Building Peta Byte Servers Jim Gray Microsoft Research

Building Peta. Byte Servers Jim Gray Microsoft Research Gray@Microsoft. com http: //www. Research. Microsoft. com/~Gray/talks Kilo Mega Giga Tera Peta Exa 103 106 109 1012 1015 1018 today, we are here 1

Outline • The challenge: Building GIANT data stores – for example, the EOS/DIS 15 PB system • Conclusion 1 – Think about MOX and SCANS • Conclusion 2: – Think about Clusters – SMP report – Cluster report 2

The Challenge -- EOS/DIS • Antarctica is melting -- 77% of fresh water liberated – – – sea level rises 70 meters Chico & Memphis are beach-front property New York, Washington, SF, LA, London, Paris • Let’s study it! Mission to Planet Earth • EOS: Earth Observing System (17 B$ => 10 B$) – 50 instruments on 10 satellites 1997 -2001 – Landsat (added later) • EOS DIS: Data Information System: – 3 -5 MB/s raw, 30 -50 MB/s processed. – 4 TB/day, – 15 PB by year 2007 3

The Process Flow • Data arrives and is pre-processed. – instrument data is calibrated, gridded averaged – Geophysical data is derived • Users ask for stored data OR to analyze and combine data. • Can make the pull-push split dynamically Pull Processing Other Data Push Processing 4

Designing EOS/DIS • Expect that millions will use the system (online) Three user categories: – – – NASA 500 -- funded by NASA to do science Global Change 10 k - other dirt bags Internet 20 m - everyone else Grain speculators Environmental Impact Reports New applications => discovery & access must be automatic • Allow anyone to set up a peer- node (DAAC & SCF) • Design for Ad Hoc queries, Not Standard Data Products If push is 90%, then 10% of data is read (on average). => A failure: no one uses the data, in DSS, push is 1% or less. => computation demand is enormous (pull: push is 100: 1) 5

Obvious Points: EOS/DIS will be a cluster of SMPs • It needs 16 PB storage – = 1 M disks in current technology – = 500 K tapes in current technology • It needs 100 Tera. Ops of processing – = 100 K processors (current technology) – and ~ 100 Terabytes of DRAM • 1997 requirements are 1000 x smaller – smaller data rate – almost no re-processing work 6

The architecture • 2+N data center design • Scaleable OR-DBMS • Emphasize Pull vs Push processing • Storage hierarchy • Data Pump • Just in time acquisition 7

2+N data center design • duplex the archive (for fault tolerance) • let anyone build an extract (the +N) • Partition data by time and by space (store 2 or 4 ways). • Each partition is a free-standing OR-DBBMS (similar to Tandem, Teradata designs). • Clients and Partitions interact via standard protocols – OLE-DB, DCOM/CORBA, HTTP, … 8

Hardware Architecture • 2 Huge Data Centers • Each has 50 to 1, 000 nodes in a cluster – Each node has about 25… 250 TB of storage – – – SMP DRAM 100 disks 10 tape robots 2 Interconnects . 5 Bips to 50 Bips 20 K$ 50 GB to 1 TB 50 K$ 2. 3 TB to 230 TB 200 K$ 25 TB to 250 TB 200 K$ 1 GBps to 100 GBps 20 K$ • Node costs 500 K$ • Data Center costs 25 M$ (capital cost) 9

Scaleable OR-DBMS • Adopt cluster approach (Tandem, Teradata, VMScluster, DB 2/PE, Informix, . . ) • System must scale to many processors, disks, links • OR DBMS based on standard object model – CORBA or DCOM (not vendor specific) • Grow by adding components • System must be self-managing 10

Storage Hierarchy • Cache hot 10% (1. 5 PB) on disk. • Keep cold 90% on near-line tape. • Remember recent results on speculation 10 -TB RAM 500 nodes 1 PB of Disk 10, 000 drives 15 PB of Tape Robot 11 4 x 1, 000 robots

Data Pump • Some queries require reading ALL the data (for reprocessing) • Each Data Center scans the data every 2 weeks. – Data rate 10 PB/day = 10 TB/node/day = 120 MB/s • Compute on demand small jobs • less than 1, 000 tape mounts • • • less than 100 M disk accesses less than 100 Tera. Ops. (less than 30 minute response time) • For BIG JOBS scan entire 15 PB database • Queries (and extracts) “snoop” this data pump. 12

Just-in-time acquisition 30% • • • Hardware prices decline 20%-40%/year So buy at last moment Buy best product that day: commodity Depreciate over 3 years so that facility is fresh. (after 3 years, cost is 23% of original). 60% decline peaks at 10 M$ 10 10 5 EOS DIS Disk Storage Size and Cost assume 40% price decline/year Data Need TB 4 3 2 Storage Cost M$ 10 1 1994 1996 1998 2000 2002 2004 2006 2008 13

Problems • HSM • Design and Meta-data • Ingest • Data discovery, search, and analysis • reorg-reprocess • disaster recovery • cost 14

What's a Terabyte 1, 000, 000 business letters 100, 000 book pages 50, 000 FAX images 10, 000 TV pictures (mpeg) 4, 000 Land. Sat images 150 miles of bookshelf 15 miles of bookshelf 7 miles of bookshelf 10 days of video Library of Congress (in ASCI) is 25 TB 1980: 200 M$ of disc 5 M$ of tape silo 1994: 1 M$ of magnetic disc 500 K$ of optical disc robot 50 K$ of tape silo 10, 000 discs 10, 000 tapes 120 discs 250 platters 50 tapes Terror Byte !!. 1% of a Peta. Byte!!!!!!!!! 16

The Cost of Storage & Access • File Cabinet: cabinet (4 drawer) 250$ paper (24, 000 sheets) 250$ space (2 x 3 @ 10$/ft 2) 180$ total 700$ 3. 0 ¢/sheet • Disk: disk (9 GB =) ASCII: • Image: 200 k pages 2, 000$ 5 m pages 0. 04 ¢/sheet (100 x cheaper) 1 ¢/sheet (similar to paper) 17

Standard Storage Metrics • Capacity: – RAM: MB and $/MB: today at 100 MB & 10 $/MB – Disk: GB and $/GB: today at 10 GB and 200 $/GB – Tape: TB and $/TB: today at. 1 TB and 100 k$/TB (nearline) • Access time (latency) – RAM: 100 ns – Disk: 10 ms – Tape: 30 second pick, 30 second position • Transfer rate – – – RAM: Disk: Tape: 1 GB/s 5 MB/s - - - Arrays can go to 1 GB/s 3 MB/s - - - not clear that striping works 18

New Storage Metrics: KOXs, MOXs, GOXs, SCANs? • KOX: How many kilobyte objects served per second – the file server, transaction processing metric • MOX: How many megabyte objects served per second – the Mosaic metric • GOX: How many gigabyte objects served per hour – the video & EOSDIS metric • SCANS: How many scans of all the data per day – the data mining and utility metric 19

Summary (of new ideas) • Storage accesses are the bottleneck • Accesses are getting larger (MOX, GOX, SCANS) • Capacity and cost are improving • BUT • Latencies and bandwidth are not improving much • SO • Use parallel access (disk and tape farms) 20

How To Get Lots of MOX, GOX, SCANS • parallelism: use many little devices in parallel At 10 MB/s: 1. 2 days to scan 1, 000 x parallel: 1. 5 1 Terabyte minute SCAN. 1 Terabyte 10 MB/s Parallelism: divide a big problem into many smaller ones to be solved in parallel. • Beware of the media myth • Beware of the access time myth 21

Meta-Message: Technology Ratios Are Important • If everything gets faster&cheaper at the same rate then nothing really changes. • Some things getting MUCH BETTER: – – – communication speed & cost 1, 000 x processor speed & cost 100 x storage size & cost 100 x • Some things staying about the same – – – speed of light (more or less constant) people (10 x worse) storage speed (only 10 x better) 22

Outline • The challenge: Building GIANT data stores – for example, the EOS/DIS 15 PB system • Conclusion 1 – Think about MOX and SCANS • Conclusion 2: – Think about Clusters – SMP report – Cluster report 23

Scaleable Computers BOTH SMP and Cluster Grow Up with SMP 4 x. P 6 is now standard SMP Super Server Grow Out with Cluster has inexpensive parts Departmental Server Cluster of PCs Personal System 24

TPC-C Current Results • Best Performance is 30, 390 tpm. C @ $305/tpm. C (Oracle/DEC) • Best Price/Perf. is 7, 693 tpm. C @ $43. 5/tpm. C (MS SQL/Dell) • Graphs show – UNIX high price – UNIX scaleup diseconomy 25

Compare SMP Performance 26

TPC C improved fast 40% hardware, 100% software, 100% PC Technology 27

Where the money goes 28

What does this mean? • PC Technology is 3 x cheaper than high-end SMPs • PC nodes performance are 1/2 of high-end SMPs – 4 x. P 6 vs 20 x. Ultra. Sparc • Peak performance is a cluster – Tandem 100 node cluster – DEC Alpha 4 x 8 cluster • Commodity solutions WILL come to this market 29

Cluster: Shared What? • Shared Memory Multiprocessor – Multiple processors, one memory – all devices are local – DEC, SG, Sun Sequent 16. . 64 nodes – easy to program, not commodity • Shared Disk Cluster – an array of nodes – all shared common disks – VAXcluster + Oracle • Shared Nothing Cluster – each device local to a node – ownership may change – Tandem, SP 2, Wolfpack 30

Clusters being built • • Teradata 1500 nodes +24 TB disk (50 k$/slice) Tandem, VMScluster 150 nodes (100 k$/slice) Intel, 9, 000 nodes @ 55 M$ ( 6 k$/slice) Teradata, Tandem, DEC moving to NT+low slice price • IBM: 512 nodes @ 100 m$ (200 k$/slice) • PC clusters (bare handed) at dozens of nodes web servers (msn, Point. Cast, …), DB servers • KEY TECHNOLOGY HERE IS THE APPS. – Apps distribute data – Apps distribute execution 31

Cluster Advantages • Clients and Servers made from the same stuff. – Inexpensive: Built with commodity components • Fault tolerance: – Spare modules mask failures • Modular growth – grow by adding small modules • Parallel data search – use multiple processors and disks 32

Clusters are winning the high end • You saw that a 4 x 8 cluster has best TPC-C performance • This year, a 95 x. Ultra. Sparc cluster won the Minute. Sort Speed Trophy (see NOWsort at www. now. cs. berkeley. edu) • Ordinal 16 x on SGI Origin is close (but the loser!). 33

Clusters (Plumbing) • Single system image – naming – protection/security – management/load balance • Fault Tolerance – Wolfpack Demo • Hot Pluggable hardware & Software 34

So, What’s New? • When slices cost 50 k$, you buy 10 or 20. • When slices cost 5 k$ you buy 100 or 200. • Manageability, programmability, usability become key issues (total cost of ownership). • PCs are MUCH easier to use and program MPP Vicious Cycle No Customers! New MPP & App New. OS CP/Commodity Virtuous Cycle: Standards allow progress and investment protection New New MPP & App New. OS Apps Standard OS & Hardware Customers 35

Windows NT Server Clustering High Availability On Standard Hardware Standard API for clusters on many platforms No special hardware required. Resource Group is unit of failover Typical resources: shared disk, printer, . . . IP address, Net. Name Service (Web, SQL, File, Print Mail, MTS API to define resource groups, dependencies, resources, GUI administrative interface A consortium of 60 HW & SW vendors (everybody who is anybody) 2 -Node Cluster in beta test now. Available 97 H 1 >2 node is next SQL Server and Oracle Demo on it today Key concepts System: a node Cluster: systems working together Resource: hard/ soft-ware module Resource dependency: resource needs another Resource group: fails over as a unit Dependencies: do not cross group boundaries 36

Wolfpack NT Clusters 1. 0 • Two node file and print failover Private Disks Shared SCSI Disk Strings Betty Alice Clients • GUI admin interface 37

Where We Are Today • Clusters moving fast – OLTP – Sort – Wolf. Pack • Technology ahead of schedule – cpus, disks, tapes, wires, . . • OR Databases are evolving • Parallel DBMSs are evolving • HSM still immature 39

Outline • The challenge: Building GIANT data stores – for example, the EOS/DIS 15 PB system • Conclusion 1 – Think about MOX and SCANS • Conclusion 2: – Think about Clusters – SMP report – Cluster report 40

Building Peta. Byte Servers Jim Gray Microsoft Research Gray@Microsoft. com http: //www. Research. Microsoft. com/~Gray/talks Kilo Mega Giga Tera Peta Exa 103 106 109 1012 1015 1018 today, we are here 41