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  • Which Edition of SQL Server 2008 R2 should you use?

    - by BuckWoody
    SQL Server 2008 R2 has just released to manufacturing (RTM’d) as I write this. With each new release, we make changes to the “Editions” we offer. A SQL Server “version” deals with the major changes in the product – and “edition” is a set of features and capabilities within that version (you’re welcome). So… definitely run over to http://msdn.microsoft.com/en-us/library/cc645993(v=SQL.105).aspx and check out the changes. For instance, did you know that SQL Server Express now supports 10GB databases? Well, then get over there and read what each edition does. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • How can I tell which page is creating a high-CPU-load httpd process?

    - by Greg
    I have a LAMP server (CentOS-based MediaTemple (DV) Extreme with 2GB RAM) running a customized Wordpress+bbPress combination . At about 30k pageviews per day the server is starting to groan. It stumbled earlier today for about 5 minutes when there was an influx of traffic. Even under normal conditions I can see that the virtual server is sometimes at 90%+ CPU load. Using Top I can often see 5-7 httpd processes that are each using 15-30% (and sometimes even 50%) CPU. Before we do a big optimization pass (our use of MySQL is probably the culprit) I would love to find the pages that are the main offenders and deal with them first. Is there a way that I can find out which specific requests were responsible for the most CPU-hungry httpd processes? I have found a lot of info on optimization in general, but nothing on this specific question. Secondly, I know there are a million variables, but if you have any insight on whether we should be at the boundaries of performance with a single dedicated virtual server with a site of this size, then I would love to hear your opinion. Should we be thinking about moving to a more powerful server, or should we be focused on optimization on the current server?

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  • SQL to select random mix of rows fairly [migrated]

    - by Matt Sieker
    Here's my problem: I have a set of tables in a database populated with data from a client that contains product information. In addition to the basic product information, there is also information about the manufacturer, and categories for those products (a product can be in one or more categories). These categories are then referred to as "Product Categories", and which stores these products are available at. These tables are updated once a week from a feed from the customer. Since for our purposes, some of the product categories are the same, or closely related for our purposes, there is another level of categories called "General Categories", a general category can have one or more product categories. For the scope of these tables, here's some rough numbers: Data Tables: Products: 475,000 Manufacturers: 1300 Stores: 150 General Categories: 245 Product Categories: 500 Mapping Tables: Product Category -> Product: 655,000 Stores -> Products: 50,000,000 Now, for the actual problem: As part of our software, we need to select n random products, given a store and a general category. However, we also need to ensure a good mix of manufacturers, as in some categories, a single manufacturer dominates the results, and selecting rows at random causes the results to strongly favor that manufacturer. The solution that is currently in place, works for most cases, involves selecting all of the rows that match the store and category criteria, partition them on manufacturer, and include their row number from within their partition, then select from that where the row number for that manufacturer is less than n, and use ROWCOUNT to clamp the total rows returned to n. This query looks something like this: SET ROWCOUNT 6 select p.Id, GeneralCategory_Id, Product_Id, ISNULL(m.DisplayName, m.Name) AS Vendor, MSRP, MemberPrice, FamilyImageName from (select p.Id, gc.Id GeneralCategory_Id, p.Id Product_Id, ctp.Store_id, Manufacturer_id, ROW_NUMBER() OVER (PARTITION BY Manufacturer_id ORDER BY NEWID()) AS 'VendorOrder', MSRP, MemberPrice, FamilyImageName from GeneralCategory gc inner join GeneralCategoriesToProductCategories gctpc ON gc.Id=gctpc.GeneralCategory_Id inner join ProductCategoryToProduct pctp on gctpc.ProductCategory_Id = pctp.ProductCategory_Id inner join Product p on p.Id = pctp.Product_Id inner join StoreToProduct ctp on p.Id = ctp.Product_id where gc.Id = @GeneralCategory and ctp.Store_id=@StoreId and p.Active=1 and p.MemberPrice >0) p inner join Manufacturer m on m.Id = p.Manufacturer_id where VendorOrder <=6 order by NEWID() SET ROWCOUNT 0 (I've tried to somewhat format it to make it cleaner, but I don't think it really helps) Running this query with an execution plan shows that for the majority of these tables, it's doing a Clustered Index Seek. There are two operations that take up roughly 90% of the time: Index Seek (Nonclustered) on StoreToProduct: 17%. This table just contains the key of the store, and the key of the product. It seems that NHibernate decided not to make a composite key when making this table, but I'm not concerned about this at this point, as compared to the other seek... Clustered Index Seek on Product: 69%. I really have no clue how I could make this one more performant. On categories without a lot of products, performance is acceptable (<50ms), however larger categories can take a few hundred ms, with the largest category taking 3s (which has about 170k products). It seems I have two ways to go from this point: Somehow optimize the existing query and table indices to lower the query time. As almost every expensive operation is already a clustered index scan, I don't know what could be done there. The inner query could be tuned to not return all of the possible rows for that category, but I am unsure how to do this, and maintain the requirements (random products, with a good mix of manufacturers) Denormalize this data for the purpose of this query when doing the once a week import. However, I am unsure how to do this and maintain the requirements. Does anyone have any input on either of these items?

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  • VM Tuning to enhance performance

    - by Tiffany Walker
    vm.bdflush = 100 1200 128 512 15 5000 500 1884 2 vm.dirty_ratio = 20 vm.min_free_kbytes = 300000 That means that the MOST dirty data that can be in RAM is 20% and that there will always be 300MB RAM that linux CANNOT use to cache files right? What I am trying to do is ensure that there is always room left for service to spawn and use RAM. I have 8GB of ram and hosting websites with PHP so I want to have more free RAM on stand by instead of seeing myself on 50MB of RAM free.

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  • Fix: Connections to SQL Server 2005 on Windows Vista suddenly stop working

    - by NTulip
    On my Vista machine at work, applications and the SQL Server Management Console work fine connecting to SQL Server 2005. Sometimes they are ok for weeks at a time, sometime for hours and then they stop connecting. I've tried everything to get it to work including the installation of SPII and running the user provisioning tool without any luck. The only way to fix it was to restart. The Error: Connections are refused with the standard error message: Cannot connect to SERVER_NAME\INSTANCE_NAME ------------------------------ ADDITIONAL INFORMATION: A network-related or instance-specific error occurred while establishing a connection to SQL Server. The server was not found or was not accessible. Verify that the instance name is correct and that SQL Server is configured to allow remote connections. (provider: SQL Network Interfaces, error: 26 - Error Locating Server/Instance Specified) (Microsoft SQL Server, Error: -1) For help, click: http://go.microsoft.com/fwlink?ProdName=Microsoft+SQL+Server&EvtSrc=MSSQLServer&EvtID=-1&LinkId=20476 The Fix: Stop and restart the Sql Server Browser, Sql Server integration, SQL Server Active Directory Helper services. Works like a charm.

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  • Advice needed: warm backup solution for SQL Server 2008 Express?

    - by Mikey Cee
    What are my options for achieving a warm backup server for a SQL Server Express instance running a single database? Sitting beside my production SQL Server 2008 Express box I have a second physical box currently doing nothing. I want to use this second box as a warm backup server by somehow replicating my production database in near real time (a little bit of data loss is acceptable). The database is very small and resources are utilized very lightly. In the case that the production server dies, I would manually reconfigure my application to point to the backup server instead. Although Express doesn't support log shipping natively, I am thinking that I could manually script a poor man's version of it, where I use batch files to take the logs and copy them across the network and apply them to the second server at 5 minute intervals. Does anyone have any advice on whether this is technically achievable, or if there is a better way to do what I am trying to do? Note that I want to avoid having to pay for the full version of SQL Server and configure mirroring as I think it is an overkill for this application. I understand that other DB platforms may present suitable options (eg. a MySQL Cluster), but for the purposes of this discussion, let's assume we have to stick to SQL Server.

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  • KVM Slow performance on XP Guest

    - by Gregg Leventhal
    The system is very slow to do anything, even browse a local folder, and CPU sits at 100% frequently. Guest is XP 32 bit. Host is Scientific Linux 6.2, Libvirt 0.10, Guest XP OS shows ACPI Multiprocessor HAL and a virtIO driver for NIC and SCSI. Installed. CPUInfo on host: processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 42 model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz stepping : 7 cpu MHz : 3200.000 cache size : 8192 KB physical id : 0 siblings : 8 core id : 0 cpu cores : 4 apicid : 0 initial apicid : 0 fpu : yes fpu_exception : yes cpuid level : 13 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good xtopology nonstop_tsc aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx lahf_lm ida arat epb xsaveopt pln pts dts tpr_shadow vnmi flexpriority ept vpid bogomips : 6784.93 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: <memory unit='KiB'>4194304</memory> <currentMemory unit='KiB'>4194304</currentMemory> <vcpu placement='static' cpuset='0'>1</vcpu> <os> <type arch='x86_64' machine='rhel6.3.0'>hvm</type> <boot dev='hd'/> </os> <features> <acpi/> <apic/> <pae/> </features> <cpu mode='custom' match='exact'> <model fallback='allow'>SandyBridge</model> <vendor>Intel</vendor> <feature policy='require' name='vme'/> <feature policy='require' name='tm2'/> <feature policy='require' name='est'/> <feature policy='require' name='vmx'/> <feature policy='require' name='osxsave'/> <feature policy='require' name='smx'/> <feature policy='require' name='ss'/> <feature policy='require' name='ds'/> <feature policy='require' name='tsc-deadline'/> <feature policy='require' name='dtes64'/> <feature policy='require' name='ht'/> <feature policy='require' name='pbe'/> <feature policy='require' name='tm'/> <feature policy='require' name='pdcm'/> <feature policy='require' name='ds_cpl'/> <feature policy='require' name='xtpr'/> <feature policy='require' name='acpi'/> <feature policy='require' name='monitor'/> <feature policy='force' name='sse'/> <feature policy='force' name='sse2'/> <feature policy='force' name='sse4.1'/> <feature policy='force' name='sse4.2'/> <feature policy='force' name='ssse3'/> <feature policy='force' name='x2apic'/> </cpu> <clock offset='localtime'> <timer name='rtc' tickpolicy='catchup'/> </clock> <on_poweroff>destroy</on_poweroff> <on_reboot>restart</on_reboot> <on_crash>restart</on_crash> <devices> <emulator>/usr/libexec/qemu-kvm</emulator> <disk type='file' device='disk'> <driver name='qemu' type='qcow2' cache='none'/> <source file='/var/lib/libvirt/images/Server-10-9-13.qcow2'/> <target dev='vda' bus='virtio'/> <alias name='virtio-disk0'/> <address type='pci' domain='0x0000' bus='0x00' slot='0x08' function='0x0'/> </disk>

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  • Performance Monitor (perfmon) showing some unusual statistics

    - by Param
    Recently i have thought to used perfmon.msc to monitor process utilization of remote computer. But i am faced with some peculiar situation. Please see the below Print-screen I have selected three computer -- QDIT049, QDIT199V6 & QNIVN014. Please observer the processor Time % which i have marked in Red Circle. How it can be more than 100%.? The Total Processor Time can never go above 100%, am i right? If i am right? than why the processor time % is showing 200% Please let me know, how it is possible or where i have done mistake. Thanks & Regards, Param

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  • SQL Server performance on VSphere 4.0

    - by Charles
    We are having a performance issue that we cannot explain with our VMWare environment and I am hoping someone here may be able to help. We have a web application that uses a databases backend. We have an SQL 2005 Cluster setup on Windows 2003 R2 between a physical node and a virtual node. Both physical servers are identical 2950's with 2x Xeaon x5460 Quad Core CPUs and 64GB of memory, 16GB allocated to the OS. We are utilizing an iSCSI San for all cluster disks. The problem is this, when utilizing the application under a repeated stress testing that adds CPUs to the cluster nodes, the Physical node scales from 1 pCPU to 8 pCPUs, meaning we see continued performance increases. When testing the node running Vsphere, we have the expected 12% performance hit for being virtual but we still scale from 1 vCPU to 4 vCPUs like the physical but beyond this performance drops off, by the time we get to 8 vCPUs we are seeing performance numbers worse than at 4 vCPUs. Again, both nodes are configured identically in terms of hardware, Guest OS, SQL Configurations etc and there is no traffic other than the testing on the system. There are no other VMs on the virtual server so there should be no competition for resources. We have contacted VMWare for help but they have not really been any suggesting things like setting SQL Processor Affinity which, while being helpful would have the same net effect on each box and should not change our results in the least. We have looked at all of VMWare's SQL Tuning guides with regards to VSphere with no benefit, please help!

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  • 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&#xD;&#xA;s.x as sx,&#xD;&#xA;e.x as ex,&#xD;&#xA;s.y as sy,&#xD;&#xA;e.y as ey,&#xD;&#xA;e.y - s.y as y_delta,&#xD;&#xA;e.x - s.x as x_delta&#xD;&#xA;from Data s &#xD;&#xA; inner join Data e&#xD;&#xA; on e.x &gt; s.x and e.x - s.x between 100 and 105&#xD;&#xA;where e.y - s.y &gt; 0.01&#xD;&#xA;" 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])&gt;=($100.0000) AND ([SomeDb].[dbo].[Data].[x] as [e].[x]-[SomeDb].[dbo].[Data].[x] as [s].[x])&lt;=($105.0000) AND ([SomeDb].[dbo].[Data].[y] as [e].[y]-[SomeDb].[dbo].[Data].[y] as [s].[y])&gt;(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>

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this 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. Why Columnstore? 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. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this 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. Why Columnstore? 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. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • SQL query performance optimization (TimesTen)

    - by Sergey Mikhanov
    Hi community, I need some help with TimesTen DB query optimization. I made some measures with Java profiler and found the code section that takes most of the time (this code section executes the SQL query). What is strange that this query becomes expensive only for some specific input data. Here’s the example. We have two tables that we are querying, one represents the objects we want to fetch (T_PROFILEGROUP), another represents the many-to-many link from some other table (T_PROFILECONTEXT_PROFILEGROUPS). We are not querying linked table. These are the queries that I executed with DB profiler running (they are the same except for the ID): Command> select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; < 1169655247309537280 > < 1169655249792565248 > < 1464837997699399681 > 3 rows found. Command> select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; < 1169655247309537280 > 1 row found. This is what I have in the profiler: 12:14:31.147 1 SQL 2L 6C 10825P Preparing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272 12:14:31.147 2 SQL 4L 6C 10825P sbSqlCmdCompile ()(E): (Found already compiled version: refCount:01, bucket:47) cmdType:100, cmdNum:1146695. 12:14:31.147 3 SQL 4L 6C 10825P Opening: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.147 4 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.148 5 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.148 6 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.228 7 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.228 8 SQL 4L 6C 10825P Closing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:35.243 9 SQL 2L 6C 10825P Preparing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928 12:14:35.243 10 SQL 4L 6C 10825P sbSqlCmdCompile ()(E): (Found already compiled version: refCount:01, bucket:44) cmdType:100, cmdNum:1146697. 12:14:35.243 11 SQL 4L 6C 10825P Opening: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; 12:14:35.243 12 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; 12:14:35.243 13 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; 12:14:35.243 14 SQL 4L 6C 10825P Closing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; It’s clear that the first query took almost 100ms, while the second was executed instantly. It’s not about queries precompilation (the first one is precompiled too, as same queries happened earlier). We have DB indices for all columns used here: T_PROFILEGROUP.M_ID, T_PROFILECONTEXT_PROFILEGROUPS.M_ID_OID and T_PROFILECONTEXT_PROFILEGROUPS.M_ID_EID. My questions are: Why querying the same set of tables yields such a different performance for different parameters? Which indices are involved here? Is there any way to improve this simple query and/or the DB to make it faster? UPDATE: to give the feeling of size: Command> select count(*) from T_PROFILEGROUP; < 183840 > 1 row found. Command> select count(*) from T_PROFILECONTEXT_PROFILEGROUPS; < 2279104 > 1 row found.

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  • SQL Server Multiple Joins Are Taxing The CPU

    - by durilai
    I have a stored procedure on SQL Server 2005. It is pulling from a Table function, and has two joins. When the query is run using a load test it kills the CPU 100% across all 16 cores! I have determined that removing one of the joins makes the query run fine, but both taxes the CPU. Select SKey From dbo.tfnGetLatest(@ID) a left join [STAGING].dbo.RefSrvc b on a.LID = b.ESIID left join [STAGING].dbo.RefSrvc c on a.EID = c.ESIID Any help is appreciated, note the join is happening on the same table in a different database on the same server.

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  • Is there a way to rewrite the SQL query efficiently

    - by user320587
    hi, I have two tables with following definition TableA TableB ID1 ID2 ID3 Value1 Value ID1 Value1 C1 P1 S1 S1 C1 P1 S2 S2 C1 P1 S3 S3 C1 P1 S5 S4 S5 The values are just examples in the table. TableA has a clustered primary key ID1, ID2 & ID3 and TableB has p.k. ID1 I need to create a table that has the missing records in TableA based on TableB The select query I am trying to create should give the following output C1 P1 S4 To do this, I have the following SQL query SELECT DISTINCT TableA.ID1, TableA.ID2, TableB.ID1 FROM TableA a, TableB b WHERE TableB.ID1 NOT IN ( SELECT DISTINCT [ID3] FROM TableA aa WHERE a.ID1 == aa.ID1 AND a.ID2 == aa.ID2 ) Though this query works, it performs poorly and my final TableA may have upto 1M records. is there a way to rewrite this more efficiently. Thanks for any help, Javid

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  • 3 Day Level 400 SQL Tuning Workshop 15 March in London, early bird and referral offer

    - by sqlworkshops
    I want to inform you that we have organized the "3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop" in London, United Kingdom during March 15-17, 2011.This is a truly level 400 hands-on workshop and you can find the Agenda, Prerequisite, Goal of the Workshop and Registration information at www.sqlworkshops.com/ruk. Charges are GBP 1800 (VAT excl.). Early bird discount of GBP 125 until 18 February. We are also introducing a new referral plan. If you refer someone who participates in the workshop you will receive an Amazon gift voucher for GBP 125.Feedback from one of the participants who attended our November London workshop:Andrew, Senior SQL Server DBA from UBS, UK, www.ubs.com, November 26, 2010:Rating: In a scale of 1 to 5 please rate each item below (1=Poor & 5=Excellent) Overall I was satisfied with the workshop 5 Instructor maintained the focus of the course 5 Mix of theory and practice was appropriate 5 Instructor answered the questions asked 5 The training facility met the requirement 5 How confident are you with SQL Server 2008 performance tuning 5 Additional comments from Andrew: The course was expertly delivered and backed up with practical examples. At the end of the course I felt my knowledge of SQL Server had been greatly enhanced and was eager to share with my colleagues. I felt there was one prerequisite missing from the course description, an open mind since the course changed some of my core product beliefs. For Additional workshop feedbacks refer to: www.sqlworkshops.com/feedbacks.I will be delivering the Level 300-400 1 Day Microsoft SQL Server 2008 Performance Monitoring and Tuning Seminar at Istanbul and Ankara, Turkey during March. This event is organized by Microsoft Turkey, let me know if you are in Turkey and would like to attend.During September 2010 I delivered this Level 300-400 1 Day Microsoft SQL Server 2008 Performance Monitoring and Tuning Seminar in Zurich, Switzerland organized by Microsoft Switzerland and the feedback was 4.85 out of 5, there were about 100 participants. During November 2010 when I delivered seminar in Lisbon, Portugal organized by Microsoft Portugal, the feedback was 8.30 out of 9, there were 130 participants.Our Mission: Empower customers to fully realize the Performance potential of Microsoft SQL Server without increasing the total cost of ownership (TCO) and achieve high customer satisfaction in every consulting engagement and workshop delivery.Our Business Plan: Provide useful content in webcasts, articles and seminars to get visibility for consulting engagements and workshop delivery opportunity. Help us by forwarding this email to your SQL Server friends and colleagues.Looking forwardR Meyyappan & Team @ www.SQLWorkshops.comLinkedIn: http://at.linkedin.com/in/rmeyyappan

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  • Peforming an Audit for SQL Server 2008

    - by Nai
    Hi all, Do you guys have any good step by step type links for performing an SQL Server 2008 Performance Audit? I know Brad McGehee has written extensively on this but for SQL Server 2005 over at http://www.sql-server-performance.com. But are any such articles for SQL Server 2008? Thanks!

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  • SQL?????!????????????????? ~ DBA????APEX

    - by Yuichi.Hayashi
    Oracle Application Express(Oracle APEX)????????????Web????????????????DBA??????·???????????????? SQL?????!????·????????????????????? SQL????????????SQL??????????????????????? Oracle Apex???????????????????????????????SQL??????????????????????????????????? Oracle DB10g????????????iSQLPLUS????????????????????????????????????? SQL?????????CSV???????????? ??·?????????????????????? ????: SQL??????????? ??????: SQL??????????? ?DESCRIBE?: ??????SQL?: SQL?????????????????????????????? ????: ?????SQL???????????? APEX?????????????????????????????????? APEX(Oracle Application Express)????~??????????????????????

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  • "If not exists" fails on SQL CE

    - by Mark Evans
    Hi I've got an unexpected problem. I'm making a script to update the schema on a SQL CE database. This won't run: if not exists ( Select column_name from information_schema.columns where column_name = 'TempTestField' and table_name = 'Inventory_Master_File' ) Alter table Inventory_Master_File add TempTestField nvarchar(10) null I think this is because I'm using stuff that isn't supported in SQL CE. Anyone know how to do this? I've tried rearranging the script and can't get anything to work. I tried "alter table ... where not exists ...". Note that the "select" part runs fine and also the "alter" part. The problem is "if not exists". I know there are some other postings regarding problems like this using SQL CE but I couldn't find an answer to this particular problem. Cheers Mark UPDATE: I've spent over an hour looking for a solution. I've found many postings asking for help with similar problems but I've still got no idea how to fix it. I really don't want to do this in C# code. I need to do this in a SQL script. I can't believe something this basic is causing so much difficulty :(

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  • SQL Performance Problem IA64

    - by Vendoran
    We’ve got a performance problem in production. QA and DEV environments are 2 instances on the same physical server: Windows 2003 Enterprise SP2, 32 GB RAM, 1 Quad 3.5 GHz Intel Xeon X5270 (4 cores x64), SQL 2005 SP3 (9.0.4262), SAN Drives Prod: Windows 2003 Datacenter SP2, 64 GB RAM, 4 Dual Core 1.6 GHz Intel Family 80000002, Model 6 Itanium (8 cores IA64), SQL 2005 SP3 (9.0.4262), SAN Drives, Veritas Cluster I am seeing excessive Signal Wait Percentages ( 250%) and Page Reads /s (50) and Page Writes /s (25) are both high occasionally. I did test this query on both QA and PROD and it has the same execution plan and even the same stats: SELECT top 40000000 * INTO dbo.tmp_tbl FROM dbo.tbl GO Scan count 1, logical reads 429564, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. As you can see it’s just logical reads, however: QA: 0:48 Prod: 2:18 So It seems like a processor related issue, however I’m not sure where to go next, any ideas? Thanks, Aaron

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  • Strange C++ performance difference?

    - by STingRaySC
    I just stumbled upon a change that seems to have counterintuitive performance ramifications. Can anyone provide a possible explanation for this behavior? Original code: for (int i = 0; i < ct; ++i) { // do some stuff... int iFreq = getFreq(i); double dFreq = iFreq; if (iFreq != 0) { // do some stuff with iFreq... // do some calculations with dFreq... } } While cleaning up this code during a "performance pass," I decided to move the definition of dFreq inside the if block, as it was only used inside the if. There are several calculations involving dFreq so I didn't eliminate it entirely as it does save the cost of multiple run-time conversions from int to double. I expected no performance difference, or if any at all, a negligible improvement. However, the perfomance decreased by nearly 10%. I have measured this many times, and this is indeed the only change I've made. The code snippet shown above executes inside a couple other loops. I get very consistent timings across runs and can definitely confirm that the change I'm describing decreases performance by ~10%. I would expect performance to increase because the int to double conversion would only occur when iFreq != 0. Chnaged code: for (int i = 0; i < ct; ++i) { // do some stuff... int iFreq = getFreq(i); if (iFreq != 0) { // do some stuff with iFreq... double dFreq = iFreq; // do some stuff with dFreq... } } Can anyone explain this? I am using VC++ 9.0 with /O2. I just want to understand what I'm not accounting for here.

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  • How to improve performance of non-scalar aggregations on denormalized tables

    - by The Lazy DBA
    Suppose we have a denormalized table with about 80 columns, and grows at the rate of ~10 million rows (about 5GB) per month. We currently have 3 1/2 years of data (~400M rows, ~200GB). We create a clustered index to best suit retrieving data from the table on the following columns that serve as our primary key... [FileDate] ASC, [Region] ASC, [KeyValue1] ASC, [KeyValue2] ASC ... because when we query the table, we always have the entire primary key. So these queries always result in clustered index seeks and are therefore very fast, and fragmentation is kept to a minimum. However, we do have a situation where we want to get the most recent FileDate for every Region, typically for reports, i.e. SELECT [Region] , MAX([FileDate]) AS [FileDate] FROM HugeTable GROUP BY [Region] The "best" solution I can come up to this is to create a non-clustered index on Region. Although it means an additional insert on the table during loads, the hit isn't minimal (we load 4 times per day, so fewer than 100,000 additional index inserts per load). Since the table is also partitioned by FileDate, results to our query come back quickly enough (200ms or so), and that result set is cached until the next load. However I'm guessing that someone with more data warehousing experience might have a solution that's more optimal, as this, for some reason, doesn't "feel right".

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  • How can I execute a .sql from C#?

    - by J. Pablo Fernández
    For some integration tests I want to connect to the database and run a .sql file that has the schema needed for the tests to actually run, including GO statements. How can I execute the .sql file? (or is this totally the wrong way to go?) I've found a post in the MSDN forum showing this code: using System.Data.SqlClient; using System.IO; using Microsoft.SqlServer.Management.Common; using Microsoft.SqlServer.Management.Smo; namespace ConsoleApplication1 { class Program { static void Main(string[] args) { string sqlConnectionString = "Data Source=(local);Initial Catalog=AdventureWorks;Integrated Security=True"; FileInfo file = new FileInfo("C:\\myscript.sql"); string script = file.OpenText().ReadToEnd(); SqlConnection conn = new SqlConnection(sqlConnectionString); Server server = new Server(new ServerConnection(conn)); server.ConnectionContext.ExecuteNonQuery(script); } } } but on the last line I'm getting this error: System.Reflection.TargetInvocationException: Exception has been thrown by the target of an invocation. --- System.TypeInitializationException: The type initializer for '' threw an exception. --- .ModuleLoadException: The C++ module failed to load during appdomain initialization. --- System.DllNotFoundException: Unable to load DLL 'MSVCR80.dll': The specified module could not be found. (Exception from HRESULT: 0x8007007E). I was told to go and download that DLL from somewhere, but that sounds very hacky. Is there a cleaner way to? Is there another way to do it? What am I doing wrong? I'm doing this with Visual Studio 2008, SQL Server 2008, .Net 3.5SP1 and C# 3.0.

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  • How to index a table with a Type 2 slowly changing dimension for optimal performance

    - by The Lazy DBA
    Suppose you have a table with a Type 2 slowly-changing dimension. Let's express this table as follows, with the following columns: * [Key] * [Value1] * ... * [ValueN] * [StartDate] * [ExpiryDate] In this example, let's suppose that [StartDate] is effectively the date in which the values for a given [Key] become known to the system. So our primary key would be composed of both [StartDate] and [Key]. When a new set of values arrives for a given [Key], we assign [ExpiryDate] to some pre-defined high surrogate value such as '12/31/9999'. We then set the existing "most recent" records for that [Key] to have an [ExpiryDate] that is equal to the [StartDate] of the new value. A simple update based on a join. So if we always wanted to get the most recent records for a given [Key], we know we could create a clustered index that is: * [ExpiryDate] ASC * [Key] ASC Although the keyspace may be very wide (say, a million keys), we can minimize the number of pages between reads by initially ordering them by [ExpiryDate]. And since we know the most recent record for a given key will always have an [ExpiryDate] of '12/31/9999', we can use that to our advantage. However... what if we want to get a point-in-time snapshot of all [Key]s at a given time? Theoretically, the entirety of the keyspace isn't all being updated at the same time. Therefore for a given point-in-time, the window between [StartDate] and [ExpiryDate] is variable, so ordering by either [StartDate] or [ExpiryDate] would never yield a result in which all the records you're looking for are contiguous. Granted, you can immediately throw out all records in which the [StartDate] is greater than your defined point-in-time. In essence, in a typical RDBMS, what indexing strategy affords the best way to minimize the number of reads to retrieve the values for all keys for a given point-in-time? I realize I can at least maximize IO by partitioning the table by [Key], however this certainly isn't ideal. Alternatively, is there a different type of slowly-changing-dimension that solves this problem in a more performant manner?

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