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PL/Container

This is an implementation of trusted language execution engine capable of bringing up Docker containers to isolate executors from the host OS, i.e. implement sandboxing.

Requirements

  1. PL/Container runs on CentOS/RHEL 7.x as this is a prerequisite for Docker
  2. PL/Container requires Docker v1.10+ as it is built with Docker API v1.22 You can find more information in the user guide

Building Environment

git clone https://github.com/greenplum-db/plcontainer.git
cd plcontainer/vagrant/centos
vagrant up

That's it, Greenplum is built and installed, database is up and running, PL/Container code is available in /plcontainer directory inside of VM

Building PL/Container Language

You can build PL/Container in a following way:

  1. Login to Vagrant: vagrant ssh
  2. Go to the PL/Container directory: cd /plcontainer
  3. Make and install it: make clean && make && make install

Database restart is not required to catch up new container execution library, you can simply connect to the database and all the calls you will make to plcontainer language would be held by new binaries you just built

Building Containers

To build containers you have to:

  1. Login to Vagrant: vagrant ssh
  2. Go to the PL/Container directory: cd /plcontainer
  3. Make it: make containers

Configuring PL/Container

You can start with the default configuration. To apply it do the following:

  1. Login to Vagrant: vagrant ssh
  2. Reset the configuration: plcontainer-config --reset

Running the tests

  1. Login to Vagrant: vagrant ssh
  2. Go to the PL/Container test directory: cd /plcontainer
  3. Make it: make installcheck

Design

The idea of PL/Container is to use containers to run user defined functions. The current implementation assume the PL function definition to have the following structure:

CREATE FUNCTION dummyPython() RETURNS text AS $$
# container: plc_python
return 'hello from Python'
$$ LANGUAGE plcontainer;

There are a couple of things that are interesting here:

  1. The function definition starts with the line # container: plc_python which defines the name of container that will be used for running this function. To check the list of containers defined in the system you can run the command plcontainer-config --show. Each container is mapped to a single docker image, you can list the ones available in your system with command docker images

  2. The implementation assumes Docker container exposes some port, i.e. the container is started by an API call similar to running docker run -d -P <image> to publish the exposed port to a random port on the host. For an example of how to create a compatible docker image see Dockerfile.R and Dockerfile.python

  3. The LANGUAGE argument to Greenplum is plcontainer

  4. At the moment two languages are supported for PL/Container: R and Python. Here is the list of supported containers:

  • plc_python - Docker image pivotaldata/plcontainer_python:0.1.0-1 with Python
  • plc_anaconda - Docker image pivotaldata/plcontainer_anaconda:0.1.0-1 implements Anaconda Python
  • plc_r - Docker image pivotaldata/plcontainer_r:0.1.0-1 with R
  • plc_python_shared - Docker image pivotaldata/plcontainer_python_shared:0.1.0-1 with Python sharing the Greenplum-provided Python execution environment
  • plc_r_shared - Docker image pivotaldata/plcontainer_r_shared:0.1.0-1 with R sharing the Greenplum-provided R execution environment

For example, to define a function that uses a container that runs the R interpreter, simply make the following definition:

CREATE FUNCTION dummyR() RETURNS text AS $$
# container: plc_r
return(log10(100))
$$ LANGUAGE plcontainer;