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An Evening with MongoDB
         Detroit
Agenda

• Overview: 10gen and MongoDB
• MongoDB and Big Data Processing
• MongoDB and Node.js




                        2
Introduction

10gen is the company behind MongoDB –
the leading NoSQL database




 Document-         General               Open-
  Oriented         Purpose              Source


                      3
10gen Overview




       170+ employees                             500+ customers




                                    Offices in New York, Palo Alto, Washington
  Over $81 million in funding       DC, London, Dublin, Barcelona and Sydney


                                4
Leading Organizations Rely on MongoDB




                  5
Global MongoDB Community

41,000+
Monthly Unique Downloads

24,000+
Online Education Registrants

12,000+
MongoDB User Group Members

10,000+
Annual MongoDB Days Attendees


                               6
Relational Database
Challenges
Relational Database Challenges
Data Types                      Agile Development
• Unstructured data             • Iterative
• Semi-structured               • Short development
  data                            cycles
• Polymorphic data              • New workloads




Volume of Data                  New Architectures
• Petabytes of data             • Horizontal scaling
• Trillions of records          • Commodity
                                  servers
• Millions of queries per
  second                        • Cloud computing


                            8
MongoDB Solution
MongoDB Solution




  Agile      Scalable   Powerful




                10
Agility

RDBMS          MongoDB


                 {
                     _id : ObjectId("4c4ba5e5e8aabf3"),
                     employee_name: "Dunham, Justin",
                     department : "Marketing",
                     title : "Product Manager, Web",
                     report_up: "Neray, Graham",
                     pay_band: “C",
                     benefits : [
                            {   type :   "Health",
                                plan : "PPO Plus" },
                            {   type :    "Dental",
                                plan : "Standard" }
                                 ]
                 }




          11
Power

Better Data Locality        In-Memory Caching




   RDBMS   MongoDB



                       12
High Availability




                    13
Scalability




              14
Voila!




         15
MongoDB Features

 JSON Data Model with             Auto-Sharding for
 Dynamic Schema                   Horizontal Scalability

 Flexible, Full Index             Rich, Document-Based
 Support                          Queries

 Built-In Replication and         Fast, In-Place
 High Availability                Updates

 Aggregation Framework and        GridFS for Large File
 Map/Reduce                       Storage



                             16
MongoDB Monitoring Service (MMS)

Free, cloud-based service for monitoring and alerts
• Charts, custom
  dashboards and
  automated alerting
• Tracks 100+ metrics –
  performance, resource
  utilization, availability
  and response times
• 10,000+ users

                              17
Best Total Cost of Ownership (TCO)

Developer and Ops Savings
•   Less code
•   More productive development
•   Easier to maintain

Hardware Savings
•   Commodity servers
•   Internal storage (no SAN)
•   Scale out, not up

Software and Support Savings
•   No upfront license – pay for value        DB Alternative
    over time
•   Cost visibility for usage growth



                                         18
10gen Products and Services

      Subscriptions
      Professional Support, Subscriber Edition and Commercial License



      Consulting
      Expert Resources for All Phases of MongoDB Implementations



      Training
      Online and In-Person for Developers and Administrators




                            19
Customer Use Cases
MongoDB Use Cases
   Content Management                Operational Intelligence




 E-Commerce         User Data Management   High Volume Data Feeds




                            21
MongoDB Adoption




              22
Case Study

  Stores user and location-based data in MongoDB
  for social networking mobile app
         Problem                   Why MongoDB                    Results

• Relational architecture     • Auto-sharding to scale   • Focus engineering on
  could not scale               high-traffic and fast-     building mobile app vs.
                                growing application        back-end
• Check-in data growth
  hit single-node capacity    • Geo-indexing for easy    • Scale efficiently with
  ceiling                       querying of location-      limited resources
                                based data
• Significant work to build                              • Increased developer
  custom sharding layer       • Simple data model          productivity




                                         23
Case Study

  Relies on a MongoDB-powered, real-time analytics
  product for SMBs
        Problem               Why MongoDB                    Results

• More than 500,000      • Ability to handle         • Shorter feature
  websites                 complex data while          development cycles
                           maintaining high            (e.g., 1 week)
• 10 years of complex      performance
  data                                               • 2.5x faster than MySQL
                         • Took 1 week for devs to
• Relational database      ramp up on MongoDB        • High-performance real-
  took several days to                                 time analytics to over
  process data           • Strong community            500,000 SMBs




                                    24
Case Study

  Stores billions of posts in myriad formats with
  MongoDB
        Problem               Why MongoDB                   Results

• 1.5M posts per day,    • Flexible document-       • Initial deployment held
  different structures     based model                over 5B documents and
• Inflexible MySQL,                                   10TB of data
                         • Horizontal scalability
  lengthy delays for       built in                 • Automated failover
  making changes
                                                      provides high
• Data piling up in      • Easy to use                availability
  production database    • Interface in familiar    • Schema changes are
• Poor performance         language                   quick and easy


                                     25
Get Involved


Speak at a MongoDB
                          meetups@10gen.com
    User Group


Get MongoDB News
                           10gen.com/signup
   and Updates


                     26
For More Information

 Resource                    Location

 MongoDB Downloads           www.mongodb.org/download

 Free Online Training        education.10gen.com

 Webinars and Events         www.10gen.com/events

 White Papers                www.10gen.com/white-papers

 Case Studies                www.10gen.com/customers

 Presentations               www.10gen.com/presentations

 Documentation               docs.mongodb.org

 Additional Info             info@10gen.com


                        27
An Evening with MongoDB Detroit 2013
Case Study

  Serves targeted content to users using MongoDB-
  powered identity system
        Problem                  Why MongoDB                 Results

• 20M+ unique visitors      • Easy-to-manage        • Rapid rollout of new
  per month                   dynamic data model      features
                              enables limitless
• Rigid relational schema     growth, interactive   • Customized, social
  unable to evolve with       content                 conversations
  changing data types                                 throughout site
  and new features          • Support for ad hoc
                              queries               • Tracks user data to
• Slow development                                    increase
  cycles                    • Highly extensible       engagement, revenue


                                       29
Case Study

  Uses MongoDB to safeguard over 6 billion images
  served to millions of customers
        Problem                   Why MongoDB                    Results

• 6B images, 20TB of         • JSON-based data          • 5x cost reduction
  data                         model
                                                        • 9x performance
• Brittle code base on top   • Agile, high                improvement
  of Oracle database –         performance, scalable
  hard to scale, add                                    • Faster time-to-market
  features                   • Alignment with
                               Shutterfly’s services-   • Dev cycles in weeks vs.
• High SW and HW costs         based architecture         tens of months




                                         30
Case Study

  Stores one of world’s largest record repositories
  and searchable catalogues in MongoDB
        Problem                   Why MongoDB                    Results

• One of world’s largest     • Fast, easy scalability   • Will scale to 100s of TB
  record repositories                                     by 2013, PB by 2020
                             • Full query language
• Move to SOA required                                  • Searchable catalogue
  new approach to data       • Complex metadata           of varied data types
  store                        storage
                                                        • Decreased SW and
• RDBMS could not                                         support costs
  support centralized data
  mgt and federation of
  information services

                                         31
Case Study

  Provides low-latency, high-scale translation
  management platform built on MongoDB
        Problem                  Why MongoDB                        Results

• Old MySQL               • Horizontal scale with built-   • Simplified scaling and
  performance               in sharding                      high-performance
  degradation and high                                       architecture
  maintenance             • High availability with
                            replica sets                   • Dramatically improved
• Complex to scale
  MySQL                                                      developer productivity
                          • Memory-mapped
• High-                     architecture for ingesting     • Increased uptime
  speed, asynchronous       content quickly w/out
  storage and fast read     separate caching layer
  requirements

                                        32
Case Study

  Uses MongoDB to power real-time ad serving
  platform
        Problem                   Why MongoDB                   Results

• Needed costly SQL        • Dynamic schema enables     • Billions of requests per
  architecture to enable     continuous algorithm         day with sub-ms latency
  real-time bidding          development and
                             customer-specific fields   • Inexpensive cost per
• Large volumes of data                                   query
  and queries              • Scalability for massive
                             data volumes and low       • TB of data stored,
• Diverse, evolving          latency                      populated on the fly and
  schema                                                  queried in real-time
                           • Visual monitoring with
                             MMS

                                         33
Case Study

  Uses MongoDB to underpin social media monitoring
  and recommendation engine
         Problem                      Why MongoDB                       Results

• HBase locked them into       • Ease of use and flexibility   • Robust queries and
  rigid data model, stifling     of data model                   dynamic schema
  ability to create                                              enable higher quality
  connections between          • Powerful indexing and ad        recommendations
  data sets                      hoc querying, plus
                                 integrated MapReduce          • Bugs fixed in hours
• Single points of failure                                       instead of days
  with master/slave            • High availability with
  topology                       replica sets                  • Dramatically improved
                                                                 uptime
• Up to 1M posts per day

                                             34
Case Study

  Stores 3.5 TB of data in MongoDB to power
  real-time dictionary
        Problem                  Why MongoDB                         Results

• Performance               • Easy to                        • Migrated 5B records in
  roadblocks with MySQL       store, locate, retrieve data     1 day, zero downtime
• Massive data ingestion    • Eliminated Memcached           • Reduced code by 75%
  led to database outages     while increasing
                              performance: up to 2M          • Sped up document
• Tables locked for tens      requests per hour, 8,000         metadata retrieval from
  of seconds during           words inserted per               30 ms to 0.1 ms
  inserts                     second                         • Significant cost
                            • Long runway for scale-out        savings, 15% reduction
                                                               in servers

                                        35
Case Study

  Self-service product built on MongoDB enables
  real-time analytics for social marketing
        Problem                   Why MongoDB                         Results

• Need for real-time         • Real-time aggregation to      • Operational cost
  aggregation and              adjust campaigns on the         savings
  analytics                    fly
                                                             • Simplified scale-out to
• Tried SQL, then            • Scalability for persistence     support 140M
  MapReduce – both             layer                           impressions
  solutions only handled
  periodic data, could not   • Ability to store large        • Data flexibility to add
  scale                        amounts of data reliably        new features and
                                                               performance gains
                                                               without overhead

                                         36
Case Study

  Runs unified data store serving hundreds of
  diverse web properties on MongoDB
        Problem                 Why MongoDB                      Results

• Hundreds of diverse       • Flexible schema          • Developers can focus on
  web properties built on                                end-user features instead
  Java-based CMS            • Rich querying and support of back-end storage
                              for secondary index
• Rich documents forced       support                  • Simplified day-to-day
  into ill-suited model                                  operations
                            • Easy to manage
• Adding new data types,      replication and scaling  • Simple to add new brands,
  tables to RDBMS killed                                 content types, etc. to
  read performance                                       platform


                                       37
Case Study

  MongoDB serves as social gaming platform for
  hundreds of millions of users
        Problem                Why MongoDB                      Results

• Legacy MySQL             • Flexible data model        • Improved game
  hindered development       applicable to wide variety performance and end-user
  speed, could not scale     of use cases                 experience
• Needed operational       • High availability through • Server cost reduction
  database that could        replica sets on commodity
  also handle real-time      servers                   • Accelerated development
  analysis                                               and time-to-market
                           • Size and strength of
• Server sprawl              MongoDB community


                                      38
Case Study

  Delivers agile automated supply chain service to
  retailers powered by MongoDB
        Problem               Why MongoDB                        Results

• RDBMS poorly-          • Document-oriented model • Decreased supplier
  equipped to handle       less complex, easier to       onboard time by 12x
  varying data types       code
  (e.g., SKUs, images)                                 • Grew from 400K records to
                         • Single data store for         40M in 12 months
• Inefficient use of       structured, semi-
  storage in RDBMS         structured and              • Significant cost reductions
  (i.e., 90% empty         unstructured data             on schema design time,
  columns)                                               ongoing developer effort,
                         • Scalability and availability and storage usage
• Complex joins
  degraded performance   • Analytics with
                           MapReduce
                                     39
Case Study

  Runs social marketing suite with real-time
  analytics on MongoDB
         Problem                    Why MongoDB                      Results

• RDBMS could not meet         • Ease of use, developer    • Decreased app
  speed and scale                ramp-up                     development from months
  requirements of                                            to weeks
  measuring massive            • Solution maturity – depth
  online activity                of functionality, failover • 30M social events per day
• Inability to provide real-                                  stored in MongoDB
                               • High-performance with
  time analytics and             write-heavy system         • 6x increase in customers
  aggregations
                                                              supported over one year
                               • Queuing and logging for
• Unpredictable peak
  loads                          easy search at app layer


                                          40
Case Study

  Real-time server and website monitoring
  solution runs on MongoDB
        Problem                   Why MongoDB                     Results

• Needed to handle           • General purpose DB        • MongoDB-first policy
  thousands of requests
  per second                 • High-write throughput     • 12+ TB ingested per
                                                           month
• MySQL resulted in          • Scales easily while
  millions of rows per         maintaining performance   • Increased performance,
  month, per server                                        decreased disk usage
                             • Easy-to-use replication
• Difficult to scale MySQL     and automated failover    • Simplified infrastructure
  with replication                                         cuts costs, frees up
                             • Native PHP and Python       resources for dev
                               drivers

                                        41
Case Study

  Social e-commerce application built on MongoDB
  offers 100M+ products from over 30K brands
        Problem                   Why MongoDB                         Results

• MySQL could not            • Flexible data model to         • Boosted developer
  accommodate growth           handle varying product           productivity
                               attributes
• Significant optimization                                    • Scaled from 5M to
  required to tune MySQL     • Scalability for global reach     100M products with
  performance                                                   minimal work
                             • Ease of maintenance
• Database maintenance                                        • Decreased product
  inhibited development      • Consistent performance           import time by 90%
                               even when adding data
                               and new features


                                         42

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An Evening with MongoDB Detroit 2013

  • 1. An Evening with MongoDB Detroit
  • 2. Agenda • Overview: 10gen and MongoDB • MongoDB and Big Data Processing • MongoDB and Node.js 2
  • 3. Introduction 10gen is the company behind MongoDB – the leading NoSQL database Document- General Open- Oriented Purpose Source 3
  • 4. 10gen Overview 170+ employees 500+ customers Offices in New York, Palo Alto, Washington Over $81 million in funding DC, London, Dublin, Barcelona and Sydney 4
  • 6. Global MongoDB Community 41,000+ Monthly Unique Downloads 24,000+ Online Education Registrants 12,000+ MongoDB User Group Members 10,000+ Annual MongoDB Days Attendees 6
  • 8. Relational Database Challenges Data Types Agile Development • Unstructured data • Iterative • Semi-structured • Short development data cycles • Polymorphic data • New workloads Volume of Data New Architectures • Petabytes of data • Horizontal scaling • Trillions of records • Commodity servers • Millions of queries per second • Cloud computing 8
  • 10. MongoDB Solution Agile Scalable Powerful 10
  • 11. Agility RDBMS MongoDB { _id : ObjectId("4c4ba5e5e8aabf3"), employee_name: "Dunham, Justin", department : "Marketing", title : "Product Manager, Web", report_up: "Neray, Graham", pay_band: “C", benefits : [ { type : "Health", plan : "PPO Plus" }, { type : "Dental", plan : "Standard" } ] } 11
  • 12. Power Better Data Locality In-Memory Caching RDBMS MongoDB 12
  • 15. Voila! 15
  • 16. MongoDB Features JSON Data Model with Auto-Sharding for Dynamic Schema Horizontal Scalability Flexible, Full Index Rich, Document-Based Support Queries Built-In Replication and Fast, In-Place High Availability Updates Aggregation Framework and GridFS for Large File Map/Reduce Storage 16
  • 17. MongoDB Monitoring Service (MMS) Free, cloud-based service for monitoring and alerts • Charts, custom dashboards and automated alerting • Tracks 100+ metrics – performance, resource utilization, availability and response times • 10,000+ users 17
  • 18. Best Total Cost of Ownership (TCO) Developer and Ops Savings • Less code • More productive development • Easier to maintain Hardware Savings • Commodity servers • Internal storage (no SAN) • Scale out, not up Software and Support Savings • No upfront license – pay for value DB Alternative over time • Cost visibility for usage growth 18
  • 19. 10gen Products and Services Subscriptions Professional Support, Subscriber Edition and Commercial License Consulting Expert Resources for All Phases of MongoDB Implementations Training Online and In-Person for Developers and Administrators 19
  • 21. MongoDB Use Cases Content Management Operational Intelligence E-Commerce User Data Management High Volume Data Feeds 21
  • 23. Case Study Stores user and location-based data in MongoDB for social networking mobile app Problem Why MongoDB Results • Relational architecture • Auto-sharding to scale • Focus engineering on could not scale high-traffic and fast- building mobile app vs. growing application back-end • Check-in data growth hit single-node capacity • Geo-indexing for easy • Scale efficiently with ceiling querying of location- limited resources based data • Significant work to build • Increased developer custom sharding layer • Simple data model productivity 23
  • 24. Case Study Relies on a MongoDB-powered, real-time analytics product for SMBs Problem Why MongoDB Results • More than 500,000 • Ability to handle • Shorter feature websites complex data while development cycles maintaining high (e.g., 1 week) • 10 years of complex performance data • 2.5x faster than MySQL • Took 1 week for devs to • Relational database ramp up on MongoDB • High-performance real- took several days to time analytics to over process data • Strong community 500,000 SMBs 24
  • 25. Case Study Stores billions of posts in myriad formats with MongoDB Problem Why MongoDB Results • 1.5M posts per day, • Flexible document- • Initial deployment held different structures based model over 5B documents and • Inflexible MySQL, 10TB of data • Horizontal scalability lengthy delays for built in • Automated failover making changes provides high • Data piling up in • Easy to use availability production database • Interface in familiar • Schema changes are • Poor performance language quick and easy 25
  • 26. Get Involved Speak at a MongoDB meetups@10gen.com User Group Get MongoDB News 10gen.com/signup and Updates 26
  • 27. For More Information Resource Location MongoDB Downloads www.mongodb.org/download Free Online Training education.10gen.com Webinars and Events www.10gen.com/events White Papers www.10gen.com/white-papers Case Studies www.10gen.com/customers Presentations www.10gen.com/presentations Documentation docs.mongodb.org Additional Info info@10gen.com 27
  • 29. Case Study Serves targeted content to users using MongoDB- powered identity system Problem Why MongoDB Results • 20M+ unique visitors • Easy-to-manage • Rapid rollout of new per month dynamic data model features enables limitless • Rigid relational schema growth, interactive • Customized, social unable to evolve with content conversations changing data types throughout site and new features • Support for ad hoc queries • Tracks user data to • Slow development increase cycles • Highly extensible engagement, revenue 29
  • 30. Case Study Uses MongoDB to safeguard over 6 billion images served to millions of customers Problem Why MongoDB Results • 6B images, 20TB of • JSON-based data • 5x cost reduction data model • 9x performance • Brittle code base on top • Agile, high improvement of Oracle database – performance, scalable hard to scale, add • Faster time-to-market features • Alignment with Shutterfly’s services- • Dev cycles in weeks vs. • High SW and HW costs based architecture tens of months 30
  • 31. Case Study Stores one of world’s largest record repositories and searchable catalogues in MongoDB Problem Why MongoDB Results • One of world’s largest • Fast, easy scalability • Will scale to 100s of TB record repositories by 2013, PB by 2020 • Full query language • Move to SOA required • Searchable catalogue new approach to data • Complex metadata of varied data types store storage • Decreased SW and • RDBMS could not support costs support centralized data mgt and federation of information services 31
  • 32. Case Study Provides low-latency, high-scale translation management platform built on MongoDB Problem Why MongoDB Results • Old MySQL • Horizontal scale with built- • Simplified scaling and performance in sharding high-performance degradation and high architecture maintenance • High availability with replica sets • Dramatically improved • Complex to scale MySQL developer productivity • Memory-mapped • High- architecture for ingesting • Increased uptime speed, asynchronous content quickly w/out storage and fast read separate caching layer requirements 32
  • 33. Case Study Uses MongoDB to power real-time ad serving platform Problem Why MongoDB Results • Needed costly SQL • Dynamic schema enables • Billions of requests per architecture to enable continuous algorithm day with sub-ms latency real-time bidding development and customer-specific fields • Inexpensive cost per • Large volumes of data query and queries • Scalability for massive data volumes and low • TB of data stored, • Diverse, evolving latency populated on the fly and schema queried in real-time • Visual monitoring with MMS 33
  • 34. Case Study Uses MongoDB to underpin social media monitoring and recommendation engine Problem Why MongoDB Results • HBase locked them into • Ease of use and flexibility • Robust queries and rigid data model, stifling of data model dynamic schema ability to create enable higher quality connections between • Powerful indexing and ad recommendations data sets hoc querying, plus integrated MapReduce • Bugs fixed in hours • Single points of failure instead of days with master/slave • High availability with topology replica sets • Dramatically improved uptime • Up to 1M posts per day 34
  • 35. Case Study Stores 3.5 TB of data in MongoDB to power real-time dictionary Problem Why MongoDB Results • Performance • Easy to • Migrated 5B records in roadblocks with MySQL store, locate, retrieve data 1 day, zero downtime • Massive data ingestion • Eliminated Memcached • Reduced code by 75% led to database outages while increasing performance: up to 2M • Sped up document • Tables locked for tens requests per hour, 8,000 metadata retrieval from of seconds during words inserted per 30 ms to 0.1 ms inserts second • Significant cost • Long runway for scale-out savings, 15% reduction in servers 35
  • 36. Case Study Self-service product built on MongoDB enables real-time analytics for social marketing Problem Why MongoDB Results • Need for real-time • Real-time aggregation to • Operational cost aggregation and adjust campaigns on the savings analytics fly • Simplified scale-out to • Tried SQL, then • Scalability for persistence support 140M MapReduce – both layer impressions solutions only handled periodic data, could not • Ability to store large • Data flexibility to add scale amounts of data reliably new features and performance gains without overhead 36
  • 37. Case Study Runs unified data store serving hundreds of diverse web properties on MongoDB Problem Why MongoDB Results • Hundreds of diverse • Flexible schema • Developers can focus on web properties built on end-user features instead Java-based CMS • Rich querying and support of back-end storage for secondary index • Rich documents forced support • Simplified day-to-day into ill-suited model operations • Easy to manage • Adding new data types, replication and scaling • Simple to add new brands, tables to RDBMS killed content types, etc. to read performance platform 37
  • 38. Case Study MongoDB serves as social gaming platform for hundreds of millions of users Problem Why MongoDB Results • Legacy MySQL • Flexible data model • Improved game hindered development applicable to wide variety performance and end-user speed, could not scale of use cases experience • Needed operational • High availability through • Server cost reduction database that could replica sets on commodity also handle real-time servers • Accelerated development analysis and time-to-market • Size and strength of • Server sprawl MongoDB community 38
  • 39. Case Study Delivers agile automated supply chain service to retailers powered by MongoDB Problem Why MongoDB Results • RDBMS poorly- • Document-oriented model • Decreased supplier equipped to handle less complex, easier to onboard time by 12x varying data types code (e.g., SKUs, images) • Grew from 400K records to • Single data store for 40M in 12 months • Inefficient use of structured, semi- storage in RDBMS structured and • Significant cost reductions (i.e., 90% empty unstructured data on schema design time, columns) ongoing developer effort, • Scalability and availability and storage usage • Complex joins degraded performance • Analytics with MapReduce 39
  • 40. Case Study Runs social marketing suite with real-time analytics on MongoDB Problem Why MongoDB Results • RDBMS could not meet • Ease of use, developer • Decreased app speed and scale ramp-up development from months requirements of to weeks measuring massive • Solution maturity – depth online activity of functionality, failover • 30M social events per day • Inability to provide real- stored in MongoDB • High-performance with time analytics and write-heavy system • 6x increase in customers aggregations supported over one year • Queuing and logging for • Unpredictable peak loads easy search at app layer 40
  • 41. Case Study Real-time server and website monitoring solution runs on MongoDB Problem Why MongoDB Results • Needed to handle • General purpose DB • MongoDB-first policy thousands of requests per second • High-write throughput • 12+ TB ingested per month • MySQL resulted in • Scales easily while millions of rows per maintaining performance • Increased performance, month, per server decreased disk usage • Easy-to-use replication • Difficult to scale MySQL and automated failover • Simplified infrastructure with replication cuts costs, frees up • Native PHP and Python resources for dev drivers 41
  • 42. Case Study Social e-commerce application built on MongoDB offers 100M+ products from over 30K brands Problem Why MongoDB Results • MySQL could not • Flexible data model to • Boosted developer accommodate growth handle varying product productivity attributes • Significant optimization • Scaled from 5M to required to tune MySQL • Scalability for global reach 100M products with performance minimal work • Ease of maintenance • Database maintenance • Decreased product inhibited development • Consistent performance import time by 90% even when adding data and new features 42