Dynamo: Amazon's Highly Available Key-value Store

1. Dynamo: Amazon's HighlyAvailable Key-value Store Guiseppe DeCandia, Deniz Hastorun,Madan Jampani, Gunavardhan Kakulapati,Avinash Lakshman, Alex Pilchin,Swami Sivasubramanian, Peter Vosshall,and Werner Vogels Presented by Steve Schlosser Big Data Reading Group October 1, 2007

2. What Dynamo is • Dynamo is a highly available distributed key-value storage system • put(), get() interface • Sacrifices consistency for availability • Provides storage for some of Amazon's key products (e.g., shopping carts, best seller lists, etc.)‏ • Uses “synthesis of well known techniques to achieve scalability and availability” • Consistent hashing, object versioning, conflict resolution, etc.

3. Scale • Amazon is busy during the holidays • Shopping cart: tens of millions of requests for 3 million checkouts in a single day • Session state system: 100,000s of concurrently active sessions • Failure is common • Small but significant number of server and network failures at all times • “Customers should be able to view and add items to their shopping cart even if disks are failing, network routes are flapping, or data centers are being destroyed by tornados.”

4. Flexibility • Minimal need for manual administration • Nodes can be added or removed without manual partitioning or redistribution • Apps can control availability, consistency, cost-effectiveness, performance • Can developers know this up front? • Can it be changed over time?

5. Assumptions & requirements • Simple query model • values are small (<1MB) binary objects • No ACID properties • Weaker consistency • No isolation guarantees • Single key updates • Stringent latency requirements • 99.9th percentile • Non-hostile environment

6. Service level agreements • SLAs are used widely at Amazon • Sub-services must meet strict SLAs • e.g., 300ms response time for 99.9% of requests at peak load of 500 requests/s • Average-case SLAs are not good enough • Mentioned a cost-benefit analysis that said 99.9% is the right number • Rendering a single page can make requests to 150 services

7. Consistency • Eventual consistency • “Always writable” • Can always write to shopping cart • Pushes conflict resolution to reads • Application-driven conflict resolution • e.g., merge conflicting shopping carts • Or Dynamo enforces last-writer-wins • How often does this work?

8. Other stuff • Incremental scalability • Minimal management overhead • Symmetry • No master/slave nodes • Decentralized • Centralized control leads to too many failures • Heterogeneity • Exploit capabilities of different nodes

9. Interface • get(key) returns object replica(s) for key, plus a context object • context encodes metadata, opaque to caller • put(key, context, object) stores object

10. Variant of consistent hashing Key K A G B Each node isassigned tomultiple pointsin the ring (e.g., B, C, Dstore keyrange(A, B) F C # of points canbe assigned basedon node’s capacity E If node becomesunavailable, load isdistributed to others D

11. Replication Key K Coordinator for key K A G B B maintains a preferencelist for each data itemspecifying nodes storingthat item F C Preference list skipsvirtual nodes in favor ofphysical nodes E D D stores (A, B], (B, C], (C, D]

12. Data versioning • put() can return before update is applied to all replicas • Subsequent get()s can return older versions • This is okay for shopping carts • Branched versions are collapsed • Deleted items can resurface • A vector clock is associated with each object version • Comparing vector clocks can determine whether two versions are parallel branches or causally ordered • Vector clocks passed by the context object in get()/put() • Application must maintain this metadata?

13. Vector clock example

14. “Quorum-likeness” • get() & put() driven by two parameters: • R: the minimum number of replicas to read • W: the minimum number of replicas to write • R + W > N yields a “quorum-like” system • Latency is dictated by the slowest R (or W) replicas • Sloppy quorum to tolerate failures • Replicas can be stored on healthy nodes downstream in the ring, with metadata specifying that the replica should be sent to the intended recipient later

15. Adding and removing nodes • Explicit commands issued via CLI or browser • Gossip-style protocol propagates changes among nodes • New node chooses virtual nodes in the hash space

16. Implementation • Persistent store either Berkeley DB Transactional Data Store, BDB Java Edition, MySQL, or in-memory buffer w/ persistent backend • All in Java! • Common N, R, W setting is (3, 2, 2) • Results are from several hundred nodes configured as (3, 2, 2) • Not clear whether they run in a single datacenter…

17. One tick= 12 hours

18. One tick= 1 hour

19. During periods of high loadpopular objects dominate During periods of low load,fewer popular objects are accessed One tick= 30 minutes

20. Quantifying divergent versions • In a 24 hour trace • 99.94% of requests saw exactly one version • 0.00057% received 2 versions • 0.00047% received 3 versions • 0.00009% received 4 versions • Experience showed that diversion came usually from concurrent writers due to automated client programs (robots), not humans

21. Conclusions • Scalable: • Easy to shovel in more capacity at Christmas • Simple: • get()/put() maps well to Amazon’s workload • Flexible: • Apps can set N, R, W to match their needs • Inflexible: • Apps have to set N, R, W to match their needs • Apps may have to do their own conflict resolution • They claim it’s easy to set these – does this mean that there aren’t many interesting points? • Interesting?