quilt

This feature requires the quilt3 API version 3.3 or higher.

Overview

A Quilt workflow is a quality gate that you set on a bucket 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.

Why metadata quality matters

Under the hood, Quilt workflows use JSON Schema 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:

Consistency — if labels and other metadata don’t use a consistent, controlled vocabulary, reuse becomes difficult and trust in data declines
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
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, metadata 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-NAME/.quilt/workflows/config.yml.

Here’s a complex example:

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: must-have-foo-bar-meta
    catalog:
      package_handle:
        files: <%= username %>/<%= directory %>
        packages: <%= username %>/production
schemas:
  superheroes:
    url: s3://quilt-dev-metadata/schemas/superheroes.schema.json
  top-secret:
    url: s3://quilt-dev-metadata/schemas/top-secret.schema.json
  must-have-foo-bar-meta:
    url: s3://quilt-dev-metadata/schemas/must-have-foo-bar-meta.schema.json

Setting a default workflow

Once a configuration file is present, you usually must specify a workflow when pushing a package from quilt3 to that bucket. You can however specify a default_workflow value at the top-level in your config.yml file:

version:
  base: "1"
  catalog: "1"
default_workflow: "experiment"
is_workflow_required: False
workflows:
  experiment:
    name: Experiment
    metadata_schema: experiment-universal
schemas:
  experiment-universal:
    url: s3://quilt-dev-metadata/.quilt/workflows/schemas/experiment-universal.json

This specifies which workflow will be used (experiment) if a workflow parameter in the Package.push() API call or CLI is not provided. Otherwise, all push calls must specify a workflow or the push will fail.

Bypassing workflow validation

If you wish for your users to be able to skip workflow validation altogether, you can make workflow validation optional with is_workflow_required: False at the top-level in your config.yml file:

version:
  base: "1"
  catalog: "1"
is_workflow_required: False

Now your users can specify workflow=None in the Python API (or --workflow '' in the CLI) when they push packages (or drop the workflow parameter altogether).

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

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

Specifying a workflow

Otherwise, once you have a configuration you must specify a workflow when pushing a package:

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

# QuiltException: Workflow required, but none specified.

Let’s retry, adding the workflow= parameter:

quilt3.Package().push('test/package', registry='s3://quilt-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:

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

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

Now let’s push with workflow='beta':

quilt3.Package().push(
        'test/package',
        registry='s3://quilt-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 located at s3://quilt-dev-metadata/schemas/superheroes.schema.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 of the package metadata, which is specified using set_meta.

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

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

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

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

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

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:

version:
  base: "1"
  catalog: "1"

Package name defaults (Quilt catalog)

By default the Quilt catalog auto-fills the package handle prefix when creating or revising a package according to the following logic:

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

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

Files tab: parent directory name. Equivalent to

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

catalog:
  # default for all workflows for Packages tab
  package_handle:
    packages: analysis/
workflows:
  my-workflow:
    name: 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.

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

Example

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

Package file validation

You can validate the names, sizes and metadata 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 relative path and name in the parent package), size (in bytes) and metadata.

Example

workflows:
  myworkflow-1:
    name: 'My workflow #1'
    entries_schema: must-contain-readme
  myworkflow-2:
    name: 'My workflow #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
  myworkflow-3:
    name: 'My workflow #3'
    entries_schema: must-have-foo-bar-meta
    description: Must contain at least one file with { foo: bar } metadata  object
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
  must-have-foo-bar-meta:
    url: s3://bucket/must-have-foo-bar-meta.json

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

Requires a README

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

`s3://bucket/must-contain-readme-summarize-at-least-1byte.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$"
          }
        }
      }
    }
  ]
}

`s3://bucket/must-have-foo-bar-meta.json`

Requires { "foo": "bar" } object as user specified metadata in README.md

{
  "type": "array",
  "contains": {
    "type": "object",
    "properties": {
      "logical_key": {
        "type": "string",
        "pattern": "^README\\.md$"
      },
      "meta": {
        "type": "object",
        "properties": {
          "foo": {
            "type": "string",
            "pattern": "^bar$"
          }
        },
        "required": ["foo"]
      }
    }
  }
}

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 feature can be enabled by adding a successors property to the config. A successor is a destination bucket.

version:
  base: "1"
workflows:
  dummy:
    name: Dummy
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.

JSON Schema

Quilt workflows support the Draft 7 JSON Schema.
JSON schemas can be stored anywhere in any accessible Amazon S3 bucket. Provided the path to the file is accessible in config.yml, the schema will successfully validate your package metadata shape.

Default values

Quilt supports the default keyword.

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.

Arrays, tuples and enums

Quilt supports the array data type. You can use array if you need to define a list of metadata values for a metadata key. These elements can be of any type.

If the order in the list is not significant, use “arrays” (using "items" and "anyOf"):

{
    "type": "array",
    "items": {
        "anyOf": [
            {
                "type": "string"
            },
            {
                "type": "number"
            }
        ]
    }
}

With this Schema you can create a list of metadata values such as: ["Any string A", 123, "Any string B"] or [123, "Any string", 456]

If the order in the list is important and the list is fixed in length, then use “tuples” (using "items", "minItems", and "maxItems"):

{
    "type": "array",
    "items": [
        {
            "type": "string"
        },
        {
            "type": "number"
        }
    ],
    "minItems": 2,
    "maxItems": 2
}

With this Schema you can create strictly ordered lists, such as ["Any string", 123].

An incorrect order will return an error [123, "Any string"] // invalid.

Remember that you should define "minItems" and "maxItems" or "minItems" and "additionalItems": false, because “tuples” must have a fixed size.

Instead of letting users set any metadata value, you can define list of available options with enum:

{
    "type": "array",
    "items": {
        "type": "string",
        "enum": ["Fixed 1", "Fixed 2"]
    }
}

With this Schema you can create a list of any length with predefined values, such as ["Fixed 1", "Fixed 2", "Fixed 1"].

{
    "type": "array",
    "items": [
        "type": "string",
        "enum": ["Fixed 1", "Fixed 2"]
    ],
    "minItems": 1,
    "additionalItems": false,
}

With this Schema users are allowed to create tuples like ["Fixed 1"] or ["Fixed 2"].

If you want to provide users with a list of predefined metadata values but additionally let them add any values outside of this list, you can use the anyOf keyword:

{
    "type": "array",
    "items": {
        "anyOf": [
            {
                "type": "string"
                "enum": ["Fixed 1", "Fixed 2"]
            },
            {
                "type": "string"
            }
        ]
    }
}

Metadata lists such as ["Fixed 1", "Fixed 2"], ["Fixed 1", "Any string"] or ["Any string 1", "Any string 2"] are all valid.

In certain use cases you may want to define metadata lists that have first-ordered items of predefined values, and the rest are any other outside of the predefined values. Then you create tuples with "additionalItems": true:

{
    "type": "array",
    "items": [
        "type": "string",
        "enum": ["Fixed 1", "Fixed 2"]
    ],
    "minItems": 1,
    "additionalItems": true,
}

With this Schema lists such as ["Fixed 1", "Any string", 123] are valid but ["Any string", 123] are invalid.

Example properties

The following examples show how you can specify complex properties such as object, array, and compound enum types.

Objects

{
    "type": "array",
    "items": {
        "type": "object",
        "properties": {
            "id": {
              "default": 123,
              "type": "number"
            },
            "name": {
              "default": "Optional default value",
              "type": "string"
            }
        }
    }
}

Compound enums: arrays

{
    "type": "array",
    "enum": [
        [1, 2, 3],
        [3, 4, 5],
        [6, 7, 8]
    ]
}

Compound enums: objects

{
    "type": "object",
    "enum": [
        {"id": 1},
        {"id": 2},
        {"id": 3}
    ]
}

Compound enums: arrays and objects

{
    "type": "array",
    "enum": [
        ["miles", {
            "format": "12h"
        }],
        ["kilometers", {
            "format": "24h"
        }],
        {
            "name": "unspecified"
        }
    ]
}

This allows for flexible and extensible schema definition, and hence validation, of complex metadata schemas to any depth.

Quilt currently uses the Draft 7 Json Schema where tuples are validated with items, and not prefixItems. The prefixItems keyword was added in Draft 2020-12, and is not currently supported.

`config.yml` JSON Schema

See workflows-config_catalog-1.0.0.json and workflows-config-1.1.0.json.

Known limitations

Only Draft 7 Json Schemas are supported
- If a workflow schema includes a non-supported keyword, the user interface displays an unknown keyword: <non-supported keyword> error
Schemas with $ref are not supported
Schemas must be in an S3 bucket for which the Quilt user has read permissions

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quilt

Overview

Why metadata quality matters

Use cases

Get started

Setting a default workflow

Bypassing workflow validation

Specifying a workflow

Data quality controls

Package name defaults (Quilt catalog)

Example

Package name validation

Example

Package file validation

Example

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

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

s3://bucket/must-have-foo-bar-meta.json

Cross-bucket package push (Quilt catalog)

JSON Schema

Default values

Auto-fill dates

Arrays, tuples and enums

Example properties

Objects

Compound enums: arrays

Compound enums: objects

Compound enums: arrays and objects

config.yml JSON Schema

Known limitations

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

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

`s3://bucket/must-have-foo-bar-meta.json`

`config.yml` JSON Schema