dag=cell_image_analysis_2channels_dag,
)
def prepare_cellprofiler_csv(ds, **kwargs):
"""Prepare the cellprofiler csv based on the args"""
df = get_cell_images_df(**kwargs)
kwargs['ti'].xcom_push(key='cell_images_df', value=df)
return
prepare_cellprofiler_csv_op = PythonOperator(
task_id='prepare_cellprofiler_csv',
provide_context=True,
python_callable=prepare_cellprofiler_csv,
dag=cell_image_analysis_2channels_dag)
prepare_cellprofiler_csv_op.set_downstream(image_conversion_dag)
cellprofiler_tasks = cell_image_analysis_generate_cellprofiler_task(
cell_image_analysis_2channels_dag)
cellprofiler_branch_tasks = cell_image_analysis_generate_decide_run_cellprofiler(
cell_image_analysis_2channels_dag)
image_conversion_dag.set_downstream(cellprofiler_branch_tasks)
cell_image_analysis_no_images_to_run_op.set_upstream(cellprofiler_branch_tasks)
for idx, cellprofiler_branch_task in enumerate(cellprofiler_branch_tasks):
cellprofiler_branch_task.set_downstream(cellprofiler_tasks[idx])
cell_image_analysis_combine_cellprofiler_csvs.set_upstream(cellprofiler_tasks)
section_1 = SubDagOperator(
task_id='section-1',
subdag=subdag(DAG_NAME, 'section-1', args),
default_args=args,
dag=dag,
)
some_other_task = DummyOperator(
task_id='some-other-task',
default_args=args,
dag=dag,
)
section_2 = SubDagOperator(
task_id='section-2',
subdag=subdag(DAG_NAME, 'section-2', args),
default_args=args,
dag=dag,
)
end = DummyOperator(
task_id='end',
default_args=args,
dag=dag,
)
start.set_downstream(section_1)
section_1.set_downstream(some_other_task)
some_other_task.set_downstream(section_2)
section_2.set_downstream(end)
dwh_script = BashOperator(
task_id='dwh_script',
bash_command=conf['bash_path']['load_to_dwh_location'] + ' ',
on_failure_callback=send_email_failure,
dag=dag)
send_email = EmailOperator(
to='*****@*****.**',
task_id='send_email_after_all_success',
subject='DAG run for {{ ds }} was successful.',
html_content=
'All the task for {{ yesterday_ds }} was successful. For more details visit http://172.20.23.251:8080/admin/airflow/tree?num_runs=25&dag_id=Jobs',
dag=dag)
brightedge_keyword_rank_data.set_downstream(integration_script)
brightedge_share_of_voice.set_downstream(integration_script)
doubleclick_search_campaign.set_downstream(integration_script)
doubleclick_search_productadvertised.set_downstream(integration_script)
doubleclick_search_keyword.set_downstream(integration_script)
doubleclick_dcm_standard_display_uk.set_downstream(integration_script)
partners_display_performance.set_downstream(integration_script)
time_wait_till_6_00am.set_downstream(partners_display_performance)
time_wait_till_6_00am.set_downstream(doubleclick_search_campaign)
time_wait_till_6_00am.set_downstream(doubleclick_dcm_standard_display_uk)
time_wait_till_6_00am.set_downstream(doubleclick_search_productadvertised)
time_wait_till_6_00am.set_downstream(sensor_to_check_ga_table_preset)
time_wait_till_6_00am.set_downstream(doubleclick_search_keyword)
sensor_to_check_ga_table_preset.set_downstream(unique_visitors)
sensor_to_check_ga_table_preset.set_downstream(gapremiuim_dailyaggregatesfull)
gapremiuim_dailyaggregatesfull.set_downstream(integration_script)
'--fix-flag',
'0',
]
t0 = mlengine_operator.MLEngineTrainingOperator(
# gcp_conn_id='project_connection',
task_id='sequential_startPoint',
project_id=PROJECT_ID,
job_id=job_id0,
package_uris=[PACKAGE_URI],
training_python_module='trainer.task',
training_args=training_args_0,
region=REGION,
runtime_version='1.9',
# scale_tier=ScaleTier,
dag=dag)
child_dag_names = ['sequential_training_12', 'sequential_training_24']
subdag = SubDagOperator(subdag=sub_dag(parent_dag_name, child_dag_names[0],
default_args, dag.schedule_interval),
task_id=child_dag_names[0],
default_args=default_args,
dag=dag)
subdag1 = SubDagOperator(subdag=sub_dag(parent_dag_name, child_dag_names[1],
default_args, dag.schedule_interval),
task_id=child_dag_names[1],
default_args=default_args,
dag=dag)
t0.set_downstream(subdag)
subdag.set_downstream(subdag1)
section_1 = SubDagOperator(
task_id='section-1',
subdag=subdag(DAG_NAME, 'section-1', args),
default_args=args,
dag=dag,
)
some_other_task = DummyOperator(
task_id='some-other-task',
default_args=args,
dag=dag,
)
section_2 = SubDagOperator(
task_id='section-2',
subdag=subdag(DAG_NAME, 'section-2', args),
default_args=args,
dag=dag,
)
end = DummyOperator(
task_id='end',
default_args=args,
dag=dag,
)
start.set_downstream(section_1)
section_1.set_downstream(some_other_task)
some_other_task.set_downstream(section_2)
section_2.set_downstream(end)
start = DummyOperator(
task_id='start',
default_args=args,
dag=dag,
)
onboard = SubDagOperator(
task_id='onboard_dag',
subdag=subdag(DAG_NAME, 'onboard_dag', args),
default_args=args,
dag=dag,
)
ongoing = SubDagOperator(
task_id='ongoing_dag',
subdag=subdag(DAG_NAME, 'ongoing_dag', args),
default_args=args,
dag=dag,
)
end = DummyOperator(
task_id='end',
default_args=args,
dag=dag,
)
start.set_downstream(onboard)
onboard.set_downstream(ongoing)
ongoing.set_downstream(end)
end.set_upstream(ssh_tasks)
# if no hive scripts generrated short circuit step in the begining of main dag
return ssh_dag
dag = DAG(
's3_convert_json_to_parquet_emr_ssh',
default_args=defautlt_args,
dagrun_timeout=timedelta(hours=1),
schedule_interval='0 3 * * *'
)
step_entities_partitions = ShortCircuitOperator(
task_id='step_entities_partitions',
python_callable=gen_hive_scripts,
provide_context=True,
dag=dag)
step_ssh_subdag = SubDagOperator(
task_id='step_jobs_submit',
subdag=get_sub_ssh_cmds_dag(dag, 'step_jobs_submit',defautlt_args),
default_args=defautlt_args,
dag=dag)
step_end = DummyOperator(
task_id='ssh_end',
dag=dag)
step_entities_partitions.set_downstream(step_ssh_subdag)
step_ssh_subdag.set_downstream(step_end)
task_id=task_id,
subdag=subdag(task_id, 'daily_items_indicator_info',
data_worker.worker.collect_some_daily_info),
executor=CeleryExecutor(),
dag=dag,
)
task_id = 'collect_daily_items_info_all'
items_all_task = SubDagOperator(
task_id=task_id,
subdag=subdag(task_id, 'daily_items_info',
data_worker.worker.collect_some_daily_info_all),
executor=CeleryExecutor(),
dag=dag,
)
task_id = 'collect_daily_items_indicator_info_all'
items_indicator_all_task = SubDagOperator(
task_id=task_id,
subdag=subdag(task_id, 'daily_items_indicator_info',
data_worker.worker.collect_some_daily_info_all),
executor=CeleryExecutor(),
dag=dag,
)
items_task.set_upstream(
[itemcodes_task, item_naver_sectors_task, item_themes_task])
items_task.set_downstream(items_all_task)
items_all_task.set_downstream(items_indicator_task)
items_indicator_task.set_downstream(items_indicator_all_task)
task_id='remove_updated_dim_grade',
python_callable=remove_updated_dim_grade,
op_kwargs={'db': db},
dag=dag)
insert_updated_dim_grade = PythonOperator(
task_id='insert_updated_dim_grade',
python_callable=insert_updated_dim_grade,
op_kwargs={'db': db},
dag=dag)
#########################################################################
########################## DAG flow #####################################
download_files.set_upstream(begin_task)
download_files.set_downstream(end_task)
end_task.set_downstream(initialize_transaction_table_dim_business)
initialize_transaction_table_dim_business.set_downstream(
prepare_and_load_transaction_table_dim_business)
prepare_and_load_transaction_table_dim_business.set_downstream(
update_transaction_table_dim_business_boro)
update_transaction_table_dim_business_boro.set_downstream(update_dim_business)
update_dim_business.set_downstream(remove_updated_dim_business)
remove_updated_dim_business.set_downstream(insert_updated_dim_business)
initialize_transaction_table_dim_grade.set_upstream(
remove_updated_dim_business)
initialize_transaction_table_dim_grade.set_downstream(
prepare_and_load_transaction_table_dim_grade)
prepare_and_load_transaction_table_dim_business.set_downstream(
f"{BASE_PACKAGE}.transactional-tables",
"OutletsByDate",
dag,
RETAIL_ID,
schema_name,
ENV_TYPE,
)
items_by_date_task = bash_operator_for_spark_submit(
f"{BASE_PACKAGE}.transactional-tables",
"ItemsByDate",
dag,
RETAIL_ID,
schema_name,
ENV_TYPE,
)
push_instruments.set_downstream(push_server_details)
branch_task.set_upstream(push_server_details)
branch_task.set_downstream(master_tables_load)
branch_task.set_downstream(history_load_done)
master_tables_load.set_downstream(create_table_structure)
history_load_done.set_downstream(create_table_structure)
create_table_structure.set_downstream(unix_chmod_task)
unix_chmod_task.set_downstream(market_baskets_task)
market_baskets_task.set_downstream(
[transaction_line_item_task, outlets_by_date_task, items_by_date_task])
data_load_done.set_upstream(
[transaction_line_item_task, outlets_by_date_task, items_by_date_task])
create_constraint_task.set_upstream(data_load_done)
dag=dag,
)
sub = SubDagOperator(subdag=subdag,
task_id="insert_and_id_pop",
trigger_rule="one_success",
dag=dag)
clear_latest = BashOperator(
bash_command="rm -rf {}/latest_links.txt".format(RAW_TWEET_DIR),
task_id="clear_latest",
dag=dag,
)
gen_search_terms.set_upstream(fill_search_terms)
for term in SEARCH_TERMS:
term_without_punctuation = re.sub(r"\W+", "", term)
simple_search = PythonOperator(
task_id="search_{}_twitter".format(term_without_punctuation),
provide_context=True,
python_callable=search_twitter,
dag=dag,
params={"query": term},
)
simple_search.set_upstream(gen_search_terms)
simple_search.set_downstream(sub)
sub.set_downstream(email_links)
email_links.set_downstream(clear_latest)
dag=dag,
)
task_concate_train_files.set_downstream(task_feature_engineering_for_train)
input_parquet_raw_file_unit = DataInputFileUnit(project_path + 'datasets/temp/X_train_final.parquet',
pandas_read_function_name='read_parquet')
task_model_learning = DataOperator(operation_function=fit_write_model,
params={'columns_selection': features_selection,
'column_target': feature_target,
'write_path': project_path + 'models/ensemble.model'
},
input_unit=input_parquet_raw_file_unit,
dag=dag, task_id='Model_learning')
task_feature_engineering_for_train.set_downstream(task_model_learning)
input_csv_files_unit = DataInputMultiFileUnit([project_path + 'datasets/input/X_tree_egc_t2.csv',
project_path + 'datasets/input/X_geoloc_egc_t2.csv',
project_path + 'datasets/input/Y_tree_egc_t2.csv'], sep=';')
output_parquet_unit = DataOutputFileUnit(project_path + 'datasets/temp/X_test_raw.parquet',
pandas_write_function_name='to_parquet')
task_concate_test_files = DataOperator(operation_function=join_dataframes,
input_unit=input_csv_files_unit,
output_unit=output_parquet_unit,
dag=dag, task_id='Join_test_data_source_files')
task_feature_engineering_for_test = SubDagOperator(
subdag=feature_engineering_sub_dag(dag.dag_id, 'Feature_engineering_for_test',
model_path=project_path + 'models/',
input_file=project_path + 'datasets/temp/X_test_raw.parquet',
