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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 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. 1.
    Consistency - if labels and other metadata don't use a consistent, controlled vocabulary, reuse becomes difficult and trust in data declines
  2. 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. 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:
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:
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:
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:
quilt3.Package().push(
'test/package',
registry='s3://quilt-sergey-dev-metadata',
message='added info about UFO',
workflow='alpha')
# Package test/[email protected] pushed to s3://quilt-sergey-dev-metadata
Now let's push with workflow='beta':
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 at s3://quilt-sergey-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:
quilt3.Package().set_meta({'superhero': 'Batman'}).push(
'test/package',
registry='s3://quilt-sergey-dev-metadata',
workflow='beta')
# Package test/[email protected] 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:
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/[email protected] 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:
quilt3.Package().push(
'test/package',
registry='s3://quilt-sergey-dev-metadata',
workflow=None)
# Package test/[email protected] 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.

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 "additonalItems": 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 4 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.

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 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 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 relative path and name in the parent package) and size (in bytes).
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
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
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$"
}
}
}
},
{
"type": "array",
"contains": {
"type": "object",
"properties": {
"logical_key": {
"type": "string",
"pattern": "^quilt_summarize\\.json$"
}
}
}
}
]
}

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.

config.yml JSON Schema

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