Google Cloud Bigtable 和 Google Cloud Datastore/App Engine 数据存储有什么区别，主要的实际优势/劣势是什么？ AFAIK Cloud Datastore 建立在 Bigtable 之上。
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标签： google-app-engine google-cloud-platform google-cloud-datastore google-cloud-bigtable
根据使用 Datastore 和阅读 Bigtable docs 的经验，主要区别在于：
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Cloud Bigtable 专为需要大量数据和复杂后端工作负载的大型公司和企业而设计。
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Bigtable 和 Datastore 截然不同。是的，数据存储是建立在 Bigtable 之上的，但这并不像它。这有点像说汽车是建立在车轮之上的，所以汽车与车轮并没有太大区别。
Bigtable 和 Datastore 提供了非常不同的数据模型和非常不同的数据更改语义。
主要区别在于，Datastore 在称为实体组的数据子集上提供类似于 SQL 数据库的 ACID 事务（尽管查询语言 GQL 比 SQL 限制性更强）。 Bigtable 是严格意义上的 NoSQL，并且具有更弱的保证。
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我将尝试总结以上所有答案以及 Coursea Google Cloud Platform Big Data and Machine Learning Fundamentals 中给出的答案
+---------------------+------------------------------------------------------------------+------------------------------------------+--+
| Category | BigTable | Datastore | |
+---------------------+------------------------------------------------------------------+------------------------------------------+--+
| Technology | Based on HBase(uses HBase API) | Uses BigTable itself | |
| ---------------- | | | |
| Access Mataphor | Key/Value (column-families) like Hbase | Persistent hashmap | |
| ---------------- | | | |
| Read | Scan Rows | Filter Objects on property | |
| ---------------- | | | |
| Write | Put Row | Put Object | |
| ---------------- | | | |
| Update Granularity | can't update row ( you should write a new row, can't update one) | can update attribute | |
| ---------------- | | | |
| Capacity | Petabytes | Terbytes | |
| ---------------- | | | |
| Index | Index key only (you should properly design the key) | You can index any property of the object | |
| Usage and use cases | High throughput, scalable flatten data | Structured data for Google App Engine | |
+---------------------+------------------------------------------------------------------+------------------------------------------+--+
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需要考虑的相对较小的一点是，截至 2016 年 11 月，bigtable python 客户端library 仍处于 Alpha 阶段，这意味着未来的更改可能无法向后兼容。此外，bigtable python 库与 App Engine 的标准环境不兼容。你必须使用灵活的。
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Cloud Datastore is a highly-scalable NoSQL database for your applications.
Like Cloud Bigtable, there is no need for you to provision database instances.
Cloud Datastore uses a distributed architecture to automatically manage
scaling. Your queries scale with the size of your result set, not the size of your
data set.
Cloud Datastore runs in Google data centers, which use redundancy to
minimize impact from points of failure. Your application can still use Cloud
Datastore when the service receives a planned upgrade.
Choose Bigtable if the data is:
Big
● Large quantities (>1 TB) of semi-structured or structured data
Fast
● Data is high throughput or rapidly changing
NoSQL
● Transactions, strong relational semantics not required
And especially if it is:
Time series
● Data is time-series or has natural semantic ordering
Big data
● You run asynchronous batch or real-time processing on the data
Machine learning
● You run machine learning algorithms on the data
Bigtable is designed to handle massive workloads at consistent low latency
and high throughput, so it's a great choice for both operational and analytical
applications, including IoT, user analytics, and financial data analysis.
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Datastore 更适合应用程序，适用于广泛的服务，尤其是微服务。
Datastore 的底层技术是 Big Table，所以你可以想象 Big Table 更强大。
Datastore 每天提供 20K 次免费操作，您可以期望以零成本托管具有可靠数据库的服务器。
您还可以查看这个 Datastore ORM 库，它有很多很棒的功能 https://www.npmjs.com/package/ts-datastore-orm
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