Implementing MongoDB

MongoSF April 2010 Kenny Gorman, Data Architect

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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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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Problems

•  Time to Market •  Cost •  Performance •  Scalability

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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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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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MongoDB

•  Open Source •  Best of RDBMS, yet not quite k,v store •  Features we need •  Commercial support •  Active community •  Performance

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MongoDB Development

•  Data modeling •  Java, .Net •  Simple, fast development •  JSON just makes sense •  Data access layer •  GridFS

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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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Going Live Plan

•  Walk before you run •  Shutterfly project/product selection •  Write through architecture

•  \

•  Good metrics •  Subset of MongoDB features

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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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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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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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Q&A

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Questions?

Contact: kg@kennygorman.com http://www.kennygorman.com http://github.com/kgorman http://www.shutterfly.com kgorman@shutterfly.com