The Architectonics of Data Clouds. Jim Baty

Transcription

1 The Architectonics of Data Clouds Jim Baty 1

2 It's time to ask: What can science learn from Google? Wired Wired The Information Factories The End of Science George Gilder Chris Anderson The desktop is dead. Welcome to the Internet cloud, where massive facilities across the globe will store all the data you'll ever use. George Gilder on the dawning of the petabyte age The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data. Welcome to the Petabyte age. 2

3 the Agenda : Clouds NOW ++ Patterns Analytics Tables PODs 3

4 So what about these 'clouds'? Cloud Computing - In this architecture, the data is mostly resident on servers 'somewhere on the Internet' and the application runs on both the 'cloud servers' and the user's browser. Eric Schmidt in Information Factories by G. Gilder. As in... David Young's 'Cloud Score' Utility computing 'pay by the drink' HW Virtualization Network Stability API for creation, deletion, cloning of instances SaaS SW as a service an application offered on-demand via multi-tenancy Application Layer Interoperability (Salesforce, GoogleApps) State Layer Interoperability PaaS Platform as a Service aka Application Application Services Stack Cloud Provider [ASCP] (Google App Automatic Scale Engine, Force.com, Morph, AWS Hardware Load Balancing packaged AMIs) IaaS Infrastructure as a Service aka HW as a Storage as a Services 'Root' if required Service (AWS - EC2/S3, BlueLock) &/or SW (ala N. Carr, the Big Switch) 4

5 What's the OS and Where's the ABI? which do I write to? app platform svc containers PaaS IaaS 'app' becomes guest svcs OS Svcs move hypervisor to OS host OS replaces 2 OSs OS emerge OS gets CPU disk 'lighter' 5

6 the net 'stack'? NOW Network Open Worksets 1998?

7 NOW who uses what? Flickr Fotolog Wikipedia/media Tailrank Twitter Amazon FeedBurner ebay ThemBid Slashdot mii.jp Feedblendr Digg LiveJournal TypePad Friendster Pownce PlentyOfFish 37signals YouTube Google MySpace lig h ttp d R u b y o n R a ils H ib e rn a te M o g ile F S A S P.N E T 2.0 E la s tic C om pute C loud (E C 2 ) S q u id S3 W in d o w s P y th o n U n i / S o la ris A W S ta ts IIS O ra c le To m c a t A kam i C D N M u n in C ++ G eo d istrib (P o w er DNS ) MySQL L in u A pache m em cached Java P e rl PHP S h a rd s Company A ttribute c ount from 7

8 NOW What should we focus on? AMMP Standard Apache, MySQL, memcached, PHP/(Perl/Python) Emerging Web Hadoop, MogileFS, Lucene, Drupal, RoR,... Networking Squid, Varnish, wackamole, XORP/Vyatta,... Open Storage MogileFS, Hadoop DFS, Lustre, SAM-QFS, pnfs,... Operations JMeter, Nagios, Snort, Mytop, DTrace, onesis, Ganglia... HPC Blender, Hadoop, Grid Engine, Lustre, OpenMPI... Some high-value components are emerging memcached, java (multi-machine), distributed data store, Hadoop... 8

9 Understanding / Redefining Architecture large scale, and dynamic Now it's dynamic ecosystems urban planning Architecture was static 'blueprints' How Buildings Learn Buildings constantly change... good: = underspecialized & adaptable Edge City Perceived as one place. office 465,000 m², retail 56,000 m², etc. A Pattern Language Patterns aren't "designed" - but patiently grown into larger global patterns... 9

10 Patterns emerge in architecture the clouds think 'patterns' 10

11 Pattern 'Mash-up' of the Flickr design derived from C. Henderson's 'Building Scalable Web Sites' 11

12 'Mash-up' of the Flickr design 'contet' drives selection of patterns and NOW components Load Balancing Unstructured Data Moving from local to global FS Moving to open sourcing enterprise patterns Sessions Keep thin for scalability Local Sessions (PHP, APC) == bad Centralized Sessions (DB) good No sessions == awesome L7 HW hi perf but epensive replication is hard Caching Applicati on server,da tabase server or both Structured Data Scale out while watching compleity tough to scale / vertical is best typical apps 80% read design schema to avoid collision Data Set Intensive Computing Eploding area modperl invalidat ion hard query cache low performan ce memcached needs app logic becoming the new 'value' 12

13 Pattern detail eample caching (memcached) 13

14 Data Set Intensive Computing (aka HPC) many shared core patterns + scheduling, management, analytics, etc. 14

15 observation 1 New 'Analytics' emerge architectural shift to the cloud and HPC-style workloads open source, general purpose datawarehouse proprietary, dedicated datawarehouse OLTP is the datawarehouse 15

16 Analytics Hi Growth, Strategic Google May 30, 2008 Facebook - June 4, MapReduce, first written in 2003, a way to make something useful of data. MapReduce is increasing use within Google. It ran 29,000 jobs in August 2004 and 2.2 million in September The average time to complete a job has dropped from 634 seconds to 395 seconds, while the output has risen from 193 terabytes to 14,018 terabytes. Each day, Google runs about 100,000 MapReduce jobs; each occupies about 400 servers and takes about 5 to 10 minutes to finish. this equates to about 200,000 servers About a year back we began playing around with Hadoop. Facebook has multiple Hadoop clusters deployed now - the biggest having 2500 cpu cores & 1 PetaByte of disk. We are loading over 2 terabytes uncompressed into the Hadoop file system every day & have hundreds of jobs running each day against this data. Projects include generating statistics about site usage, fighting spam, and determine application quality. 16

17 observation 2 Semi-Structured Data emerges 17

18 Super faster please? Data Management Projects at Google BIGTABLE - area 5. SIGMOD Record, March 2008 Attaching computation to data. To support long-running computations that need to access data in Bigtable we have been adding APIs that allow clients to run code on the same machines as their data. Although the Map-Reduce framework does provide some support for running computation near data, it does not provide any strong guarantees. 18

19 Refactoring 'Hardware' PODs/HaaS Most large scale data centers have adopted a modular approach to provisioning and managing standard server racks (& storage, & networks) Design rules for PODs specified capabilities, standardized process, limited interfaces, versionable mapped to specific workloads (e.g., http, hpc, etc.) and to specific capacities (e.g., 75,000 users, 2,000,000 transactions) Some ways of architecting PODs Sun Constellation System C-48 + Magnum Customer Ready Systems (CRS) Blackbo Refactoring affects architecture sunfire X TB, 2 Opteron dualcore, 16GB Is it 'Storever' or 'Servage'? 19

20 Putting it all together TACC X Petabytes 72 GB/sec total bandwidth X4600 Blade 6048 Switch 3, Systems 800 Cores 3,936 blades 15,744k CPUs 62,976 Cores 125 TB / RAM Dual Redundant 110 Tb/sec bisectional bandwidth 20

21 But does this really apply to me? ,010 5,550 1, ,905 4, % 24% 73% Small-toMedium Datacenters Large Datacenters Mega Datacenters Up to 2,500 Servers 3 10 MW Up to 25k Servers MW Up to 1,700 Servers 1 5 MW Source: IDC, 2012 Data Etrapolated 21

22 In Summary Web Tier DC Efcny Big Data Analytics HPC 'Applications' built on platform services / containers 'Data Center' comprised of infrastructure services Infrastructure' deployed on horizontal fail-in-place PODs 'Architecture' is coarse grained services continuous, dynamic network centric petascale rapid 22

23 Thank you 23