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XLDB South America Keynote: eScience Institute and Myria

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Keynote talk given at XLDB South America in Rio

Keynote talk given at XLDB South America in Rio

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  • 1. Myria: Scalable Analytics as a Service Bill Howe, PhD University of Washington XLDB South America 2014
  • 2. This morning • UW eScience Institute – A “Data Science Environment” • SQLShare and High Variety Data • Myria and “Relational Algorithmics” 7/10/2014 Bill Howe, UW 2
  • 3. 3 “It’s a great time to be a data geek.” -- Roger Barga, Microsoft Research “The greatest minds of my generation are trying to figure out how to make people click on ads” -- Jeff Hammerbacher, co-founder, Cloudera
  • 4. The Fourth Paradigm 1. Empirical + experimental 2. Theoretical 3. Computational 4. Data-Intensive Jim Gray 7/10/2014 Bill Howe, UW 4
  • 5. “All across our campus, the process of discovery will increasingly rely on researchers’ ability to extract knowledge from vast amounts of data… In order to remain at the forefront, UW must be a leader in advancing these techniques and technologies, and in making [them] accessible to researchers in the broadest imaginable range of fields.” 2005-2008 In other words: • Data-driven discovery will be ubiquitous • UW must be a leader in inventing the capabilities • UW must be a leader in translational activities – in putting these capabilities to work • It’s about intellectual infrastructure (human capital) and software infrastructure (shared tools and services – digital capital)
  • 6. A 5-year, US$37.8 million cross-institutional collaboration to create a data science environment 6 2014
  • 7. 7/10/2014 Bill Howe, UW 7 Data Science Kickoff Session: 137 posters from 30+ departments and units
  • 8. Establish a virtuous cycle • 6 working groups, each with • 3-6 faculty from each institution
  • 9. UW Data Science Education Efforts 7/10/2014 Bill Howe, UW 9 Students Non-Students CS/Informatics Non-Major professionals researchers undergrads grads undergrads grads UWEO Data Science Certificate MOOC Intro to Data Science IGERT: Big Data PhD Track New CS Courses Bootcamps and workshops Intro to Data Programming Data Science Masters (planned) Incubator: hands-on training
  • 10. 7/10/2014 Bill Howe, UW 10 Next Session begins June 30, 2014 https://www.coursera.org/course/datasci
  • 11. MOOC Participation numbers • “Registered”: 119,517 totally irrelevant • Clicked play in first 2 weeks: 78,589 • Turned in 1st homework: 10,663 • Completed all assignments: ~9000 typical attrition for a MOOC • “Passed”: 7022 • Forum threads: 4661 • Forum posts: 22,900 Fairly consistent with Coursera data across “hard” courses 11
  • 12. Educational transformation: A new generation of “Pi-shaped” scientists 12 PhD  πhD Educational transformation Magda Balazinska
  • 13. 13 Educational transformation Big Data access and management Big Data modeling Big Data analytics Collaborative Big Data scienceData Education and Research in Data Science • Ultimate goal: A new PhD program – Initial goal: A new certificate based on Big Data tracks in all departments – Education highlights: data science courses, co-advising, and internships • End-to-End Research Agenda – Big Data mgmt, analytics, modeling, & collaboration • Cyberinfrastructure Development – Big Data analysis service
  • 14. The Data Science Studio • An open collaborative research space • A resident data science team – Permanent staff of ~5 data scientists – applied research and development – ~15-20 data science fellows (research scientists, visitors, postdocs, students) • How to Engage: – Drop-in open workspace – Studio “Office Hours” – Incubation Program 14
  • 15. 15 6th floor Physics Astronomy Building A partnership among … • Provost • UW Libraries • Physics, Astronomy, Arts & Sciences • eScience Institute
  • 16. 16 Estimated Timeline: • Design Phase Jan-June • Construction June – Sep • Target: October 1, 2014
  • 17. 7/10/2014 Bill Howe, UW 17 The rest of this talk…
  • 18. 7/10/2014 Bill Howe, UW 18 How can we deliver 1000 little SDSSs to anyone who wants one?
  • 19. 7/10/2014 Bill Howe, UW 19 #ofbytes # of data sources telescopes spectra LSST (~100PB; images, spectra) PanSTARRS (~40PB; images, trajectories) OOI (~50TB/year; sims, RSN) IOOS (~50TB/year; sims, satellite, gliders, AUVs, vessels, more) CMOP (~10TB/year; sims, stations, gliders, AUVs, vessels, more) SDSS (~400TB; images, spectra, catalogs) n-body sims models AUVs stations cruises, CTDs flow cytometry gliders ADCP satellites Astronomy Ocean Sciences 3 V’s of Big Data Volume Variety Velocity
  • 20. How much time do you spend “handling data” as opposed to “doing science”? Mode answer: “90%” 7/10/2014 Bill Howe, UW 20 Key question: How can we reduce this “data overhead”?
  • 21. 7/10/2014 Bill Howe, UW Simple Example ###query length COG hit #1 e-value #1 identity #1 score #1 hit length #1 description #1 chr_4[480001-580000].287 4500 chr_4[560001-660000].1 3556 chr_9[400001-500000].503 4211 COG4547 2.00E-04 19 44.6 620 Cobalamin biosynthesis protein C chr_9[320001-420000].548 2833 COG5406 2.00E-04 38 43.9 1001 Nucleosome binding factor SPN, chr_27[320001-404298].20 3991 COG4547 5.00E-05 18 46.2 620 Cobalamin biosynthesis protein C chr_26[320001-420000].378 3963 COG5099 5.00E-05 17 46.2 777 RNA-binding protein of the Puf f chr_26[400001-441226].196 2949 COG5099 2.00E-04 17 43.9 777 RNA-binding protein of the Puf f chr_24[160001-260000].65 3542 chr_5[720001-820000].339 3141 COG5099 4.00E-09 20 59.3 777 RNA-binding protein of the Puf f chr_9[160001-260000].243 3002 COG5077 1.00E-25 26 114 1089 Ubiquitin carboxyl-terminal hydr chr_12[720001-820000].86 2895 COG5032 2.00E-09 30 60.5 2105 Phosphatidylinositol kinase and p chr_12[800001-900000].109 1463 COG5032 1.00E-09 30 60.1 2105 Phosphatidylinositol kinase and p chr_11[1-100000].70 2886 chr_11[80001-180000].100 1523 ANNOTATIONSUMMARY-COMBINEDORFANNOTATION16_Phaeo_genome id query hit e_value identity_ score query_start query_end hit_start hit_end hit_length 1 FHJ7DRN01A0TND.1 COG0414 1.00E-08 28 51 1 74 180 257 285 2 FHJ7DRN01A1AD2.2 COG0092 3.00E-20 47 89.9 6 85 41 120 233 3 FHJ7DRN01A2HWZ.4 COG3889 0.0006 26 35.8 9 94 758 845 872 … 2853 FHJ7DRN02HXTBY.5 COG5077 7.00E-09 37 52.3 3 77 313 388 1089 2854 FHJ7DRN02HZO4J.2 COG0444 2.00E-31 67 127 1 73 135 207 316 … 3566 FHJ7DRN02FUJW3.1 COG5032 1.00E-09 32 54.7 1 75 1965 2038 2105 … COGAnnotation_coastal_sample.txt SELECT * FROM Phaeo_genome p, coastal_sample c WHERE p.COG_hit = c.hit 21
  • 22. Data Science Workflow: 7/10/2014 Bill Howe, UW 22 1) Preparing to run a model 2) Running the model 3) Interpreting the results Gathering, cleaning, integrating, restructuring, transforming, loading, filtering, deleting, combining, merging, verifying, extracting, shaping, massaging “80% of the work” -- Aaron Kimball “The other 80% of the work”
  • 23. “[This was hard] due to the large amount of data (e.g. data indexes for data retrieval, dissection into data blocks and processing steps, order in which steps are performed to match memory/time requirements, file formats required by software used). In addition we actually spend quite some time in iterations fixing problems with certain features (e.g. capping ENCODE data), testing features and feature products to include, identifying useful test data sets, adjusting the training data (e.g. 1000G vs human-derived variants) So roughly 50% of the project was testing and improving the model, 30% figuring out how to do things (engineering) and 20% getting files and getting them into the right format. I guess in total [I spent] 6 months [on this project].” At least 3 months on issues of scale, file handling, and feature engineering. Martin Kircher, Genome SciencesWhy? 3k NSF postdocs in 2010 $50k / postdoc at least 50% overhead maybe $75M annually at NSF alone?
  • 24. 0 30 60 90 120 Benchmark 1 Benchmark 2 Old system Your system Our system A typical Computer Science paper…. slide src: Dan Halperin
  • 25. 0 2500 5000 7500 10000 12500 Benchmark 1 Benchmark 2 Old system Your system Our system What people use The reality of the situation…. slide src: Dan Halperin
  • 26. A modest goal: Expose all the world’s science data through declarative query interfaces 7/10/2014 Bill Howe, UW 26
  • 27. QUERY-AS-A-SERVICE 27 2010 - present Version 1
  • 28. 1) Upload data “as is” Cloud-hosted, secure; no need to install or design a database; no pre-defined schema; schema inference; some itegration 2) Write Queries Right in your browser, writing views on top of views on top of views ... SELECT hit, COUNT(*) FROM tigrfam_surface GROUP BY hit ORDER BY cnt DESC 3) Share the results Make them public, tag them, share with specific colleagues – anyone with access can query http://sqlshare.escience.washington.edu
  • 29. SELECT x.strain, x.chr, x.region as snp_region, x.start_bp as snp_start_bp , x.end_bp as snp_end_bp, w.start_bp as nc_start_bp, w.end_bp as nc_end_bp , w.category as nc_category , CASE WHEN (x.start_bp >= w.start_bp AND x.end_bp <= w.end_bp) THEN x.end_bp - x.start_bp + 1 WHEN (x.start_bp <= w.start_bp AND w.start_bp <= x.end_bp) THEN x.end_bp - w.start_bp + 1 WHEN (x.start_bp <= w.end_bp AND w.end_bp <= x.end_bp) THEN w.end_bp - x.start_bp + 1 END AS len_overlap FROM [koesterj@washington.edu].[hotspots_deserts.tab] x INNER JOIN [koesterj@washington.edu].[table_noncoding_positions.tab] w ON x.chr = w.chr WHERE (x.start_bp >= w.start_bp AND x.end_bp <= w.end_bp) OR (x.start_bp <= w.start_bp AND w.start_bp <= x.end_bp) OR (x.start_bp <= w.end_bp AND w.end_bp <= x.end_bp) ORDER BY x.strain, x.chr ASC, x.start_bp ASC Non-programmers can write very complex queries (rather than relying on staff programmers) Example: Computing the overlaps of two sets of blast results We see thousands of queries written by non-programmers
  • 30. Howe, et al., CISE 2013
  • 31. Steven Roberts SQL as a lab notebook: http://bit.ly/16Xj2JP Calculate # methylated CGs Calculate # all CGs Calculate methylation ratio Link methylation with gene description GFF of methylated CG locations GFF of all genes GFF of all CG locations Gene descriptions Join Reorder columns Count Count JoinJoin Reorder columns Reorder columns Compute Trim Excel Join Join misstep: join w/ wrong fill Calculate # methylated CGs Calculate # all CGs GFF of methylated CG locations GFF of all genes GFF of all CG locations Gene descriptions Calculate methylation ratio and link with gene description Popular service for Bioinformatics Workflows
  • 32. Halperin, Howe, et al. SSDBM 2013
  • 33. Two Problems with SQLShare • No help for truly big datasets • No help for “algorithmics” 33 Limitations of SQLShare
  • 34. 7/10/2014 Bill Howe, UW 34 Relational Algorithmics-as-a-Service Version 2 http://myria.cs.washington.edu
  • 35. Myria is… • MyriaQ: A compiler framework for multiple iterative RA-based languages and multiple big data back ends • MyriaX: A parallel, shared-nothing, iterative execution engine • MyriaWeb: A RESTful Analytics-as-a- Service platform and web-based interface 35 Myria is …
  • 36. Magda Balazinska, Bill Howe, and Dan Suciu Dan Halperin (technical lead) Victor Almeida Andrew Whitaker PhD Students Shumo Chu Eric Gribkoff Jeremy Hyrkas Paris Koutris Ryan Maas Dominik Moritz Laurel Orr Jennifer Ortiz Emad Soroush Jingjing Wang ShengLiang Xu Undergraduate Students Lee Lee Choo Vaspol Ruamviboonsuk Myria Team
  • 37. Myria Architecture Coordinator Language Parser Myria Compiler Logical Optimizer for RA+While REST Server Worker Catalog Catalog … json query plan netty protocols RDBMS jdbc Worker Catalog RDBMS jdbc Worker Catalog RDBMS jdbc MyriaX (Java) C Compiler Grappa Web UI MyriaQ (Python) HDFS HDFS HDFS Datalog SQL MyriaL REST SciDB
  • 38. SparkSerial C++GrappaMyriaX SQL SQLDatalogMyriaL ?? Relational Algebra + Iteration Compiler Compiler Compiler Compiler Compiler MyriaQ Oceanography, Astronomy, Biology, Medical Informatics
  • 39. Laser Microscope Objective Pine Hole Lens Nozzle d1 d2 FSC (Forward scatter) Orange fluo Red fluo EX: SeaFlow Francois Ribalet Jarred Swalwell Ginger Armbrust
  • 40. Ex: SeaFlow 10 0 10 1 10 2 10 3 10 4 100 10 1 10 2 10 3 10 4 ps3.fcs…Focus D1/FSC D2/FSC d1/FSC d2 / FSC 10 0 10 1 10 2 10 3 10 4 100 101 10 2 10 3 10 4 ps3.fcs…subset FSC 692-40REDfluorescence FSC Picoplankton Nanoplankton 100 101 102 103 104 10 0 10 1 10 2 103 104 P35-surf FSC Small Stuff 580-30 IS Ultraplankton Prochlorococcus Continuous observations of various phytoplankton groups from 1-20 mm in size Based on RED fluo: Prochlorococcus, Pico-, Ultra- and Nanoplankton Based on ORANGE fluo: Synechococcus, Cryptophytes Based on FSC: Coccolithophores Francois Ribalet Jarred Swalwell Ginger Armbrust
  • 41. Ex: SeaFlow Francois Ribalet Jarred Swalwell Ginger Armbrust
  • 42. SeaFlow in Myria • “That 5-line MyriaL program was 100x faster than my R cluster, and much simpler” Dan Halperin Sophie Clayton
  • 43. 7/10/2014 Bill Howe, UW 43 1) BD experiments are ridiculously labor-intensive – N systems x M real-world applications – Big clusters and big datasets 2) No “one size fits all solution” – Realistic environments will use more than one system 3) A return to distributed, federated databases – Erase the distinction between ETL and Analytics Why a big data middleware?
  • 44. Pregel (Malewicz) Hadoop 2008 2009 2010 2011 2012 2013 2014 HaLoop (Bu) Spark (Zakaria) Vertica (Pavlo) ~100x faster SystemML (Ghoting) Hyracks (Borkar) GraphLab (Low) faster Cumulon (Huang) comparable or inconclusive Giraph (Tian) Dremel (Melnik) SimSQL (Cai) epiC (Jiang) Impala (Cloudera) Shark (Xin) HIVE (Thusoo) “The good old days” “The age of uncertainty”
  • 45. 7/10/2014 Bill Howe, UW 45 What can we conclude? Hadoop was probably just pretty bad The rest of the story not so clear
  • 46. Relational Algebra is the Calculus of Big Data • Hadoopspawn: Pig, HIVE, blah • Hadoop contemporaries: Cascalog, Flume, blah • Post-Hadoop: Spark/Shark, Dremel, blah • etc. 7/10/2014 Bill Howe, UW 46
  • 47. HBase 7/10/2014 Bill Howe, UW 47 BigTable Dremel Tenzing 2004 Pregel Hadoop 2005 MapReduce 2006 2007 2008 2009 Spanner Megastore 2010 2011 2012 Google Big Data Systems non-Google open source implementation direct influence / shared features compatible implementation of SQL-like interface BigQuery
  • 48. Relational Algebra is the Calculus of Small Data • Galaxy – “bioinformatics workflows” • Pandas (Python) merge(left, right, on=‘key’) • dplyr (R) filter(x), select(x), arrange(x), groupby(x), inner_join(x, y), left_join(x, y), …. • Manimal, Pyxis/StatusQuo, others – Extract RA operators implemented manually in Java code 7/10/2014 Bill Howe, UW 48 “…Operate on Genomics Intervals -> Join”
  • 49. 7/10/2014 Bill Howe, UW 49 Key Idea: Algebraic Optimization N = ((z*2)+((z*3)+0))/1 Algebraic Laws: 1. (+) identity: x+0 = x 2. (/) identity: x/1 = x 3. (*) distributes: (n*x+n*y) = n*(x+y) 4. (*) commutes: x*y = y*x Apply rules 1, 3, 4, 2: N = (2+3)*z two operations instead of five, no division operator Same idea works with the Relational Algebra!
  • 50. A closer look at an example ROI(id, start, stop) is a set of “regions of interest” Read(id, start, stop) is a set of “reads” from sequencer Task: For each region of interest, count the number of reads it contains start stop stopstart
  • 51. SELECT roi.id, count(rd.id) FROM regions_of_interest roi, reads rd WHERE roi.start <= rd.start AND rd.[end] <= roi.[end] GROUP BY roi.id​ As a query “region of interest” sequence “read”
  • 52. SELECT roi.id, count(rd.start) FROM regions_of_interest roi, reads rd WHERE roi.start <= rd.start AND rd.[end] <= roi.[end] GROUP BY roi.id​ Why databases get a bad reputation many minutes SELECT roi.id, count(rd.start) as cnt FROM regions_of_interest roi, indexed_reads rd WHERE roi.start <= rd.start AND rd.start <= roi.[end] AND roi.start <= rd.[end] AND rd.[end] >= roi.[end] GROUP BY roi.id 3 seconds! roi read two-sided index scan one-sided index scan, plus filter The broken promise of declarative query…
  • 53. Lowering barrier to entry
  • 54. Giving users insight Shumo Chu Dominik Moritz
  • 55. Diagnosing problems Sourcenode Destination node Shumo Chu Dominik Moritz
  • 56. 56 A = LOAD('points.txt', id:int, x:float, y:float) E = LIMIT(A, 4); F = SEQUENCE(); Centroids = [FROM E EMIT (id=F.next, x=E.x, y=E.y)]; Kmeans = [FROM A EMIT (id=id, x=x, y=y, cluster_id=0)] DO I = CROSS(Kmeans, Centroids); J = [FROM I EMIT (Kmeans.id, Kmeans.x, Kmeans.y, Centroids.cluster_id, $distance(Kmeans.x, Kmeans.y, Centroids.x, Centroids.y))]; K = [FROM J EMIT id, distance=$min(distance)]; L = JOIN(J, id, K, id) M = [FROM L WHERE J.distance <= K.distance EMIT (id=J.id, x=J.x, y=J.y, cluster_id=J.cluster_id)]; Kmeans' = [FROM M EMIT (id, x, y, $min(cluster_id))]; Delta = DIFF(Kmeans', Kmeans) Kmeans = Kmeans' Centroids = [FROM Kmeans' EMIT (cluster_id, x=avg(x), y=avg(y))]; WHILE DELTA != {} K-Means in the language MyriaL
  • 57. 57 CurGood = SCAN(public:adhoc:sc_points); DO mean = [FROM CurGood EMIT val=AVG(v)]; std = [FROM CurGood EMIT val=STDEV(v)]; NewBad = [FROM Good WHERE ABS(Good.v - mean) > 2 * std EMIT *]; CurGood = CurGood - NewBad; continue = [FROM NewBad EMIT COUNT(NewBad.v) > 0]; WHILE continue; DUMP(CurGood); Sigma-clipping, V0
  • 58. 58 CurGood = P sum = [FROM CurGood EMIT SUM(val)]; sumsq = [FROM CurGood EMIT SUM(val*val)] cnt = [FROM CurGood EMIT CNT(*)]; NewBad = [] DO sum = sum – [FROM NewBad EMIT SUM(val)]; sumsq = sum – [FROM NewBad EMIT SUM(val*val)]; cnt = sum - [FROM NewBad EMIT CNT(*)]; mean = sum / cnt std = sqrt(1/(cnt*(cnt-1)) * (cnt * sumsq - sum*sum)) NewBad = FILTER([ABS(val-mean)>std], CurGood) CurGood = CurGood - NewBad WHILE NewBad != {} Sigma-clipping, V1: Incremental
  • 59. 59 Points = SCAN(public:adhoc:sc_points); aggs = [FROM Points EMIT _sum=SUM(v), sumsq=SUM(v*v), cnt=COUNT(v)]; newBad = [] bounds = [FROM Points EMIT lower=MIN(v), upper=MAX(v)]; DO new_aggs = [FROM newBad EMIT _sum=SUM(v), sumsq=SUM(v*v), cnt=COUNT(v)]; aggs = [FROM aggs, new_aggs EMIT _sum=aggs._sum - new_aggs._sum, sumsq=aggs.sumsq - new_aggs.sumsq, cnt=aggs.cnt - new_aggs.cnt]; stats = [FROM aggs EMIT mean=_sum/cnt, std=SQRT(1.0/(cnt*(cnt-1)) * (cnt * sumsq - _sum * _sum))]; newBounds = [FROM stats EMIT lower=mean - 2 * std, upper=mean + 2 * std]; tooLow = [FROM Points, bounds, newBounds WHERE newBounds.lower > v AND v >= bounds.lower EMIT v=Points.v]; tooHigh = [FROM Points, bounds, newBounds WHERE newBounds.upper < v AND v <= bounds.upper EMIT v=Points.v]; newBad = UNIONALL(tooLow, tooHigh); bounds = newBounds; continue = [FROM newBad EMIT COUNT(v) > 0]; WHILE continue; output = [FROM Points, bounds WHERE Points.v > bounds.lower AND Points.v < bounds.upper EMIT v=Points.v]; DUMP(output); Sigma-clipping, V2
  • 60. • Hypothesis: Loops + RA covers everything anyone wants to do – and it scales, it’s optimizable, and it’s accessible • We can smooth the ROI curve for novices – Start with simple queries… – …end up working on advanced parallel algorithms • “White Box Analytics” – Compose queries, inspect plans, monitoring, debugging, “UDRs” – user-defined optimization rules • Multiple languages, multiple backends, one data/query model – Ask me about graph data – Ask me about array data (or, rather, mesh data) “Relational Algorithmics”
  • 61. Takeaways • We hope to see “Data Science Environments” at universities worldwide – We try to make our programs and activities reusable • Software-as-a-service to reach the “long tail” of science • “Relational Algorithmics” – The relational algebra is the calculus of big data – “It’s not just for databases anymore” – Learn it, use it, teach it – Myria is a platform for “relational algorithmics” http://escience.washington.edu @billghowe billhowe@cs.washington.edu
  • 62. 62
  • 63. 63 Maslow’s Needs Hierarchy “As each need is satisfied, the next higher level in the hierarchy dominates conscious functioning.” -- Maslow 43
  • 64. A “Needs Hierarchy” of Science Data Management storage sharing 64 query integration analytics “As each need is satisfied, the next higher level in the hierarchy dominates conscious functioning.” -- Maslow 43
  • 65. A “Needs Hierarchy” of Science Data Management storage sharing 65 integration query analytics “As each need is satisfied, the next higher level in the hierarchy dominates conscious functioning.” -- Maslow 43