Kcidb is a package for submitting and querying Linux Kernel CI reports, and for maintaining the service behind that.

To install the package for the current user, run this command:

pip3 install --user <SOURCE>

Where <SOURCE> is the location of the package source, e.g. a git repo:

pip3 install --user git+https://github.com/kernelci/kcidb.git

or a directory path:

pip3 install --user .

In any case, make sure your PATH includes the ~/.local/bin directory, e.g. with:

export PATH="$PATH":~/.local/bin

Before you execute any of the tools make sure you have the path to your Google Cloud credentials stored in the GOOGLE_APPLICATION_CREDENTIALS variable. E.g.:

export GOOGLE_APPLICATION_CREDENTIALS=~/.credentials.json

To submit records use kcidb-submit, to query records - kcidb-query. Both use the same JSON schema on standard input and output respectively, which can be displayed by kcidb-schema. You can validate the data without submitting it using the kcidb-validate tool.

You can use the kcidb module to do everything the command-line tools do.

First, make sure you have the GOOGLE_APPLICATION_CREDENTIALS environment variable set and pointing at your Google Cloud credentials file. Then you can create the client with kcidb.Client(<dataset_name>) and call its submit() and query() methods.

You can find the I/O schema in kcidb.io_schema.JSON and use kcidb.io_schema.validate() to validate your I/O data.

See the source code for additional documentation.

Kcidb uses Google BigQuery for data storage. To be able to store or query anything you need to create a BigQuery dataset.

The documentation to set up a BigQuery account with a data set and a token can be found here: https://cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries

Alternatively, you may follow these quick steps:

1. Create a Google account if you don't already have one
2. Go to the "Google Cloud Console" for BigQuery: https://console.cloud.google.com/projectselector2/bigquery
3. "CREATE" a new project, enter arbitrary project name e.g. kernelci001
4. "CREATE DATASET" in that new project with an arbitrary ID e.g. kernelci001a
5. Go to "Google Cloud Platform" -> "APIs & Services" -> "Credentials", or this URL if you called your project kernelci001: https://console.cloud.google.com/apis/credentials?project=kernelci001
6. Go to "Create credentials" and select "Service Account Key"
7. Fill these values:

• Service Account Name: arbitrary e.g. admin
• Role: Owner
• Format: JSON

8. "Create" to automatically download the JSON file with your credentials.

To initialize the dataset, execute kcidb-db-init -d <DATASET>, where <DATASET> is the name of the dataset to initialize.

To cleanup the dataset (remove the tables) use kcidb-db-cleanup.

To upgrade the dataset schema, do the following.

1. Authenticate to Google Cloud with the key file (~/.kernelci-bq.json here):

    gcloud auth activate-service-account --key-file ~/.kernelci-bq.json
   

   or login with your credentials (entered via a browser window):

    gcloud auth login
   

2. Create a new dataset (kernelci02 in project kernelci here) with the new schema:

    bq mk --project_id=kernelci kernelci02
    # Using new-schema kcidb
    kcidb-db-init -d kernelci02
   

3. Switch all data submitters to using new-schema kcidb and the newly-created dataset.

4. Create a new dataset with the name of the old one (kernelci01 here), but with _archive suffix, using the old-schema kcidb:

    # Using old-schema kcidb
    kcidb-db-init -d kernelci01_archive
   

5. Using BigQuery management console, shedule copying the old dataset to the created dataset. When that is done, remove the old dataset.

6. Transfer data from the copy of the old dataset (named kernelci01_archive here) to the new dataset (named kernelci02 here) using old-schema kcidb-db-dump and new-schema kcidb-db-load.

    # Using old-schema kcidb
    kcidb-db-dump -d kernelci01_archive > kernelci01_archive.json
    # Using new-schema kcidb
    kcidb-db-load -d kernelci02 < kernelci01_archive.json
   

If you want to hack on the source code, install the package in the editable mode with the -e/--editable option, and with "dev" extra included. E.g.:

pip3 install --user --editable '.[dev]'

The latter installs kcidb executables which use the modules from the source directory, and changes to them will be reflected immediately without the need to reinstall. It also installs extra development tools, such as flake8 and pylint.

To make a release tag the release commit with v<NUMBER>, where <NUMBER> is the next release number, e.g. v3. The very next commit after the tag should update the version number in setup.py to be the next one. I.e. continuing the above example, it should be 4.