Shutterfly implemented MongoDB to address problems with their existing metadata storage architecture using an Oracle database, including slow time to market, high costs, performance issues, and lack of scalability. They developed a new data architecture using MongoDB for its simple API, open source software, and ability to partition and distribute data. Initial results showed a 500% improvement in costs and a 900% improvement in performance and latency.

1. Implementing MongoDB
MongoSF
April 2010
Kenny Gorman, Data Architect

2. Shutterfly Inc.
• Founded in December 1999
• Public company (NASDAQ: SFLY)
• Millions of customers have billions of pictures on
Shutterfly
• Photo site, books, sharing, prints, gifts
• Only photo sharing site that doesn’t down-
sample, compress, or force delete photos
• > 6B photos, adding 400TB/mo
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3. Existing Metadata Storage Architecture
• Metadata is persisted in RDBMS
• Images/media stored outside DB
• Java/Spring, C#,.Net
• Oracle™ RDBMS
• Sun™ servers and storage
• Vertically partitioned by function
• Hot Standbys used for availability
• > 20tb of RDBMS storage
• > 10000 ex/sec
• Extreme uptime requirements
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4. Problems
• Time to Market
• Cost
• Performance
• Scalability
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5. New Metadata Storage Architecture
• Performance
! Reduce complexity
! Partition data
• Scalability
! Move to clustered system
• Time to Market
! Simple API
• Cost
! OSS software
! Simple hardware
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6. New Data Architecture Fundamentals
• Partition data
• Relax consistency (where applicable)
• Data locality
• Highly available configuration
• Keep design simple/fast
• Keep hardware simple/cheap
• Keep software simple/cheap
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7. MongoDB
• Open Source
• Best of RDBMS, yet not quite k,v store
• Features we need
• Commercial support
• Active community
• Performance
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8. MongoDB Development
• Data modeling
• Java, .Net
• Simple, fast development
• JSON just makes sense
• Data access layer
• GridFS
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9. MongoDB in production
• Simple use case, simple project
• Primary and 2 replica DB’s, 1 ‘lagged’
• Manual failover
• Monitoring: http interface
• Tools: mongostat, custom rrd graphs
• Linux on Intel™
• MongoDB 1.4.2 (stable)
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10. Going Live Plan
• Walk before you run
• Shutterfly project/product selection
• Write through architecture
•
• Good metrics
• Subset of MongoDB features
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11. So how did we do?
• Time to Market
• Application developed in 1 sprint
• Cost
• 500% improvement
• Performance
• 900% improvement
• 18ms to 2ms avg latency for inserts
• Scalability
• Shard on demand
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12. The future
• More MongoDB
• Replication as durability (getLasterror(w=2))
• Replica sets
• Excitement from developers
• Lots of attribute and media metadata types
• Object mapper
• New projects and old systems
• Evaluate as they come up
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13. Lessons Learned
• Keep it simple
• Data Modeling
• Walk before you run
• Use Jira for MongoDB issues
• There is life after Larry
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14. Q&A
Questions?
Contact:
kg@kennygorman.com
http://www.kennygorman.com
http://github.com/kgorman
http://www.shutterfly.com
kgorman@shutterfly.com
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