gcf.cr provides serverless execution and deployment of crystal language code in Google Cloud Functions
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README.md

gcf.cr

CircleCI

GCF provides managed execution of crystal language code within Google Cloud Functions. GCF compiles your crystal code statically using the durosoft/crystal-alpine docker image or optionally using your local crystal installation (if it is capable of static compilation) via the --local option. It then bundles your compiled crystal code in a thin node.js wrapper function and deploys it to GCP using the options you specify. An API is also provided for writing to the console, throwing errors, and returning the final response.

Installation

  1. set up a Google Cloud Platform account if you don't have one already and create an initial project
  2. install the gcloud sdk if you haven't already
  3. log in to gcloud locally via gcloud init if you haven't already
  4. install docker (if you haven't already)
  5. set up docker to not require sudo
  6. start the docker daemon (e.g. sudo systemctl start docker) if it isn't already running
  7. clone the repo git clone git@github.com:sam0x17/gcf.cr.git
  8. run ./setup. This will compile and install a gcf binary in /usr/bin.

If you plan to use docker-based static compilation (default option), you don't need to install crystal on your system as long as you have a statically compiled gcf binary. You can use the build_static script included in the repo to build a static binary for gcf using docker. That said, having crystal locally installed will make it easier to write tests.

Getting Started

All cloud functions should consist of a crystal app project (created via crystal init app) where the main project file (e.g src/my_project.cr) meets the requirements outlined below.

Add the following to your shard.yml file and run shards install:

# shard.yml
...
dependencies:
  gcf:
    github: sam0x17/gcf.cr
    branch: master

Create a class that inherits from GCF::CloudFunction and defines a run method that accepts an argument of type JSON::Any, typically named params:

# src/example.cr
require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    # your code here
  end
end

Once you have a setup like this, you can use the various API functions from within your run method, which makes up the body of your cloud function. The available API functions are listed below. Note that methods like send, send_file, and redirect stop execution when they are run, meaning any code after these methods will not run unless it was already defined in an at_exit block. If you do not call one of these methods in your function, your function will run until it times out.

Once you are done writing your function, you can deploy it using gcf --deploy.

Crystal API

A crystal-based API is provided for communicating with the Google Cloud Function host process so you can do things like log to the console redirect the browser, or send textual or file-based data to the browser. The API is a thin layer on top of the underlying ExpressJS API used by Google Cloud Functions, and uses a combination of inter-process communication and files to send data to/from the host process.

The most basic requirement, as outlined in the getting started section, is that you create a class that inherits from GCF::CloudFunction and provide a definition for the run(params : JSON::Any) method.

params

The run method of your crystal function will provide the HTTP params object in the form of a JSON::Any object named params. You can access values in the params object as you would a hash:

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    console.log params
    send "color: #{params["color"]?}"
  end
end

If you send the http parameter color with the value of red, the function will return "color: red" with a 200 status code and you will get the following console output: console image

console.log(msg)

Logs whatever you pass it to the GCF console with info priority. Equivalent to using console.log in JavaScript. msg is interpolated so non-strings may be passed in.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    console.log "some info here"
  end
end

console.warn(msg)

Logs whatever you pass it to the GCF console with warn priority. Equivalent to using console.warn in JavaScript. msg is interpolated so non-strings may be passed in.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    console.warn "woah, warning"
  end
end

console.error(msg)

Logs whatever you pass it to the GCF console with error priority. Equivalent to using console.error in JavaScript. msg is interpolated so non-strings may be passed in.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    console.error "GASP! an error"
  end
end

send(content)

An alias for send(200, content), where 200 is the HTTP OK/ready status code.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    send "OK, done executing"
  end
end

send(status : Int, content)

Sends the interpolated version of content as output to the browser with an HTTP status code of status, and stops execution of the cloud function. This is forwarded to req.send in ExpressJS.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    send 200, "<h1>YO</h1>"
  end
end

redirect(url : String)

An alias for redirect false, url, since temporary redirects are usually preferable when used with cloud functions.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    redirect "https://google.com"
  end
end

redirect(permanent : Bool, url : String)

Redirects the browser to the specified url. If permanent is true, it will do a 301 redirect. If permanent is false, it will do a 302 redirect.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    redirect true, "https://google.com"
  end
end

send_file(path : String)

An alias for send_file 200, path, since typically you will only want to send file content with a status code of 200.

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    send_file "van_gogh.jpg"
  end
end

send_file(status : Int, path : String)

Sends the file at the specified path to the browser with an HTTP status code of status (to write files from crystal in a cloud function, you need to write to something in /tmp).

require "gcf"

class Example < GCF::CloudFunction
  def run(params : JSON::Any)
    send_file 200, "van_gogh.jpg"
  end
end

Note on puts

If you call puts directly from within a cloud function's run method, this gets mapped to console.log. This does not apply to puts calls that are made indirectly (e.g. calling code outside of this class), so the contents of these puts calls will not be handled correctly and lead to undefined behavior.

Note on exceptions

Right now exceptions work locally but not in a deployed function. We are working on this but please feel free to take a look at #1 and help out if you have any ideas. From what we can tell, all execution stops the second an exception is thrown, even from within a try-catch block, when a function is executing on GCP. For now we are logging a generic message stating that an error occurred, however we are unable to retrieve the error stacktrace or name (locally we are able to do this).

Tests / Specs

You can test your cloud function with specs the same way you would any conventional crystal-based app or library. See gcf_spec.cr for examples of how to test for particular functions outputs, redirects, etc. Also make sure to set GCF.test_mode = true in your spec_helper.cr file before any specs are loaded or the development server will attempt to run before your specs can run.

Example spec_helper.cr file:

# spec/spec_helper.cr
GCF.test_mode = true

require "spec"
require "../src/my_project.cr"

Development Mode / Test Server

A built-in test server is provided (based on Kemal) that allows you to simulate requests to your (HTTP-triggered) cloud functions on your local machine. Simply compile and run your cloud function e.g. crystal run src/*.cr and Kemal will automatically start the test server on port 8080. You can access it in a web browser by going to http://localhost:8080/, which will trigger your cloud function and load the result as if you just visited the HTTP trigger URL for the function. Note that while send_file/send/redirect methods normally stop execution of your function once they execute, this is not the case in the test server, though this is something we are trying to find a workaround for.

GET and POST params that are sent to the test server are automatically loaded into the params of your cloud function when it is invoked.

gcf_test$ crystal run src/gcf_test.cr
[development] Kemal is ready to lead at http://0.0.0.0:8080
2018-07-27 04:04:06 -04:00 200 GET / 375.0µs
{"color" => "red"}
2018-07-27 04:04:13 -04:00 200 GET /?color=red 1.41ms

Deploying

Note that GCF expects your crystal function to follow the directory structure imposed by crystal init app, in that all of your crystal code should reside in project_name/src/. During compilation, GCF uses the src/*.cr glob to compile all crystal files in the src directory.

Note also that GCF will automatically consult gcloud to discover the current GCP project id if one isn't specified.

Below you can find some basic usage examples fo rcommon use cases. For full usage information, please see the output of gcf --help.

Compile the current directory using the docker image and deploy as a function named after the current directory (default):

gcf --deploy

Specifying the source directory, static compilation using the local crystal installation, the function name, the memory capacity of the deployed function, and the google project ID respectively.

gcf --deploy --source /home/sam/proj --local --name hello-world --memory 2GB --project cool-project

Or using shorthand:

gcf -d -s /home/sam/proj -l -n hello-world -m 2GB -p cool-project

TODO

  1. attribute API so we don't need command line params
  2. fix exceptions logging bug (#1)
  3. more testing