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<!DOCTYPE html>
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<body class="article">
<div id="header">
<h1>Funcatron</h1>
<div id="toc" class="toc">
<div id="toctitle">Table of Contents</div>
<ul class="sectlevel1">
<li><a href="#_about">About</a>
<ul class="sectlevel2">
<li><a href="#_what_s_funcatron">What’s Funcatron</a></li>
<li><a href="#_software_lifecycle">Software Lifecycle</a></li>
<li><a href="#_architecture">Architecture</a></li>
<li><a href="#_contributing">Contributing</a></li>
<li><a href="#_licenses_and_support">Licenses and Support</a></li>
</ul>
</li>
<li><a href="#_documentation_versions">Documentation Versions</a></li>
</ul>
</div>
</div>
<div id="content">
<div class="sect1">
<h2 id="_about">About</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Serverless on Your Cluster:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Define Endpoints in Swagger</p>
</li>
<li>
<p>Write Functions in Java, Scala, Clojure, Python, or JavaScript</p>
</li>
<li>
<p>Deploy to Mesos, Kubernets, or Swarm</p>
</li>
<li>
<p>Autoscale</p>
</li>
</ul>
</div>
<div class="paragraph">
<p><em>Funcatron let’s you deploy serverless on any cloud provider or in your
private cloud. Focus on the functions, avoid vendor lock-in.</em></p>
</div>
<div class="paragraph">
<p>This document sets out the goals for the [Funcatron](<a href="http://funcatron.org" class="bare">http://funcatron.org</a>) project.</p>
</div>
<div class="sect2">
<h3 id="_what_s_funcatron">What’s Funcatron</h3>
<div class="paragraph">
<p>Amazon’s <a href="https://aws.amazon.com/lambda/">Lambda</a> popularized
<a href="http://www.martinfowler.com/articles/serverless.html">“serverless”</a>
code deployment. It’s dead simple: associate a “function” with an event.
Each time the event happens, the function is applied and the function’s
return value is returned to the event source. An event could be an HTTP(S)
request, something on an event queue, whatever.</p>
</div>
<div class="paragraph">
<p>Functions are ephemeral. They exist for the duration of the function call.
Once the function returns a value, all of its state and scope and everything
else about it is assumed to go away.</p>
</div>
<div class="paragraph">
<p>Scaling this kind of architecture is simple: the more frequently a function gets
applied, the more compute resources are allocated to support the function…​
and <a href="https://en.wikipedia.org/wiki/Bob%27s_your_uncle">Bob’s Your Uncle</a>.</p>
</div>
<div class="paragraph">
<p>The current popular function runners (competitors to Amazon’s Lambda), however, are
proprietary: when you write to the API for Lambda or Google’s
<a href="https://cloud.google.com/functions/docs/">Cloud Functions</a>,
<em><strong>you’re locked into that vendor</strong></em>.</p>
</div>
<div class="paragraph">
<p>There’s currently no (well, there’s <a href="https://developer.ibm.com/openwhisk/">OpenWhisk</a>)
generic way to do the auto-scale function thing on a private cloud or in a
way that can migrate from one cloud provider to another.</p>
</div>
<div class="paragraph">
<p>Funcatron addresses this. Funcatron is a cloud-provider-neutral mechanism for
developing, testing, and deploying auto-scalable functions.</p>
</div>
<div class="paragraph">
<p>Funcatron is designed to run on container orchestration clusters:
<a href="https://mesosphere.com/">Mesos</a>, <a href="http://kubernetes.io/">Kubernetes</a>, or
<a href="https://docker.com">Docker Swarm</a>.</p>
</div>
</div>
<div class="sect2">
<h3 id="_software_lifecycle">Software Lifecycle</h3>
<div class="paragraph">
<p>Software goes through a lifecycle:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Authoring</p>
</li>
<li>
<p>Testing</p>
</li>
<li>
<p>Staging</p>
</li>
<li>
<p>Production</p>
</li>
<li>
<p>Debugging</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>Funcatron addresses software at each stage of the lifecycle.</p>
</div>
<div class="sect3">
<h4 id="_authoring">Authoring</h4>
<div class="paragraph">
<p>An engineers sits down to write software. The faster the turn-around between
code written and “trying it out,” the more productive the engineer will be.</p>
</div>
<div class="paragraph">
<p>Funcatron supports a “save, reload” model where the engineer saves a file
(presuming they’re using an IDE that does compilation on save or are using a
non-compiled language), and their function endpoints are available. That’s it.
No uploading. No reconfiguration. No waiting. The endpoint is available on save.</p>
</div>
<div class="paragraph">
<p>Further, the Funcatron model requires knowledge of two things:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><a href="http://swagger.io">Swagger</a></p>
</li>
<li>
<p>How to write a simple function in Java, Scala, Clojure, Python, or JavaScript.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p><strong>That’s it.</strong></p>
</div>
<div class="paragraph">
<p>The engineer defines the endpoints in Swagger and uses the <code>operationId</code> to
specify the function (or class for Java and Scala) to apply when then endpoint
is requested. Funcatron takes care of the rest.</p>
</div>
<div class="paragraph">
<p>Between the fast turn-around and low “new stuff to learn” quotient,
it’s easy to get started with Funcatron. It’s also easy to stay productive
with Funcatron.</p>
</div>
<div class="paragraph">
<p>Also, developers need only have Docker installed on their development machine
to live-test Funcatron code.</p>
</div>
</div>
<div class="sect3">
<h4 id="_testing">Testing</h4>
<div class="paragraph">
<p>Because Funcatron endpoints are single functions (or methods on newly
instantiated classes), writing unit tests is simple. Write a unit test and
test the function.</p>
</div>
</div>
<div class="sect3">
<h4 id="_staging">Staging</h4>
<div class="paragraph">
<p>Funcatron code bundles (Funcs) contain a Swagger endpoint definition and the
functions
that are associated with the endpoint and any library code. For JVM languages,
these are bundled into an Uber JAR. For Python, a
<a href="https://github.com/pantsbuild/pex">PEX</a>
file. The Swagger definitions for an endpoint are unique based on
host name and root path.</p>
</div>
<div class="paragraph">
<p>Funcatron supports aliasing Swagger fields and values in different
environments such that a single Swagger definition can be run
in staging and testing environments without change.
Thus, there’s one deployment unit (a Func bundle) that have well defined
behaviors across test, staging, and production servers.</p>
</div>
</div>
<div class="sect3">
<h4 id="_production">Production</h4>
<div class="paragraph">
<p>Funcatron allows simple deployment and undeployment of end-point collections
defined in Swagger files and implemented in a JVM language, Python, or NodeJS.</p>
</div>
<div class="paragraph">
<p>Requests are forwarded from Nginx via a message queue to a dispatcher (a Tron).
Based on the hostname and root path, the message is placed on a queue for
a specific Func. The Func processes the request and sends the response
to a reply queue. The Nginx process dequeues the response and returns
it as an HTTP response.</p>
</div>
<div class="paragraph">
<p>The number of Func instances running on a cluster is based on the queue depth
and response time. The Func manager sends statistics back to the Trons
and the Trons change Func allocation based on these statistics by
communicating with the container orchestration substrate (Mesos, Kubernetes,
Swarm) and changing the allocation of Func running containers.</p>
</div>
<div class="paragraph">
<p>From the DevOps point of view: deploy a Func and it binds to the appropriate
HTTP endpoint and scales to handle load.</p>
</div>
</div>
<div class="sect3">
<h4 id="_debugging_test_cases">Debugging & Test Cases</h4>
<div class="paragraph">
<p>Funcatron logs a unique request ID and the SHA of the Func with every log line
related to a request. This allows correlation of requests as they fan out through
a cluster.</p>
</div>
<div class="paragraph">
<p>Funcatron allows dynamic changing log levels on a Func-by-Func basis which allows
capturing more information on demand.</p>
</div>
<div class="paragraph">
<p>All communications between the front end, Funcs, and back again are via well
defined JSON payloads. Funcatron allows capturing request and response
payloads on a Func-by-Func basis (complete streams, or random sampling).
This data can be used for testing or debugging.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_architecture">Architecture</h3>
<div class="paragraph">
<p>Funcatron has some ambitious goals…​ and has an architecture to facilitate
achieving these goals.</p>
</div>
<div class="paragraph">
<p>In all but development mode, Funcatron runs on a Docker container orchestration
system: Mesos, Kubernetes, or Docker Swarm. We call this the "container
substrate." Each of the Funcatron components can be scaled independently with
messages to the container substrate.</p>
</div>
<div class="paragraph">
<p>For HTTP requests, Funcatron uses Nginx and Lua (via the
<a href="http://openresty.org/en/">OpenResty</a> project) to handle the HTTP requests.
A small
Lua script encodes the request as a payload that’s sent to a message broker
(initially RabbitMQ, but this will be pluggable, e.g. Kafka, Redis). For large
request or response bodies, there will be a direct connection between the Front End
and the Runner.
For all but the highest volume installations, 2 Nginx instances
should be sufficient.</p>
</div>
<div class="paragraph">
<p>Based on the combination of <code>host</code> and <code>pathPrefix</code> attributes in the Swagger
module definition, the Tron enqueues the request on the appropriate queue.</p>
</div>
<div class="paragraph">
<p>A Runner module dequeues messages from a number of host/pathPrefix queues and
forwards the request to the appropriate Func. The runner then takes the function
return value and appropriately encodes it and places it on the reply queue which
dequeued by the original endpoint.</p>
</div>
<div class="paragraph">
<p>Each Func can run multiple modules. Based on queue depth, queue service time,
and CPU usage stats from the Funcs, more runners can be allocated on the substrate,
or more Funcs can be allocated across the runners.</p>
</div>
<div class="paragraph">
<p>The Lua scripts dequeues the response and turns in into an Nginx response.</p>
</div>
<div class="paragraph">
<p>Because all of the operation of the Funcs and Trons can be captured as messages
(and all the messages are in JSON), it’s possible to capture message streams for
testing and debugging purposes.</p>
</div>
<div class="paragraph">
<p>Every request has a unique ID and each log line includes the unique ID so it’s
possible to correlate a request as it moves across the cluster.</p>
</div>
<div class="imageblock">
<div class="content">
<img src="diag-7de02e942de8e578254a0d5d4c2090aa.png" alt="diag 7de02e942de8e578254a0d5d4c2090aa" width="801" height="408">
</div>
</div>
<div class="sect3">
<h4 id="_notes">Notes</h4>
<div class="paragraph">
<p>The initial implementation uses Nginx/OpenResty, RabbitMQ, Java/Scala, and Mesos
to support HTTP requests. This is not “hardcoded” but pluggable. Specifically:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Anything that can enqueue a payload and dequeue the response can
work with the rest of Funcatron. The initial implementation is HTTP via
Nginx/OpenResty, but nothing in the rest of the system depends on what enqueues
the request and dequeues the response.</p>
</li>
<li>
<p>RabbitMQ is the initial message broker, but it could be Kafka, Redis, or any other
message broker. This is pluggable.</p>
</li>
<li>
<p>Initially, dispatch from Runners to Funcs will be Java/Scala/Kotlin classes. But the
dispatch is also pluggable so other languages (Clojure) and
runtimes (Python, NodeJS, Ruby/Rails) will be supported.</p>
</li>
<li>
<p>“But Swagger is HTTP only” well…​ yes and no…​ the verb and the scheme are
HTTP-specific, but they can be ignored…​ and by the time the request is
dequeued by the Runner, the origin (HTTP or something else) of the message
is irrelevant. The power of Swagger is two-fold:</p>
<div class="ulist">
<ul>
<li>
<p>Excellent definitions of incoming and outgoing data shapes</p>
</li>
<li>
<p>Great tooling and lots of general Swagger skills</p>
</li>
</ul>
</div>
</li>
</ul>
</div>
<div class="paragraph">
<p>Because everything in Funcatron is asynchronous messages, how the messages are
passed, where the message originate and where responses are dequeued are all
pluggable and irrelevant to the other parts of the system.</p>
</div>
<div class="paragraph">
<p>The key idea in Funcatron is the Func is a well defined bundle of functionality
that’s associated with a particular message signature that maps to well HTTP via
host, pathPrefix, path, and verb, but could map to something else.</p>
</div>
<div class="paragraph">
<p>It may be possible to chain Func invocations. I don’t yet have a concrete
design, but rather than enqueuing a Func return value as a response, it may
be possible to package it as a request (the request body contains the Func
return value) and forwarding it to another Func for further processing.</p>
</div>
<div class="paragraph">
<p>Finally, if there’s no <code>reply-to</code> field in a message, the Func is applied (invoked)
but the results are discarded. This allows for side effects from the Func
rather than just computation.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_contributing">Contributing</h3>
<div class="paragraph">
<p>Please see <a href="https://github.com/funcatron/tron/blob/master/CONTRIBUTING.md">CONTRIBUTING</a> for details on
how to make a contribution.</p>
</div>
</div>
<div class="sect2">
<h3 id="_licenses_and_support">Licenses and Support</h3>
<div class="paragraph">
<p>Funcatron is licensed under an Apache 2 license.</p>
</div>
<div class="paragraph">
<p>Support is available from the project’s founder,
[David Pollak](mailto:[email protected]).</p>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_documentation_versions">Documentation Versions</h2>
<div class="sectionbody">
<div class="ulist">
<ul>
<li>
<p><a href="master/index.html">master</a></p>
</li>
<li>
<p><a href="v026/index.html">v0.2.6</a></p>
</li>
<li>
<p><a href="v025/index.html">v0.2.5</a></p>
</li>
<li>
<p><a href="v023/index.html">v0.2.3</a></p>
</li>
<li>
<p><a href="v022/index.html">v0.2.2</a></p>
</li>
<li>
<p><a href="v021/index.html">v0.2.1</a></p>
</li>
<li>
<p><a href="v020/index.html">v0.2.0</a></p>
</li>
<li>
<p><a href="v013/index.html">v0.1.3</a></p>
</li>
<li>
<p><a href="v011/index.html">v0.1.1</a></p>
</li>
<li>
<p><a href="v010/index.html">v0.1.0</a></p>
</li>
</ul>
</div>
</div>
</div>
</div>
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<div id="footer-text">
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