1. SQL Server: A Data Platform for Large-Scale Applications
José Blakeley, Software Architect
Database Systems Group, Microsoft Corporation

2. SQL Server Design Philosophy
To solve 90+% of the problems automatically
Self Tuning
Dynamic Optimization
Self Configuration
Self Management
Self Healing
The remaining percentage takes higher touch
Requires careful design for scalability
Good knowledge of how the RDBMS platform works
This talk describes how we are codifying our solution for large-scale DW into SQL Server
UCI ISG Seminar
1/8/2010

3. SQL Data Platform Any Place, Any Type, Any Scale
Enterprise Data Platform
Beyond Relational
Dynamic Development
Pervasive Insight
UCI ISG Seminar
1/8/2010

4. Outline SQL Server Data Platform Basic concepts – common ground
Assumptions
Workloads
SQL Server Parallel DW DBMS
Philosophy
System Architecture
Software Architecture
Summary
UCI ISG Seminar
1/8/2010

5. Workload Types Online Transaction Processing (OLTP)
Balanced read-update ratio (60%-40%)
Fine-grained inserts and updates
High transaction throughput e.g., 10s K/s
Usually very short transactions e.g., 1-3 tables
Sometimes multi-step e.g., financial
Relatively small data sizes e.g., few TBs
Data Warehousing and Business Analysis (DW)
Read-mostly (90%-10%)
Few updates in place, high-volume bulk inserts
Concurrent query throughput e.g., 10s K / hr
Per query response time < 2 s
Snowflake, star schemas are common e.g., 5-10 tables
Complex queries (filter, join, group-by, aggregation)
Very large data sizes e.g., 10s TB - PB
OLTP = Day-to-day business
DW = Analysis over recorded data
UCI ISG Seminar
1/8/2010

6. Star Join Schema 436,892,631 rows 41 rows 34 rows 118 rows 13,517 rows
SELECT FE0.RegionName,
FL0.FiscalYearName,
SUM (A.ActualRevenueAmt)
FROM TECSPURSL00 A
JOIN SalesDate L
ON A.SalesDateID = L.SalesDateID
JOIN UpperGeography UG
ON A.TRCreditedSubsidiaryId =
UG.CreditedSubsidiaryID
JOIN Region FE0
ON UG.CreditedRegionID =
FE0.RegionID
JOIN FiscalYear FL0
ON L.FiscalYearID = FL0.FiscalYearID
GROUP BY FE0.RegionName, FL0.FiscalYearName
118 rows
13,517 rows
UCI ISG Seminar
1/8/2010
9/18/2018

7. Why look at workloads? $B
DBMS architectures of 30 years ago designed for mixed workloads
OLTP and DW workloads have grown into big market segments
Workload-specific engines can be profitable
Innovations are driven by deeper understanding of workloads 8 6
OLTP
Mixed DW 4 2
1995
2000
2005
2010
UCI ISG Seminar
1/8/2010

8. Changing TPC-H Landscape
68x perf
12% cost
New DW startups disrupting TPC-H
Column-store, compression, and scale-out
UCI ISG Seminar
1/8/2010

9. Data Warehousing SQL Server 2008
Provides out-of-the-box SMP scale for ~10 TB
Data compression – row and page
Star join, bitmap filters, partitioned table parallelism
Minimally Logged INSERT
MERGE
Resource governor – CPU and memory
Many successful apps for TB running today via custom scale-out
E.g., Danske Bank, Clalit Health, US Dep. of Agriculture, Pan- STARRS
Building out-of-the-box scale-out for 10s-100s of TB
This is the focus of this talk
UCI ISG Seminar
1/8/2010

10. Outline SQL Server Data Platform Basic concepts – common ground
Assumptions
Workloads
SQL Server Parallel DW DBMS
Design Philosophy
Hardware Architecture
Software Architecture
Summary
UCI ISG Seminar
1/8/2010

11. Design Philosophy Shared-nothing, distributed, parallel DBMS
Implicit data and function partitioning
Appliance concept
Software + hardware solution
Optimized for DW workloads
Scalable to Petabytes
UCI ISG Seminar
1/8/2010

12. Reference Hardware Platforms
Appliance Concept
Parallel DW
DBMS
System
Software
INDUSTRY STANDARD
SERVERS
Reference Hardware Platforms
INDUSTRY STANDARD
NETWORKING
INDUSTRY STANDARD
STORAGE
UCI ISG Seminar
1/8/2010

13. Hardware Architecture
Compute Nodes
Storage Nodes
Control Nodes Active / Passive
SQL
SQL
Client Drivers
(ODBC, OLE-DB, ADO.NET)
SQL
SQL
SQL
Management Servers
SQL
Data Center
Monitoring
Dual Infiniband
SQL
Dual Fiber Channel
SQL
Landing Zone
ETL Load Interface
SQL
Backup Nodes
Spare Compute Node
Corporate Backup
Solution
1 Rack Server Appliance
UCI ISG Seminar
1/8/2010

14. Node Types Control node: Backup node: Compute nodes: Management node:
Where clients apps connect
Parallel engine runs here
Contains system metadata
Compute nodes:
Store user data
Perform query execution
Not accessible to outside world
Landing Zone:
Staging place for data loading
Accessible to outside world
Can be augmented with 3rd party HW and SW
Backup node:
Backup file storage
Accessible to outside world
Can be augmented with 3rd party HW and SW
Management node:
Windows domain controller (Active Directory)
SW upgrades staging place
Holds SW images in case a node needs reimaging

15. Software Architecture
Compute Nodes
Compute Node
Query Tool
MS BI
(AS, RS)
Control Node
Other 3rd Party Tools
DWSQL
Landing Zone Node
Internet Explorer
SQL Server
DW Authentication
DW Configuration DW
Schema
TempDB
User Data
Data Movement Service
MPP Engine Coordinator
IIS
Admin Console
Data Access (OLEDB, ODBC, ADO.NET, JDBC)
UCI ISG Seminar
1/8/2010

16. Key Components MPP Engine Coordinator Parallel Loader
Provides single system image
SQL compilation
Global metadata and appliance configuration
Global query optimization and plan generation
Global query execution coordination
Global transaction coordination
Authentication and authorization
Supportability (HW and SW status info)
Data Movement Service
Data movement across the appliance
Distributed query execution operators
Parallel Loader
Run from the Landing Zone
SSIS or command line tool
Parallel Database Copy
High performance data export
Enables Hub-Spoke scenarios
Parallel Backup/Restore
Backup files stored on Backup Nodes
Backup files may be archived into external device/system
UCI ISG Seminar
1/8/2010

17. Query Processing SQL statement compilation
Parsing, validation, optimization
Builds an MPP execution plan
A set of parallel QE steps
Executes the plan
Coordinates workflow among steps
Assembles the resultset
Returns resultset to client

18. Example – Schema TPCH UCI ISG Seminar 1/8/2010
-- Customer Table
-- distributiond on c_custkey
CREATE TABLE customer
( c_custkey bigint,
c_name varchar(25),
c_address varchar(40),
c_nationkey integer,
c_phone char(15),
c_acctbal decimal(15,2),
c_mktsegment char(10),
c_comment varchar(117))
WITH (distribution=hash(c_custkey)) ;
-- Orders Table
CREATE TABLE orders
( o_orderkey bigint,
o_custkey bigint,
o_orderstatus char(1),
o_totalprice decimal(15,2),
o_orderdate date,
o_orderpriority char(15),
o_clerk char(15),
o_shippriority integer,
o_comment varchar(79))
WITH (distribution=hash(o_orderkey)) ;   
-- LineItem Table
-- distributiond on l_orderkey
CREATE TABLE lineitem
( l_orderkey bigint,
l_partkey bigint,
l_suppkey bigint,
l_linenumber bigint,
l_quantity decimal(15,2),
l_extendedprice decimal(15,2),
l_discount decimal(15,2),
l_tax decimal(15,2),
l_returnflag char(1),
l_linestatus char(1),
l_shipdate date,
l_commitdate date,
l_receiptdate date,
l_shipinstruct char(25),
l_shipmode char(10),
l_comment varchar(44))
WITH (distribution=hash(l_orderkey)) ;
UCI ISG Seminar
1/8/2010

19. Example - Query UCI ISG Seminar 1/8/2010 SELECT TOP 10 L_ORDERKEY,
SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)) AS REVENUE,
O_ORDERDATE,
O_SHIPPRIORITY
FROM CUSTOMER,
ORDERS,
LINEITEM
WHERE C_MKTSEGMENT = 'BUILDING' AND
C_CUSTKEY = O_CUSTKEY AND
L_ORDERKEY = O_ORDERKEY AND
O_ORDERDATE < ' ' AND
O_ORDERDATE >= ' :00:00.000'
GROUP BY L_ORDERKEY,
ORDER BY REVENUE DESC,
O_ORDERDATE ;
UCI ISG Seminar
1/8/2010

20. Example – Execution Plan
-- Step 1: create temp table at control node
CREATE TABLE [tempdb].[dbo].[Q_[TEMP_ID_664]]
( [l_orderkey] BIGINT, [REVENUE] DECIMAL(38, 4),
[o_orderdate] DATE, [o_shippriority] INTEGER );
-- Step 2: create temp tables at all compute nodes
CREATE TABLE [tempdb].[dbo].[Q_[TEMP_ID_665]_[PARTITION_ID]]
( [l_orderkey] BIGINT, [l_extendedprice] DECIMAL(15, 2),
[l_discount] DECIMAL(15, 2), [o_orderdate] DATE,
[o_shippriority] INTEGER, [o_custkey] BIGINT,
[o_orderkey] BIGINT )
WITH ( DISTRIBUTION = HASH([o_custkey]) );
-- Step 3: SHUFFLE_MOVE
SELECT [l_orderkey], [l_extendedprice], [l_discount],
[o_orderdate], [o_shippriority], [o_custkey], [o_orderkey]
FROM [dwsys].[dbo].[orders] JOIN [dwsys].[dbo].[lineitem]
ON ([l_orderkey] = [o_orderkey])
WHERE ([o_orderdate] < ' '
AND [o_orderdate] >= ' :00:00.000')
INTO Q_[TEMP_ID_665]_[PARTITION_ID]
SHUFFLE ON (o_custkey);
-- Step 4: PARTITION_MOVE
SELECT [l_orderkey],
sum(([l_extendedprice] * (1 - [l_discount]))) AS REVENUE,
[o_orderdate], [o_shippriority]
FROM [dwsys].[dbo].[customer] JOIN tempdb.Q_[TEMP_ID_665]_[PARTITION_ID]
ON ([c_custkey] = [o_custkey])
WHERE [c_mktsegment] = 'BUILDING'
GROUP BY [l_orderkey], [o_orderdate], [o_shippriority]
INTO Q_[TEMP_ID_664];
-- Step 5: Drop temp tables at all compute nodes
DROP TABLE tempdb.Q_[TEMP_ID_665]_[PARTITION_ID];
-- Step 6: RETURN result to client
SELECT TOP 10 [l_orderkey], sum([REVENUE]) AS REVENUE,
FROM tempdb.Q_[TEMP_ID_664]
GROUP BY [l_orderkey], [o_orderdate], [o_shippriority]
ORDER BY [REVENUE] DESC, [o_orderdate] ;
-- Step 7: Drop temp table at control node
DROP TABLE tempdb.Q_[TEMP_ID_664];
UCI ISG Seminar
1/8/2010

21. Other Important Functionality
Fault tolerance
All HW components have redundancy:
CPUs, Disks, networks, power, storage processors
Control and Compute nodes use failover clustering
Management nodes have active & standby
Integration with SQL Server BI tools
SS Integration Services (ETL) has PDW as a destination
SS Analysis Services (OLAP) has PDW as a source
SS Reporting Services
Excel
UCI ISG Seminar
1/8/2010

22. Future Challenges Richer DW analysis
Map-reduce-like functionality inside the cluster
Data mining, embedded analytics
Richer hub-and-spoke configurations
MPP to MPP
Distributed transactions
Isolation levels, deadlocks, logs
Distributed, parallel query processing
Execution strategies (DW)
Optimization techniques (DW)
Integration with other data services
Streaming
Increased HW architecture choices
Low-power clusters
UCI ISG Seminar
1/8/2010

23. Summary SQL Server Data Platform overview Workload types
SQL Server Parallel DW DBMS
Design Philosophy
System Architecture
Software Architecture
Query Example
Future challenges
UCI ISG Seminar
1/8/2010

24. Resources Microsoft SQL Server 2008 case studies
SQL Server best practices
Dave Salch, Eric Kraemer, Umair Waheed, Paul Dyke, Fast Track Data Warehouse 2.0 Architecture, SQL Server Technical Article, MSDN, Nov
SQL Server Customer Advisory Team
Research on balanced HW architectures
Yogesh Simmhan, et. al. GrayWulf: Scalable Software Architecture for Data Intensive Computing, HICSS pp.1-10, 42nd Hawaii International Conference on System Sciences, 2009
Alexander S. Szalay, et al., Low Power Amdahl-Balanced Blades for Data Intensive Computing, SC 2009.
UCI ISG Seminar
1/8/2010

25. THANKS!
UCI ISG Seminar
1/8/2010