Management Report
October 2014
Cost/Benefit Case for IBM DB2 10.5 for
High Performance Analytics
Compared to Microsoft SQL Server 2014
International Technology Group
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International Technology Group
October 2014
Table of Contents
EXECUTIVE SUMMARY
The Players
The Landscape Changes
Lost Opportunity Costs
Technology Differentiators
Performance
Complexity
Conclusions
SOLUTIONS
SQL Server 2014
Overview
Clustered Columnstore Indexes
DB2 10.5
Overview
Overall Capabilities
DETAILED DATA
Basis of Calculations
Installations
Costs of Ownership
Costs Breakdowns
1 1 2 3 4 4
5
5 6
6 6
6 6 6
8
10
10 10
10
11 List of Figures
1.
Three-year Costs of Ownership for IBM DB2 10.5 with BLU Acceleration and
Microsoft SQL Server 2014 with Clustered Columstore Indexes
1 2.
Key Technologies Incorporated in IBM DB2 10.5 with BLU Acceleration and
Microsoft SQL Server 2014 with Clustered Columnstore Indexes
2 3.
Three-year Lost Opportunity Costs for Use of IBM DB2 10.5 with BLU Acceleration and
Microsoft SQL Server 2014 with Clustered Columnstore Indexes – All Installations
3 4.
BLU Acceleration Data Reduction Processes
5.
Principal IBM DB2 10.5 BLU Acceleration Technologies for Analytics Processing
6.
Principal Capabilities of Overall IBM DB2 10.5 Environment
7.
Installations Summary
8.
Three-year Costs of Ownership Breakdowns for IBM DB2 10.5 and Microsoft SQL Server 2014
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
4 7 8 10 11 International Technology Group
October 2014
Executive Summary
The Players
The database world is undergoing unprecedented change. Data growth continues to accelerate, and database
structures and contents to become more complex. New challenges must be met as Big Data technologies gain
traction. Cloud computing defines new deployment and operating models. Demand for increasingly powerful
analytics solutions has become pervasive.
These shifts have changed the strategies of major database vendors. IBM and Microsoft have implemented new
technologies in their mainstream databases. For high-performance analytics applications, which are the focus of
this report, key new capabilities were provided in IBM DB2 10.5 with BLU Acceleration, and in Microsoft SQL
Server 2014 with Clustered Columnstore Indexes.
There are some commonalties between these solutions. User experiences, however, indicate that BLU
Acceleration is more powerful, incorporates a broader range of technologies and is better optimized to deliver
sustained performance for high-volume analytics queries.
BLU Acceleration, moreover, employs a dramatically simplified SQL design. Application delivery times are
reduced, database administrator (DBA) staffing is lower and system overhead is less than for use of Clustered
Columnstore Indexes.
Data compression and space reclamation are also a great deal more effective. BLU Acceleration employs global
table-wide technology; i.e., the system searches for compression opportunities across entire tables. Users report
higher compression rates than for Microsoft’s segment-based approach.
In further contrast, space reclamation in BLU Acceleration is an automated online process; i.e., space is reclaimed
on an ongoing basis during production operations. Clustered Columnstore Indexes require offline administrator
intervention; i.e., it is a great deal slower. Users may not be able to realize the full potential of compression, or
may not apply it all to avoid the service interruptions and administrative overhead of the Microsoft approach.
These differences are reflected in costs of ownership. In representative installations presented in this report, threeyear costs for use of SQL Server 2014 with Clustered Columnstore Indexes ranged from 1.7 to 2.1 times more,
and averaged 1.9 times more than for DB2 10.5 with BLU Acceleration. Figure 1 illustrates these results.
IBM DB2 10.5
641.6
1,217.4
Microsoft SQL Server 2014
$ thousands
Databases
Servers
Deployment
Personnel
Facilities
Figure 1: Three-year Costs of Ownership for IBM DB2 10.5 with BLU Acceleration and
Microsoft SQL Server 2014 with Clustered Columnstore Indexes
Lost opportunity costs – meaning bottom-line business losses due to deployment delays – averaged 2.6 times
more for use of SQL Server 2014 with Clustered Columnstore Indexes than for DB2 10.5 with BLU Acceleration.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
1
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October 2014
The Landscape Changes
Growing demands for information to deal with more complex and volatile business conditions have intersected
with an acceleration of decision-making cycles at all levels of organizations. Increasingly, information must be
analyzed in real time.
Bottlenecks, however, have emerged in the ability of conventional data warehouse architectures to meet these
demands. One is that application development and deployment practices often impose delays that business users
find unacceptable. A second is that, in conventional architectures, latencies in movement of data between
processors and disks impair throughput. In high-volume environments, performance impact may be severe.
New technologies to address this constraint have emerged. Columnar data structures process only data in specific
columns, and enable significantly higher levels of compression. In-memory technologies maintain data in RAM
rather than on disk, increasing performance by wide margins. Data skipping avoids processing of data
unnecessary to specific queries, further reducing I/O loading.
Latest-generation server platforms also accelerate performance through single instruction, multiple data (SIMD)
techniques that parallel and vector processing at the microprocessor level. The is the case for Intel E7-based
systems, as well as IBM Power Systems, including new models based on IBM POWER8 processors.
POWER8-based systems offer new DB2 10.5 performance optimization capabilities, including support for largescale concurrent multithreading (up to eight threads per core) and exploitation of 128-bit registers. New reliability
features are also implemented.
The extent to which vendors have exploited new technologies varies. SQL Server 2014 with Clustered
Columnstore Indexes, and IBM DB2 10.5 with BLU Acceleration implement the technologies shown in figure 2.
Technology
IBM DB2 10.5 with
BLU Acceleration
Microsoft SQL Server 2014 with
Clustered Columnstore Indexes
High-performance compression
Table-based
Segment-based
In-memory technology
✔
✔
Data skipping
✔
Limited
SIMD exploitation
✔
N/A
Figure 2: Key Technologies Incorporated in IBM DB2 10.5 with BLU Acceleration and
Microsoft SQL Server 2014 with Clustered Columnstore Indexes
A DB2 10.5 capability introduced in August 2014 allows users to create columnar shadow tables of row-based
transactional data, and to execute queries directly on these. Shadow tables are updated automatically. Early
adopters have typically employed this capability for real-time operational queries and reporting. Users report that
DBA overhead is minimal, and that there is no impact on transactional performance.
Other distinctive DB2 10.5 BLU Acceleration features include automatic workload management (Clustered
Columnstore Indexes requires manual techniques); full separation of data management and security privileges
(not supported by Microsoft); and the ability to use key and unique constraints to avoid duplication of data in
tables (supported by SQL Server 2014 only for non-clustered columnstores, which cannot be updated).
Costs of Ownership
Costs of ownership calculations include database licenses and support; server hardware and operating systems;
personnel costs for database administration and related tasks; deployment costs and facilities (primarily energy)
costs. Acquisition, maintenance and support costs are based on discounted prices reported by users.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
2
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October 2014
Database costs are similar. Although Microsoft per core pricing is aggressive, IBM per terabyte pricing leverages
BLU Acceleration strengths in data compression to reduce overall costs. Server costs are marginally higher for
SQL Server 2014 with Clustered Columnstore Indexes, reflecting use of Windows Server rather than the less
expensive Linux distribution employed by IBM.
The largest disparities are in people-related costs for database administration (costs for use of SQL Server 2014
with Clustered Columnstore Indexes average three times more than for DB2 10.5 with BLU Acceleration) and
deployment (costs for use of SQL Server 2014 with Clustered Columnstore Indexes average 2.6 times more). In
these and other areas, the key differentiator is that DB2 10.5 with BLU Acceleration is less complex.
Lost Opportunity Costs
Analytical applications may yield significant bottom-line gains in a matter of weeks to months. The corollary is
that delays in bringing such applications into production may represent significant lost revenue and profit.
This effect is apparent in the same installations employed for costs of ownership comparisons. In these cases, lost
opportunity costs for use of SQL Server 2014 with Clustered Columnstore Indexes ranged from 2.5 to three times
more than for DB2 10.5 with BLU Acceleration. Figure 3 illustrates disparities.
Financial Services Company
Manufacturing Company
22.8
60.8
IBM DB2 10.5 with BLU Acceleration
Microsoft SQL Server 2014 with
Clustered Columnstore Indexes
87.9
263.6
426.1
IT Services Company
1,065.3
$ thousands
Figure 3: Three-year Lost Opportunity Costs for Use of IBM DB2 10.5 with BLU Acceleration and
Microsoft SQL Server 2014 with Clustered Columnstore Indexes – All Installations
These costs are for initial applications only. In practice, organizations would continue to deploy new applications.
The cumulative impact of faster deployment over multi-year periods would be a great deal larger. Disparities in
lost opportunity costs would increase by wide margins.
These and other results presented in this report are based on input from 24 companies in the same industries and
size ranges, with generally similar business profiles employing IBM DB2 10.5 with BLU Acceleration or
Microsoft SQL Server 2014 with Clustered Columnstore Indexes in comparable roles.
Further information on profiles, methodology and assumptions employed for calculations, along with cost
breakdowns for installations and platforms may be found in the Detailed Data section of this report.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
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October 2014
Technology Differentiators
Performance
In terms of performance, the two platforms are differentiated as follows:
1. SQL Server 2014 incorporates in-memory and columnar technology based on Microsoft’s earlier VertiPaq
engine and on the company’s Apollo development project. Columnar technology was introduced in SQL
Server 2008 R2 and enhanced in SQL Server 2014.
Early limitations limited adoption. For example, implementation of columnar technology was limited to
indexes, and could be employed only in read-only mode. Tables could not be changed without extensive
workarounds. In SQL Server 2014, tables may be more easily modified.
Users have reported significantly higher performance compared to use of SQL Server without Clustered
Columnstore Indexes. Microsoft benchmark tests have shown acceleration levels of 8 to 20 times using
cold buffer pools (i.e., data is fully loaded into RAM) and 4 to 10 times using warm pools (i.e., data is
divided between RAM and disk) for queries that benefit from columnar technology.
Overall performance gains are reported to be typically two to five times, with an average of slightly more
than 2.9 times. Performance increases of 300 to 800 times have been reported for individual queries.
Although Microsoft has variously claimed up to seven and up to 10 times compression, users report
between 40 and 80 percent (1.7 to 5 times). The norm appears to be two to three times compared to pagelevel compression employed in SQL Server 2014. These values, moreover, refer to raw and actual data
sizes for indexes only. SQL Server 2014 continues to employ row-based structures for other data.
2. DB2 10.5 with BLU Acceleration, introduced in April 2013, implements a range of new-generation
technologies in a more integrated and optimized manner. These include columnar and in-memory
processing, high-performance compression and caching, data skipping (i.e., the ability to avoid processing
data that is not necessary to specific queries), and microprocessor-level parallel and vector processing.
Users have reported increases of between 5 and 74 times in overall query performance compared with
previous DB2 versions (typically DB2 10 or DB2 9.7), with an overall average of 31.6 times.
Compression levels ranged from 9 to more than 15 times, and averaged around 12.6 times. In comparison
with Clustered Columnstore Indexes, which compress only at the column segment level, BLU
Acceleration extends compression across entire tables.
Key technologies in BLU Acceleration are combined in a manner that progressively reduces the amount
of data that must be processed and moved through I/O to boost performance. The sequence of processes
through which this occurs is illustrated in figure 4.
IBM BLU ACCELERATION
User Data
10 TB
Actionable
Compression
1 TB
Column
Processing
10 GB
Data
Skipping
1 GB
Parallel
Processing*
31.25 MB
Vector
Processing*
7.8 MB
*32 cores
Figure 4: BLU Acceleration Data Reduction Processes
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
4
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October 2014
These numbers should be taken as indicative – actual volumes may vary widely by database and workload. But
they highlight the efficiency of the BLU Acceleration design.
A further BLU Acceleration characteristic should be highlighted. The IT world has seen a progressive shift
toward higher-density memory media for high-performance workloads. This shift has been reflected in use or
RAM for in-memory databases. BLU Acceleration is designed to move even beyond this stage, to the point where
processing is conducted overwhelmingly in cache.
In addition, new shadow tables allow concurrent DB2 10.5 processing of queries and transactions. Row-based
transactional data is continuously and automatically replicated to columnar tables, and execution of queries to
these fully leverages BLU Acceleration in-memory technology. Users have reported the same performance
improvements of 10x or more as for dedicated DB2 10.5 query workloads.
Shadow tables are automatically synchronized with corresponding row-oriented tables. If access to other data
sources is not required, users may avoid delays due to use of extract, transformation and load (ETL) tools.
Complexity
In contrast to SQL Server 2014 with Clustered Columnstore Indexes, BLU Acceleration employs a simple SQL
design. The system does not, for example, employ schemas, indexes, or aggregate tables. Simplicity reduces the
time required for tasks such as system design, application development, testing and tuning, and ongoing
administration. Deployment also becomes a faster and more reliable process.
Applications may be created and deployed in a few comparatively simple steps. Once individuals became
proficient with the system, these took minutes…less than 20 minutes…less than an hour…a few hours. One
commented: We create tables and load data. Period. A key benefit was reported to be that end users might
develop server-based applications directly, rather than going through programming staff.
In normal operations, processes such as tuning, optimizer and compression administration, space reclamation,
database reorganizations, statistics collection and reporting, and workload management are largely automated.
Performance tuning requirements were said to be minimal…virtually non-existent. An IBM-supplied tool
automated conversion of row-based tables to columnar format.
Organizations reported that, once initial deployment had been completed, DBA overhead for BLU Acceleration
was minimal. User estimates ranged from six hours a week to maybe a quarter of an FTE (full time equivalent).
Degrees of complexity affect comparative deployment times. Users of Clustered Columnstore Indexes reported
that systems were typically brought into production in two weeks to six months – most responses were in the six
weeks to six months range – with an average of around 93 days. In comparison, BLU Acceleration users reported
eight days to three months, with an average of around 38 days.
Conclusions
The selection of DB2 10.5 or SQL Server 2014 also involves a larger choice as to how use of high-performance
analytics will evolve within organizations. The Microsoft approach focuses on end-user control, and assumes that
Microsoft will supply basic tools that will be enhanced by third parties, customized, applied and maintained by
DBAs and system administrators.
This is the traditional Microsoft approach to IT, and it will no doubt appeal to many organizations. But DB2 10.5
represents a stronger offering in terms of performance, architectural simplicity, back-end data integration,
manageability and time to bring new applications into production. Its focus is on quality and timeliness of
information. Where these are critical business priorities, DB2 10.5 with BLU Acceleration is a better option.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
5
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October 2014
Solutions
SQL Server 2014
Overview
SQL Server 2014 is the latest version of Microsoft’s core database, which originated in the 1990s. Since the mid2000s, Microsoft has positioned SQL Server aggressively for data warehousing and business intelligence (BI)
applications. Clustered Columnstore Index was added as part of Microsoft’s xVelocity solution in SQL Server
2008 R2, and enhanced in SQL Server 2014.
Other SQL Server 2014 analytics-related features include an expanded version of the Microsoft extract,
transformation and load (ETL) suite, SQL Server Integration Services. New components include Master Data
Services, a master data management tool originally developed by Stratature, which Microsoft acquired in 2007;
and Data Quality Services for data quality management.
SQL Server 2014 also supports Microsoft AlwaysOn clustering and Windows Server Core Support, which
reduces memory footprint and disk space consumption.
Clustered Columnstore Indexes
This technology organizes data in memory into columnar form, and compresses it. While not all data required for
a query must fit into memory, performance will be significantly higher when this is the case.
Data may be processed in batch mode; i.e., multiple rows of data are fetched and processed in a single operation.
According to Microsoft, this approach is most effective for queries involving large numbers of joins, filters and
aggregations. It employs a proprietary Microsoft form of vector processing.
The determination of whether to use batch or row-by-row processing for a given query is made by the SQL Server
Query Optimizer. Normally, batches contain around 1,000 rows. Smaller data blocks are processed row-by-row.
Clustered Columnstore Indexes incorporate a limited form of data skipping, which enables the system to bypass
segments of data that are not required for a specific query. Segment size is, however, comparatively large – one
million rows – which means that in practice large amounts of unnecessary data are often processed.
DB2 10.5
Overview
Introduced in 1996, DB2 for Linux, UNIX and Windows (LUW) has progressively evolved toward greater
performance and functionality. Recent versions have included DB2 9.5 (2008), DB2 9.7 (2009), DB2 9.8 (2010),
DB2 10 (2012) and DB2 10.5, which added BLU Acceleration (2013).
In its initial form, BLU Acceleration is designed primarily to support data warehouse systems with from 1 TB to
10 TBs of raw user data. It is a single-server solution, although in-memory, caching and processor optimization
technologies enable levels of analytics performance and capacity utilization that are significantly higher than for
most x86 platforms.
BLU Acceleration is supported on x86 hardware using Red Hat Enterprise Linux (RHEL) 5 or 6 x86-64 or SUSE
Linux 10 or 11 x86-64, and on IBM Power Systems, including new POWER8-based models.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
6
International Technology Group
October 2014
The principal BLU Acceleration technologies for analytics processing are summarized in figure 5.
ANALYTICS PROCESSING
DB2 integration
New runtime technology embedded in DB2 kernel.
Columnar & row-based tables can be processed simultaneously on the same system, & employ the same schema.
Can also be accessed using the same SQL & language interfaces, process model, storage, memory & utilities.
Tables can be accessed within the same SQL statement.
BLU Acceleration uses existing DB2 client server infrastructure, compilers, tablespaces, buffer pools, sort heap &
package cache, & utilities including LOAD, BACKUP, RESTORE, EXPORT, SNAPSHOT, db2top, db2pd & others.
New utility enables conversion of row-based to columnar tables in a specified database. Row-organized tables
remain online during processing. System monitors conversion processes.
DB2 tooling supports conventional & BLU Acceleration functions. Tools include Optim Query Workload Tuner (may
be employed to recommend BLU Acceleration deployments & table transformations), along with IBM Data Studio,
InfoSphere Data Architect, InfoSphere Optim Performance Manager, & InfoSphere Optim Configuration Manager.
In-Memory & Caching Dynamic in-memory technology loads & processes data in RAM.
Technologies
New memory paging architecture means entire database table does not have to reside in main memory to be
processed. Blocks of BLU data may be moved into main memory as needed to query. According to IBM,
expectation is that 70 to 80 percent of active data will reside in RAM. Performance may, however, be maintained
even when the volume of data processed exceeds RAM capacity.
Scan-friendly memory caching algorithm, unique to BLU Acceleration, optimizes cache performance for scanintensive workloads. Automatically adapts operation to data characteristics. Represents alternative to least recently
used (LRU) algorithms designed primarily for transactional applications. Enables even (egalitarian) access to
cache resources for commonly used values.
Register-friendly encoding enables compressed data to be packed into cache structures for further efficiency in use
of processor, memory & I/O resources. Encoded values are packed into bits matching CPU register width –
includes support for 128-bit wide POWER8 registers.
Data Compression &
Space Reclamation
Actionable Compression enables processing of columnar data while still compressed; i.e., analytics may be
performed without decompression. Operates on row-based & columnar structures. Automatically adapts to data
characteristics.
Combines multiple IBM compression techniques including register-friendly encoding, described above. Users have
reported compression rates of 10 times or more compared to uncompressed tables, with corresponding
performance enhancements & storage savings.
Real-time automated space reclamation extends to row-based & columnar data. Space is freed online during
processing. DBA intervention not required for space management & REORGs.
Column Store
IBM implementation of technology enabling higher performance & reduced consumption of processor, memory &
I/O resources for analytics workloads. Scans are directed to values in a particular column or columns, avoiding the
need to process all data in a table.
Data Skipping
Reduces processor, memory & I/O resource consumption by excluding data unnecessary to query from
processing. Process is automatic (no DBA intervention is required), based on system-stored metadata on parent
table columns.
Shadow Tables
New feature in DB2 Fix Pack 4 allows concurrent processing of queries & transactions. Row-based transactional
data is continuously & automatically replicated to columnar tables. Execution of queries to these fully leverages
BLU Acceleration in-memory technology. Shadow tables are automatically synchronized with corresponding roworiented tables. Shadow tables are implemented as a form of Materialized Query Table (MQT) using InfoSphere
Data Replication Change Data Capture (CDC).
Intel CPU
Optimization
Exploits latest Intel Single Instruction, Multiple Data (SIMD) parallel processing enhancements for E5 processors,
including expanded Streaming SIMD Extensions (SSE) & Advanced Vector Extensions (AVX) instructions.
According to Intel, enables parallel execution across multiple cores on single E7 processor, for up to 8x
performance boost on 8-core processor. Vector processing provides additional performance of up to 4x for floating
point-intensive applications. Actual performance boosts depend upon workloads. 10-20x improvement is common.
POWER CPU
Optimization
Exploits SIMD parallel processing & other performance-related features on Power Systems. Optimization for
POWER8-based systems includes support for concurrent multithreading; use of 128-bit registers; & enhanced
reliability features including improvements in Data Page Memory Checking & expanded integrity checking.
Workload
Management
Enables simplified operation of DB2 Workload Manager (WLM) for BLU Acceleration mode. Maintains concurrency
subject to predefined threshold criteria. Optimizes use of processor, memory & I/O resources, & performs
automatic, ongoing performance tuning based on knowledge of underlying hardware. Optionally, allows users to
define more complex policies based on DB2 WLM. Where shadow tables are employed, WLM automatically routes
queries to these, & transactions to row-based tables.
Figure 5: Principal IBM DB2 10.5 BLU Acceleration Technologies for Analytics Processing
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
7
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October 2014
BLU Acceleration leverages a number of established DB2 strengths. In particular, the core DB2 Workload
Manager (WLM) supports a new BLU Acceleration mode; and IBM- and Optim-branded DBA tools have been
adapted to support BLU Acceleration as well as conventional administration and optimization functions.
Overall Capabilities
DB2 10.5 leverages longstanding DB2 strengths in such areas as performance optimization, data compression,
workload management, high availability, and simplification and automation. Capabilities of the overall DB2 10.5
environment are summarized in figure 6.
GENERAL CAPABILITIES
Time Travel Query
Differentiates system time (when an event is logged) & business time (an alternative date &/or time
associated with the event) in maintaining & querying records. Obviates need for custom-developed
applications to analyze multiple timelines. Complies with temporal features of ANSI/ISO SQL:2011.
Continuous Data Ingest
Employs IBM parallel loading technology for extremely fast, low-overhead data transfers. Enables realtime data warehousing applications. Offers alternative to conventional batch & trickle feed techniques.
AVAILABILITY & RECOVERY
DB2 pureScale
Enables scale-out failover clustering for continuous availability. Generates <5% system overhead with
clusters of up to 64 nodes (installations are typically in this range), & <16% with 128 nodes. Based on IBM
Parallel Sysplex Data Sharing & General Parallel File System (GPFS). Migration requires no application
changes. Does not currently support BLU Acceleration, but may be employed for other workloads.
High Availability Disaster
Recovery (HADR)
Enables replication of data changes to one or more standby servers, & recovery from these. Supports up
to three hot standby servers, & allows delays to be set to prevent replication of problems.
Tivoli System Automation
Mainframe-derived high availability & policy-based automation solution. Manages failover, restart &
recovery within pureScale & HADR clusters.
Online Reorg
Reduces time when data is not available to users during reorg processes. Data remains available during
reload & rebuild phases.
PERFORMANCE-RELATED
Query Parallelism
Enables parallel query execution for more efficient use of CPU & I/O resources. Most effective for longrunning queries reading large amounts of data.
Table (Range) Partitioning
Allows data in a single table to be placed in multiple tablespaces for greater scalability, processing
efficiency & data roll-in/roll-out.
Materialized Query Tables
Query-specific table structure offering higher performance than indexes for certain types of query,
especially complex queries.
Multi-Dimensional Clustering
Enables flexible clustering of across multiple dimensions. Optimized for use in large data warehouse
environments. Typically accelerates query performance by around three times, & improvements of ten
times or more have been reported.
Scan sharing
Enables sharing of system resources by multiple scans. May significantly improve concurrency &
performance, & reduce I/O loading for high-volume scanning workloads.
DATA COMPRESSION
Actionable Compression
Enables processing of columnar data while still compressed. Operates on row-based & columnar
structures. Automatically adapts to data characteristics. Combines multiple IBM compression techniques
including register-friendly encoding. Supports real-time automated space reclamation, described above.
Adaptive Compression
Algorithms integrate table, index & compression. Overall rates are typically four to ten times, with average
of around seven times.
Backup Compression
Enables compression of data in all DB2 structures during backup.
STORAGE-RELATED
Multi-temperature Data
Management
Enables automated storage tiering for higher performance & lower overall disk costs. Obviates need for
controller-based tiering for most workloads. Tightly integrated with DB2 workload management.
pureXML Storage
Enables storage of IBM pureXML (IBM implementation of Extensible Markup Language) data in native
hierarchical mode.
Advanced Copy Services
(ACS)
Supports fast copying by IBM DS8000, Storwize V7000, SAN Volume Controller (SVC) & XIV systems
during backup & restore operations. Includes Tivoli Storage FlashCopy Manager. ACS Scripted Interface
enables use with non-IBM storage.
Figure 6: Principal Capabilities of Overall IBM DB2 10.5 Environment
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
8
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October 2014
Recent features include temporal processing (Time Travel Query), software-based storage tiering (Multi
Temperature Data Management) and an IBM parallel loading tool (Continuous Data Ingest) designed for realtime data warehouse updates.
IBM has also moved to enable integration of new Big Data types, including Hadoop and MapReduce. DB2 10.5
supports SPARQL, Resource Definition Framework (RDF), JavaScript Object Notation (JSON) data interchange
format, and other emerging standards. JSON has proved increasingly popular as an alternative to XML.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
9
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October 2014
Detailed Data
Basis of Calculations
Installations
Cost comparisons presented in this report were based on the installations summarized in figure 7.
FINANCIAL SERVICES COMPANY
MANUFACTURING COMPANY
IT SERVICES COMPANY
BUSINESS PROFILE
Diversified retail bank
$70+ billion assets
5,000+ employees
250+ branches
Contract electronics manufacturer
$6+ billion sales
40,000+ employees
50+ manufacturing plants
IT outsourcing & professional services
$500+ million sales
6,000+ employees
20+ facilities
Sales & customer profitability analysis,
demand forecasting & related
Customer financial & operational key
performance indicator (KPI) applications
4/32 x Intel E5
0.25 FTE DBA
Deployment time: 4 weeks
8/64 x Intel E5
0.55 FTE DBA
Deployment time: 10 weeks
APPLICATIONS
Risk & compliance analysis/reporting,
financial & profitability analysis
IBM DB2 10.5 WITH BLU ACCELERATION
2/16 x Intel E5
0.15 FTE DBA
Deployment time: 3 weeks
MICROSOFT SQL SERVER 2014 WITH CLUSTERED COLUMNSTORE INDEXES
2/16 x Intel E5
0.35 FTE DBA
Deployment time: 2 months
4/32 x Intel E5
0.65 FTE DBA
Deployment time: 3 months
8/64 x Intel E5
1.5 FTE DBAs
Deployment time: 6 months
Figure 7: Installations Summary
Hardware platforms for both solutions are based on x86 servers from major vendors. Configurations and FTE
DBA staffing levels were based on user-reported data.
Costs of Ownership
These were calculated as follows:
•
DB2 10.5 with BLU Acceleration costs were calculated for per terabyte licenses, plus two years of support
(the first year is included in initial licenses); hardware acquisition and three years of maintenance for x86
servers; and three-year premium Linux subscriptions.
•
SQL Server 2014 with Clustered Columnstore Indexes costs were calculated for SQL Server 2014 R2
Enterprise Edition per core licenses, and Windows Server 2012 R2 Datacenter Edition per processor
licenses and Client Access Licenses (CALs), plus three-year Microsoft Software Assurance coverage.
Calculations also include hardware acquisition and three years of maintenance for x86 servers.
All maintenance and support costs for both platforms are for 24/7 coverage with four-hour response time.
•
Personnel costs were calculated based on annual salaries of $104,316/year for DB2 10.5 DBAs with BLU
Acceleration training and $97,103/year for SQL Server 2014 DBAs with Clustered Columnstore Indexes
training. Salaries were increased by 56.7 percent for bonuses, benefits and other per capita costs, and
multiplied for three years.
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
10
International Technology Group
October 2014
Calculations also include appropriate training courses provided by Microsoft Learning Partners
(Microsoft does not offer classroom training directly) or IBM. The duration of these, and the number of
individuals trained varied between installations.
•
Deployment costs were calculated for external professional services staff, charged at $2,000 or $3,000 per
person-day, depending on required skill levels, plus travel and entertainment (T&E) expenses.
•
Facilities costs are for energy consumption. Calculations are based on vendor specifications and assume
near-24/365 operations over a three-year period. A conservative assumption for average cost per kilowatthour was employed.
All cost values are for the United States.
Costs Breakdowns
Costs of ownership breakdowns are presented in figure 8.
Financial Services
Company
Manufacturing
Company
IT Services
Company
157,920
236,880
394,800
IBM DB2 10.5 WITH BLU ACCELERATION
Databases
Servers
9,915
42,803
169,810
Deployment
69,834
95,386
238,420
Personnel
82,759
131,798
282,215
Facilities
TOTAL ($)
1,745
2,848
7,631
322,173
509,715
1,092,876
MICROSOFT SQL SERVER 2014 WITH CLUSTERED COLUMNSTORE INDEXES
Databases
115,483
230,966
461,933
26,274
75,314
233,075
Deployment
190,736
429,156
596,050
Personnel
201,698
338,643
738,632
Servers
Facilities
TOTAL ($)
2,043
3,274
8,820
536,234
1,077,353
2,038,510
Figure 8: Three-year Costs of Ownership Breakdowns for
IBM DB2 10.5 and Microsoft SQL Server 2014
Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
11
International Technology Group
October 2014
International Technology Group
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International Technology Group (ITG), established in 1983, is an independent research and management consulting firm
specializing in information technology (IT) investment strategy, cost/benefit metrics, infrastructure studies, deployment
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ITG was an early innovator and pioneer in developing total cost of ownership (TCO) and return on investment (ROI)
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Cost/Benefit Case for IBM DB2 10.5 for High Performance Analytics:
Compared to Microsoft SQL Server 2014
12