Couchbase Server 2.0 Use Cases

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The new features in Couchbase 2.0 allow an array of new use cases. We will explore in-depth some of the most common use cases.

In this webinar you will learn:
-The most common use cases for Couchbase Server 2.0
-What kind of applications are being supported by Couchbase 2.0
-How to evaluate if Couchbase is right for your application

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  • Hello everyone, thanks for joining in. I’m Dipti borkar , the product manager for Couchbase Server. Welcome to the second webinar in the Why NoSQL Now series. This webinar focuses on navigating the transition from relational to NoSQL. We’ll take a quick look at some of the key issues impacting relational databases which in turn are driving NoSQL adoption. … and then get into the details of how distributed document databases address these issues.Some house keeping notes: - To ask questions, please type them into the questions window and I’ll try to answer these along the way. In some cases I may wait until the end. We will be sending out a follow up email in a couple of days which will include links to the slides as well as the recording of the webinar.
  • Rapid app devwithouth the need to perform an expensive alter table operation.
  • JSON support – natively stored as json, whne you build an app, there is not conversion required. New doc viewing , editing capability. Indexing and querying – look inside your json, build views and query for a key, for ranges or to aggregate data Incremental mapreduce – powers indexing. Build complex views over your data. Great for real-time analytics XDCR – replicate information from one cluster to another cluster
  • These are the market segments
  • Partial listing of companies with paid production deploymentsThousands more using open source
  • For the main document select username as keyUse this as a common prefix for related dataWhen building user’s session information, fetch these itemsCreate related documents by prefixing with the same username
  • Couchbase Server 2.0 Use Cases

    1. 1. Couchbase ServerCommon use cases Dipti Borkar Director, Product Management 1
    2. 2. Couchbase Server 2.0 - Webinar Series Introducing Couchbase Server 2.0 Couchbase Server 2.0 and Indexing/Querying Couchbase Server 2.0 and Incremental Map Reduce for Real-Time Analytics Couchbase Server 2.0 and Cross Data Center Replication Couchbase Server 2.0 and Full-Text Search Integration Couchbase Server 2.0 Use Cases Overview http://www.couchbase.com/webinars 2
    3. 3. 2 major types of data management systems OLTP / ODS Analytics / EDW 3
    4. 4. 2 major types of data management systems OLTP / ODS Analytics / EDW 4
    5. 5. 2 major types of data management systems OLTP / ODS Analytics / EDW 5
    6. 6. 2 major types of data management systems NoSQL 6
    7. 7. 2 major types of data management systems Simple, fast, elastic NoSQL database with sub-millisecond performance at scale Map-reduce / batch processing against huge datasets for pattern matching, insights and answers 7
    8. 8. NoSQL Database Considerations Easy Consistent High Scalability Performance Grow cluster without application Always awesome experience changes, without downtime for your application users. when needed Always On Flexible 24x7x365 Data Model The sun never sets on the Internet, Keep developers productive and your application needs the database allow fast and easy addition of to always serve data. new features 8
    9. 9. Couchbase Server NoSQL Document Database for interactive applications 2.0 9
    10. 10. Couchbase Server Grow cluster without Easy application changes, without Scalability downtime with a single click Consistent, Consistent sub-millisecond High read and write response times Performance with consistent high throughput Always No downtime for software On upgrades, hardware maintenance, 24x7x365 etc. 10
    11. 11. Flexible Data Model { “ID”: 1, “FIRST”: “Dipti”, “LAST”: “Borkar”, “ZIP”: “94040”, “CITY”: “MV”, “STATE”: “CA” } JSON JSON JSON JSON • No need to worry about the database when changing your application • Records can have different structures, there is no fixed schema • Allows painless data model changes for rapid application development 11
    12. 12. New in Two JSON support Indexing and Querying Incremental Map Reduce Cross data center replication 12
    13. 13. Market Adoption Internet Companies Enterprises • Social Gaming • Communications • Ad Networks • Retail • Social Networks • Financial Services • Online Business • Health Care Services • Automotive/Airline • E-Commerce • Agriculture • Online Media • Content Management • Consumer Electronics • Cloud Services • Business Systems 13
    14. 14. Market Adoption – Customers Internet Companies Enterprises More than 300 customers -- 5,000 production deployments worldwide 14
    15. 15. USE CASE AND APPLICATION EXAMPLES 15
    16. 16. Application Characteristics - Data driven • 3rd party or user defined structure (Twitter feeds) • Support for unlimited data growth (Viral apps) • Data with non-homogenous structure • Need to quickly and often change data structure • Variable length documents • Sparse data records • Hierarchical data Couchbase is a good fit 16
    17. 17. Application Characteristics - Performance driven • Low latency critical (ex. 1millisecond) • High throughput (ex. 200000 ops / sec) • Large number of users • Unknown demand with sudden growth of users/data • Predominantly direct document access • Read / Mixed / Write heavy workloads Couchbase is a good fit 17
    18. 18. Use Case ExamplesWeb app or Use-case Couchbase Solution Example CustomerContent and Metadata Couchbase document store + Elastic Search McGraw-Hill…Management SystemSocial Game or Mobile Couchbase stores game and player data Zynga…AppAd Targeting Couchbase stores user information for fast AOL… accessUser Profile Store Couchbase Server as a key-value store TuneWiki…Session Store Couchbase Server as a key-value store Concur….High Availability Couchbase Server as a memcached tier Orbitz…Caching Tier replacementChat/Messaging Couchbase Server DOCOMO…Platform 18
    19. 19. Use Case: Social GamingSocial and Mobile Gaming Types of Data Application Requirements • User account information • Ability to support rapid growth • User game profile info • Fast response times for • User’s social graph awesome user experience • State of the game • Game uptime –24x7x365 • Player badges and stats • Easy to update apps with new features Why NoSQL and Couchbase • Scalability ensures that games are ready to handle the millions of users that come with viral growth. • High performance guarantees players are never left waiting to make their next move. • Always-on operations means zero interruption to game play (and revenue) • Flexible data model means games can be developed rapidly and updated easily with new features 19
    20. 20. Example: Data Profile for Players { {“UUID ”: “2 1 f7 f8 de-8 0 5 1 -5 b89 -8 6 “Time”: “2 0 1 1 -0 4 -0 1 T1 3 :0 1 :0 2.4 2 “_id”: “auser_profile”, “Server”: “A2 2 2 3 E”, “Calling Server”: “A2 2 1 3 W”, “user_id”: 7778 “Type”: “E1 0 0 ”, “Initiating Us er”: “ds allings @s py.net”, “password”: “a1004cdcaa3191b7”, “D etails ”: { ”common_name”: .2 2 ”, “IP”: “1 0 .1 .1 ”Robert User”, “API”: “Ins ertD VD QueueItem”, ”nicknames”: [”Bob”,ed”, “Trace”: “cleans ”Buddy”], “Tags ”: "sign_up_timestamp": 1224612317, [ “SERVER”, "last_login_timestamp": 1245613101 “US-Wes t”, “API” } ] } { } {“UUID ”: “ 2 1 f7 f8 d e-8 0 5 1 -5 b 8 9 -8 6 “Time”: “ 2 0 1 1 -0 4 -0 1 T1 3 :0 1 :0 2 “Server”: “A2 2 2 3 E”, .4 2 “_id”: “auser_friends”, “Callin g Server”: “Typ e”: “E1 0 0 ”, “A2 2 1 3 W ”, “In itiatin g Us er”: “d s allin gs @s p y.n et”, “friends”: [ “joe”, “D etails ”: { “IP ”: “ 1 0 .1 .1 .2 2 ”, “alan”, “AP I”: “ In s ertD VD Qu eu eItem”, “Trace”: “clean s ed ”, “Tags ”: “toru” ] [ “SERVER”, “US-Wes t”, } } “AP I” ] } 20
    21. 21. Use Case: Ad TargetingAd Targeting Types of Data Application Requirements • User profile: preferences • High performance to meet and psychographic data limited ad serving budget; time • Ad serving history by user allowance is typically <40 msec • Ad buying history by • Scalability to handle hundreds advertiser of millions of user profiles and rapidly growing amount of • Ad serving history by data advertiser • 24x7x365 availability to avoid ad revenue loss Why NoSQL and Couchbase • Sub-millisecond reads/writes means less time is needed for data access, more time is available for ad logic processing, and more highly optimized ads will be served • Ease of scalability ensures that the data cluster can be grown seamlessly as the amount of user and ad data grows • Always-on operations = always-on revenue. You will never miss the opportunity to serve an ad because downtime. 21
    22. 22. Ad and offer targeting: Data flow 40 milliseconds to Ad Targeting pick the right offer profiles, campaigns raw event data / offers, cooked insights actionable insights raw event data cooked insights 22
    23. 23. Ad Targeting: Content Recommendation content 3 oriented site targeted recommendations 1 events relational database 2 user profiles 23
    24. 24. Ad Targeting Ad Targeting Platform Logs Logs Logs Couchbase Server Cluster Logs sqoop export Logs flume flow sqoop import Hadoop Cluster 24
    25. 25. Use Case: Content and metadata store Building a self-adapting, interactive learning portal with Couchbase 25
    26. 26. The Problem As learning move online in great numbers Growing need to build interactive learning environments that 0101001001 1101010101 Scale! 0101001010 101010 Scale to millions of Serve MHE as well as third-party Including Support Self-adapt via learners content open content learning apps usage data 26
    27. 27. The ChallengeHmmm...this looks kinda Backend is an Interactive Contentlike:+ Content Caching (Scale) Delivery Cloud that must:+ Social Gaming (Stats)+ Ad Targeting (Smarts) • Allow for elastic scaling under spike periods • Ability to catalog & deliver content from many sources • Consistent low-latency for metadata and stats access • Require full-text search support for content discovery • Offer tunable content ranking & recommendation functions Experimented with a combination of: XML Databases In-memory Data Grids SQL/MR Engines Enterprise Search Servers 27
    28. 28. The Technologies 28
    29. 29. The Learning Portal • Designed and built as a collaboration between MHE Labs and Couchbase • Serves as proof-of-concept and testing harness for Couchbase + ElasticSearch integration • Available for download and further development as open source code https://github.com/couchbaselabs/learningportal 29
    30. 30. Architecture 30
    31. 31. Sample JSON document 31
    32. 32. View from the app Content stats Content tagging Content contribution 32
    33. 33. Couchbase Server Features used: Views 33
    34. 34. View definition example 34
    35. 35. Couchbase Server Features used : Full-textintegration 35
    36. 36. 2.0 Beta is Here. JSON Documents Indexing  Querying Cross Data Center Replicationhttp://www.couchbase.com/couchbase-server/beta36
    37. 37. THANK YOU @DBORKARDIPTI@COUCHBASE.COM 37
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