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MMDS 2014: Myria (and Scalable Graph Clustering with RelaxMap)

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A talk I gave at the MMDS workshop June 2014 on the Myria system as well as some of Seung-Hee Bae's work on scalable graph clustering. …

A talk I gave at the MMDS workshop June 2014 on the Myria system as well as some of Seung-Hee Bae's work on scalable graph clustering.

https://mmds-data.org/

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  • 1. Myria: Scalable Analytics as a Service Bill Howe, PhD University of Washington with Dan Suciu, Magda Balazinska, Dan Halperin, and many students MMDS 2014, Berkeley CA
  • 2. Today • Three observations about Big Data • Myria: Scalable Analytics as a Service • Parallel Flow-based Graph Clustering (if time, but there won’t be) 7/10/2014 Bill Howe, UW 2/57
  • 3. 7/10/2014 Bill Howe, UW 3 How can we deliver 1000 little SDSSs to anyone who wants one?
  • 4. How much time do you spend “handling data” as opposed to “doing science”? Mode answer: “90%” 7/10/2014 Bill Howe, UW 4
  • 5. 0 30 60 90 120 Benchmark 1 Benchmark 2 Old system Your system Our system A typical Computer Science paper…. slide src: Dan Halperin
  • 6. 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
  • 7. “[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?
  • 8. Data Science Workflow: 7/10/2014 Bill Howe, UW 8 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”
  • 9. 7/10/2014 Bill Howe, UW 9 Your cool algorithmic problem is not the bottleneck Observation 1
  • 10. 7/10/2014 Bill Howe, UW 10 Symbolic Reasoning and 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 Every database does this kind of optimization every time you issue a query
  • 11. 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
  • 12. Howe, et al., CISE 2013
  • 13. 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
  • 14. 14 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 relational algebra
  • 15. 7/10/2014 Bill Howe, UW 15 “SQL” vs. “ML” is a false dichotomy Observation 2 (Relational Algebra) (Linear Algebra)
  • 16. • SIGMOD 2009: Vertica 100x < Hadoop (Grep, Aggregation, Join) • VLDB 2010: HaLoop ~100x < Hadoop (PageRank, ShortestPath) • SIGMOD 2010: Pregel (no comparisons) • HotCloud 2010: Spark ~100x < Hadoop (logistic regression, ALS) • ICDE 2011: SystemML ~100x < Hadoop • ICDE 2011: Asterix ~100x < Hadoop (K-Means) • VLDB 2012: Graphlab ~100x < Hadoop, GraphLab 5x > Pregel, Graphlab ~ MPI (Recommendation/ALS, CoSeq/GMM, NER) • NSDI 2012: Spark 20x < Hadoop (logistic regression, PageRank) • VLDB 2012: Asterix (no comparisons) • SIGMOD 2013: Cumulon 5x < SystemML • VLDB 2013: Giraph ~ GraphLab (Connected Components) • SIGMOD 2014: SimSQL vs. Spark vs. GraphLab vs. Giraph (GMM, bayesian regression, HMM, LDA, imputation) • VLDB 2014: epiC ~ Impala, epiC ~ Shark, epiC 2x < Hadoop (Grep, Sort, TPC-H Q3, PageRank) A quick meta-analysis of some Big Data systems literature
  • 17. 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”
  • 18. 7/10/2014 Bill Howe, UW 18 Anything based on Hadoop is 100x slower than the state-of-the-art Observation 3 …but the rest of the story is not clear
  • 19. 7/10/2014 Bill Howe, UW 19 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 We need big data middleware
  • 20. 7/10/2014 Bill Howe, UW 20 Relational Analytics-as-a-Service Version 2 http://myria.cs.washington.edu
  • 21. 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
  • 22. Myria is… • MyriaQ: An optimizing compiler and middleware for multiple iterative source languages and multiple target big data systems • MyriaX: A parallel, shared-nothing, iterative execution engine • MyriaWeb: An IDE and RESTful service for algorithm development 22 Myria is …
  • 23. 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 Hadoop
  • 24. SciDBSerial C++GrappaMyriaX SQL SQLDatalogMyriaL ?? Relational Algebra + Iteration Compiler Compiler Compiler Compiler Compiler MyriaQ Oceanography, Astronomy, Biology, Medical Informatics
  • 25. Laser Microscope Objective Pine Hole Lens Nozzle d1 d2 FSC (Forward scatter) Orange fluo Red fluo EX: SeaFlow Francois Ribalet Jarred Swalwell Ginger Armbrust
  • 26. 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
  • 27. Ex: SeaFlow Francois Ribalet Jarred Swalwell Ginger Armbrust
  • 28. SeaFlow in Myria • “That 5-line MyriaL program was 100x faster than my R cluster, and much simpler” Dan Halperin Sophie Clayton
  • 29. Lowering barrier to entry
  • 30. Algorithmic insight Shumo Chu Dominik Moritz
  • 31. Performance analysis Sourcenode Destination node Shumo Chu Dominik Moritz
  • 32. 32 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
  • 33. 33 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 Sigma-Clipping (v1)
  • 34. 34 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 Sigma-Clipping (v2)
  • 35. 35 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 Sigma-Clipping (v3)
  • 36. Takeaways • Myria: Analytics-as-a-Service – Lower barrier to entry, iterative processing, state-of-the-art internals • Blur the distinction between “Query” and “Algorithm” • Relational Algebra is at least as important as Linear Algebra http://escience.washington.edu @billghowe billhowe@cs.washington.edu http://myria.cs.washington.edu https://demo.myria.cs.washington.edu/ (Relational Algebra) (Linear Algebra)
  • 37. 37
  • 38. Huffman coding refresher symbol : frequency (descending)
  • 39. Rosvall and Bergstrom 2007, 2010 A Random Walk…. http://www.mapequation.org/apps/MapDemo.html …generates a sequence of symbols with frequencies so we can generate a Huffman code for that sequence….
  • 40. A two-level coding a global index codebook indicates which module you are in a local module codebook indicates which vertex is visited Rosvall and Bergstrom 2007, 2010 codebook derived from the relative rates at which a random walker enters each module codebook derived from the relative rates at which a random walker visits each node OR exits the module
  • 41. MapEquation intuition With a bad two-level encoding, you might be frequently jumping between modules With a bad two-level encoding, your modules might have too many vertices and require long codebooks A good, short encoding means a walker spends a lot of time within modules rather than moving between them, while keeping module size to a minimum Rosvall and Bergstrom 2007, 2010 A good graph clustering
  • 42. MapEquation Rosvall and Bergstrom 2007, 2010 Describes movements between modules Describes movements within module i
  • 43. Third-party benchmarks Lancichinetti, Fortunato, “Community detection algorithms: a comparative analysis”, Phys Review 2009 “We conclude that the Infomap method by Rosvall and Bergstrom is the best performing on the set of benchmarks we have examined here.”
  • 44. Serial Algorithm (simplified) Compute visit probability of each vertex (PageRank) While the code length L has not converged: Put the vertices in random order For each vertex v: greedily move v to best neighboring module do global bookkeeping ….plus several optimizations
  • 45. How do we parallelize it? Naïve 1st Attempt: Drop all locks Compute visit probability of each vertex (PageRank) While the code length L has not converged: Put the vertices in random order For each vertex v: greedily move v to best neighboring module do global bookkeeping parallel serial, but fast serial
  • 46. Naïve lock-free scheme converges prematurely Seung-Hee Bae ICDM 2013
  • 47. How do we parallelize it? 2nd Attempt: RelaxMap Compute visit probability of each vertex (PageRank) While the code length L has not converged: Put the vertices in random order For each vertex v: greedily move v to best neighboring module grab a lock do global bookkeeping parallel serial, but fast serial ICDM 2013
  • 48. Seung-Hee Bae ICDM 2013 Converges faster Same quality
  • 49. Parallel efficiency is … ok Seung-Hee Bae ICDM 2013
  • 50. Side excursion: Prioritization Observation: Certain vertices contribute to improving the objective function more than others. Which ones? c1 n1 c2 c3 m1 v m2 mn1 mn2 mn3 n2 n3 Mc Ma Mb
  • 51. Seung-Hee Bae DMKD 2014 (submitted) vertex neighbors are red module siblings are blue vertices in neighboring modules are green
  • 52. Seung-Hee Bae DMKD 2014 (submitted)
  • 53. Seung-Hee Bae DMKD 2014 (submitted)
  • 54. How do we parallelize it? 3rd Attempt: Approximate the objective function by just moving every vertex along its heaviest edge Ignore the terms that require synchronization
  • 55. How do we parallelize it? 4th Attempt: Fully Asynchronous + Gossiping Each vertex 1) tells its neighbors when it moves 2) propagate other messages To decide whether to move, just use the information you have
  • 56. Seung-Hee Bae
  • 57. 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
  • 58. 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”
  • 59. 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…
  • 60. 60 Maslow’s Needs Hierarchy “As each need is satisfied, the next higher level in the hierarchy dominates conscious functioning.” -- Maslow 43
  • 61. A “Needs Hierarchy” of Science Data Management storage sharing 61 query integration analytics “As each need is satisfied, the next higher level in the hierarchy dominates conscious functioning.” -- Maslow 43
  • 62. A “Needs Hierarchy” of Science Data Management storage sharing 62 integration query analytics “As each need is satisfied, the next higher level in the hierarchy dominates conscious functioning.” -- Maslow 43