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  • Oracle Database 11g Express Edition(XE)????~??????????????????????

    - by Yuichi Hayashi
    Oracle Database 11g (11.2.0.2)??????????????? Oracle Database 11g Express Edition(XE)?????????????Oracle Database????????(11.2.0.2)?????????????????????? v$version??11.2.0.2???????? ?????? Oracle Technology Network(OTN)????????????? http://www.oracle.com/technetwork/database/express-edition/downloads/index.html ???? XE?????????????????????? 1CPU????(???CPU???????????1CPU???????) 1???????1????????????????1DB???????????? ????????????11GByte?? ????????(RAM)?1GByte?? ????????????Windows(x86)???Linux(x86_64) Web???????????? Oracle 11g XE??????web??????????????????????? [??????????????] ??????????????????????????????????????????????Oracle Database?????????????????????????????????? [?????????????] ???????????SQL Developer????????SQL Developer???????????????????? ??????????? ?SQL Developer????~??????????????DB???SQL??? http://blogs.oracle.com/oracle4engineer/entry/sql_developersql Web?????????????? Oracle 11g XE????????Application Express???????????????????????????APEX????????????????APEX???????????????????????? APEX?????????????????????Oracle Application Express(APEX) - ??(??), ??, ??????????????? http://blogs.oracle.com/oracle4engineer/entry/cat_apex ?????? & ??????????? ????????????(DBA)??????????????????????·????????Oracle 11g XE??????????????????????????????????????????????????????&?????????????? [??????&?????????????] [???????????????RMAN(Recovery Manager)??????] ?????????????????????????Oracle Database??????????????????????Oracle 11g XE??????????????????????? ????Oracle???:Oracle Database 11g Express Edition(XE)??????????!

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  • Oracle ZFSSA Hybrid Storage Pool Demo

    - by Darius Zanganeh
    The ZFS Hybrid Storage Pool (HSP) has been around since the ZFSSA first launched.  It is one of the main contributors to the high performance we see on the Oracle ZFSSA both in benchmarks as well as many production environments.  Below is a short video I made to show at a high level just how impactful this HSP pool is on storage performance.  We squeeze a ton of performance out of our drives with our unique use of cache, write optimized ssd and read optimized ssd.  Many have written and blogged about this technology, here it is in action. Demo of the Oracle ZFSSA Hybrid Storage Pool and how it speeds up workloads.

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  • "Optimal" game loop for 2D side-scroller

    - by MrDatabase
    Is it possible to describe an "optimal" (in terms of performance) layout for a 2D side-scroller's game loop? In this context the "game loop" takes user input, updates the states of game objects and draws the game objects. For example having a GameObject base class with a deep inheritance hierarchy could be good for maintenance... you can do something like the following: foreach(GameObject g in gameObjects) g.update(); However I think this approach can create performance issues. On the other hand all game objects' data and functions could be global. Which would be a maintenance headache but might be closer to an optimally performing game loop. Any thoughts? I'm interested in practical applications of near optimal game loop structure... even if I get a maintenance headache in exchange for great performance.

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  • What criteria would I use SQL Stream Insight vs TPL Dataflow [closed]

    - by makerofthings7
    There is an add-in to the Task Parallel Library (TPL) called TPL Dataflow that allows a variety of data processing scenarios. It seems that there are some parallels to the SQL Stream Insight product, however since SQL's Stream Insight has some interesting licensing around it, and it has a better performance depending on what license I get... I found myself asking myself should I use TPL Dataflow and not have any licensing issues, and possibly better performance. Can anyone tell me if performance is a valid criteria for comparing SQL Stream Insight vs TPL Dataflow? What other criteria should I be looking at when comparing the two?

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  • Why database partitioning didn't work? Extract from thedailywtf.com

    - by questzen
    Original link. http://thedailywtf.com/Articles/The-Certified-DBA.aspx. Article summary: The DBA suggests an approach involving rigorous partitioning, 10 partitions per disk (3 actual disks and 3 raid). The stats show that the performance is non-optimal. Then the DBA suggests an alternative of 1 partition per disk (with more added disks). This also fails. The sys-admin then sets up a single disk, single partition and saves the day. The size of disks was not mentioned but given today,s typical disk sizes (of the order of 100 GB), the partitions ; would be huge, it surprises me that a single disk with all partitions outperformed. Initially I suspect that the data was segregated and hence faster reads. But how come the performance didn't degrade as time went by with all the inserts and updates happening? Saw this on reddit, but the explanation was by far spindle/platter centered. There was no mention in the article about this. Is there any other reason? I can only guess that the tables were using a incorrect hash distribution causing non-uniform allocation across disks (wrong partitioning); this would increase fetch times. Any thoughts?

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  • How can I implement a database TableView like thing in C++?

    - by Industrial-antidepressant
    How can I implement a TableView like thing in C++? I want to emulating a tiny relation database like thing in C++. I have data tables, and I want to transform it somehow, so I need a TableView like class. I want filtering, sorting, freely add and remove items and transforming (ex. view as UPPERCASE and so on). The whole thing is inside a GUI application, so datatables and views are attached to a GUI (or HTML or something). So how can I identify an item in the view? How can I signal it when the table is changed? Is there some design pattern for this? Here is a simple table, and a simple data item: #include <string> #include <boost/multi_index_container.hpp> #include <boost/multi_index/member.hpp> #include <boost/multi_index/ordered_index.hpp> #include <boost/multi_index/random_access_index.hpp> using boost::multi_index_container; using namespace boost::multi_index; struct Data { Data() {} int id; std::string name; }; struct row{}; struct id{}; struct name{}; typedef boost::multi_index_container< Data, indexed_by< random_access<tag<row> >, ordered_unique<tag<id>, member<Data, int, &Data::id> >, ordered_unique<tag<name>, member<Data, std::string, &Data::name> > > > TDataTable; class DataTable { public: typedef Data item_type; typedef TDataTable::value_type value_type; typedef TDataTable::const_reference const_reference; typedef TDataTable::index<row>::type TRowIndex; typedef TDataTable::index<id>::type TIdIndex; typedef TDataTable::index<name>::type TNameIndex; typedef TRowIndex::iterator iterator; DataTable() : row_index(rule_table.get<row>()), id_index(rule_table.get<id>()), name_index(rule_table.get<name>()), row_index_writeable(rule_table.get<row>()) { } TDataTable::const_reference operator[](TDataTable::size_type n) const { return rule_table[n]; } std::pair<iterator,bool> push_back(const value_type& x) { return row_index_writeable.push_back(x); } iterator erase(iterator position) { return row_index_writeable.erase(position); } bool replace(iterator position,const value_type& x) { return row_index_writeable.replace(position, x); } template<typename InputIterator> void rearrange(InputIterator first) { return row_index_writeable.rearrange(first); } void print_table() const; unsigned size() const { return row_index.size(); } TDataTable rule_table; const TRowIndex& row_index; const TIdIndex& id_index; const TNameIndex& name_index; private: TRowIndex& row_index_writeable; }; class DataTableView { DataTableView(const DataTable& source_table) {} // How can I implement this? // I want filtering, sorting, signaling upper GUI layer, and sorting, and ... }; int main() { Data data1; data1.id = 1; data1.name = "name1"; Data data2; data2.id = 2; data2.name = "name2"; DataTable table; table.push_back(data1); DataTable::iterator it1 = table.row_index.iterator_to(table[0]); table.erase(it1); table.push_back(data1); Data new_data(table[0]); new_data.name = "new_name"; table.replace(table.row_index.iterator_to(table[0]), new_data); for (unsigned i = 0; i < table.size(); ++i) std::cout << table[i].name << std::endl; #if 0 // using scenarios: DataTableView table_view(table); table_view.fill_from_source(); // synchronization with source table_view.remove(data_item1); // remove item from view table_view.add(data_item2); // add item from source table table_view.filter(filterfunc); // filtering table_view.sort(sortfunc); // sorting // modifying from source_able, hot to signal the table_view? // FYI: Table view is atteched to a GUI item table.erase(data); table.replace(data); #endif return 0; }

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  • Is There any way to change Active Directory Users Database Source?

    - by Mehrdad Amini
    I need Active Directory Use My Own Custom Database (or shell or ...) for Authentication Users. Is there any extention or something like this to change User Passwords Database of active directory? I need this Because My Accounts Are In simple Database And I don't Want to Sync them periodically In Fact I can Not Change all My Applications to authenticate from Active Directory!Just I need Active Directory to Use My Database For Authentication.

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  • How can I remove unallocated space from a SQL Server database?

    - by Dynamo
    I have a database that was recently shrunk and when I run sp_spaceused I see that it has 500MB of unallocated space. I'm trying to keep this database to a certain size (do to MSDE size restrictions for my desktop users) and I'm not sure if the unallocated space affects the overall database size. Is there a way to remove this unallocated space from the database?

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  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . 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 Tagged: Koenig

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  • Can a call to WaitHandle.SignalAndWait be ignored for performance profiling purposes?

    - by Dan Tao
    I just downloaded the trial version of ANTS Performance Profiler from Red Gate and am investigating some of my team's code. Immediately I notice that there's a particular section of code that ANTS is reporting as eating up to 99% CPU time. I am completely unfamiliar with ANTS or performance profiling in general (that is, aside from self-profiling using what I'm sure are extremely crude and frowned-upon methods such as double timeToComplete = (endTime - startTime).TotalSeconds), so I'm still fiddling around with the application and figuring out how it's used. But I did call the developer responsible for the code in question and his immediate reaction was "Yeah, that doesn't surprise me that it says that; but that code calls SignalAndWait [which I could see for myself, thanks to ANTS], which doesn't use any CPU, it just sits there waiting for something to do." He advised me to simply ignore that code and look for anything ELSE I could find. My question: is it true that SignalAndWait requires NO CPU overhead (and if so, how is this possible?), and is it reasonable that a performance profiler would view it as taking up 99% CPU time? I find this particularly curious because, if it's at 99%, that would suggest that our application is often idle, wouldn't it? And yet its performance has become rather sluggish lately. Like I said, I really am just a beginner when it comes to this tool, and I don't know anything about the WaitHandle class. So ANY information to help me to understand what's going on here would be appreciated.

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  • What is the performance impact of CSS's universal selector?

    - by Bungle
    I'm trying to find some simple client-side performance tweaks in a page that receives millions of monthly pageviews. One concern that I have is the use of the CSS universal selector (*). As an example, consider a very simple HTML document like the following: <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/> <title>Example</title> <style type="text/css"> * { margin: 0; padding: 0; } </head> <body> <h1>This is a heading</h1> <p>This is a paragraph of text.</p> </body> </html> The universal selector will apply the above declaration to the body, h1 and p elements, since those are the only ones in the document. In general, would I see better performance from a rule such as: body, h1, p { margin: 0; padding: 0; } Or would this have exactly the same net effect? Essentially, what I'm asking is if these rules are effectively equivalent in this case, or if the universal selector has to perform more unnecessary work that I may not be aware of. I realize that the performance impact in this example may be very small, but I'm hoping to learn something that may lead to more significant performance improvements in real-world situations. Thanks for any help!

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  • A GUID as the MySQL table's Primary Key or as a separate column

    - by Ben
    I have a multi-process program that performs, in a 2 hour period, 5-10 million inserts to a 34GB table within a single Master/Slave MySQL setup (plus an equal number of reads in that period). The table in question has only 5 fields and 3 (single field) indexes. The primary key is auto-incrementing. I am far from a DBA, but the database appears to be crippled during this two hour period. So, I have a couple of general questions. 1) How much bang will I get out of batching these writes into units of 10? Currently, I am writing each insert serially because, after writing, I immediately need to know, in my program, the resulting primary key of each insert. The PK is the only unique field presently and approximating the order of insertion with something like a Datetime field or a multi-column value is not acceptable. If I perform a bulk insert, I won't know these IDs, which is a problem. So, I've been thinking about turning the auto-increment primary key into a GUID and enforcing uniqueness. I've also been kicking around the idea of creating a new column just for the purposes of the GUID. I don't really see the what that achieves though, that the PK approach doesn't already offer. As far as I can tell, the big downside to making the PK a randomly generated number is that the index would take a long time to update on each insert (since insertion order would not be sequential). Is that an acceptable approach for a table that is taking this number of writes? Thanks, Ben

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  • Is there a IDE/compiler PC benchmark I can use to compare my PCs performance?

    - by RickL
    I'm looking for a benchmark (and results on other PCs) which would give me an idea of the development performance gain I could get by upgrading my PC, also the benchmark could be used to justify the upgrade to my boss. I use Visual Studio 2008 for my development, so I'd like to get an idea of by what factor the build times would be improved, and also it would be good if the benchmark could incorporate IDE performance (i.e. when editing, using intellisense, opening code files etc) into its result. I currently have an AMD 3800x2, with 2GB RAM on Vista 32. For example, I'd like to know what kind of performance gain I'd see in Visual Studio 2008 with a Q6600, 4GB RAM on Vista 64. And also with other processors, and other RAM sizes... also see whether hard disk performance is a big factor. EDIT: I mentioned Vista 64 because I'm aware that Vista 32 can only use 3GB RAM maximum. So I'd presume that wanting to use more RAM would require Vista 64, but perhaps it could still be slower overall there is a large overhead in using the 32 bit VS 2008 on 64 bit OS.

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  • Performance of stored proc when updating columns selectively based on parameters?

    - by kprobst
    I'm trying to figure out if this is relatively well-performing T-SQL (this is SQL Server 2008). I need to create a stored procedure that updates a table. The proc accepts as many parameters as there are columns in the table, and with the exception of the PK column, they all default to NULL. The body of the procedure looks like this: CREATE PROCEDURE proc_repo_update @object_id bigint ,@object_name varchar(50) = NULL ,@object_type char(2) = NULL ,@object_weight int = NULL ,@owner_id int = NULL -- ...etc AS BEGIN update object_repo set object_name = ISNULL(@object_name, object_name) ,object_type = ISNULL(@object_type, object_type) ,object_weight = ISNULL(@object_weight, object_weight) ,owner_id = ISNULL(@owner_id, owner_id) -- ...etc where object_id = @object_id return @@ROWCOUNT END So basically: Update a column only if its corresponding parameter was provided, and leave the rest alone. This works well enough, but as the ISNULL call will return the value of the column if the received parameter was null, will SQL Server optimize this somehow? This might be a performance bottleneck on the application where the table might be updated heavily (insertion will be uncommon so the performance there is not a problem). So I'm trying to figure out what's the best way to do this. Is there a way to condition the column expressions with something like CASE WHEN or something? The table will be indexed up the wazoo as well for read performance. Is this the best approach? My alternative at this point is to create the UPDATE expression in code (e.g. inline SQL) and execute it against the server. This would solve my doubts about performance, but I'd rather leave this in a stored proc if possible.

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  • Having to insert a record, then update the same record warrants 1:1 relationship design?

    - by dianovich
    Let's say an Order has many Line items and we're storing the total cost of an order (based on the sum of prices on order lines) in the orders table. -------------- orders -------------- id ref total_cost -------------- -------------- lines -------------- id order_id price -------------- In a simple application, the order and line are created during the same step of the checkout process. So this means INSERT INTO orders .... -- Get ID of inserted order record INSERT into lines VALUES(null, order_id, ...), ... where we get the order ID after creating the order record. The problem I'm having is trying to figure out the best way to store the total cost of an order. I don't want to have to create an order create lines on an order calculate cost on order based on lines then update record created in 1. in orders table This would mean a nullable total_cost field on orders for starters... My solution thus far is to have an order_totals table with a 1:1 relationship to the orders table. But I think it's redundant. Ideally, since everything required to calculate total costs (lines on an order) is in the database, I would work out the value every time I need it, but this is very expensive. What are your thoughts?

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  • Recent improvements in Console Performance

    - by loren.konkus
    Recently, the WebLogic Server development and support organizations have worked with a number of customers to quantify and improve the performance of the Administration Console in large, distributed configurations where there is significant latency in the communications between the administration server and managed servers. These improvements fall into two categories: Constraining the amount of time that the Console stalls waiting for communication Reducing and streamlining the amount of data required for an update A few releases ago, we added support for a configurable domain-wide mbean "Invocation Timeout" value on the Console's configuration: general, advanced section for a domain. The default value for this setting is 0, which means wait indefinitely and was chosen for compatibility with the behavior of previous releases. This configuration setting applies to all mbean communications between the admin server and managed servers, and is the first line of defense against being blocked by a stalled or completely overloaded managed server. Each site should choose an appropriate timeout value for their environment and network latency. In the next release of WebLogic Server, we've added an additional console preference, "Management Operation Timeout", to the Console's shared preference page. This setting further constrains how long certain console pages will wait for slowly responding servers before returning partial results. While not all Console pages support this yet, key pages such as the Servers Configuration and Control table pages and the Deployments Control pages have been updated to support this. For example, if a user requests a Servers Table page and a Management Operation Timeout occurs, the table is displayed with both local configuration and remote runtime information from the responding managed servers and only local configuration information for servers that did not yet respond. This means that a troublesome managed server does not impede your ability to manage your domain using the Console. To support these changes, these Console pages have been re-written to use the Work Management feature of WebLogic Server to interact with each server or deployment concurrently, which further improves the responsiveness of these pages. The basic algorithm for these pages is: For each configuration mbean (ie, Servers) populate rows with configuration attributes from the fast, local mbean server Find a WorkManager For each server, Create a Work instance to obtain runtime mbean attributes for the server Schedule Work instance in the WorkManager Call WorkManager.waitForAll to wait WorkItems to finish, constrained by Management Operation Timeout For each WorkItem, if the runtime information obtained was not complete, add a message indicating which server has incomplete data Display collected data in table In addition to these changes to constrain how long the console waits for communication, a number of other changes have been made to reduce the amount and scope of managed server interactions for key pages. For example, in previous releases the Deployments Control table looked at the status of a deployment on every managed server, even those servers that the deployment was not currently targeted on. (This was done to handle an edge case where a deployment's target configuration was changed while it remained running on previously targeted servers.) We decided supporting that edge case did not warrant the performance impact for all, and instead only look at the status of a deployment on the servers it is targeted to. Comprehensive status continues to be available if a user clicks on the 'status' field for a deployment. Finally, changes have been made to the System Status portlet to reduce its impact on Console page display times. Obtaining health information for this display requires several mbean interactions with managed servers. In previous releases, this mbean interaction occurred with every display, and any delay or impediment in these interactions was reflected in the display time for every page. To reduce this impact, we've made several changes in this portlet: Using Work Management to obtain health concurrently Applying the operation timeout configuration to constrain how long we will wait Caching health information to reduce the cost during rapid navigation from page to page and only obtaining new health information if the previous information is over 30 seconds old. Eliminating heath collection if this portlet is minimized. Together, these Console changes have resulted in significant performance improvements for the customers with large configurations and high latency that we have worked with during their development, and some lesser performance improvements for those with small configurations and very fast networks. These changes will be included in the 11g Rel 1 patch set 2 (10.3.3.0) release of WebLogic Server.

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  • How do I find the cause for a huge difference in performance between two identical Ubuntu servers?

    - by the.duckman
    I am running two Dell R410 servers in the same rack of a data center. Both have the same hardware configuration, run Ubuntu 10.4, have the same packages installed and run the same Java web servers. No other load. One of them is 20-30% faster than the other, very consistently. I used dstat to figure out, if there are more context switches, IO, swapping or anything, but I see no reason for the difference. With the same workload, (no swapping, virtually no IO), the cpu usage and load is higher on one server. So the difference appears to be mainly CPU bound, but while a simple cpu benchmark using sysbench (with all other load turned off) did yield a difference, it was only 6%. So maybe it is not only CPU but also memory performance. I tried to figure out if the BIOS settings differ in some parameter, did a dump using dmidecode, but that yielded no difference. I compared /proc/cpuinfo, no difference. I compared the output of cpufreq-info, no difference. I am lost. What can I do, to figure out, what is going on?

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  • mysql - moving to a lower performance server, how small can I go?

    - by pedalpete
    I've been running a site for a few years now which really isn't growing in traffic, and I want to save some money on hosting, but keep it going for the loyal users of the site and api. The database has one a nearly 4 million row table, and on a 4gb dual xeon 5320 server. When I check server stats on this server with ps -aux, i get returns of mysql running at about 11% capacity, so no serious load. The main query against mysql runs in about 0.45 seconds. I popped over to linode.com to see what kind of performance I could get out of one of their tiny boxes, and their 360mb ram XEN vps returns the same query in 20 seconds. Clearly not good enough. I've looked at the mysql variables, and they are both very similar (I've included the show variables output below, if anybody is interested). Is there a good way to decide on what size server is needed based on what I'm coming from? Is it RAM that is likely making the difference with the large table size? Is there a way for me to figure out how much ram would be ideal?? Here's the output of the show variables (though I'm not sure it is important). +---------------------------------+------------------------------------------------------------+ | Variable_name | Value | +---------------------------------+------------------------------------------------------------+ | auto_increment_increment | 1 | | auto_increment_offset | 1 | | automatic_sp_privileges | ON | | back_log | 50 | | basedir | /usr/ | | bdb_cache_size | 8384512 | | bdb_home | /var/lib/mysql/ | | bdb_log_buffer_size | 262144 | | bdb_logdir | | | bdb_max_lock | 10000 | | bdb_shared_data | OFF | | bdb_tmpdir | /tmp/ | | binlog_cache_size | 32768 | | bulk_insert_buffer_size | 8388608 | | character_set_client | latin1 | | character_set_connection | latin1 | | character_set_database | latin1 | | character_set_filesystem | binary | | character_set_results | latin1 | | character_set_server | latin1 | | character_set_system | utf8 | | character_sets_dir | /usr/share/mysql/charsets/ | | collation_connection | latin1_swedish_ci | | collation_database | latin1_swedish_ci | | collation_server | latin1_swedish_ci | | completion_type | 0 | | concurrent_insert | 1 | | connect_timeout | 10 | | datadir | /var/lib/mysql/ | | date_format | %Y-%m-%d | | datetime_format | %Y-%m-%d %H:%i:%s | | default_week_format | 0 | | delay_key_write | ON | | delayed_insert_limit | 100 | | delayed_insert_timeout | 300 | | delayed_queue_size | 1000 | | div_precision_increment | 4 | | keep_files_on_create | OFF | | engine_condition_pushdown | OFF | | expire_logs_days | 0 | | flush | OFF | | flush_time | 0 | | ft_boolean_syntax | + - For some reason, that table formats properly in the preview, but apparently not when viewing the question. Hopefully it isn't needed anyway.

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  • Windows 7 host with Ubuntu Guest and a performance hit, memory locks?

    - by Cyrylski
    I have a brand new Lenovo T510 with Core i5 and 4GB of RAM with Windows 7 on it. I Installed Ubuntu 10.10 in a Virtualbox. For some reason system gets really slow on this setup which makes me really angry. There's a video card shared with full 3D support enabled and 1GB of RAM allocated for the Ubuntu machine. It may sound stupid, but WHY is the whole memory consumed in an instant when I run Virtualbox? I struggled for like 10 minutes restraining myself from a brutal reset, and now everything runs smooth but memory "in use" in Resource Monitor is 3GB flat with only Chrome running. I'm new to Windows 7, but I'm really disappointed with performance at this point... I used to work in a different environment with much slower hardware and there was no such problem (WinXP over Ubuntu, 1GB out of 2GB allocated for WinXP guest on intel GMA). This is, until I clogged RAM totally there. But I was capable of running Chrome, Firefox and Apache server on a 1GB RAM in Ubuntu there and Photoshop CS4 on Windows XP and it worked. In this case I can't go beyond setting up Ubuntu properly. I bet I'm doing something wrong.

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  • Save object states in .data or attr - Performance vs CSS?

    - by Neysor
    In response to my answer yesterday about rotating an Image, Jamund told me to use .data() instead of .attr() First I thought that he is right, but then I thought about a bigger context... Is it always better to use .data() instead of .attr()? I looked in some other posts like what-is-better-data-or-attr or jquery-data-vs-attrdata The answers were not satisfactory for me... So I moved on and edited the example by adding CSS. I thought it might be useful to make a different Style on each image if it rotates. My style was the following: .rp[data-rotate="0"] { border:10px solid #FF0000; } .rp[data-rotate="90"] { border:10px solid #00FF00; } .rp[data-rotate="180"] { border:10px solid #0000FF; } .rp[data-rotate="270"] { border:10px solid #00FF00; } Because design and coding are often separated, it could be a nice feature to handle this in CSS instead of adding this functionality into JavaScript. Also in my case the data-rotate is like a special state which the image currently has. So in my opinion it make sense to represent it within the DOM. I also thought this could be a case where it is much better to save with .attr() then with .data(). Never mentioned before in one of the posts I read. But then i thought about performance. Which function is faster? I built my own test following: <!DOCTYPE HTML> <html> <head> <title>test</title> <script type="text/javascript" src="http://ajax.googleapis.com/ajax/libs/jquery/1.7.1/jquery.min.js"></script> <script type="text/javascript"> function runfirst(dobj,dname){ console.log("runfirst "+dname); console.time(dname+"-attr"); for(i=0;i<10000;i++){ dobj.attr("data-test","a"+i); } console.timeEnd(dname+"-attr"); console.time(dname+"-data"); for(i=0;i<10000;i++){ dobj.data("data-test","a"+i); } console.timeEnd(dname+"-data"); } function runlast(dobj,dname){ console.log("runlast "+dname); console.time(dname+"-data"); for(i=0;i<10000;i++){ dobj.data("data-test","a"+i); } console.timeEnd(dname+"-data"); console.time(dname+"-attr"); for(i=0;i<10000;i++){ dobj.attr("data-test","a"+i); } console.timeEnd(dname+"-attr"); } $().ready(function() { runfirst($("#rp4"),"#rp4"); runfirst($("#rp3"),"#rp3"); runlast($("#rp2"),"#rp2"); runlast($("#rp1"),"#rp1"); }); </script> </head> <body> <div id="rp1">Testdiv 1</div> <div id="rp2" data-test="1">Testdiv 2</div> <div id="rp3">Testdiv 3</div> <div id="rp4" data-test="1">Testdiv 4</div> </body> </html> It should also show if there is a difference with a predefined data-test or not. One result was this: runfirst #rp4 #rp4-attr: 515ms #rp4-data: 268ms runfirst #rp3 #rp3-attr: 505ms #rp3-data: 264ms runlast #rp2 #rp2-data: 260ms #rp2-attr: 521ms runlast #rp1 #rp1-data: 284ms #rp1-attr: 525ms So the .attr() function did always need more time than the .data() function. This is an argument for .data() I thought. Because performance is always an argument! Then I wanted to post my results here with some questions, and in the act of writing I compared with the questions Stack Overflow showed me (similar titles) And true enough, there was one interesting post about performance I read it and run their example. And now I am confused! This test showed that .data() is slower then .attr() !?!! Why is that so? First I thought it is because of a different jQuery library so I edited it and saved the new one. But the result wasn't changing... So now my questions to you: Why are there some differences in the performance in these two examples? Would you prefer to use data- HTML5 attributes instead of data, if it represents a state? Although it wouldn't be needed at the time of coding? Why - Why not? Now depending on the performance: Would performance be an argument for you using .attr() instead of data, if it shows that .attr() is better? Although data is meant to be used for .data()? UPDATE 1: I did see that without overhead .data() is much faster. Misinterpreted the data :) But I'm more interested in my second question. :) Would you prefer to use data- HTML5 attributes instead of data, if it represents a state? Although it wouldn't be needed at the time of coding? Why - Why not? Are there some other reasons you can think of, to use .attr() and not .data()? e.g. interoperability? because .data() is jquery style and HTML Attributes can be read by all... UPDATE 2: As we see from T.J Crowder's speed test in his answer attr is much faster then data! which is again confusing me :) But please! Performance is an argument, but not the highest! So give answers to my other questions please too!

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  • SQL SERVER – Change Database Access to Single User Mode Using SSMS

    - by pinaldave
    I have previously written about how using T-SQL Script we can convert the database access to single user mode before backup. I was recently asked if the same can be done using SQL Server Management Studio. Yes! You can do it from database property (Write click on database and select database property) and follow image. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology

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  • ADNOC talks about 50x increase in performance

    - by KLaker
    If you are still wondering about how Exadata can revolutionise your business then I would recommend watching this great video which was recorded at this year's OpenWorld. First a little background...The Abu Dhabi National Oil Company for Distribution (ADNOC) is an integrated energy company that was founded in 1973. ADNOC Distribution markets and distributes petroleum products and services within the United Arab Emirates and internationally. As one of the largest and most innovative government-owned petroleum companies in the Arab Gulf, ADNOC Distribution is renowned and respected for the exceptional quality and reliability of its products and services. Its five corporate divisions include more than 200 filling stations (a number that is growing at 8% annually), more than 150 convenience stores, 10 vehicle inspection stations, as well as wholesale and retail sales of bulk fuel, gas, oil, diesel, and lubricants. ADNOC selected Oracle Exadata Database Machine after extensive research because it provided them with a single platform that can run mixed workloads in a single unified machine: "We chose Oracle Exadata Database Machine because it.offered a fully integrated and highly engineered system that was ready to deploy. With our infrastructure running all the same technology, we can operate any type of Oracle Database without restrictions and be prepared for business growth," said Ali Abdul Aziz Al-Ali, IT division manager, ADNOC Distribution. ".....we could consolidate our transaction processing and business intelligence onto one platform. Competing solutions are just not capable of doing that." - Awad Ahmed Ali El-Sidiq, Senior Database Administrator, ADNOC Distribution In this new video Awad Ahmen Ali El Sidddig, Senior DBA at ADNOC, talks about the impact that Exadata has had on his team and the whole business. ADNOC is using our engineered systems to drive and manage all their workloads: from transaction systems to payments system to data warehouse to BI environment. A true Disk-to-Dashboard revolution using Engineered Systems. This engineered approach is delivering 50x improvement in performance with one queries running 100x faster! The IT has even revolutionised some of their data warehouse related processes with the help of Exadata and now jobs that were taking over 4 hours now run in a few minutes.  To watch the video click on the image below which will take you to our Oracle YouTube page: (if the above link does not work, click here: http://www.youtube.com/watch?v=zcRpxc6u5Ic) Now that queries are running 100x faster and jobs are completing in minutes not hours, what is next for the IT team at ADNOC? Like many of our customers ADNOC is now looking to take advantage of big data to help them better align their business operations with customer behaviour and customer insights. To help deliver this next level of insight the IT team is looking at the new features in Oracle Database 12c such as the new in-memory feature to deliver even more performance gains.  The great news is that Awad Ahmen Ali El Sidddig was awarded DBA of the Year - EMEA within our Data Warehouse Global Leaders programme and you can see the badge for this award pop-up at the start of video. Well done to everyone at ADNOC and thanks for spending the time with us at OOW to create this great video.

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