airflow.example_dags.example_asset_partition

Attributes

`team_a_player_stats`
`combined_player_stats`
`regional_sales`
`region_raw_stats`
`daily_sales`
`daily_costs`
`daily_team_a`
`monthly_team_a`
`weekly_model_artifact`

Functions

`ingest_team_a_stats`()	Materialize Team A player statistics for the current hourly partition.
`team_b_player_stats`()	Produce hourly partitioned stats for Team B.
`team_c_player_stats`()	Produce hourly partitioned stats for Team C.
`combine_player_stats`([dag_run])	Merge the aligned hourly partitions into a combined dataset.
`compute_player_odds`()	Compute player odds from the combined hourly statistics.
`check_partition_alignment`()
`ingest_sales`()	Ingest regional sales data partitioned by region and time.
`aggregate_sales`([dag_run])	Aggregate sales data for the matched region-day partition.
`ingest_region`()	Materialize player statistics for a single region partition.
`regional_stats_breakdown`()	Aggregate regional player statistics.
`live_region_player_stats`(self, outlet_events)	Produce a single region partition whose key is decided at runtime.
`summarize_live_region`([dag_run])	Summarize stats for the matched runtime partition.
`multi_region_player_stats`(self, outlet_events)	Produce several region partitions from a single run.
`summarise_team_a_day`([dag_run])	Produce the full-day rollup once every hour has arrived.
`summarise_team_a_month`([dag_run])	Produce the full-month rollup once every day has arrived.
`train_model`()	Materialize the model artifact for the current weekly partition.
`run_inference`([dag_run])	Run inference for one daily partition derived from the weekly model.
`aggregate_all_regions`([dag_run])	Produce the cross-region summary once every region partition has arrived.

Module Contents

airflow.example_dags.example_asset_partition.team_a_player_stats[source]

airflow.example_dags.example_asset_partition.combined_player_stats[source]

airflow.example_dags.example_asset_partition.ingest_team_a_stats()[source]: Materialize Team A player statistics for the current hourly partition.

airflow.example_dags.example_asset_partition.team_b_player_stats()[source]: Produce hourly partitioned stats for Team B.

airflow.example_dags.example_asset_partition.team_c_player_stats()[source]: Produce hourly partitioned stats for Team C.

airflow.example_dags.example_asset_partition.combine_player_stats(dag_run=None)[source]: Merge the aligned hourly partitions into a combined dataset.

airflow.example_dags.example_asset_partition.compute_player_odds()[source]

Compute player odds from the combined hourly statistics.

This asset is partition-aware and triggered by the combined stats asset.

airflow.example_dags.example_asset_partition.check_partition_alignment()[source]

airflow.example_dags.example_asset_partition.regional_sales[source]

airflow.example_dags.example_asset_partition.ingest_sales()[source]: Ingest regional sales data partitioned by region and time.

airflow.example_dags.example_asset_partition.aggregate_sales(dag_run=None)[source]: Aggregate sales data for the matched region-day partition.

airflow.example_dags.example_asset_partition.region_raw_stats[source]

airflow.example_dags.example_asset_partition.ingest_region()[source]: Materialize player statistics for a single region partition.

airflow.example_dags.example_asset_partition.regional_stats_breakdown()[source]

Aggregate regional player statistics.

This asset demonstrates AllowedKeyMapper, which validates that upstream partition keys belong to a fixed set of allowed values (us, eu, apac) rather than time-based partitions.

airflow.example_dags.example_asset_partition.live_region_player_stats(self, outlet_events)[source]

Produce a single region partition whose key is decided at runtime.

This asset demonstrates PartitionAtRuntime, which records the partition key on the emitted event with add_partitions while the task runs rather than from a timetable.

airflow.example_dags.example_asset_partition.summarize_live_region(dag_run=None)[source]: Summarize stats for the matched runtime partition.

airflow.example_dags.example_asset_partition.multi_region_player_stats(self, outlet_events)[source]

Produce several region partitions from a single run.

This asset demonstrates runtime fan-out, where each key emits its own asset event and duplicate keys collapse to a single event.

airflow.example_dags.example_asset_partition.daily_sales[source]

airflow.example_dags.example_asset_partition.daily_costs[source]

airflow.example_dags.example_asset_partition.daily_team_a[source]

airflow.example_dags.example_asset_partition.monthly_team_a[source]

airflow.example_dags.example_asset_partition.summarise_team_a_day(dag_run=None)[source]: Produce the full-day rollup once every hour has arrived.

airflow.example_dags.example_asset_partition.summarise_team_a_month(dag_run=None)[source]: Produce the full-month rollup once every day has arrived.

airflow.example_dags.example_asset_partition.weekly_model_artifact[source]

airflow.example_dags.example_asset_partition.train_model()[source]: Materialize the model artifact for the current weekly partition.

airflow.example_dags.example_asset_partition.run_inference(dag_run=None)[source]: Run inference for one daily partition derived from the weekly model.

airflow.example_dags.example_asset_partition.aggregate_all_regions(dag_run=None)[source]: Produce the cross-region summary once every region partition has arrived.