SQL Server Parallel Data Warehouse: Architecture Overview. José Blakeley Database Systems Group, Microsoft Corporation

Transcription

1 SQL Server Parallel Data Warehouse: Architecture Overview José Blakeley Database Systems Group, Microsoft Corporation

2 Outline Motivation MPP DBMS system architecture HW and SW Key components Query processing example PDW and BI demo Upcoming capabilities Summary 2

3 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 Day-to-day business Analysis over historical data 3

4 SQL Server Parallel Data Warehouse Shared-nothing, distributed, parallel DBMS Built on Windows Server and SQL Server Built-in data and query partitioning Provides single system view over a cluster of SQL Servers Appliance concept Software + hardware solution, low TCO Choice of hardware vendors (e.g., HP, Dell) Optimized for DW workloads Bulk loads ( ~1.2 TB/hr) Sequential scans (700 TB in 3hr) Scale from 10s of TBs to PBs 1 data rack manages ~144 TB (600GB * 24 LFF * 10 nodes) 1 PB takes ~7 racks 4

5 Hardware Architecture Compute Nodes Control Nodes SQL Active / Passive SQL SQL SQL SQL SQL SQL SQL ETL Load Interface Corporate Backup Solution 5 SQL Dual Fiber Channel Data Center Monitoring SQL Dual Infiniband Client Drivers (ODBC, OLEDB, ADO.NET) Spare Compute Node 2 Rack Appliance

6 Software Architecture Query Tool MS BI (AS, RS) DWSQL 3 rd Party Tools Internet Explorer Data Access (OLEDB, ODBC, ADO.NET, JDBC) IIS Admin Console Compute Node Compute Nodes Compute Nodes Data Movement Service PDW Engine Data Movement Service User Data SQL Server Landing Zone Node DW Authentication DW Configuration DW Schema TempDB Data Movement Service SQL Server Control 6 Node

7 Key Software Functionality PDW Engine Provides single system image T-SQL compilation Global metadata and appliance configuration Global query optimization and plan generation Global T-SQL execution coordination Global transaction coordination Authentication and authorization Supportability (HW and SW status info via DMVs) Data Movement Service Data movement across the appliance Distributed query execution operators Parallel Loader Runs 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 7

8 Query Processing SQL statement compilation Parsing, validation, optimization Builds an MPP execution plan A sequence of discrete parallel QE steps Steps involve SQL queries to be executed by SQL Server at each compute node As well as data movement steps Executes the plan Coordinates workflow among steps Assembles the result set Returns result set to client 8

9 Example DW Schema 18,000,048,306 rows 4,500,000,000 rows 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 L_SHIPDATE > ' GROUP BY L_ORDERKEY, O_ORDERDATE, O_SHIPPRIORITY ORDER BY REVENUE DESC, O_ORDERDATE 30,000,000 rows 2,400,000,000 rows 600,000,000 rows 9 25 rows 5 rows 450,000,000 10/12/2011

10 Example Schema TPCH Customer Table -- distributed 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), 10 o_shippriority integer, o_comment varchar(79)) LineItem Table -- distributed 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)) ;

11 Example - Query Ten largest building orders shipped since March 5, 2010 SELECT TOP 10 L_ORDERKEY, SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)) 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 L_SHIPDATE > ' GROUP BY L_ORDERKEY, O_ORDERDATE, O_SHIPPRIORITY ORDER BY REVENUE DESC, O_ORDERDATE AS REVENUE, 11

12 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] 12 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, [o_orderdate], [o_shippriority] 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];

13 Data Movement Operations SHUFFLE_MOVE (N:N) Distributed Distributed data exchange across the appliance Result is a distributed table hashed on some column PARTITION_MOVE (N:1) Union of distributed partitions across compute nodes into a table in the control node MASTER_MOVE (1:N) Replicate a table from the control node to all compute nodes BROADCAST_MOVE (N:N) Distributed Replicated data exchange across appliance Unconditional shuffle to all compute nodes TRIM_MOVE Distribute a replicated table by trimming each copy Since all the nodes have same copy of the replicated tables the idea is that nodes keep the values that belong to the distributions in that node REPLICATE_MOVE (1:N) Moves a replicated table from 1 to N compute nodes. 13

14 PDW Demo 14

15 Other Important Functionality Fault tolerance All HW components have redundancy: CPUs, Disks, networks, power, storage processors All SW components use MS Cluster Services for failover Control, compute and management nodes have A/P Integration with Microsoft and 3 rd party BI tools SS Integration Services (ETL) has PDW as a destination SS Analysis Services (OLAP) has PDW as a source SS Reporting Services, Excel PowerPivot SAS, Business Objects, Microstrategy Hadoop connectors (ETL) Appliance health, monitoring, PDW appliance validator 15 UCI ISG Seminar 1/8/2010

16 EDW Architecture 16

17 BI Demo 17

18 Upcoming Capabilities Column-store support Data compression and query speed Enhanced distributed query processing New execution strategies (DW) New optimization techniques (DW) Data movement Faster, less CPU-intensive, more scalable Deeper analytics Map-reduce-like functionality inside the cluster Data mining, embedded analytics Enhanced HW architecture choices Low-power clusters 18 UCI ISG Seminar 1/8/2010

19 Summary Motivation MPP DBMS system architecture HW and SW Key components Query processing example PDW and BI demo Upcoming capabilities 19

20 THANKS! 20