nested queries - Page 72

How do I do "nested if" in JSTL for Java JSP?

- by user678616

I want to do something like the following: <c:choose> <c:when test="${empty example1}"> </c:when> <c:otherwise> <c:when test="${empty example2}"> </c:when> <c:otherwise> </c:otherwise> </c:otherwise> </c:choose> Is this even possible? I get an exception thrown when trying to run. Thank you.

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I am using $('#div').children().each() but want to find a nested value within one of those children

- by toddv

$('#combineDiv').children().each( function(){ if ($(this + ":first-child").attr('class') == 'combinedColumn') { alert('found ONE!'); } }); I have a hidden input in some of the divs in the children of the div #combineDiv... just dont know how to combine the THIS keyword with a selector... :(

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Tuples vs. Anonymous Types vs. Expando object. (in regards to LINQ queries)

- by punkouter

I am a beginner who finally started understanding anonymous types. (see old post http://stackoverflow.com/questions/3010147/what-is-the-return-type-for-a-anonymous-linq-query-select-what-is-the-best-way-t) So in LINQ queries you form the type of return value you want within the linq query right? It seems the way to do this is anonymous type right? Can someone explain to me if and when I could use a Tuple/Expando object instead? They all seem very simliar?

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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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General monitoring for SQL Server Analysis Services using Performance Monitor

- by Testas

A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs. File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor, a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used…… Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above 25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file

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SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

- by Pinal Dave

Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache. As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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CQRS – Questions and Concerns

- by Dylan Smith

I’ve been doing a lot of learning on CQRS and Event Sourcing over the last little while and I have a number of questions that I haven’t been able to answer. 1. What is the benefit of CQRS when compared to a typical DDD architecture that uses Event Sourcing and properly captures intent and behavior via verb-based commands? (other than Scalability) 2. When using CQRS what do you do with complex query-based logic? I’m going to elaborate on #1 in this blog post and I’ll do a follow-up post on #2. I watched through Greg Young’s video on the business benefits of CQRS + Event Sourcing and first let me say that I thought it was an excellent presentation that really drives home a lot of the benefits to this approach to architecture (I watched it twice in a row I enjoyed it so much!). But it didn’t answer some of my questions fully (I wish I had been there to ask these of Greg in person!). So let me pick apart some of the points he makes and how they relate to my first question above. I’m completely sold on the idea of event sourcing and have a clear understanding of the benefits that it brings to the table, so I’m not going to question that. But you can use event sourcing without going to a CQRS architecture, so my main question is around the benefits of CQRS + Event Sourcing vs Event Sourcing + Typical DDD architecture Architecture with Event Sourcing + Commands on Left, CQRS on Right Greg talks about how the stereotypical architecture doesn’t support DDD, but is that only because his diagram shows DTO’s coming up from the client. If we use the same diagram but allow the client to send commands doesn’t that remove a lot of the arguments that Greg makes against the stereotypical architecture? We can now introduce verbs into the system. We can capture intent now (storing it still requires event sourcing, but you can implement event sourcing without doing CQRS) We can create a rich domain model (as opposed to an anemic domain model) Scalability is obviously a benefit that CQRS brings to the table, but like Greg says, very few of the systems we create truly need significant scalability Greg talks about the ability to scale your development efforts. He says CQRS allows you to split the system into 3 parts (Client, Domain/Commands, Reads) and assign 3 teams of developers to work on them in parallel; letting you scale your development efforts by 3x with nearly linear gains. But in the stereotypical architecture don’t you already have 2 separate modules that you can split your dev efforts between: The client that sends commands/queries and receives DTO’s, and the Domain which accepts commands/queries, and generates events/DTO’s. If this is true it’s not really a 3x scaling you achieve with CQRS but merely a 1.5x scaling which while great doesn’t sound nearly as dramatic (“I can do it with 10 devs in 12 months – let me hire 5 more and we can have it done in 8 months”). Making the Query side “stupid simple” such that you can assign junior developers (or even outsource it) sounds like a valid benefit, but I have some concerns over what you do with complex query-based logic/behavior. I’m going to go into more detail on this in a follow-up blog post shortly. He also seemed to focus on how “stupid-simple” it is doing queries against the de-normalized data store, but I imagine there is still significant complexity in the event handlers that interpret the events and apply them to the de-normalized tables. It sounds like Greg suggests that because we’re doing CQRS that allows us to apply Event Sourcing when we otherwise wouldn’t be able to (~33:30 in the video). I don’t believe this is true. I don’t see why you wouldn’t be able to apply Event Sourcing without separating out the Commands and Queries. The queries would just operate against the domain model instead of the database. But you’d still get the benefits of Event Sourcing. Without CQRS the queries would only be able to operate against the current state rather than the event history, but even in CQRS the domain behaviors can only operate against the current state and I don’t see that being a big limiting factor. If some query needs to operate against something that is not captured by the current state you would just have to update the domain model to capture that information (no different than if that statement were made about a Command under CQRS). Some of the benefits I do see being applicable are that your domain model might end up being simpler/smaller since it only needs to represent the state needed to process commands and not worry about the reads (like the Deactivate Inventory Item and associated comment example that Greg provides). And also commands that can be handled in a Transaction Script style manner by the command handler simply generating events and not touching the domain model. It also makes it easier for your senior developers to focus on the command behavior and ignore the queries, which is usually going to be a better use of their time. And of course scalability. If anybody out there has any thoughts on this and can help educate me further, please either leave a comment or feel free to get in touch with me via email:

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Connection Pooling is Busted

- by MightyZot

A few weeks ago we started getting complaints about performance in an application that has performed very well for many years. The application is a n-tier application that uses ADODB with the SQLOLEDB provider to talk to a SQL Server database. Our object model is written in such a way that each public method validates security before performing requested actions, so there is a significant number of queries executed to get information about file cabinets, retrieve images, create workflows, etc. (PaperWise is a document management and workflow system.) A common factor for these customers is that they have remote offices connected via MPLS networks. Naturally, the first thing we looked at was the query performance in SQL Profiler. All of the queries were executing within expected timeframes, most of them were so fast that the duration in SQL Profiler was zero. After getting nowhere with SQL Profiler, the situation was escalated to me. I decided to take a peek with Process Monitor. Procmon revealed some “gaps” in the TCP/IP traffic. There were notable delays between send and receive pairs. The send and receive pairs themselves were quite snappy, but quite often there was a notable delay between a receive and the next send. You might expect some delay because, presumably, the application is doing some thinking in-between the pairs. But, comparing the procmon data at the remote locations with the procmon data for workstations on the local network showed that the remote workstations were significantly delayed. Procmon also showed a high number of disconnects. Wireshark traces showed that connections to the database were taking between 75ms and 150ms. Not only that, but connections to a file share containing images were taking 2 seconds! So, I asked about a trust. Sure enough there was a trust between two domains and the file share was on the second domain. Joining a remote workstation to the domain hosting the share containing images alleviated the time delay in accessing the file share. Removing the trust had no affect on the connections to the database. Microsoft Network Monitor includes filters that parse TDS packets. TDS is the protocol that SQL Server uses to communicate. There is a certificate exchange and some SSL that occurs during authentication. All of this was evident in the network traffic. After staring at the network traffic for a while, and examining packets, I decided to call it a night. On the way home that night, something about the traffic kept nagging at me. Then it dawned on me…at the beginning of the dance of packets between the client and the server all was well. Connection pooling was working and I could see multiple queries getting executed on the same connection and ethereal port. After a particular query, connecting to two different servers, I noticed that ADODB and SQLOLEDB started making repeated connections to the database on different ethereal ports. SQL Server would execute a single query and respond on a port, then open a new port and execute the next query. Connection pooling appeared to be broken. The next morning I wrote a test to confirm my hypothesis. Turns out that the sequence causing the connection nastiness goes something like this: Make a connection to the database. Open a result set that returns enough records to require multiple roundtrips to the server. For each result, query for some other data in the database (this will open a new implicit connection.) Close the inner result set and repeat for every item in the original result set. Close the original connection. Provided that the first result set returns enough data to require multiple roundtrips to the server, ADODB and SQLOLEDB will start making new connections to the database for each query executed in the loop. Originally, I thought this might be due to Microsoft’s denial of service (ddos) attack protection. After turning those features off to no avail, I eventually thought to switch my queries to client-side cursors instead of server-side cursors. Server-side cursors are the default, by the way. Voila! After switching to client-side cursors, the disconnects were gone and the above sequence yielded two connections as expected. While the real problem is the amount of time it takes to make connections over these MPLS networks (100ms on average), switching to client-side cursors made the problem go away. Believe it or not, this is actually documented by Microsoft, and rather difficult to find. (At least it was while we were trying to troubleshoot the problem!) So, if you’re noticing performance issues on slower networks, or networks with slower switching, take a look at the traffic in a tool like Microsoft Network Monitor. If you notice a high number of disconnects, and you’re using fire-hose or server-side cursors, then try switching to client-side cursors and you may see the problem go away. Most likely, Microsoft believes this to be appropriate behavior, because ADODB can’t guarantee that all of the data has been retrieved when you execute the inner queries. I’m not convinced, though, because the problem remains even after replacing all of the implicit connections with explicit connections and closing those connections in-between each of the inner queries. In that case, there doesn’t seem to be a reason why ADODB can’t use a single connection from the connection pool to make the additional queries, bringing the total number of connections to two. Instead ADO appears to make an assumption about the state of the connection. I’ve reported the behavior to Microsoft and am awaiting to hear from the appropriate team, so that I can demonstrate the problem. Maybe they can explain to us why this is appropriate behavior. :)

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CQRS - Benefits

- by Dylan Smith

Thanks to all the comments and feedback from the last post I think I have a better understanding now of the benefits of CQRS (separate from the benefits of Event Sourcing). I’m going to try and sum it up here, and point out some areas where I could still use some advice: CQRS Benefits Sounds like the primary benefit of CQRS as an architecture is it allows you to create a simpler domain model by sucking out everything related to queries. I can definitely see the benefit to this, in general the domain logic related to commands is the high-value behavior in the software, but the logic required to service the queries would add a lot of low-value “noise” to the domain model that would dilute the high-value (command) behavior – sorting, paging, filtering, pre-fetch paths, etc. Also the most appropriate domain structure for implementing commands might not be the most optimal for implementing queries. To paraphrase Greg, this usually results in a domain model that is mediocre at both, piss-poor at one, or more likely piss-poor at both commands and queries. Not only will you be able to simplify your domain model by pulling out all the query logic, but at least a handful of commands in most systems will probably be “pass-though” type commands with little to no logic that just generate events. If these can be implemented directly in the command-handler and never touch the domain model, this allows you to slim down the domain model even more. Also, if you were to do event sourcing without CQRS, you no longer have a database containing the current state (only the domain model would) which makes it difficult (or impossible) to support ad-hoc querying and/or reporting that is common in most business software. Of course CQRS provides some great scalability benefits, not only scalability but I have to assume that it provides extremely low latency for most operations, especially if you have an asynchronous event bus. I know Greg says that you get a 3x scaling (Commands, Queries, Client) of your ability to perform parallel development, but IMHO, it seems like it only provides 1.5x scaling since even without CQRS you’re going to have your client loosely coupled to your domain - which is still a great benefit to be able to realize. Questions / Concerns If all the queries against an aggregate get pulled out to the Query layer, what if the only commands for that aggregate can be handled in a “pass-through” manner with the command handler directly generating events. Is it possible to have an aggregate that isn’t modeled in the domain model? Are there any issues or downsides to this? I know in the feedback from my previous posts it was suggested that having one domain model handling both commands and queries requires implementing a lot of traversals between objects that wouldn’t be necessary if it was only servicing commands. My question is, do you include traversals in your domain model based on the needs of the code, or based on the conceptual domain model? If none of my Commands require a Customer.Orders traversal, but the conceptual domain includes the concept of a set of orders belonging to a customer – should I model that in my domain model or not? I like the idea of using the Query side of the architecture as a place to put junior devs where the risk of them screwing something up has minimal impact. But I’m not sold on the idea that you can actually outsource it. Like I said in one of my comments on my previous post, the code to handle a query and generate DTO’s is going to be dead simple, but the code to process events and apply them to the tables on the query side is going to require a significant amount of domain knowledge to know which events to listen for to update each of the de-normalized tables (and what changes need to be made when each event is processed). I don’t know about everybody else, but having Indian/Russian/whatever outsourced developers have to do anything that requires significant domain knowledge has never been successful in my experience. And if you need to spec out for each new query which events to listen to and what to do with each one, well that’s probably going to be just as much work to document as it would be to just implement it. Greg made the point in a comment that doing an aggregate query like “Total Sales By Customer” is going to be inefficient if you use event sourcing but not CQRS. I don’t understand why that would be the case. I imagine in that case you’d simply have a method/property on the Customer object that calculated total sales for that customer by enumerating over the Orders collection. Then the application services layer would generate DTO’s off of the Customers collection that included say the CustomerID, CustomerName, TotalSales, or whatever the case may be. As long as you use a snapshotting implementation, I don’t see why that would be anymore inefficient in a DDD+Event Sourcing implementation than in a typical DDD implementation. Like I mentioned in my last post I still have some questions about query logic that haven’t been answered yet, but before I start asking those I want to make sure I have a strong grasp on what benefits CQRS provides. My main concern with the query logic was that I know I could just toss it all into the query side, but I was concerned that I would be losing the benefits of using CQRS in the first place if I did that. I want to elaborate more on this though with some example situations in an upcoming post.

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JDBC query to Oracle

- by Harish

Hi, We are planning to migrate our DB to Oracle.We need to manually check each of the embedded SQL is working in Oracle as few may follow different SQL rules.Now my need is very simple. I need to browse through a file which may contain queries like this. String sql = "select * from test where name="+test+"and age="+age; There are nearly 1000 files and each file has different kind of queries like this where I have to pluck the query alone which I have done through an unix script.But I need to convert these Java based queries to Oracle compatible queries. ie. select * from test where name="name" and age="age" Basically I need to check the syntax of the queries by this.I have seen something like this in TOAD but I have more than 1000 files and can't manually change each one.Is there a way? I will explain more i the question is not clear

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issue in list of dict

- by gaggina

class MyOwnClass: # list who contains the queries queries = [] # a template dict template_query = {} template_query['name'] = 'mat' template_query['age'] = '12' obj = MyOwnClass() query = obj.template_query query['name'] = 'sam' query['age'] = '23' obj.queries.append(query) query2 = obj.template_query query2['name'] = 'dj' query2['age'] = '19' obj.queries.append(query2) print obj.queries It gives me [{'age': '19', 'name': 'dj'}, {'age': '19', 'name': 'dj'}] while I expect to have [{'age': '23' , 'name': 'sam'}, {'age': '19', 'name': 'dj'}] I thought to use a template for this list because I'm gonna to use it very often and there are some default variable who does not need to be changed. Why does doing it the template_query itself changes? I'm new to python and I'm getting pretty confused.

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Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

- by aspiringgeek

Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014. Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc. The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps. In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data. The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario Conventional Structures Columnstore ? SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically. The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes. As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics. Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible. The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators. As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing. I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

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Performance - User defined query / filter to search data

- by Cagatay Kalan

What is the best way to design a system where users can create their own criterias to search data ? By "design" i mean, data storage, data access layer and search structure. We will actually refactor an existing application which is written in C# and ASP .NET and we don't want to change the infrastructure. Our main issue is performance and we use MSSQL and DevExpress to build queries. Some queries run in 4-5 minutes and all the columns included in the queries have indexes. When i check queries, i see that DevExpress builds too many "exists" clauses and i'm not happy with that because i have doubts that some of these queries skip some indexes. What may be the alternatives to DevExpress? NHibernate or Entity Framework? Can we build dynamic criteria system and store these to database in both of them ? And also do we need any alternative storage like a lucene index or OLAP database?

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HOw do I delete record in a table by keeping certain datas??

- by mathew

my site has lots of incoming searches which is stored in a database to show recent queries into my website. due to high search queries my database is getting bigger in size. so what I want is I need to keep only recent queries in database say 10 records. this keeps my database small and queries will be faster. I am able to store incoming queries to database but don't know how to restrict or delete excess/old data from table. any help?? well I am using PHP and MySQL

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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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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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Optimzing TSQL code

- by adopilot

My job is the maintain one application which heavy use SQL server (MSSQL2005). Until now middle server stores TSQL codes in XML and send dynamic TSQL queries without using stored procs. As I am able change those XML queries I want to migrate most of my queries to stored procs. Question is folowing: Most of my queries have same Where conditions against one table Sample: Select ..... from .... where .... and (a.vrsta_id = @vrsta_id or @vrsta_id = 0) and (a.podvrsta_id = @podvrsta_id or @podvrsta_id = 0) and (a.podgrupa_2 = @podgrupa2_id or @podgrupa2_id = 0) and ( (a.id in (select art_id from osobina_veze where podosobina_id in (select ado from dbo.fn_ado_param_int(@podosobina)) group by art_id having count(art_id)= @podosobina_count )) or ('0' = @podosobina) ) They also have same where conditions on other table. How I should organize my code ? What is proper way ? Should I make table valued function that I will use in all queries or use #Temp tables and simple inner join my query to that each time when proc executing? or use #temp filed by table valued function ? or leave all queries with this large where clause and hope that index is going to do their jobs. or use WITH(statement)

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What should I use to increase performance. View/Query/Temporary Table

- by Shantanu Gupta

I want to know the performance of using Views, Temp Tables and Direct Queries Usage in a Stored Procedure. I have a table that gets created every time when a trigger gets fired. I know this trigger will be fired very rare and only once at the time of setup. Now I have to use that created table from triggers at many places for fetching data and I confirms it that no one make any changes in that table. i.e ReadOnly Table. I have to use this tables data along with multiple tables to join and fetch result for further queries say select * from triggertable By Using temp table select ... into #tx from triggertable join t2 join t3 and so on select a,b, c from #tx --do something select d,e,f from #tx ---do somethign --and so on --around 6-7 queries in a row in a stored procedure. By Using Views create view viewname ( select ... from triggertable join t2 join t3 and so on ) select a,b, c from viewname --do something select d,e,f from viewname ---do somethign --and so on --around 6-7 queries in a row in a stored procedure. This View can be used in other places as well. So I will be creating at database rather than at sp By Using Direct Query select a,b, c from select ... into #tx from triggertable join t2 join t3 join ... --do something select a,b, c from select ... into #tx from triggertable join t2 join t3 join ... --do something . . --and so on --around 6-7 queries in a row in a stored procedure. Now I can create a view/temporary table/ directly query usage in all upcoming queries. What would be the best to use in this case.

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SQL SERVER – Out of the Box – Activty and Performance Reports from SSSMS

- by pinaldave

SQL Server management Studio 2008 is wonderful tool and has many different features. Many times, an average user does not use them as they are not aware about these features. Today, we will learn one such feature. SSMS comes with many inbuilt performance and activity reports, but we do not use it to the full potential. Let us see how we can access these standard reports. Connect to SQL Server Node >> Right Click on it >> Go to Reports >> Click on Standard Reports >> Pick Any Report. Click to Enlarge You can see there are many reports, which an average users needs right away, are available there. Let me list all the reports available. Server Dashboard Configuration Changes History Schema Changes History Scheduler Health Memory Consumption Activity – All Blocking Transactions Activity – All Cursors Activity – All Sessions Activity – Top Sessions Activity – Dormant Sessions Activity - Top Connections Top Transactions by Age Top Transactions by Blocked Transactions Count Top Transactions by Locks Count Performance – Batch Execution Statistics Performance – Object Execution Statistics Performance – Top Queries by Average CPU Time Performance – Top Queries by Average IO Performance – Top Queries by Total CPU Time Performance – Top Queries by Total IO Service Broker Statistics Transactions Log Shipping Status In fact, when you look at the above list, it is fairly clear that they are very thought out and commonly needed reports that are available in SQL Server 2008. Let us run a couple of reports and observe their result. Performance – Top Queries by Total CPU Time Click to Enlarge Memory Consumption Click to Enlarge There are options for custom reports as well, which we can configure. We will learn about them in some other post. Additionally, you can right click on the reports and export in Excel or PDF. I think this tool can really help those who are just looking for some quick details. Does any of you use this feature, or this feature has some limitations and You would like to see more features? Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology

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How to find and fix performance problems in ORM powered applications

- by FransBouma

Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application. Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter. As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.

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SQL SERVER – BI Quiz Hint – Performance Tuning Cubes – Hints

- by pinaldave

I earlier wrote about SQL BI Quiz over here and here. The details of the quiz is here: Working with huge data is very common when it is about Data Warehousing. It is necessary to create Cubes on the data to make it meaningful and consumable. There are cases when retrieving the data from cube takes lots of the time. Let us assume that your cube is returning you data very quickly. Suddenly on one day it is returning the data very slowly. What are the three things will you to diagnose this. After diagnose what you will do to resolve performance issue. Participate in my question over here I required BI Expert Jason Thomas to help with few hints to blog readers. He is one of the leading SSAS expert and writes a complicated subject in simple words. If queries were executing properly before but now take a long time to return the data, it means that there has been a change in the environment in which it is running. Some possible changes are listed below:- 1) Data factors:- Compare the data size then and now. Increase in data can result in different execution times. Poorly written queries as well as poor design will not start showing issues till the data grows. How to find it out? (Ans : SQL Server profiler and Perfmon Counters can be used for identifying the issues and performance tuning the MDX queries) 2) Internal Factors:- Is some slow MDX query / multiple mdx queries running at the same time, which was not running when you had tested it before? Is there any locking happening due to proactive caching or processing operations? Are the measure group caches being cleared by processing operations? (Ans : Again, profiler and perfmon counters will help in finding it out. Load testing can be done using AS Performance Workbench (http://asperfwb.codeplex.com/) by running multiple queries at once) 3) External factors:- Is some other application competing for the same resources? HINT : Read “Identifying and Resolving MDX Query Performance Bottlenecks in SQL Server 2005 Analysis Services” (http://sqlcat.com/whitepapers/archive/2007/12/16/identifying-and-resolving-mdx-query-performance-bottlenecks-in-sql-server-2005-analysis-services.aspx) Well, these are great tips. Now win big prizes by participate in my question over here. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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WebCenter Content Web Search Performance: Do you really need that folder path info?

- by Nicolas Montoya

End-users want content at their fingertips at the speed of thought if possible. When running search operations in the WebCenter Conter Web Interface every second or fraction of a second improvement does matter. When doing some trace analysis on the systemdatabase tracing on a customer environment, we came across some SQL queries that were unnecessarily being triggered! These were related to determining the folder path for every entry part of the search result set. However, this folder path was not even being used as part of the displayed information in the user interface.Why was the folder path information being collected when it was not even displayed in the UI? We found that the configuration parameter 'FolderPathInSearchResults' was set to 'true' under Administration > Admin Server > General Configuration > Additional Configuration Variables as shown below:When executing a quicksearch by keyword we were getting 100 out of 2280 entries in the first page of the result set.When thera 'FolderPathInSearchResults' configuration parameter is set to 'true', the following queries appear in the systemdatabase tracing:100 executions for a query on the FolderFiles table for each of the documents displayed in the first page:>systemdatabase/6 12.13 11:17:48.188 IdcServer-199 1.45 ms. SELECT * FROM FolderFiles WHERE dDocName='SLC02VGVUSORAC140641' AND fLinkRank=0[Executed. Returned row(s): true]382 executions for a query of the folders tables - most of the documents that match the keyword criteria are at a folder depth level of three or four:>systemdatabase/6 12.13 11:17:48.114 IdcServer-199 2.57 ms. SELECT FolderFolders.*,FolderMetaDefaults.* FROM FolderFolders,FolderMetaDefaults WHERE FolderFolders.fFolderGUID=FolderMetaDefaults.fFolderGUID(+) AND((FolderFolders.fFolderGUID = '1EB8E527E19B09ED3FE82EE310AEA13A' ) )[Executed.Returned row(s): true]By setting this 'FolderPathInSearchResults' configuration parameter to 'false', the above queries were no longer reported in the Server Output System Audit Information.Now, let's consider a practical scenario:Search result set page = 100Average folder depth der document in the search result set: 5The number of folder path related queries will be: 100 + 5*500 = 600If each query takes slightly over 3 ms. You would have 2000 ms (2 seconds) spent in server time to get this information.The overall performance impact goes beyond seerver time execution, as this information needs to travel from the server to the browser. If the documents are further nested into the folder hierarchy, additional hundreds of queries may be executed. If folder path is not being displayed in the end-user interface profile, your system may be better of with the 'FolderPathInSearchResults' configuration parameter disabled.

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Inheritance Mapping Strategies with Entity Framework Code First CTP5 Part 1: Table per Hierarchy (TPH)

- by mortezam

A simple strategy for mapping classes to database tables might be “one table for every entity persistent class.” This approach sounds simple enough and, indeed, works well until we encounter inheritance. Inheritance is such a visible structural mismatch between the object-oriented and relational worlds because object-oriented systems model both “is a” and “has a” relationships. SQL-based models provide only "has a" relationships between entities; SQL database management systems don’t support type inheritance—and even when it’s available, it’s usually proprietary or incomplete. There are three different approaches to representing an inheritance hierarchy: Table per Hierarchy (TPH): Enable polymorphism by denormalizing the SQL schema, and utilize a type discriminator column that holds type information. Table per Type (TPT): Represent "is a" (inheritance) relationships as "has a" (foreign key) relationships. Table per Concrete class (TPC): Discard polymorphism and inheritance relationships completely from the SQL schema.I will explain each of these strategies in a series of posts and this one is dedicated to TPH. In this series we'll deeply dig into each of these strategies and will learn about "why" to choose them as well as "how" to implement them. Hopefully it will give you a better idea about which strategy to choose in a particular scenario. Inheritance Mapping with Entity Framework Code FirstAll of the inheritance mapping strategies that we discuss in this series will be implemented by EF Code First CTP5. The CTP5 build of the new EF Code First library has been released by ADO.NET team earlier this month. EF Code-First enables a pretty powerful code-centric development workflow for working with data. I’m a big fan of the EF Code First approach, and I’m pretty excited about a lot of productivity and power that it brings. When it comes to inheritance mapping, not only Code First fully supports all the strategies but also gives you ultimate flexibility to work with domain models that involves inheritance. The fluent API for inheritance mapping in CTP5 has been improved a lot and now it's more intuitive and concise in compare to CTP4. A Note For Those Who Follow Other Entity Framework ApproachesIf you are following EF's "Database First" or "Model First" approaches, I still recommend to read this series since although the implementation is Code First specific but the explanations around each of the strategies is perfectly applied to all approaches be it Code First or others. A Note For Those Who are New to Entity Framework and Code-FirstIf you choose to learn EF you've chosen well. If you choose to learn EF with Code First you've done even better. To get started, you can find a great walkthrough by Scott Guthrie here and another one by ADO.NET team here. In this post, I assume you already setup your machine to do Code First development and also that you are familiar with Code First fundamentals and basic concepts. You might also want to check out my other posts on EF Code First like Complex Types and Shared Primary Key Associations. A Top Down Development ScenarioThese posts take a top-down approach; it assumes that you’re starting with a domain model and trying to derive a new SQL schema. Therefore, we start with an existing domain model, implement it in C# and then let Code First create the database schema for us. However, the mapping strategies described are just as relevant if you’re working bottom up, starting with existing database tables. I’ll show some tricks along the way that help you dealing with nonperfect table layouts. Let’s start with the mapping of entity inheritance. -- The Domain ModelIn our domain model, we have a BillingDetail base class which is abstract (note the italic font on the UML class diagram below). We do allow various billing types and represent them as subclasses of BillingDetail class. As for now, we support CreditCard and BankAccount: Implement the Object Model with Code First As always, we start with the POCO classes. Note that in our DbContext, I only define one DbSet for the base class which is BillingDetail. Code First will find the other classes in the hierarchy based on Reachability Convention. public abstract class BillingDetail { public int BillingDetailId { get; set; } public string Owner { get; set; } public string Number { get; set; } } public class BankAccount : BillingDetail { public string BankName { get; set; } public string Swift { get; set; } } public class CreditCard : BillingDetail { public int CardType { get; set; } public string ExpiryMonth { get; set; } public string ExpiryYear { get; set; } } public class InheritanceMappingContext : DbContext { public DbSet<BillingDetail> BillingDetails { get; set; } } This object model is all that is needed to enable inheritance with Code First. If you put this in your application you would be able to immediately start working with the database and do CRUD operations. Before going into details about how EF Code First maps this object model to the database, we need to learn about one of the core concepts of inheritance mapping: polymorphic and non-polymorphic queries. Polymorphic Queries LINQ to Entities and EntitySQL, as object-oriented query languages, both support polymorphic queries—that is, queries for instances of a class and all instances of its subclasses, respectively. For example, consider the following query: IQueryable<BillingDetail> linqQuery = from b in context.BillingDetails select b; List<BillingDetail> billingDetails = linqQuery.ToList(); Or the same query in EntitySQL: string eSqlQuery = @"SELECT VAlUE b FROM BillingDetails AS b"; ObjectQuery<BillingDetail> objectQuery = ((IObjectContextAdapter)context).ObjectContext .CreateQuery<BillingDetail>(eSqlQuery); List<BillingDetail> billingDetails = objectQuery.ToList(); linqQuery and eSqlQuery are both polymorphic and return a list of objects of the type BillingDetail, which is an abstract class but the actual concrete objects in the list are of the subtypes of BillingDetail: CreditCard and BankAccount. Non-polymorphic QueriesAll LINQ to Entities and EntitySQL queries are polymorphic which return not only instances of the specific entity class to which it refers, but all subclasses of that class as well. On the other hand, Non-polymorphic queries are queries whose polymorphism is restricted and only returns instances of a particular subclass. In LINQ to Entities, this can be specified by using OfType<T>() Method. For example, the following query returns only instances of BankAccount: IQueryable<BankAccount> query = from b in context.BillingDetails.OfType<BankAccount>() select b; EntitySQL has OFTYPE operator that does the same thing: string eSqlQuery = @"SELECT VAlUE b FROM OFTYPE(BillingDetails, Model.BankAccount) AS b"; In fact, the above query with OFTYPE operator is a short form of the following query expression that uses TREAT and IS OF operators: string eSqlQuery = @"SELECT VAlUE TREAT(b as Model.BankAccount) FROM BillingDetails AS b WHERE b IS OF(Model.BankAccount)"; (Note that in the above query, Model.BankAccount is the fully qualified name for BankAccount class. You need to change "Model" with your own namespace name.) Table per Class Hierarchy (TPH)An entire class hierarchy can be mapped to a single table. This table includes columns for all properties of all classes in the hierarchy. The concrete subclass represented by a particular row is identified by the value of a type discriminator column. You don’t have to do anything special in Code First to enable TPH. It's the default inheritance mapping strategy: This mapping strategy is a winner in terms of both performance and simplicity. It’s the best-performing way to represent polymorphism—both polymorphic and nonpolymorphic queries perform well—and it’s even easy to implement by hand. Ad-hoc reporting is possible without complex joins or unions. Schema evolution is straightforward. Discriminator Column As you can see in the DB schema above, Code First has to add a special column to distinguish between persistent classes: the discriminator. This isn’t a property of the persistent class in our object model; it’s used internally by EF Code First. By default, the column name is "Discriminator", and its type is string. The values defaults to the persistent class names —in this case, “BankAccount” or “CreditCard”. EF Code First automatically sets and retrieves the discriminator values. TPH Requires Properties in SubClasses to be Nullable in the Database TPH has one major problem: Columns for properties declared by subclasses will be nullable in the database. For example, Code First created an (INT, NULL) column to map CardType property in CreditCard class. However, in a typical mapping scenario, Code First always creates an (INT, NOT NULL) column in the database for an int property in persistent class. But in this case, since BankAccount instance won’t have a CardType property, the CardType field must be NULL for that row so Code First creates an (INT, NULL) instead. If your subclasses each define several non-nullable properties, the loss of NOT NULL constraints may be a serious problem from the point of view of data integrity. TPH Violates the Third Normal FormAnother important issue is normalization. We’ve created functional dependencies between nonkey columns, violating the third normal form. Basically, the value of Discriminator column determines the corresponding values of the columns that belong to the subclasses (e.g. BankName) but Discriminator is not part of the primary key for the table. As always, denormalization for performance can be misleading, because it sacrifices long-term stability, maintainability, and the integrity of data for immediate gains that may be also achieved by proper optimization of the SQL execution plans (in other words, ask your DBA). Generated SQL QueryLet's take a look at the SQL statements that EF Code First sends to the database when we write queries in LINQ to Entities or EntitySQL. For example, the polymorphic query for BillingDetails that you saw, generates the following SQL statement: SELECT [Extent1].[Discriminator] AS [Discriminator], [Extent1].[BillingDetailId] AS [BillingDetailId], [Extent1].[Owner] AS [Owner], [Extent1].[Number] AS [Number], [Extent1].[BankName] AS [BankName], [Extent1].[Swift] AS [Swift], [Extent1].[CardType] AS [CardType], [Extent1].[ExpiryMonth] AS [ExpiryMonth], [Extent1].[ExpiryYear] AS [ExpiryYear] FROM [dbo].[BillingDetails] AS [Extent1] WHERE [Extent1].[Discriminator] IN ('BankAccount','CreditCard') Or the non-polymorphic query for the BankAccount subclass generates this SQL statement: SELECT [Extent1].[BillingDetailId] AS [BillingDetailId], [Extent1].[Owner] AS [Owner], [Extent1].[Number] AS [Number], [Extent1].[BankName] AS [BankName], [Extent1].[Swift] AS [Swift] FROM [dbo].[BillingDetails] AS [Extent1] WHERE [Extent1].[Discriminator] = 'BankAccount' Note how Code First adds a restriction on the discriminator column and also how it only selects those columns that belong to BankAccount entity. Change Discriminator Column Data Type and Values With Fluent API Sometimes, especially in legacy schemas, you need to override the conventions for the discriminator column so that Code First can work with the schema. The following fluent API code will change the discriminator column name to "BillingDetailType" and the values to "BA" and "CC" for BankAccount and CreditCard respectively: protected override void OnModelCreating(System.Data.Entity.ModelConfiguration.ModelBuilder modelBuilder) { modelBuilder.Entity<BillingDetail>() .Map<BankAccount>(m => m.Requires("BillingDetailType").HasValue("BA")) .Map<CreditCard>(m => m.Requires("BillingDetailType").HasValue("CC")); } Also, changing the data type of discriminator column is interesting. In the above code, we passed strings to HasValue method but this method has been defined to accepts a type of object: public void HasValue(object value); Therefore, if for example we pass a value of type int to it then Code First not only use our desired values (i.e. 1 & 2) in the discriminator column but also changes the column type to be (INT, NOT NULL): modelBuilder.Entity<BillingDetail>() .Map<BankAccount>(m => m.Requires("BillingDetailType").HasValue(1)) .Map<CreditCard>(m => m.Requires("BillingDetailType").HasValue(2)); SummaryIn this post we learned about Table per Hierarchy as the default mapping strategy in Code First. The disadvantages of the TPH strategy may be too serious for your design—after all, denormalized schemas can become a major burden in the long run. Your DBA may not like it at all. In the next post, we will learn about Table per Type (TPT) strategy that doesn’t expose you to this problem. References ADO.NET team blog Java Persistence with Hibernate book a { text-decoration: none; } a:visited { color: Blue; } .title { padding-bottom: 5px; font-family: Segoe UI; font-size: 11pt; font-weight: bold; padding-top: 15px; } .code, .typeName { font-family: consolas; } .typeName { color: #2b91af; } .padTop5 { padding-top: 5px; } .padTop10 { padding-top: 10px; } p.MsoNormal { margin-top: 0in; margin-right: 0in; margin-bottom: 10.0pt; margin-left: 0in; line-height: 115%; font-size: 11.0pt; font-family: "Calibri" , "sans-serif"; }

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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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