Single-Run Workflow

A Single-run Workflow represent a single-time execution of a sequence of jobs. It does not include a schedule section.

Illustration of a Single-run Workflow

Code Snippet

The following code snippet illustrates a Workflow with two jobs. The first one executes upon Flare Stack that reads data from the thirdparty01 depot in batch mode and subsequently writes to the icebase depot. Once the first job, completes it execution the second job starts execution, which does the profilling on the data using same flare stack.

Sample Single Run Workflow manifest

workflow.yml

version: v1
name: wf-city
type: workflow
owner: iamgroot
tags:
  - Tier.Gold
  - mpulse.altamed
description: The "wf-city" is a data pipeline focused on ingesting city data from icebase to icebase AltaMed healthcare provider. It involves stages such as data ingestion, tranformation and profiling.
workflow:
  title: City Data Pipeline
  dag: 
   - name: city-data-ingestion
     title: City Dimension Ingester
     description: The job ingests city data from dropzone into raw zone
     spec:
       tags:
        - Connect
        - City
     stack: flare:5.0 # The job gets executed upon the Flare Stack, so its a Flare Job
     compute: runnable-default

        # Flare Stack-specific Section
     stackSpec:
        driver:
          coreLimit: 1100m
          cores: 1
          memory: 1048m
          job:
            explain: true  #job section will contain explain, log-level, inputs, outputs and steps
            logLevel: INFO

            inputs:
              - name: city_connect
                query: SELECT
                        *,
                        date_format (NOW(), 'yyyyMMddHHmm') AS version1,
                        NOW() AS ts_city1
                      FROM
                        icebase.retail.city 
                options: 
                  SSL: "true"
                  driver: "io.trino.jdbc.TrinoDriver"
                  cluster: "system"

            outputs:
              - name: city_connect
                dataset: dataos://icebase:retail/city01?acl=rw
                format: Iceberg
                description: City data ingested from retail city
                tags:
                  - retail
                  - city
                options:
                  saveMode: overwrite

   - name: icebase-city-profiling
     title: Profiler City01
     description: The job performs profiling on city01 data
     spec:
       envs:
         DISABLE_RAW_FILE_SYSTEM_PERMISSION_SET: "true"
       tags:
       - Fides
       - City
       - has_profile
       stack: flare:5.0
       compute: runnable-default
       title: City Profile

       persistentVolume: # Define Persistent Volume
         name: persistent-v
         directory: fides 
       stackSpec:
         driver:
           coreLimit: 2400m
           cores: 2
           memory: 3072m
         executor:
           coreLimit: 2400m
           cores: 2
           instances: 1
           memory: 4096m
         job:
           explain: true
           logLevel: WARN

           inputs:
             - name: profile_city
               dataset: dataos://icebase:retail/city01 # Dataset Name
               format: iceberg

           profile:
             # basic | intermediate | advanced
             level: basic
             filters:
               - type: expression
                 expression: "state_code='AL'" # Filter Expression

         sparkConf:
         - spark.sql.shuffle.partitions: 10
         - spark.default.parallelism: 10

    dependencies:
      - city-data-ingestion

Here are it's workflow and profiling manifests to do a hands on:

Sample Workflows

workflow.yml

# Resource Section
#This is a icebase to icebase workflow hence it's way of giving input and output is diff.
version: v1 
name: wf-tmdc-01 
type: workflow 
tags:
  - Connect
  - City
description: The job ingests city data from dropzone into raw zone

# Workflow-specific Section
workflow:
  title: Connect City
  dag: 

# Job 1 Specific Section
    - name: wf-job1 # Job 1 name
      title: City Dimension Ingester
      description: The job ingests city data from dropzone into raw zone
      spec:
        tags:
          - Connect
          - City
        stack: flare:6.0 # The job gets executed upon the Flare Stack, so its a Flare Job
        compute: runnable-default

        # Flare Stack-specific Section
        stackSpec:
          driver:
            coreLimit: 1100m
            cores: 1
            memory: 1048m
          job:
            explain: true  #job section will contain explain, log-level, inputs, outputs and steps
            logLevel: INFO

            inputs:
              - name: city_connect
                query: SELECT
                        *,
                        date_format (NOW(), 'yyyyMMddHHmm') AS version1,
                        NOW() AS ts_city1
                      FROM
                        icebase.retail.city 
            #    dataset: dataos://icebase:retail/city
             #   format: Iceberg
                options: 
                  SSL: "true"
                  driver: "io.trino.jdbc.TrinoDriver"
                  cluster: "system"

              #   schemaPath: dataos://thirdparty01:none/schemas/avsc/city.avsc #schema path is not necessary for icebase to icebase

            outputs:
              - name: city_connect
                dataset: dataos://icebase:retail/city01?acl=rw
                format: Iceberg
                description: City data ingested from retail city
                tags:
                  - retail
                  - city
                options:
                  saveMode: overwrite

profiling.yaml

version: v1
name: profiler-raw-city-01
type: workflow
tags:
- Fides
- City
- has_profile
description: The job performs profiling on top of city data
workflow:
  title: City Profiler
  dag:
  - name: profiling-city01
    title: Profiler City01
    description: The job performs profiling on city01 data
    spec:
      envs:
        DISABLE_RAW_FILE_SYSTEM_PERMISSION_SET: "true"
      tags:
      - Fides
      - City
      - has_profile
      stack: flare:5.0
      compute: runnable-default
      title: City Profile

      persistentVolume: # Define Persistent Volume
        name: persistent-v
        directory: fides 
      stackSpec:
        driver:
          coreLimit: 2400m
          cores: 2
          memory: 3072m
        executor:
          coreLimit: 2400m
          cores: 2
          instances: 1
          memory: 4096m
        job:
          explain: true
          logLevel: WARN

          inputs:
            - name: profile_city
              dataset: dataos://icebase:retail/city01 # Dataset Name
              format: iceberg

          profile:
            # basic | intermediate | advanced
            level: basic
            filters:
              - type: expression
                expression: "state_code='AL'" # Filter Expression

        sparkConf:
        - spark.sql.shuffle.partitions: 10
        - spark.default.parallelism: 10