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Docker Intro at the Google Developer Group and Google Cloud Platform Meet Up

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Jérôme Petazzoni
Jérôme Petazzoni

Docker is the Open Source engine to author, run, and manage Linux Containers. This is a short introduction to Docker, what it is, what is for; it was given in the context of the Google Developer Group and Google Cloud Platform Meet-Up in San Francisco, end of March 2014.

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Introduction to Docker March 2014—Docker 0.9.0
@jpetazzo ● Wrote dotCloud PAAS deployment tools – EC2, LXC, Puppet, Python, Shell, ØMQ... ● Docker contributor – Docker-in-Docker, VPN-in-Docker, router-in-Docker... CONTAINERIZE ALL THE THINGS! ● Runs Docker in production – You shouldn't do it, but here's how anyway!
What?
Why?
Deploy everything ● Webapps ● Backends ● SQL, NoSQL ● Big data ● Message queues ● … and more
Deploy almost everywhere ● Linux servers ● VMs or bare metal ● Any distro ● Kernel 3.8 (or RHEL 2.6.32) Currently: focus on x86_64. (But people reported success on arm.)
Deploy reliably & consistently
Deploy reliably & consistently ● If it works locally, it will work on the server ● With exactly the same behavior ● Regardless of versions ● Regardless of distros ● Regardless of dependencies
Deploy efficiently ● Containers are lightweight – Typical laptop runs 10-100 containers easily – Typical server can run 100-1000 containers ● Containers can run at native speeds – Lies, damn lies, and other benchmarks: http://qiita.com/syoyo/items/bea48de8d7c6d8c73435
The performance! It's over 9000!
Is there really no overhead at all? ● Processes are isolated, but run straight on the host ● CPU performance = native performance ● Memory performance = a few % shaved off for (optional) accounting ● Network and disk I/O performance = small overhead; can be reduced to zero
… Container ?
High level approach: it's a lightweight VM ● Own process space ● Own network interface ● Can run stuff as root ● Can have its own /sbin/init (different from the host) « Machine Container »
Low level approach: it's chroot on steroids ● Can also not have its own /sbin/init ● Container = isolated process(es) ● Share kernel with host ● No device emulation (neither HVM nor PV) « Application Container »
How does it work? Isolation with namespaces ● pid ● mnt ● net ● uts ● ipc ● user
How does it work? Isolation with cgroups ● memory ● cpu ● blkio ● devices
How does it work? Copy-on-write storage ● Create a new machine instantly (Instead of copying its whole filesystem) ● Storage keeps track of what has changed ● Multiple storage plugins available (AUFS, device mapper, BTRFS, VFS...)
Union Filesystems (AUFS, overlayfs) Copy-on-write block devices Snapshotting filesystems Provisioning Superfast Supercheap Fast Cheap Fast Cheap Changing small files Superfast Supercheap Fast Costly Fast Cheap Changing large files Slow (first time) Inefficient (copy-up!) Fast Cheap Fast Cheap Diffing Superfast Slow Superfast Memory usage Efficient Inefficient (at high densities) Inefficient (but may improve) Drawbacks Random quirks AUFS not mainline Higher disk usage Great performance (except diffing) ZFS not mainline BTRFS not as nice Bottom line Ideal for PAAS and high density things Dodge Ram 3500 This is the future (Probably!) Storage options
Alright, I get this. Containers = nimble VMs.
The container metaphor
Problem: shipping goods ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
Solution: the intermodal shipping container
Solved!
Problem: shipping code ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
Solution: the Linux container
Solved!
Separation of concerns: Dave the Developer ● Inside my container: – my code – my libraries – my package manager – my app – my data
Separation of concerns: Oscar the Ops guy ● Outside the container: – logging – remote access – network configuration – monitoring
Docker-what? The Big Picture ● Open Source engine to commoditize LXC ● Using copy-on-write for quick provisioning ● Allowing to create and share images ● Standard format for containers (stack of layers; 1 layer = tarball+metadata) ● Standard, reproducible way to easily build trusted images (Dockerfile, Stackbrew...)
One-time setup ● On your servers (Linux) – Packages (Ubuntu, Debian, Fedora, Gentoo, Arch...) – Single binary install (Golang FTW!) – Easy provisioning on Rackspace, Digital Ocean, EC2, GCE... ● On your dev env (Linux, OS X, Windows) – Vagrantfile – boot2docker (25 MB VM image) – Natively (if you run Linux)
The Docker workflow 1/2 ● Work in dev environment (local machine or container) ● Other services (databases etc.) in containers (and behave just like the real thing!) ● Whenever you want to test « for real »: – Build in seconds – Run instantly
The Docker workflow 2/2 Satisfied with your local build? ● Push it to a registry (public or private) ● Run it (automatically!) in CI/CD ● Run it in production ● Happiness! Something goes wrong? Rollback painlessly!
Authoring images with run/commit
1) docker run ubuntu bash 2) apt-get install this and that 3) docker commit <containerid> <imagename> 4) docker run <imagename> bash 5) git clone git://.../mycode 6) pip install -r requirements.txt 7) docker commit <containerid> <imagename> 8) repeat steps 4-7 as necessary 9) docker tag <imagename> <user/image> 10) docker push <user/image>
Authoring images with run/commit ● Pros – Convenient, nothing to learn – Can roll back/forward if needed ● Cons – Manual process – Iterative changes stack up – Full rebuilds are boring, error-prone
Authoring images with a Dockerfile
FROM ubuntu RUN apt-get -y update RUN apt-get install -y g++ RUN apt-get install -y erlang-dev erlang-manpages erlang-base- hipe ... RUN apt-get install -y libmozjs185-dev libicu-dev libtool ... RUN apt-get install -y make wget RUN wget http://.../apache-couchdb-1.3.1.tar.gz | tar -C /tmp -zxf- RUN cd /tmp/apache-couchdb-* && ./configure && make install RUN printf "[httpd]nport = 8101nbind_address = 0.0.0.0" > /usr/local/etc/couchdb/local.d/docker.ini EXPOSE 8101 CMD ["/usr/local/bin/couchdb"] docker build -t jpetazzo/couchdb .
Authoring images with a Dockerfile ● Minimal learning curve ● Rebuilds are easy ● Caching system makes rebuilds faster ● Single file to define the whole environment!
Docker 1.0 ● Multi-arch, multi-OS ● Stable control API ● Stable plugin API ● Resiliency ● Signature ● Clustering
www.dockercon.com
DockerCon special offer! ● Promo code: docker-google15 ● 15% discount! ● Only for the first 10 people to use it! ● While supplies last! ● GET IT WHILE IT'S HOT!!!!!!!!!!
www.dockercon.com docker-google15
Thank you! Questions? http://docker.io/ http://docker.com/ @docker @jpetazzo

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Docker Intro at the Google Developer Group and Google Cloud Platform Meet Up

  • 1.
  • 2. Introduction to Docker March 2014—Docker 0.9.0
  • 3. @jpetazzo ● Wrote dotCloud PAAS deployment tools – EC2, LXC, Puppet, Python, Shell, ØMQ... ● Docker contributor – Docker-in-Docker, VPN-in-Docker, router-in-Docker... CONTAINERIZE ALL THE THINGS! ● Runs Docker in production – You shouldn't do it, but here's how anyway!
  • 6. Deploy everything ● Webapps ● Backends ● SQL, NoSQL ● Big data ● Message queues ● … and more
  • 7. Deploy almost everywhere ● Linux servers ● VMs or bare metal ● Any distro ● Kernel 3.8 (or RHEL 2.6.32) Currently: focus on x86_64. (But people reported success on arm.)
  • 8. Deploy reliably & consistently
  • 9.
  • 10. Deploy reliably & consistently ● If it works locally, it will work on the server ● With exactly the same behavior ● Regardless of versions ● Regardless of distros ● Regardless of dependencies
  • 11. Deploy efficiently ● Containers are lightweight – Typical laptop runs 10-100 containers easily – Typical server can run 100-1000 containers ● Containers can run at native speeds – Lies, damn lies, and other benchmarks: http://qiita.com/syoyo/items/bea48de8d7c6d8c73435
  • 13. Is there really no overhead at all? ● Processes are isolated, but run straight on the host ● CPU performance = native performance ● Memory performance = a few % shaved off for (optional) accounting ● Network and disk I/O performance = small overhead; can be reduced to zero
  • 15. High level approach: it's a lightweight VM ● Own process space ● Own network interface ● Can run stuff as root ● Can have its own /sbin/init (different from the host) « Machine Container »
  • 16. Low level approach: it's chroot on steroids ● Can also not have its own /sbin/init ● Container = isolated process(es) ● Share kernel with host ● No device emulation (neither HVM nor PV) « Application Container »
  • 17. How does it work? Isolation with namespaces ● pid ● mnt ● net ● uts ● ipc ● user
  • 18. How does it work? Isolation with cgroups ● memory ● cpu ● blkio ● devices
  • 19. How does it work? Copy-on-write storage ● Create a new machine instantly (Instead of copying its whole filesystem) ● Storage keeps track of what has changed ● Multiple storage plugins available (AUFS, device mapper, BTRFS, VFS...)
  • 20. Union Filesystems (AUFS, overlayfs) Copy-on-write block devices Snapshotting filesystems Provisioning Superfast Supercheap Fast Cheap Fast Cheap Changing small files Superfast Supercheap Fast Costly Fast Cheap Changing large files Slow (first time) Inefficient (copy-up!) Fast Cheap Fast Cheap Diffing Superfast Slow Superfast Memory usage Efficient Inefficient (at high densities) Inefficient (but may improve) Drawbacks Random quirks AUFS not mainline Higher disk usage Great performance (except diffing) ZFS not mainline BTRFS not as nice Bottom line Ideal for PAAS and high density things Dodge Ram 3500 This is the future (Probably!) Storage options
  • 21. Alright, I get this. Containers = nimble VMs.
  • 22.
  • 24. Problem: shipping goods ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
  • 27. Problem: shipping code ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
  • 30. Separation of concerns: Dave the Developer ● Inside my container: – my code – my libraries – my package manager – my app – my data
  • 31. Separation of concerns: Oscar the Ops guy ● Outside the container: – logging – remote access – network configuration – monitoring
  • 32.
  • 33. Docker-what? The Big Picture ● Open Source engine to commoditize LXC ● Using copy-on-write for quick provisioning ● Allowing to create and share images ● Standard format for containers (stack of layers; 1 layer = tarball+metadata) ● Standard, reproducible way to easily build trusted images (Dockerfile, Stackbrew...)
  • 34. One-time setup ● On your servers (Linux) – Packages (Ubuntu, Debian, Fedora, Gentoo, Arch...) – Single binary install (Golang FTW!) – Easy provisioning on Rackspace, Digital Ocean, EC2, GCE... ● On your dev env (Linux, OS X, Windows) – Vagrantfile – boot2docker (25 MB VM image) – Natively (if you run Linux)
  • 35. The Docker workflow 1/2 ● Work in dev environment (local machine or container) ● Other services (databases etc.) in containers (and behave just like the real thing!) ● Whenever you want to test « for real »: – Build in seconds – Run instantly
  • 36. The Docker workflow 2/2 Satisfied with your local build? ● Push it to a registry (public or private) ● Run it (automatically!) in CI/CD ● Run it in production ● Happiness! Something goes wrong? Rollback painlessly!
  • 38. 1) docker run ubuntu bash 2) apt-get install this and that 3) docker commit <containerid> <imagename> 4) docker run <imagename> bash 5) git clone git://.../mycode 6) pip install -r requirements.txt 7) docker commit <containerid> <imagename> 8) repeat steps 4-7 as necessary 9) docker tag <imagename> <user/image> 10) docker push <user/image>
  • 39. Authoring images with run/commit ● Pros – Convenient, nothing to learn – Can roll back/forward if needed ● Cons – Manual process – Iterative changes stack up – Full rebuilds are boring, error-prone
  • 41. FROM ubuntu RUN apt-get -y update RUN apt-get install -y g++ RUN apt-get install -y erlang-dev erlang-manpages erlang-base- hipe ... RUN apt-get install -y libmozjs185-dev libicu-dev libtool ... RUN apt-get install -y make wget RUN wget http://.../apache-couchdb-1.3.1.tar.gz | tar -C /tmp -zxf- RUN cd /tmp/apache-couchdb-* && ./configure && make install RUN printf "[httpd]nport = 8101nbind_address = 0.0.0.0" > /usr/local/etc/couchdb/local.d/docker.ini EXPOSE 8101 CMD ["/usr/local/bin/couchdb"] docker build -t jpetazzo/couchdb .
  • 42. Authoring images with a Dockerfile ● Minimal learning curve ● Rebuilds are easy ● Caching system makes rebuilds faster ● Single file to define the whole environment!
  • 43. Docker 1.0 ● Multi-arch, multi-OS ● Stable control API ● Stable plugin API ● Resiliency ● Signature ● Clustering
  • 45. DockerCon special offer! ● Promo code: docker-google15 ● 15% discount! ● Only for the first 10 people to use it! ● While supplies last! ● GET IT WHILE IT'S HOT!!!!!!!!!!