remove asset_partition_*_for_output from docs

docs/content/concepts/partitions-schedules-sensors/partitioning-assets.mdx

@@ -70,7 +70,7 @@ from dagster import AssetExecutionContext, DailyPartitionsDefinition, asset
 
 @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-10-01"))
 def my_daily_partitioned_asset(context: AssetExecutionContext) -> None:
-    partition_date_str = context.asset_partition_key_for_output()
+    partition_date_str = context.partition_key
 
     url = f"https://api.nasa.gov/planetary/apod?api_key=DEMO_KEY&date={partition_date_str}"
     target_location = f"nasa/{partition_date_str}.csv"
@@ -208,7 +208,7 @@ from dagster import AssetExecutionContext, DailyPartitionsDefinition, asset
 
 @asset(partitions_def=DailyPartitionsDefinition(start_date="2022-01-01"))
 def my_daily_partitioned_asset(context: AssetExecutionContext) -> pd.DataFrame:
-    partition_date_str = context.asset_partition_key_for_output()
+    partition_date_str = context.partition_key
     return pd.read_csv(f"coolweatherwebsite.com/weather_obs&date={partition_date_str}")
 ```
 

docs/content/integrations/bigquery/reference.mdx

@@ -110,7 +110,7 @@ from dagster import AssetExecutionContext, StaticPartitionsDefinition, asset
     metadata={"partition_expr": "SPECIES"},
 )
 def iris_data_partitioned(context: AssetExecutionContext) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",
@@ -165,7 +165,7 @@ from dagster import AssetExecutionContext, DailyPartitionsDefinition, asset
     metadata={"partition_expr": "TIMESTAMP_SECONDS(TIME)"},
 )
 def iris_data_per_day(context: AssetExecutionContext) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'TIME' with that stores

docs/content/integrations/dbt/reference.mdx

@@ -681,7 +681,7 @@ You can define a Dagster <PyObject module="dagster" object="PartitionDefinition"
 
 Partitioned assets will be able to access the <PyObject module="dagster" object="TimeWindow"/>'s start and end dates, and these can be passed to dbt's CLI as variables which can be used to filter incremental models.
 
-When a partition definition to passed to the <PyObject module="dagster_dbt" object="dbt_assets" decorator/> decorator, all assets are defined to operate on the same partitions. With this in mind, we can retrieve any time window from <PyObject module="dagster" object="asset_partitions_time_window_for_output"/> method in order to get the current start and end partitions.
+When a partition definition to passed to the <PyObject module="dagster_dbt" object="dbt_assets" decorator/> decorator, all assets are defined to operate on the same partitions. With this in mind, we can retrieve any time window from <PyObject module="dagster" object="partition_time_window"/> property in order to get the current start and end partitions.
 
 ```python
 import json

docs/content/integrations/deltalake/reference.mdx

@@ -81,7 +81,7 @@ from dagster import StaticPartitionsDefinition, asset
     metadata={"partition_expr": "species"},
 )
 def iris_dataset_partitioned(context) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",
@@ -134,7 +134,7 @@ from dagster import DailyPartitionsDefinition, asset
     metadata={"partition_expr": "time"},
 )
 def iris_data_per_day(context) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'time' with that stores

docs/content/integrations/duckdb/reference.mdx

@@ -113,7 +113,7 @@ from dagster import AssetExecutionContext, StaticPartitionsDefinition, asset
     metadata={"partition_expr": "SPECIES"},
 )
 def iris_dataset_partitioned(context: AssetExecutionContext) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",
@@ -168,7 +168,7 @@ from dagster import AssetExecutionContext, DailyPartitionsDefinition, asset
     metadata={"partition_expr": "TO_TIMESTAMP(TIME)"},
 )
 def iris_data_per_day(context: AssetExecutionContext) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'time' with that stores

docs/content/integrations/snowflake/reference.mdx

@@ -131,7 +131,7 @@ from dagster import AssetExecutionContext, StaticPartitionsDefinition, asset
     metadata={"partition_expr": "SPECIES"},
 )
 def iris_dataset_partitioned(context: AssetExecutionContext) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",
@@ -186,7 +186,7 @@ from dagster import AssetExecutionContext, DailyPartitionsDefinition, asset
     metadata={"partition_expr": "TO_TIMESTAMP(TIME::INT)"},
 )
 def iris_data_per_day(context: AssetExecutionContext) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'time' with that stores

examples/docs_snippets/docs_snippets/concepts/partitions_schedules_sensors/partitioned_asset.py

@@ -11,7 +11,7 @@
 
 @asset(partitions_def=DailyPartitionsDefinition(start_date="2023-10-01"))
 def my_daily_partitioned_asset(context: AssetExecutionContext) -> None:
-    partition_date_str = context.asset_partition_key_for_output()
+    partition_date_str = context.partition_key
 
     url = f"https://api.nasa.gov/planetary/apod?api_key=DEMO_KEY&date={partition_date_str}"
     target_location = f"nasa/{partition_date_str}.csv"

examples/docs_snippets/docs_snippets/concepts/partitions_schedules_sensors/partitioned_asset_uses_io_manager.py

@@ -5,5 +5,5 @@
 
 @asset(partitions_def=DailyPartitionsDefinition(start_date="2022-01-01"))
 def my_daily_partitioned_asset(context: AssetExecutionContext) -> pd.DataFrame:
-    partition_date_str = context.asset_partition_key_for_output()
+    partition_date_str = context.partition_key
     return pd.read_csv(f"coolweatherwebsite.com/weather_obs&date={partition_date_str}")

examples/docs_snippets/docs_snippets/integrations/bigquery/reference/static_partition.py

@@ -12,7 +12,7 @@
     metadata={"partition_expr": "SPECIES"},
 )
 def iris_data_partitioned(context: AssetExecutionContext) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",

examples/docs_snippets/docs_snippets/integrations/bigquery/reference/time_partition.py

@@ -14,7 +14,7 @@ def get_iris_data_for_date(*args, **kwargs):
     metadata={"partition_expr": "TIMESTAMP_SECONDS(TIME)"},
 )
 def iris_data_per_day(context: AssetExecutionContext) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'TIME' with that stores

examples/docs_snippets/docs_snippets/integrations/deltalake/static_partition.py

@@ -12,7 +12,7 @@
     metadata={"partition_expr": "species"},
 )
 def iris_dataset_partitioned(context) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",

examples/docs_snippets/docs_snippets/integrations/deltalake/time_partition.py

@@ -14,7 +14,7 @@ def get_iris_data_for_date(*args, **kwargs):
     metadata={"partition_expr": "time"},
 )
 def iris_data_per_day(context) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'time' with that stores

examples/docs_snippets/docs_snippets/integrations/duckdb/reference/static_partition.py

@@ -12,7 +12,7 @@
     metadata={"partition_expr": "SPECIES"},
 )
 def iris_dataset_partitioned(context: AssetExecutionContext) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",

examples/docs_snippets/docs_snippets/integrations/duckdb/reference/time_partition.py

@@ -14,7 +14,7 @@ def get_iris_data_for_date(*args, **kwargs):
     metadata={"partition_expr": "TO_TIMESTAMP(TIME)"},
 )
 def iris_data_per_day(context: AssetExecutionContext) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'time' with that stores

examples/docs_snippets/docs_snippets/integrations/snowflake/static_partition.py

@@ -12,7 +12,7 @@
     metadata={"partition_expr": "SPECIES"},
 )
 def iris_dataset_partitioned(context: AssetExecutionContext) -> pd.DataFrame:
-    species = context.asset_partition_key_for_output()
+    species = context.partition_key
 
     full_df = pd.read_csv(
         "https://docs.dagster.io/assets/iris.csv",

examples/docs_snippets/docs_snippets/integrations/snowflake/time_partition.py

@@ -14,7 +14,7 @@ def get_iris_data_for_date(*args, **kwargs):
     metadata={"partition_expr": "TO_TIMESTAMP(TIME::INT)"},
 )
 def iris_data_per_day(context: AssetExecutionContext) -> pd.DataFrame:
-    partition = context.asset_partition_key_for_output()
+    partition = context.partition_key
 
     # get_iris_data_for_date fetches all of the iris data for a given date,
     # the returned dataframe contains a column named 'time' with that stores

examples/project_fully_featured/project_fully_featured/assets/core/id_range_for_time.py

@@ -2,7 +2,7 @@
 from typing import Any, Mapping, Tuple
 
 from dagster import (
-    OpExecutionContext,
+    AssetExecutionContext,
     _check as check,
 )
 
@@ -89,8 +89,8 @@ def _get_item_timestamp(item_id):
 
 
 def id_range_for_time(
-    context: OpExecutionContext, hn_client: HNClient
+    context: AssetExecutionContext, hn_client: HNClient
 ) -> Tuple[Tuple[int, int], Mapping[str, Any]]:
     """For the configured time partition, searches for the range of ids that were created in that time."""
-    start, end = context.asset_partitions_time_window_for_output()
+    start, end = context.partition_time_window
     return _id_range_for_time(int(start.timestamp()), int(end.timestamp()), hn_client)

examples/project_fully_featured/project_fully_featured/assets/core/items.py

@@ -1,4 +1,4 @@
-from dagster import Output, asset
+from dagster import AssetExecutionContext, Output, asset
 from pandas import DataFrame
 from pyspark.sql import DataFrame as SparkDF
 from pyspark.sql.types import (
@@ -39,7 +39,7 @@
     partitions_def=hourly_partitions,
     key_prefix=["s3", "core"],
 )
-def items(context, hn_client: HNClient) -> Output[DataFrame]:
+def items(context: AssetExecutionContext, hn_client: HNClient) -> Output[DataFrame]:
     """Items from the Hacker News API: each is a story or a comment on a story."""
     (start_id, end_id), item_range_metadata = id_range_for_time(context, hn_client)
 

examples/with_wandb/with_wandb/assets/advanced_partitions_example.py

@@ -1,5 +1,5 @@
 import wandb
-from dagster import AssetIn, StaticPartitionsDefinition, asset
+from dagster import AssetExecutionContext, AssetIn, StaticPartitionsDefinition, asset
 
 partitions_def = StaticPartitionsDefinition(["red", "orange", "yellow", "blue", "green"])
 
@@ -17,10 +17,10 @@
         }
     },
 )
-def write_advanced_artifact(context):
+def write_advanced_artifact(context: AssetExecutionContext):
     """Example writing an Artifact with partitions and custom metadata."""
     artifact = wandb.Artifact(ARTIFACT_NAME, "dataset")
-    partition_key = context.asset_partition_key_for_output()
+    partition_key = context.partition_key
 
     if partition_key == "red":
         return "red"

examples/with_wandb/with_wandb/assets/multi_partitions_example.py

@@ -1,5 +1,6 @@
 import wandb
 from dagster import (
+    AssetExecutionContext,
     AssetIn,
     DailyPartitionsDefinition,
     MultiPartitionsDefinition,
@@ -26,9 +27,9 @@
         }
     },
 )
-def create_my_multi_partitioned_asset(context):
+def create_my_multi_partitioned_asset(context: AssetExecutionContext):
     """Example writing an Artifact with mutli partitions and custom metadata."""
-    partition_key = context.asset_partition_key_for_output()
+    partition_key = context.partition_key
     context.log.info(f"Creating partitioned asset for {partition_key}")
     if partition_key == "red|2023-01-02":
         artifact = wandb.Artifact("my_multi_partitioned_asset", "dataset")

examples/with_wandb/with_wandb/assets/simple_partitions_example.py

@@ -1,6 +1,7 @@
 import random
 
 from dagster import (
+    AssetExecutionContext,
     AssetIn,
     DailyPartitionsDefinition,
     TimeWindowPartitionMapping,
@@ -21,11 +22,11 @@
         }
     },
 )
-def create_my_daily_partitioned_asset(context):
+def create_my_daily_partitioned_asset(context: AssetExecutionContext):
     """Example writing an Artifact with daily partitions and custom metadata."""
     # Happens when the asset is materialized in multiple runs (one per partition)
     if context.has_partition_key:
-        partition_key = context.asset_partition_key_for_output()
+        partition_key = context.partition_key
         context.log.info(f"Creating partitioned asset for {partition_key}")
         return random.randint(0, 100)