1 P E R F O R M A N C E S T U D Y SQL Server Performance in a VMware Infrastructure 3 Environment
2 Contents Introduction...1 Executive Summary bit versus 64-bit Virtual Machines...2 UP versus SMP Virtual Machines...2 Experimental Setup...3 Hardware Setup...3 Virtual Machines...4 Storage Layout...4 Software Configuration...4 Test Cases...5 Performance Results for 32-bit Virtual Machines...5 Performance Results for 64-bit Virtual Machines...7 Comparing Performance Results for 32-bit and 64-bit Virtual Machines...8 Additional Observations... 1 Statistics Summary... 1 Conclusions References VMware...13 HP...13 Microsoft...13 Appendix 1: Test Environment Server...14 Server Hardware Server Software Virtual Machine Configurations Storage...14 SAN Storage Fibre Channel Switch Client...15 Benchmark Driver Machines Contents i
3 Appendix 2: Data Collection Physical Disk Object...16 Processor Object...16 SQL Server Databases Object...16 SQL Server SQL Statistics Object...16 Contents ii
4 Introduction Current data indicates that database applications running on individual physical servers represent a large consolidation opportunity. Over 5 percent of these database applications run on two-way symmetric multiprocessor (SMP) machines, and in 9 percent of the cases transaction rates are under 2 transactions per second. CPU utilization averages less than 1 percent and approaches 2 percent at peak levels. As might be expected, I/O and data transfer rates are also low. Not surprisingly, many such database applications have been successfully migrated to virtual machines running on VMware ESX Server systems (for a link to further reading, see Customer Success Stories Featuring ESX Server in References ). This paper describes transaction processing workload performance in virtual machines using Microsoft SQL Server 25 and VMware Infrastructure 3. This performance study was conducted at the HP Strategic Alliances Engineering (SAE) lab in Cupertino. The primary goal is to prove that Microsoft SQL Server 25 can successfully handle enterprise-level transaction-processing workloads when running inside VMware virtual machines. To facilitate planning for server consolidation, this study presents sizing data and data on system resource utilization at various load levels for uniprocessor (UP) virtual machines, two-way SMP virtual machines, and four-way SMP virtual machines. This study also compares the performance of UP, two-way, and four-way SMP virtual machines across 32-bit and 64-bit virtual environments. Executive Summary This performance study clearly demonstrates that VMware Infrastructure 3 provides an excellent production-ready virtualization platform for customers looking to deploy Microsoft SQL Server inside virtual machines. Furthermore, together with virtualization-based distributed infrastructure services such as VMotion, VMware High Availability, and VMware Distributed Resource Scheduler, VMware Infrastructure 3 can provide increased serviceability, efficiency, and reliability for your SQL Server deployments. This should offer transformative cost savings to your dynamic data center. The main conclusions that can be drawn from these experiments are illustrated in Figure 1 and Figure 2. Introduction 1
5 32-bit versus 64-bit Virtual Machines Virtual machine throughput scales well for both 32-bit and 64-bit virtual machines. 64-bit virtual machines provide better scalability for SMP virtual machines as user load increases bit 1 VCPU 2 VCPU 4 VCPU Figure 1: Throughput comparison for 32-bit and 64-bit virtual machines 64-bit UP versus SMP Virtual Machines SMP virtual machines provide better throughput scalability than UP virtual machines, but with a higher CPU cost. 64-bit SMP virtual machines scale better than the 32-bit SMP virtual machines as user load increases VCPU 2 VCPU 4 VCPU 32-bit 64-bit Figure 2: Throughput comparison for UP and SMP virtual machines Executive Summary 2
6 Experimental Setup The DBHammer test tool, contained in the Microsoft SQL Server 2 Resource Kit, was used to generate load for the experiments. It consists of a client application, written in Visual Basic, which generates transactions against a database server. Transactions either read or update a single record in the database. The default code generates an equal number of read and update transactions. This is a good representation of a typical transaction processing workload. To simulate decision support or business intelligence workloads, the DBHammer test should be run with appropriate read/write ratios. The kit contains source code and binaries for database creation and benchmark execution. Detailed instructions for setup, execution, and troubleshooting are also included. The client component of the test was executed on a separate physical machine. The database server was hosted on a four-way HP ProLiant DL585 server. When we performed the virtual machine tests, the database server virtual machine was the only virtual machine running on the server. The data in this paper demonstrates the performance characteristics under circumstances in which the virtual machine has sufficient resources. The standard test application setup was used. Table 1 provides the parameter values for this configuration. Application Parameter Value Read/write ratio 5/5 Database row count Interval (think time) Table 1: Benchmark parameter values 1 million rows 1 ms Figure 3: DBHammer client application Varying the Instances configuration option on the client side, as shown in Figure 3, controls the load on the system. This option determines the number of clients generating transactions that are serviced by the database server. Hardware Setup The test machine, the SAN storage array, and the client machine used to drive the workload were set up as shown in Figure 4. A four-way HP ProLiant DL585 acted as the test server, and multiple two-way HP Proliant DL36 servers were used as client machines, while an HP EVA 6 SAN fulfilled all storage needs. The test machines were connected via a gigabit Ethernet link. The link Experimental Setup 3
7 between the test server and the storage array was a Fibre Channel switch. Detailed specifications of all components are listed in Appendix 1: Test Environment. 4 HP DL36 HP DL585 HP EVA6 Figure 4: Schematic representation of test configuration In order to ensure data accuracy, all components and links were dedicated to these experiments and were not shared with other machines. Virtual Machines We set the database server to run in UP and SMP virtual machines in which the guest operating system was Microsoft Windows Server 23, Enterprise Edition with SP2. We configured the virtual machines with 3.6GB of main memory. These virtual machines were freshly created to ensure HAL correctness. We placed the guest operating system software (on the virtual C: drive) on the VMware VMFS partition created on the internal storage for the DL585 server. Storage Layout In the context of these experiments, storage layout refers to the placement of the database and log files. We placed these files on separate VMware VMFS partitions located on independent disk arrays. We placed the database files on a 1GB RAID 5 LUN for the 32-bit virtual machine and a 4GB RAID 5 LUN for the 64-bit virtual machine. We placed the log files on a1gb RAID 1 LUN for the 32-bit virtual machine and a 4GB RAID 1 LUN for the 64-bit virtual machine. This is a common configuration for general-purpose database installations. RAID 5 arrays provide reasonable fault tolerance and performance with acceptable cost and overhead unless the workload is dominated by a large number of write operations. RAID 1 provides the higher level of fault tolerance required for database log files. We left parameter settings for the SAN, HBA, and Fibre Channel switch at their default values. The SQL Server TempDB was located on the operating system partition (C: drive). It may be possible to achieve higher throughput rates by moving TempDB to a SAN partition, especially for decision support or business intelligence workloads. Software Configuration SQL Server 25 Enterprise Edition with SP2 was used for these tests. While it may be possible to obtain better system performance by manually altering some SQL Server configuration parameters based on knowledge of application characteristics, it is recommended that most SQL Server configuration options be left at their default values. This recommendation is based on the fact that SQL Server monitors system load and fine tunes many of these parameters based on this dynamic feedback. We used default values for all configuration parameters in this study. We configured VMware ESX Server to operate with default options and parameter settings. Experimental Setup 4
8 Test Cases The number of clients submitting transactions to the database determines the load on the system. We conducted tests with a large range of values for this parameter. The objective was to characterize performance for a range of load levels. We conducted these experiments for UP, twoway SMP, and four-way SMP virtual machines using both 32-bit and 64-bit versions of the guest operating system and SQL Server software. Performance Results for 32-bit Virtual Machines This section presents an overview of transaction processing workload performance as measured using the DBHammer test utility. The primary measure of transaction-processing workloads is the throughput in units of transactions per second. As with any system-level workload, resource usage is a critical aspect of database workload and is particularly important for sizing and capacity planning. CPU usage data collected within virtual machines is not useful for two reasons. First, this data does not always accurately reflect the overhead of virtualization that is incurred by the ESX Server host. Second, because of the way time is kept within virtual machines, the usage data itself may be inaccurate (for details, see Timekeeping in VMware Virtual Machines in References ). For these reasons, we use CPU usage data collected on the ESX Server host in this study. We used the esxtop tool to collect resource utilization statistics for VMware ESX Server. For further details, see Appendix 2: Data Collection. UP and SMP virtual machine performance can be compared in many different ways and along many different dimensions. In the server consolidation process, one important choice is the processor count of the virtual machines (UP, two-way SMP, or four-way SMP). SMP virtual machines usually have higher virtualization overhead than UP virtual machines, therefore the benefit to the end user must be carefully considered (for details see Performance Tuning Best Practices for ESX Server 3 in References ). In the following sections, we compare the performance of UP and SMP virtual machines in tests that feature constant load on the virtual machine. The results of these tests highlight the advantages of using SMP virtual machines. Figure 5 shows the throughput scalability we achieved for 32-bit UP virtual machine by successively increasing the number of clients. The throughput demonstrates near-linear scalability until the CPU resources for the virtual machine are saturated. Performance Results for 32-bit Virtual Machines 5
9 VM CPU % used Number of clients Batch requests/sec VM CPU % used Figure 5: Throughput for 1-VCPU 32-bit virtual machine Figure 6 and Figure 7 show similar throughput scalability for 32-bit SMP virtual machines. As with the UP virtual machine, the throughput demonstrates near-linear scalability until the CPU resources of the virtual machine are saturated. Furthermore, the SMP virtual machines can support higher transaction rates because of the additional CPU resources available. Note: The measurement of virtual machine CPU used is cumulative and reflects the fact that there are two virtual CPUs in the virtual machine. If both virtual CPUs were fully saturated, the measurement would show 2 percent CPU used. In a virtual machine with four VCPUs, the maximum would be 4 percent CPU used Number of clients VM CPU % used Batch requests/sec VM CPU % used Figure 6: Throughput for 2-VCPU 32-bit virtual machine Performance Results for 32-bit Virtual Machines 6
10 Number of clients VM CPU % used Batch requests/sec VM CPU % used Figure 7: Throughput for 4-VCPU 32-bit virtual machine Performance Results for 64-bit Virtual Machines This section presents performance results for 64-bit virtual machines. As in the 32-bit performance results sections, we compare the performance characteristics of UP and SMP virtual machines to analyze behavior under constant load (that is, with the same number of clients). The results of these tests highlight the advantages of using SMP virtual machines. Figure 8 shows the throughput scalability we achieved for a 64-bit UP virtual machine by successively increasing the number of clients. The throughput demonstrates near-linear scalability until the CPU resources for the virtual machine are saturated VM CPU % used Number of clients Batch requests/sec VM CPU % used Figure 8: Throughput for 1-VCPU 64-bit virtual machine Figure 9 and Figure 1 show similar throughput scalability for 64-bit SMP virtual machines. As with the UP virtual machine, the throughput demonstrates near-linear scalability until the CPU Performance Results for 64-bit Virtual Machines 7
11 resources of the virtual machine are saturated. Furthermore, the SMP virtual machines can support higher transaction rates because of the additional CPU resources available. Note: The measurement of virtual machine CPU used is cumulative and reflects the fact that there are multiple virtual CPUs in the virtual machine. If both virtual CPUs in a two-vcpu virtual machine were fully saturated, the measurement would show 2 percent CPU used. In a virtual machine with four VCPUs, the maximum would be 4 percent CPU used Number of clients 64-bit 2-VCPU VM CPU % used VM CPU % used Figure 9: Throughput for 2-VCPU 64-bit virtual machine VM CPU % used Number of clients 64-bit 4-VCPU VM CPU % used Figure 1: Throughput for 4-VCPU 64-bit virtual machine Comparing Performance Results for 32-bit and 64-bit Virtual Machines In this section, we compare the performance characteristics of 32-bit and 64-bit virtual machines to analyze behavior under constant load (that is, with the same number of clients). This set of comparisons highlights the benefits, if any, that a 64-bit environment may provide over a 32-bit environment. In these comparisons, the transaction processing rate is similar when using UP virtual machine in both 32-bit and 64-bit environments, as shown in Figure 11. Comparing Performance Results for 32-bit and 64-bit Virtual Machines 8
12 Number of clients 32-bit 1-VCPU 64-bit 1-VCPU Figure 11: Throughput for 4-VCPU 64-bit virtual machine For SMP virtual machines, the throughput rate is consistently higher in a 64-bit environment compared to a 32-bit environment, under constant load. Because of time constraints, we could not investigate this difference further. Figure 11 and Figure 12 show these results for 2-VCPU and 4-VCPU virtual machines Number of clients 32-bit 2-VCPU 64-bit 2-VCPU Figure 12: Throughput for 2-VCPU 64-bit virtual machine Number of clients 32-bit 4-VCPU 64-bit 4-VCPU Figure 13: Throughput for 4-VCPU 64-bit virtual machine Comparing Performance Results for 32-bit and 64-bit Virtual Machines 9
13 Additional Observations During these tests, we found that the DBHammer client seemed to saturate when running more than 25 instances. Consequently, we used a separate benchmark client for every 3 clients simulated by DBHammer. This DBHammer saturation behavior is particularly evident in the 4- VCPU SMP graphs above for the 5- and 1-client data points. The transaction throughput shown in these results represents the highest steady-state throughput achievable for the virtual machine until its CPU resources are saturated. However, note that while the CPU resources inside the virtual machine were exhausted, the physical CPU resources used on the ESX Server host were only used corresponding to the number of virtual CPUs because of the excellent isolation capability offered by VMware ESX Server. In other words, for UP virtual machines, only 13 percent of total physical CPU resources were utilized. Likewise, for 2-VCPU virtual machine, total physical CPU utilization was about 29 percent, and for the 4-VCPU virtual machine, the maximum total physical CPU utilization was about 52 percent. This implies that configurations running multiple instances of SQL Server virtual machines can achieve higher transaction throughput rates than those running only a single instance. It is possible that the transaction throughput in a configuration with multiple instances may be limited by the IOPS that the storage can handle. In such a situation, you may want to configure storage adapter queue depths and ESX Server outstanding disk requests parameters to minimize any queued disk I/O requests. Statistics Summary The following tables summarize the performance data for the UP and SMP virtual machines in both 32-bit and 63-bit environments presented in this study. Number of Clients Transaction Rate Host % CPU Busy Table 2: 32-bit 1-VCPU virtual machine performance summary Additional Observations 1
14 Number of Clients Transaction Rate Host % CPU Busy Table 3: 32-bit 2-VCPU SMP virtual machine performance summary Number of Clients Transaction Rate Host % CPU Busy Table 4: 32-bit 4-VCPU SMP virtual machine performance summary Number of Clients Transaction Rate Host % CPU Busy Table 5: 64-bit 1-VCPU virtual machine performance summary Statistics Summary 11
15 Number of Clients Transaction Rate Host % CPU Busy Table 6: 64-bit 2-VCPU virtual machine performance summary Number of Clients Transaction Rate Host % CPU Busy Table 7: 64-bit 4-VCPU SMP virtual machine performance summary Conclusions The performance results we describe in this paper support the conclusion that running Microsoft SQL Server 25 inside VMware virtual machines can provide an effective production-ready platform for running transaction-processing workloads. Your IT organization can leverage the sizing data we present in this white paper to deploy Microsoft SQL Server successfully using VMware Infrastructure 3. By comparing effective workload throughput and resource utilization for your datacenter with the results presented in this paper, you can design and size your virtualized environment to meet your SLA targets. The number of actual users and transactions you can support in a production environment will, of course, depend on the specific applications used and the intensity of user activity. The results of Conclusions 12
16 our tests clearly demonstrate that in a VMware Infrastructure 3 environment, the transaction throughput scales almost linearly even as the number of clients increases, for every configuration we tested. Our results show that the throughput rate of UP virtual machine is comparable for 32- bit and 64-bit environments. However, for SMP virtual machines, the throughput rate is slightly higher in a 64-bit environment compared to a 32-bit environment. Further, SMP virtual machines can service a higher number of users. One disadvantage of SMP virtual machines is that they have higher CPU costs per transaction than UP virtual machines. From a performance perspective, we recommend using SMP virtual machines rather than UP virtual machines for Microsoft SQL Server, and if possible, a 64-bit environment if your application stack supports it. References VMware VMware Virtual Performance Web site Customer Success Stories Featuring ESX Server Timekeeping in VMware Virtual Machines Performance Tuning Best Practices for ESX Server 3 Server Configuration Guide SAN Configuration Guide HP HP ProLiants HP Resource Web site on VMware HP whitepapers on Microsoft SQL Microsoft DBHammer test utility, in Microsoft SQL Server 2 Resource Kit SQL Server 25 Books Online References 13
17 Appendix 1: Test Environment This section provides details about the hardware and software environment in which the tests described in this study were run. Server Server Hardware HP ProLiant DL585 G2 Processor (four-way): Dual-Core AMD Opteron Processor 822 SE x86 Family 15 Model 65 Stepping 2, AuthenticAMD 2.8GHz L2 Cache 512KB Memory: 4GB Local storage: HP SmartArray P GB 1Krpm SAS hard disk drives Ethernet adapters (2): HP NC371i Multifunction Gigabit Server Adapter HBA: Emulex LightPulse LP15 Server Software VMware Software: ESX Server 3..1 Build 3239 Virtual Machine Configurations CPU: UP, two-way and four-way SMP Memory: 3.6GB Connectivity: vmxnet Guest Operating System: Windows Server 23 Enterprise Edition with SP2 (32-bit or 64-bit) SQL Server: SQL Server 25 Enterprise Edition with SP2 (32-bit or 64-bit) Storage SAN Storage HP EVA 6 SAN; 2C4D Configuration SPA: HSV2-61 SPB: HSV2-61 Total GB FC SCSI 15Krpm disks Fibre Channel Switch HP 4/64 Switch Link speed: 4Gb/sec Appendix 1: Test Environment 14
18 Client Benchmark Driver Machines Windows Server 23 Enterprise Edition (32-bit) with SP1 HP Proliant 36 G4 Two-way Intel Xeon 2.8GHz CPU HT enabled Memory: 3.5GB Local storage: 33GB Ethernet adapter: HP NC371i Multifunction Gigabit Server Adapter Appendix 1: Test Environment 15
19 Appendix 2: Data Collection During these tests, performance data was collected at two levels. The esxtop tool was used to collect resource utilization statistics for VMware ESX Server. Furthermore, esxtop data helps us understand how the ESX Server host handles a virtual machine. This information may be useful in diagnosing performance issues. We also used the Windows MMC snap-in Performance Monitor (Perfmon) as a data collection tool. Perfmon categorizes data by objects (for example, processor or physical disk) and provides counters relevant to each object (for example, disk writes/sec for physical disk objects). The default instance for such counters sums the data for all instances of the object and may not be useful. It may therefore be necessary in some cases to select the proper instance of the counter. For example for the Physical Disk Object it is necessary to specify the disk for which the statistics are to be collected. The Perfmon objects and counters relevant to this report are listed below. Physical Disk Object Disk Bytes/sec: Shows the data transfer rate to or from the disk during write or read operations. Disk Read Bytes/sec: Shows the data transfer rate from the disk during read operations. Disk Reads/sec: Shows the rate of read operations on the disk. Disk Transfers/sec: Shows the rate of read and write operations on the disk. Disk Write Bytes/sec: Shows the data transfer rate to the disk during write operations. Disk Writes/sec: Shows the rate of write operations on the disk. Processor Object % Processor time: This counter is the primary indicator of processor activity and displays the average percentage of busy time observed during the sample interval. SQL Server Databases Object Transactions/sec: Shows the number of transactions started for the database. SQL Server SQL Statistics Object Batch requests/sec: Shows the number of SQL Batch requests received by server. Because both esxtop and Perfmon report data averaged over the sampling interval, it is important to use the same sampling interval for both. Appendix 2: Data Collection 16
20 Revision: Item: PS-29-PRD-1-1 VMware, Inc. 341 Hillview Ave. Palo Alto CA 9434 USA Tel Fax VMware, Inc. All rights reserved. Protected by one or more of U.S. Patent Nos. 6,397,242, 6,496,847, 6,74,925, 6,711,672, 6,725,289, 6,735,61, 6,785,886, 6,789,156, 6,795,966, 6,88,22, 6,961,941, 6,961,86, 6,944,699, 7,69,413; 7,82,598, 7,89,377, 7,111,86, 7,111,145, 7,117,481, 7,149, 843, 7,155,558, 7,222,221, 7,26,815, 7,26,82, 7,269,683, 7,275,136, 7,277,998, 7,277,999, 7,278,3, and 7,281,12; patents pending. VMware, the VMware boxes logo and design, Virtual SMP and VMotion are registered trademarks or trademarks of VMware, Inc. in the United States and/or other jurisdictions. All other marks and names mentioned herein may be trademarks of their respective companies.
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Technical white paper HP high availability solutions for Microsoft SQL Server Fast Track Data Warehouse using SQL Server 2012 failover clustering Table of contents Executive summary 2 Fast Track reference
Technical Note Scalability Tuning vcenter Operations Manager for View 1.0 Scalability Overview The scalability of vcenter Operations Manager for View 1.0 was tested and verified to support 4000 concurrent