Does anyone have a good suggestion as to what database I should use, to achieve replication across a variable number of targets? I have a mesh network of Raspberry Pi servers, each of which can contain a database. I want the contents of each database to be replicated across the network, but I can't guarantee what nodes are available at any point in time.
对于我应该使用什么数据库来实现跨不同目标数量的复制，有人有好的建议吗?我有一个网状的树莓Pi服务器，每个都可以包含一个数据库。我希望每个数据库的内容都可以通过网络进行复制，但是我不能保证在任何时间点上都有哪些节点可用。
Most nosql databases (CouchDB, Cassandra for example) appear to only support defined targets in the configuration.
大多数nosql数据库(例如CouchDB、Cassandra)似乎只支持配置中定义的目标。
So (assuming nosql is the best database option); is there a nosql database that can replicate to variable number of targets?
(假设nosql是最好的数据库选项);是否有一个nosql数据库可以复制到不同数量的目标?
5 个解决方案
#1
4
For this scenario I would recommend the Hadoop Distributed File System (HDFS).
对于这个场景，我建议使用Hadoop分布式文件系统(HDFS)。
Features that make HDFS attractive to your scenario:
使HDFS对您的场景有吸引力的特性:
- It is a distributed file system with variable replication factor (the default is 3 which is nearly impossible to lose data with).
- 它是一个具有可变复制因子的分布式文件系统(默认值是3，几乎不可能丢失数据)。
- Can scale up to thousands of different machines
- 能扩展到上千台不同的机器吗
- Does not depend on high availability of individual nodes -- automatically handles node failure and replicates any data from downed nodes
- 是否不依赖于单个节点的高可用性——自动处理节点故障并从已关闭的节点复制任何数据
As for the actual database... HBase, Mongo, or Cassandra are all good options here, pick whatever you are most comfortable with -- HDFS will take care of all of the replication for you.
至于实际的数据库……HBase、Mongo或Cassandra都是不错的选择，您可以选择您最喜欢的——HDFS将为您处理所有复制。
#2
3
According to this SO response:
根据这一回应:
https://*.com/a/8787999/2020565
https://*.com/a/8787999/2020565
And upon cheking their website, maybe you should check Elliptics: http://www.ioremap.net/projects/elliptics/
并且在他们的网站上，也许你应该检查椭圆:http://www.ioremap.net/projects/椭球/。
The network does not use dedicated servers to maintain the metadata information, it supports redundant objects storage. Small to medium sized write benchmarks can be found on eblob page.
网络不使用专用服务器来维护元数据信息，它支持冗余对象存储。可以在eblob页面上找到中小型写基准。
#3
3
In my experience Elasticsearch has great and easy-to-use cluster management, it supports out of the box nice features such as node autodiscovery, data replication, auto-rebalancing etc., have a look at docs. Usually it is used to replicate data from an other database to make it searchable but I don't see why it couldn't be used in this context as well.
根据我的经验，Elasticsearch有很好的、易于使用的集群管理，它支持开箱即用的优秀特性，如节点自动发现、数据复制、自动再平衡等。通常，它用于复制来自其他数据库的数据，使其可搜索，但我不明白为什么不能在此上下文中使用它。
Basically when you create a "table" (called "index" in ES) you get to decide that in how many "partitions" (called "shards") the data should be partitioned, and ad-hoc set that how many replicas of that table you want to have (this doesn't 100% match the correct terminology since an "index" can consist of multiple "types" but I think this is the best analogy).
基本上,当您创建一个“表”(称为“指数”在ES)你来决定,有多少“分区”(称为“碎片”)数据应该分区,和特别设置表你想要多少个副本(这并不100%匹配正确的术语,因为一个“索引”可以包含多个“类型”,但我认为这是最好的类比)。
An example project with three Pis is here.
这里有一个具有三个Pis的示例项目。
I have read a bit about Cassandra as well and I imagine it would have similar features, for example partitions and replicas are mentioned here.
我也读过一些关于Cassandra的文章，我想它应该有类似的特性，例如这里提到的分区和副本。
#4
2
I'd recommend taking a look at Hazelcast. They do pretty good in memory replication across a cluster that might change. You'd have to write a custom client to store the data into a local database of your choice if you want disk backed persistence, but Hazelcast can take care of replication across a cluster in memory and has a lot of flexibility.
我建议你去看看榛子酱。它们在跨集群的内存复制方面做得很好，这可能会改变。如果需要磁盘支持的持久性，则必须编写一个自定义客户机将数据存储到您选择的本地数据库中，但是Hazelcast可以在内存中处理跨集群的复制，并且具有很大的灵活性。
#5
0
-
You should consider Erlang OTP platform and Mnesia database
您应该考虑Erlang OTP平台和Mnesia数据库
-
If you prefer C language you can consider SQlite in memory database together with nanomsg framework
如果您喜欢C语言，可以考虑使用nanomsg框架在内存数据库中使用SQlite
#1
4
For this scenario I would recommend the Hadoop Distributed File System (HDFS).
对于这个场景，我建议使用Hadoop分布式文件系统(HDFS)。
Features that make HDFS attractive to your scenario:
使HDFS对您的场景有吸引力的特性:
- It is a distributed file system with variable replication factor (the default is 3 which is nearly impossible to lose data with).
- 它是一个具有可变复制因子的分布式文件系统(默认值是3，几乎不可能丢失数据)。
- Can scale up to thousands of different machines
- 能扩展到上千台不同的机器吗
- Does not depend on high availability of individual nodes -- automatically handles node failure and replicates any data from downed nodes
- 是否不依赖于单个节点的高可用性——自动处理节点故障并从已关闭的节点复制任何数据
As for the actual database... HBase, Mongo, or Cassandra are all good options here, pick whatever you are most comfortable with -- HDFS will take care of all of the replication for you.
至于实际的数据库……HBase、Mongo或Cassandra都是不错的选择，您可以选择您最喜欢的——HDFS将为您处理所有复制。
#2
3
According to this SO response:
根据这一回应:
https://*.com/a/8787999/2020565
https://*.com/a/8787999/2020565
And upon cheking their website, maybe you should check Elliptics: http://www.ioremap.net/projects/elliptics/
并且在他们的网站上，也许你应该检查椭圆:http://www.ioremap.net/projects/椭球/。
The network does not use dedicated servers to maintain the metadata information, it supports redundant objects storage. Small to medium sized write benchmarks can be found on eblob page.
网络不使用专用服务器来维护元数据信息，它支持冗余对象存储。可以在eblob页面上找到中小型写基准。
#3
3
In my experience Elasticsearch has great and easy-to-use cluster management, it supports out of the box nice features such as node autodiscovery, data replication, auto-rebalancing etc., have a look at docs. Usually it is used to replicate data from an other database to make it searchable but I don't see why it couldn't be used in this context as well.
根据我的经验，Elasticsearch有很好的、易于使用的集群管理，它支持开箱即用的优秀特性，如节点自动发现、数据复制、自动再平衡等。通常，它用于复制来自其他数据库的数据，使其可搜索，但我不明白为什么不能在此上下文中使用它。
Basically when you create a "table" (called "index" in ES) you get to decide that in how many "partitions" (called "shards") the data should be partitioned, and ad-hoc set that how many replicas of that table you want to have (this doesn't 100% match the correct terminology since an "index" can consist of multiple "types" but I think this is the best analogy).
基本上,当您创建一个“表”(称为“指数”在ES)你来决定,有多少“分区”(称为“碎片”)数据应该分区,和特别设置表你想要多少个副本(这并不100%匹配正确的术语,因为一个“索引”可以包含多个“类型”,但我认为这是最好的类比)。
An example project with three Pis is here.
这里有一个具有三个Pis的示例项目。
I have read a bit about Cassandra as well and I imagine it would have similar features, for example partitions and replicas are mentioned here.
我也读过一些关于Cassandra的文章，我想它应该有类似的特性，例如这里提到的分区和副本。
#4
2
I'd recommend taking a look at Hazelcast. They do pretty good in memory replication across a cluster that might change. You'd have to write a custom client to store the data into a local database of your choice if you want disk backed persistence, but Hazelcast can take care of replication across a cluster in memory and has a lot of flexibility.
我建议你去看看榛子酱。它们在跨集群的内存复制方面做得很好，这可能会改变。如果需要磁盘支持的持久性，则必须编写一个自定义客户机将数据存储到您选择的本地数据库中，但是Hazelcast可以在内存中处理跨集群的复制，并且具有很大的灵活性。
#5
0
-
You should consider Erlang OTP platform and Mnesia database
您应该考虑Erlang OTP平台和Mnesia数据库
-
If you prefer C language you can consider SQlite in memory database together with nanomsg framework
如果您喜欢C语言，可以考虑使用nanomsg框架在内存数据库中使用SQlite