NoSQL

این مبحث مربوط به مبحث پایگاه داده های غیر رابطه ای است که در حال حاضر شرکت گوگل و خیلی شرکت بزرگ دنیا بدلیل مشکلاتی که با پایگاه های رابطه ای دارند  رو به این نوع پایگاه ها می آورند البته این نوع پایگاه ها در مقایس کوچک به بدترین راندمان خود می رسند یعنی در مقیاس خیلی بزرگ عالی کار می کنند و
 در مقیاس کوچک خیلی ضعیف.این قسمتی از یک مقاله انگلیسی هست که شرکت اوراکل در مورد پایگاه داد های NOSQL ارائه داده است.

امیدوارم مورد رضایتتان واقع شود..

                                                                                          

NoSQL databases represent a recent evolution in enterprise application architecture

continuing the evolution of the past twenty years. In the 1990’s, vertically integrated

applications gave way to client-server architectures, and more recently, client-server

architectures gave way to three-tier web application architectures. In parallel, the demands of

web-scale data analysis added map-reduce processing into the mix and data architects started

eschewing transactional consistency in exchange for incremental scalability and large-scale

distribution. The NoSQL movement emerged out of this second ecosystem.

NoSQL is often characterized by what it’s not – depending on whom you ask, it’s either not

only a SQL-based relational database management system or it’s simply not a SQL-based

RDBMS. While those definitions explain what NoSQL is not, they do little to explain what

NoSQL is. Consider the fundamentals that have guided data management for the past forty

years. RDBMS systems and large-scale data management have been characterized by the

transactional ACID properties of Atomicity, Consistency, Isolation, and Durability. In contrast,

NoSQL is sometimes characterized by the BASE acronym:

Basically Available: Use replication to reduce the likelihood of data unavailability and use

sharding, or partitioning the data among many different storage servers, to make any

remaining failures partial. The result is a system that is always available, even if subsets of

the data become unavailable for short periods of time.

Soft state: While ACID systems assume that data consistency is a hard requirement,

NoSQL systems allow data to be inconsistent and relegate designing around such

inconsistencies to application developers.

Eventually consistent: Although applications must deal with instantaneous consistency,

NoSQL systems ensure that at some future point in time the data assumes a consistent

state. In contrast to ACID systems that enforce consistency at transaction commit, NoSQL

guarantees consistency only at some undefined future time.

NoSQL emerged as companies, such as Amazon, Google, LinkedIn and Twitter struggled to

deal with unprecedented data and operation volumes under tight latency constraints. Analyzing

high-volume, real time data, such as web-site click streams, provides significant business

advantage by harnessing unstructured and semi-structured data sources to create more

business value. Traditional relational databases were not up to the task, so enterprises built

upon a decade of research on distributed hash tables (DHTs) and either conventional

relational database systems or embedded key/value stores, such as Oracle’s Berkeley DB, to

develop highly available, distributed key-value stores

Although some of the early NoSQL solutions built their systems atop existing relational

database engines, they quickly realized that such systems were designed for SQL-based

access patterns and latency demands that are quite different from those of NoSQL systems,

so these same organizations began to develop brand new storage layers. In contrast, Oracle’s

Berkeley DB product line was the original key/value store; Oracle Berkeley DB Java Edition

has been in commercial use for over eight years. By using Oracle Berkeley DB Java Edition as

the underlying storage engine beneath a NoSQL system, Oracle brings enterprise robustness

.and stability to the NoSQL landscape.

Furthermore, until recently, integrating

NoSQL solutions with an enterprise

application architecture required manual

integration and custom development;

Oracle’s NoSQL Database provides all

the desirable features of NoSQL solutions

necessary for seamless integration into

an enterprise application architecture.

Figure 1 shows a canonical acquireorganize-

analyze data cycle,

demonstrating how Oracle’s NoSQL

Database fits into such an ecosystem.

Oracle-provided adapters allow the

Oracle NoSQL Database to integrate with

a Hadoop MapReduce framework or with

the Oracle Database in-database

MapReduce, Data Mining, R-based

analytics, or whatever business needs

demand.

The Oracle NoSQL Database, with its “No Single Point of Failure” architecture is the right

solution when data access is “simple” in nature and application demands exceed the volume or

latency capability of traditional data management solutions. For example, click-stream data

from high volume web sites, high-throughput event processing, and social networking

communications all represent application domains that produce extraordinary volumes of

simple keyed data. Monitoring online retail behavior, accessing customer profiles, pulling up

appropriate customer ads and storing and forwarding real-time communication are examples

of domains requiring the ultimate in low-latency access. Highly distributed applications such as

real-time sensor aggregation and scalable authentication also represent domains well-suited to

Oracle NoSQL Database.