Building BI Solutions with SQL Server PDW AU3 Ruwen Hess Senior Program Manager Microsoft Corporation DBI321

Source: TDWI Report – Next Generation DW Data Warehousing has shifted almost entirely towards the appliance model due to speed of the balanced appliance and scalability of scale out (MPP) solutions. Jim Cobelius, Forrester Research

Source: MS internal analysis, DBSMIT Cloud Market Opportunity Forecast CAGR -0.3% 26.2% 7.1% Share(‘15) 4.6% 5.0% 30.0% 60.4% 7.1%

Scale out Scalable Standards Based Flexible Cost Effective

CONTROL RACK DATA RACK Control Node (query submitted here) Management Node Landing Zone Backup Node Query is executed on all nodes Multiple queries are simultaneously executed across all nodes PDW supports querying while data is loading

Time Dim Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Store Dim Store Dim ID Store Name Store Mgr Store Size Store Dim ID Store Name Store Mgr Store Size Product Dim Prod Dim ID Prod Category Prod Sub Cat Prod Desc Prod Dim ID Prod Category Prod Sub Cat Prod Desc Mktg Campaign Dim Mktg Campaign Dim Mktg Camp ID Camp Name Camp Mgr Camp Start Camp End PDW Compute Nodes Sales Facts Date Dim ID Store Dim ID Prod Dim ID Mktg Camp Id Qty Sold Dollars Sold

Time Dim Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Sales Facts Date Dim ID Store Dim ID Prod Dim ID Mktg Camp Id Qty Sold Dollars Sold Store Dim Store Dim ID Store Name Store Mgr Store Size Store Dim ID Store Name Store Mgr Store Size Product Dim Prod Dim ID Prod Category Prod Sub Cat Prod Desc Prod Dim ID Prod Category Prod Sub Cat Prod Desc Mktg Campaign Dim Mktg Campaign Dim Mktg Camp ID Camp Name Camp Mgr Camp Start Camp End PD TD MD SD PD TD MD SD PD TD MD SD PD TD MD SD Smaller Dimension Tables are Replicated on Every Compute Node

Time Dim Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Store Dim Store Dim ID Store Name Store Mgr Store Size Store Dim ID Store Name Store Mgr Store Size Product Dim Prod Dim ID Prod Category Prod Sub Cat Prod Desc Prod Dim ID Prod Category Prod Sub Cat Prod Desc Sales Facts Date Dim ID Store Dim ID Prod Dim ID Mktg Camp Id Qty Sold Dollars Sold Mktg Campaign Dim Mktg Campaign Dim Mktg Camp ID Camp Name Camp Mgr Camp Start Camp End PD TD MD SD PD TD MD SD PD TD MD SD PD TD MD SD SF-1 SF-2 SF-3 SF-4 Larger Fact Table is Hash Distributed Across All Compute Nodes SF-1 SF-2 SF-3 SF-4

SQL Server PDW Appliance

Shuffle Movement DMS Redistributes the data by color values in parallel. Compute Node 1 Compute Node 2 Example: Select [color], SUM([qty]) from [Store Sales] group by [color]; Example: Select [color], SUM([qty]) from [Store Sales] group by [color]; Return Ss_idcolorqty Store Sales 1 Red5 3 Blue11 5 Red12 7 Green7 Ss_idcolorqty Store Sales 2 Red8 4 Blue10 6 Yellow12 Distributed Table Temp_1 Red5 12 Red8 Green7 Temp_1 Blue11 Yellow12 Blue10 colorqty colorqty Hash Blue21 Red25 Green7 Yellow12 colorqty Hash Parallel Merge and Aggregate

Legend: Control Node Client Interface (JDBC, ODBC, OLE-DB, ADO.NET) Client Interface (JDBC, ODBC, OLE-DB, ADO.NET) DMS Manager PDW Engine … Compute Node 1 DMS Core PDW Agent Landing Zone Node Bulk Data Loader PDW Agent Management Node Active Directory PDW Agent Compute Node 2 DMS Core PDW Agent Compute Node 10 DMS Core PDW Agent PDW service Data Movement ServiceDMS= Parallel Data WarehousePDW= ETL Interface ETL Interface Data Rack (up to 4)Control Rack

SQL Server Compatibility BI, Analytics, & ETL Integration Performance At Scale Broader functionality Full Alignment Less work for the same results Do the same work more efficiently Native Support for -Analysis Services -Reporting Services -PowerPivot Lay the foundation for broad connectivity support

Control Node

Shell Appliance (SQL Server) Shell Appliance (SQL Server) Engine Service Plan Steps Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Control Node SELECT foo

Shell Appliance (SQL Server) Shell Appliance (SQL Server) Engine Service Plan Steps MEMO Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Compute Node (SQL Server) Control Node SELECT Return

1. Simplification and space exploration Query standardization and simplification (e.g. column reduction, predicates push-down) Logical space exploration (e.g. join re-ordering, local/global aggregation) Space expansion (e.g. bushy trees – dealing with intermediate resultsets) Physical space exploration Serializing MEMO into binary XML (logical plans) De-serializing binary XML into PDW Memo 2. Parallel optimization and pruning Injecting data move operations (expansion) Costing different alternatives Pruning and selecting lowest cost distributed plan 3. SQL Generation Generating SQL Statements to be executed

(l_o = o_o) O (o_o) LI (l_o) (l_o = o_o) shuffle (l_pk) O (o_o) LI (l_o) (l_pk = p_pk) broadcast P (p_pk) SELECT * from orders JOIN lineitem on (o_orderkey = l_orderkey) JOIN part on (l_partkey = p_partkey) WHERE p_name like '%smoke%'; P (p_pk)

Seconds Queries 5x improvement in terms of total elapsed time out of the box

Goal Eliminate CPU utilization spent on data conversions Further parallelize operations during data moves Functionality Using ODBC instead of ADO.NET for reading and writing data Minimizing appliance resource utilization for data moves Benefits Better resource, CPU, utilization 6x or more faster move operations Increased concurrency Mixed workload (loads + queries)

SQL PDW Clients (ODBC, OLE-DB, ADO.NET) SQL Server Clients (ADO.NET, ODBC, OLE-DB, JDBC) TDS Server: , Server: , SequeLink Goal ‘Look’ just like a normal SQL Server Better integration with other BI tools Functionality Use existing SQL Server drivers to connect to SQL Server PDW Implement SQL Server TDS protocol Named Parameter support SQLCMD connectivity to PDW Benefits Use known tools and proven technology stack Existing SQL Server ’eco-system’ 2x performance improvement for return operations 5x reduction of connection time

Goal Support common scenarios of code encapsulation and reuse in Reporting and ETL Functionality System and user-defined stored procedures Invocation using RPC or EXECUTE Control flow logic, input parameters Benefits Enables common logic re-use Big impact for Reporting Services scenarios Allows porting existing scripts Increases compatibility with SQL Server Syntax CREATE { PROC | PROCEDURE } [dbo.]procedure_name [ data_type } [ = default ] ] [,...n ] AS { [ BEGIN ] sql_statement [;] [...n ] [ END ] } [;] ALTER { PROC | PROCEDURE } [dbo.]procedure_name [ data_type } [ = default ] ] [,...n ] AS { [ BEGIN ] sql_statement [;] [...n ] [ END ] } [;] DROP { PROC | PROCEDURE } { [dbo.]procedure_name } [;] [ { EXEC | EXECUTE } ] { { [database_name.][schema_name.]procedure_name } [{ value }] [,...n ] } [;] { EXEC | EXECUTE } ( | [ N ]'tsql_string' } [ +...n ] ) [;] Unsupported Functionality Stored Proc NestingOutput Params Return Try-Catch

Goal Support local and international data Functionality Fixed server level collation User-defined column level collation Supporting all Windows collations Allow COLLATE clauses in Queries and DML Benefits Store all the data in PDW w/ additional querying flexibility Existing T-SQL DDL and Query scripts SQL Server alignment and functionality Syntax CREATE TABLE T ( c1 varchar(3) COLLATE traditional_Spanish_ci_ai, c2 varchar(10) COLLATE …) SELECT c1 COLLATE Latin1_General_Bin2 FROM T SELECT * FROM T ORDER BY c1 COLLATE Latin1_General_Bin2 Unsupported Functionality  Cannot specify DB collation during DB creation  Cannot alter column collations for existing tables

Connector for Hadoop Bi-directional (import/export) interface between MSFT Hadoop and PDW Delimited file support Adapter uses existing PDW tools (bulk loader, dwsql) Low cost solution that handles all the data: structured and unstructured Additional agility, flexibility and choice Connector for Informatica Connector providing PDW source and target (mappings, transformations) Informatica uses PDW bulk loader for fast loads Leverage existing toolset and knowledge Connector for Business Objects

Seconds Queries

Portal ETL PDW Operational DB’s

Infiniband GBit link

demo PowerPivot with SQL Server PDW … just like any other SQL Server

Sensor/ RFID Data Blogs, Docs Web Data HADOOP

Sensor/ RFID Data Blogs, Docs Web Data SQL Server PDW Interactive BI/Data Visualization SQOOP Application Programmers DBMS Admin Power BI Users

… Landing Zone Compute Node 1 Compute Node 8 HDFS … PDW- configuration file PDW Hadoop Connector SQOOP export with source (HDFS path) & target (PDW DB & table) 1. FTP Server Copies incoming data on Landing Zone Read HDFS data via mappers Invokes ‘DWLoader’ Telnet Server 4.4. Control Node Compute Nodes Windows/ PDW Linux/ Hadoop 5.5.

demo Hadoop Sqoop Connector with SQL Server PDW … integrating unstructured data into your end-to-end DW/BI solution

Q1 Q2 Q3 Q4Q1 Q2 Improved node manageability Better performance and reduced overhead OEM requests Programmability Batches Control flow Variables Temp tables QDR infiniband switch Onboard Dell Columnar store index Stored procedures Integrated Authentication PowerView integration Workload management LZ/BU redundancy Windows 8 SQL Server 2012 Hardware refresh CALENDAR YEAR 2011 CALENDAR YEAR 2012 Cost based optimizer Native SQL Server drivers, including JDBC Collations More expressive query language Data Movement Services performance SCOM pack Stored procedures (subset) Half-rack 3rd party integration (Informatica, MicroStrategy, Business Objects, HADOOP) Q4 V-Next Appliance Update 3 Appliance Update 1 Shipped Appliance Update 2 Q3 Shipped

DBI209 – Big Data, Big Deal Lots of BI Tool Specific Related Sessions (PowerPivot, Analysis services, Etc.) Breakthrough Insights: Big Data Analytics & Data Warehousing Demo Station PDW Deep Dive Session Online from TechEd 2010

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