Announcing Hot Functions for IronFunctions

IronFunctions is a serverless application platform. Unlike AWS Lambda it’s open-source, can run on any cloud — public, on-premise, or hybrid, and language agnostic, while maintaining AWS Lambda compatibility.

The initial release of IronFunctions received some amazing feedback and the past few weeks were spent addressing outstanding issues. In this post I will be highlighting the biggest feature with the upcoming release, Hot Functions.

TL;DR:

Hot Functions improves IronFunctions throughput by 8x (depending on duration of task). By re-using containers or what we call Hot Functions each call is reduced by 300ms.

Details:

Before Hot Functions, IronFunctions would spin up a new container to handle every job. This led to a 300ms overhead per job due to container startup time.

With Hot Functions, long-lived containers are able to serve the same type of task without incurring the startup time penalty. They do this by taking incoming workloads and feeding in through standard input and writing to standard output. In addition, permanent network connections are reused. For more information on implementing Hot Functions, see the Github docs.

We ran our benchmark on a 1 GB Digital Ocean instance and used honeycomb.io to plot the results.


Simple function printing “Hello World” called for 10s (MAX CONCURRENCY = 1).

Hot Functions have 162x higher throughput.


Complex function pulling image and md5 checksumming called for 10s (MAX CONCURRENCY = 1).

Hot Functions have 139x higher throughput.


By combining Hot Functions with concurrency we saw even better results: 

Complex function pulling image and md5 checksumming called for 10s (MAX CONCURRENCY = 7).

Hot Functions have 7.84x higher throughput.


There’s more to this release as well. IronFunctions brings Single Flight pattern for DB calls as well as stability and optimization fixes across the board.

IronFunctions is maturing quickly and our community is growing. To get involved, please join our Slack community and check out IronFunctions today!

Also stay tuned for upcoming announcements by following this blog and our developer blog.

Hacker News conversation here.

Announcing Project Picasso – OpenStack Functions as a Service

We are pleased to announce a new project to enable Functions as a Service (FaaS) on OpenStack — Picasso.

The mission is to provide an API for running FaaS on OpenStack, abstracting away the infrastructure layer while enabling simplicity, efficiency, and scalability for both developers and operators.

Picasso can be used to trigger functions from OpenStack services, such as Telemetry (via HTTP callback) or Swift notifications. This means no long running applications, as functions are only executed when called.

Picasso is comprised of two main components:

  • Picasso API
    • The Picasso API server uses Keystone authentication and authorization through its middleware.
  • IronFunctions
    • Picasso leverages the backend container engine provided by IronFunctions, an open-source Serverless/FaaS platform based on Docker.

Resources

We’ve created some initial blueprints to show what the future roadmap looks like for the project.

You can try out Picasso now on DevStack by following the quick start guide here. Let us know what you think!

If you’re interested in contributing or just have any questions, please join us on the #OpenStack channel in Slack.

Announcing IronFunctions Open Source

logo-black-400wToday we’re excited to announce IronFunctions, our first major open source project.

IronFunctions is a serverless microservices platform that you can run anywhere; on public, private, and hybrid clouds, even on your own laptop. The world is moving towards hybrid/multi-cloud, so should your serverless platform.

It runs on top of the popular orchestration frameworks (Kubernetes, Mesosphere), inside PaaS runtime environments (CloudFoundry, OpenShift), and on bare metal.

Functions are packaged using Docker so it supports any language, any dependencies, and can run anywhere. It will also eventually support other container technologies, and today it supports the Lambda function format for easy portability and will soon support others as well.

IronFunctions is written in Go, extremely fast, and written with scalability and operability in mind.

Finally, it’s being driven by our team at Iron.io that is unashamedly taking credit for coining the term serverless dating back to 2011 and 2012. We’ve launched billions of containers through our flagship serverless job processing service IronWorker, and now bring this knowledge and experience to IronFunctions to round out our portfolio of products with synchronous capabilities.

So without further ado, we’d love your help in building an amazing platform and community. Fork the repo and please give us pull requests and create issues!

The Project: https://github.com/iron-io/functions

Join our Slack room: http://get.iron.io/open-slack

The Press Release: http://www.marketwired.com/press-release/ironio-releases-first-open-source-project-2175887.htm

Join the conversation: https://news.ycombinator.com/item?id=12961296

Thanks for supporting Iron.io for the past 5+ years.

Chad Arimura
CEO, Iron.io

The Overhead of Docker Run

First published on Medium on 10/11/2016.

We use Docker a lot. Like a lot, lot. While we love it for a lot of things, it still has a lot of room for improvement. One of those areas that could use improvement is the startup/teardown time of running a container.

The Test

To test the overhead of running a Docker container, I made a script that compares execution times for various docker run options vs not using Docker at all. The script that I’m running is a simple hello world shell script that consists of the following:

echo "Hello World!"

The base Docker image is the official Alpine linux image plus the script above.

4 Things to Compare

  1. As a baseline, the first measurement is sans Docker. This is just running the hello.sh script directly.
  2. The second measure is just docker run IMAGE.
  3. The third measure adds the “rm” flag to remove the container after execution.
  4. The final one is to use docker start instead of run, so we can see the effect of reusing an already created container.

Docker for Mac

Server Version: 1.12.2-rc1

Running: ./hello.sh
avg: 5.897752ms
Running: docker run treeder/hello:sh
avg: 988.098391ms
Running: docker run — rm treeder/hello:sh
avg: 999.637832ms
Running: docker start -a reuse
avg: 986.875089ms

(Note: looks like using Ubuntu as a base image is slightly faster than Alpine, in the 10–50ms range).

Docker on Ubuntu

Server Version: 1.12.1

Running: ./hello.sh
avg: 2.139666ms
Running: docker run treeder/hello:sh
avg: 391.171656ms
Running: docker run — rm treeder/hello:sh
avg: 396.385453ms
Running: docker start -a reuse
each: 340.793602ms

Results

As you can see from the results above, using Docker adds nearly a full second to the execution time of our script on Mac and ~390ms on Linux (~175x slower than running the script without Docker).

Now this may not be much of an issue if your script/application runs for a long period of time, but it is certainly an issue if you run short lived programs.

Try it yourself

Feel free to try running the script on your system and share the results! You can find everything you need here:https://github.com/treeder/dockers/tree/master/hello

Just clone that repo, cd into the hello directory and run:

go run time.go

Delivering on the Promise of Multicloud Lambda-like Functionality

multicloud-takeoff

In February, we launch a beta called Project Kratos. It promised to bring Lambda-like functionality to any cloud – public, private, hybrid or on-premises. As we quickly approach Q4, February seems like a long time ago, but so much has happened since then.

Over the past seven months, serverless computing has gained momentum as more than just the hot topic of the moment. Because it allows enterprises to build and deploy applications and services at scale on flexible platforms that abstract away physical infrastructure, it’s quickly becoming a must have for the modern enterprise. It will soon be a competitive advantage for those already implementing it.

Our journey with serverless has also moved from a project announcement full of promises to the solution that is widely available today.  First, in April, we announced the general availability of its multicloud solution. Since then, we’ve systematically partnered with leading cloud providers to support multicloud development.

In April, Iron.io announced its partnership with Mirantis to bring event-driven, serverless functionality to the OpenStack community. The joint solution enables enterprise developers using OpenStack to deliver applications and services faster through the serverless experience provided by Iron.io.

In May, Iron.io announced its collaboration with Cloud Foundry Foundation, home of the industry-standard multi-cloud platform, to integrate the Iron.io API with the Cloud Foundry platform.

In June, Iron.io brought the serverless experience to Red Hat OpenShift — a pairing that provided users with an end-to-end environment for building and deploying applications at scale, without the headaches of complex operations.

And in August, Iron.io announced its strategic partnership with Mesosphere, enabling microservices and serverless computing for modern data centers. Joint customers using Mesosphere’s Data Center Operating System (DC/OS) with Iron.io could experience enhanced flexibility to develop their hybrid cloud strategy and run distributed job processing across heterogeneous environments.

Yesterday, we added an announcement that serverless functionality is now available on Cloud Foundry and Iron.io supports Diego as a runtime for Iron.io workloads. Iron.io is now able to be deployed on top of Cloud Foundry, run inside of Cloud Foundry, and scale out Cloud Foundry containers.

Wow. I was here for all of it and it still seems like a lot, but it’s only the beginning. The Iron.io team is committed to bringing a serverless experiences to developers and companies far and wide.

If you want information on how we define serverless and why the world is moving this way, check out Chad Arimura’s presentation Best Practices for Implementing Serverless Architecture from the O’Reilly Software Architect conference or Dave Nugent and Ivan Dywer’s great Fireside Chat about serverless computing.