Host Based Inventory

You've arrived at the repo for the backend of the Host Based Inventory (HBI). If you're looking for API, integration or user documentation for HBI please see the Inventory section in our Platform Docs site.

Getting Started

Snappy

This project uses Snappy compression to enhance its Kafka usage. The python-snappy package requires the core Snappy library to be installed on the machine, so start off by running the following:

sudo dnf install snappy

pg_config

Local development also requires the pg_config file, which is installed with the postgres developer library. To install this, use the command appropriate to your system:

Fedora/Centos

sudo dnf install libpq-devel

Debian/Ubuntu

sudo apt-get install libpq-dev

MacOS (using Homebrew)

brew install postgresql

Install dependencies

This project uses pipenv to manage the development and deployment environments. To set the project up for development, we recommend using pyenv to install/manage the appropriate python (currently 3.8.x), pip and pipenv version. Once you have pipenv, do the following:

pipenv install --dev

Afterwards you can activate the virtual environment by running:

pipenv shell

Included is a docker-compose file dev.yml that will start a postgres database that is useful for development.

docker-compose -f dev.yml up

Initialize the database

Run the following commands to run the db migration scripts which will maintain the db tables.

The database migration scripts determine the DB location, username, password and db name from the INVENTORY_DB_HOST, INVENTORY_DB_USER, INVENTORY_DB_PASS and INVENTORY_DB_NAME environment variables.

make upgrade_db

By default the database container will use a bit of local storage so that data you enter will be persisted across multiple starts of the container. If you want to destroy that data do the following:

docker-compose down

Running the Tests

You can run the tests using pytest:

pytest --cov=.

Or you can run the tests individually:

./test_api.py
pytest test_db_model.py
./test_unit.py
pytest test_json_validators.py

Depending on the environment, it might be necessary to set the DB related environment variables (INVENTORY_DB_NAME, INVENTORY_DB_HOST, etc).

Sonar Integration

This project uses SonarQube to perform static code analysis, monitor test coverage, and find potential issues in the Host Inventory codebase. The analysis is run automatically for each PR by the "host-inventory pr security scan" Jenkins job. The results are uploaded to RedHat's SonarQube server, on the console.redhat.com:insights-host-inventory project.

Contributing

This repository uses pre-commit to check and enforce code style. It uses Black to reformat the Python code and Flake8 to check it afterwards. Other formats and text files are linted as well.

Install pre-commit hooks to your local repository by running:

pre-commit install

After that, all your commited files will be linted. If the checks don’t succeed, the commit will be rejected, but the altered files from the linting will be ready for you to commit again if the issue was automatically correctable.

If you're inside the Red Hat network, please also make sure you have rh-pre-commit installed; instructions on installation can be found here. Then, verify the installation by following the Testing the Installation section. If you follow the instructions for Quickstart Install, and then re-enable running hooks in the repo's .pre-commit-config.yaml (instructions in the Manual Install section), both hooks should run upon making a commit.

Please make sure all checks pass before submitting a pull request. Thanks!

Running the server locally

Prometheus was designed to run in a multi-threaded environment whereas gunicorn uses a multi-process architecture. As a result, there is some work to be done to make prometheus integrate with gunicorn.

A temp directory for prometheus needs to be created before the server starts. The prometheus_multiproc_dir environment needs to point to this directory. The contents of this directory need to be removed between runs.

If running the server in a cluster, you can use this command:

gunicorn -c gunicorn.conf.py run

When running the server locally for development, the Prometheus configuration is done automatically. You can run the server locally using this command:

python3 run_gunicorn.py

Running all services locally

Honcho provides a command to run MQ and web services at once:

honcho start

Config env vars

 prometheus_multiproc_dir=/path/to/prometheus_dir
 APP_NAME="inventory"
 PATH_PREFIX="/r/insights/platform"
 INVENTORY_DB_USER="insights"
 INVENTORY_DB_PASS="insights"
 INVENTORY_DB_HOST="localhost"
 INVENTORY_DB_NAME="insights"
 INVENTORY_DB_POOL_TIMEOUT="5"
 INVENTORY_DB_POOL_SIZE="5"
 INVENTORY_LOGGING_CONFIG_FILE=logconf.ini
 INVENTORY_DB_SSL_MODE=""
 INVENTORY_DB_SSL_CERT=""
 UNLEASH_TOKEN='*:*.dbffffc83b1f92eeaf133a7eb878d4c58231acc159b5e1478ce53cfc'
 UNLEASH_CACHE_DIR=./.unleash
 UNLEASH_URL="http://localhost:4242/api"

To force an ssl connection to the db set INVENTORY_DB_SSL_MODE to "verify-full" and provide the path to the certificate you'd like to use.

Identity

It is necessary to provide a valid Identity both when testing the API, and when producing messages via Kafka. For Kafka messages, the Identity must be set in the platform_metadata.b64_identity field of the message. When testing the API, it must be provided in the authentication header x-rh-identity on each call to the service. For testing purposes, this required identity header can be set to the following:

x-rh-identity: eyJpZGVudGl0eSI6eyJvcmdfaWQiOiJ0ZXN0IiwidHlwZSI6IlVzZXIiLCJhdXRoX3R5cGUiOiJiYXNpYy1hdXRoIiwidXNlciI6eyJ1c2VybmFtZSI6InR1c2VyQHJlZGhhdC5jb20iLCJlbWFpbCI6InR1c2VyQHJlZGhhdC5jb20iLCJmaXJzdF9uYW1lIjoidGVzdCIsImxhc3RfbmFtZSI6InVzZXIiLCJpc19hY3RpdmUiOnRydWUsImlzX29yZ19hZG1pbiI6ZmFsc2UsImlzX2ludGVybmFsIjp0cnVlLCJsb2NhbGUiOiJlbl9VUyJ9fX0=

This is the Base64 encoding of the following JSON document:

{"identity":{"org_id":"test","type":"User","auth_type":"basic-auth","user":{"username":"[email protected]","email":"[email protected]","first_name":"test","last_name":"user","is_active":true,"is_org_admin":false,"is_internal":true,"locale":"en_US"}}}

The above header has the "User" identity type, but it's possible to use a "System" type header as well.

x-rh-identity: eyJpZGVudGl0eSI6eyJvcmdfaWQiOiAidGVzdCIsICJhdXRoX3R5cGUiOiAiY2VydC1hdXRoIiwgInN5c3RlbSI6IHsiY2VydF90eXBlIjogInN5c3RlbSIsICJjbiI6ICJwbHhpMTN5MS05OXV0LTNyZGYtYmMxMC04NG9wZjkwNGxmYWQifSwidHlwZSI6ICJTeXN0ZW0ifX0=

This is the Base64 encoding of the following JSON document:

{"identity":{"org_id": "test", "auth_type": "cert-auth", "system": {"cert_type": "system", "cn": "plxi13y1-99ut-3rdf-bc10-84opf904lfad"},"type": "System"}}

If you want to encode other JSON documents, you can use the following command:

echo -n '{"identity": {"org_id": "0000001", "type": "System"}}' | base64 -w0

Identity Enforcement

The Identity provided limits access to specific hosts. For API requests, the user can only access Hosts which have the same Org ID as the provided Identity. For Host updates via Kafka messages, A Host can only be updated if not only the Org ID matches, but also the Host.system_profile.owner_id matches the provided identity.system.cn value.

Using the legacy api

Some apps still need to use the legacy API path, which by default is /r/insights/platform/inventory/v1/. In case legacy apps require this prefix to be changed, it can be modified using this environment variable:

 export INVENTORY_LEGACY_API_URL="/r/insights/platform/inventory/api/v1"

Payload Tracker Integration

The inventory service has been integrated with the Payload Tracker service. The payload tracker integration can be configured using the following environment variables:

KAFKA_BOOTSTRAP_SERVERS=localhost:29092
PAYLOAD_TRACKER_KAFKA_TOPIC=platform.payload-status
PAYLOAD_TRACKER_SERVICE_NAME=inventory
PAYLOAD_TRACKER_ENABLED=true

The payload tracker can be disabled by setting the PAYLOAD_TRACKER_ENABLED environment variable to false. The payload tracker will also be disabled for add/delete operations that do not include a request_id. When the payload tracker is disabled, a NullObject implementation (NullPayloadTracker) of the PayloadTracker interface is used. The NullPayloadTracker implements the PayloadTracker interface but the methods are no-op methods.

The PayloadTracker purposefully eats all exceptions that it generates. The exceptions are logged. A failure/exception within the PayloadTracker should not cause a request to fail.

The payload status is a bit "different" due to each "payload" potentially containing multiple hosts. For example, the add_host operation will only log an error for the payload if the entire payload fails (catastrophic failure during processing...db down, etc). One or more of the hosts could fail during the add_host method. These will get logged as a "processing_error". If a host is successfully added/updated, then it will be logged as a "processing_success". Having one or more hosts get logged as "processing_error" will not cause the payload to be flagged as "error" overall.

The payload tracker status logging for the delete operation is similar. The overall status of the payload will only be logged as an "error" if the entire delete operation fails (a 404 due to the hosts not existing, db down, etc).

Generating a database migration script

Run this command to generate a new revision in migrations/versions

python3 manage.py db revision -m "Description of revision"

Building a docker container image

A Dockerfile is provided for building local Docker containers. The container image built this way is only intended for development purposes (e.g. orchestrating the container using docker-compose) and must not be used for production deployment.

Note some of the packages require a subscription. Make sure the host building the image is attached to a valid subscription providing RHEL.

docker build . -f dev.dockerfile -t inventory:dev

By default, the container runs the database migrations and then starts the inventory-mq service.

Metrics

The application provides some management information about itself. These endpoints are exposed at the root path / and thus are accessible only from inside of the cluster.

• /health responds with 200 to any GET requests; point your liveness or readiness probe here.
• /metrics offers metrics and monitoring intended to be pulled by Prometheus.
• /version responds with a json doc that contains the build version info (the value of the OPENSHIFT_BUILD_COMMIT environment variable)

Cron jobs such as reaper and sp-validator push their metrics to a Prometheus Pushgateway instance running at PROMETHEUS_PUSHGATEWAY. Defaults to localhost:9091.

Release process

This section describes the process of getting a code change from a pull request all the way to production.

1. Pull request

It all starts with a pull request. When a new pull request is opened, some jobs are run automatically. These jobs are defined in app-interface here.

• host-inventory pr-checker runs the following:
  • database migrations
  • code style checks
  • unit tests
• ci.ext.devshift.net PR build - All tests runs all of the IQE tests on the PR's code.
• host-inventory build-master builds the container image, and pushes it to Quay, where it is scanned for vulnerabilities.

Should any of these fail this is indicated directly on the pull request.

When all of these checks pass and a reviewer approves the changes the pull request can be merged by someone from the @RedHatInsights/host-based-inventory-committers team.

2. Latest image and smoke tests

When a pull request is merged to master, a new container image is built and tagged as insights-inventory:latest. This image is then automatically deployed to the Stage environment.

3. QE testing in the Stage environment

Once the image lands in the Stage environment, the QE testing can begin. People in @platform-inventory-qe run the full IQE test suite against Stage, and then report the results in the #platform-inventory-standup channel.

4. Promoting the image to the production environment

In order to promote a new image to the production environment, it is necessary to update the deploy-clowder.yml file. The ref parameter on the prod-host-inventory-prod namespace needs to be updated to the SHA of the validated image.

Once the change has been made, submit a merge request to app-interface. For the CI pipeline to run tests on your fork, you'll need to add @devtools-bot as a Maintainer. See this guide on how to do that.

After the MR has been opened, somebody from AppSRE/insights-host-inventory will review and approve the MR by adding a /lgtm comment. Afterwards, the MR will be merged automatically and the changes will be deployed to the production environment. The engineer who approved the MR is then responsible for monitoring of the rollout of the new image.

Once that happens, contact @platform-inventory-qe and request the image to be re-tested in the production environment. The new image will also be tested automatically when the Full Prod Check pipeline is run (twice daily).

Monitoring of deployment

It is essential to monitor the health of the service during and after the production deployment. A non-exhaustive list of things to watch includes:

• Deployments in the host-inventory-prod OSD namespace
  • primarily ensure that the new pods are spun up properly
• #team-consoledot-inventory Slack channel for any inventory-related Prometheus alerts
• Inventory Dashboard for any anomalies such as error rate or consumer lag
• Kibana for any error-level log entries

Deployment rollback

Should unexpected problems occur during the deployment, it is possible to do a rollback. This is done by updating the ref parameter in deploy-clowder.yml to point to the previous commit SHA, or by reverting the MR that triggered the production deployment.

Updating the System Profile

In order to add or update a field on the System Profile, first follow the instructions in the inventory-schemas repo. After an inventory-schemas PR has been accepted and merged, HBI must be updated to keep its own schema in sync. To do this, simply run this command:

make update-schema

This will pull the latest version of the System Profile schema from inventory-schemas and update files as necessary. Open a PR with these changes, and it will be reviewed and merged as per the standard process.

Deploying Host Inventory and Xjoin-search to Kubernetes Namespaces

A list of high level steps is provided here

Debugging Local Code with Services Deployed to Kubernetes Namespaces

This section relies on successful deployment of host-inventory and xjoin-search following the instructions in the previous section. To make local code work with the services running in Kubernetes requires some actions provided here

Running ad hoc jobs using a different image

There may be a job (ClowdJobInvocation) which requires using a special image that is different from the one used by the parent application, i.e. host-inventory. Clowder out-of-the-box does not allow it. Running a Special Job describes how to accomplish it.