edited sensors to account for a fresh start, edited README

README.md

@@ -2,24 +2,31 @@
 
 Dagster setup that scrapes GTFS and GTFS-RT for specified transit agencies and adds them to a DuckDB
 
-## Input
-You define which agencies and feeds to scrape with the file`config/agency_list.csv`
+## Quick start
 
-To include the transit agencies that you want to scrape, add the relevant IDs from mobilitydatabase.org
-
-See `config/agency_list.csv.sample` for an example.
-
-## set your environment
-
-### .env file
+1. Edit the .env file.
 copy `env.sample` to `.env` and change:
-- Postgres database password - make it something random before the first run
-- MobilityDatabase.org API token
-- Location of data, config, and postgres_data directories (default is in working directory)
+  - Postgres database password - make it something random before the first run
+  - MobilityDatabase.org API token
+  - Location of `data`, `config`, and `postgres_data` directories (default is in working directory). `config` is part of the repo as it comes with sample configuration files.
 
+2. Edit `config/agency_list.csv`
+  - See `config/agency_list.csv.sample` for an example.
+  - Define which agencies and feeds to scrape with the file.
+  - To include the transit agencies that you want to scrape, add the relevant Feed IDs from mobilitydatabase.org
 
-
-# Run it
+3. Build the docker containers
 `docker compose build`
+
+4. Run the docker containers
 `docker compose up -d`
-access the Dagster web ui at 127.0.0.1:3001
+
+5. Access the Dagster web ui at 127.0.0.1:3001
+
+6. Materialize the first asset: `agency_list`
+
+## To-do:
+1. Change mobilitydata from using the API with a key, to using the csv on their GitHub page.
+2. Load data into duckdb
+3. Transform data in duckdb
+4. Analyze data

user_code/assets/config.py

@@ -1,4 +1,5 @@
 import pandas as pd
+from pathlib import Path
 from dagster import (
     asset,
 )
@@ -11,6 +12,10 @@ from dagster_duckdb import DuckDBResource
 def agency_list(duckdb: DuckDBResource) -> None:
     """Load agency list from CSV into DuckDB."""
 
+    # Ensure the database directory exists
+    db_path = Path(duckdb.database)
+    db_path.parent.mkdir(parents=True, exist_ok=True)
+
     # Read the CSV (path is relative to container working directory)
     df = pd.read_csv('config/agency_list.csv')
 
@@ -20,4 +25,3 @@ def agency_list(duckdb: DuckDBResource) -> None:
             CREATE OR REPLACE TABLE agency_list AS
             SELECT * FROM df
         """)
-

user_code/sensors/gtfs_realtime.py

@@ -1,13 +1,13 @@
 from dagster import (
     sensor,
+    SensorEvaluationContext,
     RunRequest,
     SkipReason,
-    SensorEvaluationContext,
     DefaultSensorStatus,
+    AssetKey,
 )
 from dagster_duckdb import DuckDBResource
 
-
 @sensor(
     name="gtfs_rt_vehicles_sensor",
     minimum_interval_seconds=60,
@@ -22,6 +22,17 @@ def gtfs_rt_vehicles_sensor(
     Sensor that triggers gtfs_rt_vehicles_downloads every 60 seconds.
     Fetches feed metadata once and passes it to each partition run.
     """
+    # Check if upstream asset has been materialized at least once
+    upstream_asset_key = AssetKey("gtfs_rt_vehicles_partitions")
+    latest_materialization = context.instance.get_latest_materialization_event(upstream_asset_key)
+
+    if latest_materialization is None:
+        return SkipReason(
+            "Waiting for upstream asset 'gtfs_rt_vehicles_partitions' to be materialized. "
+            "Run the upstream assets first."
+        )
+
+    try:
         with duckdb.get_connection() as conn:
             # Get all active feeds with their metadata in one query
             feeds = conn.execute("""
@@ -59,3 +70,8 @@ def gtfs_rt_vehicles_sensor(
             context.log.info(f"Triggering downloads for {len(run_requests)} GTFS-RT vehicle feeds")
 
             return run_requests
+
+    except Exception as e:
+        # Handle case where table doesn't exist yet or other DB errors
+        context.log.warning(f"Database query failed: {e}")
+        return SkipReason(f"Database not ready or query failed: {e}")

user_code/sensors/gtfs_static.py

@@ -6,6 +6,7 @@ from dagster import (
     DefaultSensorStatus,
     SensorResult,
     EventLogEntry,
+    AssetKey,
 )
 from dagster_duckdb import DuckDBResource
 
@@ -24,6 +25,18 @@ def gtfs_static_hourly_sensor(
     Sensor that triggers gtfs_feed_downloads every 60 minutes.
     Fetches feed metadata once and passes it to each partition run.
     """
+    # Check if upstream asset has been materialized at least once
+    # Update this asset name if your upstream asset has a different name
+    upstream_asset_key = AssetKey("gtfs_feed_partitions")
+    latest_materialization = context.instance.get_latest_materialization_event(upstream_asset_key)
+
+    if latest_materialization is None:
+        return SkipReason(
+            "Waiting for upstream asset 'gtfs_feed_partitions' to be materialized. "
+            "Run the upstream assets first."
+        )
+
+    try:
         with duckdb.get_connection() as conn:
             # Get all active feeds with their metadata in one query
             feeds = conn.execute("""
@@ -62,6 +75,12 @@ def gtfs_static_hourly_sensor(
 
             return run_requests
 
+    except Exception as e:
+        # Handle case where table doesn't exist yet or other DB errors
+        context.log.warning(f"Database query failed: {e}")
+        return SkipReason(f"Database not ready or query failed: {e}")
+
+
 @sensor(
     name="gtfs_static_partition_update_sensor",
     asset_selection=["gtfs_feed_downloads"],