A La Mode

A tool for describing pure data pipelines that enables avoiding repeating work (incrementality) and keeping old data around (provenance)

You may want to watch the talk from Yow for more context to the project

The DSL is inspired by airflow, the file generated is a bencoded description of the flow with unique IDs for the outputs of each step, that will change if the task description or any of its inputs change. What "output" means is left vague, you might do files or folders on a local disk, blobs in Blob store, store them in a database.

The expectation is that different "execution engines" can parse this description and perform the work described, avoiding outputting to locations that have successfully† been used before.

† Success detection is not trivial, something existing in the location is not enough, some metadata or record of the task executions is needed.

Example

If our workflow looks like this

Create the 4 tasks

from a_la_mode import Task, Dag, pp
from deepdiff import DeepDiff

dag = Dag({
    'schedule': '@daily'
})

dag.task('download_images',
            {
                'image': 'collage',
                'sha': '1qe34',
                'command': "python dl_images.py"
            })

dag.task('blur',
            {
                'image': 'collage',
                'sha': '6f09d',
                'command': "python transform_images.py blur"
            })

dag.task('edge_enhance',
            {
                'image': 'collage',
                'sha': '1qe34',
                'command': "python transform_images.py edge_enhance"

            })

dag.task('collage',
            {
                'image': 'collage',
                'sha': '1qe34',
                'command': "python collage.py"
            })

Connect them up with the basic DSL

for task in [dag.blur, dag.edge_enhance]:
    task.add_dep(dag.download_images)

for task in [dag.blur, dag.edge_enhance]:
    dag.collage.add_dep(task)

encode the initial dag

encoded_dag = dag.encode()
pp(encoded_dag)

{ 'meta': {'schedule': '@daily'},
  'tasks': { 'blur': { 'command': 'python transform_images.py blur',
                       'image': 'collage',
                       'inputs': { 'download_images': '6075b3951568e2f31df204a59eb92bbb17d84e6b49f1275216bc0b6deb0e223d'},
                       'output': '82695e5805a9cd091e42ae94469efe77a90907c96190c793cee3c7d58dc2bbe0',
                       'sha': '6f09d'},
             'collage': { 'command': 'python collage.py',
                          'image': 'collage',
                          'inputs': { 'blur': '82695e5805a9cd091e42ae94469efe77a90907c96190c793cee3c7d58dc2bbe0',
                                      'edge_enhance': '7a764d984ce66d23a69c87645b47fcf7be79711f40be95fb631308ee01ca2a04'},
                          'output': '566a13d45eeebdefc50de118f5031278b20171ce76deb51791a4f084668e6d16',
                          'sha': '1qe34'},
             'download_images': { 'command': 'python dl_images.py',
                                  'image': 'collage',
                                  'inputs': {},
                                  'output': '6075b3951568e2f31df204a59eb92bbb17d84e6b49f1275216bc0b6deb0e223d',
                                  'sha': '1qe34'},
             'edge_enhance': { 'command': 'python transform_images.py '
                                          'edge_enhance',
                               'image': 'collage',
                               'inputs': { 'download_images': '6075b3951568e2f31df204a59eb92bbb17d84e6b49f1275216bc0b6deb0e223d'},
                               'output': '7a764d984ce66d23a69c87645b47fcf7be79711f40be95fb631308ee01ca2a04',
                               'sha': '1qe34'}}}

Change it and re-encode it after (this is just for demo, is not the expected usage)

dag.blur.spec['sha'] = 'qwetr3'
encoded_changed_dag = dag.encode()
pp(encoded_changed_dag)

{ 'meta': {'schedule': '@daily'},
  'tasks': { 'blur': { 'command': 'python transform_images.py blur',
                       'image': 'collage',
                       'inputs': { 'download_images': '6075b3951568e2f31df204a59eb92bbb17d84e6b49f1275216bc0b6deb0e223d'},
                       'output': '4df7200d1f836f7372ce01761c9cb9e0d2bc7d1abc7ee9c1abe8dc7df9a67e5e',
                       'sha': 'qwetr3'},
             'collage': { 'command': 'python collage.py',
                          'image': 'collage',
                          'inputs': { 'blur': '4df7200d1f836f7372ce01761c9cb9e0d2bc7d1abc7ee9c1abe8dc7df9a67e5e',
                                      'edge_enhance': '7a764d984ce66d23a69c87645b47fcf7be79711f40be95fb631308ee01ca2a04'},
                          'output': '25adad1924719ff4d5d2c3f4839f0ff1b4e21537c3c358f7d10b5e5d7c9c6de5',
                          'sha': '1qe34'},
             'download_images': { 'command': 'python dl_images.py',
                                  'image': 'collage',
                                  'inputs': {},
                                  'output': '6075b3951568e2f31df204a59eb92bbb17d84e6b49f1275216bc0b6deb0e223d',
                                  'sha': '1qe34'},
             'edge_enhance': { 'command': 'python transform_images.py '
                                          'edge_enhance',
                               'image': 'collage',
                               'inputs': { 'download_images': '6075b3951568e2f31df204a59eb92bbb17d84e6b49f1275216bc0b6deb0e223d'},
                               'output': '7a764d984ce66d23a69c87645b47fcf7be79711f40be95fb631308ee01ca2a04',
                               'sha': '1qe34'}}}

To save you time examining the output, we can take a look with DeepDiff

pp(DeepDiff(encoded_dag, encoded_changed_dag))

{ 'values_changed': { "root['tasks']['blur']['output']": { 'new_value': '4df7200d1f836f7372ce01761c9cb9e0d2bc7d1abc7ee9c1abe8dc7df9a67e5e',
                                                           'old_value': '82695e5805a9cd091e42ae94469efe77a90907c96190c793cee3c7d58dc2bbe0'},
                      "root['tasks']['blur']['sha']": { 'new_value': 'qwetr3',
                                                        'old_value': '6f09d'},
                      "root['tasks']['collage']['inputs']['blur']": { 'new_value': '4df7200d1f836f7372ce01761c9cb9e0d2bc7d1abc7ee9c1abe8dc7df9a67e5e',
                                                                      'old_value': '82695e5805a9cd091e42ae94469efe77a90907c96190c793cee3c7d58dc2bbe0'},
                      "root['tasks']['collage']['output']": { 'new_value': '25adad1924719ff4d5d2c3f4839f0ff1b4e21537c3c358f7d10b5e5d7c9c6de5',
                                                              'old_value': '566a13d45eeebdefc50de118f5031278b20171ce76deb51791a4f084668e6d16'}}}

You can see the sha and the output have changed for the blur task and the corresponding input to collage and its output are changed.

franxan/a-la-mode

A La Mode

Example