Tools and Services for Data Intensive Research
Roger Barga, Architect
eXtreme Computing Group, Microsoft Research
An Elephant Through the Eye of a Needle
Select eXtreme Computing Group (XCG) Initiatives
• Cloud Computing Futures– ab initio R&D on cloud hardware/software infrastructure
• Multicore academic engagement– Universal Parallel Computing Research Centers (UPCRCs)
• Software incubations– Multicore applications, power management, scheduling
• Quantum computing– Topological quantum computing investigations
• Security and cryptography– Theoretical explorations and software tools
• Research cloud engagement– Worldwide government and academic research partnerships
– Inform next generation cloud computing infrastructure
Data Intensive Research• The nature of scientific computing is changing
– It is about the data…
• Hypothesis-driven research
– “I have an idea, let me verify it...”
• Exploratory
– “What correlations can I glean from everyone’s data?”
• Requires different tools and techniques– Exploratory analysis relies on data mining, viz analytics– “grep” is not a data mining tool, and neither is a DBMS…
• Massive, multidisciplinary data– Rising rapidly and at unprecedented scale
Research1. Have good idea
2. Write proposal
3. Wait 6 months4. If successful, wait 3
months
5. Install Computers
6. Start Work
Science Start-ups1. Have good idea
2. Write Business Plan
3. Ask VCs to fund
4. If successful..
5. Install Computers
6. Start Work
Cloud Computing Model1. Have good idea
2. Grab nodes from Cloud provider
3. Start Work
4. Pay for what you used
also scalability, cost, sustainability
Why Commercial Clouds are Important*
* Slide used with permission of Paul Watson, University of Newcastle (UK)
Moore’s “Law” favored consumer commoditiesEconomics drove enormous improvementsSpecialized processors and mainframes falteredThe commodity software industry was born
The Pull of Economics (follow the money)
Today’s economicsUnprecedented economies of scaleEnterprise moving to PaaS, SaaS, cloud computingOpportunities for Analysis as a Service, multi-disciplinary data sets,…
This will drive changes in research computing and cloud infrastructureJust as did “killer micros” and inexpensive clusters
Drinking from the Twitter Fire Hose
• Assume the order of magnitude of the twitter user base is in the 10-50MM range, let’s crank this up to the 500M range.
• The average Twitter user is generating a relatively low incomingmessage rate right now, assume that a user’s devices (phone, car,PC) are enhanced to begin auto-generating periodic Twittermessages on their behalf, e.g. with location ‘pings’ and solving otherproblems that twitterbots are emerging to address. So let’s say theinput rate grows again to 10x-100x what it was in the previous step.
On the “input” end
• Start with the ‘twitter fire hose’, messages flowing inbound at specific rate.
• Enrich each element with significantly more metadata, e.g. geolocation.
On the “input” end
On the “output” end: three different usage modalities• Each user has one or more ‘agents’ they run on their behalf, monitoring this input
stream. This might just be a client that displays a stream that is incoming fromthe @friends or #topics or the #interesting&@queries (user standing queries).
• A user can do more general queries from a search page. This query may havemore unstructured search terms than the above, and it is expected not just to begoing against incoming stream but against much larger corpus of messages fromthe entire input stream that has been persisted for days, weeks, months, years…
• Finally, analytical tools or bots whose purpose is to do trend analysis on theknowledge popping out of the stream, in real-time. Whether seeded with aninterest (“let me know when a problem pops up with <product> that will damagemy company’s reputation”) or just discovering a topic from the noise (“let meknow when a new hot news item emerges”), both must be possible.
Drinking from the Twitter Fire Hose
Pause for Moment…Defining representative challenges or quests to focus group
attention is an excellent way to proceed as a community
Publishing a whitepaper articulating these
challenges is a great way to allow others
to contribute to a shared research agenda
Make simulated and reference data sets available
to ground such a distributed research effort
On the “input” end
On the “output” end: three different usage modalities
A combination of live data, including streaming, and historical data
Lots of necessary technology, but no single technology is sufficient
If this is going to be successful it must be accessible to the masses
Simple to use and highly scalable, which is extremely difficult because in actuality it is not simple…
Drinking from the Twitter Fire Hose
This Talk is About
Intersection of four fundamental strategies 1. Distribute Data and perform Parallel Processing
2. Parallel operations to take advantage of multiple cores;
3. Reduce the size of the data accessed
– Data compression
– Data structures that limit the amount of data required for queries;
4. Stream data processing to extract information before storage
Effort to build & port tools for data intensive research in the cloud– None have run in the cloud to date or at scale we are targeting…
Able to handle torrential streams of live and historical data– Goal is simplicity and ease-of-use combined with scalability
Microsoft’s Dryad
• Continuously deployed since 2006
• Running on >> 104 machines
• Sifting through > 10Pb data daily
• Runs on clusters > 3000 machines
• Handles jobs with > 105 processes each
• Used by >> 100 developers
• Rich platform for data analysis
Microsoft Research, Silicon ValleyMichael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, Dennis Fetterly
Pause for Moment…Data-Intensive Computing Symposium, 2007
Dryad is now freely availablehttp://research.microsoft.com/en-us/collaboration/tools/dryad.aspxThanks to Geoffrey Fox (Indiana) and Magda Balazinska (UW) as early adoptersCommitment by External Research (MSR) to support research community use
Simple Programming ModelTerasort, well known benchmark, time to sort time 1 TB data [J. Gray 1985]
• Sequential scan/disk = 4.6 hours
• DryadLINQ provides simple but powerful programming model
• Only few lines of code needed to implement Terasort, benchmark May 2008
• DryadLINQ result: 349 seconds (5.8 min)
• Cluster of 240 AMD64 (quad) machines, 920 disks
• Code: 17 lines of LINQ
DryadDataContext ddc = new DryadDataContext(fileDir);
DryadTable<TeraRecord> records =
ddc.GetPartitionedTable<TeraRecord>(file);
var q = records.OrderBy(x => x);
q.ToDryadPartitionedTable(output);
LINQ• Microsoft’s Language INtegrated Query
– Available in Visual Studio 2008
• A set of operators to manipulate datasets in .NET– Support traditional relational operators
• Select, Join, GroupBy, Aggregate, etc.
• Data model– Data elements are strongly typed .NET objects
– Much more expressive than SQL tables
• Extremely extensible– Add new custom operators
– Add new execution providers
Dryad Generalizes Unix Pipes
Unix Pipes: 1-Dgrep | sed | sort | awk | perl
Dryad: 2-D, multi-machine, virtualized
grep1000 | sed500 | sort1000 | awk500 | perl50
Dryad Job Structure
grep
sed
sortawk
perlgrep
grepsed
sort
sortawk
Inputfiles
Vertices (processes)
Outputfiles
Channels
Stage
Channel is a finite streams of items• NTFS files (temporary)• TCP pipes (inter-machine)• Memory FIFOs (intra-machine)
Dryad System Architecture
Files, TCP, FIFO, Networkjob schedule
data plane
control plane
NS PD PDPD
V V V
Job manager cluster
JM code
Vertex Code
Dryad Job Staging1. Build
2. Send .exe
3. Start JM
5. Generate graph
7. Serialize vertices
8. Monitor vertex execution
4. Query cluster resources
Cluster services6. Initialize vertices
Dryad Scheduler is a State Machine
• Static optimizer builds execution graph– Vertex can run anywhere once all its inputs are ready.
• Dynamic optimizer mutates running graph – Distributes code, routes data;– Schedules processes on machines near data;– Adjusts available compute resources at each stage;– Automatically recovers computation, adjusts for overload
o If A fails, run it again;
o If A’s inputs are gone, run upstream vertices again (recursively);
o If A is slow, run a copy elsewhere and use output from one that finishes first.
– Masks failures in cluster and network;
Combining Query Providers
PLINQ
Local Machine
.Netprogram(C#, VB, F#, etc)
Execution Engines
Query
Objects
LINQ-to-IMDB
DryadLINQ
LINQ-to-CEPLIN
Q p
rovi
de
r in
terf
ace
Scalability
Single-core
Multi-core
Cluster
LINQ == Tree of Operators• A query is comprised of a tree of operators• As with a program AST, these trees can be analyzed, rewritten• This is why PLINQ can safely introduce parallelism
q = from x in A where p(x) select x3;
• Intra-operator:
• Inter-operator:
• Both composed:
• Nesting queries inside of others is common
PLINQ can fuse partitionsvar q1 = from x in A select x*2;
var q2 = q1.Sum();
Combining with PLINQ
Query
DryadLINQ
PLINQ
subquery
Combining with LINQ-to-IMDB
DryadLINQ
Subquery Subquery Subquery Subquery
Query
LINQ-to-IMDBHistoricalReference
Data
Combining with LINQ-to-CEP
DryadLINQ
Subquery Subquery Subquery Subquery
Query
LINQ-to-IMDB
Subquery
LINQ-to-CEP‘Live’Streaming
Data
Cost of storing data –few cents/month/MB
Cost of acquiring data – negligible
Extracting insight while acquiring data - priceless
Mining historical data for ways to extract insight – precious
CEDR CEP – the engine that makes it possible
Consistent Streaming Through Time: A Vision for Event Stream ProcessingRoger S. Barga, Jonathan Goldstein, Mohamed H. Ali, Mingsheng HongIn the proceedings of CIDR 2007
Complex Event ProcessingComplex Event Processing (CEP) is the continuous and incremental processing ofevent (data) streams from multiple sources based on declarative query and patternspecifications with near-zero latency.
The CEDR (Orinoco) Algebra
Leverages existing SQL understanding
– Streaming extensions to relational algebra
– Query integration with host languages (LINQ)
Semantics are independent of order of arrival
– Specify a standing event query
– Separately specify desired disorder handling strategy
– Many interesting repercussions
Consistent Streaming Through Time: A Vision for Event Stream ProcessingRoger S. Barga, Jonathan Goldstein, Mohamed H. Ali, Mingsheng HongIn the proceedings of CIDR 2007
CEDR (Orinoco) Overview
Currently processing over 400M events per day for internal application (5000 events/sec)
Reference Data on AzureOcean Science data on Azure SDS-relational
• Two terabytes of coastal and model data
• Collaboration with Bill Howe (Univ of Washington)
Computational finance data on Azure SDS-relational
• BATS, daily tick data for stocks (10 years)
• XBRL call report for banks (10,000 banks)
Working with IRIS to store select seismic data onAzure. IRIS consortium based in Seattle (NSF)collects and distributes global seismological data.
• Data sets requested by researchers worldwide
• Includes HD videos, seismograms, images, data from major seismic events.
• Data growing exponentially: big data, with big implications…• Implications for research environments and cloud infrastructure
• Building cloud analysis & storage tools for data intensive research– Implementing key services for science (PhyloD for HIV researchers)– Host select data sets for multidisciplinary data analysis
• Ongoing discussions for research access to Azure– Many PB of storage and hundreds of thousands of core-hours– Internet2/ESnet connections, w/ service peering at high bandwidth– Drive negotiations with ISVs for pay-as-you-go licensing (MATLAB)
• Academic access to Azure through our MSDN program
• Technical engagement team to onboard research groups– Tools for data analysis, data storage services, and visual analytics
Summary
Questions
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