Example single function in FaaSr

Overview

This example guides you through the setup and execution of a simple FaaSr function. You will learn how to describe, configure, and execute a FaaSr function using GitHub Actions for cloud execution of functions, and a public Minio S3 “bucket” for cloud data storage. With the knowledge gained from this tutorial, you will be able to also run FaaSr workflows in OpenWhisk and Amazon Lambda, as well as use an S3-compliant bucket of your choice.

Requirements

The main requirements to follow this vignette are: * FaaSr installed in RStudio in your desktop * Git installed in your computer * minioclient installed in your desktop (optional, but recommended - you can also use other S3 clients) * A GitHub account

For FaaSr to use your GitHub account, you need a GitHub personal access token (PAT), configured to enable at least the “workflow” and “read:org” (under admin:org) scopes. With your GitHub PAT in hand, you can configure Rstudio and FaaSr to use it:

Configure your Rstudio environment with your credentials

Ensure your Rstudio session can access GitHub account using your PAT so FaaSr can register and invoke functions on your behalf.

Replace YOUR_GITHUB_USERNAME and YOUR_GITHUB_EMAIL with your account information in the command below:

usethis::use_git_config(user.name = "YOUR_GITHUB_USERNAME", user.email = "YOUR_GITHUB_EMAIL")

This function will prompt you for your GitHub PAT token; copy and paste it to the pop-up window:

credentials::set_github_pat()

note: make sure your git installation is set to use main as the default branch. This is typically the default in most modern systems, but if your git version uses master as the default, you can change with this terminal command: git config --global init.defaultBranch main

Add your credentials to faasr_env file

You also need to add your GitHub and S3 credentials to a faasr_env file. Create a new faasr_env file with the following information, replacing with your GitHub PAT token, and save it to your current working directory. The S3 credentials below are pre-set to use the Minio Play server.

"My_GitHub_Account_TOKEN"="REPLACE_WITH_YOUR_GITHUB_TOKEN"
"My_Minio_Bucket_ACCESS_KEY"="Q3AM3UQ867SPQQA43P2F"
"My_Minio_Bucket_SECRET_KEY"="zuf+tfteSlswRu7BJ86wekitnifILbZam1KYY3TG"

Create GitHub repo and R function

Use the GitHub web site to create a new repository, named FaaSr_single_function_code This is where your R source code will go.

Create a file create_sample_data.R file in this repository, copying and pasting the code below. This function creates two synthetic .csv files and uploads to an S3 bucket:

create_sample_data <- function(folder, output1, output2) {

  # Create sample files for FaaSr example and stores in an S3 bucket
  #
  # The function uses the default S3 bucket name, configured in the FaaSr JSON
  # folder: name of the folder where the sample data is to be stored
  # output1, output2: names of the sample files to be created 
 
  df1 <- NULL
  for (e in 1:10)
    rbind(df1,data.frame(v1=e,v2=e^2,v3=e^3)) -> df1
  df2 <- NULL
  for (e in 1:10)
    rbind(df2,data.frame(v1=e,v2=2*e,v3=3*e)) -> df2

  # Now we export these data frames to CSV files df1.csv and df2.csv stored in a local directory
  #
  write.table(df1, file="df1.csv", sep=",", row.names=F, col.names=T)
  write.table(df2, file="df2.csv", sep=",", row.names=F, col.names=T)
  
  # Now, upload the these file to the S3 bucket with folder name and file name provided by user
  #
  faasr_put_file(local_file="df1.csv", remote_folder=folder, remote_file=output1)
  faasr_put_file(local_file="df2.csv", remote_folder=folder, remote_file=output2)

  # Print a log message
  # 
  log_msg <- paste0('Function create_sample_data finished; outputs written to folder ', folder, ' in default S3 bucket')
  faasr_log(log_msg)
}   

Create JSON configuration file

You will use the FaaSr workflow builder Shiny app to create your JSON configuration file.

Define a Data Server

  1. From the drop-down menu on the left, select Data Server.
  2. For Data Server Name, enter My_Minio_Bucket - this is the name your S3 server will be referred to for any upload/download operations.
  3. For Data Server endpoint, enter https://play.min.io - this is the Internet address of the S3 server.
  4. For Data Server Bucket, enter faasr
  5. Leave the remaining fields as the default, and click the Apply button. You will see an icon for this data server in the main window to the right.
Workflow builder - defining data server
Workflow builder - defining data server

Define a FaaS Server

  1. From the drop-down menu on the left, select FaaS Server.
  2. For FaaS Server Name, enter My_GitHub_Account - this is the name your FaaS server will be referred to for any functions that are executed.
  3. For the Select Type drop-down, select GitHubActions.
  4. For GitHubActions user name, enter your GitHub username. This will ensure FaaSr runs under your account.
  5. For GitHubActions Action Repository name, enter FaaSr_single_function_action - this is where GitHub Actions will be configured by FaaSr.
  6. For GitHubActions Branch, enter main
  7. Leave the remaining fields as the default, and click the Apply button. You will see an icon for this FaaS server in the main window to the right.
Workflow builder - defining FaaS server after data server has been configured
Workflow builder - defining FaaS server after data server has been configured

Define the Workflow

  1. From the drop-down menu on the left, select Functions.
  2. Under Action Name, enter Action1 - this is the name that will be used for your GitHub Action.
  3. Under Function Name, enter create_sample_data - this is the name of the R function you created in the previous section.
  4. Under Function FaaS Server, leave the default My_GitHub_Account - this is the name of the server you configured in the previous step.
  5. Under Function Arguments, enter the following arguments, which will be passed to the create_sample_data() function:
folder="myexample",
output1="sample1.csv",
output2="sample2.csv"
  1. Under Next Actions to Invoke, leave it blank (the default). This single function does not invoke any other functions.
  2. Under Function's Action Container, leave it blank (the default). This example will use the default Rocker/Tidyverse FaaSr container.
  3. Under Repository/Path, where the function is stored, enter your GitHub username and repository you created in a previous step, e.g. username/FaaSr_single_function_code
  4. Leave the remaining fields as the default, and click the Apply button. You will see an icon for this function in the main window to the right.
Workflow builder - defining the workflow after both data and FaaS servers have been configured
Workflow builder - defining the workflow after both data and FaaS servers have been configured

Configure starting function and log server

  1. From the drop-down menu on the left, select General Configuration.
  2. For the First Function to be executed drop-down, select Action1. This is the first (and only) function in this workflow graph.
  3. For the Logging Data Server drop-down, select My_Minio_Bucket. This defines the S3 server where logs will be directed to with the faasr_log() functions in the R code above.
  4. For the Default Data Server drop down, also select My_Minio_Bucket. This defines the S3 server where files are stored/retrieved from with the faasr_put_file() and faasr_get_file() functions in the R code above.
  5. Leave the remaining fields as the default, and click the Apply button.