Transcript of Fast Track, Microsoft SQL Server 2008 Parallel Data Warehouse and Traditional Data Warehouse Design...
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- Fast Track, Microsoft SQL Server 2008 Parallel Data Warehouse
and Traditional Data Warehouse Design BI Best Practices and Tuning
for Scaling SQL Server 2008
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- Data Warehouse
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- Fast Track
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- PDW
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- Traditional MD design SSAS PDW SSAS
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- Characteristic Typical BI (DWs & DMs)OLTP (Operational
Database) Data Activity Large reads (disjoint sequential scans)
Large writes (new data appends) Indexed reads and writes Large
scale hashing Small transactions Constant small index reads,
writes, and updates Database sweet spot size 100s of Gigabytes to
Terabytes (need medium to large storage farms) Gigabytes (require
smaller to medium sized storage farms) Time period Historical
(contributes to large data volumes) Current Queries Largely
unpredictablePredictable I/O throughput requirement Up to 20 GB/sec
sustained throughput IOPS is more important than sustained
throughput
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- Microsoft/HP Fast Track reference configurations OR SQL Server
Parallel Data Warehouse (PDW) SQL Server/HP Traditional DW design
reference configurations Different logical and physical DB design
philosophies Mmm, what will my logical & physical DB design
look like ? Lower hardware costs
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- It is not uncommon to have hundreds of disk drives to support
the I/O throughput requirements in a traditional DW environment
RAID 5
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- How does Fast Track and PDW get its speed ? X-Ray view at the
physical disk level First lets look at a traditional DW..
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- Data is stored wherever it happens to land Sequential data Fact
table Initial load Fact table 2 nd day load Fact table 3 rd day
load Fact table 5 th day load Fact table 6 th day load
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- Column Index / Column Index / Column Pre-Calculated data
Pre-Calculated data Duplicate data
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- Disk throughput is slower with indexes, aggregates and summary
tables Index-lite is faster because there is less disk head
movement Eliminating indexes and storing data sequentially will
provide the fastest disk throughput rates Index Summary table
Traditional DW design with indexes & summary tables Fast Track
& PDW Index-lite Fast Track & PDW Fastest sequential scan
rates
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- Example: Average disk Seek time is typically about 4ms; Full
stroke is about 7.5ms. At 15K RPM = 250 revolutions/sec. = 4ms for
a full revolution = Average latency is about 2ms. Fast Track &
PDW are designed to stream large blocks of data sequentially which
is even faster than average latency because disk heads are directly
over the streaming data.
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- Seek time is typically 2 - 4x longer than average latency. By
eliminating seek time you can have approximately 2 4x fewer disk
drives in order to maintain a given throughput level. Fast Track
& PDW are designed to stream large blocks of data sequentially!
Why does PDW and Fast Track want data to be stored sequentially
?
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- Fast Track and PDW get its speed from FAST scan rates ! In
addition, HP and SQL Server PDW uses Massively Parallel Processing
(MPP) to expand Fast Track concepts in a BI appliance Fast scan
rates
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- Traditional DB design Fast Track or PDW
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- Basic 6 12TB DL38x w/ MSA2000 Mainstream 12 24TB DL585 G6 w/
MSA2000 Mainstream 16 32 TB DL580 G5 w/ MSA2000 G2 Premium 24 48 TB
DL785 G6 w/ MSA2000 G2
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- HP SQL Server 2008 Parallel Data Warehouse (PDW) Control Rack
Data Rack
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- ProLiant Servers
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- Miscellaneous Techniques to Improve SQL Server BI
Performance
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- SQL Server Analysis Services 2008
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- SQL Server Analysis Services 2008 Techniques to Improve
Performance SSAS SSAS has to major components Formula Engine (does
most of the analysis work and tries to keep cells in memory) Fast
clock speeds are best Storage Engine (if cells are not in memory,
the Storage Engine gets the data from disk) Goal is to minimize
Storage Engine use and keep data in memory for the Formula Engine
to use Faster Storage (SSD) OR more disk drives for quicker
responses to Storage Engine Manage your partitions in your AS
Database by query performance required Because Large Cubes > 100
GB may not fit in memory. So we design the partitions to get into
memory as quickly as possible. Best Practice less than 4 million
cells per partition
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- Tune memory
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- Buffers are allocated via Execution Trees Each of these
Numbered Steps represents a new Execution Tree Spawning multiple
copies of the package with a horizontal partition of data will
create more process space and execution trees
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- www.microsoft.com/teched www.microsoft.com/learning
http://microsoft.com/technet http://microsoft.com/msdn
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