Search Results

Search found 13341 results on 534 pages for 'obiee performance tuning'.

Page 137/534 | < Previous Page | 133 134 135 136 137 138 139 140 141 142 143 144 | Next Page >

Will PHP Die In Web Page Development World?

- by Morgan Cheng

I know that PHP is still the most popular web programming language in the world. This question just want to bring some of my concerns about PHP. PHP is naturally bound to C10K problem. Since PHP (generally run in Apache) cannot be event-driven or asynchronous, each HTTP request will occupy at least one thread or process. This makes it resistant to be more scalable. Currently, a lot of web sites (like Facebook) with high performance and scalability still depends on PHP in their front end servers. I suppose it is due to legacy reason. Is it possible that PHP will be replaced by language more suitable for C10K?

Read the article
Is str.replace(..).replace(..) ad nauseam a standard idiom in Python?

- by meeselet

For instance, say I wanted a function to escape a string for use in HTML (as in Django's escape filter): def escape(string): """ Returns the given string with ampersands, quotes and angle brackets encoded. """ return string.replace('&', '&').replace('<', '<').replace('>', '>').replace("'", ''').replace('"', '"') This works, but it gets ugly quickly and appears to have poor algorithmic performance (in this example, the string is repeatedly traversed 5 times). What would be better is something like this: def escape(string): """ Returns the given string with ampersands, quotes and angle brackets encoded. """ # Note that ampersands must be escaped first; the rest can be escaped in # any order. return replace_multi(string.replace('&', '&'), {'<': '<', '>': '>', "'": ''', '"': '"'}) Does such a function exist, or is the standard Python idiom to use what I wrote before?

Read the article
Python faster way to read fixed length fields form a file into dictionary

- by Martlark

I have a file of names and addresses as follows (example line) OSCAR ,CANNONS ,8 ,STIEGLITZ CIRCUIT And I want to read it into a dictionary of name and value. Here self.field_list is a list of the name, length and start point of the fixed fields in the file. What ways are there to speed up this method? (python 2.6) def line_to_dictionary(self, file_line,rec_num): file_line = file_line.lower() # Make it all lowercase return_rec = {} # Return record as a dictionary for (field_start, field_length, field_name) in self.field_list: field_data = file_line[field_start:field_start+field_length] if (self.strip_fields == True): # Strip off white spaces first field_data = field_data.strip() if (field_data != ''): # Only add non-empty fields to dictionary return_rec[field_name] = field_data # Set hidden fields # return_rec['_rec_num_'] = rec_num return_rec['_dataset_name_'] = self.name return return_rec

Read the article
Reading from a file not line-by-line

- by MadH

Assigning a QTextStream to a QFile and reading it line-by-line is easy and works fine, but I wonder if the performance can be inreased by first storing the file in memory and then processing it line-by-line. Using FileMon from sysinternals, I've encountered that the file is read in chunks of 16KB and since the files I've to process are not that big (~2MB, but many!), loading them into memory would be a nice thing to try. Any ideas how can I do so? QFile is inhereted from QIODevice, which allows me to ReadAll() it into QByteArray, but how to proceed then and divide it into lines?

Read the article
pipelined function

- by user289429

Can someone provide an example of how to use parallel table function in oracle pl/sql. We need to run massive queries for 15 years and combine the result. SELECT * FROM Table(TableFunction(cursor(SELECT * FROM year_table))) ...is what we want effectively. The innermost select will give all the years, and the table function will take each year and run massive query and returns a collection. The problem we have is that all years are being fed to one table function itself, we would rather prefer the table function being called in parallel for each of the year. We tried all sort of partitioning by hash and range and it didn't help. Also, can we drop the keyword PIPELINED from the function declaration? because we are not performing any transformation and just need the aggregate of the resultset.

Read the article
Should I use integer primary IDs?

- by arthurprs

For example, I always generate an auto-increment field for the users table, but I also specify a UNIQUE index on their usernames. There are situations that I first need to get the userId for a given username and then execute the desired query, or use a JOIN in the desired query. It's 2 trips to the database or a JOIN vs. a varchar index. Should I use integer primary IDs? Is there a real performance benefit on INT over small VARCHAR indexes?

Read the article
perfomance checking of system by using FreshDiagnose tool

- by peter

I installed a FreshDiagnose software in my machine.I can see all information about my machine.But i need to check system perfomance and to show in a graph view.Means i need to show how an application running in my machine means how fast it is like that

Read the article
Under what circumstances does Groovy use AbstractConcurrentMap?

- by Electrons_Ahoy

(Specifically, org.codehaus.groovy.util.AbstractConcurrentMap) While doing some profiling of our application thats mixed Java/Groovy, I'm seeing a lot of references to the AbstractConcurrentMap class, none of which are explicit in the code base. Does groovy use this class when maps are instantiated in the groovy dynamic def myMap = [:] style? Are there rules somewhere about when groovy chooses to use this as opposed to, say, java.util.HashMap? And does anyone have any performance information comparing the two? My rough "eyeball check" says that AbstractConcurrentMap seems to be much slower - anyone know if I'm right?

Read the article
C++ Asymptotic Profiling

- by Travis

I have a performance issue where I suspect one standard C library function is taking too long and causing my entire system (suite of processes) to basically "hiccup". Sure enough if I comment out the library function call, the hiccup goes away. This prompted me to investigate what standard methods there are to prove this type of thing? What would be the best practice for testing a function to see if it causes an entire system to hang for a sec (causing other processes to be momentarily starved)? I would at least like to definitively correlate the function being called and the visible freeze. Thanks

Read the article
Simple Self Join Query Bad Performance

- by user1514042

Could anyone advice on how do I improve the performance of the following query. Note, the problem seems to be caused by where clause. Data (table contains a huge set of rows - 500K+, the set of parameters it's called with assums the return of 2-5K records per query, which takes 8-10 minutes currently): USE [SomeDb] GO SET ANSI_NULLS ON GO SET QUOTED_IDENTIFIER ON GO CREATE TABLE [dbo].[Data]( [x] [money] NOT NULL, [y] [money] NOT NULL, CONSTRAINT [PK_Data] PRIMARY KEY CLUSTERED ( [x] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO The Query select top 10000 s.x as sx, e.x as ex, s.y as sy, e.y as ey, e.y - s.y as y_delta, e.x - s.x as x_delta from Data s inner join Data e on e.x > s.x and e.x - s.x between xFrom and xTo --where e.y - s.y > @yDelta -- when uncommented causes a huge delay Update 1 - Execution Plan <?xml version="1.0" encoding="utf-16"?> <ShowPlanXML xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" Version="1.2" Build="11.0.2100.60" xmlns="http://schemas.microsoft.com/sqlserver/2004/07/showplan"> <BatchSequence> <Batch> <Statements> <StmtSimple StatementCompId="1" StatementEstRows="100" StatementId="1" StatementOptmLevel="FULL" StatementOptmEarlyAbortReason="GoodEnoughPlanFound" StatementSubTreeCost="0.0263655" StatementText="select top 100
s.x as sx,
e.x as ex,
s.y as sy,
e.y as ey,
e.y - s.y as y_delta,
e.x - s.x as x_delta
from Data s 
 inner join Data e
 on e.x > s.x and e.x - s.x between 100 and 105
where e.y - s.y > 0.01
" StatementType="SELECT" QueryHash="0xAAAC02AC2D78CB56" QueryPlanHash="0x747994153CB2D637" RetrievedFromCache="true"> <StatementSetOptions ANSI_NULLS="true" ANSI_PADDING="true" ANSI_WARNINGS="true" ARITHABORT="true" CONCAT_NULL_YIELDS_NULL="true" NUMERIC_ROUNDABORT="false" QUOTED_IDENTIFIER="true" /> <QueryPlan DegreeOfParallelism="0" NonParallelPlanReason="NoParallelPlansInDesktopOrExpressEdition" CachedPlanSize="24" CompileTime="13" CompileCPU="13" CompileMemory="424"> <MemoryGrantInfo SerialRequiredMemory="0" SerialDesiredMemory="0" /> <OptimizerHardwareDependentProperties EstimatedAvailableMemoryGrant="52199" EstimatedPagesCached="14561" EstimatedAvailableDegreeOfParallelism="4" /> <RelOp AvgRowSize="55" EstimateCPU="1E-05" EstimateIO="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row" EstimateRows="100" LogicalOp="Compute Scalar" NodeId="0" Parallel="false" PhysicalOp="Compute Scalar" EstimatedTotalSubtreeCost="0.0263655"> <OutputList> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> <ColumnReference Column="Expr1004" /> <ColumnReference Column="Expr1005" /> </OutputList> <ComputeScalar> <DefinedValues> <DefinedValue> <ColumnReference Column="Expr1004" /> <ScalarOperator ScalarString="[SomeDb].[dbo].[Data].[y] as [e].[y]-[SomeDb].[dbo].[Data].[y] as [s].[y]"> <Arithmetic Operation="SUB"> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </Identifier> </ScalarOperator> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> </Identifier> </ScalarOperator> </Arithmetic> </ScalarOperator> </DefinedValue> <DefinedValue> <ColumnReference Column="Expr1005" /> <ScalarOperator ScalarString="[SomeDb].[dbo].[Data].[x] as [e].[x]-[SomeDb].[dbo].[Data].[x] as [s].[x]"> <Arithmetic Operation="SUB"> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> </Identifier> </ScalarOperator> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> </Identifier> </ScalarOperator> </Arithmetic> </ScalarOperator> </DefinedValue> </DefinedValues> <RelOp AvgRowSize="39" EstimateCPU="1E-05" EstimateIO="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row" EstimateRows="100" LogicalOp="Top" NodeId="1" Parallel="false" PhysicalOp="Top" EstimatedTotalSubtreeCost="0.0263555"> <OutputList> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </OutputList> <RunTimeInformation> <RunTimeCountersPerThread Thread="0" ActualRows="100" ActualEndOfScans="1" ActualExecutions="1" /> </RunTimeInformation> <Top RowCount="false" IsPercent="false" WithTies="false"> <TopExpression> <ScalarOperator ScalarString="(100)"> <Const ConstValue="(100)" /> </ScalarOperator> </TopExpression> <RelOp AvgRowSize="39" EstimateCPU="151828" EstimateIO="0" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row" EstimateRows="100" LogicalOp="Inner Join" NodeId="2" Parallel="false" PhysicalOp="Nested Loops" EstimatedTotalSubtreeCost="0.0263455"> <OutputList> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </OutputList> <RunTimeInformation> <RunTimeCountersPerThread Thread="0" ActualRows="100" ActualEndOfScans="0" ActualExecutions="1" /> </RunTimeInformation> <NestedLoops Optimized="false"> <OuterReferences> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </OuterReferences> <RelOp AvgRowSize="23" EstimateCPU="1.80448" EstimateIO="3.76461" EstimateRebinds="0" EstimateRewinds="0" EstimatedExecutionMode="Row" EstimateRows="1" LogicalOp="Clustered Index Scan" NodeId="3" Parallel="false" PhysicalOp="Clustered Index Scan" EstimatedTotalSubtreeCost="0.0032831" TableCardinality="1640290"> <OutputList> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </OutputList> <RunTimeInformation> <RunTimeCountersPerThread Thread="0" ActualRows="15225" ActualEndOfScans="0" ActualExecutions="1" /> </RunTimeInformation> <IndexScan Ordered="false" ForcedIndex="false" ForceScan="false" NoExpandHint="false"> <DefinedValues> <DefinedValue> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> </DefinedValue> <DefinedValue> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </DefinedValue> </DefinedValues> <Object Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Index="[PK_Data]" Alias="[e]" IndexKind="Clustered" /> </IndexScan> </RelOp> <RelOp AvgRowSize="23" EstimateCPU="0.902317" EstimateIO="1.88387" EstimateRebinds="1" EstimateRewinds="0" EstimatedExecutionMode="Row" EstimateRows="100" LogicalOp="Clustered Index Seek" NodeId="4" Parallel="false" PhysicalOp="Clustered Index Seek" EstimatedTotalSubtreeCost="0.0263655" TableCardinality="1640290"> <OutputList> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> </OutputList> <RunTimeInformation> <RunTimeCountersPerThread Thread="0" ActualRows="100" ActualEndOfScans="15224" ActualExecutions="15225" /> </RunTimeInformation> <IndexScan Ordered="true" ScanDirection="FORWARD" ForcedIndex="false" ForceSeek="false" ForceScan="false" NoExpandHint="false" Storage="RowStore"> <DefinedValues> <DefinedValue> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> </DefinedValue> <DefinedValue> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> </DefinedValue> </DefinedValues> <Object Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Index="[PK_Data]" Alias="[s]" IndexKind="Clustered" /> <SeekPredicates> <SeekPredicateNew> <SeekKeys> <EndRange ScanType="LT"> <RangeColumns> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> </RangeColumns> <RangeExpressions> <ScalarOperator ScalarString="[SomeDb].[dbo].[Data].[x] as [e].[x]"> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> </Identifier> </ScalarOperator> </RangeExpressions> </EndRange> </SeekKeys> </SeekPredicateNew> </SeekPredicates> <Predicate> <ScalarOperator ScalarString="([SomeDb].[dbo].[Data].[x] as [e].[x]-[SomeDb].[dbo].[Data].[x] as [s].[x])>=($100.0000) AND ([SomeDb].[dbo].[Data].[x] as [e].[x]-[SomeDb].[dbo].[Data].[x] as [s].[x])<=($105.0000) AND ([SomeDb].[dbo].[Data].[y] as [e].[y]-[SomeDb].[dbo].[Data].[y] as [s].[y])>(0.01)"> <Logical Operation="AND"> <ScalarOperator> <Compare CompareOp="GE"> <ScalarOperator> <Arithmetic Operation="SUB"> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> </Identifier> </ScalarOperator> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> </Identifier> </ScalarOperator> </Arithmetic> </ScalarOperator> <ScalarOperator> <Const ConstValue="($100.0000)" /> </ScalarOperator> </Compare> </ScalarOperator> <ScalarOperator> <Compare CompareOp="LE"> <ScalarOperator> <Arithmetic Operation="SUB"> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="x" /> </Identifier> </ScalarOperator> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="x" /> </Identifier> </ScalarOperator> </Arithmetic> </ScalarOperator> <ScalarOperator> <Const ConstValue="($105.0000)" /> </ScalarOperator> </Compare> </ScalarOperator> <ScalarOperator> <Compare CompareOp="GT"> <ScalarOperator> <Arithmetic Operation="SUB"> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[e]" Column="y" /> </Identifier> </ScalarOperator> <ScalarOperator> <Identifier> <ColumnReference Database="[SomeDb]" Schema="[dbo]" Table="[Data]" Alias="[s]" Column="y" /> </Identifier> </ScalarOperator> </Arithmetic> </ScalarOperator> <ScalarOperator> <Const ConstValue="(0.01)" /> </ScalarOperator> </Compare> </ScalarOperator> </Logical> </ScalarOperator> </Predicate> </IndexScan> </RelOp> </NestedLoops> </RelOp> </Top> </RelOp> </ComputeScalar> </RelOp> </QueryPlan> </StmtSimple> </Statements> </Batch> </BatchSequence> </ShowPlanXML>

Read the article
Speed up math code in C# by writing a C dll?

- by Projectile Fish

I have a very large nested for loop in which some multiplications and additions are performed on floating point numbers. for (int i = 0; i < length1; i++) { s = GetS(i); c = GetC(i); for(int j = 0; j < length2; j++) { double oldU = u[j]; u[j] = c * oldU + s * omega[i][j]; omega[i][j] = c * omega[i][j] - s * oldU; } } This loop is taking up the majority of my processing time and is a bottleneck. Would I be likely to see any speed improvements if I rewrite this loop in C and interface to it from C#?

Read the article
StringBuilder/StringBuffer vs. "+" Operator

- by matt.seil

I'm reading "Better, Faster, Lighter Java" (by Bruce Tate and Justin Gehtland) and am familiar with the readability requirements in agile type teams, such as what Robert Martin discusses in his clean coding books. On the team I'm on now, I've been told explicitly not to use the "+" operator because it creates extra (and unnecessary) string objects during runtime. But this article: http://www.ibm.com/developerworks/java/library/j-jtp01274.html Written back in '04 talks about how object allocation is about 10 machine instructions. (essentially free) It also talks about how the GC also helps to reduce costs in this environment. What is the actual performance tradeoffs between using "+," "StringBuilder," or "StringBuffer?" (In my case it is StringBuffer only as we are limited to Java 1.4.2.) StringBuffer to me results in ugly, less readable code, as a couple of examples in Tate's book demonstrates. And StringBuffer is thread-synchronized which seems to have its own costs that outweigh the "danger" in using the "+" operator. Thoughts/Opinions?

Read the article
Pros and cons of sorting data in DB?

- by Roman

Let's assume I have a table with field of type VARCHAR. And I need to get data from that table sorted alphabetically by that field. What is the best way (for performance): add sort by field to the SQL-query or sort the data when it's already fetched? I'm using Java (with Hibernate), but I can't tell anything about DB engine. It could be any popular relational database (like MySQL or MS Sql Server or Oracle or HSQL DB or any other). The amount of records in table can vary greatly but let's assume there are 5k records.

Read the article
Managing StringBuilder Resources in C#

- by Jim Fell

Hello. My C# (.NET 2.0) application has a StringBuilder variable with a capacity of 2.5MB. Obviously, I do not want to copy such a large buffer to a larger buffer space every time it fills. By that point, there is so much data in the buffer anyways, removing the older data is a viable option. Can anyone see any obvious problems with how I'm doing this (i.e. am I introducing more performance problems than I'm solving), or does it look okay? tText_c = new StringBuilder(2500000, 2500000); private void AppendToText(string text) { if (tText_c.Length * 100 / tText_c.Capacity > 95) { tText_c.Remove(0, tText_c.Length / 2); } tText_c.Append(text); } Thanks.

Read the article
Is this a valid benefit of using embedded SQL over stored procedures?

- by George

Here's an argument for SPs that I haven't heard. Flamers, be gentle with the down tick, Since there is overhead associated with each trip to the database server, I would suggest that a POSSIBLE reason for placing your SQL in SPs over embedded code is that you are more insulated to change without taking a performance hit. For example. Let's say you need to perform Query A that returns a scalar integer. Then, later, the requirements change and you decide that it the results of the scalar is x that then, and only then, you need to perform another query. If you performed the first query in a SP, you could easily check the result of the first query and conditionally execute the 2nd SQL in the same SP. How would you do this efficiently in embedded SQL w/o perform a separate query or an unnecessary query? Here's an example: --This SP may return 1 or two queries. SELECT @CustCount = COUNT(*) FROM CUSTOMER IF @CustCount 10 SELECT * FROM PRODUCT Can this/what is the best way to do this in embedded SQL?

Read the article
Speed-up of readonly MyISAM table

- by Ozzy

We have a large MyISAM table that is used to archive old data. This archiving is performed every month, and except from these occasions data is never written to the table. Is there anyway to "tell" MySQL that this table is read-only, so that MySQL might optimize the performance of reads from this table? I've looked at the MEMORY storage engine, but the problem is that this table is so large that it would take a large portion of the servers memory, which I don't want. Hope my question is clear enough, I'm a novice when it comes to db administration so any input or suggestions are welcome.

Read the article
How to profile object creation in Java?

- by gooli

The system I work with is creating a whole lot of objects and garbage collecting them all the time which results in a very steeply jagged graph of heap consumption. I would like to know which objects are being generated to tune the code, but I can't figure out a way to dump the heap at the moment the garbage collection starts. When I tried to initiate dumpHeap via JConsole manually at random times, I always got results after GC finished its run, and didn't get any useful data. Any notes on how to track down excessive temporary object creation are welcome.

Read the article
When calling CRUD check if "parent" exists with read or join?

- by Trick

All my entities can not be deleted - only deactivated, so they don't appear in any read methods (SELECT ... WHERE active=TRUE). Now I have some 1:M tables on this entities on which all CRUD operations can be executed. What is more efficient or has better performance? My first solution: To add to all CRUD operations: UPDATE ... JOIN entity e ... WHERE e.active=TRUE My second solution: Before all CRUD operations check if entity is active: if (getEntity(someId) != null) { //do some CRUD } In getEntity there's just SELECT * FROM entity WHERE id=? AND active=TRUE. Or any other solution, recommendation,...?

Read the article
SQL query: how to translate IN() into a JOIN?

- by tangens

I have a lot of SQL queries like this: SELECT o.Id, o.attrib1, o.attrib2 FROM table1 o WHERE o.Id IN ( SELECT DISTINCT Id FROM table1, table2, table3 WHERE ... ) These queries have to run on different database engines (MySql, Oracle, DB2, MS-Sql, Hypersonic), so I can only use common SQL syntax. Here I read, that with MySql the IN statement isn't optimized and it's really slow, so I want to switch this into a JOIN. I tried: SELECT o.Id, o.attrib1, o.attrib2 FROM table1 o, table2, table3 WHERE ... But this does not take into account the DISTINCT keyword. Question: How do I get rid of the duplicate rows using the JOIN approach?

Read the article
MongoDB efficient dealing with embedded documents

- by Sebastian Nowak

I have serious trouble finding anything useful in Mongo documentation about dealing with embedded documents. Let's say I have a following schema: { _id: ObjectId, ... data: [ { _childId: ObjectId // let's use custom name so we can distinguish them ... } ] } What's the most efficient way to remove everything inside data for particular _id? What's the most efficient way to remove embedded document with particular _childId inside given _id? What's the performance here, can _childId be indexed in order to achieve logarithmic (or similar) complexity instead of linear lookup? If so, how? What's the most efficient way to insert a lot of (let's say a 1000) documents into data for given _id? And like above, can we get O(n log n) or similar complexity with proper indexing? What's the most efficient way to get the count of documents inside data for given _id?

Read the article
DataReader or DataSet when pulling multiple recordsets in ASP.NET

- by Gern Blandston

I've got an ASP.NET page that has a bunch of controls that need to be populated (e.g. dropdown lists). I'd like to make a single trip to the db and bring back multiple recordsets instead of making a round-trip for each control. I could bring back multiple tables in a DataSet, or I could bring back a DataReader and use '.NextResult' to put each result set into a custom business class. Will I likely see a big enough performance advantage using the DataReader approach, or should I just use the DataSet approach? Any examples of how you usually handle this would be appreciated.

Read the article
How to keep Hibernate mapping use under control as requirements grow

- by David Plumpton

I've worked on a number of Java web apps where persistence is via Hibernate, and we start off with some central class (e.g. an insurance application) without any time being spent considering how to break things up into manageable chunks. Over time as features are added we add more mappings (rates, clients, addresses, etc.) and then amount of time spent saving and loading an insurance object and everything it connects to grows. In particular you get close to a go-live date and performance testing with larger amounts of data in each table is starting to demonstrate that it's all too slow. Obviously there are a number of ways that we could attempt to partition things up, e.g. map only the client classes for the client CRUD screens, etc., which would have been better to get in place earlier rather than trying to work it in at the end of the dev cycle. I'm just wondering if there are recommendations about ways to handle/mitigate this.

Read the article
Scalable way of doing self join with many to many table

- by johnathan

I have a table structure like the following: user id name profile_stat id name profile_stat_value id name user_profile user_id profile_stat_id profile_stat_value_id My question is: How do I evaluate a query where I want to find all users with profile_stat_id and profile_stat_value_id for many stats? I've tried doing an inner self join, but that quickly gets crazy when searching for many stats. I've also tried doing a count on the actual user_profile table, and that's much better, but still slow. Is there some magic I'm missing? I have about 10 million rows in the user_profile table and want the query to take no longer than a few seconds. Is that possible?

Read the article
In .NET which loop runs faster for or foreach

- by Binoj Antony

In c#/VB.NET/.NET which loop runs faster for or foreach? Ever since I read that for loop works faster than foreach a long time ago I assumed it stood true for all collections, generic collection all arrays etc. I scoured google and found few articles but most of them are inconclusive (read comments on the articles) and open ended. What would be ideal is to have each scenarios listed and the best solution for the same e.g: (just example of how it should be) for iterating an array of 1000+ strings - for is better than foreach for iterating over IList (non generic) strings - foreach is better than for Few references found on the web for the same: Original grand old article by Emmanuel Schanzer CodeProject FOREACH Vs. FOR Blog - To foreach or not to foreach that is the question asp.net forum - NET 1.1 C# for vs foreach [Edit] Apart from the readability aspect of it I am really interested in facts and figures, there are applications where the last mile of performance optimization squeezed do matter.

Read the article
Why do a small amount of add/deletes take several seconds in EF4?

- by TomWij

Using the Entity Framework 4. I have created a Code First database in the past and a piece of code needs to delete and add 16 objects, this takes 6 seconds each. That's 300+ ms for each query! The deletes/adds occur in a foreach scope and there is a SaveChanges() outside the foreach. In the above image you see that each takes 6 seconds, which is 34% of the time, for 16 calls. That doesn't sound normal to me... Why is this and how can I improve the performance? If there is no solution: Are there any workarounds I can use? It would be a pain to rewrite my project...

Read the article

< Previous Page | 133 134 135 136 137 138 139 140 141 142 143 144 | Next Page >