Tuesday, September 18, 2018

AWS - Developer Tools

This post summarizes the AWS services that are used to help you write code and reliably build, test and deploy it faster that things would be manually. The overall concept of doing all this automatically is usually summarized as Continuous Integration Continuous Deployment. Here is a simple post that nicely explains these concepts.

If you don't want to read any more the tl;dr is this:

* Write code using AWS Cloud 9 
* Debug code using AWS XRay
* Store code using AWS Code Commit
* Build and test code using AWS Code Build
* Deploy code using AWS Code Deploy
* Watch task progression at runtime from a single interface using AWS Code 
* Use an integrated dashboard for all your tools including issue tracking using
   AWS Code Star.

If you're not familiar with Git, I'd strongly recommend reading a little about it before proceeding and playing with all these shiny new AWS tools. A great source is this chapter from the ProGit book. Once that's done, come back here. It's fine to read through this post as well, even without Git knowledge - it's just easier with that background knowledge.

Cloud 9 IDE

Once you have an idea in mind and want to write software to actualize it, you need a place to write it. A simple text editor works just fine, but as your programs get more complex an IDE is really helpful. A couple you might be familiar with are Eclipse and IntelliJ. However, since this post is about AWS, I must mention the Cloud9 IDE. It is a browser based IDE that gives you the familiar environment. I haven't played with it too much, but it's good to know there is a web-based option now.


This looks like a code-profiler to me. I did not use it so do not have much to say about it. But I'd think the way to use it, will be to write your code and use this to figure out which calls are really slow and see if you can optimize your code further. All the rest I did try out and can confirm they are all very cool tools. So read on.

Code Commit

Once you finish writing all your code, you need a place to store the code. This is where all the VCS come in. Git is what everyone use these days. The AWS equivalent of Git is CodeCommit. It's so similar that you do not need to learn any new commands. Once you've set your repository up, all the old Git commands work perfectly well. You can add files, commit them and push them to your Code Commit repository.

All you need to do is install Git on your machine, create a key pair and configure your IAM user to use this to authenticate to Code Commit. Clicking the "Connect" button inside the interface gives you instructions per platform if you get stuck.

The coolest thing here is that you can create triggers that'll run as soon as you push code to your repository. Maybe you want to build, test and deploy your code to your test environment as soon as every single commit is pushed. You can do that here by setting up a Lambda function that will be called as soon a commit is made. Which nicely flows into Code Build..

Code Build

Once you have a workflow going where you can write code in an IDE and push commits to a CodeCommit repository, the next step is to make sure that your code builds properly. This is where CodeBuild comes in. All you do is point CodeBuild to the Code Commit repository where you stored your code and tell it where you want to dump any output artifacts of the program (usually S3).

It supports branches too, so you can tell it which branch to pull code from in Code Commit. You select your runtime environment, which you need to build code in (Java/Python/whatever), configure a bunch of other options and then build your project. The result is whatever you get after you hit Code - Build in whatever IDE you use.

The big advantages here are that you do have to spend very little time configuring your software development environment. Also, like I touched upon a bit in the Code Commit section, you could have that Lambda function you wrote as a CodeCommit trigger automatically run Code Build against your code each time a commit is made.

Code Deploy

Once the code is compiled, tests are run and your entire project is built, the last step is usually to deploy it to a web server so your users can then access it. That's where Code Deploy comes in. You can configure it so it uses the build output (with a deployable project) and puts it onto every web server you want to have it on.

You have options of using a load balancer as well, if you want traffic to be evenly distributed. Once deployment is complete, the output should appear on all the servers in that group.

Again, remember you can further extend your Lambda function to build and deploy now as soon as a commit hits Code Commit. Pretty cool :)

Code Pipeline

Code Pipeline isn't something new but it certainly makes life much easier. It helps though if you understand the the 3 previous services I talked briefly about earlier - since the screens in Code Pipeline deal with these 3 services and ask you for input. So I'd recommend understanding those Code Commit, Code Build and Code Deploy really well before using Code Pipeline.

Pipeline basically is a wizard for the other 3 services. So it'll prompt you to tell it where your code is (Code Commit) , what to build it with (Code Build) and what to deploy it with (Coce Deploy). If you already have roles and resources set up successfully when you played with the other 3 services - this should feel very intuitive when you do it. A couple of great tutorials are here and here. Also, a nice writeup on how someone automated the whole process is here.

The coolest thing about Pipeline is that you can see everything, stage by stage and where each stage is once you create it. For example: Once your code is pushed to Code Commit (as usual) and you have the Pipeline dashboard open, you can actually see each stage succeeding or failing, after which you can troubleshoot accordingly.


Managers should love this one. I used it just a bit but it has this fantastic looking dashboard that gives you a unified view of every single service that you are using. So in short, it has links to C9, CC, CB, CD and CP. So if you didn't cheat and did everything step by step :) you should see all your commits, builds and pipelines by clicking on the buttons on the fancy dashboard that is CodeStar.

The additional feature here is integration with Jira and Github where you can see all your issues as well.

So in short CodeStar is a one stop shop if you've bought into the AWS development environment and want to be tied into it for years to come, while parting with your money bit by bit :)

Friday, September 14, 2018

Sample Architecture - AWS Example

A quick post this time on how you can use the AWS CLI or SDK to create an entire network, without using the GUI wizards (which are great, but sometimes irritatingly slow :)).

Relevant code to do everything in this post and a bit more is all uploaded here.

First up, almost certainly you want a VPC, because some services are public and some are private. A VPC will help you separate these. So you use the CreateVPC call to create one.

Make sure you enable private DNS so your external clients can reach your private hosts.

Then you create public and private subnets, so you can put your public and private hosts into each of those.

Your public subnet needs an Internet gateway to talk to the Internet, so you create one and attach a gateway to it.

Once you have your VPC, subnets and Internet gateway ready you need to setup routes between them. The wizard would do this automatically but we have to do it manually. So you first create a route table for both subnets and add routes to each route table. Note here, that you don't need your private subnet hosts to talk to the Internet. If you do, for some reason you will need to create a NAT gateway in the public subnet and modify your routing table in the private subnet to send traffic to it.

Now everything is sort of setup. So you then think of access control everywhere. For starters you create a security group allowing only inbound SSH and HTTPS access for an EC2 instance in the public subnet and only MySQL access for an RDS instance in the private subnet.

Create a key pair (I reused an old one is this was just a test) so you can use it for your new EC2. Identify an AMI to run on your EC2 instance. I used the console for this but you can apparently use the CLI or the SDK to find this out if you want to.

Once that's done you launch an EC2 instance in the public subnet, with the SSH-HTTPS security group and your key pair. Make sure you assign it a public IP otherwise you won't be able to reach it. Login to the instance with your keypair and confirm access works.

Now you start thinking of things you want to keep in your private subnet. The 3 things I was working with were RDS so my EC2 could talk to it, Secrets Manager to store my RDS credentials and a Lambda function that is needed to rotate the credentials in SecretsManager. All of these should be in the private subnet.

A cool thing here is that you can create a private endpoint for SecretsManager so that all traffic to it is always over an AWS network and doesn't go to the Internet at all.

RDS only needs inbound access from EC2 and Lambda on port 3306. I'm not sure what SecretsManager needs but I gave it inbound 443 only (You should test this more). Lambda doesn't need any inbound access. Setup security groups similar to how you did it before.

Create a secret in Secrets Manager. Use a random name if you're testing, you can't reuse old names for a while, even if you have deleted the secret. This secret should contain all the information you need to connect to the RDS database and used when you actually create the database.

Create a DB subnet group, retrieve the secrets you stored earlier from secrets manager and the security group that you created earlier (*3306 inbound access*) and then create the actual RDS itself.

Once the database is created, the only task remaining is to create the Lambda function that will rotate the credentials for you in Secrets Manager.

Saturday, September 8, 2018

Confused Deputy

The confused deputy problem is one of the best named issues. Not for any deep philosophical reason, but just because it is truly confusing :). To me anyway, but then, most things are confusing to me, until I spend way-above-normal amounts of time re-reading and re-writing it in my own words. The link above (AWS) is an excellent resource, which I learnt most of it from, so go there first - and if you find that confusing, come over here and I'll try and explain it in my own words. As always, there's nothing wonderfully new here - just my attempt to make sure I remember, have fun writing and hopefully help anyone else along the way.

Let us just keep it simple here. The 3 people in question are Alice, Bob and Eve. Alice has software called MyBackup hosted on the cloud that lets you back up your images that are stored in the service called MyImages. Each time you use Alice's software you have to pay her 100$. Sure that's ridiculous, but stick with me. For some reason Bob thinks this is a great idea and pings Alice to use this service.

Alice creates an account and gives him a unique string called BobAliceBackup1987. She says that all Bob needs to do is to login when he wants to backup, paste the string into a text box on the website and click "Create Backup". This will automatically (details are not important here) let Alice into Bob's account and copy them all to her secret storage box that is very hard to hack and send Bob an Email when it is all done. Don't think about how lame this system is at this point :).

Eve now hears that Bob is using this service and likes it a lot. She subscribes to the service too and gets her key EveAliceBackup1991. Everything is good and everyone is happy.

One day Bob and Eve have a fight and stop talking to each other. Eve feels that Bob is wrong and wants to teach him a lesson. Frustrated, she logs into MyBackup to look at her backups. (WTF who even does this??). While typing in her "secret string" she suddenly wonders if she can make Bob spend his Britney Spears concert money on Backups instead. Can she predict Bob's key? Will Alice find out? Only one way to find out...

She guesses Bob's key (what a shock :/) and sends that key to Alice. Alice hasn't spent much time developing any kind of authorization models, so all she sees is a string come in and think - well there's another 100$ for me :). She just assumes (pay attention here) that whoever sends the string is the owner of the string and actually wants to back their images up. And she backs Bob's images up, 20 times in a row without thinking that something's wrong. Bob gets back at night (no there are no Instant Mobile alerts here for payment debits) and finds out he has backed his stupid car_bumper_dented images up 20 times. Alice is no help, she has proof he sent a string...and sure enough when Bob logs in and checks backup history he sees 20 requests too. Meanwhile Eve feels vindicated. Eventually she might get caught, eventually Bob might get his money back and eventually Alice will learn to write better software but that's beside the point. And yes, it's a made up example but one that hopefully helps you understand the point of the attack better.

In a nutshell, confused deputy occurs when a service with multiple users makes a decision based on user input that is predictable without asking for further authorization. In AWS world, the predictable input is a Role ARN that a service can assume in your account to do something in it. While it looks really big, it is not considered secret and if someone guesses it, they can make a service do things in your account - without your permission. Does that make sense? I hope so. But if not...

... go and read that excellent AWS blog again and see if it makes more sense.