Post on 08-Jan-2017
transcript
Using Flink with MongoDB to enhance relevancy in personalization
“How to use Flink with MongoDB?”
Marc Schwering Sr. Solution Architect – EMEA
marc@mongodb.com @m4rcsch
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Agenda For This Session
• Personalization Process Review • The Life of an Application • Separation of Concerns / Real World Architecture • Apache Spark and Flink Data Processing Projects • Clustering with Apache Flink • Next Steps
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High Level Personalization Process
1. Profile created
2. Enrich with public data
3. Capture ac9vity
4. Clustering analysis
5. Define Personas
6. Tag with personas
7. Personalize interac9ons
Batch analytics
Public data
Common technologies • R • Hadoop • Spark • Python • Java • Many other
options Personas changed much less often than tagging
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Evolution of a Profile (1)
{ "_id" : ObjectId("553ea57b588ac9ef066428e1"),
"ipAddress" : "216.58.219.238",
"referrer" : ”kay.com",
"firstName" : "John",
"lastName" : "Doe",
"email" : "johndoe@gmail.com"
}
• <sample> – Originating IP – Demographic info – Location – Name – Sex – Email
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Evolution of a Profile (n+1) {
"_id" : ObjectId("553e7dca588ac9ef066428e0"),
"firstName" : "John",
"lastName" : "Doe", "address" : "229 W. 43rd St.",
"city" : "New York",
"state" : "NY",
"zipCode" : "10036", "age" : 30,
"email" : "john.doe@mongodb.com",
"twitterHandle" : "johndoe",
"gender" : "male", "interests" : [ "electronics", "basketball",
"weightlifting", "ultimate frisbee", "traveling", "technology" ], "visitedCounts" : {
"watches" : 3, "shirts" : 1, "sunglasses" : 1,
"bags" : 2 }, "purchases" : [ { "id" : 1, "desc" : "Power Oxford Dress Shoe",
"category" : "Mens shoes" }, { "id" : 2, "desc" : "Striped Sportshirt", "category" : "Mens shirts"
} ], "persona" : "shoe-fanatic” }
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One size/document fits all?
• Profile Data – Preferences – Personal information
• Contact information • DOB, gender, ZIP...
• Customer Data – Purchase History – Marketing History
• „Session Data“ – View History – Shopping Cart Data – Information Broker Data
• Personalisation Data – Persona Vectors – Product and Category recommendations
Application
Batch analytics
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Separation of Concerns
• Profile Data – Preferences – Personal information
• Contact information • DOB, gender, ZIP...
• Customer Data – Purchase History – Marketing History
• „Session Data“ – View History – Shopping Cart Data – Information Broker Data
• Personalisation Data – Persona Vectors – Product and Category recommendations
Batch analytics Layer
Frontend - System
Profile Service Customer Service Session Service Persona Service
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Benefits
• Code does less, Document and Code stays focused • Split ability
– Different Teams – New Languages – Defined Dependencies
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Advice for Developers (1)
• Code does less, Document and Code stays focused • Split ability
– Different Teams – New Languages – Defined Dependencies
KISS => Keep it simple and save!
=> Clean Code <=
• Robert C. Marten: https://cleancoders.com/ • M. Fowler / B. Meyer. et. al.: Command Query Separation
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Separation of Concerns
• Profile Data – Preferences – Personal information
• Contact information • DOB, gender, ZIP...
• Customer Data – Purchase History – Marketing History
• „Session Data“ – View History – Shopping Cart Data – Information Broker Data
• Personalisation Data – Persona Vectors – Product and Category recommendations
Batch analytics Layer
Frontend – System
Profile Service Customer Service Session Service Persona Service
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Separation of Concerns
• Profile Data – Preferences – Personal information
• Contact information • DOB, gender, ZIP...
• Customer Data – Purchase History – Marketing History
• „Session Data“ – View History – Shopping Cart Data – Information Broker Data
• Personalisation Data – Persona Vectors – Product and Category recommendations
Batch analytics Layer
Frontend – System
Profile Service Customer Service Session Service Persona Service
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Architecture revised
Profile Service Customer Service Session Service Persona Service
Frontend – System Backend– Systems
Data Processing
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Advice for Developers (2)
• OWN YOUR DATA! (but only relevant Data) • Say no! (to direct Data ie. DB Access)
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Hadoop in a Nutshell
• An open source distributed storage and distributed batch oriented processing framework
• Hadoop Distributed File System (HDFS) to store data on commodity hardware
• Yarn as resource management platform • MapReduce as programming model working on top of HDFS
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Spark in a Nutshell
• Spark is a top-level Apache project
• Can be run on top of YARN and can read any Hadoop API data, including HDFS or MongoDB
• Fast and general engine for large-scale data processing and analytics
• Advanced DAG execution engine with support for data locality and in-memory computing
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Flink in a Nutshell
• Flink is a top-level Apache project
• Can be run on top of YARN and can read any Hadoop API data, including HDFS or MongoDB
• A distributed streaming dataflow engine • Streaming and batch • Iterative in memory execution and handling • Cost based optimizer
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Latency of query operations
Query Aggregation MapReduce Cluster Algorithms
time
MongoDB Hadoop Spark/Flink
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Iterations in Flink
• Dedicated iteration operators • Tasks keep running for the iterations, not redeployed for each step • Caching and optimizations done automatically
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Takeaways
• Evolution is amazing and exiting! – Be ready to learn new things, ask questions across Silos!
• Stay focused => Start and stay small – Evaluate with BigDocuments but do a PoC focussed on the topic
• Extending functionality could be challenging – Evolution is outpacing help channels – A lot of options (Spark, Flink, Storm, Hadoop….) – More than just a binary
• Extending functionality is easy – Aggregation, MapReduce – Connectors opening a new variety of Use Cases
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Next Steps
• Try out Flink – http://flink.apache.org/ – https://github.com/mongodb/mongo-hadoop – https://github.com/m4rcsch/flink-mongodb-example
• Participate and ask Questions! – @m4rcsch – marc@mongodb.com
• We are hiring!! J