bas-web-map-inventory 0.4.0

Inventory of geospatial layers and web maps provided by the BAS Mapping and Geographic Information Centre (MAGIC), visualised in Airtable.

MAGIC Web Map Inventory

Inventory of geospatial layers and web maps provided by the BAS Mapping and Geographic Information Centre (MAGIC), visualised in Airtable.

• view the inventory in Airtable (Internal)

See the Data model section for more information about what this inventory holds.

Note: This project is designed for internal use within MAGIC, but is offered to anyone with similar needs.

Usage

This project runs in a container. See the Setup section for setup instructions.

If running on the BAS central workstations:

$ ssh [e-mail]
$ web-map-inventory [task]

Configuration, logs and data output are stored in /users/geoweb/.config/web-map-inventory/.

If any errors occur they will be reported to Sentry and relevant individials alerted by email.

Commands

version

Reports the current application version.

data fetch

Fetches information about servers, namespaces, repositories, styles, layers and layer groups from servers defined in a data sources file. Fetched information is saved to an output data file.

Options:

• -s, --data-sources-file-path:
  • path to a data sources file
  • default: data/sources.json
• -d, --data-output-file-path:
  • path to a data sources file
  • default: data/data.json

Note: Currently this task results in new IDs being generated for each resource, even if it already exists. This will lead to resources being removed and re-added unnecessarily but will always remain internally consistent.

data validate

Validates protocols offered by servers defined in a data sources file (by default data/sources.json).

Options:

• -s, --data-sources-file-path:
  • path to a data sources file
  • default: data/sources.json
• -i, --data-source-identifier:
  • identifier of a server in the data sources file
  • use special value all to select all data sources
• -p, --validation-protocol:
  • protocol to validate
  • default: wms

Note: Currently this task is limited to the WMS (OGC Web Map Service) protocol.

airtable status

Checks local items against Airtable to check whether they are up-to-date (current), outdated, missing or orphaned.

airtable sync

Creates, updates or removes items in Airtable to match local items.

airtable reset

Removes all data from Airtable.

Managing data sources

Each data source is represented as an object in the server list in data/sources.json [1]. The structure of each source depends on its type. For more general information, see the Data sources section.

[1] This file is either in the runtime path created during Setup or ~/.config/web-map-inventory/ on the BAS central servers).

Adding new data sources

Note: See Supported data sources for currently supported data sources.

Once added use the data fetch task.

Adding a GeoServer data source

Property	Required	Data Type	Allowed Values	Example Value	Description	Notes
id	Yes	String	A ULID (Universally Unique Lexicographically Sortable Identifier)	01DRS53XAJNH0TNBW5161B6EWJ	Unique identifier for server/source	See below for how to generate
label	Yes	String	Any combination of a-Z, A-Z, 0-9, -, _	a-1_A	Using a short, well-known identifier	-
hostname	Yes	String	Any valid hostname	example.com	-	-
type	Yes	String	geoserver	See allowed value	-	-
port	Yes	String	Any valid port number	8080	-	Usually 80 or 8080
api-path	Yes	String	/geoserver/rest	See allowed value	Defined by GeoServer	-
wms-path	Yes	String	/geoserver/ows?service=wms&version=1.3.0&request=GetCapabilities	See allowed value	Defined by GeoServer	-
wfs-path	Yes	String	/geoserver/ows?service=wfs&version=2.0.0&request=GetCapabilities	See allowed value	Defined by GeoServer	-
username	Yes	String	Any valid GeoServer username	admin	Usually the GeoServer admin user	-
password	Yes	String	Password for GeoServer user	password	Usually the GeoServer admin user	-

Note: Use ulidgenerator.com to generate ULIDs manually.

Example:

{
  "id": "xxx",
  "label": "example",
  "hostname": "example.com",
  "type": "geoserver",
  "port": "80",
  "api-path": "/geoserver/rest",
  "wms-path": "/geoserver/ows?service=wms&version=1.3.0&request=GetCapabilities",
  "wfs-path": "/geoserver/ows?service=wfs&version=2.0.0&request=GetCapabilities",
  "username": "admin",
  "password": "password"
}

Note: Make sure to include a comma between each source (but don't include a trailing comma) - e.g.:

{
    "servers": [
        {
            "id": "xxx",
            "label": "existing-server"
            ...
        },
        {
            "id": "xxx",
            "label": "new-server"
            ...
        }
    ]
}

Implementation

Flask application using the airtable-python-wrapper library to interact with the Airtable API.

Airtable

Data is synced to the MAGIC Maps and Layers Inventory Base in the BAS MAGIC Workspace.

Data model

This project, an inventory, consists of information held in geospatial services. The data model is intended to be generic to support different data sources and technologies.

This data model consists of:

• Servers: Represent a source of geospatial information, such as an instance of a technology or a whole platform
• Namespaces: Represent a logical grouping of resources within a server/endpoint
• Repositories: Represent a data source that backs one or more layers
• Styles: Represent a definition for how data in a layer should be represented/presented
• Layers: Represent a logical unit of geospatial information
• Layer Groups: Represent a logical grouping of one or more layers that should be treated as a single, indivisible unit

A JSON Schema describes this schema. It is used internally for validating data prior to use but is also published for use by others if needed as: data-schema-v1.json.

Data sources

Data sources are servers in the project Data model and define connection details for APIs and services each server type provides for fetching information about components they contain (e.g. listing layers).

A data sources file, data/sources.json, is used for recording these details. An example is available in data/sources.example.json. See the Adding a data source section for more information.

A JSON Schema, bas_web_map_inventory/resources/json_schemas/data-sources-schema.json, validates this file.

Supported data sources

• GeoServer
  • Using a combination of its admin API and WMS/WFS OGC endpoints

Configuration

Configuration options are set within bas_web_map_inventory/config.py.

All Options are defined in a Config base class, with per-environment sub-classes overriding and extending these options as needed. The active configuration is set using the FLASK_ENV environment variable.

Most options can be Set using environment variables or files.

Configuration options

Option	Required	Environments	Data Type (Cast)	Source	Allowed Values	Default Value	Example Value	Description	Notes
FLASK_APP	Yes	All	String	.flaskenv	Valid FLASK_APP value	manage.py	See default value	See Flask documentation	-
APP_ENABLE_SENTRY	No	All	Boolean	.flaskenv	True/False	False (for development/testing), True (for staging/production)	True	Feature flag for Error reporting	-
APP_ENABLE_FILE_LOGGING	No	All	Boolean	.flaskenv	True/False	False	False	Feature flag for writing Application Logs to a file in addition to standard out	-
SENTEY_DSN	Yes	Yes	String	.flaskenv	Sentry DSN for this project	https://[e-mail]/1832548	See default value	Sentry Data Source Name	This value is not a secret
APP_LOG_FILE_PATH	No	All	String	.flaskenv	Valid file path	/var/log/app/app.log	/var/log/app/app.log	Path to application log file, if enabled	-
AIRTABLE_API_KEY	Yes	All	String	.env	Valid AirTable API key	-	keyxxxxxxxxxxxxxx	AirTable API Key	-
AIRTABLE_BASE_ID	Yes	All	String	.env	Valid AirTable Base ID	-	appxxxxxxxxxxxxxx	ID of the AirTable Base to populate/use	-

Options are set as strings and then cast to the data type listed above. See Environment variables for information about an options 'Source'.

Flask also has a number of builtin configuration options.

Setting configuration options

Variable configuration options can be set using environment variables or environment files:

Source	Priority	Purpose	Notes
OS environment variables	1st	General/Runtime	-
.env	2nd	Secret/private variables	Generate by copying .env.example
.flaskenv	3rd	Non-secret/public variables	Generate by copying .flaskenv.example

Note: these sources are a Flask convention.

Error tracking

Errors in this service are tracked with Sentry:

• Sentry dashboard
• GitLab dashboard

Error tracking will be enabled or disabled depending on the environment. It can be manually controlled by setting the APP_ENABLE_SENTRY variable in .flaskenv.

Logging

Logs for this service are written to stdout and a log file, /var/log/app/app.py, depending on the environment.

File based logging can be manually controlled by setting the APP_ENABLE_FILE_LOGGING and APP_LOG_FILE_PATH variables in .flaskenv.

Note: If APP_LOG_FILE_PATH is changed, the user the container runs as must be granted suitable write permissions.

XML Catalogue

An XML Catalog is used to cache XML files locally (typically XSD's for schemas). This drastically speeds up XML parsing and removes a dependency on remote endpoints.

XML files in the catalogue are typically stored in bas_web_map_inventory/resources/xml_schemas/.

Different catalogue files are used for different container variants due to differences in the applications location:

• :latest: ./support/xml-schemas/catalogue.xml
• /deploy: provisioning/docker/catalog.xml

In either case, the catalogue is available within the container at the conventional path, /etc/xml/catalog, and will be used automatically by most XML libraries and tools (such as lxml and xmllint).

Setup

The application for this project runs as a Docker container.

Once setup, see the Data sources and Usage sections for how to use and run the application.

Note: This project can run locally or on the BAS central workstations using Podman. You will need access to the private BAS Docker Registry (part of gitlab.data.bas.ac.uk) and for IT to enable Podman in your user account. Unless noted, docker commands listed here can be replaced with podman.

$ docker login docker-registry.data.bas.ac.uk
$ docker pull docker-registry.data.bas.ac.uk/magic/web-map-inventory/deploy:stable

Note: Other image tags are available if you want to run pre-release versions, or a specific, previous, version.

Before you can run the container, you will need to create a runtime directory that will live outside of the container. You will need to create the required Configuration files. Any generated output will also be saved here.

$ mkdir -p ~/.config/web-map-inventory

Optional wrapper script

If using podman, a wrapper script, support/container-wrapper/podman-wrapper.sh, is available to make running the container easier.

To use, copy this script and enable it to be executed:

$ mkdir ~/bin
# copy `support/container-wrapper/podman-wrapper.sh` as `~/bin/web-map-inventory`
$ chmod +x ~/bin/web-map-inventory

Then ensure ~/bin is part of the user's path (use echo $PATH to check), if it isn't edit the user's shell to include it (these instructions assume the bash shell and the absolute path to the user's home directory is /home/foo):

$ vi ~/.bash_rc
# add `export PATH="/home/foo/bin:$PATH" then save the file and reload the user's shell

You should now be able to run web-map-inventory to run the container.

Terraform

Terraform is used to provision resources required to allow JSON Schemas for data resources and data sources to be accessed externally.

Access to the BAS AWS account is needed to provisioning these resources.

Note: This provisioning should have already been performed (and applies globally). If changes are made to this provisioning it only needs to be applied once.

# start terraform inside a docker container
$ cd provisioning/terraform
$ docker-compose run terraform
# setup terraform
$ terraform init
# apply changes
$ terraform validate
$ terraform fmt
$ terraform apply
# exit container
$ exit
$ docker-compose down

Terraform remote state

State information for this project is stored remotely using a Backend.

Specifically the AWS S3 backend as part of the BAS Terraform Remote State project.

Remote state storage will be automatically initialised when running terraform init. Any changes to remote state will be automatically saved to the remote backend, there is no need to push or pull changes.

Remote state authentication

Permission to read and/or write remote state information for this project is restricted to authorised users. Contact the BAS Web & Applications Team to request access.

See the BAS Terraform Remote State project for how these permissions to remote state are enforced.

Development

$ git clone https://gitlab.data.bas.ac.uk/MAGIC/web-map-inventory
$ cd map-layer-index

Development environment

The :latest container image is used for developing this project. It can run locally using Docker and Docker Compose:

$ docker login docker-registry.data.bas.ac.uk
$ docker-compose pull app

Then create/configure required Configuration files:

$ cp .env.example .env
$ cp .flaskenv.example .flaskenv
$ cp data/sources.example.json data/sources.json

To run/test application commands:

$ docker-compose run app flask [task]

[1] You will need access to the private BAS Docker Registry (part of gitlab.data.bas.ac.uk) to pull this image. If you don't, you can build the relevant image/tag locally instead.

Code Style

PEP-8 style and formatting guidelines must be used for this project, with the exception of the 80 character line limit.

Black is used to ensure compliance, configured in pyproject.toml.

Black can be integrated with a range of editors, such as PyCharm, to perform formatting automatically.

To apply formatting manually:

$ docker-compose run app black bas_web_map_inventory/

To check compliance manually:

$ docker-compose run app black --check bas_web_map_inventory/

Checks are ran automatically in Continuous Integration.

Python version

When upgrading to a new version of Python, ensure the following are also checked and updated where needed:

• Dockerfile:
  • base stage image (e.g. FROM python:3.X-alpine as base to FROM python:3.Y-alpine as base)
  • pre-compiled wheels (e.g. https://.../linux_x86_64/cp3Xm/lxml-4.5.0-cp3X-cp3X-linux_x86_64.whl to http://.../linux_x86_64/cp3Ym/lxml-4.5.0-cp3Y-cp3Y-linux_x86_64.whl)
• provisioning/docker/Dockerfile:
  • base stage image (e.g. FROM python:3.X-alpine as base to FROM python:3.Y-alpine as base)
  • pre-compiled wheels (e.g. http://.../linux_x86_64/cp3Xm/lxml-4.5.0-cp3X-cp3X-linux_x86_64.whl to http://.../linux_x86_64/cp3Ym/lxml-4.5.0-cp3Y-cp3Y-linux_x86_64.whl)
• provisioning/docker/catalog.xml:
  • update the path to the Python package (e.g. file://.../lib/python3.X/site-packages/... to file://.../lib/python3.Y/site-packages/...)
• pyproject.toml
  • [tool.poetry.dependencies]
    • python (e.g. python = "^3.X" to python = "^3.Y")
  • [tool.black]
    • target-version (e.g. target-version = ['py3X'] to target-version = ['py3Y'])

Dependencies

Python dependencies for this project are managed with Poetry in pyproject.toml.

The development container image installs both runtime and development dependencies. Deployment images only install runtime dependencies.

Non-code files, such as static files, can also be included in the Python package using the include key in pyproject.toml.

To add a new (development) dependency:

$ docker-compose run app ash
$ poetry add [dependency] (--dev)

Then rebuild the development container and push to GitLab (GitLab will rebuild other images automatically as needed):

$ docker-compose build app
$ docker-compose push app

Static security scanning

To ensure the security of this API, source code is checked against Bandit for issues such as not sanitising user inputs or using weak cryptography. Bandit is configured in .bandit.

Warning: Bandit is a static analysis tool and can't check for issues that are only be detectable when running the application. As with all security tools, Bandit is an aid for spotting common mistakes, not a guarantee of secure code.

To run checks manually:

$ docker-compose run app bandit -r .

Checks are ran automatically in Continuous Integration.

Logging

Use the Flask default logger. For example:

app.logger.info('Log message')

When outside of a route/command use current_app:

from flask import current_app

current_app.logger.info('Log message')

File paths

Use Python's pathlib library for managing file paths.

Where displaying a file path to the user ensure it is always absolute to aid in debugging:

from pathlib import Path

foo_path = Path("foo.txt")
print(f"foo_path is: {str(foo_path.absolute())}")

JSON Schemas

JSON Schema's can be developed using jsonschemavalidator.net.

XML Catalogue additions

If new functionality is added that depends on XML files, such as XSDs, it is strongly recommended to add them to the XML catalogue, especially where they are used in tests.

Once added, you will need to rebuild and push the project Docker image (see the Dependencies section for more information).

Editor support

PyCharm

A run/debug configuration, App, is included in the project.

Testing

All code in the bas_web_map_inventory module must be covered by tests, defined in tests/. This project uses PyTest which should be ran in a random order using pytest-random-order.

To run tests manually from the command line:

$ docker-compose run app -e FLASK_ENV=testing app pytest --random-order

To run tests manually using PyCharm, use the included App (Integration) run/debug configuration.

Tests are ran automatically in Continuous Integration.

Test coverage

pytest-cov is used to measure test coverage.

To prevent noise, .coveragerc is used to omit empty __init__.py files from reports.

To measure coverage manually:

$ docker-compose run -e FLASK_ENV=testing app pytest --cov=bas_web_map_inventory --cov-fail-under=100 --cov-report=html .

Continuous Integration will check coverage automatically and fail if less than 100%.

Continuous Integration

All commits will trigger a Continuous Integration process using GitLab's CI/CD platform, configured in .gitlab-ci.yml.

Deployment

Python package

This project is distributed as a Python package, hosted in PyPi.

Source and binary packages are built and published automatically using Poetry in Continuous Delivery.

Package versions are determined automatically using the support/python-packaging/parse_version.py script.

Docker image

The project Python package is available as a Docker/OCI image, hosted in the private BAS Docker Registry (part of gitlab.data.bas.ac.uk).

Continuous Delivery will automatically:

• build a /deploy:latest image for commits to the master branch
• build a /deploy:release-stable and /deploy:release-[release] image for tags
• deploy new images to the BAS central workstations (by running podman pull [image] from the workstations)

Continuous Deployment

All commits will trigger a Continuous Deployment process using GitLab's CI/CD platform, configured in .gitlab-ci.yml.

Release procedure

For all releases:

1. create a release branch
2. close release in CHANGELOG.md
3. push changes, merge the release branch into master and tag with version

Feedback

The maintainer of this project is the BAS Mapping and Geographic Information Centre (MAGIC), they can be contacted at: [e-mail].

Issue tracking

This project uses issue tracking, see the Issue tracker for more information.

Note: Read & write access to this issue tracker is restricted. Contact the project maintainer to request access.

License

© UK Research and Innovation (UKRI), 2019 - 2020, British Antarctic Survey.

You may use and re-use this software and associated documentation files free of charge in any format or medium, under the terms of the Open Government Licence v3.0.

You may obtain a copy of the Open Government Licence at http://www.nationalarchives.gov.uk/doc/open-government-licence/