Cloud

boot2docker

Introduction to boot2docker

Josh Reichardt 7 Comments

If you work on a Mac (or Windows) and use Docker then you probably have heard of boot2docker.  If you haven’t heard of it before, boot2docker is basically a super lightweight Linux VM that is designed to run Docker containers.  Unfortunately there is no support (yet) in Mac OS X or Windows kernels for running Docker natively so this lightweight VM must be used as an intermediary layer that allows the host Operating Systems to communicate with the Docker daemon running inside the VM.  This solution really is not that limiting once you get introduced to and become comfortable with boot2docker and how to work around some of the current limitations.

Because Docker itself is such a new piece of software, the ecosystem and surrounding environment is still expanding and growing rapidly.  As such, the tooling has not had a great deal of time to mature.  So with pretty much anything that’s new, especially in the software and Open Source world, there are definitely some nuances and some things to be aware of when working with boot2docker.

That being said, the boot2docker project bridges a gap and does a great job of bringing Docker to an otherwise incompatible platform as well as making it easy to use across platforms, which especially useful for furthering the adoption of Docker among Mac and Windows users.

When getting started with boot2docker, it is important to note that there are a few different things going on under the hood.

Components

The first component is VirtualBox.  If you are familiar with virtual machines, there’s pretty much nothing new here.  It is the underpinning of running the VM and is a common tool for creating and managing VM’s.  One important note here about VBox.  This is currently the key to make volume sharing work with boot2docker to allow a user to pass local directories and files in to containers using its shared folder implementation.  Unfortunately it has been pretty well documented that vboxsf (shared folders) have not great performance when compared to other solutions.  This is something that the boot2docker team is aware of and working on for future reference.  I have a workaround that I will outline below if anyone happens to hit some of these performance issues.

The next component is the VM.  This is a super light weight image based on Tiny Core Linux and the 3.16.4 Linux kernel with AUFS to leverage Docker.  Other than that there is pretty much nothing else to it.  The TCL image is about 27MB and boots up in about 5 seconds, making it very fast, which is nice to get going with quickly.  There are instructions on the boot2docker site for creating custom .iso’s if you are interested as well if you are’t interested in building your own customized TCL.

The final component is called boot2docker-cli, which is normally referred to as the management tool.  This tools does a lot of the magic to get your host talking to the VM with minimal interaction.  It is basically the glue or the duct tape that allows users to pass commands from a local shell in to the container and get Docker to do stuff.

Installation

It is pretty dead simple to get boot2docker set up and configured.  You can download everything in one shot from the links on their site http://boot2docker.io or you can install manually on OSX with brew and a few other tools.  I highly recommend the packaged installer, it is very straight forward and easy to follow and there is a good video depiction of the process on the boot2docker site.

If you choose to install everything with brew you can use the following commands as a reference.  Obviously it will be assumed that brew is already installed and set up on your OSX system.  The first step is to install boot2docker.

brew install boot2docker

You might need to install Virtualbox separately using this method, depending on whether or not you already have a good version of Virtualbox to use with boot2docker.

The following commands will assume you are starting from scratch and do not have VBox installed.

brew update
brew tap phinze/homebrew-cask
brew install brew-cask
brew cask install virtualbox

That is pretty much it for installation.

Usage

The boot2docker CLI is pretty straight forward to use.  There are a bunch of commands to help users interface with the boot2docker VM from the command line.  The most basic and simple usage to initialize and create a vanilla boot2docker VM can be done with the following command.

boot2docker init

This will pull down the correct image and get the environment set up.  Once the VM has been created (see the tricks sections for a bit of customization) you are ready to bring up the VM.

boot2docker start

This command will simply start up the boot2docker VM and run some behind the scenes  tasks to help make using the VM seamless.  Sometimes you will be asked to set ENV variables here, just go ahead and follow the instructions to add them.

There are a few other nice commands that help you interact with the boot2docker VM.  For example if you are having trouble communicating with the VM you can run the ip command to gather network information.

boot2docker ip

If the VM somehow gets shut off or you cannot access it you can check its status.

boot2docker status

Finally there is a nice help command that serves as a good guide for interacting with the VM in various ways.

boot2docker help

The commands listed in this section will for the most part cover 90% of interaction and usage of the boot2docker VM.   There is a little bit of advanced usage with the cli covered below in the tricks section.

Tricks

You can actually modify some of the default the behavior of your boot2docker VM by altering some of the underlying boot2docker configurations.  For example, boot2docker will look in $HOME/.boot2docker/profile for any custom settings you may have.  If you want to change any network settings, adjust memory or cpu or a number of settings, you would simply change the profile to reflect the updated changes.

You can also override the defaults when you create your boot2docker VM by passing some arguments in.  If you want to change the memory or disk size by default, you would run something like

boot2docker init --memory=4096 --disksize=60000

Notice the –disksize=60000.  Docker likes to take up a lot of disk space for some of its operations, so if you can afford to, I would very highly recommending that you adjust the default disk size for the VM to avoid any strange running out of disk issues.  Most Macbooks or Windows machines have plenty of extra resources and big disks so usually there isn’t a good reason to not leverage the extra horsepower for your VM.

Troubleshooting

One very useful command for gathering information about your boot2docker environment is the boot2docker config command.  This command will give you all the basic information about the running config.  This can be very valuable when you are trying to troubleshoot different types of errors.

If you are familiar with boot2docker already you might have noticed that it isn’t a perfect solution and there are some weird quirks and nuances.  For example, if you put your host machine to sleep while the boot2docker VM is still running and then attempt to run things in Docker you may get some quirky results or things just won’t work.  This is due to the time skew that occurs when you put the machine to sleep and wake it up again, you can check the github issue for details.  You can quickly check if the boot2docker VM is out of sync with this command.

date -u; boot2docker ssh date -u

If you notice that the times don’t match up then you know to update your time settings.  The best fix for now that I have found for now is to basically reset the time settings by wrapping the following commands in to a script.

#!/bin/sh
 
# Fix NTP/Time
boot2docker ssh -- sudo killall -9 ntpd
boot2docker ssh -- sudo ntpclient -s -h pool.ntp.org
boot2docker ssh -- sudo ntpd -p pool.ntp.org

For about 95% of the time skew issues you can simply run sudo ntpclient -s -h pool.ntp.org to take care of the issue.

Another interesting boot2docker oddity is that sometimes you will not be able to connect to the Docker daemon or will sometimes receive other strange errors.  Usually this indicates that the environment variables that get set by boot2docker have disappeared,  if you close your terminal window or something similar for example.  Both of the following errors indicate the issue.

dial unix /var/run/docker.sock: no such file or directory

or

Cannot connect to the Docker daemon. Is 'docker -d' running on this host?

The solution is to either add the ENV variables back in to the terminal session by hand or just as easily modify your bashrc config file to read the values in when the terminal loads up.  Here are the variables that need to be reset, or appended to your bashrc.

export DOCKER_CERT_PATH=/Users/jmreicha/.boot2docker/certs/boot2docker-vm
export DOCKER_TLS_VERIFY=1
export DOCKER_HOST=tcp://192.168.59.103:2376

Assuming your boot2docker VM has an address of 192.168.59.103 and a port of 2376 for communication.

Shared folders

This has been my biggest gripe so far with boot2docker as I’m sure it has been for others as well.  Mostly I am upset that vboxsf are horrible and in all fairness the boot2docker people have been awesome getting this far to get things working with vboxsf as of release 1.3.  Another caveat to note if you aren’t aware is that currently, if you pass volumes to docker with “-v”, the directory you share must be located within the “/Users” directory on OSX.  Obviously not a huge issue but something to be aware if you happen to have problems with volume sharing.

The main issue with vboxsf is that it does not do any sort of caching sort of caching so when you are attempting to share a large amount of small files (big git repo’s) or anything that is filesystem read heavy (grunt) performance becomes a factor.  I have been exploring different workarounds because of this limitation but have not found very many that I could convince our developers to use.  Others have had luck by creating a container that runs SMB or have been able to share a host directory in to the boot2docker vm with sshfs but I have not had great success with these options.  If anybody has these working please let me know I’d love to see how to get them working.

The best solution I have come up with so far is using vagrant with a customized version of boot2docker with NFS support enabled, which has very little “hacking” to get working which is nice.  And a good enough selling point for me is the speed increase by using NFS instead of vboxsf, it’s pretty staggering actually.

This is the project that I have been using https://vagrantcloud.com/yungsang/boxes/boot2docker.  Thanks to @yungsang for putting this project together.  Basically it uses a custom vagrant-box based off of the boot2docker iso to accomplish its folder sharing with the awesome customization that Vagrant provides.

To get this workaround to work, grab the vagrantfile from the link provided above and put that in to the location you would like to run Vagrant from.  The magic sauce in the volume sharing is in this line.

onfig.vm.synced_folder ".", "/vagrant", type: "nfs"

Which tells Vagrant to share your current directory in to the boot2docker VM in the /vagrant directory, using NFS.  I would suggest modifying the default CPU and memory as well if your machine is beefy enough.

v.cpus = 4
v.memory = 4096

After you make your adjustments, you just need to spin up the yungsang version of boot2docker and jump in to the VM.

vagrant up
vagrant ssh

From within the VM you can run your docker commands just like you normally would.  Ports get forwarded through to your local machine like magic and everybody is happy.

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docker developer tools

Useful tools for Docker development

Josh Reichardt 17 Comments

Docker is still a young project, and as such the ecosystem around it hasn’t quite matured to the point that many people feel quite comfortable using it at this point.  It is nice to have such a fast growing set of tools, however the downside to all of this is that many of the tools are not production ready.  I think as the ecosystem solidifies and Docker adoption grows we will see a healthy set of solid, production ready tools that are built off of the current generation of tools.

Once you get introduced to the concepts and ideas behind Docker you quickly realize the power and potential that it holds.   Inevitably though, there comes a “now what?” moment where you basically realize that Docker can do some interesting things but get stuck because there are barriers to simply dropping Docker into a production environment.

One problem is that you can’t simply “turn on” Docker in your environment, so you need tools to help manage images and containers, manage orchestration, development, etc.  So there are a number of challenged to take Docker and start doing useful and interesting things with it once you get past the introductory novelty of building an image and deploying simple containers.

I will attempt to make sense of the current state of Docker and to help take some of the guesswork out of which tools to use in which situations and scenarios for those that are hesitant to adopt Docker.  This post will focus mostly around the development aspects of the Docker ecosystem because that is a nice gateway to working with and getting acquainted with Docker.

Boot2Docker

As you may be aware, Docker does not (yet) support MacOSX or Windows.  This can definitely be a hindrance for adopting and building Docker acceptance amongst developers.  Boot2Docker massively simplifies this issue by essentially creating a sandbox to work with Docker as a thin layer between Docker and Mac (or Windows) via the boot2docker VM.

You can check it out here, but essentially you will download a package and install it and you are ready to start hacking away on Docker on your Mac.  Definitely a must for Mac OS as well as Windows users that are looking to begin their Docker journey, because the complexity is completely removed.

Behind the scenes, a number of things get abstracted away and simplified with Boot2docker, like setting up SSH keys, managing network interfaces, setting up VM integrations and guest additions, etc.  Boot2docker also bundles together with a cli for managing the VM that manages docker so it is easy to manage and configure the VM from the terminal.

CoreOS

It would take many blog posts to try to describe everything that CoreOS and its tooling can do.  The reason I am mentioning it here is because CoreOS is one of those core building blocks that are recently becoming necessary in any Docker environment.  Docker as it is today, is not specifically designed for distributed workloads and as such doesn’t provide much of the tooling around how to solve challenges that accompany distributed systems.  However, CoreOS bridges this gap very well.

CoreOS is a minimal Linux distribution that aims to help with a number of Docker related tasks and challenges.  It is distributed by its design so can do some really interesting things with images and containers using etcd, systemd, fleetd, confd and others as the platform continues to evolve.

Because of this tooling and philosophy, CoreOS machines can be rebooted on the fly without interrupting services and clustered processes across machines.  This means that maintenance can occur whenever and wherever, which makes the resiliency factor very high for CoreOS servers.

Another highlight is its security model, which is a push based model.  For example, instead of manually updating servers with security patches, the CoreOS maintainers periodically push updates to servers, alleviating the need to update all the time.  This was very nice when the latest shellshock vulnerability was released because within a day or so, a patch was automatically pushed to all CoreOS servers, automating the otherwise tedious process of updating servers, especially without config management tools.

Fig

Fig is a must have for anybody that works with Docker on a regular basis, ie developers.  Fig allows you to define your environment in a simple YAML config file and then bring up an entire development environment in one command, with fig up.

Fig works very well for a development work flow because you can rapidly prototype and test how Docker images will work together and eliminate issues that might crop up without being able to test things so easily.  For example if you are working on an application stack you can simply define how the different containers should work and interact together from the fig file.

The downside to fig is that in its current form, it isn’t really equipped to deal with distributed Docker hosts, something that you will find a large number of projects are attempting to solve and simplify.  This shouldn’t be an issue though, if you are aware of its limitations beforehand and know that there are some workloads that fig is not built for.

Panamax

This is a cool project out of Century Link Labs that aims to solve problems around docker app development and orchestration.  Panamax is similar to Fig in that it stitches containers together logically but is slightly different in  a few regards.  First, Panamax builds off of CoreOS to leverage some of its built in tools, etcd, fleetd, etc.  Another thing to note is that currently Panamax only supports single host deployments.  The creators of Panamax have stated that clustered support and multi host tenancy is in the works but for now you will have to use Panamax on a single host.

Panamax simplifies Docker images and application orchestration (kind of), in the background and additionally places a nice layer of abstraction on top of this process so that managing the Docker image “stack” becomes even easier, through a slick GUI.  With the GUI you can set environment variables, link containers together, bind ports and volumes.

Panamax draws a number of its concepts from Fig.  It uses templates as the underlying way to compose containers and applications, which is similar to the Fig config files, as both use YAML files to compose and orchestrate Docker container behavior.  Another cool thing about Panamax is that there is a public template repo for getting different application and container stacks up and running, so the community participation is a really nice aspect of the project.

If setting up a command line config file isn’t an ideal solution in your environment, this tool is definitely worth a look.  Panamax is a great way to quickly develop and prototype Docker containers and applications.

Flocker

This is a very young but interesting project.  The project looks interesting because of the way that it handles and deals volume management.  Right now one of the biggest challenges to widespread Docker adoption is exactly the problem that Flocker solves in its ability to persist storage across distributed hosts.

From their github page:

Flocker is a data volume manager and multi-host Docker cluster management tool. With it you can control your data using the same tools you use for your stateless applications by harnessing the power of ZFS on Linux.

Basically, Flocker is using some ZFS magic behind the scenes to allow volumes to float between servers, to allow for persistent storage across machines and containers.  That is a huge win for building distributed systems that require persisnt data and storage, eg databases.

Definitely keep an eye on this project for improvements and look for them to push this area in the future.  The creators have said it isn’t production ready just quite yet but is  a great tool to use in a test or staging environment.

Flynn

Flynn touts itself as a Platform as a Service (PasS) built on Docker, in a very similar vein to Heroku.  Having a Docker PaaS is a huge win for developers because it simplifies developer workflow.  There are some great benefits of having a PaaS in your environment, the subject could easily expand to be its own topic of conversation.

The approach that Flynn takes (and Paas in general) is that operations should be a product team.  With Flynn, ops can provide the platform and developers can focus on their tasks much more easily, developing software, testing and generally freeing developers the time to focus on development tasks instead of fighting operations.  Flynn does a nice job of decoupling operations tasks from dev tasks so that the developers don’t need to rely on operations to do their work and operations don’t need to concern themselves with development tasks which can cause friction and create efficiency issues.

Flynn works by basically tying a number of different tools together created specifically to solve challenges of building a PaaS to perform their workloads via Docker (scheduling, persistent storage, orchestration, clustering, etc) as one single entity.

Currently its developers state that Flynn is not quite suitable for production use yet, but it is still mature enough to use and play around with and even deploy apps to.

Deis

Deis is another PaaS for Docker, aiming to solve the same problems and challenges that Flynn does, so there is definitely some overlap in the projects, as far as end users are concerned.  There is a nice CLI tool for manaing and intereacting with Deis and it offers much of the same functionality that either heroku or Flynn offer.  Deis can do things like horizontal application scaling, supports many different application frameworks and is Open Source.

Deis is similar in concept to Flynn in that it aims to solve PaaS challenges but they are quite different in their implementation and how they actually achieve their goals.

Both Flynn and Deis aim to create platforms to build Docker apps on top of but do so in somewhat different means.  As the creator Deis explains, Deis is very much more practical in its approach to solving PaaS issues because it is basically taking a number of available technologies and tools that have already been created and is fitting them together only creating the pieces that are missing,  while Flynn seems to be very much more ambitious in its approach due to the fact that it is implementing a number of its own tooling and solutions, including its own scheduler, registration service, etc and only relying on a few tools that are already in existence.  For example, while Flynn does all of these different things, Deis leverages CoreOS to do many of the tasks it needs to operate and work correctly while minimally bolting on tooling that it needs to function correctly.

Conclusion

As the Docker ecosystem continues to evolve, more and more options seem to be sprouting up.  There are already a number of great tools in the space but as the community continues to evolve I believe that the current tools will continue to improve and new and useful tools will be built for Docker specific workloads.  It is really cool to see how the Docker ecosystem is growing and how the tools and technologies are disrupting traditional views on a number of areas in tech including virtualization, DevOps, development, deployments and application development, among others.

I anticipate the adoption of Docker to continue growing for the foreseeable future as the core Docker project continues to improve and stabilize as well as the tools tools built around it that I have discussed here.  It will be interesting to see where things are even six months from now in regards to the adoption and use cases that Docker has created.

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Autosnap AWS snapshot and volume management tool

Josh Reichardt 19 Comments

This is my first serious attempt at a Python tool on github.  I figured it was about time, as I’ve been leveraging Open Source tools for a long time, I might as well try to give a little bit back.  Please check out the project and leave feedback by emailing, opening a github or issue or commenting here, I’d love to see what can be done with this tool, there are lots of bugs to shake out and things to improve.  Even better if you have some code you’d like to contribute, this is very much a work in progress!

Here is the project – https://github.com/jmreicha/autosnap.

Introduction

Essentially, this tool is designed to ease the management of the snapshot and volume lifecycle in an AWS environment.  I have discovered that snapshots and volumes can be used together to form a simple backup management system, so by simplifying the management of these resources, by utilizing the power of the AWS API, you can easily manage backups of your AWS data.

While this obviously isn’t a full blown backup tool, it can do a few handy things like leverage tags to create and destroy backups based on custom expiration dates and create snapshots based on a few other criteria, all managed with tags.  Another cool thing about handling backups this way is that you get amazing resiliency by storing snapshots to S3, as well as dirt cheap storage.  Obviously if you have a huge number of servers and volumes your mileage will vary, but this solution should scale up in to the hundreds, if not thousands pretty easily.  The last big bonus is that you can nice granularity for backups.

For example, if you wanted to keep a weeks worth of backups across all your servers in a region, you would simply use this tool to set an expiration tag of 7 days and voila.  You will have rolling backups, based on snapshots for the previous seven days.  You can get the backup schedule fairly granular, because the snapshots are tagged down to the hour. It would be easy to get them down to the second if that is something people would find useful, I could see DB snapshots being important enough but for now it is set to the hour.

The one drawback is that this needs to be run on a daily basis so you would need to add it to a cron job or some other tool that runs tasks periodically.  Not a drawback really as much of a side note to be aware of.

Configuration

There is a tiny bit of overhead to get started, so I will show you how to get going.  You will need to either set up a config file or let autosnap build you one.  By default, autosnap will help create one the first time you run it, so you can use this command to build it:

autosnap

If you would like to provide your own config, create a file called ‘.config‘ in the base directory of this project.  Check the README on the github page for the config variables and for any clarifications you may need.

Usage

Use the –help flag to get a feeling for some of the functions of this tool.

$ autosnap --help

usage: autosnap [--config] [--list-vols] [--manage-vols] [--unmanage-vols]
 [--list-snaps] [--create-snaps] [--remove-snaps] [--dry-run]
 [--verbose] [--version] [--help]

optional arguments:
 --config          create or modify configuration file
 --list-vols       list managed volumes
 --manage-vols     manage all volumes
 --unmanage-vols   unmanage all volumes
 --list-snaps      list managed snapshots
 --create-snaps    create a snapshot if it is managed
 --remove-snaps    remove a snapshot if it is managed
 --version         show program's version number and exit
 --help            display this help and exit

The first thing you will need to do is let autosnap manage the volumes in a region:

autosnap --manage-vols

This command will simply add some tags to help with the management of the volumes.  Next, you can take a look and see what volumes got  picked up and are now being managed by autosnap

autosnap --list-vols

To take a snapshot of all the volumes that are being managed:

autosnap --create-snaps

And you can take a look at your snapshots:

autosnap --list-snaps

Just as easily you can remove snapshots older than the specified expiration date:

autosnap --remove-snaps

There are some other useful features and flags but the above commands are pretty much the meat and potatoes of how to use this tool.

Conclusion

I know this is not going to be super useful for everybody but it is definitely a nice tool to have if you work with AWS volumes and snapshots on a semi regular basis.  As I said, this can easily be improved so I’d love to hear what kinds of things to add or change to make this a great tool.  I hope to start working on some more interesting projects and tools in the near future, so stay tuned.

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Analyzing cloud costs

Josh Reichardt 19 Comments

Knowing about and controlling the costs of a cloud environment is not only good to know how to do as an admin/engineer, it also greatly helps others inside your organization.  Knowing your environment and cost overhead also makes you (or your team) look better when you can pinpoint bottlenecks, as well as anomalies in your environment, and create solutions to mitigating costs or otherwise track cloud resource utilization.  Plus, it can even get you some extra credit.

So with this in mind, I’d like to talk about a few strategies and tools I have been experimenting with to help road map and accurately model different costs and utilization for different workloads spread out accross an AWS environment.

ICE

The first tool I’d like to mention is ICE and is probably my favorite tool. It is a tool developed by Netflix and analyzes costs across your AWS infrastructure.  It gives you nice graphs and advanced breakdowns of prices, including spot pricing vs on demand and many other permutations across your AWS infrastructure.

This is the best explanation I can find, pulled right from their github page:

The ability to trend usage patterns on a global scale, yet decompose them down to a region, availability zone, or service team provides incredible flexibility. Ice allows us to quantify our AWS footprint and to make educated decisions regarding reservation purchases and reallocation of resources.

Amazon ICE

It has a nice interface and some slick filtering, so breaking things down on a region by region level becomes easy, which is otherwise not the case for the other tools.  This tool is also great for spotting trends and anomalies in your environment which can sometimes go undetected if not viewed in the correct context.

The downside is the overhead associated with getting this up and running bu there is a Chef cookbook that will pretty much do the installation for you, if you are comfortable with Chef.  You will need to override some attributes but otherwise it is pretty straight forward.  If you need assistance let me know and I’d be glad to walk you through getting it set up.

AWS Calculator

This is a handy tool to help ballpark and model various costs for AWS services.  One disappointing discovery of this tool is that it doesn’t help model spot instance prices.

AWS calculator

This is great for mocking out what the TCO of a server or group of servers might look like.  It is also good for getting a general feel for what different server costs will be for a certain number of months and/or years.

Be sure to check this out to help stay current on the most recent news because AWS moves quickly with seemingly constant updates and have been dropping prices steadily over the past 3 years.  Especially with the increased competition from Microsoft (Azure) and Google (Google Cloud), AWS seems to be constantly slashing prices and adding new improvement and features to their product.

AWS Billing and Cost Management

This one is pretty self explanatory.  It is built right in to AWS and as such, it can be a very powerful tool that can easily be overlooked.  It offers a variety of detailed information about costs and billing.  It offers some nice graphs and charts for trend spotting and can be exported for analysis, which is also nice (even though I haven’t got that far yet).

The major downside (in my opinion) is that you can’t get the granular price breakdowns that are available with a tool like ICE.  For example, there isn’t an easy way to find a price comparison breakdown for cost per region or other more detailed information.

Trusted Advisor

This tool is great and is free for basic usage.  This offering from AWS is really nice for helping to find and optimize settings according to a number of good practice recommendations created by Amazon.  Not only does it give you some really nice price breakdowns but it also reports things like security and performance which can be equally useful.  Use this often to tighten up areas of your infrastructure and to optimize costs.

One down for this one is that to unlock all of the features and functionality you need to upgrade to the enterprise version which is obviously more expensive.

AWS ELK Billing

I just found out about this one but it looks like it might be a very nice solution, leveraging the Logstash + Kibana stack.  I have written a post about getting started with the ELK stack so it shouldn’t be difficult at all to begin playing around with this solution if you are interested.

If you get this tool up and working I would love to hear about it.

Cost saving tips

I have compiled a list of simple yet powerful tips to help control costs in AWS.  Ideally a combination of all of these tips would be used to help control costs.

  • Upgrade server and service instance generations as often as possible for automatic improved performance and reduced price.  For example gen 1 to gen 3, m1.xxx -> m3.xxx.
  • Try to size servers correctly by keeping them busy.  Servers that are running but aren’t doing anything are essentially wasting money.  Either run them according to time of day or bump up the amount of utilization per box, either by downsizing the server or upping the workload.
  • On that note, size servers correctly according to workload.  For example a workload that demands CPU cylces should not be deployed as a memory optimized server.
  • Adopt on demand instances and utilize them early on.  On demand prices are significantly lower than standard prices.  Just be careful because your on demand instances can disappear.
  • In the same ilk of on demand instances, use reserved instances.  These instance types can significantly reduce prices, and have the advantage that they won’t disappear so long running servers and services benefit from this type of cost control.
  • Set up granular billing as early as possible.  Create and optimize alerts based on expected usage for tighter control of costs.  It’s better to start off knowing and controlling environment costs sooner than later.
  • Delete unused EBS volumes.  Servers and volumes can come and go, but often times EBS volumes can become orphaned and essentially no good.  Therefore it is a good idea to clean up unused EBS volumes whenever you can.  Of course this process can and should be automated.

Conclusion

Managing cost and optimizing your cloud infrastructure really could be considered its own discipline in some regards.  Environments can become complex quite quickly with instances, services and resources spinning up and down as well as dynamically growing up and down to accommodate workloads as well as ever evolving environments can lead to what some call “Cloud Sprawl”.

The combination of the tools and cost savings tips mentioned above can be a real lifesaver when you are looking to squeeze out the most bang for your buck out of your cloud environment.  It can also lead to a much more solid understanding of all the moving pieces in your environment and can help determine exactly is going on at any given time, which is especially useful for DevOps admins and engineers.

If you have any other cool tips or tips for controlling AWS costs or other cloud environment costs let me know, I’ll be sure to add them here!

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test kitchen

Test Kitchen Tricks

Josh Reichardt 27 Comments

I have been working a lot with Chef and Test kitchen lately and thus have learned a few interesting tricks when running tests with these tools.  Test Kitchen is one of my favorite tools when working with Chef configuration management because it is very easy to use and has a number of powerful features that make testing things in Chef simple and easy.

Test Kitchen itself sits on top of Vagrant and Virtualbox by default so to get started with the most basic usage example of Test Kitchen you will need to have Vagrant installed along with a few other items.

Then to install Test Kitchen.

gem install test-kitchen

That’s pretty much it.  The official docs have some pretty detailed usage and in fact I have learned many of the tricks that I will be writing about today from the docs.

Once you are comfortable with Test Kitchen you can begin leveraging some of the more powerful features, which is what the remainder of this post will cover.  There is a great talk given by the creator of test kitchen at this year’s Chefconf by the creator of Test Kitchen about some of the lessons learned and cool things that you can do with the tool.  If you haven’t already seen it, it is worth a watch.

Anyway, let’s get started.

1) Fuzzy matching

This one is great for the lazy people out there.  It basically allows you match a certain unique part of a command instead of typing out an entire command.  So for example, you can just type in a partial name for a command to return the desired full command.  Since Test Kitchen uses regular expression matching, this can be a very powerful feature.

2) Custom drivers

One reason that Test Kitchen is so flexible is because it can leverage many different plugins and drivers.  And, since it is open source, if there is functionality missing from a driver you can simply write your own.  Currently there is an awesome list of drivers available for Test Kitchen to use, and a wide variety of options available to hopefully suit most testing scenarios.

Of course, there are others as well.  These just happen to be the drivers that I have tried and can verify.  There is even support for alternate configuration management tool testing, which can be handy for those that are not using Chef specifically.  For example there is a salt driver available.

3) .kitchen.local.yml

This is a nice handy little bit that is often overlooked but allows a nice amount of control by overriding the default .kitchen.yml configuration file with specific options.  So for example, if you are using the ec2 driver in your configuration but need to test locally with Vagrant you can simply drop a .kitchen.local.yml on your dev machine and override the driver (and any other settings you might need to change). I have created the following .kitchen.local.yml for testing on a local Vagrant box using 32 bit Ubuntu to highlight the override capabilities of Test Kitchen.

driver: 
 name: vagrant 
 
platforms: 
 - name: ubuntu-1310-i386 
 - name: ubuntu-1404-i386

4) Kitchen diagnose

An awesome tool for diagnosing issues with Test Kitchen.  Running the diagnose will give you lots of juicy info about what your test machines are doing (or should be doing) and a ton of configuration information about them.  Basically, if something is misbehaving this is the first place you should look for clues.

If you want to blast info and settings for all your configurations, just run the following,

kitchen diagnose

5) Concurrency

If you have a large number of systems that need to have tests run on them then running your Test Kitchen tests in parallel is a great way to speed up your total testing time.  Turning on concurrency is pretty straight forward, just add the “-c” flag and the number of instances to run on (the default is 9999).

kitchen converge -c 5

6) Verbose logging

This one can be helpful if your kitchen run is failing with no real clues or helpful information provided by the diagnose command.  It seems obvious but getting this one to work gave me some trouble initially.  To turn on verbosity simply add the debug flag to your test kitchen command.

So for example, if you want to converge a node with verbosity turned on, you would use this command.

kitchen converge -l debug

I recommend taking a look at some or all of these tricks to help improve your integration testing with Test Kitchen.  Of course as I stated, all of this is pretty well documented.  Even if you are already familiar with this tool, sometimes it just helps to have a refresher to remind you of a great tool and to jar your memory.  Let me know if you have any other handy tricks and I will be sure to post them here.

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