Search Engines use web search queries to collect information and present it to the user. How do you go about building a search engine in the first place?

1. Building Search Engines
Comparing Lucene / SolR / Elastic & Cloud Search Providers

2. Business Platform Success
We design, build, and manage business
platforms by leveraging DataStax,
Sitecore, Salesforce, Quickbooks and
other cloud software.

3. Agenda
• Challenge - Why does this matter?
• Info Retrieval - Retrieval / Routing
• Lucene - More than meets the eye ...
• Search Engine - 30k Foot View
• On Premise - Lucene / SolR / Elastic
• Cloud Providers - Amazon / Azure

4. Challenge - Why does this matter?
Knowledge
Project
Information
Client
Service
Information
Corporate
Guides
Collaborativ
e
Documents
Assets
& Files
Corporate
Resources
Appleseed Framework (Portal, Base, Search)
G Drive
Delta
DropBox
G Drive
Delta
Nutshell
Dropbox
Freshbooks
G Drive
G Sites (KB)
G Drive
Workflowy
Evernote
G Drive
DropBox
OwnCloud
Pocket
Leaves
AIC (WP)
Anant (WP)

5. Information Retrieval
● Document Retrieval
○ Google Search
○ Amazon Search
○ LinkedIN Search
○ *CMS Search
○ *Portal Search
○ *CRM Search
○ * Search
Document Routing
● Google Alerts
● Amazon’s Recommendations
● Netflix Recommendations
● LinkedIN Recommendations

6. Lucene - Inverted Index

7. Lucene - More than meets the eye
Who
Next?
Think of it like a “NoSQL” Database that has great indexing everywhere.

8. Search Engine - 30 Thousand Foot View
The search index is only as good as your processed data. If you put everything you
find in your index, you are going to spend a lot of time telling people how to search.

9. Lucene
• Library
• File System
• Format
• Fast
• Embeddable*
• Indexing Anywhere
• Need to really know
Lucene
• No Interface
• No server
• Lots of house keeping
On Premise - Lucene/ ES / SolR
SolR
• Server
• Admin / REST
Interface
• Configurable
• Scalable
• Great at Text*
• Truly Open
• 10+ Years
• Good ecosystem
• Too customizable
• Schemas*
• Zookeeper Needed
ElasticSearch
• Server
• Configurable
• Scalable
• Good ecosystem
• Built in Clustering
• Grouping / Filtering
• Great for Logs
• Started as a Cloud
Tool
• No great OTS Interface
• Only REST Interface

10. Amazon
• SolRCloud*
• AWS* Ecosystem
• 5 QParsers
• Dynamic Fields
• 100% Completely
Managed
• Been Around for a
While
• Data / Read Writes
• No nested Objects
Cloud Search - Amazon / Azure
Azure
• ElasticSearch*
• Azure* Ecosystem
• 2 QParsers
• 100% Completely
Managed
• Good SDK
• Few Years Old
• Data / Read Writes
• No nested Objects
• Not so Dynamic Fields

11. Data & Analytics
Cassandra, DataStax, Kafka, Spark
Customer Experience
Sitecore
Information Systems
Salesforce, Quickbooks, and more
www.anant.us | solutions@anant.us | (855) 262-6826
3 Washington Circle, NW | Suite 301 | Washington, DC 20037