Dynamo Amazons Highly Available Keyvalue Store Guiseppe De

Dynamo: Amazon's Highly Available Key-value Store Guiseppe De. Candia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swami Sivasubramanian, Peter Vosshall, and Werner Vogels

Cloud Data Services & Relational Database Systems go hand in hand Oracle, Microsoft SQL Server and even My. SQL have traditionally powered enterprise and online data clouds Clustered - Traditional Enterprise RDBMS provide the ability to cluster and replicate data over multiple servers – providing reliability Highly Available – Provide Synchronization (“Always Consistent”), Load-Balancing and High-Availability features to provide nearly 100% Service Uptime Structured Querying – Allow for complex data models and structured querying – It is possible to off-load much of data processing and manipulation to the back-end database

Traditional RDBMS clouds are: EXPENSIVE! To maintain, license and store large amounts of data The service guarantees of traditional enterprise relational databases like Oracle, put high overheads on the cloud Complex data models make the cloud more expensive to maintain, update and keep synchronized Load distribution often requires expensive networking equipment To maintain the “elasticity” of the cloud, often requires expensive upgrades to the network

The Solution Downgrade some of the service guarantees of traditional RDBMS Replace the highly complex data models Oracle and SQL Server offer, with a simpler one – This means classifying service data models based on the complexity of the data model they may required Replace the “Always Consistent” guarantee synchronization model with an “Eventually Consistent” model – This means classifying services based on how “updated” its data set must be Redesign or distinguish between services that require a simpler data model and lower expectations on consistency. We could then offer something different from traditional RDBMS!

The Solution Amazon’s Dynamo – Used by Amazon’s EC 2 Cloud Hosting Service. Powers their Elastic Storage Service called S 2 as well as their E-commerce platform Offers a simple Primary-key based data model. Stores vast amounts of information on distributed, lowcost virtualized nodes Google’s Big. Table – Google’s principle data cloud, for their services – Uses a more complex column-family data model compared to Dynamo, yet much simpler than traditional RMDBS Google’s underlying file-system provides the distributed architecture on low-cost nodes Facebook’s Cassandra – Facebook’s principle data cloud, for their services. This project was recently open-sourced. Provides a data-model similar to Google’s Big. Table, but the distributed characteristics of Amazon’s Dynamo

What is Dynamo is a highly available distributed keyvalue 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, 000 s 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, costeffectiveness, performance Can developers know this up front? Can it be changed over time?

System Assumptions & Requirements Query Model: simple read and write operations to a data item that is uniquely identified by a key. values are small (<1 MB) binary objects No ACID Properties: Atomicity, Consistency, Isolation, Durability. Efficiency: latency requirements which are in general measured at the 99. 9 th percentile of the distribution. Other Assumptions: operation environment is assumed to be non-hostile and there are no security related requirements such as authentication and authorization.

Service level agreements SLAs are used widely at Amazon Sub-services must meet strict SLAs e. g. , 300 ms 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 Service-oriented architecture of Amazon’s platform

Sacrifice strong consistency for availability 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 Design Principles Incremental scalability Symmetry No master/slave nodes Decentralized Minimal management overhead Centralized control leads to too many failures Heterogeneity Exploit capabilities of different nodes

Summary of techniques used in Dynamo and their advantages Problem Technique Advantage Partitioning Consistent Hashing Incremental Scalability High Availability for writes Vector clocks with reconciliation during reads Version size is decoupled from update rates. Handling temporary failures Sloppy Quorum and hinted handoff Provides high availability and durability guarantee when some of the replicas are not available. Recovering from permanent failures Anti-entropy using Merkle trees Membership and failure detection Gossip-based membership protocol and failure detection. Synchronizes divergent replicas in the background. Preserves symmetry and avoids having a centralized registry for storing membership and node liveness information.

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 Key K A G Each node is assigned to multiple points in the ring (e. g. , B, C, D store keyrange (A, B) B F C E D # of points can be assigned based on node’s capacity If node becomes unavailable, load is distributed to others

Replication Key K Coordinator for key K A G B F B maintains a preference list for each data item specifying nodes storing that item C E D Preference list skips virtual nodes in favor of physical nodes D stores (A, B], (B, C], (C, D]

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 A vector clock is a list of (node, counter) pairs. Every version of every object is associated with one vector clock. If the counters on the first object’s clock are less-than-or-equal to all of the nodes in the second clock, then the first is an ancestor of the second and can be forgotten.

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, My. SQL, 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

During periods of high load popular objects dominate One tick = 30 minutes 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: Simple: Apps can set N, R, W to match their needs Inflexible: get()/put() maps well to Amazon’s workload Flexible: Easy to shovel in more capacity at Christmas 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?