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a leading commercial database solution for their internal architecture in
conjunction with some Hadoop. Eventually, the tsunami of users led the
company to start thinking in terms of unlimited scalability and focus on
availability and distribution. The nature of the data and its producers and
consumers did not mandate consistency but needed unlimited availabil-
ity and scalable performance. The team at Facebook built an architecture
that combines the data model approaches of BigTable and the infrastruc-
ture approaches of Dynamo with scalability and performance capabilities,
named Cassandra. Cassandra is often referred to as hybrid architecture
since it combines the column-oriented data model from BigTable with
Hadoop MapReduce jobs, and it implements the patterns from Dynamo
like eventually consistent, gossip protocols, a master-master way of serv-
ing both read and write requests. Cassandra supports a full replication
model based on NoSQL architectures.
The Cassandra team had a few design goals to meet, considering the archi-
tecture at the time of first development and deployment was primarily being
done at Facebook. The goals included
• High availability
• Eventual consistency
• Incremental scalability
• Optimistic replication
• Tunable trade-offs between consistency, durability, and latency
• Low cost of ownership
• Minimal administration
Amazon Dynamo is the distributed key-value store that supports the man-
agement of information of several of the business services offered by Amazon
Inc. The main goal of Dynamo is to provide an incrementally scalable and
highly available storage system. This goal helps in achieving reliability at a
massive scale, where thousands of servers and network components build
an infrastructure serving 10 million requests per day. Dynamo provides a
simplified interface based on get/put semantics, where objects are stored
and retrieved with a unique identifier (key). The main goal of achieving
an extremely reliable infrastructure has imposed some constraints on the
properties of these systems. For example, ACID properties on data have been
sacrificed in favor of a more reliable and efficient infrastructure. This creates
what it is called an eventually consistent model (i.e., in the long term, all the
users will see the same data).
21.4.3 Document-Oriented Databases
Document-oriented databases or document databases can be defined as a
schema-less and flexible model of storing data as documents, rather than
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