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Heterogeneous Workflows With Spark At Netflix

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Jen Aman

Spark Summit 2016 talk by Antony Arokiasamy (Netflix) and Kedar Sadekar (Netflix)

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Heterogeneous Workflows with Spark at Netflix 0 Antony Arokiasamy | Kedar Sadekar | Personalization Infrastructure
1 Help members find content to watch and enjoy to maximize member sa8sfac8on and reten8on
Everything is a Recommendation 2 Recommenda)ons are driven by Machine Learning Ranking Rows
Machine Learning Pipeline 3 User Selec8on Feature Genera8on Model Valida8on Publish Model Model Training
Machine Learning Pipeline Challenges 4 •  Innova8on •  Heterogeneous Environments •  Spark •  Na8ve Support •  Separate Orchestra8on and Execu8on •  Mul8 Tenancy •  Machine Learning Constructs •  Parameter Sweep – 30k Dockers
Meson Workflow System 5 •  General Purpose Workflow Orchestra8on and Scheduling framework •  Delegates execu8on to resource managers like Mesos •  Op8mized for Machine Learning Pipelines and Visualiza8on •  Checkout the Blog •  hTp://bit.ly/mesonws or techblog.neXlix.com •  Plan to Open Sourced soon
Meson Architecture 6
Standard and Custom Step Types 7
Parameter Passing 8 Hive Query User DataSet Regional DataSet Global DataSet Get Users Regional Model Global Model User DataSet Wrangle Data
Structured Constructs 9
Top Down or Bottom Up 10
Two Way Communication 11
Spark Step 12
Artifacts 13 •  Step outputs tracked as Ar8facts •  Visualiza8on •  Memoiza8on
Multi Tenancy 14 •  Resource ATributes •  spark.cores.max •  spark.executor.memory •  spark.mesos.constraints •  Dynamic Resource Alloca8on
Cluster Management 15 •  Red-Black soware updates •  Scale up/Scale down
Meson/Spark Cluster 16 •  100s of Concurrent Jobs •  700 Nodes •  5000 Cores •  25 TB Memory •  Apps: Meson Workflow System, Spark and Dockers •  Few smaller clusters
17 Antony Arokiasamy Kedar Sadekar @aasamy /aasamy aarokiasamy@netflix.com @kedar_sadekar /kedar-sadekar ksadekar@netflix.com

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