Conference PaperPDF Available

Dynamo: Amazon's highly available key-value store


Abstract and Figures

Reliability at massive scale is one of the biggest challenges we face at, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems. This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
Content may be subject to copyright.
Dynamo: Amazon’s Highly Available Key-value Store
Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash
Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall and Werner Vogels
Reliability at massive scale is one of the biggest challenges we
face at, one of the largest e-commerce operations in
the world; even the slightest outage has significant financial
consequences and impacts customer trust. The
platform, which provides services for many web sites worldwide,
is implemented on top of an infrastructure of tens of thousands of
servers and network components located in many datacenters
around the world. At this scale, small and large components fail
continuously and the way persistent state is managed in the face
of these failures drives the reliability and scalability of the
software systems.
This paper presents the design and implementation of Dynamo, a
highly available key-value storage system that some of Amazon’s
core services use to provide an “always-on” experience. To
achieve this level of availability, Dynamo sacrifices consistency
under certain failure scenarios. It makes extensive use of object
versioning and application-assisted conflict resolution in a manner
that provides a novel interface for developers to use.
Categories and Subject Descriptors
D.4.2 [Operating Systems]: Storage Management; D.4.5
[Operating Systems]: Reliability; D.4.2 [Operating Systems]:
General Terms
Algorithms, Management, Measurement, Performance, Design,
Amazon runs a world-wide e-commerce platform that serves tens
of millions customers at peak times using tens of thousands of
servers located in many data centers around the world. There are
strict operational requirements on Amazon’s platform in terms of
performance, reliability and efficiency, and to support continuous
growth the platform needs to be highly scalable. Reliability is one
of the most important requirements because even the slightest
outage has significant financial consequences and impacts
customer trust. In addition, to support continuous growth, the
platform needs to be highly scalable.
One of the lessons our organization has learned from operating
Amazon’s platform is that the reliability and scalability of a
system is dependent on how its application state is managed.
Amazon uses a highly decentralized, loosely coupled, service
oriented architecture consisting of hundreds of services. In this
environment there is a particular need for storage technologies
that are always available. For example, 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. Therefore, the service responsible for
managing shopping carts requires that it can always write to and
read from its data store, and that its data needs to be available
across multiple data centers.
Dealing with failures in an infrastructure comprised of millions of
components is our standard mode of operation; there are always a
small but significant number of server and network components
that are failing at any given time. As such Amazon’s software
systems need to be constructed in a manner that treats failure
handling as the normal case without impacting availability or
To meet the reliability and scaling needs, Amazon has developed
a number of storage technologies, of which the Amazon Simple
Storage Service (also available outside of Amazon and known as
Amazon S3), is probably the best known. This paper presents the
design and implementation of Dynamo, another highly available
and scalable distributed data store built for Amazon’s platform.
Dynamo is used to manage the state of services that have very
high reliability requirements and need tight control over the
tradeoffs between availability, consistency, cost-effectiveness and
performance. Amazon’s platform has a very diverse set of
applications with different storage requirements. A select set of
applications requires a storage technology that is flexible enough
to let application designers configure their data store appropriately
based on these tradeoffs to achieve high availability and
guaranteed performance in the most cost effective manner.
There are many services on Amazon’s platform that only need
primary-key access to a data store. For many services, such as
those that provide best seller lists, shopping carts, customer
preferences, session management, sales rank, and product catalog,
the common pattern of using a relational database would lead to
inefficiencies and limit scale and availability. Dynamo provides a
simple primary-key only interface to meet the requirements of
these applications.
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists,
requires prior specific permission and/or a fee.
SOSP 2007, Oct., 2004, Stevenson, WA, USA.
Copyright 2007 ACM XXX…$5.00.
Dynamo uses a synthesis of well known techniques to achieve
scalability and availability: Data is partitioned and replicated
using consistent hashing [10], and consistency is facilitated by
object versioning [12]. The consistency among replicas during
updates is maintained by a quorum-like technique and a
decentralized replica synchronization protocol. Dynamo employs
a gossip based distributed failure detection and membership
protocol. Dynamo is a completely decentralized system with
minimal need for manual administration. Storage nodes can be
added and removed from Dynamo without requiring any manual
partitioning or redistribution.
In the past year, Dynamo has been the underlying storage
technology for a number of the core services in Amazon’s e-
commerce platform. It was able to scale to extreme peak loads
efficiently without any downtime during the busy holiday
shopping season. For example, the service that maintains
shopping cart (Shopping Cart Service) served tens of millions
requests that resulted in well over 3 million checkouts in a single
day and the service that manages session state handled hundreds
of thousands of concurrently active sessions.
The main contribution of this work for the research community is
the evaluation of how different techniques can be combined to
provide a single highly-available system. It demonstrates that an
eventually-consistent storage system can be used in production
with demanding applications. It also provides insight into the
tuning of these techniques to meet the requirements of production
systems with very strict performance demands.
The paper is structured as follows. Section 2 presents the
background and Section 3 presents the related work. Section 4
presents the system design and Section 5 describes the
implementation. Section 6 details the experiences and insights
gained by running Dynamo in production and Section 7 concludes
the paper. There are a number of places in this paper where
additional information may have been appropriate but where
protecting Amazon’s business interests require us to reduce some
level of detail. For this reason, the intra- and inter-datacenter
latencies in section 6, the absolute request rates in section 6.2 and
outage lengths and workloads in section 6.3 are provided through
aggregate measures instead of absolute details.
Amazon’s e-commerce platform is composed of hundreds of
services that work in concert to deliver functionality ranging from
recommendations to order fulfillment to fraud detection. Each
service is exposed through a well defined interface and is
accessible over the network. These services are hosted in an
infrastructure that consists of tens of thousands of servers located
across many data centers world-wide. Some of these services are
stateless (i.e., services which aggregate responses from other
services) and some are stateful (i.e., a service that generates its
response by executing business logic on its state stored in
persistent store).
Traditionally production systems store their state in relational
databases. For many of the more common usage patterns of state
persistence, however, a relational database is a solution that is far
from ideal. Most of these services only store and retrieve data by
primary key and do not require the complex querying and
management functionality offered by an RDBMS. This excess
functionality requires expensive hardware and highly skilled
personnel for its operation, making it a very inefficient solution.
In addition, the available replication technologies are limited and
typically choose consistency over availability. Although many
advances have been made in the recent years, it is still not easy to
scale-out databases or use smart partitioning schemes for load
This paper describes Dynamo, a highly available data storage
technology that addresses the needs of these important classes of
services. Dynamo has a simple key/value interface, is highly
available with a clearly defined consistency window, is efficient
in its resource usage, and has a simple scale out scheme to address
growth in data set size or request rates. Each service that uses
Dynamo runs its own Dynamo instances.
2.1 System Assumptions and Requirements
The storage system for this class of services has the following
Query Model: simple read and write operations to a data item that
is uniquely identified by a key. State is stored as binary objects
(i.e., blobs) identified by unique keys. No operations span
multiple data items and there is no need for relational schema.
This requirement is based on the observation that a significant
portion of Amazon’s services can work with this simple query
model and do not need any relational schema. Dynamo targets
applications that need to store objects that are relatively small
(usually less than 1 MB).
ACID Properties: ACID (Atomicity, Consistency, Isolation,
Durability) is a set of properties that guarantee that database
transactions are processed reliably. In the context of databases, a
single logical operation on the data is called a transaction.
Experience at Amazon has shown that data stores that provide
ACID guarantees tend to have poor availability. This has been
widely acknowledged by both the industry and academia [5].
Dynamo targets applications that operate with weaker consistency
(the “C” in ACID) if this results in high availability. Dynamo
does not provide any isolation guarantees and permits only single
key updates.
Efficiency: The system needs to function on a commodity
hardware infrastructure. In Amazon’s platform, services have
stringent latency requirements which are in general measured at
the 99.9th percentile of the distribution. Given that state access
plays a crucial role in service operation the storage system must
be capable of meeting such stringent SLAs (see Section 2.2
below). Services must be able to configure Dynamo such that they
consistently achieve their latency and throughput requirements.
The tradeoffs are in performance, cost efficiency, availability, and
durability guarantees.
Other Assumptions: Dynamo is used only by Amazon’s internal
services. Its operation environment is assumed to be non-hostile
and there are no security related requirements such as
authentication and authorization. Moreover, since each service
uses its distinct instance of Dynamo, its initial design targets a
scale of up to hundreds of storage hosts. We will discuss the
scalability limitations of Dynamo and possible scalability related
extensions in later sections.
2.2 Service Level Agreements (SLA)
To guarantee that the application can deliver its functionality in a
bounded time, each and every dependency in the platform needs
to deliver its functionality with even tighter bounds. Clients and
services engage in a Service Level Agreement (SLA), a formally
negotiated contract where a client and a service agree on several
system-related characteristics, which most prominently include
the client’s expected request rate distribution for a particular API
and the expected service latency under those conditions. An
example of a simple SLA is a service guaranteeing that it will
provide a response within 300ms for 99.9% of its requests for a
peak client load of 500 requests per second.
In Amazon’s decentralized service oriented infrastructure, SLAs
play an important role. For example a page request to one of the
e-commerce sites typically requires the rendering engine to
construct its response by sending requests to over 150 services.
These services often have multiple dependencies, which
frequently are other services, and as such it is not uncommon for
the call graph of an application to have more than one level. To
ensure that the page rendering engine can maintain a clear bound
on page delivery each service within the call chain must obey its
performance contract.
Figure 1 shows an abstract view of the architecture of Amazon’s
platform, where dynamic web content is generated by page
rendering components which in turn query many other services. A
service can use different data stores to manage its state and these
data stores are only accessible within its service boundaries. Some
services act as aggregators by using several other services to
produce a composite response. Typically, the aggregator services
are stateless, although they use extensive caching.
A common approach in the industry for forming a performance
oriented SLA is to describe it using average, median and expected
variance. At Amazon we have found that these metrics are not
good enough if the goal is to build a system where all customers
have a good experience, rather than just the majority. For
example if extensive personalization techniques are used then
customers with longer histories require more processing which
impacts performance at the high-end of the distribution. An SLA
stated in terms of mean or median response times will not address
the performance of this important customer segment. To address
this issue, at Amazon, SLAs are expressed and measured at the
99.9th percentile of the distribution. The choice for 99.9% over an
even higher percentile has been made based on a cost-benefit
analysis which demonstrated a significant increase in cost to
improve performance that much. Experiences with Amazon’s
production systems have shown that this approach provides a
better overall experience compared to those systems that meet
SLAs defined based on the mean or median.
In this paper there are many references to this 99.9th percentile of
distributions, which reflects Amazon engineers’ relentless focus
on performance from the perspective of the customers’
experience. Many papers report on averages, so these are included
where it makes sense for comparison purposes. Nevertheless,
Amazon’s engineering and optimization efforts are not focused on
averages. Several techniques, such as the load balanced selection
of write coordinators, are purely targeted at controlling
performance at the 99.9th percentile.
Storage systems often play an important role in establishing a
service’s SLA, especially if the business logic is relatively
lightweight, as is the case for many Amazon services. State
management then becomes the main component of a service’s
SLA. One of the main design considerations for Dynamo is to
give services control over their system properties, such as
durability and consistency, and to let services make their own
tradeoffs between functionality, performance and cost-
Figure 1: Service-oriented architecture of Amazon’s
2.3 Design Considerations
Data replication algorithms used in commercial systems
traditionally perform synchronous replica coordination in order to
provide a strongly consistent data access interface. To achieve this
level of consistency, these algorithms are forced to tradeoff the
availability of the data under certain failure scenarios. For
instance, rather than dealing with the uncertainty of the
correctness of an answer, the data is made unavailable until it is
absolutely certain that it is correct. From the very early replicated
database works, it is well known that when dealing with the
possibility of network failures, strong consistency and high data
availability cannot be achieved simultaneously [2, 11]. As such
systems and applications need to be aware which properties can
be achieved under which conditions.
For systems prone to server and network failures, availability can
be increased by using optimistic replication techniques, where
changes are allowed to propagate to replicas in the background,
and concurrent, disconnected work is tolerated. The challenge
with this approach is that it can lead to conflicting changes which
must be detected and resolved. This process of conflict resolution
introduces two problems: when to resolve them and who resolves
them. Dynamo is designed to be an eventually consistent data
store; that is all updates reach all replicas eventually.
An important design consideration is to decide when to perform
the process of resolving update conflicts, i.e., whether conflicts
should be resolved during reads or writes. Many traditional data
stores execute conflict resolution during writes and keep the read
complexity simple [7]. In such systems, writes may be rejected if
the data store cannot reach all (or a majority of) the replicas at a
given time. On the other hand, Dynamo targets the design space
of an “always writeable” data store (i.e., a data store that is highly
available for writes). For a number of Amazon services, rejecting
customer updates could result in a poor customer experience. For
instance, the shopping cart service must allow customers to add
and remove items from their shopping cart even amidst network
and server failures. This requirement forces us to push the
complexity of conflict resolution to the reads in order to ensure
that writes are never rejected.
The next design choice is who performs the process of conflict
resolution. This can be done by the data store or the application. If
conflict resolution is done by the data store, its choices are rather
limited. In such cases, the data store can only use simple policies,
such as “last write wins” [22], to resolve conflicting updates. On
the other hand, since the application is aware of the data schema it
can decide on the conflict resolution method that is best suited for
its client’s experience. For instance, the application that maintains
customer shopping carts can choose to “merge” the conflicting
versions and return a single unified shopping cart. Despite this
flexibility, some application developers may not want to write
their own conflict resolution mechanisms and choose to push it
down to the data store, which in turn chooses a simple policy such
as “last write wins”.
Other key principles embraced in the design are:
Incremental scalability: Dynamo should be able to scale out one
storage host (henceforth, referred to as “node”) at a time, with
minimal impact on both operators of the system and the system
Symmetry: Every node in Dynamo should have the same set of
responsibilities as its peers; there should be no distinguished node
or nodes that take special roles or extra set of responsibilities. In
our experience, symmetry simplifies the process of system
provisioning and maintenance.
Decentralization: An extension of symmetry, the design should
favor decentralized peer-to-peer techniques over centralized
control. In the past, centralized control has resulted in outages and
the goal is to avoid it as much as possible. This leads to a simpler,
more scalable, and more available system.
Heterogeneity: The system needs to be able to exploit
heterogeneity in the infrastructure it runs on. e.g. the work
distribution must be proportional to the capabilities of the
individual servers. This is essential in adding new nodes with
higher capacity without having to upgrade all hosts at once.
3.1 Peer to Peer Systems
There are several peer-to-peer (P2P) systems that have looked at
the problem of data storage and distribution. The first generation
of P2P systems, such as Freenet and Gnutella1, were
predominantly used as file sharing systems. These were examples
of unstructured P2P networks where the overlay links between
peers were established arbitrarily. In these networks, a search
query is usually flooded through the network to find as many
peers as possible that share the data. P2P systems evolved to the
next generation into what is widely known as structured P2P
networks. These networks employ a globally consistent protocol
to ensure that any node can efficiently route a search query to
some peer that has the desired data. Systems like Pastry [16] and
Chord [20] use routing mechanisms to ensure that queries can be
answered within a bounded number of hops. To reduce the
additional latency introduced by multi-hop routing, some P2P
systems (e.g., [14]) employ O(1) routing where each peer
maintains enough routing information locally so that it can route
requests (to access a data item) to the appropriate peer within a
constant number of hops.
Various storage systems, such as Oceanstore [9] and PAST [17]
were built on top of these routing overlays. Oceanstore provides a
global, transactional, persistent storage service that supports
serialized updates on widely replicated data. To allow for
concurrent updates while avoiding many of the problems inherent
with wide-area locking, it uses an update model based on conflict
resolution. Conflict resolution was introduced in [21] to reduce
the number of transaction aborts. Oceanstore resolves conflicts by
processing a series of updates, choosing a total order among them,
and then applying them atomically in that order. It is built for an
environment where the data is replicated on an untrusted
infrastructure. By comparison, PAST provides a simple
abstraction layer on top of Pastry for persistent and immutable
objects. It assumes that the application can build the necessary
storage semantics (such as mutable files) on top of it.
3.2 Distributed File Systems and Databases
Distributing data for performance, availability and durability has
been widely studied in the file system and database systems
community. Compared to P2P storage systems that only support
flat namespaces, distributed file systems typically support
hierarchical namespaces. Systems like Ficus [15] and Coda [19]
replicate files for high availability at the expense of consistency.
Update conflicts are typically managed using specialized conflict
resolution procedures. The Farsite system [1] is a distributed file
system that does not use any centralized server like NFS. Farsite
achieves high availability and scalability using replication. The
Google File System [6] is another distributed file system built for
hosting the state of Google’s internal applications. GFS uses a
simple design with a single master server for hosting the entire
metadata and where the data is split into chunks and stored in
chunkservers. Bayou is a distributed relational database system
that allows disconnected operations and provides eventual data
consistency [21].
Among these systems, Bayou, Coda and Ficus allow disconnected
operations and are resilient to issues such as network partitions
and outages. These systems differ on their conflict resolution
procedures. For instance, Coda and Ficus perform system level
conflict resolution and Bayou allows application level resolution.
All of them, however, guarantee eventual consistency. Similar to
these systems, Dynamo allows read and write operations to
continue even during network partitions and resolves updated
conflicts using different conflict resolution mechanisms.
Distributed block storage systems like FAB [18] split large size
objects into smaller blocks and stores each block in a highly
available manner. In comparison to these systems, a key-value
store is more suitable in this case because: (a) it is intended to
store relatively small objects (size < 1M) and (b) key-value stores
are easier to configure on a per-application basis. Antiquity is a
wide-area distributed storage system designed to handle multiple
server failures [23]. It uses a secure log to preserve data integrity,
replicates each log on multiple servers for durability, and uses
Byzantine fault tolerance protocols to ensure data consistency. In
contrast to Antiquity, Dynamo does not focus on the problem of
data integrity and security and is built for a trusted environment.
Bigtable is a distributed storage system for managing structured
data. It maintains a sparse, multi-dimensional sorted map and
allows applications to access their data using multiple attributes
[2]. Compared to Bigtable, Dynamo targets applications that
require only key/value access with primary focus on high
availability where updates are not rejected even in the wake of
network partitions or server failures.
Traditional replicated relational database systems focus on the
problem of guaranteeing strong consistency to replicated data.
Although strong consistency provides the application writer a
convenient programming model, these systems are limited in
scalability and availability [7]. These systems are not capable of
handling network partitions because they typically provide strong
consistency guarantees.
3.3 Discussion
Dynamo differs from the aforementioned decentralized storage
systems in terms of its target requirements. First, Dynamo is
targeted mainly at applications that need an “always writeable”
data store where no updates are rejected due to failures or
concurrent writes. This is a crucial requirement for many Amazon
applications. Second, as noted earlier, Dynamo is built for an
infrastructure within a single administrative domain where all
nodes are assumed to be trusted. Third, applications that use
Dynamo do not require support for hierarchical namespaces (a
norm in many file systems) or complex relational schema
(supported by traditional databases). Fourth, Dynamo is built for
latency sensitive applications that require at least 99.9% of read
and write operations to be performed within a few hundred
milliseconds. To meet these stringent latency requirements, it was
imperative for us to avoid routing requests through multiple nodes
(which is the typical design adopted by several distributed hash
table systems such as Chord and Pastry). This is because multi-
hop routing increases variability in response times, thereby
increasing the latency at higher percentiles. Dynamo can be
characterized as a zero-hop DHT, where each node maintains
enough routing information locally to route a request to the
appropriate node directly.
The architecture of a storage system that needs to operate in a
production setting is complex. In addition to the actual data
persistence component, the system needs to have scalable and
robust solutions for load balancing, membership and failure
detection, failure recovery, replica synchronization, overload
handling, state transfer, concurrency and job scheduling, request
marshalling, request routing, system monitoring and alarming,
and configuration management. Describing the details of each of
the solutions is not possible, so this paper focuses on the core
distributed systems techniques used in Dynamo: partitioning,
replication, versioning, membership, failure handling and scaling.
Table 1 presents a summary of the list of techniques Dynamo uses
and their respective advantages.
4.1 System Interface
Dynamo stores objects associated with a key through a simple
interface; it exposes two operations: get() and put(). The get(key)
operation 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. The put(key, context,
object) operation determines where the replicas of the object
should be placed based on the associated key, and writes the
replicas to disk. The context encodes system metadata about the
object that is opaque to the caller and includes information such as
the version of the object. The context information is stored along
with the object so that the system can verify the validity of the
context object supplied in the put request.
Dynamo treats both the key and the object supplied by the caller
as an opaque array of bytes. It applies a MD5 hash on the key to
generate a 128-bit identifier, which is used to determine the
storage nodes that are responsible for serving the key.
4.2 Partitioning Algorithm
One of the key design requirements for Dynamo is that it must
scale incrementally. This requires a mechanism to dynamically
partition the data over the set of nodes (i.e., storage hosts) in the
system. Dynamo’s partitioning scheme relies on consistent
hashing to distribute the load across multiple storage hosts. In
consistent hashing [10], the output range of a hash function is
treated as a fixed circular space or “ring” (i.e. the largest hash
value wraps around to the smallest hash value). Each node in the
system is assigned a random value within this space which
represents its “position” on the ring. Each data item identified by
a key is assigned to a node by hashing the data item’s key to yield
its position on the ring, and then walking the ring clockwise to
find the first node with a position larger than the item’s position.
Table 1: Summary of techniques used in Dynamo and
their advantages.
Key K
Nodes B, C
and D store
keys in
range (A,B)
Problem Technique Advantage
Partitioning Consistent Hashing Incremental
High Availability
for writes
Vector clocks with
reconciliation during
Version size is
decoupled from
update rates.
Handling temporary
Sloppy Quorum and
hinted handoff
Provides high
availability and
durability guarantee
when some of the
replicas are not
Figure 2: Partitioning and replication of keys in Dynamo
ring. Recovering from
permanent failures
Anti-entropy using
Merkle trees
divergent replicas in
the background.
Membership and
failure detection
membership protocol
and failure detection.
Preserves symmetry
and avoids having a
centralized registry
for storing
membership and
node liveness
Thus, each node becomes responsible for the region in the ring
between it and its predecessor node on the ring. The principle
advantage of consistent hashing is that departure or arrival of a
node only affects its immediate neighbors and other nodes remain
The basic consistent hashing algorithm presents some challenges.
First, the random position assignment of each node on the ring
leads to non-uniform data and load distribution. Second, the basic
algorithm is oblivious to the heterogeneity in the performance of
nodes. To address these issues, Dynamo uses a variant of
consistent hashing (similar to the one used in [10, 20]): instead of
mapping a node to a single point in the circle, each node gets
assigned to multiple points in the ring. To this end, Dynamo uses
the concept of “virtual nodes”. A virtual node looks like a single
node in the system, but each node can be responsible for more
than one virtual node. Effectively, when a new node is added to
the system, it is assigned multiple positions (henceforth, “tokens”)
in the ring. The process of fine-tuning Dynamo’s partitioning
scheme is discussed in Section 6.
Using virtual nodes has the following advantages:
If a node becomes unavailable (due to failures or routine
maintenance), the load handled by this node is evenly
dispersed across the remaining available nodes.
When a node becomes available again, or a new node is
added to the system, the newly available node accepts a
roughly equivalent amount of load from each of the other
available nodes.
The number of virtual nodes that a node is responsible can
decided based on its capacity, accounting for heterogeneity
in the physical infrastructure.
4.3 Replication
To achieve high availability and durability, Dynamo replicates its
data on multiple hosts. Each data item is replicated at N hosts,
where N is a parameter configured “per-instance”. Each key, k, is
assigned to a coordinator node (described in the previous section).
The coordinator is in charge of the replication of the data items
that fall within its range. In addition to locally storing each key
within its range, the coordinator replicates these keys at the N-1
clockwise successor nodes in the ring. This results in a system
where each node is responsible for the region of the ring between
it and its Nth predecessor. In Figure 2, node B replicates the key k
at nodes C and D in addition to storing it locally. Node D will
store the keys that fall in the ranges (A, B], (B, C], and (C, D].
The list of nodes that is responsible for storing a particular key is
called the preference list. The system is designed, as will be
explained in Section 4.8, so that every node in the system can
determine which nodes should be in this list for any particular
key. To account for node failures, preference list contains more
than N nodes. Note that with the use of virtual nodes, it is possible
that the first N successor positions for a particular key may be
owned by less than N distinct physical nodes (i.e. a node may
hold more than one of the first N positions). To address this, the
preference list for a key is constructed by skipping positions in the
ring to ensure that the list contains only distinct physical nodes.
4.4 Data Versioning
Dynamo provides eventual consistency, which allows for updates
to be propagated to all replicas asynchronously. A put() call may
return to its caller before the update has been applied at all the
replicas, which can result in scenarios where a subsequent get()
operation may return an object that does not have the latest
updates.. If there are no failures then there is a bound on the
update propagation times. However, under certain failure
scenarios (e.g., server outages or network partitions), updates may
not arrive at all replicas for an extended period of time.
There is a category of applications in Amazon’s platform that can
tolerate such inconsistencies and can be constructed to operate
under these conditions. For example, the shopping cart application
requires that an “Add to Cart” operation can never be forgotten or
rejected. If the most recent state of the cart is unavailable, and a
user makes changes to an older version of the cart, that change is
still meaningful and should be preserved. But at the same time it
shouldn’t supersede the currently unavailable state of the cart,
which itself may contain changes that should be preserved. Note
that both “add to cart” and “delete item from cart” operations are
translated into put requests to Dynamo. When a customer wants to
add an item to (or remove from) a shopping cart and the latest
version is not available, the item is added to (or removed from)
the older version and the divergent versions are reconciled later.
In order to provide this kind of guarantee, Dynamo treats the
result of each modification as a new and immutable version of the
data. It allows for multiple versions of an object to be present in
the system at the same time. Most of the time, new versions
subsume the previous version(s), and the system itself can
determine the authoritative version (syntactic reconciliation).
However, version branching may happen, in the presence of
failures combined with concurrent updates, resulting in
conflicting versions of an object. In these cases, the system cannot
reconcile the multiple versions of the same object and the client
must perform the reconciliation in order to collapse multiple
branches of data evolution back into one (semantic
reconciliation). A typical example of a collapse operation is
“merging” different versions of a customer’s shopping cart. Using
this reconciliation mechanism, an “add to cart” operation is never
lost. However, deleted items can resurface.
It is important to understand that certain failure modes can
potentially result in the system having not just two but several
versions of the same data. Updates in the presence of network
partitions and node failures can potentially result in an object
having distinct version sub-histories, which the system will need
to reconcile in the future. This requires us to design applications
that explicitly acknowledge the possibility of multiple versions of
the same data (in order to never lose any updates).
Dynamo uses vector clocks [12] in order to capture causality
between different versions of the same object. A vector clock is
effectively a list of (node, counter) pairs. One vector clock is
associated with every version of every object. One can determine
whether two versions of an object are on parallel branches or have
a causal ordering, by examine their vector clocks. 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. Otherwise, the two changes are considered to be
in conflict and require reconciliation.
In Dynamo, when a client wishes to update an object, it must
specify which version it is updating. This is done by passing the
context it obtained from an earlier read operation, which contains
the vector clock information. Upon processing a read request, if
Dynamo has access to multiple branches that cannot be
syntactically reconciled, it will return all the objects at the leaves,
with the corresponding version information in the context. An
update using this context is considered to have reconciled the
divergent versions and the branches are collapsed into a single
new version.
To illustrate the use of vector clocks, let us consider the example
shown in Figure 3. A client writes a new object. The node (say
Sx) that handles the write for this key increases its sequence
number and uses it to create the data's vector clock. The system
now has the object D1 and its associated clock [(Sx, 1)]. The
client updates the object. Assume the same node handles this
request as well. The system now also has object D2 and its
associated clock [(Sx, 2)]. D2 descends from D1 and therefore
over-writes D1, however there may be replicas of D1 lingering at
nodes that have not yet seen D2. Let us assume that the same
client updates the object again and a different server (say Sy)
handles the request. The system now has data D3 and its
associated clock [(Sx, 2), (Sy, 1)].
Next assume a different client reads D2 and then tries to update it,
and another node (say Sz) does the write. The system now has D4
(descendant of D2) whose version clock is [(Sx, 2), (Sz, 1)]. A
node that is aware of D1 or D2 could determine, upon receiving
D4 and its clock, that D1 and D2 are overwritten by the new data
and can be garbage collected. A node that is aware of D3 and
receives D4 will find that there is no causal relation between
them. In other words, there are changes in D3 and D4 that are not
reflected in each other. Both versions of the data must be kept and
presented to a client (upon a read) for semantic reconciliation.
Now assume some client reads both D3 and D4 (the context will
reflect that both values were found by the read). The read's
context is a summary of the clocks of D3 and D4, namely [(Sx, 2),
(Sy, 1), (Sz, 1)]. If the client performs the reconciliation and node
Sx coordinates the write, Sx will update its sequence number in
the clock. The new data D5 will have the following clock: [(Sx,
3), (Sy, 1), (Sz, 1)].
A possible issue with vector clocks is that the size of vector
clocks may grow if many servers coordinate the writes to an
object. In practice, this is not likely because the writes are usually
handled by one of the top N nodes in the preference list. In case of
network partitions or multiple server failures, write requests may
be handled by nodes that are not in the top N nodes in the
preference list causing the size of vector clock to grow. In these
scenarios, it is desirable to limit the size of vector clock. To this
end, Dynamo employs the following clock truncation scheme:
Along with each (node, counter) pair, Dynamo stores a timestamp
that indicates the last time the node updated the data item. When
the number of (node, counter) pairs in the vector clock reaches a
threshold (say 10), the oldest pair is removed from the clock.
Clearly, this truncation scheme can lead to inefficiencies in
reconciliation as the descendant relationships cannot be derived
accurately. However, this problem has not surfaced in production
and therefore this issue has not been thoroughly investigated.
Figure 3: Version evolution of an object over time.
4.5 Execution of get () and put () operations
Any storage node in Dynamo is eligible to receive client get and
put operations for any key. In this section, for sake of simplicity,
we describe how these operations are performed in a failure-free
environment and in the subsequent section we describe how read
and write operations are executed during failures.
Both get and put operations are invoked using Amazon’s
infrastructure-specific request processing framework over HTTP.
There are two strategies that a client can use to select a node: (1)
route its request through a generic load balancer that will select a
node based on load information, or (2) use a partition-aware client
library that routes requests directly to the appropriate coordinator
nodes. The advantage of the first approach is that the client does
not have to link any code specific to Dynamo in its application,
whereas the second strategy can achieve lower latency because it
skips a potential forwarding step.
A node handling a read or write operation is known as the
coordinator. Typically, this is the first among the top N nodes in
the preference list. If the requests are received through a load
balancer, requests to access a key may be routed to any random
node in the ring. In this scenario, the node that receives the
request will not coordinate it if the node is not in the top N of the
requested key’s preference list. Instead, that node will forward the
request to the first among the top N nodes in the preference list.
Read and write operations involve the first N healthy nodes in the
preference list, skipping over those that are down or inaccessible.
When all nodes are healthy, the top N nodes in a key’s preference
list are accessed. When there are node failures or network
partitions, nodes that are lower ranked in the preference list are
To maintain consistency among its replicas, Dynamo uses a
consistency protocol similar to those used in quorum systems.
This protocol has two key configurable values: R and W. R is the
minimum number of nodes that must participate in a successful
read operation. W is the minimum number of nodes that must
participate in a successful write operation. Setting R and W such
that R + W > N yields a quorum-like system. In this model, the
latency of a get (or put) operation is dictated by the slowest of the
R (or W) replicas. For this reason, R and W are usually
configured to be less than N, to provide better latency.
Upon receiving a put() request for a key, the coordinator generates
the vector clock for the new version and writes the new version
locally. The coordinator then sends the new version (along with
the new vector clock) to the N highest-ranked reachable nodes. If
at least W-1 nodes respond then the write is considered
Similarly, for a get() request, the coordinator requests all existing
versions of data for that key from the N highest-ranked reachable
nodes in the preference list for that key, and then waits for R
responses before returning the result to the client. If the
coordinator ends up gathering multiple versions of the data, it
returns all the versions it deems to be causally unrelated. The
divergent versions are then reconciled and the reconciled version
superseding the current versions is written back.
4.6 Handling Failures: Hinted Handoff
If Dynamo used a traditional quorum approach it would be
unavailable during server failures and network partitions, and
would have reduced durability even under the simplest of failure
conditions. To remedy this it does not enforce strict quorum
membership and instead it uses a “sloppy quorum”; all read and
write operations are performed on the first N healthy nodes from
the preference list, which may not always be the first N nodes
encountered while walking the consistent hashing ring.
Consider the example of Dynamo configuration given in Figure 2
with N=3. In this example, if node A is temporarily down or
unreachable during a write operation then a replica that would
normally have lived on A will now be sent to node D. This is done
to maintain the desired availability and durability guarantees. The
replica sent to D will have a hint in its metadata that suggests
which node was the intended recipient of the replica (in this case
A). Nodes that receive hinted replicas will keep them in a
separate local database that is scanned periodically. Upon
detecting that A has recovered, D will attempt to deliver the
replica to A. Once the transfer succeeds, D may delete the object
from its local store without decreasing the total number of replicas
in the system.
Using hinted handoff, Dynamo ensures that the read and write
operations are not failed due to temporary node or network
failures. Applications that need the highest level of availability
can set W to 1, which ensures that a write is accepted as long as a
single node in the system has durably written the key it to its local
store. Thus, the write request is only rejected if all nodes in the
system are unavailable. However, in practice, most Amazon
services in production set a higher W to meet the desired level of
durability. A more detailed discussion of configuring N, R and W
follows in section 6.
It is imperative that a highly available storage system be capable
of handling the failure of an entire data center(s). Data center
failures happen due to power outages, cooling failures, network
failures, and natural disasters. Dynamo is configured such that
each object is replicated across multiple data centers. In essence,
the preference list of a key is constructed such that the storage
nodes are spread across multiple data centers. These datacenters
are connected through high speed network links. This scheme of
replicating across multiple datacenters allows us to handle entire
data center failures without a data outage.
4.7 Handling permanent failures: Replica
Hinted handoff works best if the system membership churn is low
and node failures are transient. There are scenarios under which
hinted replicas become unavailable before they can be returned to
the original replica node. To handle this and other threats to
durability, Dynamo implements an anti-entropy (replica
synchronization) protocol to keep the replicas synchronized.
To detect the inconsistencies between replicas faster and to
minimize the amount of transferred data, Dynamo uses Merkle
trees [13]. A Merkle tree is a hash tree where leaves are hashes of
the values of individual keys. Parent nodes higher in the tree are
hashes of their respective children. The principal advantage of
Merkle tree is that each branch of the tree can be checked
independently without requiring nodes to download the entire tree
or the entire data set. Moreover, Merkle trees help in reducing the
amount of data that needs to be transferred while checking for
inconsistencies among replicas. For instance, if the hash values of
the root of two trees are equal, then the values of the leaf nodes in
the tree are equal and the nodes require no synchronization. If not,
it implies that the values of some replicas are different. In such
cases, the nodes may exchange the hash values of children and the
process continues until it reaches the leaves of the trees, at which
point the hosts can identify the keys that are “out of sync”. Merkle
trees minimize the amount of data that needs to be transferred for
synchronization and reduce the number of disk reads performed
during the anti-entropy process.
Dynamo uses Merkle trees for anti-entropy as follows: Each node
maintains a separate Merkle tree for each key range (the set of
keys covered by a virtual node) it hosts. This allows nodes to
compare whether the keys within a key range are up-to-date. In
this scheme, two nodes exchange the root of the Merkle tree
corresponding to the key ranges that they host in common.
Subsequently, using the tree traversal scheme described above the
nodes determine if they have any differences and perform the
appropriate synchronization action. The disadvantage with this
scheme is that many key ranges change when a node joins or
leaves the system thereby requiring the tree(s) to be recalculated.
This issue is addressed, however, by the refined partitioning
scheme described in Section 6.2.
4.8 Membership and Failure Detection
4.8.1 Ring Membership
In Amazon’s environment node outages (due to failures and
maintenance tasks) are often transient but may last for extended
intervals. A node outage rarely signifies a permanent departure
and therefore should not result in rebalancing of the partition
assignment or repair of the unreachable replicas. Similarly,
manual error could result in the unintentional startup of new
Dynamo nodes. For these reasons, it was deemed appropriate to
use an explicit mechanism to initiate the addition and removal of
nodes from a Dynamo ring. An administrator uses a command
line tool or a browser to connect to a Dynamo node and issue a
membership change to join a node to a ring or remove a node
from a ring. The node that serves the request writes the
membership change and its time of issue to persistent store. The
membership changes form a history because nodes can be
removed and added back multiple times. A gossip-based protocol
propagates membership changes and maintains an eventually
consistent view of membership. Each node contacts a peer chosen
at random every second and the two nodes efficiently reconcile
their persisted membership change histories.
When a node starts for the first time, it chooses its set of tokens
(virtual nodes in the consistent hash space) and maps nodes to
their respective token sets. The mapping is persisted on disk and
initially contains only the local node and token set. The mappings
stored at different Dynamo nodes are reconciled during the same
communication exchange that reconciles the membership change
histories. Therefore, partitioning and placement information also
propagates via the gossip-based protocol and each storage node is
aware of the token ranges handled by its peers. This allows each
node to forward a key’s read/write operations to the right set of
nodes directly.
4.8.2 External Discovery
The mechanism described above could temporarily result in a
logically partitioned Dynamo ring. For example, the
administrator could contact node A to join A to the ring, then
contact node B to join B to the ring. In this scenario, nodes A and
B would each consider itself a member of the ring, yet neither
would be immediately aware of the other. To prevent logical
partitions, some Dynamo nodes play the role of seeds. Seeds are
nodes that are discovered via an external mechanism and are
known to all nodes. Because all nodes eventually reconcile their
membership with a seed, logical partitions are highly unlikely.
Seeds can be obtained either from static configuration or from a
configuration service. Typically seeds are fully functional nodes
in the Dynamo ring.
4.8.3 Failure Detection
Failure detection in Dynamo is used to avoid attempts to
communicate with unreachable peers during get() and put()
operations and when transferring partitions and hinted replicas.
For the purpose of avoiding failed attempts at communication, a
purely local notion of failure detection is entirely sufficient: node
A may consider node B failed if node B does not respond to node
A’s messages (even if B is responsive to node C's messages). In
the presence of a steady rate of client requests generating inter-
node communication in the Dynamo ring, a node A quickly
discovers that a node B is unresponsive when B fails to respond to
a message; Node A then uses alternate nodes to service requests
that map to B's partitions; A periodically retries B to check for the
latter's recovery. In the absence of client requests to drive traffic
between two nodes, neither node really needs to know whether the
other is reachable and responsive.
Decentralized failure detection protocols use a simple gossip-style
protocol that enable each node in the system to learn about the
arrival (or departure) of other nodes. For detailed information on
decentralized failure detectors and the parameters affecting their
accuracy, the interested reader is referred to [8]. Early designs of
Dynamo used a decentralized failure detector to maintain a
globally consistent view of failure state. Later it was determined
that the explicit node join and leave methods obviates the need for
a global view of failure state. This is because nodes are notified of
permanent node additions and removals by the explicit node join
and leave methods and temporary node failures are detected by
the individual nodes when they fail to communicate with others
(while forwarding requests).
4.9 Adding/Removing Storage Nodes
When a new node (say X) is added into the system, it gets
assigned a number of tokens that are randomly scattered on the
ring. For every key range that is assigned to node X, there may be
a number of nodes (less than or equal to N) that are currently in
charge of handling keys that fall within its token range. Due to the
allocation of key ranges to X, some existing nodes no longer have
to some of their keys and these nodes transfer those keys to X. Let
us consider a simple bootstrapping scenario where node X is
added to the ring shown in Figure 2 between A and B. When X is
added to the system, it is in charge of storing keys in the ranges
(F, G], (G, A] and (A, X]. As a consequence, nodes B, C and D no
longer have to store the keys in these respective ranges.
Therefore, nodes B, C, and D will offer to and upon confirmation
from X transfer the appropriate set of keys. When a node is
removed from the system, the reallocation of keys happens in a
reverse process.
Operational experience has shown that this approach distributes
the load of key distribution uniformly across the storage nodes,
which is important to meet the latency requirements and to ensure
fast bootstrapping. Finally, by adding a confirmation round
between the source and the destination, it is made sure that the
destination node does not receive any duplicate transfers for a
given key range.
In Dynamo, each storage node has three main software
components: request coordination, membership and failure
detection, and a local persistence engine. All these components
are implemented in Java.
Dynamo’s local persistence component allows for different
storage engines to be plugged in. Engines that are in use are
Berkeley Database (BDB) Transactional Data Store2, BDB Java
Edition, MySQL, and an in-memory buffer with persistent
backing store. The main reason for designing a pluggable
persistence component is to choose the storage engine best suited
for an application’s access patterns. For instance, BDB can handle
objects typically in the order of tens of kilobytes whereas MySQL
can handle objects of larger sizes. Applications choose Dynamo’s
local persistence engine based on their object size distribution.
The majority of Dynamo’s production instances use BDB
Transactional Data Store.
The request coordination component is built on top of an event-
driven messaging substrate where the message processing pipeline
is split into multiple stages similar to the SEDA architecture [24].
All communications are implemented using Java NIO channels.
The coordinator executes the read and write requests on behalf of
clients by collecting data from one or more nodes (in the case of
reads) or storing data at one or more nodes (for writes). Each
client request results in the creation of a state machine on the node
that received the client request. The state machine contains all the
logic for identifying the nodes responsible for a key, sending the
requests, waiting for responses, potentially doing retries,
processing the replies and packaging the response to the client.
Each state machine instance handles exactly one client request.
For instance, a read operation implements the following state
machine: (i) send read requests to the nodes, (ii) wait for
minimum number of required responses, (iii) if too few replies
were received within a given time bound, fail the request, (iv)
otherwise gather all the data versions and determine the ones to be
returned and (v) if versioning is enabled, perform syntactic
reconciliation and generate an opaque write context that contains
the vector clock that subsumes all the remaining versions. For the
sake of brevity the failure handling and retry states are left out.
After the read response has been returned to the caller the state
machine waits for a small period of time to receive any
outstanding responses. If stale versions were returned in any of
the responses, the coordinator updates those nodes with the latest
version. This process is called read repair because it repairs
replicas that have missed a recent update at an opportunistic time
and relieves the anti-entropy protocol from having to do it.
As noted earlier, write requests are coordinated by one of the top
N nodes in the preference list. Although it is desirable always to
have the first node among the top N to coordinate the writes
thereby serializing all writes at a single location, this approach has
led to uneven load distribution resulting in SLA violations. This is
because the request load is not uniformly distributed across
objects. To counter this, any of the top N nodes in the preference
list is allowed to coordinate the writes. In particular, since each
write usually follows a read operation, the coordinator for a write
is chosen to be the node that replied fastest to the previous read
operation which is stored in the context information of the
request. This optimization enables us to pick the node that has the
data that was read by the preceding read operation thereby
increasing the chances of getting “read-your-writes” consistency.
It also reduces variability in the performance of the request
handling which improves the performance at the 99.9 percentile.
Dynamo is used by several services with different configurations.
These instances differ by their version reconciliation logic, and
read/write quorum characteristics. The following are the main
patterns in which Dynamo is used:
Business logic specific reconciliation: This is a popular use
case for Dynamo. Each data object is replicated across
multiple nodes. In case of divergent versions, the client
application performs its own reconciliation logic. The
shopping cart service discussed earlier is a prime example of
this category. Its business logic reconciles objects by
merging different versions of a customer’s shopping cart.
Timestamp based reconciliation: This case differs from the
previous one only in the reconciliation mechanism. In case of
divergent versions, Dynamo performs simple timestamp
based reconciliation logic of “last write wins”; i.e., the object
with the largest physical timestamp value is chosen as the
correct version. The service that maintains customer’s
session information is a good example of a service that uses
this mode.
High performance read engine: While Dynamo is built to be
an “always writeable” data store, a few services are tuning its
quorum characteristics and using it as a high performance
read engine. Typically, these services have a high read
request rate and only a small number of updates. In this
configuration, typically R is set to be 1 and W to be N. For
these services, Dynamo provides the ability to partition and
replicate their data across multiple nodes thereby offering
incremental scalability. Some of these instances function as
the authoritative persistence cache for data stored in more
heavy weight backing stores. Services that maintain product
catalog and promotional items fit in this category.
The main advantage of Dynamo is that its client applications can
tune the values of N, R and W to achieve their desired levels of
performance, availability and durability. For instance, the value of
N determines the durability of each object. A typical value of N
used by Dynamo’s users is 3.
The values of W and R impact object availability, durability and
consistency. For instance, if W is set to 1, then the system will
never reject a write request as long as there is at least one node in
the system that can successfully process a write request. However,
low values of W and R can increase the risk of inconsistency as
write requests are deemed successful and returned to the clients
even if they are not processed by a majority of the replicas. This
also introduces a vulnerability window for durability when a write
request is successfully returned to the client even though it has
been persisted at only a small number of nodes.
Figure 4: Average and 99.9 percentiles of latencies for read and
write requests during our peak request season of December 2006.
The intervals between consecutive ticks in the x-axis correspond
to 12 hours. Latencies follow a diurnal pattern similar to the
request rate and 99.9 percentile latencies are an order of
magnitude higher than averages
Figure 5: Comparison of performance of 99.9th percentile
latencies for buffered vs. non-buffered writes over a period of
24 hours. The intervals between consecutive ticks in the x-axis
correspond to one hour.
Traditional wisdom holds that durability and availability go hand-
in-hand. However, this is not necessarily true here. For instance,
the vulnerability window for durability can be decreased by
increasing W. This may increase the probability of rejecting
requests (thereby decreasing availability) because more storage
hosts need to be alive to process a write request.
The common (N,R,W) configuration used by several instances of
Dynamo is (3,2,2). These values are chosen to meet the necessary
levels of performance, durability, consistency, and availability
All the measurements presented in this section were taken on a
live system operating with a configuration of (3,2,2) and running
a couple hundred nodes with homogenous hardware
configurations. As mentioned earlier, each instance of Dynamo
contains nodes that are located in multiple datacenters. These
datacenters are typically connected through high speed network
links. Recall that to generate a successful get (or put) response R
(or W) nodes need to respond to the coordinator. Clearly, the
network latencies between datacenters affect the response time
and the nodes (and their datacenter locations) are chosen such that
the applications target SLAs are met.
6.1 Balancing Performance and Durability
While Dynamo’s principle design goal is to build a highly
available data store, performance is an equally important criterion
in Amazon’s platform. As noted earlier, to provide a consistent
customer experience, Amazon’s services set their performance
targets at higher percentiles (such as the 99.9th or 99.99th
percentiles). A typical SLA required of services that use Dynamo
is that 99.9% of the read and write requests execute within 300ms.
Since Dynamo is run on standard commodity hardware
components that have far less I/O throughput than high-end
enterprise servers, providing consistently high performance for
read and write operations is a non-trivial task. The involvement of
multiple storage nodes in read and write operations makes it even
more challenging, since the performance of these operations is
limited by the slowest of the R or W replicas. Figure 4 shows the
average and 99.9th percentile latencies of Dynamo’s read and
write operations during a period of 30 days. As seen in the figure,
the latencies exhibit a clear diurnal pattern which is a result of the
diurnal pattern in the incoming request rate (i.e., there is a
significant difference in request rate between the daytime and
night). Moreover, the write latencies are higher than read latencies
obviously because write operations always results in disk access.
Also, the 99.9th percentile latencies are around 200 ms and are an
order of magnitude higher than the averages. This is because the
99.9th percentile latencies are affected by several factors such as
variability in request load, object sizes, and locality patterns.
While this level of performance is acceptable for a number of
services, a few customer-facing services required higher levels of
performance. For these services, Dynamo provides the ability to
trade-off durability guarantees for performance. In the
optimization each storage node maintains an object buffer in its
main memory. Each write operation is stored in the buffer and
gets periodically written to storage by a writer thread. In this
scheme, read operations first check if the requested key is present
in the buffer. If so, the object is read from the buffer instead of the
storage engine.
Figure 6: Fraction of nodes that are out-of-balance (i.e., nodes
whose request load is above a certain threshold from the
average system load) and their corresponding request load.
The interval between ticks in x-axis corresponds to a time
period of 30 minutes. This optimization has resulted in lowering the 99.9th percentile
latency by a factor of 5 during peak traffic even for a very small
buffer of a thousand objects (see Figure 5). Also, as seen in the
figure, write buffering smoothes out higher percentile latencies.
Obviously, this scheme trades durability for performance. In this
scheme, a server crash can result in missing writes that were
queued up in the buffer. To reduce the durability risk, the write
operation is refined to have the coordinator choose one out of the
N replicas to perform a “durable write”. Since the coordinator
waits only for W responses, the performance of the write
operation is not affected by the performance of the durable write
operation performed by a single replica.
6.2 Ensuring Uniform Load distribution
Dynamo uses consistent hashing to partition its key space across
its replicas and to ensure uniform load distribution. A uniform key
distribution can help us achieve uniform load distribution
assuming the access distribution of keys is not highly skewed. In
particular, Dynamo’s design assumes that even where there is a
significant skew in the access distribution there are enough keys
in the popular end of the distribution so that the load of handling
popular keys can be spread across the nodes uniformly through
partitioning. This section discusses the load imbalance seen in
Dynamo and the impact of different partitioning strategies on load
To study the load imbalance and its correlation with request load,
the total number of requests received by each node was measured
for a period of 24 hours - broken down into intervals of 30
minutes. In a given time window, a node is considered to be “in-
balance”, if the node’s request load deviates from the average load
by a value a less than a certain threshold (here 15%). Otherwise
the node was deemed “out-of-balance”. Figure 6 presents the
fraction of nodes that are “out-of-balance” (henceforth,
“imbalance ratio”) during this time period. For reference, the
corresponding request load received by the entire system during
this time period is also plotted. As seen in the figure, the
imbalance ratio decreases with increasing load. For instance,
during low loads the imbalance ratio is as high as 20% and during
high loads it is close to 10%. Intuitively, this can be explained by
the fact that under high loads, a large number of popular keys are
accessed and due to uniform distribution of keys the load is
evenly distributed. However, during low loads (where load is 1/8th
of the measured peak load), fewer popular keys are accessed,
resulting in a higher load imbalance.
This section discusses how Dynamo’s partitioning scheme has
evolved over time and its implications on load distribution.
Strategy 1: T random tokens per node and partition by token
value: This was the initial strategy deployed in production (and
described in Section 4.2). In this scheme, each node is assigned T
tokens (chosen uniformly at random from the hash space). The
tokens of all nodes are ordered according to their values in the
hash space. Every two consecutive tokens define a range. The last
token and the first token form a range that "wraps" around from
the highest value to the lowest value in the hash space. Because
the tokens are chosen randomly, the ranges vary in size. As nodes
join and leave the system, the token set changes and consequently
the ranges change. Note that the space needed to maintain the
membership at each node increases linearly with the number of
nodes in the system.
While using this strategy, the following problems were
encountered. First, when a new node joins the system, it needs to
“steal” its key ranges from other nodes. However, the nodes
handing the key ranges off to the new node have to scan their
local persistence store to retrieve the appropriate set of data items.
Note that performing such a scan operation on a production node
is tricky as scans are highly resource intensive operations and they
need to be executed in the background without affecting the
customer performance. This requires us to run the bootstrapping
task at the lowest priority. However, this significantly slows the
bootstrapping process and during busy shopping season, when the
nodes are handling millions of requests a day, the bootstrapping
has taken almost a day to complete. Second, when a node
joins/leaves the system, the key ranges handled by many nodes
change and the Merkle trees for the new ranges need to be
recalculated, which is a non-trivial operation to perform on a
production system. Finally, there was no easy way to take a
snapshot of the entire key space due to the randomness in key
ranges, and this made the process of archival complicated. In this
scheme, archiving the entire key space requires us to retrieve the
keys from each node separately, which is highly inefficient.
The fundamental issue with this strategy is that the schemes for
data partitioning and data placement are intertwined. For instance,
in some cases, it is preferred to add more nodes to the system in
order to handle an increase in request load. However, in this
scenario, it is not possible to add nodes without affecting data
partitioning. Ideally, it is desirable to use independent schemes for
partitioning and placement. To this end, following strategies were
Figure 7: 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.
Strategy 2: T random tokens per node and equal sized partitions:
In this strategy, the hash space is divided into Q equally sized
partitions/ranges and each node is assigned T random tokens. Q is
usually set such that Q >> N and Q >> S*T, where S is the
number of nodes in the system. In this strategy, the tokens are
only used to build the function that maps values in the hash space
to the ordered lists of nodes and not to decide the partitioning. A
partition is placed on the first N unique nodes that are encountered
while walking the consistent hashing ring clockwise from the end
of the partition. Figure 7 illustrates this strategy for N=3. In this
example, nodes A, B, C are encountered while walking the ring
from the end of the partition that contains key k1. The primary
advantages of this strategy are: (i) decoupling of partitioning and
partition placement, and (ii) enabling the possibility of changing
the placement scheme at runtime.
Strategy 3: Q/S tokens per node, equal-sized partitions: Similar to
strategy 2, this strategy divides the hash space into Q equally
sized partitions and the placement of partition is decoupled from
the partitioning scheme. Moreover, each node is assigned Q/S
tokens where S is the number of nodes in the system. When a
node leaves the system, its tokens are randomly distributed to the
remaining nodes such that these properties are preserved.
Similarly, when a node joins the system it "steals" tokens from
nodes in the system in a way that preserves these properties.
The efficiency of these three strategies is evaluated for a system
with S=30 and N=3. However, comparing these different
strategies in a fair manner is hard as different strategies have
different configurations to tune their efficiency. For instance, the
load distribution property of strategy 1 depends on the number of
tokens (i.e., T) while strategy 3 depends on the number of
partitions (i.e., Q). One fair way to compare these strategies is to
evaluate the skew in their load distribution while all strategies use
the same amount of space to maintain their membership
information. For instance, in strategy 1 each node needs to
maintain the token positions of all the nodes in the ring and in
strategy 3 each node needs to maintain the information regarding
the partitions assigned to each node.
In our next experiment, these strategies were evaluated by varying
the relevant parameters (T and Q). The load balancing efficiency
of each strategy was measured for different sizes of membership
information that needs to be maintained at each node, where Load
balancing efficiency is defined as the ratio of average number of
requests served by each node to the maximum number of requests
served by the hottest node.
The results are given in Figure 8. As seen in the figure, strategy 3
achieves the best load balancing efficiency and strategy 2 has the
worst load balancing efficiency. For a brief time, Strategy 2
served as an interim setup during the process of migrating
Dynamo instances from using Strategy 1 to Strategy 3. Compared
to Strategy 1, Strategy 3 achieves better efficiency and reduces the
size of membership information maintained at each node by three
orders of magnitude. While storage is not a major issue the nodes
gossip the membership information periodically and as such it is
desirable to keep this information as compact as possible. In
addition to this, strategy 3 is advantageous and simpler to deploy
for the following reasons: (i) Faster bootstrapping/recovery:
Since partition ranges are fixed, they can be stored in separate
files, meaning a partition can be relocated as a unit by simply
transferring the file (avoiding random accesses needed to locate
specific items). This simplifies the process of bootstrapping and
recovery. (ii) Ease of archival: Periodical archiving of the dataset
is a mandatory requirement for most of Amazon storage services.
Archiving the entire dataset stored by Dynamo is simpler in
strategy 3 because the partition files can be archived separately.
By contrast, in Strategy 1, the tokens are chosen randomly and,
archiving the data stored in Dynamo requires retrieving the keys
from individual nodes separately and is usually inefficient and
slow. The disadvantage of strategy 3 is that changing the node
membership requires coordination in order to preserve the
properties required of the assignment.
6.3 Divergent Versions: When and How
0 5000 10000 15000 20000 25000 30000 35000
Size of me tadata ma intained a t each node (in abstract units)
Efficieny (mean lo ad/max l oad)
Strategy 1
Strategy 2
Strategy 3
Figure 8: Comparison of the load distribution efficiency o
different strategies for system with 30 nodes and N=3 with
equal amount of metadata maintained at each node. The
values of the system size and number of replicas are based on
the typical configuration deployed for majority of our
As noted earlier, Dynamo is designed to tradeoff consistency for
availability. To understand the precise impact of different failures
on consistency, detailed data is required on multiple factors:
outage length, type of failure, component reliability, workload etc.
Presenting these numbers in detail is outside of the scope of this
paper. However, this section discusses a good summary metric:
the number of divergent versions seen by the application in a live
production environment.
Divergent versions of a data item arise in two scenarios. The first
is when the system is facing failure scenarios such as node
failures, data center failures, and network partitions. The second is
when the system is handling a large number of concurrent writers
to a single data item and multiple nodes end up coordinating the
updates concurrently. From both a usability and efficiency
perspective, it is preferred to keep the number of divergent
versions at any given time as low as possible. If the versions
cannot be syntactically reconciled based on vector clocks alone,
they have to be passed to the business logic for semantic
reconciliation. Semantic reconciliation introduces additional load
on services, so it is desirable to minimize the need for it.
In our next experiment, the number of versions returned to the
shopping cart service was profiled for a period of 24 hours.
During this period, 99.94% of requests saw exactly one version;
0.00057% of requests saw 2 versions; 0.00047% of requests saw 3
versions and 0.00009% of requests saw 4 versions. This shows
that divergent versions are created rarely.
Experience shows that the increase in the number of divergent
versions is contributed not by failures but due to the increase in
number of concurrent writers. The increase in the number of
concurrent writes is usually triggered by busy robots (automated
client programs) and rarely by humans. This issue is not discussed
in detail due to the sensitive nature of the story.
6.4 Client-driven or Server-driven
As mentioned in Section 5, Dynamo has a request coordination
component that uses a state machine to handle incoming requests.
Client requests are uniformly assigned to nodes in the ring by a
load balancer. Any Dynamo node can act as a coordinator for a
read request. Write requests on the other hand will be coordinated
by a node in the key’s current preference list. This restriction is
due to the fact that these preferred nodes have the added
responsibility of creating a new version stamp that causally
subsumes the version that has been updated by the write request.
Note that if Dynamo’s versioning scheme is based on physical
timestamps, any node can coordinate a write request.
An alternative approach to request coordination is to move the
state machine to the client nodes. In this scheme client
applications use a library to perform request coordination locally.
A client periodically picks a random Dynamo node and
downloads its current view of Dynamo membership state. Using
this information the client can determine which set of nodes form
the preference list for any given key. Read requests can be
coordinated at the client node thereby avoiding the extra network
hop that is incurred if the request were assigned to a random
Dynamo node by the load balancer. Writes will either be
forwarded to a node in the key’s preference list or can be
coordinated locally if Dynamo is using timestamps based
An important advantage of the client-driven coordination
approach is that a load balancer is no longer required to uniformly
distribute client load. Fair load distribution is implicitly
guaranteed by the near uniform assignment of keys to the storage
nodes. Obviously, the efficiency of this scheme is dependent on
how fresh the membership information is at the client. Currently
clients poll a random Dynamo node every 10 seconds for
membership updates. A pull based approach was chosen over a
push based one as the former scales better with large number of
clients and requires very little state to be maintained at servers
regarding clients. However, in the worst case the client can be
exposed to stale membership for duration of 10 seconds. In case,
if the client detects its membership table is stale (for instance,
when some members are unreachable), it will immediately refresh
its membership information.
Table 2 shows the latency improvements at the 99.9th percentile
and averages that were observed for a period of 24 hours using
client-driven coordination compared to the server-driven
approach. As seen in the table, the client-driven coordination
approach reduces the latencies by at least 30 milliseconds for
99.9th percentile latencies and decreases the average by 3 to 4
milliseconds. The latency improvement is because the client-
driven approach eliminates the overhead of the load balancer and
the extra network hop that may be incurred when a request is
assigned to a random node. As seen in the table, average latencies
tend to be significantly lower than latencies at the 99.9th
percentile. This is because Dynamo’s storage engine caches and
write buffer have good hit ratios. Moreover, since the load
balancers and network introduce additional variability to the
response time, the gain in response time is higher for the 99.9th
percentile than the average.
6.5 Balancing background vs. foreground
Each node performs different kinds of background tasks for
replica synchronization and data handoff (either due to hinting or
adding/removing nodes) in addition to its normal foreground
put/get operations. In early production settings, these background
tasks triggered the problem of resource contention and affected
the performance of the regular put and get operations. Hence, it
became necessary to ensure that background tasks ran only when
the regular critical operations are not affected significantly. To
this end, the background tasks were integrated with an admission
control mechanism. Each of the background tasks uses this
controller to reserve runtime slices of the resource (e.g. database),
shared across all background tasks. A feedback mechanism based
on the monitored performance of the foreground tasks is
employed to change the number of slices that are available to the
background tasks.
The admission controller constantly monitors the behavior of
resource accesses while executing a "foreground" put/get
operation. Monitored aspects include latencies for disk operations,
failed database accesses due to lock-contention and transaction
timeouts, and request queue wait times. This information is used
to check whether the percentiles of latencies (or failures) in a
given trailing time window are close to a desired threshold. For
example, the background controller checks to see how close the
99th percentile database read latency (over the last 60 seconds) is
to a preset threshold (say 50ms). The controller uses such
comparisons to assess the resource availability for the foreground
operations. Subsequently, it decides on how many time slices will
be available to background tasks, thereby using the feedback loop
to limit the intrusiveness of the background activities. Note that a
similar problem of managing background tasks has been studied
in [4].
6.6 Discussion
This section summarizes some of the experiences gained during
the process of implementation and maintenance of Dynamo.
Many Amazon internal services have used Dynamo for the past
two years and it has provided significant levels of availability to
its applications. In particular, applications have received
successful responses (without timing out) for 99.9995% of its
requests and no data loss event has occurred to date.
Moreover, the primary advantage of Dynamo is that it provides
the necessary knobs using the three parameters of (N,R,W) to tune
their instance based on their needs.. Unlike popular commercial
data stores, Dynamo exposes data consistency and reconciliation
logic issues to the developers. At the outset, one may expect the
application logic to become more complex. However, historically,
Amazon’s platform is built for high availability and many
applications are designed to handle different failure modes and
inconsistencies that may arise. Hence, porting such applications to
use Dynamo was a relatively simple task. For new applications
that want to use Dynamo, some analysis is required during the
initial stages of the development to pick the right conflict
resolution mechanisms that meet the business case appropriately.
Finally, Dynamo adopts a full membership model where each
node is aware of the data hosted by its peers. To do this, each
node actively gossips the full routing table with other nodes in the
system. This model works well for a system that contains couple
of hundreds of nodes. However, scaling such a design to run with
tens of thousands of nodes is not trivial because the overhead in
maintaining the routing table increases with the system size. This
limitation might be overcome by introducing hierarchical
extensions to Dynamo. Also, note that this problem is actively
addressed by O(1) DHT systems(e.g., [14]).
This paper described Dynamo, a highly available and scalable
data store, used for storing state of a number of core services of’s e-commerce platform. Dynamo has provided the
desired levels of availability and performance and has been
successful in handling server failures, data center failures and
network partitions. Dynamo is incrementally scalable and allows
service owners to scale up and down based on their current
Table 2: Performance of client-driven and server-driven
coordination approaches.
driven 68.9 68.5 3.9 4.02
driven 30.4 30.4 1.55 1.9
request load. Dynamo allows service owners to customize their
storage system to meet their desired performance, durability and
consistency SLAs by allowing them to tune the parameters N, R,
and W.
The production use of Dynamo for the past year demonstrates that
decentralized techniques can be combined to provide a single
highly-available system. Its success in one of the most
challenging application environments shows that an eventual-
consistent storage system can be a building block for highly-
available applications.
The authors would like to thank Pat Helland for his contribution
to the initial design of Dynamo. We would also like to thank
Marvin Theimer and Robert van Renesse for their comments.
Finally, we would like to thank our shepherd, Jeff Mogul, for his
detailed comments and inputs while preparing the camera ready
version that vastly improved the quality of the paper.
[1] Adya, A., Bolosky, W. J., Castro, M., Cermak, G., Chaiken,
R., Douceur, J. R., Howell, J., Lorch, J. R., Theimer, M., and
Wattenhofer, R. P. 2002. Farsite: federated, available, and
reliable storage for an incompletely trusted environment.
SIGOPS Oper. Syst. Rev. 36, SI (Dec. 2002), 1-14.
[2] Bernstein, P.A., and Goodman, N. An algorithm for
concurrency control and recovery in replicated distributed
databases. ACM Trans. on Database Systems, 9(4):596-615,
December 1984
[3] Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach,
D. A., Burrows, M., Chandra, T., Fikes, A., and Gruber, R.
E. 2006. Bigtable: a distributed storage system for structured
data. In Proceedings of the 7th Conference on USENIX
Symposium on Operating Systems Design and
Implementation - Volume 7 (Seattle, WA, November 06 - 08,
2006). USENIX Association, Berkeley, CA, 15-15.
[4] Douceur, J. R. and Bolosky, W. J. 2000. Process-based
regulation of low-importance processes. SIGOPS Oper. Syst.
Rev. 34, 2 (Apr. 2000), 26-27.
[5] Fox, A., Gribble, S. D., Chawathe, Y., Brewer, E. A., and
Gauthier, P. 1997. Cluster-based scalable network services.
In Proceedings of the Sixteenth ACM Symposium on
Operating Systems Principles (Saint Malo, France, October
05 - 08, 1997). W. M. Waite, Ed. SOSP '97. ACM Press,
New York, NY, 78-91.
[6] Ghemawat, S., Gobioff, H., and Leung, S. 2003. The Google
file system. In Proceedings of the Nineteenth ACM
Symposium on Operating Systems Principles (Bolton
Landing, NY, USA, October 19 - 22, 2003). SOSP '03. ACM
Press, New York, NY, 29-43.
[7] Gray, J., Helland, P., O'Neil, P., and Shasha, D. 1996. The
dangers of replication and a solution. In Proceedings of the
1996 ACM SIGMOD international Conference on
Management of Data (Montreal, Quebec, Canada, June 04 -
06, 1996). J. Widom, Ed. SIGMOD '96. ACM Press, New
York, NY, 173-182.
[8] Gupta, I., Chandra, T. D., and Goldszmidt, G. S. 2001. On
scalable and efficient distributed failure detectors. In
Proceedings of the Twentieth Annual ACM Symposium on
Principles of Distributed Computing (Newport, Rhode
Island, United States). PODC '01. ACM Press, New York,
NY, 170-179.
[9] Kubiatowicz, J., Bindel, D., Chen, Y., Czerwinski, S., Eaton,
P., Geels, D., Gummadi, R., Rhea, S., Weatherspoon, H.,
Wells, C., and Zhao, B. 2000. OceanStore: an architecture
for global-scale persistent storage. SIGARCH Comput.
Archit. News 28, 5 (Dec. 2000), 190-201.
[10] Karger, D., Lehman, E., Leighton, T., Panigrahy, R., Levine,
M., and Lewin, D. 1997. Consistent hashing and random
trees: distributed caching protocols for relieving hot spots on
the World Wide Web. In Proceedings of the Twenty-Ninth
Annual ACM Symposium on theory of Computing (El Paso,
Texas, United States, May 04 - 06, 1997). STOC '97. ACM
Press, New York, NY, 654-663.
[11] Lindsay, B.G., et. al., “Notes on Distributed Databases”,
Research Report RJ2571(33471), IBM Research, July 1979
[12] Lamport, L. Time, clocks, and the ordering of events in a
distributed system. ACM Communications, 21(7), pp. 558-
565, 1978.
[13] Merkle, R. A digital signature based on a conventional
encryption function. Proceedings of CRYPTO, pages 369–
378. Springer-Verlag, 1988.
[14] Ramasubramanian, V., and Sirer, E. G. Beehive: O(1)lookup
performance for power-law query distributions in peer-to-
peer overlays. In Proceedings of the 1st Conference on
Symposium on Networked Systems Design and
Implementation, San Francisco, CA, March 29 - 31, 2004.
[15] Reiher, P., Heidemann, J., Ratner, D., Skinner, G., and
Popek, G. 1994. Resolving file conflicts in the Ficus file
system. In Proceedings of the USENIX Summer 1994
Technical Conference on USENIX Summer 1994 Technical
Conference - Volume 1 (Boston, Massachusetts, June 06 - 10,
1994). USENIX Association, Berkeley, CA, 12-12..
[16] Rowstron, A., and Druschel, P. Pastry: Scalable,
decentralized object location and routing for large-scale peer-
to-peer systems. Proceedings of Middleware, pages 329-350,
November, 2001.
[17] Rowstron, A., and Druschel, P. Storage management and
caching in PAST, a large-scale, persistent peer-to-peer
storage utility. Proceedings of Symposium on Operating
Systems Principles, October 2001.
[18] Saito, Y., Frølund, S., Veitch, A., Merchant, A., and Spence,
S. 2004. FAB: building distributed enterprise disk arrays
from commodity components. SIGOPS Oper. Syst. Rev. 38, 5
(Dec. 2004), 48-58.
[19] Satyanarayanan, M., Kistler, J.J., Siegel, E.H. Coda: A
Resilient Distributed File System. IEEE Workshop on
Workstation Operating Systems, Nov. 1987.
[20] Stoica, I., Morris, R., Karger, D., Kaashoek, M. F., and
Balakrishnan, H. 2001. Chord: A scalable peer-to-peer
lookup service for internet applications. In Proceedings of
the 2001 Conference on Applications, Technologies,
Architectures, and Protocols For Computer Communications
(San Diego, California, United States). SIGCOMM '01.
ACM Press, New York, NY, 149-160.
[21] Terry, D. B., Theimer, M. M., Petersen, K., Demers, A. J.,
Spreitzer, M. J., and Hauser, C. H. 1995. Managing update
conflicts in Bayou, a weakly connected replicated storage
system. In Proceedings of the Fifteenth ACM Symposium on
Operating Systems Principles (Copper Mountain, Colorado,
United States, December 03 - 06, 1995). M. B. Jones, Ed.
SOSP '95. ACM Press, New York, NY, 172-182.
[22] Thomas, R. H. A majority consensus approach to
concurrency control for multiple copy databases. ACM
Transactions on Database Systems 4 (2): 180-209, 1979.
[23] Weatherspoon, H., Eaton, P., Chun, B., and Kubiatowicz, J.
2007. Antiquity: exploiting a secure log for wide-area
distributed storage. SIGOPS Oper. Syst. Rev. 41, 3 (Jun.
2007), 371-384.
[24] Welsh, M., Culler, D., and Brewer, E. 2001. SEDA: an
architecture for well-conditioned, scalable internet services.
In Proceedings of the Eighteenth ACM Symposium on
Operating Systems Principles (Banff, Alberta, Canada,
October 21 - 24, 2001). SOSP '01. ACM Press, New York,
NY, 230-243.
AWS Glue is Amazon's serverless data integration cloud service that makes it simple and cost effective to extract, clean, enrich, load, and organize data. Originally launched in August 2017, AWS Glue began as an extract-transform-load (ETL) service designed to relieve developers and data engineers of the undifferentiated heavy lifting needed to load databases, data warehouses, and build data lakes on Amazon S3. Since then, it has evolved to serve a larger audience including ETL specialists and data scientists, and includes a broader suite of data integration capabilities. Today, hundreds of thousands of customers use AWS Glue every month. In this paper, we describe the use cases and challenges cloud customers face in preparing data for analytics and the tenets we chose to drive Glue's design. We chose early on to focus on ease-of-use, scale, and extensibility. At its core, Glue offers serverless Apache Spark and Python engines backed by a purpose-built resource manager for fast startup and auto-scaling. In Spark, it offers a new data structure --- DynamicFrames --- for manipulating messy schema-free semi-structured data such as event logs, a variety of transformations and tooling to simplify data preparation, and a new shuffle plugin to offload to cloud storage. It also includes a Hivemetastore compatible Data Catalog with Glue crawlers to build and manage metadata, e.g. for data lakes on Amazon S3. Finally, Glue Studio is its visual interface for authoring Spark and Python-based ETL jobs. We describe the innovations that differentiate AWS Glue and drive its popularity and how it has evolved over the years.
A single-master database has limited update capacity because a single node handles all updates. A multi-master database potentially has higher update capacity because the load is spread across multiple nodes. However, the need to coordinate updates and ensure durability can generate high network traffic. Reducing network load is particularly important in a cloud environment where the network infrastructure is shared among thousands of tenants. In this paper, we present Taurus MM, a shared-storage multi-master database optimized for cloud environments. It implements two novel algorithms aimed at reducing network traffic plus a number of additional optimizations. The first algorithm is a new type of distributed clock that combines the small size of Lamport clocks with the effective support of distributed snapshots of vector clocks. The second algorithm is a new hybrid page and row locking protocol that significantly reduces the number of lock requests sent over the network. Experimental results on a cluster with up to eight masters demonstrate superior performance compared to Aurora multi-master and CockroachDB.
Full-text available
Most practical systems are negatively affected by uncertainties, such as unknown system parameters, nonlinearity, structural variation, unpredicted component failures, etc. The traditional certainty-equivalence-based adaptive control treats the estimated parameters as the truth values and drives the control law directly upon this estimation. However, this method does not consider the parameter estimation error, leading to the designed controller making the closed-loop system far from optimal. In contrast, the adaptive dual control method studied in this paper can drive systems not only to track the target trajectory cautiously, but also fully motivate the system to actively learn more information about the system dynamics, so that the control law can be closer to the optimal one. Although previous adaptive dual controllers have handled the mentioned uncertainties, other types of uncertainties, such as random noise with non-Gaussian distributions, outliers, random disturbances, gained insufficient attention in the adaptive dual control. This paper conducts research on adaptive dual control methods with active learning for systems suffering from different types of uncertainties. Our research efforts are as follows: (1) Although the traditional reinforcement Q-learning based adaptive control method for linear systems with unknown parameters can derive the optimal control law by trial-and-error learning, however, their exploration signal needs manually setting, and the large exploration signal will bring large overshoots. We present an active learning exploration signal algorithm, resulting that the system state can cautiously track the target trajectory, and the system is stimulated toward enriched information, where the controller can actively explore unknown parameters. The actively adjusted exploration signal is calculated by optimizing the state tracking performance and the Q-function kernel matrix estimation performance simultaneously, so as to avoid overshoot and insufficient exploration in exploration period and optimally balance the conflict between exploration and exploitation in Q-learning. (2) Outliers, resulting from sensor malfunctions, network data transmission errors, device malfunctions, adversary attacks, etc., exist in most practical systems. The existing adaptive dual control methods for uncertain systems are aimed at systems polluted by white Gaussian noise, yet they are no longer valid for outliers. We use the real-time observation data to design two criteria for distance and direction, and detect outliers in the data stream before deriving the adaptive dual control law. On the other hand, we devise uncontrollable system excitation components in our method, and propose a dual adaptive control with active learning to reduce the uncertainty of system parameters. (3) Random noises in practical systems, such as economic systems, social decision-making systems, and ecosystems, are usually characterized by peak, thick-tail, and asymmetric, rather than Gaussian white noises. As such, Kalman Filter based adaptive dual control cannot cope with this kind of random noise. We proposed an adaptive quantile control method with a Bayesian quantile sum estimator, which can synthesize the unknown parameters estimations under different quantiles, so as to improve the parameter estimation accuracy quality and the control performance of the system. (4) The traditional neural network based adaptive control for completely unknown nonlinear systems uses historical input-output data for modeling, and then calculates the control law without considering the approximation error, which will degrade the control performance. We design an adaptive dual control method with active learning, which uses resource allocated neural network model to learn the system online, and leverage the information entropy to quantify the model learning performance. The active learning featured control law is derived by considering the optimal tracking control performance and the model learning performance, which can further improve the control performance. (5) The traditionally robust control for the system with unmeasurable disturbances is designed under the worst-case assumption, which is too conservative for desirable control performance. This thesis designs an anti-disturbance controller with active learning, where we propose a neural network model that integrates both additive and multiplicative disturbances, and then design a finite candidate set according to the prior knowledge of multiplicative and additive disturbances. We use the Bayesian theory to learn which candidate value is closest to the ground truth value, and drive the anti-disturbance control law by the learned disturbed neural network. In the candidate values learning, the learning error is fully considered, and the control law is derived based on the active learning property for unknown disturbance, which aims to reduce the system uncertainties and improving the control performance.
A gossip protocol is a distributed communication paradigm inspired by the gossip phenomenon that can be observed in social networks. Initially born to efficiently disseminate information, as its human counterpart, it has been later used to solve several other problems, such as failure detection, data aggregation, distributed topology construction, and resource allocation –to name just a few. Gossip protocols tend to be used in contexts where both the scale and the dynamism of the underlying communication network make the adoption of traditional communication protocols highly unpractical. In this article, we first introduce a collection of gossip protocols for information diffusion, and we provide an analytical model to study their performance with respect to speed and quality of the diffusion. We then introduce three representative examples of gossip‐based protocols that solve the most diverse problems, namely, membership management, aggregation, and overlay topology construction.
Full-text available
p>This paper finds that existing mutex locks suffer from throughput collapses or latency collapses, or both in the over-subscribed scenarios where applications create more threads than the core number, e.g., database applications like mysql uses per thread per connection. We make an in-depth performance analysis on existing locks and then identify three design rules for the lock primitive to achieve scalable performance in over-subscribed scenarios. First, to achieve ideal throughput, the lock design should keep adequate number of active competitors. Second, the active competitors should be arranged carefully to avoid preemption problems. Third, to meet latency requirements, the lock design should track the latency of each competitor and reorder the competitors according to the latency requirement. We propose a new lock design called HTLL that satisfies these rules and achieves both high throughput and low latency even when the cores are oversubscribed. HTLL only requires minimal human effort to annotate the latency requirement. Evaluation results show that HTLL achieves scalable performance in the over-subscribed scenarios. Specifically, in lmdb, HTLL can reduce latency by up to 97% with only an average 5% degradation in throughput, when compared to state-of-the-art locks such as Malthusian, CST, and Mutexee locks. In comparison to pthread mutex lock, HTLL increases the throughput by up to 22% and decreases the latency by up to 80%. Meanwhile, for the under-subscribed scenarios, it also has similar or even better performance than state-of-the-arts.</p
Full-text available
p>This paper finds that existing mutex locks suffer from throughput collapses or latency collapses, or both in the over-subscribed scenarios where applications create more threads than the core number, e.g., database applications like mysql uses per thread per connection. We make an in-depth performance analysis on existing locks and then identify three design rules for the lock primitive to achieve scalable performance in over-subscribed scenarios. First, to achieve ideal throughput, the lock design should keep adequate number of active competitors. Second, the active competitors should be arranged carefully to avoid preemption problems. Third, to meet latency requirements, the lock design should track the latency of each competitor and reorder the competitors according to the latency requirement. We propose a new lock design called HTLL that satisfies these rules and achieves both high throughput and low latency even when the cores are oversubscribed. HTLL only requires minimal human effort to annotate the latency requirement. Evaluation results show that HTLL achieves scalable performance in the over-subscribed scenarios. Specifically, in lmdb, HTLL can reduce latency by up to 97% with only an average 5% degradation in throughput, when compared to state-of-the-art locks such as Malthusian, CST, and Mutexee locks. In comparison to pthread mutex lock, HTLL increases the throughput by up to 22% and decreases the latency by up to 80%. Meanwhile, for the under-subscribed scenarios, it also has similar or even better performance than state-of-the-arts.</p
Full-text available
Farsite is a secure, scalable file system that logically functions as a centralized file server but is physically distributed among a set of untrusted computers. Farsite provides file availability and reliability through randomized replicated storage; it ensures the secrecy of file contents with cryptographic techniques; it maintains the integrity of file and directory data with a Byzantine-fault-tolerant protocol; it is designed to be scalable by using a distributed hint mechanism and delegation certificates for pathname translations; and it achieves good performance by locally caching file data, lazily propagating file updates, and varying the duration and granularity of content leases. We report on the design of Farsite and the lessons we have learned by implementing much of that design.
Conference Paper
Full-text available
We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/IP, and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes minimally as the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.
Conference Paper
Full-text available
A new digital signature based only on a conventional encryption function (such as DES) is described which is as secure as the underlying encryption function -- the security does not depend on the difficulty of factoring and the high computational costs of modular arithmetic are avoided. The signature system can sign an unlimited number of messages, and the signature size increases logarithmically as a function of the number of messages signed. Signature size in a ‘typical’ system might range from a few hundred bytes to a few kilobytes, and generation of a signature might require a few hundred to a few thousand computations of the underlying conventional encryption function.
Conference Paper
Full-text available
Antiquity is a wide-area distributed storage system designed to provide a simple storage service for applications like file systems and back-up. The design assumes that all servers eventually fail and attempts to maintain data despite those failures. Antiquity uses a secure log to maintain data integrity, replicates each log on multiple servers for durability, and uses dynamic Byzantine fault- tolerant quorum protocols to ensure consistency among replicas. We present Antiquity's design and an experimental evaluation with global and local testbeds. Antiquity has been running for over two months on 400+ PlanetLab servers storing nearly 20,000 logs totaling more than 84 GB of data. Despite constant server churn, all logs remain durable.
Conference Paper
Full-text available
This paper describes the design, implementation, and evaluation of a Federated Array of Bricks (FAB), a distributed disk array that provides the reliability of traditional enterprise arrays with lower cost and better scalability. FAB is built from a collection of , small storage appliances containing commodity disks, CPU, NVRAM, and network interface cards. FAB deploys a new majority-voting-based algorithm to replicate or erasure-code logical blocks across bricks and a reconfiguration algorithm to move data in the background when bricks are added or decommissioned. We argue that voting is practical and necessary for reliable, high-throughput storage systems such as FAB. We have implemented a FAB prototype on a 22-node Linux cluster. This prototype sustains 85MB/second of throughput for a database workload, and 270MB/second for a bulk-read workload. In addition, it can outperform traditional master-slave replication through performance decoupling and can handle brick failures and recoveries smoothly without disturbing client requests.
This paper describes the design, implementation, and evaluation of a Federated Array of Bricks (FAB), a distributed disk array that provides the reliability of traditional enterprise arrays with lower cost and better scalability. FAB is built from a collection of bricks , small storage appliances containing commodity disks, CPU, NVRAM, and network interface cards. FAB deploys a new majority-voting-based algorithm to replicate or erasure-code logical blocks across bricks and a reconfiguration algorithm to move data in the background when bricks are added or decommissioned. We argue that voting is practical and necessary for reliable, high-throughput storage systems such as FAB. We have implemented a FAB prototype on a 22-node Linux cluster. This prototype sustains 85MB/second of throughput for a database workload, and 270MB/second for a bulk-read workload. In addition, it can outperform traditional master-slave replication through performance decoupling and can handle brick failures and recoveries smoothly without disturbing client requests.
Antiquity is a wide-area distributed storage system designed to provide a simple storage service for applications like file systems and back-up. The design assumes that all servers eventually fail and attempts to maintain data despite those failures. Antiquity uses a secure log to maintain data integrity, replicates each log on multiple servers for durability, and uses dynamic Byzantine fault-tolerant quorum protocols to ensure consistency among replicas. We present Antiquity's design and an experimental evaluation with global and local testbeds. Antiquity has been running for over two months on 400+ PlanetLab servers storing nearly 20,000 logs totaling more than 84 GB of data. Despite constant server churn, all logs remain durable.
Conference Paper
This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for wide-area peer-to-peer applications. Pastry performs application-level routing and object location in a potentially very large overlay network of nodes connected via the Internet. It can be used to support a variety of peer-to-peer applications, including global data storage, data sharing, group communication and naming. Each node in the Pastry network has a unique identifier (nodeId). When presented with a message and a key, a Pastry node efficiently routes the message to the node with a nodeId that is numerically closest to the key, among all currently live Pastry nodes. Each Pastry node keeps track of its immediate neighbors in the nodeId space, and notifies applications of new node arrivals, node failures and recoveries. Pastry takes into account network locality; it seeks to minimize the distance messages travel, according to a to scalar proximity metric like the number of IP routing hops. Pastry is completely decentralized, scalable, and self-organizing; it automatically adapts to the arrival, departure and failure of nodes. Experimental results obtained with a prototype implementation on an emulated network of up to 100,000 nodes confirm Pastry’s scalability and efficiency, its ability to self-organize and adapt to node failures, and its good network locality properties.