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Riak on Drugs(and the other way around)
Kresten Krab Thorup, Jørn LarsenTrifork
@drkrab
onsdag den 12. oktober 2011
In this talk...
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■ About Common Medicine Card
■ Building a Decentralized Architecture
■ Conclusion
onsdag den 12. oktober 2011
A Medicine Card
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■ For a person
■ List of current drug treatments
■ With prescriptions and related events
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Common Medicine Card
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Common Medicine Card
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15-30 existing systems+150k users
SOAP
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Master
MySQL
Slave
MySQL
Front Endweb services/app
Front Endweb services/app
Front Endweb services/app
Architecture
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Overload
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Database
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Prescriptions Replica
Treatments+ Events
Front Ends
SOAP endpointWeb applicationBusiness Logic
Prescription Service
Front Ends
SOAP endpointWeb applicationBusiness Logic
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Distributed Architecture
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■ Availability: Run in multiple data centers
■ Scalability: Prepare the system forexpected growth
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Riak Data Store
■ Fit the general requirements
■ Availability + Scalability
■ Operational improvements
■ Challenges
■ Key/Value Store, vs Relational Model
■ New technology, many unknowns
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coordinate
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sync
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scalable and available
system captures write conflicts
resolve lazily (read repair)
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Database
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Prescriptions Replica
Drug Treatments
Front Ends
SOAP endpointWeb applicationBusiness Logic
Prescription Service
Front Ends
SOAP endpointWeb applicationBusiness Logic
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MySQL
Master/Slave
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Front End
SOAP endpointWeb applicationBusiness Logic
Prescription Service
Riak
4 nodes x2 data centers
Front Ends
SOAP endpointWeb applicationBusiness Logic
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Challenges
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■ Data model: To go from Relational model to Key/Value model
■ Experiences with Riak’s backends
■ A true war story
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Data Model
■ Integrity without transactions
■ Riak’s default storage keeps all keys in memory
■ Dealing with Write Conflicts
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First Attempt: Using Links
■ Ran very poorly on Virtual Hardware
■ Trying to figure out how to handle conflicts
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PersonKey: Person-IDLinks: Prescription-ID
PrescriptionKey: Prescription-IDContent: Protobuf+GZip
~5 million ~200 million
onsdag den 12. oktober 2011
Second Take
■ Very simple: read - resolve - modify - write
■ Integrity: 1 person ⬌ 1 record
■ Performance good: 1 disk access
■ All keys fit in memory22
PrescriptionsKey: Person-IDContent: Medicine Record
~5 million
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Read Repair
■ On every read, we handle write conflicts
■ If so, auto-merge[*], store and re-read
■ Resolve: Merging is business logic; some merge actions need user attention, others don’t.
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Conclusions
■ 1 key per person 500MB in memory
■ Availability, hot replace of HW
■ Continuous Deployment
■ 10x in performance gain and Scalable
■ 2500 TPS with 10% load of nodes
■ Fully deployed 5000 TPS needed
■ Dramatic reduction in cost of HW and Licences24
onsdag den 12. oktober 2011
Thank You.
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@drkrab
onsdag den 12. oktober 2011
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