bi303 data warehousing with fast track and pdw - assaf fraenkel

Data Warehousing

with

FastTrack and PDW

Assaf Fraenkel Oded Shihor

Lead Architect, MCS Senior Solution Architect, HP

?איזה מכונית כדאי לקנות !?האם זו שאלה של מחיר

Agenda

Motivation

Fast Track Offering

– Balanced Architecture Approach for DW

– Example FastTrack Reference Architectures

– Optimizing Storage, Load and Maintenance

– Case Studies

Parallel Data Warehouse Offering Overview

Some SQL Data Warehouses today

Big SAN

Big SMP Server

Connected together

What’s wrong with this picture?

Answer: system out of balance

This server can consume 16 GB/Sec of IO, but the SAN can only deliver 2 GB/Sec

– Even when the SAN is dedicated to the SQL Data Warehouse, which it often isn’t

– Lots of disks for Random IOPS BUT

– Limited controllers Limited IO bandwidth

System is typically IO bound

Queries are slow

Result: significant investment, not delivering performance

You can get more sophisticated…

Realize that queries performing complex calculations,

format conversions, multi-dimension hash joins, etc. will be

more cpu-intensive than others

– Complex queries will consume data at a slower per-core rate

than simpler queries

Alternative: Measure per-core data consumption for a

variety of queries, and take the weighted average

– A standard approach to capacity planning

Or you can leave it to us…

We’ve measured a mix of TPCH queries that reflect a

‘prototype’ Data Warehouse workload

Concluded that SQL Sever 2008 R2 on current x64 cores

consume ~200 MB/Sec per core on average for this

workload

We use this as a basis for the published reference

architectures

Your mileage will vary!

– For precise system sizing, measure your own workload

Potential Performance Bottlenecks

FC HBA

A

B

FC HBA

A

B

FC SWITCH

STORAGE CONTROLLER

A

B

A

B CA

CH

E

SERVER

CACHE

SQL SERVER

WINDOWS

CPU CORES

CPU Feed Rate HBA Port Rate Switch Port Rate SP Port Rate

A

B DISK DISK

LUN

DISK DISK

LUN

SQL Server Read Ahead Rate

LUN Read Rate Disk Feed Rate

The Alternative: A Balanced System

Design a server + storage configuration that can deliver all the IO bandwidth that CPUs can consume when executing a SQL Relational DW workload

Avoid sharing storage devices among servers

Avoid overinvesting in disk drives – Focus on scan performance, not IOPS

Layout and manage data to maximize range scan performance and minimize fragmentation

Microsoft Data Warehousing – Product Offering

Scale Complexity HA by default SW-HW integration

1

2

3

SQL Server 2008 R2 with Fast Track

Reference Architecture

PDW with Hub-and-spoke

SQL Server 2008 R2

4

PDW

1

2

3

Minimal HW tune up/optimization. Supports mixed workloads Balanced solution for mostly scan centric workloads.

Max HW tune up for most DW scenarios.

4 Most flexible Architecture for handling all DW scenarios.

Agenda

Motivation

Fast Track Offering





SQL Server Fast Track Data Warehouse

A for designing a cost-effective, balanced system for Data

Warehouse workloads

Reference hardware developed in conjunction with

hardware partners using this method

for data layout, loading and management

Relational Database Only – Not SSAS, IS, RS

Solution to help customers and partners accelerate their data warehouse deployments

Fast Track Scope

Data

Path

Data Warehouse

Analysis Services

Cubes

PerformancePoint

SAN, Storage Array

Reporting Services

Web Analytic Tools

Integration

Services ETL

SharePoint Services

Microsoft Office SharePoint

Data Staging,

Bulk Loading

Subject Area

Data Marts

Supporting Systems BI Data Storage Systems Presentation Layer Systems

Reference Architecture Scope (dashed)

Excel Services

Pre

sen

tati

on

Data

P

resen

tati

on

Data

HP Fast Track DL785 G6 Demo

Fast Track SQL DW Architecture vs. Traditional DW

SQL 2008 Data Warehouse

4 Processor 16 + Core Server

Shared Network

Bandwidth

Enterprise Shared

SAN Storage

Dedicated Network

Bandwidth

Traditional SQL DW Architecture

Shared Infrastructure

Fast Track SQL DW Architecture

Dedicated DW Infrastructure

Architecture modeled after DW Appliances

Scalability from 4TB to 80TB

Dedicated Low Cost

SAN Arrays 1 for every

4 CPU Cores

OLTP Applications

Benefits:

-Lower TCO

-Balanced CPU to I/O Channel Optimized for DW

-Modular Building Block Approach

-Scale Out or Up within limits of Server and San

HP SQL Server Fast Track Data Warehousing

Fast Track G7 Configurations

Scales from SMB to Enterprise

– Prescriptive guidance and optimized methodology for deploying a data warehouse

– Targeted at query workloads patterned for large sequential data reads

– Balanced hardware approach

HP provides

– Configurations, tested performance, guidance and

– Best practices for deploying/operating/managing

– Packaged and custom support

Basic 8– 16TB

DL38x G7w/ MSA2000 G3

Mainstream 8 – 16TB

DL38x G7 w/ MSA P2000 G3

Mainstream 20 – 60TB

DL58x G7 w/ MSA P2000 G3

Premium 40– 80 TB

DL980 G7 w/ MSA P2000 G3

Coming soon

Server: HP ProLiant DL980 G7 with 8x 8-core Intel Xeon

Storage: HP P2000 G3

Scalability: 40 – 80TB

Server: HP ProLiant DL380 G7 with 2x 6-core Intel Xeon

Storage : HP P2000 G3


Small SMP:

2- Socket Processor Configuration

Server: HP ProLiant DL 580 G7 with 4x 8-core Intel Xeon

Storage : HP P2000 G3


Medium SMP: 4- Socket Processor

Configuration

Large SMP:

8- Socket Processor Configuration

2p; 12 core, 64-192GB RAM

8p; 64 core, 2TB RAM

4p; 32 core, 144-512GB RAM

Coming soon

HP SQL Server Fast Track Data Warehousing

Fast Track G7 configurations in test

Fast Track Component Architecture

Server

Windows Server OS

SQL Server

Sto

rag

e In

terc

on

nect

Storage Enclosure

Disk Array

Host Storage Adaptor

Storage Processor

CPU

Core Evaluation Metrics

These metrics are used to both validate and position Fast Track Reference Architectures

– Maximum Consumption Rate – Ability of SQL Server to process data for a specific CPU and Server combination and a standard SQL query.

– Benchmark Consumption Rate – Ability of SQL Server to process data for a specific CPU and Server combination and a user workload or query.

– User Data Capacity – Maximum available SQL Server storage for a specific Fast Track RA assuming 2.5:1 page compression factor.

Scaling the IO stack

Server

Fiber Switch

HBA

HBA

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

CPU Socket (4 Core)

CPU Socket (4 Core)

CPU Socket (4 Core)

CPU Socket (4 Core)

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

Storage Enclosure

Storage Processor

Storage Processor

RAID-1 RAID-1

RAID-1 RAID-1

RAID-1

CPU Socket (4 Core)

CPU Socket (4 Core)

CPU Socket (4 Core)

CPU Socket (4 Core)

HBA

HBA

HBA

HBA

HBA

HBA

User Data Capacity

UDC is the data capacity required

– Plan for projected growth • Based on your projections

• Needs to be allocated up-front

– Allocate for data management needs • Staging database requirements

• Temporary objects

– Allocate for TempDB • Typically 20-30% of primary data space

• Tempdb is not compressed

Storage Layout Implications for SQL Server

LUN16 LUN 2 LUN 3

Local Drive 1

Log LUN 1

Permanent DB Log

LUN 1

Te

mp

DB

TempDB.mdf (25GB) TempDB_02.ndf (25GB) TempDB_03ndf (25GB) TempDB_16.ndf (25GB)

Permanent FG

Permanent_1.ndf

Pe

rma

na

nt_

DB

S

tag

e

Data

ba

se

Stage FG

Stage_1.ndf Stage_2.ndf Stage_3.ndf Stage_16.ndf

Stage DB Log

Permanent_2.ndf Permanent_3.ndf Permanent_16.ndf

Sequential Scan Components

Contiguous allocation, data striping, pre-fetch, and read-ahead work to create efficient Sequential IO

– Data stripe width is balanced against read-ahead “Depth”

– Combined, these elements provide effective access to the full data stripe from a single thread

Each element is necessary to maximize efficiency

ARY01D1v01

ARY01D2v02

ARY02D1v03

ARY02D2v04

ARY03D1v05

ARY03D2v06

ARY04D1v07

ARY04D2v08

DB1-1.ndf DB1-7.ndf DB1-5.ndf DB1-3.ndf

DB1-2.ndf DB1-4.ndf DB1-6.ndf DB1-8.ndf

4MB

4MB 4MB

4MB 4MB 4MB

4MB 4MB

loading

One of the important topics

I hope you saw the session yesterday

If not – you can watch the video

OR

There is Appendix to this presentation -

Minimizing File fragmentation

Pre-allocate database files

• Size files correctly to prevent growth

• Do not shrink files

Do not use NTFS file fragmentation tools

– Rebuild table to ensure disk block level optimal organization

Writing data

– Concurrent load operations to the same file will induce fragmentation

– DML change operations (Update/Delete) may induce fragmentation

Use Filegroups and Partitioning to manage concurrent writes

for large tables

What’s next? My car is too small

•

•

Agenda

Motivation

Fast Track Offering





– Scale Out Architecture Approach for DW

– SQL Server in Scale Out Story

HP Enterprise Data Warehouse Appliance

Transforming today’s SQL

The world’s most scalable,

easy-to-manage enterprise

data warehousing solution

BEFORE AFTER


FOR ANY SCALE COMPLETE SIMPLIFIED


Description

Scale-Out of SQL Server: 10s TB ►100s TB ►PB

Uses massively parallel processing (MPP)

Highly optimised for DW workload at each layer of the stack

Uses index-Light

Deliver predictable performance at low cost

Simplified deployment and maintenance via appliance model

Integration with existing SQL Server 2008 DW via Hub & Spoke Architecture

Lower total cost of ownership

HP Parallel Data Warehouse Appliance -

Hardware Architecture Database Nodes

HP ProLiant DL

Control Nodes HP ProLiant DL

Active / Passive

Landing Zone

Backup Node

Spare Database Node

Management Servers

Client Drivers

ETL Load Interface

Data Center

Monitoring

Corporate Network Private Network

SQL

SQL

SQL

SQL

SQL

SQL

SQL

SQL

SQL

SQL

Control node

Where clients apps connect

MPP engine runs here

Controls DMS on all nodes

Central point for all HW

monitoring

Management node

S/W upgrades and patch

deployment staging place

Holds S/W images in case a

node needs reimaging

SQL

Storage Nodes HP MSA P2000 G3

Landing Zone

Staging place for data

loading

Accessible to outside world

Corporate Backup

Solution

Backup node

Backup file storage

Accessible to outside world

Data Rack

Control Rack

Du

al F

ibe

r C

han

ne

l

Dua

l In

fin

iban

d

Compute nodes

Store user data

Perform local query processing

Run data movement service

Not accessible to outside world

SMP (SQL Server, Fast Track) MPP (PDW)

OLTP, Transactional, Data Warehousing

Parallel Data Warehousing (esp. VLDB, complex workloads)

Symmetric Multi-Processing vs. Massively

Parallel Processing

HP Enterprise Parallel Data Warehouse –

Impressive live demo

Massive parallel query processing

106 billion rows; 10 TB table

High performance report without indexing and aggregations

Agenda

Motivation

Fast Track Offering





– Scale Out Architecture Approach for DW

– SQL Server in Scale Out Story

Date Dim D_DATE_SK D_DATE_ID D_DATE D_MONTH …

Item I_ITEM_SK I_ITEM_ID I_REC_START_DATE I_ITEM_DESC …

Store Sales Ss_sold_date_sk Ss_item_sk Ss_customer_sk Ss_cdemo_sk Ss_store_sk Ss_promo_sk Ss_quantity …

Promotion P_PROMO_SK P_PROMO_ID P_START_DATE_SK P_END_DATE_SK … Store

S_STORE_SK S_STORE_ID S_REC_START_DATE S_REC_END_DATE S_STORE_NAME …

Customer C-CUSTOMER_SK C_CUSTOMER_ID C_CURRENT_ADDR …

Customer Demographics

CD_DEMO_SK CD_GENDER CD_MARITAL_STATUS CD_EDUCATION …

Database

SS[1]

SS[2]

SS[3]

SS[4]

Data Distribution with replication

Distributed Data Warehouse Architecture

Central Enterprise

DW Hub

Regional Reporting

Departmental Reporting

ETL Tools Hub= unified EDW Spoke= Federated data marts

Enterprise data

can be maintained

on a PDW hub

MS Office 2010

Distributed Data Warehouse Approach

Hub & Spoke model

Enables DW architecture to more closely match the structure of large enterprises.

Separates user and data workloads eliminating traditional process and resource conflicts

Integrate both SMP and MPP systems with “Shared Nothing”

All systems connect via a dedicated high speed netwok

Dual high speed Infiniband

Supports multiple workloads on different systems

Microsoft Data Warehousing – Product Offering

Scale Complexity HA by default SW-HW integration

1

2

3

SQL Server 2008 R2 with Fast Track

Reference Architecture

PDW with Hub-and-spoke

SQL Server 2008 R2

4

PDW

1

2

3

Minimal HW tune up/optimization. Supports mixed workloads Balanced solution for mostly scan centric workloads.

Max HW tune up for most DW scenarios.

4 Most flexible Architecture for handling all DW scenarios.

Resources

SQL Server Fast Track DW Home Page

– http://www.microsoft.com/sqlserver/2008/en/us/fasttrack.aspx

Fast Track DW 2.0 Architecture Whitepaper

– http://msdn.microsoft.com/en-us/library/dd459178.aspx

Use minimal logged BULK operation (Trace Flag –T 610)


http://www.microsoft.com/sqlserver/2008/en/us/fasttrack.aspx





http://msdn.microsoft.com/en-us/library/dd459178.aspx













Perspectives: 2010

משובים ופייסבוק

השלמה -מירב

Let’s Party!

18:30-20:30בין השעות –ארוחת ערב

20:30 -שאטלים החל מ –למסיבה תחבורה

במעטפות בקבלת החדרים מקבלים - צמידים לכניסה

Alternatives for loading

Use a heap

– Practical if queries need to scan whole partitions

or…Use a batchsize = 0

– Fine if no parallelism is needed during load

or…Use a Two-Step Load

1. Load to a Staging Table (heap) with constraint for Deltas

2. INSERT-SELECT from Staging Table into Target CI

Resulting rows are not fragmented

Can use Parallelism in step 1 – essential for large data volumes

Two-Step Load Variations

To achieve high parallelism during historical load

– Typically into a partitioned table

– Use a Staging Table (heap) that is partitioned identically to the Target

Table

– Use multiple concurrent streams to load the Staging Table with

moderate batchsize (SSIS, Bulk Insert, etc)

– INSERT-SELECT separate partitions into the Target Table –

potentially in parallel

• Use ALTER TABLE SET ( LOCK_ESCALATION = AUTO)

– Note: If memory is limited, TempDB could be heavily used for sorting

Two-Step Load Variations (cont.)

To avoid most TempDB space and TempDB IO during load

– Use a partitioned Staging Table that is also indexed identically to

Target Table

– Load Staging Table using moderate batchsize (< 1M rows)

– Final INSERT-SELECTs will avoid any sort!

• However the staging loads will be logged

– Note: Parallelism will be limited if load batches overlap

Loading Data

Goal: maximize read performance

– Minimizes Disk head movement

– Maintains high average request size (Think ~400k not 8k)

– Sustain high average scan rates

Key considerations for a Fast Track data load

– Data Architecture: Destination table, partitioning, and filegroup

– Source Data: Format & size

– System Resources: CPU & Memory

Use minimal logged BULK operation (Trace Flag –T 610)







bi303 data warehousing with fast track and pdw - assaf fraenkel

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