# pip install --upgrade domolibraryTutorial - Generate a DomoStats dataset using DomoLibrary
DomoStats
MagicETL
import domolibrary
domolibrary.__version__'4.2.105'retrieve Authentication credentials
Don’t store your password on the internet!!!
import os
domo_instance = os.environ['DOMO_INSTANCE']
domo_access_token = os.environ['DOMO_ACCESS_TOKEN']
TEST_DATAFLOW_ID = 108import domolibrary.client.DomoAuth as dmda
auth = dmda.DomoTokenAuth(
domo_instance=domo_instance,
domo_access_token=domo_access_token
)
await auth.print_is_token()
assert auth.token🎉 token_auth token retrieved from domo-community ⚙️Retrieve Data
How do you handle looping??
How do you handle ‘fast’ code execution? (asynchronous code execution)
Will one request be enough?
- get all dataflow
- for each dataflow retrieve the correct information”
import domolibrary.classes.DomoDatacenter as dmdc
from typing import List
async def get_dataflow_ids(auth: dmda.DomoAuth) -> List[int]:
"""searches domo datacenter and returns a list of dataflow_ids"""
domo_datacenter = dmdc.DomoDatacenter(auth=auth)
dataflows_ls = await domo_datacenter.search_datacenter(
auth=auth,
entity_type="DATAFLOW",
additional_filters_ls=[
{
"filterType": "term",
"field": "data_flow_type",
"value": "MAGIC",
"name": "Magic ETL v2",
"not": False,
}
],
)
return [dataflow_obj["databaseId"] for dataflow_obj in dataflows_ls]
dataflow_ids = await get_dataflow_ids(auth)
dataflow_ids[0:5]['131', '227', '37', '185', '38']import domolibrary.classes.DomoDataflow as dmdf
import domolibrary.utils.chunk_execution as ce
from typing import List
async def get_dataflows(dataflow_ids_ls: List[int]) -> List[dmdf.DomoDataflow]:
"""retrieves dataflow metadata from a list of dataflows"""
return await ce.gather_with_concurrency(
*[
dmdf.DomoDataflow.get_by_id(dataflow_id=dataflow_id, auth=auth)
for dataflow_id in dataflow_ids_ls
],
n=20
)
domo_dataflow_ls = await get_dataflows([dataflow_ids[0]])
domo_dataflow = domo_dataflow_ls[0]
domo_dataflowDomoDataflow(id=131, name='BeastModesGovernance_01_AccumulateHistory', owner=None, description=None, tags=None, actions=[DomoDataflow_Action(id='40554fc4-428a-4117-8820-b3f4f4927021', type='LoadFromVault', name='Governance_Beast Modes', datasource_id='b7998b45-ccb5-4183-830e-8c2bdd489fd0', sql=None, depends_on=None, parent_actions=None), DomoDataflow_Action(id='05082103-d42e-49ab-a66e-bd31b4c0e3df', type='ExpressionEvaluator', name='Add Formula', datasource_id=None, sql=None, depends_on=['40554fc4-428a-4117-8820-b3f4f4927021'], parent_actions=[DomoDataflow_Action(id='40554fc4-428a-4117-8820-b3f4f4927021', type='LoadFromVault', name='Governance_Beast Modes', datasource_id='b7998b45-ccb5-4183-830e-8c2bdd489fd0', sql=None, depends_on=None, parent_actions=None)]), DomoDataflow_Action(id='fe77761b-eb11-4491-b19c-ebb56d009b76', type='PublishToVault', name='Governance_BeastMode_historical', datasource_id='e6559952-30ce-41a1-b831-f29ebe95d6e5', sql=None, depends_on=['05082103-d42e-49ab-a66e-bd31b4c0e3df'], parent_actions=[DomoDataflow_Action(id='05082103-d42e-49ab-a66e-bd31b4c0e3df', type='ExpressionEvaluator', name='Add Formula', datasource_id=None, sql=None, depends_on=['40554fc4-428a-4117-8820-b3f4f4927021'], parent_actions=[DomoDataflow_Action(id='40554fc4-428a-4117-8820-b3f4f4927021', type='LoadFromVault', name='Governance_Beast Modes', datasource_id='b7998b45-ccb5-4183-830e-8c2bdd489fd0', sql=None, depends_on=None, parent_actions=None)])])], version_id=None, version_number=None, versions=None, History=DomoDataflow_History(execution_history=None), Lineage=DomoLineage(parent_type='DATAFLOW', parent_id=131))import pandas as pd
async def generate_version_action_pdf(domo_dataflow: dmdf.DomoDataflow) -> pd.DataFrame:
"""retrieves dataflow definition version history of a dataflow"""
dataflow_versions = await domo_dataflow.get_versions()
if not domo_dataflow.versions:
return pd.DataFrame()
df = pd.DataFrame(
[
{
"dataflow_id": dataflow_version.id,
"dataflow_version": dataflow_version.version_id,
"dataflow_name": dataflow_version.name,
**domo_action.__dict__,
}
for dataflow_version in dataflow_versions
for domo_action in dataflow_version.actions
]
)
df.drop(columns=["parent_actions", "datasource_id", "sql"], inplace=True)
df.rename(columns={"id": "tile_id", "type": "tile_type"}, inplace=True)
return df
await generate_version_action_pdf(domo_dataflow)| dataflow_id | dataflow_version | dataflow_name | tile_id | tile_type | name | depends_on | |
|---|---|---|---|---|---|---|---|
| 0 | 131 | 520 | BeastModesGovernance_01_AccumulateHistory | 40554fc4-428a-4117-8820-b3f4f4927021 | LoadFromVault | Governance_Beast Modes | None |
| 1 | 131 | 520 | BeastModesGovernance_01_AccumulateHistory | 05082103-d42e-49ab-a66e-bd31b4c0e3df | ExpressionEvaluator | Add Formula | [40554fc4-428a-4117-8820-b3f4f4927021] |
| 2 | 131 | 520 | BeastModesGovernance_01_AccumulateHistory | fe77761b-eb11-4491-b19c-ebb56d009b76 | PublishToVault | Governance_BeastMode_historical | [05082103-d42e-49ab-a66e-bd31b4c0e3df] |
| 3 | 131 | 519 | New ETL Transform | 40554fc4-428a-4117-8820-b3f4f4927021 | LoadFromVault | Governance_Beast Modes | None |
| 4 | 131 | 519 | New ETL Transform | 05082103-d42e-49ab-a66e-bd31b4c0e3df | ExpressionEvaluator | Add Formula | [40554fc4-428a-4117-8820-b3f4f4927021] |
| 5 | 131 | 519 | New ETL Transform | fe77761b-eb11-4491-b19c-ebb56d009b76 | PublishToVault | Governance_BeastMode_historical | [05082103-d42e-49ab-a66e-bd31b4c0e3df] |
| 6 | 131 | 518 | New ETL Transform | 40554fc4-428a-4117-8820-b3f4f4927021 | LoadFromVault | Governance_Beast Modes | None |
| 7 | 131 | 518 | New ETL Transform | fe77761b-eb11-4491-b19c-ebb56d009b76 | PublishToVault | Governance_BeastMode_historical | [05082103-d42e-49ab-a66e-bd31b4c0e3df] |
| 8 | 131 | 518 | New ETL Transform | 05082103-d42e-49ab-a66e-bd31b4c0e3df | ExpressionEvaluator | Add Formula | [40554fc4-428a-4117-8820-b3f4f4927021] |
async def generate_action_stats_df(
domo_dataflow: dmdf.DomoDataflow,
maximum_history: int = 10, # number of history to look back
) -> pd.DataFrame:
"""returns execution history by tile"""
await domo_dataflow.History.get_execution_history(maximum=maximum_history)
if (
not domo_dataflow.History
or not domo_dataflow.History.execution_history
or len(domo_dataflow.History.execution_history) == 0
):
print(
f"⚠️ dataflow {domo_dataflow.id} - {domo_dataflow.name} has never been executed"
)
return pd.DataFrame()
history_ls = [
domo_history
for domo_history in domo_dataflow.History.execution_history
if domo_history.action_results and len(domo_history.action_results) > 0
]
df = pd.DataFrame(
[
{
"dataflow_execution_id": domo_history.dataflow_execution_id,
"dataflow_version": domo_history.dataflow_version,
"dataflow_id": domo_history.dataflow_id,
"dataflow_begin_time": domo_history.begin_time,
**domo_action.__dict__,
}
for domo_history in history_ls
for domo_action in domo_history.action_results
]
)
df.drop(columns=["name"], inplace=True)
df.rename(columns={"id": "tile_id", "type": "tile_type"}, inplace=True)
return df
stats_df = None
try:
stats_df = await generate_action_stats_df(domo_dataflow)
except Exception as e:
print(e)
stats_df| dataflow_execution_id | dataflow_version | dataflow_id | dataflow_begin_time | tile_id | tile_type | is_success | rows_processed | begin_time | end_time | duration_in_sec | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4759ab00-3f9d-4d55-9087-32aa38655a51 | 519 | 131 | 2023-03-07 00:07:39 | 40554fc4-428a-4117-8820-b3f4f4927021 | DataHubManifestLoaderAction | True | 1239 | 2023-03-07 00:07:46.011 | 2023-03-07 00:07:46.919 | 0.908 |
| 1 | 4759ab00-3f9d-4d55-9087-32aa38655a51 | 519 | 131 | 2023-03-07 00:07:39 | 05082103-d42e-49ab-a66e-bd31b4c0e3df | ExpressionEvaluator | True | 1239 | 2023-03-07 00:07:46.630 | 2023-03-07 00:07:46.919 | 0.289 |
| 2 | 4759ab00-3f9d-4d55-9087-32aa38655a51 | 519 | 131 | 2023-03-07 00:07:39 | fe77761b-eb11-4491-b19c-ebb56d009b76 | PublishToVault | True | 1239 | 2023-03-07 00:07:46.631 | 2023-03-07 00:07:47.434 | 0.803 |
| 3 | 99808eee-9d74-4c9f-8256-17dff1fe44fe | 518 | 131 | 2023-03-06 23:57:25 | 40554fc4-428a-4117-8820-b3f4f4927021 | DataHubManifestLoaderAction | True | 1238 | 2023-03-06 23:57:33.196 | 2023-03-06 23:57:33.999 | 0.803 |
| 4 | 99808eee-9d74-4c9f-8256-17dff1fe44fe | 518 | 131 | 2023-03-06 23:57:25 | 05082103-d42e-49ab-a66e-bd31b4c0e3df | ExpressionEvaluator | True | 1238 | 2023-03-06 23:57:33.872 | 2023-03-06 23:57:33.999 | 0.127 |
| 5 | 99808eee-9d74-4c9f-8256-17dff1fe44fe | 518 | 131 | 2023-03-06 23:57:25 | fe77761b-eb11-4491-b19c-ebb56d009b76 | PublishToVault | True | 1238 | 2023-03-06 23:57:33.873 | 2023-03-06 23:57:34.520 | 0.647 |
async def process_instance(auth):
dataflow_ids = await get_dataflow_ids(auth)
domo_dataflows = await get_dataflows(dataflow_ids)
actions_df_ls = await ce.gather_with_concurrency(
*[
generate_version_action_pdf(domo_dataflow)
for domo_dataflow in domo_dataflows
],
n=20
)
stats_df_ls = await ce.gather_with_concurrency(
*[generate_action_stats_df(domo_dataflow) for domo_dataflow in domo_dataflows],
n=20
)
actions_df = pd.concat(actions_df_ls)
stats_df = pd.concat(stats_df_ls)
# import domojupyter as dj
# dj.write_dataframe(actions_df, 'MONIT_Dataflow_Tiles')
# dj.write_dataframe(stats_df, 'MONIT_Dataflow_Stats')
return actions_df, stats_df
base_actions_df, base_stats_df = await process_instance(auth)⚠️ dataflow 69 - Copy of gov_datasets_INT has never been executed
⚠️ dataflow 27 - JW_Simple Sample Set_Rolling Averages has never been executed
⚠️ dataflow 394 - ETL Forecasting has never been executed
⚠️ dataflow 85 - Working Hours Test has never been executed
⚠️ dataflow 322 - Forecast Sales - HV has never been executed
⚠️ dataflow 323 - New ETL Transform has never been executed
⚠️ dataflow 83 - Working Hours Example has never been executed
⚠️ dataflow 410 - Domain Visits - SEA has never been executeddef generate_datasets(actions_df, stats_df, dataflow_id=None, execution_id=None):
### filter and configure actions_df
actions_df = actions_df.copy()
if dataflow_id:
actions_df = actions_df[actions_df["dataflow_id"] == dataflow_id]
actions_df.rename(columns={"name": "tile_name"}, inplace=True)
#### filter and configure stats_df
stats_df = stats_df.copy()
if execution_id:
stats_df = stats_df[stats_df["dataflow_execution_id"] == execution_id]
if dataflow_id:
stats_df = stats_df[stats_df["dataflow_id"] == dataflow_id]
stats_df = pd.merge(
stats_df,
actions_df[
["dataflow_id", "dataflow_name", "dataflow_version", "tile_id", "tile_name"]
],
how="inner",
)
#### handle generate facts_df with one row per action and parent
explode_df = actions_df[
["dataflow_id", "dataflow_version", "tile_id", "depends_on"]
].explode("depends_on")
facts_df = stats_df[
[
"dataflow_name",
"dataflow_execution_id",
"dataflow_version",
"dataflow_id",
"tile_id",
"tile_name",
"tile_type",
"rows_processed",
"dataflow_begin_time",
"begin_time",
"end_time",
"duration_in_sec",
]
]
parent_df = facts_df[
[
"dataflow_execution_id",
"dataflow_version",
"dataflow_id",
"tile_id",
"tile_name",
"tile_type",
"rows_processed",
"end_time",
]
].rename(
columns={
"tile_id": "depends_on",
"tile_name": "parent_tile_name",
"tile_type": "parent_tile_type",
"rows_processed": "parent_rows_processed",
"end_time": "parent_end_time",
}
)
facts_by_parent_df = pd.merge(
facts_df,
explode_df,
how="inner",
on=["dataflow_id", "tile_id", "dataflow_version"],
)
facts_by_parent_df = pd.merge(
facts_by_parent_df,
parent_df,
how="left",
on=["dataflow_id", "dataflow_version", "dataflow_execution_id", "depends_on"],
).rename(columns={"depends_on": "parent_tile_id"})
facts_by_parent_df["parent_end_time"] = facts_by_parent_df[
"parent_end_time"
].fillna(facts_by_parent_df["begin_time"])
facts_by_parent_df["actual_duration_in_sec"] = (
facts_by_parent_df["end_time"] - facts_by_parent_df["parent_end_time"]
).dt.total_seconds()
facts_by_parent_df["tile_delay_rank"] = facts_by_parent_df.groupby(
["dataflow_execution_id", "dataflow_version", "dataflow_id", "tile_id"]
)["actual_duration_in_sec"].rank(ascending=False)
return facts_by_parent_df.sort_values(
by=["dataflow_id", "begin_time", "parent_tile_id"], ascending=True
)
generate_datasets(
base_actions_df,
base_stats_df,
TEST_DATAFLOW_ID,
execution_id="1e331f2b-1db8-460d-9860-334aedc88e93",
)| dataflow_name | dataflow_execution_id | dataflow_version | dataflow_id | tile_id | tile_name | tile_type | rows_processed | dataflow_begin_time | begin_time | end_time | duration_in_sec | parent_tile_id | parent_tile_name | parent_tile_type | parent_rows_processed | parent_end_time | actual_duration_in_sec | tile_delay_rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | dce3487e-6a2d-49a2-afd4-1f7867cb3b95 | Governance_datasets | DataHubManifestLoaderAction | 570.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:05.717 | 2022-11-03 09:33:06.614 | 0.897 | None | NaN | NaN | NaN | 2022-11-03 09:33:05.717 | 0.897 | 1.0 |
| 1 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | df709f9b-0e6f-4f69-8b66-aa7411028db8 | Governance_dataflow_details | DataHubManifestLoaderAction | 361.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:05.717 | 2022-11-03 09:33:06.656 | 0.939 | None | NaN | NaN | NaN | 2022-11-03 09:33:05.717 | 0.939 | 1.0 |
| 2 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | 3b3396dd-a673-4211-868f-abbbcc4ce0a3 | Datasets | SelectValues | 570.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:06.609 | 2022-11-03 09:33:06.614 | 0.005 | dce3487e-6a2d-49a2-afd4-1f7867cb3b95 | Governance_datasets | DataHubManifestLoaderAction | 570.0 | 2022-11-03 09:33:06.614 | 0.000 | 1.0 |
| 4 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | 33a5f316-cb1e-4c2c-bf10-911396a5ae97 | Python Script | PythonEngineAction | 931.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:06.610 | 2022-11-03 09:33:15.408 | 8.798 | 3b3396dd-a673-4211-868f-abbbcc4ce0a3 | Datasets | SelectValues | 570.0 | 2022-11-03 09:33:06.614 | 8.794 | 1.0 |
| 5 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | 33a5f316-cb1e-4c2c-bf10-911396a5ae97 | Python Script | PythonEngineAction | 931.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:06.610 | 2022-11-03 09:33:15.408 | 8.798 | 7e839c17-3e1a-4938-86d4-1b19d61c3662 | Dataflow Details | SelectValues | 361.0 | 2022-11-03 09:33:06.656 | 8.752 | 2.0 |
| 3 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | 7e839c17-3e1a-4938-86d4-1b19d61c3662 | Dataflow Details | SelectValues | 361.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:06.656 | 2022-11-03 09:33:06.656 | 0.000 | df709f9b-0e6f-4f69-8b66-aa7411028db8 | Governance_dataflow_details | DataHubManifestLoaderAction | 361.0 | 2022-11-03 09:33:06.656 | 0.000 | 1.0 |
| 6 | Datasets_lineage | 1e331f2b-1db8-460d-9860-334aedc88e93 | 441 | 108 | dffae0ba-4e21-457f-b190-9af841e5f771 | Datasets_lineage | PublishToVault | 1391.0 | 2022-11-03 09:33:00 | 2022-11-03 09:33:15.348 | 2022-11-03 09:33:15.490 | 0.142 | 33a5f316-cb1e-4c2c-bf10-911396a5ae97 | Python Script | PythonEngineAction | 931.0 | 2022-11-03 09:33:15.408 | 0.082 | 1.0 |
generate_datasets(base_actions_df, base_stats_df, None, None)
# dj.write_dataframe(exploded_df , 'MONIT_Dataflow_Tiles_Exploded')| dataflow_name | dataflow_execution_id | dataflow_version | dataflow_id | tile_id | tile_name | tile_type | rows_processed | dataflow_begin_time | begin_time | end_time | duration_in_sec | parent_tile_id | parent_tile_name | parent_tile_type | parent_rows_processed | parent_end_time | actual_duration_in_sec | tile_delay_rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16884 | MetaData_Stage 1 | 8f77dfc7-b2fb-4f3c-9d24-1d94e0e32424 | 19 | 8 | d312578c-e9cb-4e3a-9dc6-662c00c93ba0 | Governance_Users and Groups | DataHubManifestLoaderAction | 35.0 | 2020-07-09 16:00:07 | 2020-07-09 16:00:17.000 | 2020-07-09 16:00:21.000 | 4.000 | None | NaN | NaN | NaN | 2020-07-09 16:00:17.000 | 4.000 | 1.0 |
| 16885 | MetaData_Stage 1 | 8f77dfc7-b2fb-4f3c-9d24-1d94e0e32424 | 19 | 8 | d316ddf3-9917-4542-bf68-e1cd8397d894 | Governance_Groups | DataHubManifestLoaderAction | 3.0 | 2020-07-09 16:00:07 | 2020-07-09 16:00:17.000 | 2020-07-09 16:00:21.000 | 4.000 | None | NaN | NaN | NaN | 2020-07-09 16:00:17.000 | 4.000 | 1.0 |
| 16887 | MetaData_Stage 1 | 8f77dfc7-b2fb-4f3c-9d24-1d94e0e32424 | 19 | 8 | eebdf394-fc6d-40c0-895e-b325044db506 | MetaData_Groups | PublishToVault | 3.0 | 2020-07-09 16:00:07 | 2020-07-09 16:00:21.000 | 2020-07-09 16:00:22.000 | 1.000 | 0fe45cc8-7c88-48cb-a437-4656954767a6 | PDP? | Constant | 3.0 | 2020-07-09 16:00:21.000 | 1.000 | 1.0 |
| 16886 | MetaData_Stage 1 | 8f77dfc7-b2fb-4f3c-9d24-1d94e0e32424 | 19 | 8 | e048f809-08ae-49c2-84be-5b7b76a99944 | MetaData_Users and Groups | PublishToVault | 35.0 | 2020-07-09 16:00:07 | 2020-07-09 16:00:21.000 | 2020-07-09 16:00:22.000 | 1.000 | 8e52e4dc-fd3a-455b-9ec1-c2a404d342d0 | PDP? 1 | Constant | 35.0 | 2020-07-09 16:00:21.000 | 1.000 | 1.0 |
| 16883 | MetaData_Stage 1 | 8f77dfc7-b2fb-4f3c-9d24-1d94e0e32424 | 19 | 8 | 8e52e4dc-fd3a-455b-9ec1-c2a404d342d0 | PDP? 1 | Constant | 35.0 | 2020-07-09 16:00:07 | 2020-07-09 16:00:21.000 | 2020-07-09 16:00:21.000 | 0.000 | d312578c-e9cb-4e3a-9dc6-662c00c93ba0 | Governance_Users and Groups | DataHubManifestLoaderAction | 35.0 | 2020-07-09 16:00:21.000 | 0.000 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18775 | Sample Store Sales Flow | b95e7a38-cc77-49db-9c28-5923991fe556 | 1551 | 426 | e895ae72-acbf-4e7c-8b9a-3341ba8e944f | Select Columns | SelectValues | 19378.0 | 2025-01-24 16:37:32 | 2025-01-24 16:37:41.729 | 2025-01-24 16:37:42.627 | 0.898 | fd921644-a6ce-45db-9482-576ed9638f55 | Alter Columns | Metadata | 19378.0 | 2025-01-24 16:37:42.627 | 0.000 | 1.0 |
| 18776 | Sample Store Sales Flow | b95e7a38-cc77-49db-9c28-5923991fe556 | 1551 | 426 | 1d6404a2-7810-4595-961e-0e6c00eb545d | Sample Store Sales Flow | PublishToVault | 19378.0 | 2025-01-24 16:37:32 | 2025-01-24 16:37:41.736 | 2025-01-24 16:37:42.836 | 1.100 | e895ae72-acbf-4e7c-8b9a-3341ba8e944f | Select Columns | SelectValues | 19378.0 | 2025-01-24 16:37:42.627 | 0.209 | 1.0 |
| 18865 | HeartVoronoi | e55abc80-eadf-4d79-a057-49a1593a8e51 | 1553 | 427 | b16e8be9-62b5-4e40-8d03-ee405886e179 | HeartVoron 1 | DataHubManifestLoaderAction | 9643.0 | 2025-01-31 16:06:23 | 2025-01-31 16:06:28.519 | 2025-01-31 16:06:28.849 | 0.330 | None | NaN | NaN | NaN | 2025-01-31 16:06:28.519 | 0.330 | 1.0 |
| 18866 | HeartVoronoi | e55abc80-eadf-4d79-a057-49a1593a8e51 | 1553 | 427 | 1d555ba4-520e-4e75-9a41-a3a3e7f417ab | Python Script | PythonEngineAction | 9643.0 | 2025-01-31 16:06:23 | 2025-01-31 16:06:28.793 | 2025-01-31 16:06:31.155 | 2.362 | b16e8be9-62b5-4e40-8d03-ee405886e179 | HeartVoron 1 | DataHubManifestLoaderAction | 9643.0 | 2025-01-31 16:06:28.849 | 2.306 | 1.0 |
| 18867 | HeartVoronoi | e55abc80-eadf-4d79-a057-49a1593a8e51 | 1553 | 427 | d9c960ce-241b-4bf3-ba55-6e8537cfd4ca | HeartVoron 1 | PublishToVault | 1.0 | 2025-01-31 16:06:23 | 2025-01-31 16:06:31.350 | 2025-01-31 16:06:31.536 | 0.186 | 1d555ba4-520e-4e75-9a41-a3a3e7f417ab | Python Script | PythonEngineAction | 9643.0 | 2025-01-31 16:06:31.155 | 0.381 | 1.0 |
21648 rows × 19 columns