MLconf seattle 2015 presentation

Spark and GraphX in the Netflix Recommender System

Ehtsham Elahi and Yves Raimond

Algorithms EngineeringNetflix

MLconf Seattle 2015

Machine Learning @ Netflix

Introduction● Goal: Help members find

content that they’ll enjoy to maximize satisfaction and retention

● Core part of product○ Every impression is a

recommendation

Main Challenge - Scale● Algorithms @ Netflix Scale

○ > 62 M Members○ > 50 Countries○ > 1000 device types○ > 100M Hours / day

● Distributed Machine Learning algorithms help with Scale

Main Challenge - Scale● Algorithms @ Netflix Scale

○ > 62 M Members○ > 50 Countries○ > 1000 device types○ > 100M Hours / day

● Distributed Machine Learning algorithms help with Scale○ Spark And GraphX

Spark and GraphX

Spark And GraphX● Spark- Distributed in-memory computational engine

using Resilient Distributed Datasets (RDDs)

● GraphX - extends RDDs to Multigraphs and provides graph analytics

● Convenient and fast, all the way from prototyping (iSpark, Zeppelin) to Production

Two Machine Learning Problems● Generate ranking of items with respect to a given item

from an interaction graph

○ Graph Diffusion algorithms (e.g. Topic Sensitive Pagerank)

● Find Clusters of related items using co-occurrence data

○ Probabilistic Graphical Models (Latent Dirichlet Allocation)

Iterative Algorithms in GraphX

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Edge Attribute


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GraphX represents the graph as RDDs. e.g. VertexRDD, EdgeRDD


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GraphX provides APIs to propagate and update attributes


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Iterative Algorithm proceeds by creating updated graphs

Graph Diffusion algorithms

● Popular graph diffusion algorithm

● Capturing vertex importance with regards to a particular vertex

● e.g. for the topic “Seattle”

Topic Sensitive Pagerank @ Netflix

Iteration 0

We start by activating a single node

“Seattle”

related to

shot in

featured in

related to

cast

cast

cast

related to

Iteration 1

With some probability, we follow outbound edges, otherwise we go back to the origin.

Iteration 2

Vertex accumulates higher mass

Iteration 2

And again, until convergence

GraphX implementation● Running one propagation for each possible starting

node would be slow

● Keep a vector of activation probabilities at each vertex

● Use GraphX to run all propagations in parallel

Topic Sensitive Pagerank in GraphX

activation probability, starting from vertex 1



...

Activation probabilities as vertex attributes

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... ...

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Example graph diffusion results

“Matrix”

“Zombies”

“Seattle”

Distributed Clustering algorithms

LDA @ Netflix● A popular clustering/latent factors model● Discovers clusters/topics of related videos from Netflix

data● e.g, a topic of Animal Documentaries

LDA - Graphical Model

Question: How to parallelize inference?

LDA - Graphical Model

Question: How to parallelize inference?Answer: Read conditional independenciesin the model

Gibbs Sampler 1 (Semi Collapsed)

Gibbs Sampler 1 (Semi Collapsed)

Sample Topic Labels in a given document SequentiallySample Topic Labels in different documents In parallel

Gibbs Sampler 2 (UnCollapsed)


Sample Topic Labels in a given document In parallelSample Topic Labels in different documents In parallel


Suitable For GraphX

Sample Topic Labels in a given document In parallelSample Topic Labels in different documents In parallel

Distributed Gibbs Sampler

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A distributed parameterized graph for LDA with 3 Topics


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Edge: if word appeared in the document


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Categorical distributionfor the triplet usingvertex attributes


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Categorical distributions forall triplets


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Neighborhood aggregation for topic histograms


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Realize samples from Dirichlet to update the graph

Example LDA Results

Cluster of Bollywood Movies

Cluster of Kids shows

Cluster of Western movies

GraphX performance comparison

Algorithm Implementations● Topic Sensitive Pagerank

○ Distributed GraphX implementation○ Alternative Implementation: Broadcast graph adjacency matrix,

Scala/Breeze code, triggered by Spark

● LDA○ Distributed GraphX implementation○ Alternative Implementation: Single machine, Multi-threaded Java code

● All implementations are Netflix Internal Code

Performance Comparison


Open Source DBPedia dataset


Alternative Implementation: Scala code triggered by Spark on a cluster


Sublinear rise in time with GraphX Vs Linear rise in the Alternative


Doubling the size of cluster:2.0 speedup in the Alternative Impl Vs 1.2 in GraphX


Large number of vertices propagated in parallel lead to large shuffle data, causing failures in GraphX for small clusters


Netflix datasetNumber of Topics = 100


Multi-core implementation:Single machine Multi-threaded Java Code


GraphX setup:8 x Resources than the Multi-Core setup


Wikipedia dataset, 100 Topic LDACluster: (16 x r3.2xl)(source: Databricks)


GraphX for very large datasets outperforms the multi-core unCollapsed Impl

Lessons Learned

What we learned so far ...

● Where is the cross-over point for your iterative ML algorithm?○ GraphX brings performance benefits if you’re on the right side of that

point○ GraphX lets you easily throw more hardware at a problem

● GraphX very useful (and fast) for other graph processing tasks○ Data pre-processing○ Efficient joins


● Regularly save the state○ With a 99.9% success rate, what’s the probability of successfully

running 1,000 iterations?

● Multi-Core Machine learning (r3.8xl, 32 threads, 220 GB) is very efficient○ if your data fits in memory of single machine !


● Regularly save the state○ With a 99.9% success rate, what’s the probability of successfully

running 1,000 iterations?○ ~36%

● Multi-Core Machine learning (r3.8xl, 32 threads, 220 GB) is very efficient○ if your data fits in memory of single machine !

We’re hiring!(come talk to us)

https://jobs.netflix.com/



Appendix

Using GraphXscala> val edgesFile = "/data/mlconf-graphx/edges.txt"

scala> sc.textFile(edgesFile).take(5).foreach(println)

0 12 32 42 52 6

scala> val mapping = sc.textFile("/data/mlconf-graphx/uri-mapping.csv")

scala> mapping.take(5).foreach(println)

http://dbpedia.org/resource/Drew_Finerty,3663393http://dbpedia.org/resource/1998_JGTC_season,4148403http://dbpedia.org/resource/Eucalyptus_bosistoana,3473416http://dbpedia.org/resource/Wilmington,234049http://dbpedia.org/resource/Wetter_(Ruhr),884940

Creating a GraphX graphscala> val graph = GraphLoader.edgeListFile(sc, edgesFile, false, 100)

graph: org.apache.spark.graphx.Graph[Int,Int] = org.apache.spark.graphx.impl.GraphImpl@547a8dc1

scala> graph.edges.count

res3: Long = 16090021

scala> graph.vertices.count

res4: Long = 4548083

Pagerank in GraphXscala> val ranks = graph.staticPageRank(10, 0.15).vertices

scala> val resources = mapping.map { row =>

val fields = row.split(",")

(fields.last.toLong, fields.first)

}

scala> val ranksByResource = resources.join(ranks).map {

case (id, (resource, rank)) => (resource, rank)

}

scala> ranksByResource.top(3)(Ordering.by(_._2)).foreach(println)

(http://dbpedia.org/resource/United_States,15686.671749384182)

(http://dbpedia.org/resource/Animal,6530.621240073025)

(http://dbpedia.org/resource/United_Kingdom,5780.806077968981)

References

● Topic Sensitive Pagerank [Haveliwala, 2002]● Latent Dirichlet Allocation [Blei, 2003]

http://infolab.stanford.edu/~taherh/papers/topic-sensitive-pagerank-tkde.pdf

https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf

MLconf seattle 2015 presentation

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