什么是通过http和json同步2个sqlite表的最好的方法？ [英] What is the best way to sync 2 sqlite tables over http and json?

问题描述

我有一个相当简单的同步问题。我有一个表，大约10列，我想保持同步在一个sqlite文件在3个不同的客户端：Iphone客户端，浏览器客户端和一个Ruby on Rails客户端。所以我需要一个简单的sycing解决方案，将工作所有3，即，我可以很容易地实现它在Javascript，Objective C和Ruby和它的工作与JSON通过HTTP。我看过其他同步解决方案的各种组件，如git中的一些，一些来自谷歌齿轮社区的教程，以及一个rails插件，名为acts_as_replica。我天真的方法是简单地在数据库中创建最后一个同步的时间戳，然后在创建所有删除的更改日志。 （我不允许更新表中的条目）。然后，我可以检索自上次时间戳以来的所有新条目，然后与删除组合，并在3个解决方案之间通过http发送一个changelog作为json。

I have a fairly simple sync problem. I have a table with about 10 columns that I want to keep in sync between a sqlite file on 3 different clients: an Iphone client, a browser client, and a Ruby on Rails client. So I need a simple sycing solution that will work for all 3, i.e. I can easily implement it in Javascript, Objective C, and Ruby and it works with JSON over HTTP. I have looked at various components of other syncing solutions like the one in git, some of the tutorials that have come out of the Google gears community, and a rails plugin called acts_as_replica. My naive approach would be to simply create a last synced timestamp in the database and then create a changelog of all deletes as they are made. (I don't allow updates to entries in the table). I can then retrieve all the new entries since the last timestamp, combine then with the deletes, and send a changelog as json over http between the 3 solutions.

我应该考虑使用SHA1哈希或每个条目的UUID还是最后一个同步的时间戳足够吗？如何确保没有重复的条目？有没有更简单的算法我可以跟着？

Should I consider the use of SHA1 hash or a UUID of each entry or is a last synced timestamp sufficient? How do I make sure there are no duplicate entries? Is there a simpler algorithm I could follow?

推荐答案

我假设改变可能在结束。我不知道插入和更新的性质，但这是我的想法;

I am assuming changes are likely to be at the end. I don't know the character of insert and updates but here is my idea;

• 我会SHA1（或MD5，事件在这种情况下）当前月份和月份的天数。与这些指纹比较是一种快速的方式来看待差异是什么。


• 如果以前的月份有差异，
  
  
  
  
  • 如果一个月的量过大，我们可以拆分月份，只需生成每日指纹，而不是整个月的比较。
  
  
  • 否则，我们可以按照处理每日变更的方式处理每月更改。
  
  

  时间类别（日，月）可以根据数据量进行调整。

  The time categories (day, month) can be adjusted according to the data volume.

  当然这是一个朴素而简单的算法。如果我正在处理敏感/关键数据，我会查找事务算法。

  Of course this is a naive and simple algorithm. If I was processing sensitive/critical data I would look for a transactional algorithm.

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