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Distributed processing of large graphs in python
The Data Science Retreat (DSR), directed by Jose Quesada, accepts fewer than 5% of applicants and emphasizes commercial awareness, with participants completing portfolio projects. The document details graph theory concepts and the implementation of distributed graph processing using Apache Spark's GraphX and GraphFrames, highlighting their advantages and challenges. Additionally, it discusses the importance of formulating effective questions in data science and offers resources for further learning.
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Distributed processing of large graphs in python
- 1. Jose Quesada Director, DataScience Retreat jose@datascienceretreat.com @quesada
- 3. • Mentors areworld-class. CTOs, library authors, inventors, founders of fast-growing companies, etc • DSR accepts fewer than 5% of the applications • Strong focus on commercial awareness • 5 years of working experience on average • 30+ partner companies in Europe
- 5. DSR participants doa portfolio project
- 7. Why is DSRtalking about Scala/Spark? They are b IBM is behind this They hired
- 9. What is agood question?
- 10. What is agood question? • Business case • Data available • Technology to answer the question is available • We know when the solution worked
- 11. Does he looklike a bitch?
- 12. What is agood question? • Business case • Data available • Technology to answer the question is available • We know when the solution worked
- 13. The question: Whenshould I tweet to influence the right account? Or ‘beat Buffer at their own game’
- 14. What is agood question? • Business case
- 15. DJ J &MAX RECORDS
- 16. DJ J &MAX RECORDS
- 17. DJ J &MAX RECORDS
- 18. DJ J &MAX RECORDS
- 19. DJ J &MAX RECORDS
- 22. DJ J &MAX RECORDS
- 23. Overlap Tweet hours Tweetfrequency per UTC hour
- 24. What is agood question? • Business case • Data available
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- 26. What is agood question? • Business case • Data available • Technology to answer the question is available
- 27. What is agood question? • Business case • Data available • Technology to answer the question is available • We know when the solution worked
- 28. Graph theory partswe can use to solve this problem
- 29. Graph theory primer •Random walk • Shortest path • Sampling
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- 31. Sampling in Networks Notethat sampling in Networks is fraught with difficulties. One cannot simply sample the edges and nodes and expect that the sample be representative of the original network. In the graph below, a sample that missed node 1 or 2 would disconnect the two clusters, and would not have the same properties as the original Node 11 Node 2
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- 37. Random surfer A B C D E Visited moreoften: • Nodes with many links • Coming from frequently visited nodes
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- 48. Teleport A B C D E At regular node:invoke teleport operation with probability α and standard random walk with probability (1-α) (1-α) α
- 49. Personalized pagerank A B C D E At regularnode: invoke teleport operation with probability α and standard random walk with probability (1-α). When teleporting, go to target node (1-α)
- 50. Personalized pagerank A B C D E At regularnode: invoke teleport operation with probability α and standard random walk with probability (1-α). When teleporting, go to target node (1-α) α
- 51. Personalized pagerank • Specialcase of Pagerank with priors (distribution of weights over the nodes)
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- 101. A partitioned, distributedgraph processing engine is significantly more complex and difficult to build
- 102. GraphX and graphframes(new in spark 2.0) • GraphX is to RDD as graphframe is to dataframe • GraphX is lower level, and the API is scala-only. Graphframe is very new: • It’s not designed to be a graph database, as neo4J. Nodes and edges can contain metadata, but the query engine is not as complete as cypher
- 103. Advantages of graphframes •Graphframes have a python API • Graphframes give you simple querying for free. GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries • They contain most of the algorithms in graphX, but the API is less well-tested • Pyspark shell instead of spark-shell
- 104. Distributed PageRank • Problem:Computing PageRank on graph too large for one machine • Algorithm: – Shard edges randomly, – compute on each machine – average results • Basic idea: Duplicate edges from low-degree nodes. Gives an unbiased estimator
- 105. • Nodes: 41.652.230 •Edges: 1.468.365.182
- 108. Summary of implementation,benefits • Graph theory is a really flexible way to represent a problem • Data structures to represent graphs are mature • You can do now out-of-core, distributed graph analysis for cheap • Implementations are there for even state-of-the-art methods
- 109. Summary, finding aproblem • We live in an age of abundance (methods, data, hardware, ideas) • Finding the question is more than half of the battle • I had about a week to prepare this talk, but I managed to put together something that showcases what you can do with large graphs today, and it could be effective as a startup idea • My question is not great because you cannot demonstrate that it works till you use it (common problem for unsupervised methods)
- 110. The question: Whenshould I tweet to influence the right account? Or ‘beat Buffer at their own game’
- 111. References: Drawing graphs •Graphs in this slide set have been drawn with Gephi • If you use Zeppelin notebook, you can draw graphs with: drawGraph(org.apache.spark.graphx.util. GraphGenerators.rmatGraph(sc,32,60))
- 112. 25 videos explainingML on spark, 50 more to come. A bunch on graphX • For people who already know ML • http://datascienceretreat.com/videos/data-science-with- scala-and-spark
- 113. About learning newtech over seven weekends…
- 114. About learning newtech over seven weekends • You have time and enjoy using it to learn alone: learn it ‘the hard way’ • You are extremely motivated and talented, have money: Apply for DSR • You want your weekends for yourself. You are already very good but want to switch jobs. Apply for codekitt
- 115. Thanks! Jose Quesada Director, DataScience Retreat jose@datascienceretreat.com @quesada http://datascienceretreat.com/ codekitt.com