aws fargate_我如何在AWS Fargate上部署＃100DaysOfCloud Twitter Bot

aws fargate

After passing my last certification, I asked myself how much time I spent studying cloud computing.

通过上一份认证后，我问自己自己花了多少时间研究云计算。

More than 100 days!

超过100天！

It also made me realize two things:

这也让我意识到两件事：

There was no #100DaysOfCloud challenge没有＃100DaysOfCloud挑战
We have now enough content to create this challenge现在，我们有足够的内容来应对这一挑战

So I immediately contacted Alex Kallaway, the creator of #100DaysOfCode, to ask him if it was possible to create #100DaysOfCloud based on his challenge.

因此，我立即联系＃100DaysOfCode的创建者Alex Kallaway，询问他是否有可能根据他的挑战创建＃100DaysOfCloud。

And a few days later, the #100DaysOfCloud challenge was official:

几天后，＃100DaysOfCloud挑战赛正式开始：

But something was missing.

但是缺少了一些东西。

If you already used the #100DaysOfCode hashtag, you know that your tweets are going to be retweeted at least three times by Twitter bots.

如果您已经使用＃100DaysOfCode主题标签，则知道Twitter机器人将至少转发3次您的推文。

Because there were no bots for the new #100DaysOfCloud challenge, I decided to fix that problem.

因为没有新的＃100DaysOfCloud挑战机器人，所以我决定解决此问题。

In this post, we're gonna see how I deployed my Twitter bot and why I choose to deploy it on AWS Fargate.

在本文中，我们将看到我如何部署Twitter机器人以及为什么选择在AWS Fargate上部署它。

Let's start!

开始吧！

先决条件 (Prerequisites)

If you want to follow along and run the commands below, be sure to:

如果要继续并运行以下命令，请确保：

Have an AWS account with access key and secret key

拥有一个具有访问密钥和秘密密钥的AWS账户
Have Twitter API Authentication Credentials

拥有Twitter API身份验证凭据
Install Docker on your machine

在您的机器上安装Docker
Clone the repository from https://github.com/johanrin/100-days-of-cloud-bot

从https://github.com/johanrin/100-days-of-cloud-bot克隆存储库

为什么选择AWS Fargate？ (Why AWS Fargate?)

If you check my code, you’ll see that:

如果检查我的代码，您会看到：

The code is written in Python and use tweepy package该代码是用Python编写的，并使用tweepy包
The bot is always running (using a while loop)

机器人始终处于运行状态(使用while循环)
There is a Dockerfile to build my image

有一个Dockerfile构建我的映像

I will not explain all the code because that's beyond the scope of this post. But I did mention all the sources I used in the GitHub repository.

我不会解释所有代码，因为这超出了本文的范围。但是我确实提到了我在GitHub存储库中使用的所有资源。

My idea was to deploy the Docker image in a container in the cloud with the following constraints:

我的想法是将Docker映像部署在云中的容器中，并具有以下限制：

I don't want to spend much money我不想花很多钱
I don't want to manage and operate servers我不想管理和操作服务器

Because I have credits on AWS, the first constraint was easy — go with AWS.

因为我对AWS有信誉，所以第一个约束很容易-选择AWS。

As for constraint number 2, I knew from the AWS Certified Developer - Associate course, that we could deploy serverless containers with AWS Fargate.

关于约束2，我从AWS Certified Developer-Associate课程知道，我们可以使用AWS Fargate部署无服务器容器。

Depending on your region, the pricing for AWS services can vary. It's often just a matter of a few cents, but I planned to run my bot for at least one year. So, every cent mattered.

根据您所在的地区，AWS服务的价格可能有所不同。通常只需几美分，但我计划将我的机器人运行至少一年。因此，每一分都很重要。

To see AWS Fargate pricing and minimize your cost, you can check the AWS documentation. I found that the cheapest region close to me was Ireland, so I decided to deploy my bot to AWS Fargate there.

要查看AWS Fargate定价并最大程度地降低成本，您可以查看AWS文档。我发现距离我最近的最便宜的地区是爱尔兰，因此我决定将我的机器人部署到那里的AWS Fargate。

我如何部署我的机器人？ (How did I deploy my bot?)

Now that I've explained why I used AWS Fargate, let's see how I deployed my bot.

现在，我已经解释了为什么使用AWS Fargate的原因，让我们看看如何部署我的机器人。

There are two big steps to deploy a Docker image on AWS Fargate:

在AWS Fargate上部署Docker映像有两个重要步骤：

Push the Docker image to Amazon Elastic Container Registry (ECR)将Docker映像推送到Amazon Elastic Container Registry(ECR)
Deploy the Docker image on Fargate在Fargate上部署Docker映像

Let's explain each step in detail.

让我们详细解释每个步骤。

将Docker映像推送到Amazon Elastic Container Registry(ECR) (Push the Docker image to Amazon Elastic Container Registry (ECR))

In the root directory, build your image from the Dockerfile.

在根目录中，从Dockerfile构建映像。

docker build . -t 100-days-of-cloud-bot

Authenticate your Docker to Amazon ECR.对您的Docker进行Amazon ECR身份验证。

aws ecr get-login-password --region region | docker login --username AWS --password-stdin aws_account_id.dkr.ecr.region.amazonaws.com

Tag your image with the Amazon ECR repository.用Amazon ECR存储库标记图像。

docker tag 100-days-of-cloud-bot aws_account_id.dkr.ecr.region.amazonaws.com/100-days-of-cloud-bot

Push your image to Amazon ECR.将您的图像推送到Amazon ECR。

docker push aws_account_id.dkr.ecr.region.amazonaws.com/100-days-of-cloud-bot

在Amazon Fargate上部署Docker映像 (Deploy the Docker image on Amazon Fargate)

Open the Amazon ECS console first run wizard.

打开Amazon ECS控制台首次运行向导。

Click Configure in the custom container, complete with the following settings, and then select Update.

单击自定义容器中的“ 配置 ”，完成以下设置，然后选择“ 更新” 。

Property	Value
Container name	container-100-days-of-cloud-bot
Image	aws_account_id.dkr.ecr.region.amazonaws.com/100-days-of-cloud-bot
Memory Limits (MiB)	Soft Soft limit \| 512
Port mappings	Container port: 80Protocol: tcp
CPU units	256
Environment variables (Twitter API Authentication Credentials)	CONSUMER_KEY \| Value \| consumer_key CONSUMER_SECRET \| Value \| consumer_secret ACCESS_TOKEN \| Value \| access_token ACCESS_TOKEN_SECRET \| Value \| access_token_secret

属性	值
容器名称	容器-100天的云机器人
图片	aws_account_id.dkr.ecr.region.amazonaws.com/100天云漫游器
内存限制(MiB)	软限制 512
端口映射	货柜码头：80 通讯协议：tcp
CPU单元	256
环境变量 (Twitter API身份验证凭据)	CONSUMER_KEY \| 价值\| Consumer_key CONSUMER_SECRET \| 价值\| Consumer_secret ACCESS_TOKEN \| 价值\| access_token ACCESS_TOKEN_SECRET \| 价值\| access_token_secret

Click Edit in the Task definition section, complete with the following settings, and then select Save.

单击“ 任务定义”部分中的“ 编辑 ”，完成以下设置，然后选择“ 保存” 。

Property	Value
Task definition name	task-definition-100-days-of-cloud-bot
Task memory	0.5GB (512)
Task CPU	0.25 vCPU (256)

属性	值
任务定义名称	任务定义100天云机器人
任务记忆	0.5GB(512)
任务CPU	0.25 vCPU(256)

Click Next.

单击下一步 。
Click Edit in the Define your service section, complete with the following settings, and then select Save.

点击定义您的服务部分中的编辑，完成以下设置，然后选择保存。

Property	Value
Service name	service-100-days-of-cloud-bot
Number of desired tasks	1
Load balancer type	None

属性	值
服务名称	服务100天的云机器人
所需任务数	1个
负载均衡器类型	没有

We don't need a load balancer here because of the Twitter API rate limit. Even if we scale-out our containers, Twitter's API will send us a 420 error message because the bot is being rate limited for making too many requests.

由于Twitter API速率限制，我们在这里不需要负载均衡器。即使我们向外扩展容器，Twitter的API也会向我们发送420错误消息，因为该漫游器由于发出过多请求而受到速率限制。

Click Next.

单击下一步 。
Edit your Cluster name with cluster-100-days-of-cloud-bot.

使用cluster-100-days-cloud-bot编辑您的集群名称 。

Click Next.

单击下一步 。
Review your configuration and click Create.

查看您的配置，然后单击创建。

That's it, the container is deployed on AWS Fargate!

就是这样，容器已部署在AWS Fargate上！

结论 (Conclusion)

You deployed your Twitter bot on AWS Fargate with only four steps.