CEDA Near-Line Data Store

This is the server code for the CEDA Near-Line Data Store (NLDS), consisting of an HTTP API and a cluster of rabbit consumer microservices. The NLDS client is required to communicate with the API, either via the command line interface or python client library.

The NLDS is a unified storage solution, allowing easy use of disk, s3 object storage, and tape from a single interface. It utilises object storage as a cache for the tape backend allowing for low-latency backup

The NLDS server is built upon FastAPI for the API, RabbitMQ for the message broker, minio for the s3 client, SQLAlchemy for the database client and xrootd for the tape interactions.

Documentation can be found here.

Installation

If installing locally we strongly recommend the use of a virtual environment to manage the dependencies.

1. Create a Python virtual environment:

   python3 -m venv nlds-venv
   

2. Activate the nlds-venv:

   source nlds-venv/bin/activate
   

3. You could either install the nlds package with editing capability from a locally cloned copy of this repo (note the inclusion of the editable flag -e), e.g.

   pip install -e ~/Coding/nlds
   

   or install this repo directly from github:

   pip install git+https://github.com/cedadev/nlds.git
   

4. (Optional) There are several more requirements/dependencies defined:

   • requirements-dev.txt - contains development-specific (i.e. not production appropriate) dependencies. Currently this consists of a psycopg2 binary python package for interacting with PostgeSQL from a local NLDS instance.
   • requirements-deployment.txt - contains deployment-specific dependencies, excluding XRootD. Currently this consists of the psycopg2 package but built from source instead of a precompiled binary.
   • requirements-tape.txt - contains tape-specific dependencies, notably XRootD.
   • tests/requirements.txt - contains the dependencies for the test suite.
   • docs/requirements.txt - contains the dependencies required for building the documentation with sphinx.

Server Config

To interface with the JASMIN accounts portal, for the OAuth2 authentication, a .server_config file has to be created. This contains infrastructure information and so is not included in the GitHub repository. See the relevant documentation and examples for more information.

A Jinja-2 template for the .server_config file can also be found in the templates/ directory.

Running the Server

1. The NLDS API requires something to serve the API, usually uvicorn in a local development environment:

   uvicorn nlds.main:nlds --reload
   

   This will create a local NLDS API server at http://127.0.0.1:8000/. FastAPI displays automatically generated documentation for the REST-API, to browse this go to http://127.0.0.1:8000/docs/

2. To run the microservices, you have two options:

   • In individual terminals, after activating the virtual env, (e.g. source ~/nlds-venv/bin/activate), start each of the microservice consumers:

     nlds_q
     index_q
     catalog_q  
     transfer_put_q   
     transfer_get_q
     logging_q
     archive_put_q
     archive_get_q
     

     This will send the output of each consumer to its own terminal (as well as whatever is configured in the logger).

   • Alternatively, you can use the scripts in the test_run/ directory, notably start_test_run.py to start and stop_test_run.py to stop. This will start a screen session with all 8 processors (+ api server) in, sending each output to a log in the ./nlds_log/ directory.

Tests

The NLDS uses pytest for its unit test suite. Once test/requirements.txt have been installed, you can run the tests with

pytest

in the root directory. Pytest is also used for integration testing in the separate nlds-test repo.

The pytest test-coverage report can (hopefully) be found here.

License

The NLDS is available on a BSD 2-Clause License, see the license for more info.

Acknowledgements

NLDS was developed at the Centre for Environmental Data Analysis and supported through the ESiWACE2 project. The project ESiWACE2 has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 823988.