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Couchbase数据库设计

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11#
 楼主| 发表于 2014-4-19 22:12 | 只看该作者
    Note

    All nodes within Couchbase Server are created equally. There is no hierarchy or topology, and no single node is a ‘master’ of the rest of the cluster. Each node is responsible only for the data it stores and the requests made to it by clients.
Cluster

    A cluster is a collection of one or more instances of Couchbase Server that are configured as a logical cluster. All nodes within the cluster are identical and provide the same functionality and information. The entire cluster shares data across the individual nodes, with each node being responsible for only a portion of the entire data set.

    Clusters operate in a completely horizontal fashion. To increase the size of a cluster, you add another node. There are no parent/child relationships or hierarchical structures involved. This means that Couchbase Server scales linearly, both in terms of increasing the storage capacity and the performance and scalability.

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日期:2011-05-07 01:45:08
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 楼主| 发表于 2014-4-19 22:12 | 只看该作者
Cluster Manager

Every node within a Couchbase Server Cluster includes the Cluster Manager component. The Cluster Manager is responsible for the following within a cluster:

    Cluster management

    Node administration

    Node monitoring

    REST API for management

    Statistics gathering and aggregation

    Runtime logging

    Multitenancy

    Security for administrative and client access

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日期:2011-05-07 01:45:08
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 楼主| 发表于 2014-4-19 22:13 | 只看该作者
Buckets

Couchbase Server provides data management services using named buckets. These are isolated virtual containers for data. A bucket is a logical grouping of physical resources within a cluster of Couchbase Servers. They can be used by multiple client applications across a cluster. Buckets provide a secure mechanism for organizing, managing, and analyzing data storage resources.

Couchbase Server provides the two core types of buckets that can be created and managed, summarized in Table 1-1. Couchbase Server collects and reports on runtime statistics by bucket type.

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日期:2011-05-07 01:45:08
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 楼主| 发表于 2014-4-19 22:13 | 只看该作者
Buckets can be used to isolate individual applications to provide multitenancy, or to isolate data types to enhance performance and visibility. Couchbase Server allows you to configure different ports to access different buckets. Password authentication is also available on individual buckets.

Note

Smart clients discover changes in the cluster structure automatically by using the Couchbase Management REST API. This ensures that the client application continues to communicate to the appropriate node for the data being accessed.

Couchbase Server allows you to use and mix different types of buckets (Couchbase and Memcached) as appropriate in your environment. Quotas for RAM and disk usage are configurable per bucket so that resource usage can be managed across the cluster. Quotas can be modified on a running cluster so that administrators can reallocate resources as usage patterns or priorities change over time.

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日期:2014-02-19 10:05:27现任管理团队成员
日期:2011-05-07 01:45:08
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 楼主| 发表于 2014-4-19 22:13 | 只看该作者
vBuckets

A vBucket is defined as the owner of a subset of the key space of a Couchbase cluster. These vBuckets are used to allow information to be distributed effectively across the cluster. The vBucket system is used both for distributing data, and for supporting replicas (copies of bucket data) on more than one node.

Clients access the information stored in a bucket by communicating directly with the node response for the corresponding vBucket. This direct access enables clients to communicate with the node storing the data, rather than using a proxy or redistribution architecture. The result is abstracting the physical topology from the logical partitioning of data. This architecture is what gives Couchbase Server elasticity.

This architecture differs from the method used by memcached, which uses client-side key hashes to determine the server from a defined list. This requires active management of the list of servers, and specific hashing algorithms such as Ketama to cope with changes to the topology. The structure is also more flexible and able to cope with changes than the typical sharding arrangement used in an RDBMS environment.

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