Google Cloud Bigtable与Google Cloud Datastore

本文介绍了Google Cloud Bigtable与Google Cloud Datastore的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

Google Cloud Bigtable 有什么区别？和Google Cloud Datastore / App Engine数据存储，以及主要的实际优势/劣势是什么？ AFAIK Cloud Datastore建立在Bigtable之上。

解决方案

基于Datastore的经验并阅读Bigtable docs ，主要区别在于：

• Bigtable似乎设计用于HBase兼容性，而Datastore更适合于Python / Java / Go Web应用程序开发人员（原为App Engine）。 Bigtable'有点比数据存储更多的IaaS，因为它不是仅存在，但需要将群集配置为配置
• Bigtable仅支持一个索引 - 行键（数据存储中的实体键）
  • 这意味着查询位于Key上，与数据存储的索引属性不同
  • Bigtable仅在单行上支持原子性 - 没有事务
  • 在Bigtable中，突变和删除似乎不是原子性的，而数据存储提供了最终和强大的一致性，具体取决于读取/查询方法。
  • 计费模式非常不同：
    • 数据存储为读/写操作，存储和带宽收费
    • Bigtable 针对'节点'，存储空间和带宽收取费用
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  What is the difference between Google Cloud Bigtable and Google Cloud Datastore / App Engine datastore, and what are the main practical advantages/disadvantages? AFAIK Cloud Datastore is build on top of Bigtable.

  解决方案

  Based on experience with Datastore and reading the Bigtable docs, the main differences are:

  • Bigtable seems to be designed for HBase compatibility, whereas Datastore is more geared towards Python/Java/Go web app developers (originally App Engine)
  • Bigtable is 'a bit more IaaS' than Datastore in that it's not 'just there' but requires a cluster to be configured.
  • Bigtable supports only one index - the 'row key' (the entity key in Datastore)
    • This means queries are on the Key, unlike Datastore's indexed properties
  • Bigtable supports atomicity only on a single row - there are no transactions
  • Mutations and deletions appear not to be atomic in Bigtable, whereas Datastore provides eventual and strong consistency, depending on the read/query method
  • The billing model is very different:
    • Datastore charges for read/write operations, storage and bandwidth
    • Bigtable charges for 'nodes', storage and bandwidth