Heterogeneous Workflows With Spark At Netflix

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

Data & Analytics

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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Cross Site Collection Navigation using SPFx, Powershell PnP & PnP-JS

Cross Site Collection Navigation using SPFx, Powershell PnP & PnP-JS

Cross Site Collection Navigation using SPFx, Powershell PnP & PnP-JS

Software design with Domain-driven design

Software design with Domain-driven design

Software design with Domain-driven design

Getting started with SparkSQL - Desert Code Camp 2016

Getting started with SparkSQL - Desert Code Camp 2016

Getting started with SparkSQL - Desert Code Camp 2016

01/2009 - Portral development with liferay

01/2009 - Portral development with liferay

01/2009 - Portral development with liferay

Machine Learning Platform Life-Cycle Management

Machine Learning Platform Life-Cycle Management

Machine Learning Platform Life-Cycle Management

Cross Site Collection Navigation

Cross Site Collection Navigation

Cross Site Collection Navigation

Create great cncf user base from lessons learned from other open source com...

Create great cncf user base from lessons learned from other open source com...

Create great cncf user base from lessons learned from other open source com...

Office 365 Developer Bootcamp Melbourne

Office 365 Developer Bootcamp Melbourne

Office 365 Developer Bootcamp Melbourne

Productionzing ML Model Using MLflow Model Serving

Productionzing ML Model Using MLflow Model Serving

Productionzing ML Model Using MLflow Model Serving

Concept Maps in TeSS

Concept Maps in TeSS

Concept Maps in TeSS

Exposing services with Azure API Management

Exposing services with Azure API Management

Exposing services with Azure API Management

Portal and Intranets

Portal and Intranets

Portal and Intranets

Discover Microsoft's Cloud Automation Suite

Discover Microsoft's Cloud Automation Suite

Discover Microsoft's Cloud Automation Suite

Presto Strata Hadoop SJ 2016 short talk

Presto Strata Hadoop SJ 2016 short talk

Presto Strata Hadoop SJ 2016 short talk

More from Jen Aman

2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

Deep Learning on Apache® Spark™ : Workflows and Best Practices

Deep Learning on Apache® Spark™ : Workflows and Best Practices

Deep Learning on Apache® Spark™ : Workflows and Best Practices

Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

A Graph-Based Method For Cross-Entity Threat Detection

A Graph-Based Method For Cross-Entity Threat Detection

A Graph-Based Method For Cross-Entity Threat Detection

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

Livy: A REST Web Service For Apache Spark

Livy: A REST Web Service For Apache Spark

Livy: A REST Web Service For Apache Spark

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

Spark And Cassandra: 2 Fast, 2 Furious

Spark And Cassandra: 2 Fast, 2 Furious

Spark And Cassandra: 2 Fast, 2 Furious

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Spark on Mesos

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Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™ : Workflows and Best Practices

Deep Learning on Apache® Spark™ : Workflows and Best Practices

Deep Learning on Apache® Spark™ : Workflows and Best Practices

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

A Graph-Based Method For Cross-Entity Threat Detection

A Graph-Based Method For Cross-Entity Threat Detection

A Graph-Based Method For Cross-Entity Threat Detection

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

Livy: A REST Web Service For Apache Spark

Livy: A REST Web Service For Apache Spark

Livy: A REST Web Service For Apache Spark

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

Spark And Cassandra: 2 Fast, 2 Furious

Spark And Cassandra: 2 Fast, 2 Furious

Spark And Cassandra: 2 Fast, 2 Furious

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

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2. 1 Help members ﬁnd content to watch and enjoy to maximize member sa8sfac8on and reten8on

3. Everything is a Recommendation 2 Recommenda)ons are driven by Machine Learning Ranking Rows

4. Machine Learning Pipeline 3 User Selec8on Feature Genera8on Model Valida8on Publish Model Model Training

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6. 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

7. Meson Architecture 6

8. Standard and Custom Step Types 7

9. Parameter Passing 8 Hive Query User DataSet Regional DataSet Global DataSet Get Users Regional Model Global Model User DataSet Wrangle Data

10. Structured Constructs 9

11. Top Down or Bottom Up 10

12. Two Way Communication 11

13. Spark Step 12

14. Artifacts 13 •  Step outputs tracked as Ar8facts •  Visualiza8on •  Memoiza8on

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16. Cluster Management 15 •  Red-Black soware updates •  Scale up/Scale down

17. 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

18. 17 Antony Arokiasamy Kedar Sadekar @aasamy /aasamy aarokiasamy@netflix.com @kedar_sadekar /kedar-sadekar ksadekar@netflix.com