几个优点： • 应用程序的整个逻辑流程都存储在一个分层的文本文件中。这使得人们更容易查看和理解它，尤其是对于大型应用程序而言。 • 网页设计人员不必费力地阅读 Java 代码来理解应用程序的流程。 • Java 开发人员也不必在更改流程以后重新编译代码。可以通过扩展 ActionServlet 来添加 Command 功能。 ActionForm 类 ActionForm 维护 Web 应用程序的会话状态。 ActionForm 是一个抽象类，必须为每个输入表单模型创建该类的子类。当我说输入表单模型时,是指 ActionForm 表示的是由 HTML 表单设置或更新的一般意义上的数据。例如，您可能有一个由 HTML 表单设置的 UserActionForm 。Struts 框架将执行以下操作： • 检查 UserActionForm 是否存在；如果不存在，它将创建该类的一个实例。 • Struts 将使用 HttpServletRequest 中相应的域设置 UserActionForm 的状态。没有太多讨厌的 request.getParameter() 调用。例如，Struts 框架将从请求流中提取 fname ，并调用 UserActionForm.setFname() 。 • Struts 框架在将 UserActionForm 传递给业务包装 UserAction 之前将更新它的状态。 • 在将它传递给 Action 类之前，Struts 还会对 UserActionForm 调用 validation() 方法进行表单状态验证。注：这并不总是明智之举。别的网页或业务可能使用 UserActionForm ，在这些地方，验证可能有所不同。在 UserAction 类中进行状态验证可能更好。 • 可在会话级维护 UserActionForm 。注： • struts-config.xml 文件控制 HTML 表单请求与 ActionForm 之间的映射关系。 • 可将多个请求映射到 UserActionForm 。 • UserActionForm 可跨多页进行映射，以执行诸如向导之类的操作。 Action 类 Action 类是业务逻辑的一个包装。 Action 类的用途是将 HttpServletRequest 转换为业务逻辑。要使用 Action ，请创建它的子类并覆盖 process() 方法。 ActionServlet (Command) 使用 perform() 方法将参数化的类传递给 ActionForm 。仍然没有太多讨厌的 request.getParameter() 调用。当事件进展到这一步时，输入表单数据（或 HTML 表单数据）已被从请求流中提取出来并转移到 ActionForm 类中。注：扩展 Action 类时请注意简洁。 Action 类应该控制应用程序的流程，而不应该控制应用程序的逻辑。通过将业务逻辑放在单独的包或 EJB 中，我们就可以提供更大的灵活性和可重用性。考虑 Action 类的另一种方式是 Adapter 设计模式。 Action 的用途是“将类的接口转换为客户机所需的另一个接口。Adapter 使类能够协同工作，如果没有 Adapter，则这些类会因为不兼容的接口而无法协同工作。”（摘自 Gof 所著的 Design Patterns - Elements of Reusable OO Software ）。本例中的客户机是 ActionServlet ，它对我们的具体业务类接口一无所知。因此，Struts 提供了它能够理解的一个业务接口，即 Action 。通过扩展 Action ，我们使得我们的业务接口与 Struts 业务接口保持兼容。（一个有趣的发现是， Action 是类而不是接口）。 Action 开始为一个接口，后来却变成了一个类。真是金无足赤。） ActionMapping 类输入事件通常是在 HTTP 请求表单中发生的，servlet 容器将 HTTP 请求转换为 HttpServletRequest 。控制器查看输入事件并将请求分派给某个 Action 类。 struts-config.xml 确定 Controller 调用哪个 Action 类。 struts-config.xml 配置信息被转换为一组 ActionMapping ，而后者又被放入 ActionMappings 容器中。（您可能尚未注意到这一点，以 s结尾的类就是容器） ActionMapping 包含有关特定事件如何映射到特定 Action 的信息。 ActionServlet (Command) 通过 perform() 方法将 ActionMapping 传递给 Action 类。这样就使 Action 可访问用于控制流程的信息。 ActionMappings ActionMappings 是 ActionMapping 对象的一个集合。 Struts 的优点 • JSP 标记机制的使用标记特性从 JSP 文件获得可重用代码和抽象 Java 代码。这个特性能很好地集成到基于 JSP 的开发工具中，这些工具允许用标记编写代码。 • 标记库为什么要另发明一种轮子，或标记库呢？如果您在库中找不到您所要的标记，那就自己定义吧。此外，如果您正在学习 JSP 标记技术，则 Struts 为您提供了一个起点。 • 开放源码您可以获得开放源码的全部优点，比如可以查看代码并让使用库的每个人检查代码。许多人都可以进行很好的代码检查。 • MVC 实现样例如果您希望创建您自己的 MVC 实现，则 Struts 可增加您的见识。 • 管理问题空间分治是解决问题并使问题可管理的极好方法。当然，这是一把双刃剑。问题越来越复杂，并且需要越来越多的管理。 Struts 的缺点 • 仍处于发展初期 Struts 开发仍处于初级阶段。他们正在向着发行版本 1.0 而努力，但与任何 1.0 版本一样，它不可能尽善尽美。 • 仍在变化中这个框架仍在快速变化。Struts 1.0 与 Struts 0.5 相比变化极大。为了避免使用不赞成使用的方法，您可能隔一天就需要下载最新的 Struts。在过去的 6 个月中，我目睹 Struts 库从 90K 增大到 270K 以上。由于 Struts 中的变化，我不得不数次修改我的示例，但我不保证我的示例能与您下载的 Struts 协同工作。 • 正确的抽象级别 Struts 是否提供了正确的抽象级别？对于网页设计人员而言，什么是正确的抽象级别呢？这是一个用 $64K 的文字才能解释清楚的问题。在开发网页的过程中，我们是否应该让网页设计人员访问 Java 代码？某些框架（如 Velocity）说不应该，但它提供了另一种 Web 开发语言让我们学习。在 UI 开发中限制访问 Java 有一定的合理性。最重要的是，如果让网页设计人员使用一点 Java，他将使用大量的 Java。在 Microsoft ASP 的开发中，我总是看到这样的情况。在 ASP 开发中，您应该创建 COM 对象，然后编写少量的 ASP 脚本将这些 COM 对象联系起来。但是，ASP 开发人员会疯狂地使用 ASP 脚本。我会听到这样的话，“既然我可以用 VBScript 直接编写 COM 对象，为什么还要等 COM 开发人员来创建它呢？”通过使用标记库，Struts 有助于限制 JSP 文件中所需的 Java 代码的数量。Logic Tag 就是这样的一种库，它对有条件地生成输出进行管理，但这并不能阻止 UI 开发人员对 Java 代码的狂热。无论您决定使用哪种类型的框架，您都应该了解您要在其中部署和维护该框架的环境。当然，这项任务真是说起来容易做起来难。 • 有限的适用范围 Struts 是一种基于 Web 的 MVC 解决方案，所以必须用 HTML、JSP 文件和 servlet 来实现它。 • J2EE 应用程序支持 Struts 需要支持 JSP 1.1 和 Servlet 2.2 规范的 servlet 容器。仅凭这一点远不能解决您的全部安装问题，除非使用 Tomcat 3.2。我用 Netscape iPlanet 6.0 安装这个库时遇到一大堆问题，按理说它是第一种符合 J2EE 的应用程序服务器。我建议您在遇到问题时访问 Struts 用户邮件列表的归档资料。 • 复杂性在将问题分为几个部分的同时也引入了复杂性。毫无疑问，要理解 Struts 必须接受一定的培训。随着变化的不断加入，这有时会令人很沮丧。欢迎访问本网站。 Struts 的前景在这个软件开发的新时代，一切都变得很快。在不到 5 年的时间内，我已经目睹了从 cgi/perl 到 ISAPI/NSAPI、再到使用 VB 的 ASP、一直到现在的 Java 和 J2EE 的变迁。Sun 正在尽力将新的变化反映到 JSP/servlet 体系结构中，正如他们对 Java 语言和 API 所作的更改一样。您可以从 Sun 的网站获得新的 JSP 1.2 和 Servlet 2.3 规范的草案。此外，一个标准 JSP 标记库即将出现。 3：外文出处 [1]Malcolm Davis. Struts——an open-source MVC implementation [2]IBM System Journal,2006

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