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.

The architecture of PL/Container is described at PL/Container-Architecture

Requirements

1. PL/Container runs on CentOS/RHEL 7.x or CentOS/RHEL 6.6+
2. PL/Container requires Docker version 17.05 for CentOS/RHEL 7.x and Docker version 1.7 CentOS/RHEL 6.6+
3. GPDB version should be 5.2.0 or later. For PostgreSQL

Building PL/Container

Get the code repo

git clone https://github.com/greenplum-db/plcontainer.git

You can build PL/Container in the following way:

1. Go to the PL/Container directory: cd plcontainer
2. PL/Container needs libcurl >=7.40. If the libcurl version on your system is low, you need to upgrade at first. For example, you could download source code and then compile and install, following this page: Install libcurl from source. Note you should make sure the libcurl library path is in the list for library lookup. Typically you might want to add the path into LD_LIBRARY_PATH and export them in shell configuration or greenplum_path.sh on all nodes (Note you need to restart the Greenplum cluster).
3. Make and install it: make clean && make && make install
4. Make with code coverage enabled (For dev and test only): make clean && make ENABLE_COVERAGE=yes && make install. After running test, generate code coverage report: make coverage-report

Configuring PL/Container

To configure PL/Container environment, you need to enable PL/Container for specific databases by running

psql -d your_database -c 'create extension plcontainer;'

Running the regression tests

1. Prepare docker images for R & Python environment. Refer How to build docker image for docker file examples. You can also download PLContainer images from pivotal networks

2. Tests require some images and runtime configurations are installed.

   Install the PL/Container R & Python docker images by running

   plcontainer image-add -f /home/gpadmin/plcontainer-r-images.tar.gz;
   plcontainer image-add -f /home/gpadmin/plcontainer-python-images.tar.gz

   Add runtime configurations as below

   plcontainer runtime-add -r plc_r_shared -i pivotaldata/plcontainer_r_shared:devel -l r
   plcontainer runtime-add -r plc_python_shared -i pivotaldata/plcontainer_python_shared:devel -l python

3. Go to the PL/Container test directory: cd plcontainer/tests

4. Make it: make tests

Note that if you just want to test or run your own R or Python code, you do just need to install the image and runtime for that language.

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 $$
# container: plc_python_shared
return 'hello from Python'
$$ LANGUAGE plcontainer;

There are a couple of things you need to pay attention to:

1. The LANGUAGE argument to Greenplum is plcontainer

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

PL/Container supports various parameters for docker run, and also it supports some useful UDFs for monitoring or debugging. Please read the official document for details.

Contributing

PL/Container is maintained by a core team of developers with commit rights to the plcontainer repository on GitHub. At the same time, we are very eager to receive contributions and any discussions about it from anybody in the wider community.

Everyone interests PL/Container can subscribe gpdb-dev mailist list, send related topics to gpdb-dev, create issues or submit PR.