Dynamo: Amazon’s Highly Available Key-value Store

Presented By: Devarsh Patel

1 CS5204 – Operating Systems

Dynamo

CS5204 – Operating Systems

Introduction

Amazon’s e-commerce platform Requires performance, reliability and efficiency To support continuous growth, platform needs to be highly scalable

Dynamo – A highly available and scalable distributed data store built for Amazon’s platform

Dynamo is used to manage services that have very high reliability requirements and need tight control over the tradeoffs between availability, consistency, cost-effectiveness and performance.

Dynamo provides a simple primary-key only interface to meet

requirements of applications like best seller lists, shopping carts, customer preferences, session management, etc.

A completely decentralized system with minimal need for manual

administration.

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Dynamo

System Assumptions and Requirements Simple key-value interface

Highly available Efficient in resource usage Simple scale out scheme to address growth in data set size or

request rates Each service that uses Dynamo runs its own Dynamo

instances Used only by Amazon’s internal services

Non-hostile environment No security requirements like authentication and authorization

Targets applications that operate with weaker consistency in favor of high availability

Service level agreements (SLA) Measured at the 99.9th percentile of the distribution Key factors: service latency at a given request rate Example: response time of 300ms for 99.9% of requests at peak

client load of 500 requests per second State management is the main component of a service’s SLAs

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Dynamo

Design Considerations

Designed to be an eventually consistent data store “Always writeable” data store Consistency vs. availability

To achieve a level of consistency, replication algorithms are forced to tradeoff the availability of the data under certain failure scenarios.

To improve availability, Dynamo uses weaker form of consistency (eventual consistency) Allows optimistic replication techniques

Can lead to conflicting changes which must be detected and resolved

Data store or application performs conflict resolution to the reads Other key principles

Incremental scalability – One storage node at a time Symmetry – Every node has same set of responsibilities Decentralization – Favor decentralized peer-to-peer techniques Heterogeneity – Work distribution must be proportional

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Dynamo

System Architecture

Core distributed system techniques used in Dynamo: Partitioning, Replication, Versioning, Membership, Failure

handling and Scaling

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Dynamo

System Interface

Two operations: get() and put() get(key) – Locates the object replicas associated with

the key in the storage system and returns a single object or a list of objects with conflicting versions along with a context

put(key, context, object) - Determines where the replicas of the object should be placed based on the associated key, and writes the replicas to disk

context – encodes system metadata about the object MD5 hash on the key generates 128-bit identifier to

identify storage nodes

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Dynamo

Partitioning Algorithm

Consistent Hashing Output range is a fixed circular space or “ring” Advantage

Departure or arrival of a node only affects immediate neighbors

Issues Non-uniform data and load distribution

Dynamo uses a variant of consistent hashing by using concept of “virtual nodes”

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Dynamo

Replication

Replicate data on multiple hosts Reason – To achieve high availability and durability

“per-instance” Preference list – List of nodes responsible for storing particular key Figure 1: Partitioning and replication of keys in Dynamo ring.

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Dynamo

Data Versioning

Dynamo treats the result of each modification as a new and immutable version of the data

Allows for multiple versions of an object to be present in the system at the same time.

Problem Version branching due to failures combined with

concurrent updates, resulting in conflicting versions of object

Updates in the presence of network partitions and node failures result in an object having distinct version sub-histories

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Dynamo

Data Versioning

Uses vector clocks – A list of (node, counter) pairs

Determines two version of an object are on parallel branches or have causal ordering

Conflict requires reconciliation Conflicting versions passed to application as

output of get operation Application resolves conflicts and puts a new

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Dynamo

Data Versioning

Figure: Version evolution of an object over time

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Dynamo

Execution of get/put operations

Two strategies to select a node: Request through a load balancer Request directly to the coordinator nodes

Coordinator – Node handling read and write operation First among the top N nodes in the preference list

Quorum system

Two key configurable values: R and W R - minimum nodes participated in successful read operation W - minimum nodes participated in successful write operation Quorum like system requires, R+W > N (N, R, W) can be chosen to achieve desired tradeoff R and W are usually configured to be less than N, to provide

better latency. Write is successful – If W-1 nodes respond to put() request Read is successful – If R noes respond to get() request

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Dynamo

Hinted Handoff

“Sloppy quorum” All read and write operations are done on Top N

healthy nodes in the preference list Coordinator is first in this group Replicas sent to node will have a “hint” in its

metadata indicating the original node that should hold the replica

Hinted replicas are stored by available node and sent forwarded when original node recovers.

Ensures read and write operations are not failed due to node or network failures

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Dynamo

Replica synchronization

Detect the inconsistencies between replicas faster and to minimize the amount of transferred data using Merkle tree.

Separate tree maintained by each node for each key range

Advantage: each branch of the tree can be checked

independently without requiring nodes to download the entire tree or the entire data set

Disadvantage: Adds overhead to maintain Merkle trees when a

node joins or leaves the system

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Dynamo

Membership and Failure Detection

Ring Membership Explicit mechanism to add or remove node from a ring Done by administrator using command line tool or browser Gossip-based protocol propagates membership, partitioning,

and placement information via periodic exchanges Nodes eventually know key ranges of its peers and can

forward requests to them

External Discovery To prevent logical partitions, some nodes play role of seeds “Seed” nodes discovered via external mechanism are known

to all nodes

Failure Detection Nodes failures are detected by lack of responsiveness and

recovery detected by periodic retry

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Dynamo

Experiences & Lessons Learned

Main patterns in which Dynamo is used: Business logic specific reconciliation Timestamp based reconciliation High performance read engine

Client applications can tune values of N, R and W Common (N,R,W) configuration used by several

instances of Dynamo is (3,2,2)

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Dynamo

Experiences & Lessons Learned

Balancing performance and Durability

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Dynamo

Experiences & Lessons Learned

Ensuring Uniform Load Distribution

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Dynamo

Partitioning & Placement Strategies

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Partitioning and placement of keys in the three strategies. A, B, and C depict the three unique nodes that form the preference list for the key k1 on the consistent hashing ring (N=3). The shaded area indicates the key range for which nodes A, B, and C form the preference list. Dark arrows indicate the token locations for various nodes.

Dynamo

Partitioning & Placement Strategies

Strategy 1 T random tokens per node and partition by token

value: It needs to steal its key ranges from other nodes Bootstrapping of new node is lengthy Other nodes process scanning/transmission of key

ranges for new node as background activities Disadvantages:

Numerous nodes have to adjust their Merkle trees when a new node joins or leaves system

Archiving entire key space is highly inefficient

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Dynamo

Partitioning & Placement Strategies

Strategy 2 T random tokens per node and equal sized partitions:

Divided into Q equally sized partitions Q >> N and Q >> S*T, where S is no. of nodes in the system Advantages:

Decoupling of partition and partition placement Allows changing of placement scheme at run-time

Strategy 3 Q/S tokens per node, equal sized partitions:

Decoupling of partition and placement Advantages:

Faster bootstrapping/recovery Ease of archival

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Dynamo

Partitioning & Placement Strategies

Strategies have different tuning parameters Fair way to compare strategies is to evaluate the skew in their load

distributions for a fixed amount of space to maintain membership information Strategy 3 achieves best load balancing efficiency

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Dynamo

Client-driven or Server-driven Coordination

Any node can coordinate read requests; write requests handled by coordinator State-machine for coordination can be in load balancing server or incorporated

into client Client-driven coordination has lower latency because it avoids extra network

hop (redirection)

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Dynamo

Thank You

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