mongodb et hadoop
TRANSCRIPT
Agenda
Evolving Data Landscape
MongoDB & Hadoop Use Cases
MongoDB Connector Features
Demo
Evolving Data Landscape
• Terabyte and Petabyte datasets• Data warehousing• Advanced analytics
Hadoop
“The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.”
http://hadoop.apache.org
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Enterprise IT Stack
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Operational vs. Analytical: Enrichment
Warehouse, AnalyticsApplications, Interactions
Operational: MongoDB
First-Level Analytics
Internet of Things
Social
Mobile Apps
Product/Asset Catalog
Security & Fraud
Single View
Customer Data Management
Churn Analysis
Risk Modeling
Sentiment Analysis
Trade Surveillance
Recommender
Warehouse & ETL
Ad Targeting
Predictive Analytics
Analytical: Hadoop
First-Level Analytics
Internet of Things
Social
Mobile Apps
Product/Asset Catalog
Security & Fraud
Single View
Customer Data Management
Churn Analysis
Risk Modeling
Sentiment Analysis
Trade Surveillance
Recommender
Warehouse & ETL
Ad Targeting
Predictive Analytics
Operational & Analytical: Lifecycle
First-Level Analytics
Internet of Things
Social
Mobile Apps
Product/Asset Catalog
Security & Fraud
Single View
Customer Data Management
Churn Analysis
Risk Modeling
Sentiment Analysis
Trade Surveillance
Recommender
Warehouse & ETL
Ad Targeting
Predictive Analytics
MongoDB & Hadoop Use Cases
Commerce
Applicationspowered by
Analysispowered by
Products & Inventory
Recommended products
Customer profile
Session management
Elastic pricing
Recommendation models
Predictive analytics
Clickstream history
MongoDB Connectorfor Hadoop
Insurance
Applicationspowered by
Analysispowered by
Customer profiles
Insurance policies
Session data
Call center data
Customer action analysis
Churn analysis
Churn prediction
Policy rates
MongoDB Connectorfor Hadoop
Fraud Detection
MongoDB Connectorfor Hadoop
Payments Nightly Analysis
3rd Party
Data Sources
Results CacheFraud
Detection
Qu
ery
On
ly
Query Only
MongoDB Connector for Hadoop
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Connector Overview
DATA
• Read/Write MongoDB• Read/Write BSON
TOOLS
• MapReduce• Pig• Hive• Spark
PLATFORMS
• Apache Hadoop• Cloudera CDH• Hortonworks HDP• MapR• Amazon EMR
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Connector Features and Functionality
• Computes splits to read data• Single Node, Replica Sets, Sharded Clusters
• Mappings for Pig and Hive• MongoDB as a standard data source/destination
• Support for• Filtering data with MongoDB queries• Authentication• Reading from Replica Set tags• Appending to existing collections
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MapReduce Configuration
• MongoDB input/outputmongo.job.input.format = com.mongodb.hadoop.MongoInputFormatmongo.input.uri = mongodb://mydb:27017/db1.collection1mongo.job.output.format = com.mongodb.hadoop.MongoOutputFormatmongo.output.uri = mongodb://mydb:27017/db1.collection2
• BSON input/outputmongo.job.input.format = com.hadoop.BSONFileInputFormatmapred.input.dir = hdfs:///tmp/database.bsonmongo.job.output.format = com.hadoop.BSONFileOutputFormatmapred.output.dir = hdfs:///tmp/output.bson
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Pig Mappings
• Input: BSONLoader and MongoLoaderdata = LOAD ‘mongodb://mydb:27017/db.collection’ using com.mongodb.hadoop.pig.MongoLoader
• Output: BSONStorage and MongoInsertStorageSTORE records INTO ‘hdfs:///output.bson’ using com.mongodb.hadoop.pig.BSONStorage
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Hive Support
• Access collections as Hive tables• Use with MongoStorageHandler or BSONStorageHandler
CREATE TABLE mongo_users (id int, name string, age int)STORED BY "com.mongodb.hadoop.hive.MongoStorageHandler"WITH SERDEPROPERTIES("mongo.columns.mapping” = "_id,name,age”) TBLPROPERTIES("mongo.uri" = "mongodb://host:27017/test.users”)
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Spark
• Use with MapReduce input/output formats
• Create Configuration objects with input/output formats and data URI
• Load/save data using SparkContext Hadoop file API
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Data Movement
Dynamic queries to MongoDB vs. BSON snapshots in HDFS
Dynamic queries with most recent data
Puts load on operational database
Snapshots move load to Hadoop
Snapshots add predictable load to MongoDB
Demo : Recommendation Platform
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Movie Web
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MovieWeb Web Application
• Browse - Top movies by ratings count- Top genres by movie count
• Log in to - See My Ratings- Rate movies
• Recommendations- Movies You May Like- Recommendations
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MovieWeb Components
• MovieLens dataset– 10M ratings, 10K movies, 70K users– http://grouplens.org/datasets/movielens/
• Python web app to browse movies, recommendations– Flask, PyMongo
• Spark app computes recommendations– MLLib collaborative filter
• Predicted ratings are exposed in web app– New predictions collection
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Spark Recommender
• Apache Hadoop (2.3) - HDFS & YARN- Top genres by movie count
• Spark (1.0)- Execute within YARN- Assign executor resources
• Data- From HDFS, MongoDB- To MongoDB
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MovieWeb Workflow
Snapshot dbas BSON
Predict ratings for all pairings
Write Prediction to MongoDB collection
Store BSON in HDFS
Read BSON into Spark App
Create user movie pairing
Web Application exposes
recommendationsRepeat Process
Train Model from existing ratings
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Execution
$ spark-submit --master local \ --driver-memory 2G --executor-memory 2G \ --jars mongo-hadoop-core.jar,mongo-java-driver.jar \ --class com.mongodb.workshop.SparkExercise \ ./target/spark-1.0-SNAPSHOT.jar \ hdfs://localhost:9000 \ mongodb://127.0.0.1:27017/movielens \ predictions \
Should I use MongoDB or Hadoop?
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Business First!
First-Level Analytics
Internet of Things
Social
Mobile Apps
Product/Asset
Catalog
Security & Fraud
Single View
Customer Data
Management
Churn Analysis
Risk Modeling
Sentiment Analysis
Trade Surveillance
Recommender
Warehouse & ETL
Ad Targeting
Predictive Analytics
What/Why How
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The good tool for the task
• Dataset size• Data processing complexity• Continuous improvement
V1.0
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The good tool for the task
• Dataset size• Data processing complexity• Continuous improvement
V2.0
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Resources / Questions
• MongoDB Connector for Hadoop- http://github.com/mongodb/mongo-hadoop
• Getting Started with MongoDB and Hadoop - http://docs.mongodb.org/ecosystem/tutorial/getting-s
tarted-with-hadoop/
• MongoDB-Spark Demo- https://github.com/crcsmnky/mongodb-hadoop-work
shop
