A Low Cost Data Warehouse Using SQL Server 2008

Transcription

1 A Low Cost Data Warehouse Using SQL Server 2008 Database Solutions Engineering By Jisha J Dell Product Group July 2009

2 Executive Summary A Data warehouse refers to a data repository which stores huge amount of data and is predominantly used for analytical purposes like reporting and data mining. From a performance point of view, organizations prefer to have separate decision support systems (DSS) and Online Transaction Processing (OLTP) environments. Separating the DSS environment from the OLTP environment helps customers to manage these systems efficiently and this helps to perform better capacity planning. This paper outlines different considerations for separating DSS from OLTP environments and touches upon technical procedures for achieving the same. A low cost data warehouse solution which comprises Dell PowerEdge T710, Dell PowerVault MD1120 and PERC6/E RAID controller is proposed. This paper highlights the capability of Dell PowerVault MD1120 to handle the data warehouse workloads. This paper also analyses different storage topologies and discusses some of the storage interface optimizations based on a standard DSS workload. THIS WHITE PAPER IS FOR INFORMATIONAL PURPOSES ONLY, AND MAY CONTAIN TYPOGRAPHICAL ERRORS AND TECHNICAL INACCURACIES. THE CONTENT IS PROVIDED AS IS, WITHOUT EXPRESS OR IMPLIED WARRANTIES OF ANY KIND Dell Inc. All rights reserved. Reproduction of this material in any manner whatsoever without the express written permission of Dell Inc. is strictly forbidden. For more information, contact Dell. Dell, the DELL logo, PowerEdge, PowerVault, PowerConnect, and OpenManage are trademarks of Dell Inc. Microsoft, SQL Server, and Windows Server are registered trademarks of Microsoft Corporation in the United States and/or other countries. Other trademarks and trade names may be used in this document to refer to either the entities claiming the marks and names or their products. Dell disclaims proprietary interest in the marks and names of others. Page ii

3 Contents Introduction... 4 Separating OLTP and OLAP Database Systems... 5 Transporting Data from an OLTP Database to an OLAP Database... 5 Dell Advantages for Deploying a Data Warehouse Solution... 7 SQL Server 2008 Partitioning... 9 Test Methodology Test Configuration OLAP (DSS) Workload Single MD1120 Enclosure Test Setup Results Analysis Daisy chaining Multiple MD1120 Enclosures Test Setup Results Analysis Daisy Chaining Multiple Enclosures vs. Multiple PERC MD1120 Connections Test Setup Results Analysis Implementing SQL Server Partitioning Test Setup Results Analysis PERC 6/E Controller Optimizations for a Typical Data Warehouse Test Setup Results Analysis Conclusions Glossary References... 23

4 Introduction For data warehouse deployments, one of the challenges that the customers are facing is how to have a cost effective data warehouse solution which can meet their performance requirements. Most of the data centers have a mix of OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) databases, which have different performance and criticality requirements based on the business requirement. The OLTP databases are customer facing systems, where a number of clients log in and execute a number of small transactions. The I/O pattern is more of a random nature. These databases handle the most immediate data of the organization and are therefore constrained by the RPO (Recovery Point Objective) and RTO (Recovery Time Objective) considerations. These systems are usually implemented as part of a SAN (Storage Attached Network) to satisfy the No Downtime requirement. Additional business continuity measures such as DR (Disaster Recovery) solutions are considered on the subject of the OLTP database design. In other words, the whole OLTP infrastructure requires a huge capital investment. A typical transaction entry database at a retail store may be considered as an example of a simple OLTP database. The Transaction Processing systems are often complemented by one or more legacy databases for reporting and analysis purposes. These databases are also known as DSS (Decision Support Systems) or Data warehouses. (The term Data warehouse refers to a custom designed data repository of an organization mainly meant for reporting and analysis.) These decision support systems are usually derived from the live OLTP systems and are characterized by a number of large queries to collect the statistical data for a particular period of time. The data is in turn used in the decision making process of the organization. Since the queries are run on a particular set of data, the I/O pattern is mostly sequential in this case. A database which holds the sales information which is aggregated by region, period and sales channel may be considered as an example of an OLAP database. In today s economy, the organizations are more focused on reducing the cost without compromising the performance requirements. The OLTP database deployments involve lots of cost due to its criticality and performance requirements. Whereas customers may prefer to have a low cost data warehouse solution which can meet their desired performance requirements. This paper presents a low cost data warehousing solution from Dell with Dell PowerEdge 11 th generation servers and Dell PowerVault storage. This paper provides a high level discussion on how to transport data from OLTP to DSS environments. A detailed analysis is performed on Dell PowerVault MD1120 storage capability (which includes a scalability aspect) to handle the data warehouse workload. Several best practices are discussed and recommended to get optimal performance from data warehouse deployment with the MD1120 storage array. Microsoft SQL Server data partitioning capability is tested and expected performance enhancements are analyzed in this paper. Page 4

5 Separating OLTP and OLAP Database Systems It is often a recommended practice to separate the DSS or Data warehousing environment from the OLTP environment. Some of the different factors favoring this configuration are as follows: Workload: The DSS workload usually consists of full table scans, long running queries and different backup jobs, which tend to utilize a major portion of the database server memory and processor time. On the other hand, the OLTP workload is characterized by small insert, update and delete statements. I/O pattern: DSS is characterized by large sequential I/Os compared to small random I/Os on the OLTP side. Therefore, the database server I/O optimizations become very simple on separating both the environments. I/O size: The typical I/O size for DSS systems is very huge usually in the range of 1MB, where as for OLTP systems the typical I/O size is 4KB or 8KB. I/O Requirements: In OLTP systems, performance is measured in Transactions per second (TPS) within the permissible response time, whereas DSS systems expect high throughput measured in Megabytes per second (MBps). Database tuning: The database server tuning becomes very flexible in separating the OLTP and DSS environments. Database Schema: There is much difference between the OLTP and OLAP database schemas. The OLTP schema is designed for facilitating fast insert, update and delete operations. It is usually highly normalized. An OLTP database is usually characterized by a number of small lookup or reference tables. The foreign keys and the referential integrity constraints play an important role in the OLTP database design. On the other hand, the OLAP schema is designed to facilitate easy retrieval of data. In this case, the tables are highly denormalized. The most common OLAP schema is the Star schema. It consists of a number of large tables called Fact tables. These huge tables are surrounded by a number of small tables called Dimensions. Fact tables contain the data that can be used arithmetically for analysis purposes. Each dimension table provides the reference to the data in the Fact tables. Transporting Data from an OLTP Database to an OLAP Database The data from OLTP databases can be moved to a DSS environment in a number of ways. Several ETL (Extraction Transformation and Loading) tools are available in the market which serves the purpose of populating the DSS database. Microsoft SQL Server comes bundled with a number of resources which facilitates ETL. SQL Server Database snapshot, introduced in Microsoft SQL Server 2005, combined with the Database mirroring is one of the options that can be considered in transporting data from OLTP systems for reporting purposes. A database snapshot is a read only copy of the database. The OLTP database may be mirrored to a separate location and database snapshots can be incorporated in the mirrored database to make the mirrored data available for reporting. Page 5

6 Microsoft SQL Server Integration Services (SSIS) is a powerful ETL Tool which comes packaged with Microsoft SQL Server 2008 Installation wizard. SSIS can be used in environments where the DSS data need to be standardized based on the organization requirements. SSIS can perform data integration using functions like reformatting data, integrity checking, correcting errors, etc. The data from different online production databases of different brands can be integrated in a single data warehouse using any of the ETL Tools. This enables the organization to have a consolidated view of all the organization data. The steps involved in a typical ETL operation are as follows: 1. Data from different databases are brought in to a standard staging area. 2. Based on the organizational requirements, the data from different environments is integrated, checked for errors and reformatted in the staging area. 3. After doing the required modifications, the formatted data is loaded into the central data warehouse. 4. Data from the central data warehouse may be segregated and loaded into smaller databases, called data marts. This is an optional step which enables more focused data analysis and reporting. In some cases, formatted data is directly loaded into different data marts, thereby eliminating Step 3 above. Page 6

7 Figure 1 provides an overview of the above specified overall ETL operation. Dell Advantages for Deploying a Data Warehouse Solution Dell provides a number of hardware components that can be integrated to form a low cost data warehousing environment. This section discusses the advantages of deploying a data warehouse solution with Dell hardware components (PowerEdge Server T710, PowerVault MD1120, PERC6/E RAID controller) and Microsoft software components (Windows Server 2008 and SQL Server 2008). The PowerEdge server T710 is well suited for deploying a data warehouse. The 64 bit extended memory architecture provides significant benefits for an OLAP application, which requires sufficient memory to perform ad hoc querying of large databases. Also, the T710 server supports up to 96GB of RAM, which is very beneficial in handling large amounts of live data. It comes with a large processing power, being able to accommodate up to two dual core or quad core processors. The Dell PowerVault MD1120 is a direct attached SAS storage array that delivers the performance required for a data warehouse at an attractive price point. It can accommodate up to 24 SAS drives and can be expanded with up to 6 additional MD1120 arrays to accommodate up to 144 total drives behind a RAID PERC 6/E connection. It can deliver up to 3 Gbps dedicated throughput for each drive with Page 7

8 12Gbps throughput available on external wide links. Also, it is the first array from Dell to feature disks, which make it capable of storing up to 3.5TB per enclosure, which can be expanded up to 21TB per raid controller. The Dell PERC 6/E RAID controller allows redundant connection from the database server to the Dell PowerVault MD1120 storage. A few of the PERC6/E features which are of interest for DSS performance enhancements are as follows: Load balancing feature: When enabled on redundant storage connection, the load will be balanced equally on both of the controller ports. Read policy: The PERC6/E Controller offers three read policies: No Read Ahead, Read Ahead, and Adaptive Read Ahead (ARA). For a DSS (large sequential) workload, setting the read policy to the Read Ahead or Adaptive Read Ahead may be beneficial over the No Read Ahead policy. The benefits from the Adaptive Read Ahead policy are analyzed and discussed later in this paper. Microsoft Windows Server 2008 comes with a number of storage optimization features. It attempts to simplify the storage partition alignment setting by default. In all the versions prior to Windows 2008, the data partitions needed to be aligned explicitly with the underlying storage sectors. Failing to do so may degrade the storage performance up to 40%. In Windows Server 2008, the default partition alignment of all the newly created partitions has been set to 1024 KB, thus aligning to most of the storage stripe unit sizes such as 64 KB, 128 KB, 256 KB, 512 KB and 1024 KB. Microsoft SQL Server 2008 provides a number of features for optimizing the data warehouse performance. The new feature, Partitioned Table Parallelism, is built on a strong background of the table partitioning feature introduced in SQL Server Star join Optimizations are also introduced in SQL Server 2008 to improve the DSS query performance. The new feature, Resource Governor, enables the organizations to run multiple different workloads like ad hoc queries, reporting and analysis on the same system. The SQL Server Integration Services (SSIS), an integrated component of SQL Server 2008 can be used as a powerful ETL tool to transport the data from the OLTP system to a Data warehouse environment. Dell provides a high performing low cost data warehouse solution integrating Dell PowerEdge Server T710, Dell PowerVault MD1120, Microsoft Windows Server 2008 and Microsoft SQL Server The data from any OLTP system may be easily transported to the Dell data warehouse using the SQL Server Integration Services, the SQL Server 2008 component. To summarize, Dell s data warehouse solution provides a bundle which consists of high performance hardware and software components. This Dell solution is cost effective and at the same time can meet the customer s performance requirements. This will be discussed in the subsequent sections. Page 8

9 Figure 2 illustrates the sample data transportation from a cost intensive highly available OLTP system to the low cost high performing Dell Data Warehouse solution. SQL Server 2008 Partitioning Apart from the latest feature support from SQL Server, it provides an excellent performance enhancement feature called Table Partitioning. Based on a particular partition scheme, the large fact tables within the DSS database can be divided into smaller subsets of data, which can be spread across different disk drives. Partitions are basically mapped to filegroups, which hold the datafiles that contain the table data. Partitions may be implemented for large fact tables within the data warehouse to achieve performance enhancements. However, for small tables, partitioning creates more management overhead which in turn may degrade the performance. Indexes can be rebuilt, reorganized and realigned on the individual partitions for better management. In a data warehouse, the partitioning scheme is decided based on the application and the query behavior. If the query filtering key matches with the partitioning key, faster response times can be expected. In SQL Server 2008, there can be multiple threads accessing the same table partition. It allocates all the available threads to each table partition, accessed by the query, in a round robin fashion. This improves the query performance significantly. Page 9

10 Microsoft recommends aligning the related tables and indexes on the same partition scheme. The tables and indexes created on the same partitioning scheme are said to be aligned. When the SQL Server detects the aligned tables and indexes are accessed, it can join the data that resides on the same partitions first and then combine the results. This allows efficient use of multiple processor cores within the database server. Test Methodology This section provides the PowerVault MD1120 performance characterization with Microsoft SQL Server for a standard DSS Workload. The analysis can be used for optimizing the DSS Workload with the Dell PowerVault MD1120 storage arrays. Test Configuration For the MD1120 storage characterization, a dedicated database test server was setup with SQL Server 2008 Enterprise Edition with direct SAS connection to the MD1120 storage box. The detailed list of components is given in Table 1. Components Details Model : Dell PowerEdge T710 Server Processor : 2 *Quad core Intel Xeon Processors 2.40GHz, L3 8MB Hardware Storage RAID Controller Memory : 12 GB(3*4GB RDIMM 1067MHz) Model : Dell PowerVault MD1120 Enclosure Management Module(EMM) Firmware : A.01 Hard drives : 73GB 15k drives Model : PowerEdge RAID Controller 6E(PERC 6/E) Firmware version: Driver version : Network Switch Dell PowerConnect 6248 Software Operating System Microsoft Windows Server 2008 Enterprise Edition x64 Page 10

11 Components Database Details Microsoft SQL Server 2008 SP1 x64 Benchmarking Tools Quest Benchmark Factory Version : 5.8 Windows Reliability and Performance Monitor(perfmon) Version : Table 1. Test Components OLAP (DSS) Workload There are number of standard benchmarks defined by the Transaction Processing Council (TPC) such as TPC App, TPC C, TPC E and TPC H. The TPC H benchmark is a decision support benchmark and it simulates the decision support system that examines huge volumes of data, performs a number of complex ad hoc queries and answers critical business questions. Therefore, the TPC H benchmark was selected for the storage performance characterization efforts for this paper. The benchmarking tool, Quest Benchmark Factory for Databases, provides a TPC H framework to simulate the real time TPC H workload. The standard TPC H workload was executed for each of the test iterations. The details of the TPC H workload are listed in Table 2. TPC H I/O Pattern 100% Read Database Scale 100 Streams 5 Table 2. TPCH Workload Details The TPC H workload was run for the following test scenarios and the test results were analyzed to evaluate the performance of the MD1120 storage array: Single MD1120 Enclosure Multiple MD1120 Enclosures o Daisy chaining to the maximum supported configuration per the PERC 6/E port o Daisy chaining to two enclosures on a single PERC 6/E card vs. connecting two enclosures to different PERC 6/E cards Implementing SQL Server partitioning for a large fact table in the TPC H schema PERC 6/E Controller optimizations for a typical data warehouse The performance counters were captured using the Windows Performance and Reliability Monitor tool. The performance of any storage may be analyzed based on the storage throughput, i.e., the capability of the storage system to send/receive data. The metric, Megabytes per Second (MBps), is usually used to Page 11

12 denote the throughput. Since the TPC H workload is characterized by 100% read, the Read Bytes per Sec counters for the database disks were used to capture the storage throughput. The Processor utilization was captured using the % Processor time counter and the disk response time was analyzed using the Avg. Disk Sec/Read counter. For the PERC 6/E controller optimization activity, the Avg. Disk Bytes/Read counter was also captured and analyzed. Single MD1120 Enclosure This section provides the performance analysis of a single MD1120 for the TPC H workload. The TPC H workload was run on the database with the redundant connection to the storage. Test Setup A single MD1120 enclosure was connected to the database server and database volume was created out of all the 24 MD1120 disks with RAID10 redundancy. Figure 3 provides the pictorial representation of the test setup: The TPC H workload was carried out with a single SAS connection to the PowerVault MD1120 storage. Test iterations were carried with and without the PERC Load balancing feature enabled. Results With the above mentioned TPC H workload, the MD1120 was able to deliver a maximum throughput of around 600 MBps. During the sampling period, the CPU utilization averaged to a value of around 28% and the disk response time averaged to be around 0.25 sec for both the iterations. The test results are captured in Table 3. Page 12

13 Experiment Max Throughput (MBps) Avg. Response Time(sec) Avg. CPU Utilization (%) PERC 6/E Load Balancing On PERC 6/E Load Balancing Off Table 3. Single MD1120 Enclosure Performance Figure 4 provides a comparative analysis of the disk throughput, CPU utilization and disk response time. Analysis The PowerVault MD1120 delivers a satisfactory throughput of around 575 MBps throughout the sampling time. The redundant connection to the storage provides the failover in case the active connection fails. Enabling the load balancing feature of the PERC6 card may give added benefits in this case. Of course, those benefits will be obvious only when the workload is able to saturate a single PCIe bus at any point of time. In the above experiment, the used TPC H workload could not saturate the PCIe bus. A single PCIe card is enough to handle the above mentioned workload; therefore, the benefit of the load balancing feature could not be noticed. Daisy chaining Multiple MD1120 Enclosures This section analyzes and showcases the TPC H performance capability of MD1120 daisy chained to a maximum limit per PERC 6/E port. Page 13

14 Test Setup Three fully loaded MD1120 enclosures were daisy chained to the database server. This is the maximum configuration supported per PERC 6/E port. A single volume, with RAID 10 redundancy, was created out of all the PowerVault MD1120 disks to house the database. Figure 5 depicts the setup details: Results During the TPC H workload, the database disks were able to deliver a maximum throughput of 1185 MBps. The average CPU utilization was around 44% and the average disk response time was around 0.22 seconds. The test result is summed up in Table 4. Experiment Max Throughput (MBps) Avg. Response Time(sec) Avg. CPU Utilization (%) Daisy Chaining three MD1120 enclosures per PERC 6/E port Table 4. Daisy Chaining Multiple Enclosures Performance Page 14

15 Figure 6 gives the plot for the disk throughput, CPU utilization and disk response time. Analysis The impressive throughput results for the particular experiment seems to be because of the large number of backend disks available to process the read requests. The daisy chained MD1120 configuration supports up to 72 disks per SAS port. A PERC 6/E card with dual SAS ports can support up to 144 disks at the backend. The 75GB and 146GB 15k RPM disk support enables the Dell PowerVault MD1120 storage to perform immensely well in the large data warehousing scenarios. The tests were carried out on a standard database schema, with absolutely no database tuning. The throughput may be improved significantly by tuning the internal database tables based on the nature of the queries. Daisy Chaining Multiple Enclosures vs. Multiple PERC MD1120 Connections This section analyses the scenario of connecting two MD1120 enclosures either by daisy chain or using two separate PERC 6/E cards. Page 15

16 Test Setup Two MD1120 enclosures were used to house the entire database. In the first iteration of the TPC H workload, both the enclosures were connected to a single PERC 6/E card in a daisy chain fashion, as shown in Figure 7.1. In the next TPC H workload iteration, both the MD1120 enclosures were connected to separate PERC 6/E cards, as in Figure 7.2. Page 16

17 Results The experiment showed improvements in performance numbers, over daisy chaining, when an additional controller was added to the storage configuration. The results are captured in the Table 5. The results are plotted in figure 8. Experiment Max Throughput (MBps) Avg. Response Time(sec) Avg. CPU Utilization (%) Daisy chained dual MD1120s Dual MD1120s connected via separate PERCs Table 5. Single vs. Multiple PERC with Multiple MD1120 enclosures Analysis The improvement in storage performance in the above activity may be due to the fact that the entire load is processed by two individual controllers at the backend. In the case of daisy chaining the MD11120 enclosures, the single PCIe bus, on which the PERC operates, also poses a bottleneck. This is overcome when we use separate PERC controllers to house the database. The CPU utilization pattern is almost the same in both cases. It can be observed that there is a significant improvement in the response time in the case of using separate PERC cards as compared to daisy chaining the enclosures. Page 17

18 Implementing SQL Server Partitioning This section provides insight into the performance benefits that can be achieved by implementing SQL server partitioning in a data warehouse environment. This section also discusses on the importance of aligning the indexes with table partitions. Test Setup Two MD1120 enclosures connected to two separate PCIe cards were used for the test. The largest table in the TPC H schema was identified by using the sp_spaceused stored procedure. For the Quest Benchmark factory TPC H schema, Lineitem was found to be the largest table and the same was selected as a partitioning candidate. The largest fact table, Lineitem, of the standard TPC H schema was entirely stored on a single MD1120 and the other MD1120 enclosure was used to contain the rest of the database. Before partitioning, the Lineitem table was created on a single filegroup with a single datafile. The table was partitioned based on the range partitioning scheme, on a particular date time column namely l_shipdate. The partitioning scheme was chosen based on l_shipdate, as a few of the TPC H queries were accessing the Lineitem table on a particular l_shipdate range. On partitioning, the entire Lineitem table was divided into four filegroups, each created on six MD1120 disks with RAID 10 redundancy. Each filegroup had its own datafile. The separate datafile was implemented on every filegroup to avoid the read requests getting spanned across different partitions. Three TPC H workload iterations were carried out as part of the exercise. The first iteration was without table partitioning, the next with partitioned table and the last TPC H workload execution was done after realigning the table indexes based on the partitioning scheme. Results During the sampling period, the TPC H query throughput and response time was improved on implementing partitioning on the table, Lineitem, the largest table in the schema. At the same time, the results showed an increase in the overall CPU utilization on introducing partition. The query throughput, response time and CPU utilization showed significant performance enhancement, after aligning the indexes according to the table partitioning scheme. The experiment results are listed below, in Table 6. Experiment Max Throughput (MBps) Avg. Response Time(sec) Avg. CPU Utilization (%) Before Partitioning After Partitioning the table After aligning the table indexes according to the partition scheme Table 6. SQL Server Table Partitioning The results are plotted as shown in Figure 9. Page 18

19 Analysis SQL Server Table Partitioning splits the whole table into a number of partitions based on specified partitioning parameters, thereby segregating the data. This makes it easier in case of sequential access of data. This in turn results in an increase in throughput. But partitioning may have an adverse effect, if the partitioning scheme is not favoring the query behavior. The performance gets degraded if a number of read requests are getting split across the partitions. So the query behavior has to be taken into account on designing the table partitions. The table index partition alignment also plays a major role in query performance improvement. If the indexes are not properly aligned with the table partitions, this may create a CPU overhead, which in turn results in increased query response time. To summarize, query performance may be enhanced by implementing table partitions according to the query behavior. The table indexes also need to be aligned with the partitions to yield the maximum benefits. PERC 6/E Controller Optimizations for a Typical Data Warehouse This section discusses on some of the PERC 6/E optimizations that were carried out to improve the TPC H workload performance. Test Setup The setup is similar to the one used for the partitioning exercise. Two MD1120s were used to house the database, with one entire MD1120 reserved for the largest fact table, Lineitem. The different controller Page 19

20 optimizations were carried out on the PERC 6/E MD1120 combination which contains the Lineitem table. Results During the course of the TPC H workload, the setup with both the ARA (Adaptive Read Ahead) and 1MB stripe size outperformed all the other configurations. The configuration with No ARA and the 64KB stripe size showed the lesser performance figures. The main highlights of the activity are given in Table 7. Experiment ARA & 1 MB Stripe Size No ARA & 1 MB Stripe Size ARA & 64 KB Stripe Size No ARA & 64 KB Stripe Size Max Disk Kilo Bytes/Read Max Throughput (MBps) Avg. Disk Read Response Time(secs) Avg. CPU Utilization (%) Table 7. PERC 6/E Optimizations Page 20

21 The results of the experiment are plotted as given in Figure 10. Analysis Based on the experimental results, it may be concluded that the DSS performance is very much impacted by the stripe size of the storage volume. The larger the stripe size, the better the performance. We can see nearly 40% improvement over the 64KB stripe size, when 1MB stripe size is used. When ARA is enabled on the 64KB stripe, it seemed to boost the performance. But the 1MB stripe size performance degraded when ARA was enabled. This may be due to the fact that the DSS sequential block I/O size was less than the 1MB stripe size; therefore, when ARA was enabled, it posed some overhead on the performance. Page 21

22 Conclusions Dell offers the low cost data warehousing solution comprised of Dell PowerEdge Servers, Dell PowerVault Storage, Microsoft Windows Server and Microsoft SQL Server. The long running DSS workloads can be seamlessly downloaded to the Dell solution using the powerful SSIS ETL tool provided by Microsoft SQL Server This eliminates the purchase of a separate ETL tool for the OLTP to DSS data transportation. The Dell PowerVault MD1120 provides good performance for this type of workload, despite the relatively low cost of the hardware. The storage performance can be further improved by implementing the PERC 6/E controller optimizations discussed in this paper. The results from experiments with a single enclosure and multiple enclosures are summarized in Table 8. Using multiple PERC 6/E controllers to connect separate MD1120 arrays may boost the performance of a data warehousing solution, as it avoids PCIe bus saturation to a great extent. Experiment Maximum Throughput (MBps) Single Enclosure 612 Daisy Chain up to three enclosures per PERC 6/E port 1185 Dual MD enclosures daisy chained together 1197 Dual MD enclosures using separate PERC 6/E 1504 Table 8. Maximum Test Throughput on various Storage Configurations SQL Server Partitioning may be implemented to complement the underlying storage performance. The partitioning scheme and structure should be decided based on nature of the queries, in line with the business requirements. Incorrect or unaligned table partitions may adversely affect the overall query performance, which in turn makes your queries run longer. To get the maximum benefit out of partitioning, the indexes should also be aligned to your table partitions. Based on all the above analysis, it can be concluded that Dell PowerVault MD1120 is a good choice for a low cost data warehouse implementation. The performance figures for the MD1120 array are notable, particularly considering the price point of the array. The Dell PowerEdge Servers, Dell PowerVault Storage, Microsoft Windows Server and Microsoft SQL Server database can be integrated into a good solution for low cost data warehouses. Page 22

23 Glossary Response Time: Response time indicates the time elapsed for the SQL round trip. Throughput: Measures the traffic flow between the storage device and the application servers. Read ahead: The RAID controller reads the whole stripe containing the requested data block and will keep it in cache. For sequential read requests, it can substantially reduce the amount of read requests to the hard drives and can substantially increase performance. Adaptive Read Ahead: The RAID controller initiates read ahead only if the two most recent read requests accessed sequential sectors of the disk. References High Performance Data Warehouse with SQL Server e 484c abd7 29f31282b04d/RelDWPerf.doc Optimizing the Dell PowerVault MD1xxx Family SAS Solution Dell PowerEdge T710 Dell PowerVault MD s=bsd Designing Partitions to Improve Query Performance us/library/ms aspx Disk Partition Alignment Best Practices for SQL Server us/library/dd aspx Dell MD PERC6/E Performance md1120 perc6e performance/ Dell OpenManage Server Administrator Version 1.9 Command Line Interface User's Guide Page 23

24 Table and Index Partitioning in SQL Server 2008 Enterprise Edition bloggers.blogspot.com/2009/04/table and index partitioning in sql.html Strategies for Partitioning Relational Data Warehouses in Microsoft SQL Server us/library/cc aspx Partitioning enhancements in SQL Server enhancements in sql server 2008.aspx Partitioned Tables and Indexes in SQL Server us/library/ms345146(sql.90).aspx TPC Benchmarks Page 24