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openeo-r-udf

Introduction

This repository contains a R package for implementing the concept of User-Defined Functions for processing Earth Observation (EO) data in cloud backends. This will allow users to run their custom code written in R to be executed on EO data such as satellite imageries with the help of processing backends conforming to the openEO API. The openEO API is being developed as part of the project "openEO".

Background

This repository is meant to be part of the H2020 funded project openEO. The objective of this project is to develop an uniform API to allow processing of Earth Observation (EO) data in cloud-based processing backends from various client nodes. In this API framework, User-Defined Functions (UDFs) is a concept that would allow users to run their own scripts on EO data in these cloud backends.

User-Defined Functions

The UDFs are implemented by developing an UDF API which work hand-in-hand with the openEO core API. The main idea is that there are UDF (web-) services which could be used by the backends as required. The typical workflow is:

  1. The user uploads his/her script from the client nodes to the backends along with the process graph
  2. The backend executes the process graph and encounters the UDF in the process graph
  3. The backend seeks the services of the UDF service to execute the user's script and sends the script and intermediate data to the service through appropriate means
  4. The UDF service executes the script on the data and sends the result back to the backend.
  5. The backend receives the data and continues executing the process graph until the final result is obtained.
  6. The backend sends the completed result to the user's client node.

These UDF service is being developed for two different languages - Python and R. This repository concerns with the implementation using R.

Architecture

openEO UDF Architecture

In the openEO API, the different clients interact with the different backends through the openEO API which acts as a common language understood by both the clients and the backends. The UDF service is not accessible to the clients directly but only through the backends and hence the UDF service's internal operations are abstracted to the user.

Installation

Dependencies

To run the API you need to have the following packages installed:

  • stars (>= 0.4-0)
  • jsonlite
  • plumber
  • lubridate
  • sf

Depending on what analysis a potential UDF user can run this list needs to be extended.

These can be installed by running the following:

install.packages(c("stars", "plumber", "lubridate","sf"), dependencies = TRUE)

Additionally on Linux systems you need to install the following libraries to allow the "stars" package to function properly:

sudo apt-get install libudunits2-dev libgdal-dev -y

Running the API locally

Clone this repository and use RStudio (>= 1.2) to "plumb" api.R. Since version 1.2 RStudio allows to run plumbers "plumb" natively. But you can run the plumb also manually by executing the code in server_start.R. It will require the file api.R and data_transformation.R.

Using Docker

Docker provides a virtual containerized environment for running software. In order to install this R package in a Docker environment, please follow these steps:

  1. Install Docker on your machine. The installation instructions vary according to the Operating System. Detailed instructions for all common Operating Systems may be found here: https://docs.docker.com/install/.
  2. Make sure that Docker has been installed correctly using the following command. Details on containers and Docker version will be shown.
docker info
  1. Run the provided docker-compose.yml. In the shell / console change directory to this cloned GitHub repository. Run docker-compose up -d.

Usage

For first code examples, please have a look at /examples/introduction.Rmd. In combination with the two test data sets /examples/hypercube.json and /examples/raster_collection_tile.json you can start experimenting with OpenEO UDFs. In the markdown file there is a run_udf function defined that combines the code, the serialized data and sends it to the service for processing. The request is build according to the API description. Currently the services offers the functionality for POST /udf with HyperCubes and RasterCollectionTiles as supported input data. The results are provided as a Hypercube.