Merge pull request #965 from neuroglia-io/feat-task-descriptor-reference

Add `reference` property to the `TaskDescriptor` object

dsl.md

@@ -296,18 +296,6 @@ This argument contains information about the runtime executing the workflow.
 | version | `string` | The version of the runtime. This can be an arbitrary string | a incrementing positive integer (`362`), semantic version (`1.4.78`), commit hash (`04cd3be6da98fc35422c8caa821e0aa1ef6b2c02`) or container image label (`v0.7.43-alpine`) |
 | metadata | `map` | An object/map of implementation specific key-value pairs. This can be chosen by runtime implementors and usage of this argument signals that a given workflow definition might not be runtime agnostic | A Software as a Service (SaaS) provider might choose to expose information about the tenant the workflow is executed for e.g. `{ "organization": { "id": "org-ff51cff2-fc83-4d70-9af1-8dacdbbce0be", "name": "example-corp" }, "featureFlags": ["fastZip", "arm64"] }`.  |
 
-##### Task Descriptor
-
-| Name | Type | Description | Example |
-|:-----|:----:|:------------|:--------|
-| name | `string` | The task's name. | `getPet` |
-| definition | `map` | The tasks definition (specified under the name) as a parsed object | `{ "call": "http", "with": { ... } }` |
-| input | `any` | The task's *raw* input (i.e. *BEFORE* the `input.from` expression). For the result of `input.from` expression use the context of the runtime expression (for jq `.`) | - |
-| output | `any` | The task's *raw* output (i.e. *BEFORE* the `output.as` expression). | - |
-| startedAt.iso8601 | `string` | The start time of the task as a ISO 8601 date time string. It uses `T` as the date-time delimiter, either UTC (`Z`) or a time zone offset (`+01:00`). The precision can be either seconds, milliseconds or nanoseconds | `2022-01-01T12:00:00Z`, `2022-01-01T12:00:00.123456Z`, `2022-01-01T12:00:00.123+01:00` |
-| startedAt.epochMillis | `integer` | The start time of the task as a integer value of milliseconds since midnight of 1970-01-01 UTC | `1641024000123` (="2022-01-01T08:00:00.123Z") |
-| startedAt.epochNanos | `integer` | The start time of the task as a integer value of nanoseconds since midnight of 1970-01-01 UTC | `1641024000123456` (="2022-01-01T08:00:00.123456Z") |
-
 ##### Workflow Descriptor
 
 | Name | Type | Description | Example |
@@ -331,6 +319,19 @@ The following table shows which arguments are available for each runtime express
 | Task `export.as` | Transformed task output | `$context` | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
 | Workflow `output.as` | Last task's transformed output | Transformed workflow output | ✔ | | | ✔ | | ✔ |
 
+##### Task Descriptor
+
+| Name | Type | Description | Example |
+|:-----|:----:|:------------|:--------|
+| name | `string` | The task's name. | `getPet` |
+| reference | `string` | The task's reference. | `/do/2/myTask` |
+| definition | `map` | The tasks definition (specified under the name) as a parsed object | `{ "call": "http", "with": { ... } }` |
+| input | `any` | The task's *raw* input (i.e. *BEFORE* the `input.from` expression). For the result of `input.from` expression use the context of the runtime expression (for jq `.`) | - |
+| output | `any` | The task's *raw* output (i.e. *BEFORE* the `output.as` expression). | - |
+| startedAt.iso8601 | `string` | The start time of the task as a ISO 8601 date time string. It uses `T` as the date-time delimiter, either UTC (`Z`) or a time zone offset (`+01:00`). The precision can be either seconds, milliseconds or nanoseconds | `2022-01-01T12:00:00Z`, `2022-01-01T12:00:00.123456Z`, `2022-01-01T12:00:00.123+01:00` |
+| startedAt.epochMillis | `integer` | The start time of the task as a integer value of milliseconds since midnight of 1970-01-01 UTC | `1641024000123` (="2022-01-01T08:00:00.123Z") |
+| startedAt.epochNanos | `integer` | The start time of the task as a integer value of nanoseconds since midnight of 1970-01-01 UTC | `1641024000123456` (="2022-01-01T08:00:00.123456Z") |
+
 ### Fault Tolerance
 
 Serverless Workflow is designed with resilience in mind, acknowledging that errors are an inevitable part of any system. The DSL provides robust mechanisms to identify, describe, and handle errors effectively, ensuring the workflow can recover gracefully from failures.