使用Amazon Redshift进行本地开发和暂存 [英] Local development and staging with Amazon Redshift

问题描述

我喜欢通过生产，暂存和本地开发来设置工具和服务.我想使用Amazon Redshift，对于列式商店数据库，每月180美元起价似乎很合理，但实际上我是否必须将其视为180美元x环境/月?有什么办法可以为Redshift提供免费的暂存和本地环境?

I like to set up tools and services with production, staging, and local development. I'd like to use Amazon Redshift, and starting at $180 a month seems pretty reasonable for a columnar store database, but do I actually have to think about it as $180 x # of environments / month? Is there any way to have a free staging and local environment for Redshift?

能够针对本地实例进行开发而不是依赖于网络也很不错.我认为Redshift无法实现.

It's also nice to be able to do development against a local instance rather than relying on the network. I assume that's not possible with Redshift.

与Redshift合作时，您如何做才能使本地开发更容易，更快和更便宜?

What do you do to make local development easier, faster and cheaper when working with Redshift?

推荐答案

Amazon Redshift是专门为在AWS基础架构上运行而创建的.不可下载. (有趣的是，Amazon DynamoDB确实有可下载的版本用于开发目的.)

Amazon Redshift was specifically created to run on AWS infrastructure. It is not available as a download. (Interestingly, Amazon DynamoDB does have a downloadable version for development purposes.)

最便宜的选择可能是关闭您的Dev&每天晚上和周末测试实例.在删除群集之前先做一个快照，然后在第二天早上根据快照创建一个群集.可以通过AWS命令行界面(CLI)自动执行此操作，从而可以轻松地通过cron或Scheduled Tasks进行计划.

The cheapest option might be to shutdown your Dev & Test instances each night and on weekends. Take a snapshot before deleting the cluster, then create a cluster the next morning based on the snapshot. This can be automated via the AWS Command-Line Interface (CLI), making it easy to schedule with cron or Scheduled Tasks.

您还可以具有测试数据的快照，并每天早晨恢复该快照，这意味着测试数据库中不会填满测试用例.

You could also have a snapshot of Test data and restore that snapshot each morning, which means the test database doesn't fill-up with test cases.

另一种节省成本的方法可能是减少非生产系统的节点数量.查询运行速度会变慢，存储总量将减少，但可能更具成本效益.甚至甚至可以使用密集存储" 2TB节点而不是几个密集计算" SSD实例-它们将在更少的节点上提供更多存储.

Another cost saving might be to reduce the number of nodes for the non-production systems. Queries will run slower and the total amount of storage will be reduced, but it could be more cost-effective. Or even use a "Dense Storage" 2TB node instead of several "Dense Compute" SSD instances -- they will provide more storage on less nodes.

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