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 SchlosserBig Data Reading Group

October 1, 2007

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.

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.”

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?

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

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

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?

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

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

Variant of consistent hashing

A

B

C

DE

F

G

Key K

Each node isassigned tomultiple pointsin the ring(e.g., B, C, Dstore keyrange(A, B)

# of points canbe assigned basedon node’s capacity

If node becomesunavailable, load isdistributed to others

Replication

A

B

C

DE

F

G

Key KCoordinator for key K

D stores (A, B], (B, C], (C, D]

B maintains a preferencelist for each data itemspecifying nodes storingthat item

Preference list skipsvirtual nodes in favor ofphysical nodes

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?

Vector clock example

“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

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

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…

One tick= 12 hours

One tick= 1 hour

One tick= 30 minutes

During periods of high loadpopular objects dominate

During periods of low load,fewer popular objects are accessed

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

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?