# Workflows

*New in Quilt 3.3*

## Workflows

A Quilt *workflow* is a quality gate that you set to ensure the quality of your data and metadata *before* it becomes a Quilt package. You can create as many workflows as you like to accommodate all of your data creation patterns.

### On data quality

Under the hood, Quilt workflows use [JSON Schema](https://json-schema.org) to check that package metadata have the right *shape*. Metadata shape determines which keys are defined, their values, and the types of the values.

Ensuring the quality of your data has long-lasting implications:

1. Consistency - if labels and other metadata don't use a consistent, controlled vocabulary, reuse becomes difficult and trust in data declines
2. Completeness - if your workflows do not require users to include files, documentation, labels, etc. then your data is on its way towards becoming mystery data and ultimately junk data that no one can use
3. Context - data can only be reused if users know where it came from, what it means, who touched it, and what the related datasets are

From the standpoint of querying engines like Amazon Athena, data that lacks consistency and completeness is extremely difficult to query longitudinally and depreciates over time (as team members change, platforms change, and tribal knowledge is lost).

### Use cases

* Ensure that labels are correct and drawn from a controlled vocabulary (e.g. ensure that the only labels in a package of images are either "bird" or "not bird"; avoid data entry errors like "birb")
* Ensure that users provide a README.md for every new package
* Ensure that included files are non-empty
* Ensure that every new package (or dataset) has enough labels so that it can be reused (e.g. Date, Creator, Type, etc.)

### Get started

To get started, create a configuration file in your Quilt S3 bucket at `s3://BUCKET/.quilt/workflows/config.yml`.

Here's an example:

```yaml
version:
  base: "1"
  catalog: "1"
workflows:
  alpha:
    name: Search for aliens
    is_message_required: true
  beta:
    name: Studying superpowers
    metadata_schema: superheroes
  gamma:
    name: Nothing special
    description: TOP SECRET
    is_message_required: true
    metadata_schema: top-secret
    handle_pattern: ^(employee1|employee2)/(staging|production)$
    entries_schema: validate-secrets
    catalog:
      package_handle:
        files: <%= username %>/<%= directory %>
        packages: <%= username %>/production
schemas:
  superheroes:
    url: s3://quilt-sergey-dev-metadata/schemas/superheroes.schema.json
  top-secret:
    url: s3://quilt-sergey-dev-metadata/schemas/top-secret.schema.json
  validate-secrets:
    url: s3://quilt-sergey-dev-metadata/schemas/validate-secrets.schema.json
```

With the above configuration, you must specify a workflow before you can push:

```python
>>> import quilt3
>>> quilt3.Package().push('test/package', registry='s3://quilt-sergey-dev-metadata')

QuiltException: Workflow required, but none specified.
```

Let's try with the `workflow=` parameter:

```python
>>> quilt3.Package().push('test/package', registry='s3://quilt-sergey-dev-metadata', workflow='alpha')

QuiltException: Commit message is required by workflow, but none was provided.
```

The above `QuiltException` is caused by `is_message_required: true`. Here's how we can pass the workflow:

```python
>>> quilt3.Package().push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        message='added info about UFO',
        workflow='alpha')

Package test/package@bc9a838 pushed to s3://quilt-sergey-dev-metadata
```

Now let's push with `workflow='beta'`:

```python
>>> quilt3.Package().push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        workflow='beta')

QuiltException: Metadata failed validation: 'superhero' is a required property.
```

We encountered another exception because the `beta` workflow specifies `metadata_schema: superheroes`. Therefore, the `test/package` metadata must validate against the [JSON Schema](https://json-schema.org/) at `s3://quilt-sergey-dev-metadata/schemas/superheroes.schema.json`:

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "$id": "http://example.com/superheroes.schema.json",
  "properties": {
    "superhero": {
      "enum": [
        "Spider-Man",
        "Superman",
        "Batman"
      ]
    }
  },
  "required": [
    "superhero"
  ]
}
```

Note that `superhero` is a required property:

```python
>>> quilt3.Package().set_meta({'superhero': 'Batman'}).push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        workflow='beta')

Package test/package@c4691d8 pushed to s3://quilt-sergey-dev-metadata
```

For the `gamma` workflow, both `is_message_required: true` and `metadata_schema` are set, so both `message` and package metadata are validated:

```python
>>> quilt3.Package().push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        workflow='gamma')

QuiltException: Metadata failed validation: 'answer' is a required property.

>>> quilt3.Package().set_meta({'answer': 42}).push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        workflow='gamma')

QuiltException: Commit message is required by workflow, but none was provided.

>>> quilt3.Package().set_meta({'answer': 42}).push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        message='at last all is set up',
        workflow='gamma')

Package test/package@6331508 pushed to s3://quilt-sergey-dev-metadata
```

If you wish for your users to be able to skip workflows altogether, you can make workflow validation optional with `is_workflow_required: false` in your `config.yml`, and specify `workflow=None` in the API:

```python
>>> quilt3.Package().push(
        'test/package',
        registry='s3://quilt-sergey-dev-metadata',
        workflow=None)

Package test/package@06b2815 pushed to s3://quilt-sergey-dev-metadata
```

Also `default_workflow` can be set in the config to specify which workflow will be used if `workflow` parameter is not provided.

### JSON Schema

Quilt workflows support the [Draft 7 JSON Schema](https://json-schema.org/specification-links.html#draft-7).

#### Default values

Quilt supports the [`default` keyword](https://json-schema.org/understanding-json-schema/reference/generic.html?highlight=default).

#### Auto-fill dates

If you wish to pre-populate dates in the Quilt catalog, you can use the custom keyword `dateformat` in your schemas. For example:

```
{
    "type": "string",
    "format": "date",
    "dateformat": "yyyy-MM-dd"
}
```

The `dateformat` template follows [Unicode Technical Standard #35](https://www.unicode.org/reports/tr35/tr35-dates.html#Date_Field_Symbol_Table).

### Data quality controls

In addition to package-level metadata. Quilt workflows enable you to validate package names, and basic file metadata.

> You must include the following schema version at the root of your config.yml in order for any catalog-specific features to function:

```yaml
version:
  base: "1"
  catalog: "1"
```

#### Package name defaults (Quilt catalog)

By default the Quilt catalog auto-fills the package handle **prefix** according to the following logic:

* Packages tab: username (everything before the @ in your sign-in email). Equivalent to

```yaml
catalog:
  package_handle:
    packages: <%= username %>
```

* Files tab: parent directory name. Equivalent to

```yaml
catalog:
  package_handle:
    files: <%= directory %>
```

You can customize the default prefix with `package_handle` key in one or both of the following places:

* Set `catalog.package_handle.(files|packages)` at the root of config.yml to affect all workflows
* Set `workflows.WORKFLOW.catalog.package_handle.(files|packages)` to affect the tabs and workflow in question

**Example**

```yaml
catalog:
  # default for all workflows for Packages tab
  package_handle:
    packages: analysis/
workflows:
  my-workflow:
    catalog:
      # defaults for my-workflow, different for each tab
      package_handle:
        files: <%= username %>/<%= directory %>
        packages: <%= username %>/production
```

#### Package name validation

You can validate package names with `WORKFLOW.handle_pattern`, which accepts [JavaScript regular expression](https://datatracker.ietf.org/doc/html/draft-handrews-json-schema-validation-01#section-6.3.3).

> By default, patterns are not anchored. You can explicitly add start (`^`) and end (`$`) markers as needed.

**Example**

```yaml
workflows:
  my-workflow:
    handle_pattern: ^(employee1|employee2)/(production|staging)$
```

#### Package file validation

You can validate the names and sizes of files in the package with `WORkFLOW.entries_schema`. The provided schema runs against an array of objects known as *package entries*. Each package entry defines a logical key (its releative path and name in the parent package) and size (in bytes).

**Example**

```yaml
workflows:
  myworkflow-1:
    entries_schema: must-contain-readme
  myworkflow-2:
    entries_schema: must-contain-readme-summarize-at-least-1byte
    description: Must contain non-empty README.md and quilt_summarize.json at package root; no more than 4 files
schemas:
  must-contain-readme:
    url: s3://bucket/must-contain-readme.json
  must-contain-readme-summarize-at-least-1byte:
    url: s3://bucket/must-contain-readme-summarize-at-least-1byte.json
```

**`s3://bucket/must-contain-readme.json`**

```json
{
  "type": "array",
  "items": {
  "contains": {
    "type": "object",
    "properties": {
      "logical_key": {
        "type": "string",
        "pattern": "^README\\.md$"
      }
    }
  }
}
```

**`s3://bucket/must-contain-readme-summarize-at-least-1byte.json`**

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "allOf": [
    {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "size": {
            "type": "number",
            "minimum": 1,
            "maximum": 100000
          }
        }
      },
      "minItems": 2,
      "maxItems": 4
    },
    {
      "type": "array",
      "contains": {
        "type": "object",
        "properties": {
          "logical_key": {
            "type": "string",
            "pattern": "^README\\.md$"
          }
        }
      }
    },
    {
      "type": "array",
      "contains": {
        "type": "object",
        "properties": {
          "logical_key": {
            "type": "string",
            "pattern": "^quilt_summarize\\.json$"
          }
        }
      }
    }
  ]
}
```

#### Cross-bucket package push (Quilt catalog)

In Quilt, S3 buckets are like git branches but for data. With `quilt3` you can `browse` any package and then `push` it to any bucket that you choose.

As a rule, cross-bucket pushes or "merges" reflect change in a package's lifecycle. For example, you might push a package from *my-staging-bucket* to *my-production-bucket* as it matures and becomes trusted.

The catalog's [Push to bucket](https://docs.quilt.bio/version-5.0.x/walkthrough/working-with-the-catalog) feature can be enabled by adding a `successors` property to the config. A *successor* is a destination bucket.

```yaml
successors:
  s3://bucket1:
    title: Staging
    copy_data: false
  s3://bucket2:
    title: Production
```

If `copy_data` is `true` (the default), all package entries will be copied to the destination bucket. If `copy_data` is `false`, all entries will remain in their current locations.

### `config.yml` JSON Schema

See [workflows-config\_catalog-1.0.0.json](https://github.com/quiltdata/quilt/blob/master/shared/schemas/workflows-config_catalog-1.0.0.json) and [workflows-config-1.1.0.json](https://github.com/quiltdata/quilt/blob/master/shared/schemas/workflows-config-1.1.0.json).

### Known limitations

* Only [Draft 7 Json Schemas](https://json-schema.org/specification-links.html#draft-7) are supported
* Schemas with [`$ref`](https://json-schema.org/draft-07/json-schema-core.html#rfc.section.8.3) are not supported
* Schemas must be in an S3 bucket for which the Quilt user has read permissions