make dag where operators are split

2025/census-data-airflow-bigframes/census_to_bigquery.py

@@ -42,7 +42,7 @@
     "start_date": datetime.datetime(2025, 6, 30),
 }
 
-GCS_LOCATION = "gs://us-central1-bigframes-orche-b70f2a52-bucket/data/us-census/cc-est2024-agesex-all.csv"
+GCS_LOCATION = "gs://us-central1-bigframes-orche-5b3ec9ed-bucket/data/us-census/cc-est2024-agesex-all.csv"
 
 # Define a DAG (directed acyclic graph) of tasks.
 # Any task you create within the context manager is automatically added to the
@@ -63,19 +63,13 @@
         """,
     )
 
-    def callable_virtualenv():
-        """
-        Example function that will be performed in a virtual environment.
-
-        Importing at the module level ensures that it will not attempt to import the
-        library before it is installed.
-        """
+    def callable_python():
         import datetime
 
         import bigframes.pandas as bpd
 
         BIGQUERY_DESTINATION = "swast-scratch.airflow_demo.us_census_by_county2020_to_present"
-        GCS_LOCATION = "gs://us-central1-bigframes-orche-b70f2a52-bucket/data/us-census/cc-est2024-agesex-all.csv"
+        GCS_LOCATION = "gs://us-central1-bigframes-orche-5b3ec9ed-bucket/data/us-census/cc-est2024-agesex-all.csv"
 
         #=============================
         # Setup bigframes
@@ -149,7 +143,7 @@ def callable_virtualenv():
 
     bf_to_gbq = PythonOperator(
         task_id="bf_to_gbq",
-        python_callable=callable_virtualenv,
+        python_callable=callable_python,
     )
 
 

2025/census-data-airflow-bigframes/census_to_bigquery_split.py

@@ -0,0 +1,154 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+An example DAG for loading data from the US Census using BigQuery DataFrames
+(aka bigframes). This DAG uses PythonVirtualenvOperator for environments where
+bigframes can't be installed for use from PythonOperator.
+
+I have tested this DAG on Cloud Composer 3 with Apache Airflow 2.10.5.
+
+For local development:
+
+    pip install 'apache-airflow[google]==2.10.5' bigframes
+"""
+
+import datetime
+import json
+
+from airflow import models
+from airflow.operators import bash
+from airflow.operators.python import (
+    PythonOperator,
+)
+from airflow.providers.google.cloud.operators.bigquery import BigQueryDeleteTableOperator
+
+
+default_dag_args = {
+    # The start_date describes when a DAG is valid / can be run. Set this to a
+    # fixed point in time rather than dynamically, since it is evaluated every
+    # time a DAG is parsed. See:
+    # https://airflow.apache.org/faq.html#what-s-the-deal-with-start-date
+    "start_date": datetime.datetime(2025, 6, 30),
+}
+
+GCS_LOCATION = "gs://us-central1-bigframes-orche-5b3ec9ed-bucket/data/us-census/cc-est2024-agesex-all.csv"
+
+# Define a DAG (directed acyclic graph) of tasks.
+# Any task you create within the context manager is automatically added to the
+# DAG object.
+with models.DAG(
+    "census_from_http_to_bigquery_split_once",
+    schedule_interval="@once",
+    default_args=default_dag_args,
+) as dag:
+    download_upload = bash.BashOperator(
+        task_id="download_upload",
+        # See
+        # https://www.census.gov/data/tables/time-series/demo/popest/2020s-counties-detail.html
+        # for file paths and methodologies.
+        bash_command=f"""
+        wget https://www2.census.gov/programs-surveys/popest/datasets/2020-2024/counties/asrh/cc-est2024-agesex-all.csv -P ~;
+        gcloud storage cp ~/cc-est2024-agesex-all.csv {GCS_LOCATION}
+        """,
+    )
+
+    def preprocess(task_instance):
+        import datetime
+
+        import bigframes.pandas as bpd
+
+        GCS_LOCATION = "gs://us-central1-bigframes-orche-5b3ec9ed-bucket/data/us-census/cc-est2024-agesex-all.csv"
+
+        bpd.options.bigquery.ordering_mode = "partial"
+        bpd.options.compute.maximum_result_rows = 10_000
+        # bpd.options.bigquery.project = "my-project-id"
+
+        try:
+            # By loading with the BigQuery engine, you can avoid having to read
+            # the file into memory. This is because BigQuery is responsible for
+            # parsing the file.
+            df = bpd.read_csv(GCS_LOCATION, engine="bigquery")
+
+            # Perform preprocessing. For example, you can map some coded data
+            # into a form that is easier to understand.
+            df_dates = df.assign(
+                ESTIMATE_DATE=df["YEAR"].case_when(
+                    caselist=[
+                        (df["YEAR"].eq(1), datetime.date(2020, 4, 1)),
+                        (df["YEAR"].eq(2), datetime.date(2020, 7, 1)),
+                        (df["YEAR"].eq(3), datetime.date(2021, 7, 1)),
+                        (df["YEAR"].eq(4), datetime.date(2022, 7, 1)),
+                        (df["YEAR"].eq(5), datetime.date(2023, 7, 1)),
+                        (df["YEAR"].eq(6), datetime.date(2024, 7, 1)),
+                        (True, None),
+                    ]
+                ),
+            ).drop(columns=["YEAR"])
+
+            task_instance.xcom_push(key="census_preprocessed_table", value=df_dates.to_gbq())
+        finally:
+            bpd.close_session()
+
+    def validate_and_write(task_instance):
+        import bigframes.pandas as bpd
+        bigquery_destination = "swast-scratch.airflow_demo.us_census_by_county2020_to_present"
+
+        bpd.options.bigquery.ordering_mode = "partial"
+        bpd.options.compute.maximum_result_rows = 10_000
+        # bpd.options.bigquery.project = "my-project-id"
+
+        try:
+            # Get the table ID from the previous step.
+            bigquery_source = task_instance.xcom_pull(
+                task_ids="bf_preprocess",
+                key="census_preprocessed_table",
+            )
+            df_dates = bpd.read_gbq(bigquery_source)
+
+            row_count, column_count = df_dates.shape
+            assert row_count > 0
+            assert column_count > 0
+            assert not df_dates["ESTIMATE_DATE"].hasnans
+
+            # TODO(developer): Add additional validations as needed.
+
+            df_dates.to_gbq(
+                bigquery_destination,
+                if_exists="replace",
+                clustering_columns=["ESTIMATE_DATE", "STATE", "COUNTY"],
+            )
+        finally:
+            bpd.close_session()
+
+
+    bf_preprocess = PythonOperator(
+        task_id="bf_preprocess",
+        python_callable=preprocess,
+    )
+
+    bf_validate_and_write = PythonOperator(
+        task_id="bf_validate_and_write",
+        python_callable=validate_and_write,
+    )
+
+    cleanup_preprocess_table = BigQueryDeleteTableOperator(
+        task_id="cleanup_preprocess_table",
+        deletion_dataset_table="{{ task_instance.xcom_pull(task_ids='bf_preprocess', key='census_preprocessed_table' }}",
+        # Always execute, even if the previous task failed.
+        # https://stackoverflow.com/a/44441890/101923
+        trigger_rule="all_done",
+    )
+
+    download_upload >> bf_preprocess >> bf_validate_and_write >> cleanup_preprocess_table