# ckanext-validation
[](https://github.com/frictionlessdata/ckanext-validation/actions)
[](http://codecov.io/github/frictionlessdata/ckanext-validation?branch=master)
Data description and validation for CKAN with [Frictionless Data](https://frictionlessdata.io) tools.
## Table of Contents
* [Overview](#overview)
* [Versions supported and Requirements](#versions-supported-and-requirements)
* [Installation](#installation)
* [Configuration](#configuration)
* [How it works](#how-it-works)
* [Data Validation](#data-validation)
* [Data Schema](#data-schema)
* [Validation Options](#validation-options)
* [Operation modes](#operation-modes)
* [Asynchronous validation](#asynchronous-validation)
* [Synchronous validation](#synchronous-validation)
* [Changes in the metadata schema](#changes-in-the-metadata-schema)
* [Extending via interfaces](#extending-via-interfaces)
* [Action functions](#action-functions)
* [resource_validation_run](#resource_validation_run)
* [resource_validation_show](#resource_validation_show)
* [resource_validation_delete](#resource_validation_delete)
* [resource_validation_run_batch](#resource_validation_run_batch)
* [Command Line Interface](#command-line-interface)
* [Starting the validation process manually](#starting-the-validation-process-manually)
* [Data validation reports](#data-validation-reports)
* [Running the Tests](#running-the-tests)
* [Copying and License](#copying-and-license)
## Overview
This extension brings data validation powered by the [Frictionless
Framework](https://github.com/frictionlessdata/framework) library to CKAN. It
provides out of the box features to validate tabular data and integrate
validation reports to the CKAN interface.
Data validation can be performed automatically on the background or during
dataset creation, and the results are stored against each resource.
Comprehensive reports are created describing issues found with the data, both
at the structure level (missing headers, blank rows, etc) and at the data
schema level (wrong data types, values out of range, etc).
The extension also exposes all the underlying [actions](#action-functions) so
data validation can be integrated in custom workflows from other extensions.
If you are eager to get started, jump to the [Installation](#installation) and
[Configuration](#configuration) instructions. To learn more about data
validation and how the extension works, read the next section.
## Versions supported and Requirements
Compatibility with core CKAN versions:
| CKAN version | Compatibility |
| -------------- |------------------------------------------------------------|
| 2.7 | no longer supported |
| 2.8 | no longer supported (last supported 1.x) |
| 2.9 | yes (Python3) Must: `pip install "setuptools>=44.1.0,<71"` |
| 2.10 | no |
| 2.11 | no |
It is strongly recommended to use it alongside
[ckanext-scheming](https://github.com/ckan/ckanext-scheming) to define the
necessary extra fields in the default CKAN schema.
## Installation
To install ckanext-validation, activate your CKAN virtualenv and run:
git clone https://github.com/ckan/ckanext-validation.git
cd ckanext-validation
pip install -r requirements.txt
python setup.py develop
Create the database tables running:
paster validation init-db -c ../path/to/ini/file
## Configuration
Once installed, add the `validation` plugin to the `ckan.plugins` configuration option in your INI file. If using ckanext-scheming, the `validation` plugins should be loaded **before** the `scheming_datasets` one:
ckan.plugins = ... validation scheming_datasets
### Adding schema fields to the Resource metadata
The extension requires changes in the CKAN metadata schema. The easiest way to
add those is by using ckanext-scheming. Use these two configuration options to
link to the dataset schema (replace with your own if you need to customize it)
and the required presets:
scheming.dataset_schemas = ckanext.validation.examples:ckan_default_schema.json
scheming.presets = ckanext.scheming:presets.json
ckanext.validation:presets.json
Read more below about how to [change the CKAN metadata schema](#changes-in-the-metadata-schema)
### Operation modes
Use the following configuration options to choose the [operation modes](#operation-modes):
ckanext.validation.run_on_create_async = True|False (Defaults to True)
ckanext.validation.run_on_update_async = True|False (Defaults to True)
ckanext.validation.run_on_create_sync = True|False (Defaults to False)
ckanext.validation.run_on_update_sync = True|False (Defaults to False)
### Formats to validate
By default validation will be run against the following formats: `CSV`, `XLSX`
and `XLS`. You can modify these formats using the following option:
ckanext.validation.formats = csv xlsx
You can also provide [validation options](#validation-options) that will be
used by default when running the validation:
ckanext.validation.default_validation_options={
"skip_errors": ["blank-row", "duplicate-label"],
}
Make sure to use indentation if the value spans multiple lines otherwise it
won't be parsed.
If you are using a cloud-based storage backend for uploads, check [Private
datasets](#private-datasets) for other configuration settings that might be
relevant.
### Display badges
To prevent the extension from adding the validation badges next to the
resources use the following option:
ckanext.validation.show_badges_in_listings = False
### Clean validation reports
To prevent the extension from keeping validation reports for unsupported Resource formats. Defaults to False:
ckanext.validation.clean_validation_reports = True
Once a Resource is updated and its format is not supported in ckanext.validation.formats, a job will be enqueued to remove the validation reports from the Resource.
## How it works
### Data Validation
CKAN users will be familiar with the validation performed against the metadata
fields when creating or updating datasets. The form will return an error, for
instance, if a field is missing or it doesn't have the expected format.
Data validation follows the same principle, but against the actual data
published in CKAN, that is the contents of tabular files (Excel, CSV, etc)
hosted in CKAN itself or elsewhere. Whenever a resource of the appropriate
format is created or updated, the extension will validate the data against a
collection of checks. This validation is powered by
[Frictionless Framework](https://github.com/frictionlessdata/framework), a very
powerful data validation library developed by the [Open Knowledge Foundation](https://okfn.org)
as part of the [Frictionless Data](https://frictionlessdata.io) project.
Frictionless Framework provides an extensive suite of [checks](https://framework.frictionlessdata.io/docs/checks/baseline.html)
that cover common issues with tabular data files.
These checks include structural problems like missing headers or values, blank
rows, etc., but also can validate the data contents themselves (see
[Data Schemas](#data-schemas)) or even run [custom checks](https://framework.frictionlessdata.io/docs/guides/validating-data.html#custom-checks).
The result of this validation is a JSON report. This report contains all the
issues found (if any) with their relevant context (row number, columns
involved, etc). The reports are stored in the database and linked to the
CKAN resources, and can be retrieved [via the API](#resource_validation_show).
If there is a report available for a particular resource, a status badge will
be displayed in the resource listing and on the resource page, showing whether
validation passed or failed for the resource.
Clicking on the badge will take you to the validation report page, where the
report will be rendered.
Whenever possible, the report will provide a preview of the cells, rows or
columns involved in an error, to make it easy to identify and fix it.
### Data Schema
As mentioned before, data can be validated against a schema. Much in the same
way as the standard CKAN schema for metadata fields, the schema describes the
data and what its values are expected to be.
These schemas are defined following the [Table Schema](http://frictionlessdata.io/specs/table-schema/)
specification, a really simple and flexible standard for describing tabular data.
Let's see an example. Consider the following table (that could be stored as a
CSV or Excel file):
| id | location | date | measurement | observations |
| --- | -------- | ---------- | ----------- | -------------- |
| 1 | 'A' | 01/02/2017 | 23.65 | |
| 2 | 'B' | 21/03/2017 | 22.90 | |
| 3 | 'A' | 15/06/2017 | 21.79 | Severe drought |
| 4 | 'C' | 10/10/2017 | 24.12 | |
| 5 | 'C' | 31/10/2017 | 24.21 | |
The following schema describes the expected data:
```json
{
"primaryKey": "id",
"fields": [
{
"name": "id",
"title": "Measurement identifier",
"type": "integer"
},
{
"name": "location",
"title": "Measurement location code",
"type": "string",
"constraints": {
"enum": ["A", "B", "C", "D"]
}
},
{
"name": "date",
"title": "Measurement date",
"type": "date",
"format": "%d/%m/%Y"
},
{
"name": "measurement",
"title": "Measure of the oblique fractal impedance at noon",
"type": "number",
"constraints": {
"required": true
}
},
{
"name": "observations",
"title": "Extra observations",
"type": "string"
}
]
}
```
If we store this schema against a resource, it will be used to perform a more
thorough validation. For instance, updating the resource with the following
data would fail validation with a variety of errors, even if the general
structure of the file is correct:
| id | location | date | measurement | observations |
| --- | -------- | ---------- | ----------- | -------------- |
| ... | ... | ... | ... | ... |
| 5 | 'E' | 2017-11-01 | missing | |
| 'a' | 'B' | 21/03/2017 | | |
With the extension enabled and configured, schemas can be attached to the
`schema` field on resources via the UI form or the API. If present in a
resource, they will be used when performing validation on the resource file.
### Validation Options
As we saw before, the validation process involves many different checks and
it's very likely that what "valid" data actually means will vary across CKAN
instances or datasets. The validation process can be tweaked by passing any of
the [supported
options](https://framework.frictionlessdata.io/docs/guides/validating-data.html)
to Frictionless Framework. These can be used to add or remove specific checks, control
limits, etc.
For instance, the following file would fail validation using the default
options, but it may be valid in a given context, or the issues may be known to
the publishers:
```
<blank line>
<blank line>
id;group;measurement
# 2017
1;A;23
2;B;24
# 2016
3;C;23
4;C;25
<blank line>
```
The following validation options would make validation pass:
```json
{
"skip_errors": ["blank-row"]
"dialect": {
"header": True,
"headerRows": [2],
"commentChar": "#",
"csv": {
"delimiter": ";"
}
},
"checks": [
{"type": "table-dimensions", "minRows": 3},
{"type": 'ascii-value'}
]
}
```
Validation options can be defined (as a JSON object like the above) on each
resource (via the UI form or the API on the `validation_options` field) or can
be set globally by administrators on the CKAN INI file (see [Configuration](#configuration)).
### Private datasets
Validation can be performed on private datasets. When validating a locally
uploaded resource, the extension uses the actual physical path to read the
file, so internally there is no need for authorization. But when the upload is
on a cloud-based backend (like the ones provided by [ckanext-cloudstorage](https://github.com/TkTech/ckanext-cloudstorage) or
[ckanext-s3filestore](https://github.com/okfn/ckanext-s3filestore)) we need
to request the file via an HTTP request to CKAN. If the resource is private
this will require an `Authorization` header in order to avoid a `Not Authorized` error.
In these cases, the API key for the site user will be passed as part of the
request (or alternatively `ckanext.validation.pass_auth_header_value` if set in
the configuration).
As this involves sending API keys to other extensions, this behaviour can be
turned off by setting `ckanext.validation.pass_auth_header` to `False`.
Again, these settings only affect private resources when using a cloud-based
backend.
### Operation modes
The data validation process described above can be run in two modes:
asynchronously in the background or synchronously while the resource is being
created or updated. You can choose the mode for each of the create and update
actions, but in most cases you will probably need just one of the two modes for
both actions.
#### Asynchronous validation
Asynchronous validation is run in the background whenever a resource of a
supported format is created or updated. Validation won't affect the action
performed, so if there are validation errors found the resource will be created
or updated anyway.
This mode might be useful for instances where datasets are harvested from other
sources, or where multiple publishers create datasets and as a maintainer you
only want to give visibility to the quality of data, encouraging publishers to
fix any issues.
You will need to run the `worker` commmand to pick up validation jobs. Please
refer to the [background jobs documentation](http://docs.ckan.org/en/latest/maintaining/background-tasks.html)
for more details:
paster jobs worker -c /path/to/ini/file
Use `ckanext.validation.run_on_create_async` and
`ckanext.validation.run_on_update_async` to enable this mode (See [Configuration](#configuration)).
#### Synchronous validation
Synchronous validation is performed at the same time a resource of the
supported formats is being created or updated. Currently, if data validation
errors are found, a `ValidationError` will be raised and you won't be able to
create or update the resource.
Validation at creation or update time can be useful to ensure that data quality
is maintained or that published data conforms to a particular schema.
When using the UI form, validation errors will be displayed as normal CKAN
validation errors:
Clicking the link on the error message will bring up a modal window with the
validation report rendered:
Use `ckanext.validation.run_on_create_sync` and `ckanext.validation.run_on_update_sync`
to enable this mode (See [Configuration](#configuration)).
### Changes in the metadata schema
The extension requires changes in the default CKAN resource metadata schema to
add some fields it requires. It is strongly recommended to use
[ckanext-scheming](https://github.com/ckan/ckanext-scheming) to define your
CKAN schema. This extension provides all the necessary presets and validators
to get up and running just by adding the following fields to the
`resource_fields` section of a ckanext-scheming schema:
```json
{
"field_name": "schema",
"label": "Schema",
"preset": "resource_schema"
},
{
"field_name": "validation_options",
"label": "Validation options",
"preset": "validation_options"
},
{
"field_name": "validation_status",
"label": "Validation status",
"preset": "hidden_in_form"
},
{
"field_name": "validation_timestamp",
"label": "Validation timestamp",
"preset": "hidden_in_form"
}
```
Here's more detail on the fields added:
* `schema`: This can be a [Table Schema](http://frictionlessdata.io/specs/table-schema/)
JSON object or an URL pointing to one. In the UI form you can upload a JSON file, link to one
providing a URL or enter it directly. If uploaded, the file contents will be
read and stored in the `schema` field. In all three cases the contents will be
validated against the Table Schema specification.
* `validation_options`: A JSON object with validation options that will be
passed to Frictionless Framework [validate](https://framework.frictionlessdata.io/docs/guides/validating-data.html)
function.
Additionally, two read-only fields are added to resources:
* `validation_status`: Stores the last validation result for the resource.
Can be one of `success`, `failure` or `error`.
* `validation_timestamp`: Date and time of the last validation run.
### Extending via interfaces
The plugin provides the `IDataValidation` interface so other plugins can modify
its behaviour.
Currently it only provides the `can_validate()` method, that plugins can use to
determine if a specific resource should be validated or not:
```
class IDataValidation(Interface):
def can_validate(self, context, data_dict):
'''
When implemented, this call can be used to control whether the
data validation should take place or not on a specific resource.
Implementations will receive a context object and the data_dict of
the resource.
If it returns False, the validation won't be performed, and if it
returns True there will be a validation job started.
Note that after this methods is called there are further checks
performed to ensure the resource has one of the supported formats.
This is controlled via the `ckanext.validation.formats` config option.
Here is an example implementation:
from ckan import plugins as p
from ckanext.validation.interfaces import IDataValidation
class MyPlugin(p.SingletonPlugin):
p.implements(IDataValidation, inherit=True)
def can_validate(self, context, data_dict):
if data_dict.get('my_custom_field') == 'xx':
return False
return True
'''
return True
```
The plugin also provides the `IPipeValidation` interface so other plugins can receive the dictized validation reports in a Data Pipeline way. This would allow plugins to perform actions once a validation job is finished.
Example:
```
import ckan.plugins as plugins
from ckanext.validation.interfaces import IPipeValidation
class MyPlugin(plugins.SingletonPlugin):
plugins.implements(IPipeValidation)
def receive_validation_report(self, validation_report):
if validation_report.get('status') == 'success':
# Do something when the resource successfully passes validation
```
## Action functions
The `validation` plugin adds new API actions to create and display validation
reports. By default `resource_validation_run`, `resource_validation_delete` and
`resource_validation_show` inherit whatever auth is in place for
`resource_update` and `resource_show` respectively.
There is an extra action which only sysadmins can access:
`resource_validation_run_batch`.
#### `resource_validation_run`
```python
def resource_validation_run(context, data_dict):
u'''
Start a validation job against a resource.
Returns the identifier for the job started.
Note that the resource format must be one of the supported ones,
currently CSV or Excel.
:param resource_id: id of the resource to validate
:type resource_id: string
:rtype: string
'''
```
#### `resource_validation_show`
```python
def resource_validation_show(context, data_dict):
u'''
Display the validation job result for a particular resource.
Returns a validation object, including the validation report or errors
and metadata about the validation like the timestamp and current status.
Validation status can be one of:
* `created`: The validation job is in the processing queue
* `running`: Validation is under way
* `error`: There was an error while performing the validation, eg the file
could not be downloaded or there was an error reading it
* `success`: Validation was performed, and no issues were found
* `failure`: Validation was performed, and there were issues found
:param resource_id: id of the resource to validate
:type resource_id: string
:rtype: dict
'''
```
#### `resource_validation_delete`
```python
def resource_validation_delete(context, data_dict):
u'''
Remove the validation job result for a particular resource.
It also deletes the underlying Validation object.
:param resource_id: id of the resource to remove validation from
:type resource_id: string
:rtype: None
'''
```
#### `resource_validation_run_batch`
```python
def resource_validation_run_batch(context, data_dict):
u'''
Start asynchronous data validation on the site resources. If no
options are provided it will run validation on all resources of
the supported formats (`ckanext.validation.formats`). You can
specify particular datasets to run the validation on their
resources. You can also pass arbitrary search parameters to filter
the selected datasets.
Only sysadmins are allowed to run this action.
Examples::
curl -X POST http://localhost:5001/api/action/resource_validation_run_batch \
-d '{"dataset_ids": "ec9bfd88-f90a-45ca-b024-adc8854b49bd"}' \
-H Content-type:application/json \
-H Authorization:API_KEY
curl -X POST http://localhost:5001/api/action/resource_validation_run_batch \
-d '{"dataset_ids": ["passenger-data-2018", "passenger-data-2017]}}' \
-H Content-type:application/json \
-H Authorization:API_KEY
curl -X POST http://localhost:5001/api/action/resource_validation_run_batch \
-d '{"query": {"fq": "res_format:XLSX"}}' \
-H Content-type:application/json \
-H Authorization:API_KEY
:param dataset_ids: Run data validation on all resources for a
particular dataset or datasets. Not to be used with ``query``.
:type dataset_ids: string or list
:param query: Extra search parameters that will be used for getting
the datasets to run validation on. It must be a JSON object like
the one used by the `package_search` API call. Supported fields
are ``q``, ``fq`` and ``fq_list``. Check the documentation for
examples. Note that when using this you will have to specify
the resource formats to target your Not to be used with
``dataset_ids``.
:type query: dict
:rtype: string
'''
```
## Command Line Interface
### Starting the validation process manually
You can start (asynchronous) validation jobs from the command line using the
`paster validation run` command. If no parameters are provided it will start a
validation job for all resources in the site of suitable format (ie
`ckanext.validation.formats`):
paster validation run -c /path/to/ckan/ini
You can limit the resources by specifying a dataset id or name:
paster validation run -c /path/to/ckan/ini -d statistical-data-2018
Or providing arbitrary search parameters:
paster validation run -c ../ckan/development.ini -s '{"fq":"res_format:XLSX"}'
### Data validation reports
The extension provides two small utilities to generate a global report with all the current data validation reports:
paster validation report -c /path/to/ckan/ini
paster validation report-full -c /path/to/ckan/ini
Both commands will print an overview of the total number of datasets and
tabular resources, and a breakdown of how many have a validation status of
success, failure or error. Additionally they will create a CSV report. `paster
validation report` will create a report with all failing resources, including
the following fields:
* Dataset name
* Resource id
* Resource format
* Resource URL
* Status
* Validation report URL
`paster validation report-full` will add a row on the output CSV for each error
found on the validation report (limited to ten occurrences of the same error
type per file). So the fields in the generated CSV report will be:
* Dataset name
* Resource id
* Resource format
* Resource URL
* Status
* Error code
* Error message
In both cases you can define the location of the output CSV passing the `-o` or
`--output` option:
paster validation report-full -c /path/to/ckan/ini -o /tmp/reports/validation_full.csv
Check the command help for more details:
paster validation --help
Usage: paster validation [options] Utilities for the CKAN data validation extension
Usage:
paster validation init-db
Initialize database tables
paster validation run [options]
Start asynchronous data validation on the site resources. If no
options are provided it will run validation on all resources of
the supported formats (`ckanext.validation.formats`). You can
specify particular datasets to run the validation on their
resources. You can also pass arbitrary search parameters to filter
the selected datasets.
paster validation report [options]
Generate a report with all current data validation reports. This
will print an overview of the total number of tabular resources
and a breakdown of how many have a validation status of success,
failure or error. Additionally it will create a CSV report with all
failing resources, including the following fields:
* Dataset name
* Resource id
* Resource URL
* Status
* Validation report URL
paster validation report-full [options]
Generate a detailed report. This is similar to the previous command
but on the CSV report it will add a row for each error found on the
validation report (limited to ten occurrences of the same error
type per file). So the fields in the generated CSV report will be:
* Dataset name
* Resource id
* Resource URL
* Status
* Error code
* Error message
Options:
-h, --help show this help message and exit
-v, --verbose
-c CONFIG, --config=CONFIG
Config file to use.
-f FILE_PATH, --file=FILE_PATH
File to dump results to (if needed)
-y, --yes Automatic yes to prompts. Assume "yes" as answer to
all prompts and run non-interactively
-r RESOURCE_ID, --resource=RESOURCE_ID
Run data validation on a particular resource (if the
format is suitable). It can be defined multiple times.
Not to be used with -d or -s
-d DATASET_ID, --dataset=DATASET_ID
Run data validation on all resources for a particular
dataset (if the format is suitable). You can use the
dataset id or name, and it can be defined multiple
times. Not to be used with -r or -s
-s SEARCH_PARAMS, --search=SEARCH_PARAMS
Extra search parameters that will be used for getting
the datasets to run validation on. It must be a JSON
object like the one used by the `package_search` API
call. Supported fields are `q`, `fq` and `fq_list`.
Check the documentation for examples. Note that when
using this you will have to specify the resource
formats to target yourself. Not to be used with -r or
-d.
-o OUTPUT_FILE, --output=OUTPUT_FILE
Location of the CSV validation report file on the
relevant commands.
## Running the Tests
To run the tests, do:
pip install -r dev-requirements.txt
pytest --ckan-ini=test-custom.ini
## Copying and License
This material is copyright (c) [Open Knowledge Foundation](https://okfn.org).
It is open and licensed under the GNU Affero General Public License (AGPL) v3.0
whose full text may be found at:
http://www.fsf.org/licensing/licenses/agpl-3.0.html
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"description": "# ckanext-validation\n\n[](https://github.com/frictionlessdata/ckanext-validation/actions)\n[](http://codecov.io/github/frictionlessdata/ckanext-validation?branch=master)\n\n\nData description and validation for CKAN with [Frictionless Data](https://frictionlessdata.io) tools.\n\n\n## Table of Contents\n\n * [Overview](#overview)\n * [Versions supported and Requirements](#versions-supported-and-requirements)\n * [Installation](#installation)\n * [Configuration](#configuration)\n * [How it works](#how-it-works)\n * [Data Validation](#data-validation)\n * [Data Schema](#data-schema)\n * [Validation Options](#validation-options)\n * [Operation modes](#operation-modes)\n * [Asynchronous validation](#asynchronous-validation)\n * [Synchronous validation](#synchronous-validation)\n * [Changes in the metadata schema](#changes-in-the-metadata-schema)\n * [Extending via interfaces](#extending-via-interfaces)\n * [Action functions](#action-functions)\n\t* [resource_validation_run](#resource_validation_run)\n\t* [resource_validation_show](#resource_validation_show)\n\t* [resource_validation_delete](#resource_validation_delete)\n\t* [resource_validation_run_batch](#resource_validation_run_batch)\n * [Command Line Interface](#command-line-interface)\n * [Starting the validation process manually](#starting-the-validation-process-manually)\n * [Data validation reports](#data-validation-reports)\n * [Running the Tests](#running-the-tests)\n * [Copying and License](#copying-and-license)\n\n\n## Overview\n\nThis extension brings data validation powered by the [Frictionless\nFramework](https://github.com/frictionlessdata/framework) library to CKAN. It\nprovides out of the box features to validate tabular data and integrate\nvalidation reports to the CKAN interface.\n\nData validation can be performed automatically on the background or during\ndataset creation, and the results are stored against each resource.\n\n\n\nComprehensive reports are created describing issues found with the data, both\nat the structure level (missing headers, blank rows, etc) and at the data\nschema level (wrong data types, values out of range, etc).\n\n\nThe extension also exposes all the underlying [actions](#action-functions) so\ndata validation can be integrated in custom workflows from other extensions.\n\nIf you are eager to get started, jump to the [Installation](#installation) and\n[Configuration](#configuration) instructions. To learn more about data\nvalidation and how the extension works, read the next section.\n\n## Versions supported and Requirements\n\nCompatibility with core CKAN versions:\n\n | CKAN version | Compatibility |\n | -------------- |------------------------------------------------------------|\n | 2.7 | no longer supported |\n | 2.8 | no longer supported (last supported 1.x) | \n | 2.9 | yes (Python3) Must: `pip install \"setuptools>=44.1.0,<71\"` |\n | 2.10 | no |\n | 2.11 | no |\n\n\nIt is strongly recommended to use it alongside\n[ckanext-scheming](https://github.com/ckan/ckanext-scheming) to define the\nnecessary extra fields in the default CKAN schema.\n\n\n## Installation\n\nTo install ckanext-validation, activate your CKAN virtualenv and run:\n\n git clone https://github.com/ckan/ckanext-validation.git\n cd ckanext-validation\n pip install -r requirements.txt\n python setup.py develop\n\nCreate the database tables running:\n\n paster validation init-db -c ../path/to/ini/file\n\n\n## Configuration\n\nOnce installed, add the `validation` plugin to the `ckan.plugins` configuration option in your INI file. If using ckanext-scheming, the `validation` plugins should be loaded **before** the `scheming_datasets` one:\n\n ckan.plugins = ... validation scheming_datasets\n\n\n### Adding schema fields to the Resource metadata\n\nThe extension requires changes in the CKAN metadata schema. The easiest way to\nadd those is by using ckanext-scheming. Use these two configuration options to\nlink to the dataset schema (replace with your own if you need to customize it)\n and the required presets:\n\n\tscheming.dataset_schemas = ckanext.validation.examples:ckan_default_schema.json\n\tscheming.presets = ckanext.scheming:presets.json\n \t ckanext.validation:presets.json\n\nRead more below about how to [change the CKAN metadata schema](#changes-in-the-metadata-schema)\n\n### Operation modes\n\nUse the following configuration options to choose the [operation modes](#operation-modes):\n\n\tckanext.validation.run_on_create_async = True|False (Defaults to True)\n\tckanext.validation.run_on_update_async = True|False (Defaults to True)\n\n\tckanext.validation.run_on_create_sync = True|False (Defaults to False)\n\tckanext.validation.run_on_update_sync = True|False (Defaults to False)\n\n### Formats to validate\n\nBy default validation will be run against the following formats: `CSV`, `XLSX`\nand `XLS`. You can modify these formats using the following option:\n\n\tckanext.validation.formats = csv xlsx\n\nYou can also provide [validation options](#validation-options) that will be\nused by default when running the validation:\n\n\tckanext.validation.default_validation_options={\n\t \"skip_errors\": [\"blank-row\", \"duplicate-label\"],\n \t}\n\nMake sure to use indentation if the value spans multiple lines otherwise it\nwon't be parsed.\n\nIf you are using a cloud-based storage backend for uploads, check [Private\ndatasets](#private-datasets) for other configuration settings that might be\nrelevant.\n\n### Display badges\n\nTo prevent the extension from adding the validation badges next to the\nresources use the following option:\n\n ckanext.validation.show_badges_in_listings = False\n\n### Clean validation reports\n\nTo prevent the extension from keeping validation reports for unsupported Resource formats. Defaults to False:\n\n ckanext.validation.clean_validation_reports = True\n\nOnce a Resource is updated and its format is not supported in ckanext.validation.formats, a job will be enqueued to remove the validation reports from the Resource.\n\n## How it works\n\n### Data Validation\n\nCKAN users will be familiar with the validation performed against the metadata\nfields when creating or updating datasets. The form will return an error, for\ninstance, if a field is missing or it doesn't have the expected format.\n\nData validation follows the same principle, but against the actual data\npublished in CKAN, that is the contents of tabular files (Excel, CSV, etc)\nhosted in CKAN itself or elsewhere. Whenever a resource of the appropriate\nformat is created or updated, the extension will validate the data against a\ncollection of checks. This validation is powered by\n[Frictionless Framework](https://github.com/frictionlessdata/framework), a very\npowerful data validation library developed by the [Open Knowledge Foundation](https://okfn.org)\nas part of the [Frictionless Data](https://frictionlessdata.io) project.\nFrictionless Framework provides an extensive suite of [checks](https://framework.frictionlessdata.io/docs/checks/baseline.html)\nthat cover common issues with tabular data files.\n\nThese checks include structural problems like missing headers or values, blank\nrows, etc., but also can validate the data contents themselves (see\n[Data Schemas](#data-schemas)) or even run [custom checks](https://framework.frictionlessdata.io/docs/guides/validating-data.html#custom-checks).\n\nThe result of this validation is a JSON report. This report contains all the\nissues found (if any) with their relevant context (row number, columns\n involved, etc). The reports are stored in the database and linked to the\nCKAN resources, and can be retrieved [via the API](#resource_validation_show).\n\nIf there is a report available for a particular resource, a status badge will\nbe displayed in the resource listing and on the resource page, showing whether\nvalidation passed or failed for the resource.\n\n\n\nClicking on the badge will take you to the validation report page, where the\nreport will be rendered.\n\n\n\nWhenever possible, the report will provide a preview of the cells, rows or\ncolumns involved in an error, to make it easy to identify and fix it.\n\n### Data Schema\n\nAs mentioned before, data can be validated against a schema. Much in the same\nway as the standard CKAN schema for metadata fields, the schema describes the\ndata and what its values are expected to be.\n\nThese schemas are defined following the [Table Schema](http://frictionlessdata.io/specs/table-schema/)\nspecification, a really simple and flexible standard for describing tabular data.\n\nLet's see an example. Consider the following table (that could be stored as a\n CSV or Excel file):\n\n| id | location | date | measurement | observations |\n| --- | -------- | ---------- | ----------- | -------------- |\n| 1 | 'A' | 01/02/2017 | 23.65 | |\n| 2 | 'B' | 21/03/2017 | 22.90 | |\n| 3 | 'A' | 15/06/2017 | 21.79 | Severe drought |\n| 4 | 'C' | 10/10/2017 | 24.12 | |\n| 5 | 'C' | 31/10/2017 | 24.21 | |\n\n\nThe following schema describes the expected data:\n\n```json\n{\n \"primaryKey\": \"id\",\n \"fields\": [\n {\n \"name\": \"id\",\n \"title\": \"Measurement identifier\",\n \"type\": \"integer\"\n },\n {\n \"name\": \"location\",\n \"title\": \"Measurement location code\",\n \"type\": \"string\",\n \"constraints\": {\n \"enum\": [\"A\", \"B\", \"C\", \"D\"]\n }\n },\n {\n \"name\": \"date\",\n \"title\": \"Measurement date\",\n \"type\": \"date\",\n \"format\": \"%d/%m/%Y\"\n },\n {\n \"name\": \"measurement\",\n \"title\": \"Measure of the oblique fractal impedance at noon\",\n \"type\": \"number\",\n \"constraints\": {\n \"required\": true\n }\n },\n {\n \"name\": \"observations\",\n \"title\": \"Extra observations\",\n \"type\": \"string\"\n }\n ]\n}\n\n```\n\nIf we store this schema against a resource, it will be used to perform a more\nthorough validation. For instance, updating the resource with the following\ndata would fail validation with a variety of errors, even if the general\nstructure of the file is correct:\n\n\n| id | location | date | measurement | observations |\n| --- | -------- | ---------- | ----------- | -------------- |\n| ... | ... | ... | ... | ... |\n| 5 | 'E' | 2017-11-01 | missing | |\n| 'a' | 'B' | 21/03/2017 | | |\n\nWith the extension enabled and configured, schemas can be attached to the\n`schema` field on resources via the UI form or the API. If present in a\nresource, they will be used when performing validation on the resource file.\n\n\n### Validation Options\n\nAs we saw before, the validation process involves many different checks and\nit's very likely that what \"valid\" data actually means will vary across CKAN\ninstances or datasets. The validation process can be tweaked by passing any of\nthe [supported\noptions](https://framework.frictionlessdata.io/docs/guides/validating-data.html)\nto Frictionless Framework. These can be used to add or remove specific checks, control\nlimits, etc.\n\nFor instance, the following file would fail validation using the default\noptions, but it may be valid in a given context, or the issues may be known to\nthe publishers:\n\n```\n<blank line>\n<blank line>\nid;group;measurement\n# 2017\n1;A;23\n2;B;24\n# 2016\n3;C;23\n4;C;25\n<blank line>\n```\n\nThe following validation options would make validation pass:\n\n```json\n{\n \"skip_errors\": [\"blank-row\"]\n \"dialect\": {\n \"header\": True,\n \"headerRows\": [2],\n \"commentChar\": \"#\",\n \"csv\": {\n \"delimiter\": \";\"\n }\n },\n \"checks\": [\n {\"type\": \"table-dimensions\", \"minRows\": 3},\n {\"type\": 'ascii-value'}\n ]\n}\n\n```\n\nValidation options can be defined (as a JSON object like the above) on each\nresource (via the UI form or the API on the `validation_options` field) or can\nbe set globally by administrators on the CKAN INI file (see [Configuration](#configuration)).\n\n\n### Private datasets\n\nValidation can be performed on private datasets. When validating a locally\nuploaded resource, the extension uses the actual physical path to read the\nfile, so internally there is no need for authorization. But when the upload is\non a cloud-based backend (like the ones provided by [ckanext-cloudstorage](https://github.com/TkTech/ckanext-cloudstorage) or\n[ckanext-s3filestore](https://github.com/okfn/ckanext-s3filestore)) we need\nto request the file via an HTTP request to CKAN. If the resource is private\nthis will require an `Authorization` header in order to avoid a `Not Authorized` error.\n\nIn these cases, the API key for the site user will be passed as part of the\nrequest (or alternatively `ckanext.validation.pass_auth_header_value` if set in\nthe configuration).\n\nAs this involves sending API keys to other extensions, this behaviour can be\nturned off by setting `ckanext.validation.pass_auth_header` to `False`.\n\nAgain, these settings only affect private resources when using a cloud-based\nbackend.\n\n\n### Operation modes\n\nThe data validation process described above can be run in two modes:\nasynchronously in the background or synchronously while the resource is being\ncreated or updated. You can choose the mode for each of the create and update\nactions, but in most cases you will probably need just one of the two modes for\nboth actions.\n\n#### Asynchronous validation\n\nAsynchronous validation is run in the background whenever a resource of a\nsupported format is created or updated. Validation won't affect the action\nperformed, so if there are validation errors found the resource will be created\nor updated anyway.\n\nThis mode might be useful for instances where datasets are harvested from other\nsources, or where multiple publishers create datasets and as a maintainer you\nonly want to give visibility to the quality of data, encouraging publishers to\nfix any issues.\n\nYou will need to run the `worker` commmand to pick up validation jobs. Please\nrefer to the [background jobs documentation](http://docs.ckan.org/en/latest/maintaining/background-tasks.html)\nfor more details:\n\n paster jobs worker -c /path/to/ini/file\n\nUse `ckanext.validation.run_on_create_async` and\n`ckanext.validation.run_on_update_async` to enable this mode (See [Configuration](#configuration)).\n\n\n#### Synchronous validation\n\nSynchronous validation is performed at the same time a resource of the\nsupported formats is being created or updated. Currently, if data validation\nerrors are found, a `ValidationError` will be raised and you won't be able to\ncreate or update the resource.\n\nValidation at creation or update time can be useful to ensure that data quality\nis maintained or that published data conforms to a particular schema.\n\nWhen using the UI form, validation errors will be displayed as normal CKAN\nvalidation errors:\n\n\n\nClicking the link on the error message will bring up a modal window with the\nvalidation report rendered:\n\n\n\nUse `ckanext.validation.run_on_create_sync` and `ckanext.validation.run_on_update_sync`\nto enable this mode (See [Configuration](#configuration)).\n\n\n### Changes in the metadata schema\n\nThe extension requires changes in the default CKAN resource metadata schema to\nadd some fields it requires. It is strongly recommended to use\n[ckanext-scheming](https://github.com/ckan/ckanext-scheming) to define your\nCKAN schema. This extension provides all the necessary presets and validators\nto get up and running just by adding the following fields to the\n`resource_fields` section of a ckanext-scheming schema:\n\n```json\n {\n \"field_name\": \"schema\",\n \"label\": \"Schema\",\n \"preset\": \"resource_schema\"\n },\n {\n \"field_name\": \"validation_options\",\n \"label\": \"Validation options\",\n \"preset\": \"validation_options\"\n },\n {\n \"field_name\": \"validation_status\",\n \"label\": \"Validation status\",\n \"preset\": \"hidden_in_form\"\n },\n {\n \"field_name\": \"validation_timestamp\",\n \"label\": \"Validation timestamp\",\n \"preset\": \"hidden_in_form\"\n }\n\n```\n\n\nHere's more detail on the fields added:\n\n* `schema`: This can be a [Table Schema](http://frictionlessdata.io/specs/table-schema/)\nJSON object or an URL pointing to one. In the UI form you can upload a JSON file, link to one\nproviding a URL or enter it directly. If uploaded, the file contents will be\nread and stored in the `schema` field. In all three cases the contents will be\nvalidated against the Table Schema specification.\n* `validation_options`: A JSON object with validation options that will be\npassed to Frictionless Framework [validate](https://framework.frictionlessdata.io/docs/guides/validating-data.html)\nfunction.\n\n\n\nAdditionally, two read-only fields are added to resources:\n\n* `validation_status`: Stores the last validation result for the resource.\nCan be one of `success`, `failure` or `error`.\n* `validation_timestamp`: Date and time of the last validation run.\n\n\n### Extending via interfaces\n\nThe plugin provides the `IDataValidation` interface so other plugins can modify\nits behaviour.\n\nCurrently it only provides the `can_validate()` method, that plugins can use to\ndetermine if a specific resource should be validated or not:\n\n```\nclass IDataValidation(Interface):\n\n def can_validate(self, context, data_dict):\n '''\n When implemented, this call can be used to control whether the\n data validation should take place or not on a specific resource.\n\n Implementations will receive a context object and the data_dict of\n the resource.\n\n If it returns False, the validation won't be performed, and if it\n returns True there will be a validation job started.\n\n Note that after this methods is called there are further checks\n performed to ensure the resource has one of the supported formats.\n This is controlled via the `ckanext.validation.formats` config option.\n\n Here is an example implementation:\n\n\n from ckan import plugins as p\n\n from ckanext.validation.interfaces import IDataValidation\n\n\n class MyPlugin(p.SingletonPlugin):\n\n p.implements(IDataValidation, inherit=True)\n\n def can_validate(self, context, data_dict):\n\n if data_dict.get('my_custom_field') == 'xx':\n return False\n\n return True\n\n '''\n return True\n```\n\nThe plugin also provides the `IPipeValidation` interface so other plugins can receive the dictized validation reports in a Data Pipeline way. This would allow plugins to perform actions once a validation job is finished.\n\nExample:\n```\nimport ckan.plugins as plugins\nfrom ckanext.validation.interfaces import IPipeValidation\n\nclass MyPlugin(plugins.SingletonPlugin):\n plugins.implements(IPipeValidation)\n\n def receive_validation_report(self, validation_report):\n if validation_report.get('status') == 'success':\n # Do something when the resource successfully passes validation\n```\n\n## Action functions\n\nThe `validation` plugin adds new API actions to create and display validation\nreports. By default `resource_validation_run`, `resource_validation_delete` and\n`resource_validation_show` inherit whatever auth is in place for\n`resource_update` and `resource_show` respectively.\n\nThere is an extra action which only sysadmins can access:\n`resource_validation_run_batch`.\n\n#### `resource_validation_run`\n\n```python\ndef resource_validation_run(context, data_dict):\n u'''\n Start a validation job against a resource.\n Returns the identifier for the job started.\n\n Note that the resource format must be one of the supported ones,\n currently CSV or Excel.\n\n :param resource_id: id of the resource to validate\n :type resource_id: string\n\n :rtype: string\n\n '''\n```\n\n#### `resource_validation_show`\n\n```python\ndef resource_validation_show(context, data_dict):\n u'''\n Display the validation job result for a particular resource.\n Returns a validation object, including the validation report or errors\n and metadata about the validation like the timestamp and current status.\n\n Validation status can be one of:\n\n * `created`: The validation job is in the processing queue\n * `running`: Validation is under way\n * `error`: There was an error while performing the validation, eg the file\n could not be downloaded or there was an error reading it\n * `success`: Validation was performed, and no issues were found\n * `failure`: Validation was performed, and there were issues found\n\n :param resource_id: id of the resource to validate\n :type resource_id: string\n\n :rtype: dict\n\n '''\n```\n\n#### `resource_validation_delete`\n\n```python\n\ndef resource_validation_delete(context, data_dict):\n u'''\n Remove the validation job result for a particular resource.\n It also deletes the underlying Validation object.\n\n :param resource_id: id of the resource to remove validation from\n :type resource_id: string\n\n :rtype: None\n\n '''\n\n```\n\n#### `resource_validation_run_batch`\n\n```python\n\ndef resource_validation_run_batch(context, data_dict):\n u'''\n Start asynchronous data validation on the site resources. If no\n options are provided it will run validation on all resources of\n the supported formats (`ckanext.validation.formats`). You can\n specify particular datasets to run the validation on their\n resources. You can also pass arbitrary search parameters to filter\n the selected datasets.\n\n Only sysadmins are allowed to run this action.\n\n Examples::\n\n curl -X POST http://localhost:5001/api/action/resource_validation_run_batch \\\n -d '{\"dataset_ids\": \"ec9bfd88-f90a-45ca-b024-adc8854b49bd\"}' \\\n -H Content-type:application/json \\\n -H Authorization:API_KEY\n\n curl -X POST http://localhost:5001/api/action/resource_validation_run_batch \\\n -d '{\"dataset_ids\": [\"passenger-data-2018\", \"passenger-data-2017]}}' \\\n -H Content-type:application/json \\\n -H Authorization:API_KEY\n\n\n curl -X POST http://localhost:5001/api/action/resource_validation_run_batch \\\n -d '{\"query\": {\"fq\": \"res_format:XLSX\"}}' \\\n -H Content-type:application/json \\\n -H Authorization:API_KEY\n\n :param dataset_ids: Run data validation on all resources for a\n particular dataset or datasets. Not to be used with ``query``.\n :type dataset_ids: string or list\n :param query: Extra search parameters that will be used for getting\n the datasets to run validation on. It must be a JSON object like\n the one used by the `package_search` API call. Supported fields\n are ``q``, ``fq`` and ``fq_list``. Check the documentation for\n examples. Note that when using this you will have to specify\n the resource formats to target your Not to be used with\n ``dataset_ids``.\n :type query: dict\n\n :rtype: string\n '''\n```\n\n\n## Command Line Interface\n\n### Starting the validation process manually\n\nYou can start (asynchronous) validation jobs from the command line using the\n`paster validation run` command. If no parameters are provided it will start a\nvalidation job for all resources in the site of suitable format (ie\n `ckanext.validation.formats`):\n\n paster validation run -c /path/to/ckan/ini\n\nYou can limit the resources by specifying a dataset id or name:\n\n paster validation run -c /path/to/ckan/ini -d statistical-data-2018\n\nOr providing arbitrary search parameters:\n\n paster validation run -c ../ckan/development.ini -s '{\"fq\":\"res_format:XLSX\"}'\n\n\n### Data validation reports\n\nThe extension provides two small utilities to generate a global report with all the current data validation reports:\n\n\tpaster validation report -c /path/to/ckan/ini\n\n\tpaster validation report-full -c /path/to/ckan/ini\n\n\nBoth commands will print an overview of the total number of datasets and\ntabular resources, and a breakdown of how many have a validation status of\nsuccess, failure or error. Additionally they will create a CSV report. `paster\nvalidation report` will create a report with all failing resources, including\nthe following fields:\n\n* Dataset name\n* Resource id\n* Resource format\n* Resource URL\n* Status\n* Validation report URL\n\n`paster validation report-full` will add a row on the output CSV for each error\nfound on the validation report (limited to ten occurrences of the same error\n type per file). So the fields in the generated CSV report will be:\n\n* Dataset name\n* Resource id\n* Resource format\n* Resource URL\n* Status\n* Error code\n* Error message\n\nIn both cases you can define the location of the output CSV passing the `-o` or\n`--output` option:\n\n\n\tpaster validation report-full -c /path/to/ckan/ini -o /tmp/reports/validation_full.csv\n\n\nCheck the command help for more details:\n\n\tpaster validation --help\n\n\tUsage: paster validation [options] Utilities for the CKAN data validation extension\n\n Usage:\n paster validation init-db\n Initialize database tables\n\n paster validation run [options]\n\n Start asynchronous data validation on the site resources. If no\n options are provided it will run validation on all resources of\n the supported formats (`ckanext.validation.formats`). You can\n specify particular datasets to run the validation on their\n resources. You can also pass arbitrary search parameters to filter\n the selected datasets.\n\n paster validation report [options]\n\n Generate a report with all current data validation reports. This\n will print an overview of the total number of tabular resources\n and a breakdown of how many have a validation status of success,\n failure or error. Additionally it will create a CSV report with all\n failing resources, including the following fields:\n * Dataset name\n * Resource id\n * Resource URL\n * Status\n * Validation report URL\n\n paster validation report-full [options]\n\n Generate a detailed report. This is similar to the previous command\n but on the CSV report it will add a row for each error found on the\n validation report (limited to ten occurrences of the same error\n type per file). So the fields in the generated CSV report will be:\n\n * Dataset name\n * Resource id\n * Resource URL\n * Status\n * Error code\n * Error message\n\n\n\n\tOptions:\n\t -h, --help show this help message and exit\n\t -v, --verbose\n\t -c CONFIG, --config=CONFIG\n\t\t\t\t\t\t\tConfig file to use.\n\t -f FILE_PATH, --file=FILE_PATH\n\t\t\t\t\t\t\tFile to dump results to (if needed)\n\t -y, --yes Automatic yes to prompts. Assume \"yes\" as answer to\n\t\t\t\t\t\t\tall prompts and run non-interactively\n\t -r RESOURCE_ID, --resource=RESOURCE_ID\n\t\t\t\t\t\t\t Run data validation on a particular resource (if the\n\t\t\t\t\t\t\tformat is suitable). It can be defined multiple times.\n\t\t\t\t\t\t\tNot to be used with -d or -s\n\t -d DATASET_ID, --dataset=DATASET_ID\n\t\t\t\t\t\t\t Run data validation on all resources for a particular\n\t\t\t\t\t\t\tdataset (if the format is suitable). You can use the\n\t\t\t\t\t\t\tdataset id or name, and it can be defined multiple\n\t\t\t\t\t\t\ttimes. Not to be used with -r or -s\n\t -s SEARCH_PARAMS, --search=SEARCH_PARAMS\n\t\t\t\t\t\t\tExtra search parameters that will be used for getting\n\t\t\t\t\t\t\tthe datasets to run validation on. It must be a JSON\n\t\t\t\t\t\t\tobject like the one used by the `package_search` API\n\t\t\t\t\t\t\tcall. Supported fields are `q`, `fq` and `fq_list`.\n\t\t\t\t\t\t\tCheck the documentation for examples. Note that when\n\t\t\t\t\t\t\tusing this you will have to specify the resource\n\t\t\t\t\t\t\tformats to target yourself. Not to be used with -r or\n\t\t\t\t\t\t\t-d.\n\t -o OUTPUT_FILE, --output=OUTPUT_FILE\n\t\t\t\t\t\t\tLocation of the CSV validation report file on the\n\t\t\t\t\t\t\trelevant commands.\n\n\n## Running the Tests\n\nTo run the tests, do:\n\n pip install -r dev-requirements.txt\n pytest --ckan-ini=test-custom.ini\n\n\n## Copying and License\n\nThis material is copyright (c) [Open Knowledge Foundation](https://okfn.org).\n\nIt is open and licensed under the GNU Affero General Public License (AGPL) v3.0\nwhose full text may be found at:\n\nhttp://www.fsf.org/licensing/licenses/agpl-3.0.html\n",
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