GraphX: Graph analytics for insights about developer communities
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The document provides an overview of Graph Analytics in Spark. It discusses Spark components and key distinctions from MapReduce. It also covers GraphX terminology and examples of composing node and edge RDDs into a graph. The document provides examples of simple traversals and routing problems on graphs. It discusses using GraphX for topic modeling with LDA and provides further reading resources on GraphX, algebraic graph theory, and graph analysis tools and frameworks.
![GraphX: Compose Node + Edge RDDs into a Graph
val nodeRDD: RDD[(Long, ND)] = sc.parallelize(…)
val edgeRDD: RDD[Edge[ED]] = sc.parallelize(…)
val g: Graph[ND, ED] = Graph(nodeRDD, edgeRDD)](https://image.slidesharecdn.com/pacographanalytics-150609121430-lva1-app6891/85/GraphX-Graph-analytics-for-insights-about-developer-communities-13-320.jpg)
![// http://spark.apache.org/docs/latest/graphx-programming-guide.html
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
case class Peep(name: String, age: Int)
val nodeArray = Array(
(1L, Peep("Kim", 23)), (2L, Peep("Pat", 31)),
(3L, Peep("Chris", 52)), (4L, Peep("Kelly", 39)),
(5L, Peep("Leslie", 45))
)
val edgeArray = Array(
Edge(2L, 1L, 7), Edge(2L, 4L, 2),
Edge(3L, 2L, 4), Edge(3L, 5L, 3),
Edge(4L, 1L, 1), Edge(5L, 3L, 9)
)
val nodeRDD: RDD[(Long, Peep)] = sc.parallelize(nodeArray)
val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)
val g: Graph[Peep, Int] = Graph(nodeRDD, edgeRDD)
val results = g.triplets.filter(t => t.attr > 7)
for (triplet <- results.collect) {
println(s"${triplet.srcAttr.name} loves ${triplet.dstAttr.name}")
}
GraphX: Example – simple traversals](https://image.slidesharecdn.com/pacographanalytics-150609121430-lva1-app6891/85/GraphX-Graph-analytics-for-insights-about-developer-communities-14-320.jpg)
![TextBlob
tag and
lemmatize
words
TextBlob
segment
sentences
TextBlob
sentiment
analysis
Py
generate
skip-grams
parsed
JSON
message
JSON Treebank,
WordNet
Data Prep: Parser pipeline
{
"graf": [ [1, "Only", "only", "RB", 1, 0], [2, "fit", "fit", "VBP", 1, 1 ] ... ],
"id": “CA+B-+fyrBU1yGZAYJM_u=gnBVtzB=sXoBHkhmS-6L1n8K5Hhbw",
"polr": 0.2,
"sha1": "178b7a57ec6168f20a8a4f705fb8b0b04e59eeb7",
"size": 14,
"subj": 0.7,
"tile": [ [1, 2], [2, 3], [3, 4] ... ]
]
}
{
"date": "2014-10-01T00:16:08+00:00",
"id": "CA+B-+fyrBU1yGZAYJM_u=gnBVtzB=sXoBHkhmS-6L1n8K5Hhbw",
"next_thread": "CALEj8eP5hpQDM=p2xryL-JT-x_VhkRcD59Q+9Qr9LJ9sYLeLVg",
"next_url": "http://mail-archives.apache.org/mod_mbox/spark-user/201410.mbox/%3cCALEj8eP5hpQDM=p
"prev_thread": "",
"sender": "Debasish Das <debasish.da...@gmail.com>",
"subject": "Re: memory vs data_size",
"text": "nOnly fit the data in memory where you want to run the iterativenalgorithm....nnFor
}](https://image.slidesharecdn.com/pacographanalytics-150609121430-lva1-app6891/85/GraphX-Graph-analytics-for-insights-about-developer-communities-34-320.jpg)
![TextRank: data results
"Compatibility of systems of linear constraints"
[{'index': 0, 'stem': 'compat', 'tag': 'NNP','word': 'compatibility'},
{'index': 1, 'stem': 'of', 'tag': 'IN', 'word': 'of'},
{'index': 2, 'stem': 'system', 'tag': 'NNS', 'word': 'systems'},
{'index': 3, 'stem': 'of', 'tag': 'IN', 'word': 'of'},
{'index': 4, 'stem': 'linear', 'tag': 'JJ', 'word': 'linear'},
{'index': 5, 'stem': 'constraint', 'tag': 'NNS','word': 'constraints'}]
compat
system
linear
constraint
1:
2:
3:](https://image.slidesharecdn.com/pacographanalytics-150609121430-lva1-app6891/85/GraphX-Graph-analytics-for-insights-about-developer-communities-41-320.jpg)
![Social Graph: use GraphX to run graph analytics
// run graph analytics
val g: Graph[String, Int] = Graph(nodes, edges)
val r = g.pageRank(0.0001).vertices
r.join(nodes).sortBy(_._2._1, ascending=false).foreach(println)
// define a reduce operation to compute the highest degree vertex
def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = {
if (a._2 > b._2) a else b
}
// compute the max degrees
val maxInDegree: (VertexId, Int) = g.inDegrees.reduce(max)
val maxOutDegree: (VertexId, Int) = g.outDegrees.reduce(max)
val maxDegrees: (VertexId, Int) = g.degrees.reduce(max)
// connected components
val scc = g.stronglyConnectedComponents(10).vertices
node.join(scc).foreach(println)](https://image.slidesharecdn.com/pacographanalytics-150609121430-lva1-app6891/85/GraphX-Graph-analytics-for-insights-about-developer-communities-46-320.jpg)
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GraphX: Graph analytics for insights about developer communities
- 1. Graph Analytics in Spark 2015-06-08 • Scala Days • Amsterdam Paco Nathan, @pacoid Licensed under a Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International License
- 4. • generalized patterns unified engine for many use cases • lazy evaluation of the lineage graph reduces wait states, better pipelining • generational differences in hardware off-heap use of large memory spaces • functional programming / ease of use reduction in cost to maintain large apps • lower overhead for starting jobs • less expensive shuffles Spark Overview: Key Distinctions vs. MapReduce
- 5. databricks.com/blog/2014/11/05/spark-officially- sets-a-new-record-in-large-scale-sorting.html TL;DR: SmashingThe Previous Petabyte Sort Record
- 6. Spark is one of the most active Apache projects ohloh.net/orgs/apache TL;DR: Sustained Exponential Growth
- 7. oreilly.com/data/free/2014-data-science- salary-survey.csp TL;DR: Spark ExpertiseTops Median Salaries within Big Data
- 8. twitter.com/dberkholz/status/ 568561792751771648 TL;DR: Spark on StackOverflow
- 9. databricks.com/blog/2015/01/27/big-data-projects-are- hungry-for-simpler-and-more-powerful-tools-survey- validates-apache-spark-is-gaining-developer-traction.html TL;DR: Spark Survey 2015 by Databricks +Typesafe
- 11. GraphX: spark.apache.org/docs/latest/graphx- programming-guide.html Key Points: • graph-parallel systems • emphasis on integrated workflows • optimizations
- 12. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs J. Gonzalez, Y. Low, H. Gu, D. Bickson, C. Guestrin graphlab.org/files/osdi2012-gonzalez-low-gu-bickson- guestrin.pdf Pregel: Large-scale graph computing at Google Grzegorz Czajkowski, et al. googleresearch.blogspot.com/2009/06/large-scale-graph- computing-at-google.html GraphX: Graph Analytics in Spark Ankur Dave, Databricks spark-summit.org/east-2015/talk/graphx-graph- analytics-in-spark Topic modeling with LDA: MLlib meets GraphX Joseph Bradley, Databricks databricks.com/blog/2015/03/25/topic-modeling-with- lda-mllib-meets-graphx.html GraphX: Further Reading…
- 13. GraphX: Compose Node + Edge RDDs into a Graph val nodeRDD: RDD[(Long, ND)] = sc.parallelize(…) val edgeRDD: RDD[Edge[ED]] = sc.parallelize(…) val g: Graph[ND, ED] = Graph(nodeRDD, edgeRDD)
- 14. // http://spark.apache.org/docs/latest/graphx-programming-guide.html import org.apache.spark.graphx._ import org.apache.spark.rdd.RDD case class Peep(name: String, age: Int) val nodeArray = Array( (1L, Peep("Kim", 23)), (2L, Peep("Pat", 31)), (3L, Peep("Chris", 52)), (4L, Peep("Kelly", 39)), (5L, Peep("Leslie", 45)) ) val edgeArray = Array( Edge(2L, 1L, 7), Edge(2L, 4L, 2), Edge(3L, 2L, 4), Edge(3L, 5L, 3), Edge(4L, 1L, 1), Edge(5L, 3L, 9) ) val nodeRDD: RDD[(Long, Peep)] = sc.parallelize(nodeArray) val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray) val g: Graph[Peep, Int] = Graph(nodeRDD, edgeRDD) val results = g.triplets.filter(t => t.attr > 7) for (triplet <- results.collect) { println(s"${triplet.srcAttr.name} loves ${triplet.dstAttr.name}") } GraphX: Example – simple traversals
- 15. GraphX: Example – routing problems cost 4 node 0 node 1 node 3 node 2 cost 3 cost 1 cost 2 cost 1 What is the cost to reach node 0 from any other node in the graph? This is a common use case for graph algorithms, e.g., Dijkstra
- 16. GraphX: code examples… Let’s check some code!
- 17. Graph Analytics
- 18. Graph Analytics: terminology • many real-world problems are often represented as graphs • graphs can generally be converted into sparse matrices (bridge to linear algebra) • eigenvectors find the stable points in a system defined by matrices – which may be more efficient to compute • beyond simpler graphs, complex data may require work with tensors
- 19. Suppose we have a graph as shown below: We call x a vertex (sometimes called a node) An edge (sometimes called an arc) is any line connecting two vertices Graph Analytics: example v u w x
- 20. We can represent this kind of graph as an adjacency matrix: • label the rows and columns based on the vertices • entries get a 1 if an edge connects the corresponding vertices, or 0 otherwise Graph Analytics: representation v u w x u v w x u 0 1 0 1 v 1 0 1 1 w 0 1 0 1 x 1 1 1 0
- 21. An adjacency matrix always has certain properties: • it is symmetric, i.e., A = AT • it has real eigenvalues Therefore algebraic graph theory bridges between linear algebra and graph theory Graph Analytics: algebraic graph theory
- 22. Sparse Matrix Collection… for when you really need a wide variety of sparse matrix examples, e.g., to evaluate new ML algorithms University of Florida Sparse Matrix Collection cise.ufl.edu/ research/sparse/ matrices/ Graph Analytics: beauty in sparsity
- 23. Algebraic GraphTheory Norman Biggs Cambridge (1974) amazon.com/dp/0521458978 Graph Analysis andVisualization Richard Brath, David Jonker Wiley (2015) shop.oreilly.com/product/9781118845844.do See also examples in: Just Enough Math Graph Analytics: resources
- 24. Although tensor factorization is considered problematic, it may provide more general case solutions, and some work leverages Spark: TheTensor Renaissance in Data Science Anima Anandkumar @UC Irvine radar.oreilly.com/2015/05/the-tensor- renaissance-in-data-science.html Spacey RandomWalks and Higher Order Markov Chains David Gleich @Purdue slideshare.net/dgleich/spacey-random-walks- and-higher-order-markov-chains Graph Analytics: tensor solutions emerging
- 25. Data Preparation
- 26. Data Prep: Exsto Project Overview • insights about dev communities, via data mining their email forums • works with any Apache project email archive • applies NLP and ML techniques to analyze message threads • graph analytics surface themes and interactions • results provide feedback for communities, e.g., leaderboards
- 27. Data Prep: Exsto Project Overview – four links https://github.com/ceteri/spark-exercises/tree/master/exsto/dbc http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf http://mail-archives.apache.org/mod_mbox/spark-user/ https://class01.cloud.databricks.com/#notebook/67011
- 28. Data Prep: Scraper pipeline github.com/ceteri/spark-exercises/tree/master/exsto/dbc +
- 29. Data Prep: Scraper pipeline Typical data rates, e.g., for [email protected]: • ~2K msgs/month • ~18 MB/month parsed in JSON Six months’ list activity represents a graph of: • 1882 senders • 1,762,113 nodes • 3,232,174 edges A large graph?! In any case, it satisfies definition of a graph-parallel system – lots of data locality to leverage
- 30. Data Prep: Microservices meet Parallel Processing services email archives community leaderboards SparkSQL Data Prep Features Explore Scraper / Parser NLTK data Unique Word IDs TextRank, Word2Vec, etc. community insights not so big data… relatively big compute…
- 31. Data Prep: Scraper pipeline message JSON Py filter quoted content Apache email list archive urllib2 crawl monthly list by date Py segment paragraphs
- 32. Data Prep: Scraper pipeline message JSON Py filter quoted content Apache email list archive urllib2 crawl monthly list by date Py segment paragraphs { "date": "2014-10-01T00:16:08+00:00", "id": "CA+B-+fyrBU1yGZAYJM_u=gnBVtzB=sXoBHkhmS-6L1n8K5Hhbw", "next_thread": "CALEj8eP5hpQDM=p2xryL-JT-x_VhkRcD59Q+9Qr9LJ9sYLeLVg", "next_url": "http://mail-archives.apache.org/mod_mbox/spark-user/201410.mbox/%3cCALEj8eP5hpQ "prev_thread": "", "sender": "Debasish Das <[email protected]>", "subject": "Re: memory vs data_size", "text": "nOnly fit the data in memory where you want to run the iterativenalgorithm....n }
- 33. TextBlob tag and lemmatize words TextBlob segment sentences TextBlob sentiment analysis Py generate skip-grams parsed JSON message JSON Treebank, WordNet Data Prep: Parser pipeline
- 34. TextBlob tag and lemmatize words TextBlob segment sentences TextBlob sentiment analysis Py generate skip-grams parsed JSON message JSON Treebank, WordNet Data Prep: Parser pipeline { "graf": [ [1, "Only", "only", "RB", 1, 0], [2, "fit", "fit", "VBP", 1, 1 ] ... ], "id": “CA+B-+fyrBU1yGZAYJM_u=gnBVtzB=sXoBHkhmS-6L1n8K5Hhbw", "polr": 0.2, "sha1": "178b7a57ec6168f20a8a4f705fb8b0b04e59eeb7", "size": 14, "subj": 0.7, "tile": [ [1, 2], [2, 3], [3, 4] ... ] ] } { "date": "2014-10-01T00:16:08+00:00", "id": "CA+B-+fyrBU1yGZAYJM_u=gnBVtzB=sXoBHkhmS-6L1n8K5Hhbw", "next_thread": "CALEj8eP5hpQDM=p2xryL-JT-x_VhkRcD59Q+9Qr9LJ9sYLeLVg", "next_url": "http://mail-archives.apache.org/mod_mbox/spark-user/201410.mbox/%3cCALEj8eP5hpQDM=p "prev_thread": "", "sender": "Debasish Das <[email protected]>", "subject": "Re: memory vs data_size", "text": "nOnly fit the data in memory where you want to run the iterativenalgorithm....nnFor }
- 35. Data Prep: code examples… Let’s check some code!
- 37. TextRank: original paper TextRank: Bringing Order intoTexts Rada Mihalcea, Paul Tarau Conference on Empirical Methods in Natural Language Processing (July 2004) https://goo.gl/AJnA76 http://web.eecs.umich.edu/~mihalcea/papers.html http://www.cse.unt.edu/~tarau/
- 38. TextRank: other implementations Jeff Kubina (Perl / English): http://search.cpan.org/~kubina/Text-Categorize- Textrank-0.51/lib/Text/Categorize/Textrank/En.pm Paco Nathan (Hadoop / English+Spanish): https://github.com/ceteri/textrank/ Karin Christiasen (Java / Icelandic): https://github.com/karchr/icetextsum
- 39. TextRank: Spark-based pipeline Spark create word graph RDD word graph NetworkX visualize graph GraphX run TextRank Spark extract phrases ranked phrases parsed JSON
- 40. TextRank: raw text input
- 41. TextRank: data results "Compatibility of systems of linear constraints" [{'index': 0, 'stem': 'compat', 'tag': 'NNP','word': 'compatibility'}, {'index': 1, 'stem': 'of', 'tag': 'IN', 'word': 'of'}, {'index': 2, 'stem': 'system', 'tag': 'NNS', 'word': 'systems'}, {'index': 3, 'stem': 'of', 'tag': 'IN', 'word': 'of'}, {'index': 4, 'stem': 'linear', 'tag': 'JJ', 'word': 'linear'}, {'index': 5, 'stem': 'constraint', 'tag': 'NNS','word': 'constraints'}] compat system linear constraint 1: 2: 3:
- 44. TextRank: code examples… Let’s check some code!
- 45. Social Graph
- 46. Social Graph: use GraphX to run graph analytics // run graph analytics val g: Graph[String, Int] = Graph(nodes, edges) val r = g.pageRank(0.0001).vertices r.join(nodes).sortBy(_._2._1, ascending=false).foreach(println) // define a reduce operation to compute the highest degree vertex def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = { if (a._2 > b._2) a else b } // compute the max degrees val maxInDegree: (VertexId, Int) = g.inDegrees.reduce(max) val maxOutDegree: (VertexId, Int) = g.outDegrees.reduce(max) val maxDegrees: (VertexId, Int) = g.degrees.reduce(max) // connected components val scc = g.stronglyConnectedComponents(10).vertices node.join(scc).foreach(println)
- 47. Social Graph: PageRank of top dev@spark email, 4Q2014 (389,(22.690229478710016,Sean Owen <[email protected]>)) (857,(20.832469059298248,Akhil Das <[email protected]>)) (652,(13.281821379806798,Michael Armbrust <[email protected]>)) (101,(9.963167550803664,Tobias Pfeiffer <[email protected]>)) (471,(9.614436778460558,Steve Lewis <[email protected]>)) (931,(8.217073486575732,shahab <[email protected]>)) (48,(7.653814912512137,ll <[email protected]>)) (1011,(7.602002681952157,Ashic Mahtab <[email protected]>)) (1055,(7.572376489758199,Cheng Lian <[email protected]>)) (122,(6.87247388819558,Gerard Maas <[email protected]>)) (904,(6.252657820614504,Xiangrui Meng <[email protected]>)) (827,(6.0941062762076115,Jianshi Huang <[email protected]>)) (887,(5.835053915864531,Davies Liu <[email protected]>)) (303,(5.724235650446037,Ted Yu <[email protected]>)) (206,(5.430238461114108,Deep Pradhan <[email protected]>)) (483,(5.332452537151523,Akshat Aranya <[email protected]>)) (185,(5.259438927615685,SK <[email protected]>)) (636,(5.235941228955769,Matei Zaharia <matei.zaha…@gmail.com>)) // seaaaaaaaaaan! maxInDegree: (org.apache.spark.graphx.VertexId, Int) = (389,126) maxOutDegree: (org.apache.spark.graphx.VertexId, Int) = (389,170) maxDegrees: (org.apache.spark.graphx.VertexId, Int) = (389,296)
- 48. Social Graph: code examples… Let’s check some code!
- 49. Misc., Etc., Maybe: Feature learning withWord2Vec Matt Krzus www.yseam.com/blog/WV.html ranked phrases GraphX run Con.Comp. MLlib run Word2Vec aggregated by topic MLlib run KMeans topic vectors better than LDA? features… models… insights…
- 50. Resources
- 51. Spark Developer Certification • go.databricks.com/spark-certified-developer • defined by Spark experts @Databricks • assessed by O’Reilly Media • establishes the bar for Spark expertise
- 52. • 40 multiple-choice questions, 90 minutes • mostly structured as choices among code blocks • expect some Python, Java, Scala, SQL • understand theory of operation • identify best practices • recognize code that is more parallel, less memory constrained Overall, you need to write Spark apps in practice Developer Certification: Overview
- 53. community: spark.apache.org/community.html events worldwide: goo.gl/2YqJZK YouTube channel: goo.gl/N5Hx3h video+preso archives: spark-summit.org resources: databricks.com/spark/developer-resources workshops: databricks.com/spark/training
- 54. MOOCs: Anthony Joseph UC Berkeley early June 2015 edx.org/course/uc-berkeleyx/uc- berkeleyx-cs100-1x- introduction-big-6181 Ameet Talwalkar UCLA late June 2015 edx.org/course/uc-berkeleyx/ uc-berkeleyx-cs190-1x- scalable-machine-6066
- 55. Resources: Spark Packages Looking for other libraries and features? There are a variety of third-party packages available at: http://spark-packages.org/
- 56. http://spark-summit.org/ Resources: Spark Summit conferences discount code datamining15 for 15% off registration
- 57. http://strataconf.com/ Resources: Strata + HadoopWorld conferences
- 58. Resources: O’Reilly Podcast https://itunes.apple.com/podcast/id944929220
- 59. books+videos: Learning Spark Holden Karau, Andy Konwinski, Parick Wendell, Matei Zaharia O’Reilly (2015) shop.oreilly.com/ product/ 0636920028512.do Intro to Apache Spark Paco Nathan O’Reilly (2015) shop.oreilly.com/ product/ 0636920036807.do Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills O’Reilly (2015) shop.oreilly.com/ product/ 0636920035091.do Data Algorithms Mahmoud Parsian O’Reilly (2014) shop.oreilly.com/ product/ 0636920033950.do
- 60. presenter: Just Enough Math O’Reilly (2014) justenoughmath.com preview: youtu.be/TQ58cWgdCpA monthly newsletter for updates, events, conf summaries, etc.: liber118.com/pxn/ Enterprise Data Workflows with Cascading O’Reilly (2013) shop.oreilly.com/product/ 0636920028536.do