This document summarizes presentations from three companies - ZipList, Zumobi, and Viddy - on using Amazon CloudSearch for search capabilities in their mobile and social applications. ZipList discusses using CloudSearch to enable unified search across global recipes and individual user recipe boxes. Zumobi provides an example of using CloudSearch to power search within a news app. Viddy talks about how CloudSearch allowed them to focus on innovation rather than rebuilding search infrastructure, reducing costs and improving performance.

1. Jon Handler
Delivering Search for Today's Local, Social, and Mobile
Applications
CloudSearch Solution Architect

2. Agenda
• Search for social and mobile applications
• Amazon CloudSearch
• Customer stories and panel
– Tim Ramsey, CTO/Co-founder for Ziplist / Conde Nast
– Pat Binkley, VP Engineering for Zumobi
– Ken Chung, CTO for Viddy

3. Time Spent Online (source: go-gulf.com)

4. Social Activities
• People are talking!
• Word of mouth is the primary factor behind
20% to 50% of all purchasing decisions
(Jonah Berger, "Contagious")
• Search brings relevance to the process of
pulling in friends

5. The Rise of Mobile Search (Source: BIA / Kelsey)
• In 2011, Search was 75% desktop,
25% mobile
• In 2013, they project 60% / 40%
• By 2016, 57% mobile, 43% desktop

6. In the Mobile Space, Local Is Growing

7. The Path to Search
• Search is central to user experience
• Options for building a search experience: BYO, open source, legacy enterprise
• Challenges

8. Amazon CloudSearch: Simple Search Simply
• Pay for infrastructure as you need it
• Lower total cost of operation
• No need to guess at capacity
• Increase innovation – low risk, inexpensive, simple experimentation
• Does the undifferentiated heavy lifting
• Available in 5 regions: go global in minutes

9. Amazon CloudSearch Overview
DNS / Load Balancing AWS Query
Search API Console Config
API
Command
Line Interface
ConsoleDoc
Svc API
Command
Line Interface
Console
SEARCH SERVICE
Search Documents
DOCUMENT SERVICE
Add Documents
Update Documents
Delete Documents
Create Domains
Configure Domains
Delete Domains
CONFIG SERVICE
Search Domain

10. CloudSearch in Context

11. Automatic Scaling
SEARCH INSTANCE
Index Partition n
Copy 1
SEARCH INSTANCE
Index Partition 2
Copy 2
SEARCH INSTANCE
Index Partition n
Copy 2
SEARCH INSTANCE
Index Partition 2
Copy n
SEARCH INSTANCE
DATA Document Quantity and Size
TRAFFIC
Search
Request
Volume and
Complexity
Index Partition n
Copy n
SEARCH INSTANCE
Index Partition 1
Copy 1
SEARCH INSTANCE
Index Partition 2
Copy 1
SEARCH INSTANCE
Index Partition 1
Copy 2
SEARCH INSTANCE
Index Partition 1
Copy n

12. Pricing
• Get started for just $2.40/day; $75/month
• AWS Calculator http://calculator.s3.amazonaws.com/calc5.html
Get started now – 30 days free

13. Customer Stories

14. Searching Recipes and Recipe Boxes on ZipList
Tim Ramsey
Chief Architect and Co-Founder,
Ziplist Inc.

15. ZipList.com - High-Level Description and Context
• Shopping List
• Recipes and Recipe Box
o 1.6M Recipes in Global Index
o 25M Recipes in Boxes
• 3.5M Users
• iPhone and Android Mobile Apps –
315K Downloads
• Partners
o 300+ White Label Partners
(SkinnyTaste.ZipList.com)
o 1,500+ Recipe Partners
(DarcyDiva.com)
• Hardware/Software Platform
o Amazon EC2
o Amazon RDS
o Amazon CloudSearch
o Ruby on Rails

16. ZipList Recipes
Indexed from Around the Web
• Structured (AllRecipes.com) or
Unstructured (Saveur.com)
• Recipes collected with and without
website cooperation
• Pushes to API or JIT with the Recipe
Clipper
* Attributes used in recipe box search/filtering
Attributes Collected and Indexed
• Title*
• Description
• Photo* (yes/no)
• Publisher* (Martha Stewart, Serious
Eats, etc.)
• Ingredients (text and number*)
• Instructions
• Publisher Tags*
• Course* (main, dessert, breakfast, etc.)
• Season (Thanksgiving, summer,
birthday, etc.)
• Etc.

17. Searching ZipList Recipes
• First try – Ferret
o Ability to index on any attribute without DB changes
o Zero latency between insert and available in index
o Runtime load and stability
• Next try – Sphinx
o Incremental indexing load and time
o Indexing DB load
o Search daemon load and latency
• Current – CloudSearch
o SDF allows for synthetic fields (like ferret)
o Fast query results
o Indexing latency issues

18. ZipList Platform
ZipList Recipe Indexing
WWW
Recipes
ZL
REcipes
User A
Recipe Box
User B
Recipe Box
Recipes
CloudSearch
SDF Documents
Save Recipe
Save Recipe
Recipe Box
Cloudsearch
SDF Documents
Recipes Clipped or
Pushed

19. Recipe Box Indexing Latency
• Desire to unify the UX for Global Recipe Search and Recipe Box Search
o Filtering
o Facet Counts
o Pagination
• Scenario
o User finds a recipe on Epicurious.com and adds it to their box
o They then go immediately to ZipList.com to view their Recipe Box and filter by
Epicurious publisher
o The time it takes for the recipe to appear is LTOT = LSDF + LCSI
o Even 10s is unacceptable

20. ZipList Platform
Handling Indexing Latency
ZL
Recipes
Recipe
Box
Recipe Box
Cloudsearch
SDF Documents
Save Recipe
Query
Module
RB
Cache
Search
Query
Search
Results

21. Summary and Conclusions
• With the latency issues resolved, CloudSearch works very well for our
searching needs
• Searching, filtering, and faceting provided by CloudSearch fit in well with our
application
• We have a consistent interface / capabilities between Global Recipe Search
and Recipe Box Search
• Positions ZipList well for moving to NoSQL solutions

22. Zumobi
Pat Binkley, VP Engineering

23. Zumobi is the leading mobile media company that partners with top media brands to publish premium applications
and provide integrated native advertising experiences on smartphones and connected devices.
The Zumobi Brand Integration (ZBi) native advertising platform offers an SDK to drive a wide
array of features and formats enabling seamlessly integrated brand experiences on mobile.
Zumobi’s Long History of Mobile Innovation

24. CloudSearch Example: THE WEEK App
• Why? Parity with Desktop experience
– Most news apps are not yet integrated with search
capabilities, but desktop is. Mobile is quickly catching up
with desktop functionalities
• Problem? Volume of data on device
– Zumobi apps store content locally for performance and
offline experience
– Too much data to store on phone to provide meaningful
search results
• Solution? AWS CloudSearch
– Store documents in the cloud and query

25. New Capabilities
• Keyword Searching
• Relevant Results Display
• Article Viewing
• Social sharing
– Facebook & Twitter
• Save
– Save your favorite articles to review later

26. Architecture
Zumobi
Platform
(Rails App)
CloudSearch
DOCUMENT
SERVICERSS
Optional: >2K items
Document
Request
Response
CloudSearch
SEARCH
SERVICEStep 1: Search for Item/List Returned
Step 2: Fetch optional > 2K items
App Content
SQLite/S3
CDN
** Implementation Time:
- 5 days

27. Impact
• Feature parity with THEWEEK.com website
• Enhanced App Performance
– Increased Usage
• Amplified User Engagement
• More Pageviews
• New Users
• Data Collection
– Find out what your audience is searching for in order to provide more relevant
content
Impact

28. Zumobi App NetworkFuture Search Integrations…

29. Learn More – Check Out
• Zumobi.com
• Zumobi.com/AdLab

30. Viddy
Ken Chung, CTO

31. Viddy
Viddy is a simple way to capture,
create, and share short, 30-second
social videos with friends.

33. Viddy – Search Use Cases
• Users want to discover content around them
• Users want to find their friends and celebrities
• Users want to discover content with certain #hashtags and keywords
• Users want to discover more relevant content by language, country, and more
context

34. Viddy – Unique Problem? No
• Problems not unique to Viddy
• With rise of social, mobile, and geo-aware mobile apps, search is basically
everyone’s challenge
• Why re-invent the wheel?
• Let the platform handle the headaches and let us focus on innovations!

35. Viddy – B.C. aka “Before CloudSearch”
• Custom managed Lucene + Indexers + Search API Servers
• A full dedicated backend development effort for search product (out of two
backend engineers)
• Lack of tooling for optimizing queries and improving search relevancy by
non-engineers
• Lack of monitoring and reporting around search

36. … and then 300K users to 40 million users in less than 6 months

37. Viddy – Hello, CloudSearch!
• Migrated entirely from Lucene to CloudSearch in 3 days
• Simple CloudSearch REST API for Indexing and Querying
• 2–5 hrs/wk of maintenance effort for search product
• Interns improving search result through CloudSearch console
• Better monitoring and report

38. Viddy – Aftermath
• Operation cost: $5–6K/mo to $1600/mo
• More development resources for innovation
• 5x increase in search usage
• Faster incremental index: avg. delay from 15 mins to 30 secs

39. Customer Panel

40. Thank You!
• Learn more at http://aws.amazon.com/cloudsearch
• Please fill out your evaluation forms