# 267 Automation 4db36136

> Automation Patterns Reference

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- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/267-automation-4db36136

---

# Automation Patterns Reference

## Pattern Summary

| Pattern                  | When to Use                                       |
| ------------------------ | ------------------------------------------------- |
| `AutomationCondition`    | Modern asset-centric automation with conditions   |
| `ScheduleDefinition`     | Fixed time intervals (daily, hourly, monthly)     |
| `@dg.schedule` decorator | Custom schedule logic with dynamic job selection  |
| `@dg.sensor`             | Event-driven triggers (file changes, API updates) |
| `PartitionsDefinition`   | Time-series or categorical data splits            |
| Partitioned schedules    | Automate partition materialization                |

**Modern Recommendation**: Use `AutomationCondition` for new asset-centric pipelines. Schedules and
sensors remain useful for ops-based jobs and event-driven automation.

---

## Declarative Automation

**Modern automation pattern** that sets conditions on assets rather than scheduling jobs.

### What is Declarative Automation?

Instead of defining schedules or sensors that trigger jobs, you define **conditions** directly on
assets. Dagster evaluates these conditions and automatically materializes assets when conditions are
met.

### Basic AutomationCondition Patterns

```python
import dagster as dg
from dagster import AutomationCondition

# Materialize when missing (never been created)
@dg.asset(
    automation_condition=AutomationCondition.on_missing()
)
def initial_load() -> None:
    pass

# Materialize on cron schedule
@dg.asset(
    automation_condition=AutomationCondition.on_cron("0 9 * * *")
)
def daily_report() -> None:
    pass

# Materialize when upstream assets update
@dg.asset(
    deps=["raw_data"],
    automation_condition=AutomationCondition.eager()
)
def processed_data() -> None:
    """Automatically updates when raw_data changes."""
    pass
```

### Combining Conditions

```python
# OR: Materialize if ANY condition is true
@dg.asset(
    automation_condition=(
        AutomationCondition.on_missing()
        | AutomationCondition.on_cron("0 0 * * *")
    )
)
def flexible_asset() -> None:
    """Updates if missing OR daily at midnight."""
    pass

# AND: Materialize only if ALL conditions are true
@dg.asset(
    automation_condition=(
        AutomationCondition.on_cron("0 9 * * *")
        & AutomationCondition.any_deps_updated()
    )
)
def conditional_report() -> None:
    """Only updates at 9am if upstream deps changed."""
    pass
```

### Common AutomationCondition Patterns

| Condition                           | Behavior                                      |
| ----------------------------------- | --------------------------------------------- |
| `on_missing()`                      | Materialize if never created                  |
| `on_cron(schedule)`                 | Materialize on cron schedule                  |
| `eager()`                           | Materialize when upstream assets update       |
| `any_deps_updated()`                | True if any upstream assets updated           |
| `any_deps_missing()`                | True if any upstream assets are missing       |
| `all_deps_updated_since_cron(cron)` | True if all deps updated since last cron tick |

### Benefits of Declarative Automation

1. **Asset-Native**: No separate job definitions needed
2. **Expressive**: Combine conditions with boolean logic
3. **Dependency-Aware**: Automatically understands asset graph
4. **Better for Complex Logic**: More readable than schedule/sensor combinations
5. **Centralized**: Automation logic lives with asset definition

### When to Use

- **Declarative Automation**: Asset-centric pipelines with condition-based triggers
- **Schedules**: Simple time-based triggers, ops-based jobs, or when you need exact cron control
- **Sensors**: Event-driven automation with external systems, complex state tracking

---

## Jobs

Jobs select which assets to materialize together:

### Basic Job Definition

```python
import dagster as dg

trip_update_job = dg.define_asset_job(
    name="trip_update_job",
    selection=["taxi_trips", "taxi_zones"],
)
```

### Asset Selection Patterns

```python
# Select specific assets
dg.AssetSelection.assets("asset_a", "asset_b")

# Select all assets
dg.AssetSelection.all()

# Select by group
dg.AssetSelection.groups("analytics")

# Select with dependencies
dg.AssetSelection.assets("final_report").upstream()  # Include upstream
dg.AssetSelection.assets("raw_data").downstream()    # Include downstream

# Combine selections
dg.AssetSelection.all() - dg.AssetSelection.assets("excluded_asset")
dg.AssetSelection.groups("a") | dg.AssetSelection.groups("b")  # Union
dg.AssetSelection.groups("a") & dg.AssetSelection.assets("specific")  # Intersection
```

### Job with Tags

```python
daily_job = dg.define_asset_job(
    name="daily_job",
    selection=dg.AssetSelection.all(),
    tags={"team": "data-eng", "priority": "high"},
)
```

---

## Schedules

### Basic Schedule

```python
import dagster as dg
from my_project.defs.jobs import trip_update_job

trip_update_schedule = dg.ScheduleDefinition(
    job=trip_update_job,
    cron_schedule="0 0 5 * *",  # 5th of each month at midnight
)
```

### Common Cron Patterns

| Pattern        | Meaning                        |
| -------------- | ------------------------------ |
| `* * * * *`    | Every minute                   |
| `0 * * * *`    | Every hour (at minute 0)       |
| `0 0 * * *`    | Daily at midnight              |
| `0 6 * * *`    | Daily at 6:00 AM               |
| `0 0 * * 1`    | Weekly on Monday at midnight   |
| `0 0 * * 1-5`  | Weekdays at midnight           |
| `0 0 1 * *`    | Monthly on the 1st at midnight |
| `0 0 5 * *`    | Monthly on the 5th at midnight |
| `15 5 * * 1-5` | Weekdays at 5:15 AM            |

**Tip**: Use [Crontab Guru](https://crontab.guru/) to create and test cron expressions.

### Schedule with Timezone

```python
my_schedule = dg.ScheduleDefinition(
    job=my_job,
    cron_schedule="0 9 * * *",  # 9:00 AM
    execution_timezone="America/New_York",
)
```

### Custom Schedule with Decorator

```python
@dg.schedule(cron_schedule="0 0 * * *", job=my_job)
def custom_schedule(context: dg.ScheduleEvaluationContext):
    """Schedule with custom logic."""
    scheduled_date = context.scheduled_execution_time.strftime("%Y-%m-%d")

    return dg.RunRequest(
        run_key=f"daily_{scheduled_date}",
        run_config={
            "ops": {
                "my_asset": {
                    "config": {"date": scheduled_date}
                }
            }
        },
    )
```

### Partitioned Schedule

Automatically run for new partitions:

```python
from my_project.defs.partitions import monthly_partition
from my_project.defs.jobs import partitioned_job

monthly_schedule = dg.build_schedule_from_partitioned_job(
    job=partitioned_job,
    description="Materializes data for the previous month",
)
```

---

## Sensors

### Sensor Anatomy

Sensors follow this lifecycle:

1. Read cursor (previous state)
2. Observe current state
3. Compare states and create run requests for changes
4. Update cursor

### Basic Sensor Pattern

```python
import dagster as dg
import json

@dg.sensor(job=my_job)
def file_sensor(context: dg.SensorEvaluationContext):
    # 1. Read cursor (previous state)
    previous_state = json.loads(context.cursor) if context.cursor else {}
    current_state = {}
    runs_to_request = []

    # 2. Observe current state
    for filepath in get_files_to_watch():
        last_modified = os.path.getmtime(filepath)
        filename = os.path.basename(filepath)
        current_state[filename] = last_modified

        # 3. Check for changes
        if filename not in previous_state or previous_state[filename] != last_modified:
            runs_to_request.append(dg.RunRequest(
                run_key=f"file_{filename}_{last_modified}",
                run_config={
                    "ops": {
                        "process_file": {
                            "config": {"filename": filename}
                        }
                    }
                }
            ))

    # 4. Return result with updated cursor
    return dg.SensorResult(
        run_requests=runs_to_request,
        cursor=json.dumps(current_state),
    )
```

### File Sensor

```python
import os
import json

@dg.sensor(job=adhoc_request_job)
def adhoc_request_sensor(context: dg.SensorEvaluationContext):
    PATH_TO_REQUESTS = os.path.join(
        os.path.dirname(__file__),
        "../../../data/requests",
    )

    previous_state = json.loads(context.cursor) if context.cursor else {}
    current_state = {}
    runs_to_request = []

    for filename in os.listdir(PATH_TO_REQUESTS):
        file_path = os.path.join(PATH_TO_REQUESTS, filename)
        if filename.endswith(".json") and os.path.isfile(file_path):
            last_modified = os.path.getmtime(file_path)
            current_state[filename] = last_modified

            if filename not in previous_state or previous_state[filename] != last_modified:
                with open(file_path, "r") as f:
                    request_config = json.load(f)

                runs_to_request.append(dg.RunRequest(
                    run_key=f"request_{filename}_{last_modified}",
                    run_config={
                        "ops": {
                            "adhoc_request": {
                                "config": {
                                    "filename": filename,
                                    **request_config
                                }
                            }
                        }
                    }
                ))

    return dg.SensorResult(
        run_requests=runs_to_request,
        cursor=json.dumps(current_state),
    )
```

### Asset Sensor

Trigger when another asset materializes:

```python
@dg.asset_sensor(asset_key=dg.AssetKey("upstream_asset"), job=downstream_job)
def upstream_sensor(context: dg.SensorEvaluationContext, asset_event):
    """Triggers when upstream_asset is materialized."""
    return dg.RunRequest(
        run_key=f"downstream_{asset_event.dagster_event.event_specific_data.materialization.run_id}",
    )
```

### Sensor with Skip Reason

```python
@dg.sensor(job=my_job, minimum_interval_seconds=60)
def conditional_sensor(context: dg.SensorEvaluationContext):
    new_files = check_for_new_files()

    if not new_files:
        return dg.SkipReason("No new files found")

    return dg.RunRequest(run_key=f"files_{len(new_files)}")
```

---

## Partitions

### Time-Based Partitions

```python
import dagster as dg

# Daily partitions
daily_partition = dg.DailyPartitionsDefinition(
    start_date="2023-01-01",
    end_date="2024-12-31",  # Optional
)

# Weekly partitions
weekly_partition = dg.WeeklyPartitionsDefinition(
    start_date="2023-01-01",
)

# Monthly partitions
monthly_partition = dg.MonthlyPartitionsDefinition(
    start_date="2023-01-01",
    end_date="2023-12-31",
)

# Hourly partitions
hourly_partition = dg.HourlyPartitionsDefinition(
    start_date="2023-01-01-00:00",
)
```

### Static Partitions

```python
# Fixed set of partitions
region_partition = dg.StaticPartitionsDefinition([
    "us-east",
    "us-west",
    "eu-west",
    "ap-south",
])

# Category partitions
category_partition = dg.StaticPartitionsDefinition([
    "electronics",
    "clothing",
    "home",
    "sports",
])
```

### Dynamic Partitions

Partitions created at runtime based on external data:

```python
from dagster import DynamicPartitionsDefinition

# Define dynamic partition
customer_partition = DynamicPartitionsDefinition(name="customers")

@dg.asset(partitions_def=customer_partition)
def customer_data(context: dg.AssetExecutionContext) -> None:
    customer_id = context.partition_key
    # Process data for this customer
    pass

# Add partitions at runtime
def add_new_customer(customer_id: str):
    from dagster import get_dagster_instance
    instance = get_dagster_instance()
    instance.add_dynamic_partitions("customers", [customer_id])
```

**Use Cases**:

- Customer-specific processing where customers are added dynamically
- File-based partitioning where files arrive unpredictably
- Any scenario where partition keys aren't known at definition time

### Multi-Dimensional Partitions

```python
multi_partition = dg.MultiPartitionsDefinition({
    "date": dg.DailyPartitionsDefinition(start_date="2023-01-01"),
    "region": dg.StaticPartitionsDefinition(["us", "eu", "ap"]),
})

@dg.asset(partitions_def=multi_partition)
def multi_partitioned_asset(context: dg.AssetExecutionContext) -> None:
    partition_key = context.partition_key
    # partition_key is a MultiPartitionKey with .keys_by_dimension
    date = partition_key.keys_by_dimension["date"]
    region = partition_key.keys_by_dimension["region"]
```

### Using Partitions in Assets

```python
@dg.asset(partitions_def=monthly_partition)
def monthly_data(context: dg.AssetExecutionContext) -> None:
    """Process data for a specific month."""
    partition_date_str = context.partition_key  # "2023-01-01"
    month = partition_date_str[:-3]  # "2023-01"

    context.log.info(f"Processing partition: {month}")

    data = fetch_data_for_month(month)
    save_data(data, month)
```

### Partition Window Access

```python
@dg.asset(partitions_def=daily_partition)
def windowed_data(context: dg.AssetExecutionContext) -> None:
    """Access partition time window."""
    time_window = context.partition_time_window

    start = time_window.start  # datetime
    end = time_window.end      # datetime

    context.log.info(f"Processing from {start} to {end}")
```

---

## Partitioned Jobs

### Define Partitioned Job

```python
partitioned_job = dg.define_asset_job(
    name="partitioned_job",
    selection=["monthly_data"],
    partitions_def=monthly_partition,
)
```

### Run Specific Partition

```python
# In launchpad or programmatically
result = partitioned_job.execute_in_process(
    partition_key="2023-06-01",
)
```

---

## Backfills

Backfills materialize multiple partitions at once:

### UI Backfill

1. Navigate to the asset in the Dagster UI
2. Click "Materialize" dropdown
3. Select "Backfill"
4. Choose partition range

### Programmatic Backfill

```python
from dagster import DagsterInstance

instance = DagsterInstance.get()
instance.submit_run(
    partition_key="2023-01-01",
    job_name="partitioned_job",
)
```

---

## Combining Automation

### Partitioned Schedule

```python
# Create job with partition
monthly_job = dg.define_asset_job(
    name="monthly_job",
    selection=["monthly_data"],
    partitions_def=monthly_partition,
)

# Build schedule that runs for new partitions
monthly_schedule = dg.build_schedule_from_partitioned_job(
    job=monthly_job,
)
```

### Sensor with Partitions

```python
@dg.sensor(job=partitioned_job)
def partition_sensor(context: dg.SensorEvaluationContext):
    missing_partitions = get_missing_partitions()

    return [
        dg.RunRequest(
            run_key=f"backfill_{partition}",
            partition_key=partition,
        )
        for partition in missing_partitions
    ]
```

---

## Anti-Patterns to Avoid

| Anti-Pattern              | Better Approach                        |
| ------------------------- | -------------------------------------- |
| No cursor in sensors      | Always use cursor for state tracking   |
| Polling too frequently    | Set `minimum_interval_seconds`         |
| Giant backfills at once   | Use `max_runtime` on backfill policies |
| Hardcoded partition dates | Use dynamic start/end with constants   |
| Ignoring timezone         | Set `execution_timezone` on schedules  |

---

## References

- [Schedules](https://docs.dagster.io/guides/automate/schedules)
- [Sensors](https://docs.dagster.io/guides/automate/sensors)
- [Partitions](https://docs.dagster.io/guides/build/partitions)
- [Asset Selection Syntax](https://docs.dagster.io/concepts/assets/asset-selection-syntax)