Is there comparation which NoSQL is designed for what purpose?
NoSQL是否有针对什么目的进行比较?
I'm especially interested in querying large amount of data practically read-only.
我特别感兴趣的是查询实际上只读的大量数据。
Edit: I mean comparation between NoSQL databases not about comparation with SQL databases.
编辑:我的意思是NoSQL数据库之间的比较,而不是与SQL数据库的比较。
2 个解决方案
#1
4
Soft NoSQL Systems: [not the original intention of "NoSQL" but mostly worth a look for great non relational solutions] Object Databases
软NoSQL系统:[不是“NoSQL”的初衷,但主要值得寻找伟大的非关系解决方案]对象数据库
db4o: API: Java, C#, .Net Langs, Protocol: language, Query Method: QBE (by Example), Soda, Native Queries, LINQ (.NET), Replication: db4o2db4o & dRS to relationals, Written in: Java, Cuncurrency: ACID serialized, Misc: embedded lib, Links: DZone Refcard #53 », Book »,
db4o:API:Java,C#,。Net Langs,协议:语言,查询方法:QBE(通过示例),Soda,本机查询,LINQ(.NET),复制:db4o2db4o和dRS到关系,编写于:Java,Cuncurrency :ACID序列化,杂项:嵌入式lib,链接:DZone Refcard#53»,Book»,
Versant: Languages/Protocol: Java, C#, C++, Python. Schema: language class model (easy changable). Modes: always consistent and eventually consistent Replication: synchronous fault tolerant and peer to peer asynchronous. Concurrency: optimistic and object based locks. Scaling: can add physical nodes on fly for scale out/in and migrate objects between nodes without impact to application code. Misc: MapReduce via parallel SQL like query across logical database groupings.
Versant:语言/协议:Java,C#,C ++,Python。架构:语言类模型(易于改变)。模式:始终保持一致且最终一致复制:同步容错和对等异步。并发:乐观和基于对象的锁。扩展:可以快速添加物理节点以进行扩展/扩展,并在节点之间迁移对象,而不会影响应用程序代码。其他:MapReduce通过并行SQL,如跨逻辑数据库分组查询。
Objectivity: Languages: Java, C#, C++, Python, Smalltalk, SQL access through ODBC. Schema: native language class model, direct support for references, interoperable across all language bindings. 64 bit unique object ID (OID) supports multi exa-byte. Platforms: 32 and 64 bit Windows, Linux, Mac OSX, *Unix. Modes: always consistent (ACID). Concurrency: locks at cluster of objects (container) level. Scaling: unique distributed architecture, dynamic addition/removal of clients & servers, cloud environment ready. Replication: synchronous with quorum fault tolerant across peer to peer partitions.
客观性:语言:Java,C#,C ++,Python,Smalltalk,通过ODBC访问SQL。架构:本机语言类模型,直接支持引用,可跨所有语言绑定进行互操作。 64位唯一对象ID(OID)支持多个exa-byte。平台:32位和64位Windows,Linux,Mac OSX,* Unix。模式:始终一致(ACID)。并发:锁定对象集群(容器)级别。扩展:独特的分布式架构,动态添加/删除客户端和服务器,云环境就绪。复制:与对等分区之间的仲裁容错同步。
[Gemstone, Progress ]
[宝石,进步]
Perst: API: Java,Java ME,C#,Mono. Query method: OO via Perst collections, QBE, Native Queries, LINQ, native full-text search, JSQL Replication: Async+sync (master-slave) Written in: Java, C#. Caching: Object cache (LRU, weak, strong), page pool, in-memory database Concurrency: Pessimistic+optimistic (MVCC) + async or sync (ACID) Index types: Many tree models + Time Series. Misc.: Embedded lib., encryption, automatic recovery, native full text search, on-line or off-line backup.
Perst:API:Java,Java ME,C#,Mono。查询方法:OO通过Perst集合,QBE,本机查询,LINQ,本机全文搜索,JSQL复制:异步+同步(主从)写入:Java,C#。缓存:对象缓存(LRU,弱,强),页面池,内存数据库并发:悲观+乐观(MVCC)+异步或同步(ACID)索引类型:许多树模型+时间序列。其他:嵌入式lib。,加密,自动恢复,本机全文搜索,在线或离线备份。
ZODB: API: Python, Protocol: Internal, ZEO, Query Method: Direct object access, zope.catalog, gocept.objectquery, Replication: ZEO, ZEORAID, RelStorage Written in: Python, C Concurrency: MVCC, License: Zope Public License (OSI approved) Misc:Used in production since 1998
ZODB:API:Python,协议:内部,ZEO,查询方法:直接对象访问,zope.catalog,gocept.objectquery,复制:ZEO,ZEORAID,RelStorage编写于:Python,C并发:MVCC,许可证:Zope公共许可证( OSI批准)杂项:自1998年起用于生产
NEO: API: Python - ZODB "Storage" interface, Protocol: native, Query Method: transactional key-value, Replication: native, Written in: Python, Concurrency: MVCC at ZODB level, License: GPL "v2 or later", Misc: Load balancing, fault tolerant, hot-extensible.
NEO:API:Python - ZODB“存储”接口,协议:本机,查询方法:事务键值,复制:本机,写入:Python,并发:ZODB级别的MVCC,许可证:GPL“v2或更高版本”,其他:负载平衡,容错,热扩展。
StupidDB », KiokuDB » (Perl solution),
StupidDB»,KiokuDB»(Perl解决方案),
I found this nice list of NoSQL's at:
我找到了这个很好的NoSQL列表:
If you notice a little past midway down the page they have a nice list of noSQL's and appear to go into detail about each.
如果您注意到页面中间稍微过了一点,那么他们会有一个很好的noSQL列表,并且似乎详细介绍了每个。
#2
0
I think that you greatly underestimate the power of RDBMS. Why use noSQL?
我认为你大大低估了RDBMS的强大功能。为什么要使用noSQL?
I've worked on ~10tb databases.. and that was 8 years ago, on ancient hardware compared to today.
我已经研究了〜10tb的数据库..那是8年前,与现在相比,在古代硬件上。
Do you really have more than 10tb of data? Is it properly normalized?
你真的拥有超过10tb的数据吗?它是否正确归一化?
I can scan against a billion rows in SQL and gives instant answers to almost anything I need.
我可以扫描SQL中的十亿行,并为我需要的几乎任何东西提供即时答案。
#1
4
Soft NoSQL Systems: [not the original intention of "NoSQL" but mostly worth a look for great non relational solutions] Object Databases
软NoSQL系统:[不是“NoSQL”的初衷,但主要值得寻找伟大的非关系解决方案]对象数据库
db4o: API: Java, C#, .Net Langs, Protocol: language, Query Method: QBE (by Example), Soda, Native Queries, LINQ (.NET), Replication: db4o2db4o & dRS to relationals, Written in: Java, Cuncurrency: ACID serialized, Misc: embedded lib, Links: DZone Refcard #53 », Book »,
db4o:API:Java,C#,。Net Langs,协议:语言,查询方法:QBE(通过示例),Soda,本机查询,LINQ(.NET),复制:db4o2db4o和dRS到关系,编写于:Java,Cuncurrency :ACID序列化,杂项:嵌入式lib,链接:DZone Refcard#53»,Book»,
Versant: Languages/Protocol: Java, C#, C++, Python. Schema: language class model (easy changable). Modes: always consistent and eventually consistent Replication: synchronous fault tolerant and peer to peer asynchronous. Concurrency: optimistic and object based locks. Scaling: can add physical nodes on fly for scale out/in and migrate objects between nodes without impact to application code. Misc: MapReduce via parallel SQL like query across logical database groupings.
Versant:语言/协议:Java,C#,C ++,Python。架构:语言类模型(易于改变)。模式:始终保持一致且最终一致复制:同步容错和对等异步。并发:乐观和基于对象的锁。扩展:可以快速添加物理节点以进行扩展/扩展,并在节点之间迁移对象,而不会影响应用程序代码。其他:MapReduce通过并行SQL,如跨逻辑数据库分组查询。
Objectivity: Languages: Java, C#, C++, Python, Smalltalk, SQL access through ODBC. Schema: native language class model, direct support for references, interoperable across all language bindings. 64 bit unique object ID (OID) supports multi exa-byte. Platforms: 32 and 64 bit Windows, Linux, Mac OSX, *Unix. Modes: always consistent (ACID). Concurrency: locks at cluster of objects (container) level. Scaling: unique distributed architecture, dynamic addition/removal of clients & servers, cloud environment ready. Replication: synchronous with quorum fault tolerant across peer to peer partitions.
客观性:语言:Java,C#,C ++,Python,Smalltalk,通过ODBC访问SQL。架构:本机语言类模型,直接支持引用,可跨所有语言绑定进行互操作。 64位唯一对象ID(OID)支持多个exa-byte。平台:32位和64位Windows,Linux,Mac OSX,* Unix。模式:始终一致(ACID)。并发:锁定对象集群(容器)级别。扩展:独特的分布式架构,动态添加/删除客户端和服务器,云环境就绪。复制:与对等分区之间的仲裁容错同步。
[Gemstone, Progress ]
[宝石,进步]
Perst: API: Java,Java ME,C#,Mono. Query method: OO via Perst collections, QBE, Native Queries, LINQ, native full-text search, JSQL Replication: Async+sync (master-slave) Written in: Java, C#. Caching: Object cache (LRU, weak, strong), page pool, in-memory database Concurrency: Pessimistic+optimistic (MVCC) + async or sync (ACID) Index types: Many tree models + Time Series. Misc.: Embedded lib., encryption, automatic recovery, native full text search, on-line or off-line backup.
Perst:API:Java,Java ME,C#,Mono。查询方法:OO通过Perst集合,QBE,本机查询,LINQ,本机全文搜索,JSQL复制:异步+同步(主从)写入:Java,C#。缓存:对象缓存(LRU,弱,强),页面池,内存数据库并发:悲观+乐观(MVCC)+异步或同步(ACID)索引类型:许多树模型+时间序列。其他:嵌入式lib。,加密,自动恢复,本机全文搜索,在线或离线备份。
ZODB: API: Python, Protocol: Internal, ZEO, Query Method: Direct object access, zope.catalog, gocept.objectquery, Replication: ZEO, ZEORAID, RelStorage Written in: Python, C Concurrency: MVCC, License: Zope Public License (OSI approved) Misc:Used in production since 1998
ZODB:API:Python,协议:内部,ZEO,查询方法:直接对象访问,zope.catalog,gocept.objectquery,复制:ZEO,ZEORAID,RelStorage编写于:Python,C并发:MVCC,许可证:Zope公共许可证( OSI批准)杂项:自1998年起用于生产
NEO: API: Python - ZODB "Storage" interface, Protocol: native, Query Method: transactional key-value, Replication: native, Written in: Python, Concurrency: MVCC at ZODB level, License: GPL "v2 or later", Misc: Load balancing, fault tolerant, hot-extensible.
NEO:API:Python - ZODB“存储”接口,协议:本机,查询方法:事务键值,复制:本机,写入:Python,并发:ZODB级别的MVCC,许可证:GPL“v2或更高版本”,其他:负载平衡,容错,热扩展。
StupidDB », KiokuDB » (Perl solution),
StupidDB»,KiokuDB»(Perl解决方案),
I found this nice list of NoSQL's at:
我找到了这个很好的NoSQL列表:
If you notice a little past midway down the page they have a nice list of noSQL's and appear to go into detail about each.
如果您注意到页面中间稍微过了一点,那么他们会有一个很好的noSQL列表,并且似乎详细介绍了每个。
#2
0
I think that you greatly underestimate the power of RDBMS. Why use noSQL?
我认为你大大低估了RDBMS的强大功能。为什么要使用noSQL?
I've worked on ~10tb databases.. and that was 8 years ago, on ancient hardware compared to today.
我已经研究了〜10tb的数据库..那是8年前,与现在相比,在古代硬件上。
Do you really have more than 10tb of data? Is it properly normalized?
你真的拥有超过10tb的数据吗?它是否正确归一化?
I can scan against a billion rows in SQL and gives instant answers to almost anything I need.
我可以扫描SQL中的十亿行,并为我需要的几乎任何东西提供即时答案。