airflow-state-store

Airflow Task State Store (AIP-103)

Airflow 3.3 ships two key/value stores and a crash-safety mixin for operators that submit external jobs.

task_state_store, asset_state_store, and ResumableJobMixin's crash-safety guarantee require Airflow 3.3+. Check first:

af config version

Below 3.3: task_state_store/asset_state_store are unavailable, and durable=True is a no-op — provider operators ship a pre-3.3 ResumableJobMixin shim that always submits fresh (see Section 5). Tell the user those specific features aren't available yet and link the AIP-103 tracking issue. This does not gate Section 6's Triggerer-vs-mode="reschedule" decision, or the general "green submit ≠ success" anti-pattern — those apply on any Airflow version. On a pre-3.3 DAG, give that guidance in full; only drop the "durable=True adds crash-safety" half of it.

Section 1 — Pick the right primitive

When NOT to use these:

• Passing data between tasks -> use XCom
• Large payloads (model weights, dataframes) -> use XCom with an object storage backend
• Config or secrets shared across DAGs -> use Variables or Connections

Section 2 — Detect anti-patterns in existing DAGs (on demand)

When the user asks to review a DAG or asks "is there a better way", scan for these patterns and flag them:

Show a before/after snippet when flagging. Use the canonical examples in Steps 3–5 as the "after".

The submit+sensor split is worth a comment even when the sensor code is bug-free — reviewing the sensor's code quality (correct mode=, correct terminal-state handling, cached hook) is a separate question from whether the two-task split is the right architecture. Follow Section 6, "Submit-and-poll DAGs: one task or two?" for that decision; don't re-derive it here.

Section 3 — task_state_store: per-task coordination state

task_state_store is a key/value store scoped to a single task instance identity (dag_id + run_id + task_id + map_index). It survives retries — a new retry on the same task reads the same store.

from airflow.sdk import dag, task
from pendulum import datetime

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def etl_with_checkpoint():

    @task(retries=3)
    def process_records(**context):
        task_state_store = context["task_state_store"]  # injected by Airflow, no setup needed
        cursor = task_state_store.get("last_cursor", default=0)
        records = fetch_records_after(cursor)
        for record in records:
            process(record)
            cursor = record["id"]
            task_state_store.set("last_cursor", cursor)   # checkpoint after each record

    process_records()

etl_with_checkpoint()

API:

from airflow.sdk import NEVER_EXPIRE

task_state_store.get(key, default=None)                        # returns a JsonValue or default
task_state_store.set(key, value)                               # uses default_retention_days
task_state_store.set(key, value, retention=timedelta(days=7))  # per-key TTL override
task_state_store.set(key, value, retention=NEVER_EXPIRE)       # never expires regardless of config
task_state_store.delete(key)                                   # no-op if key does not exist
task_state_store.clear()                                       # delete all keys for this task instance

Key rules:

• Values must be JSON-serializable (str, int, float, bool, list, dict — None values are rejected).
• Default expiry is controlled by [state_store] default_retention_days (0 = never expire).
• Use NEVER_EXPIRE for keys that must outlive the default retention window (e.g. a job ID for a multi-day Spark job).
• Max value size defaults to 64 KB; configurable via [state_store] max_value_storage_bytes (0 = no limit). For larger payloads, configure a custom [state_store] backend or a worker side backend configured via: [workers] state_store_backend.

Mapped tasks — each index has its own namespace:

When a task is dynamically mapped (task.expand(...)), each map index gets an isolated task_state_store scoped to its own map_index. Indices do not share state.

@task(retries=2)
def process_partition(partition_id, **context):
    task_state_store = context["task_state_store"]
    # Scoped to THIS index only — other indices have their own copy
    cursor = task_state_store.get("cursor", default=0)
    task_state_store.set("cursor", new_cursor)

process_partition.expand(partition_id=[0, 1, 2, 3])

clear() clears only the current index. To wipe state across all map indices of a task group, use the CLI or core API.

Before (anti-pattern):

@task
def process(**context):
    cursor = Variable.get("etl_cursor", default_var=0)
    # ... process ...
    Variable.set("etl_cursor", new_cursor)  # global, any task can overwrite

After:

@task(retries=3)
def process(**context):
    task_state_store = context["task_state_store"]
    cursor = task_state_store.get("cursor", default=0)
    # ... process ...
    task_state_store.set("cursor", new_cursor)    # scoped to this task instance

Section 4 — asset_state_store: per-asset metadata across DAG runs

asset_state_store is scoped to an asset, not a task instance. It persists across DAG runs — the same key on the same asset is readable and writable by any task that produces or consumes it.

from airflow.sdk import DAG, Asset, task
from datetime import datetime, timezone

ORDERS = Asset(name="orders/daily", uri="s3://warehouse/orders/daily")

with DAG(dag_id="producer", schedule=None, start_date=datetime(2026, 1, 1), catchup=False):

    @task(inlets=[ORDERS], outlets=[ORDERS])
    def load(asset_state_store=None):        # asset_state_store injected by Airflow — declare as a kwarg
        asset_state_store = asset_state_store[ORDERS]

        watermark = asset_state_store.get("watermark", default="2026-01-01T00:00:00+00:00")
        records = fetch_records_since(watermark)

        now = datetime.now(tz=timezone.utc).isoformat()
        asset_state_store.set("watermark", now)
        asset_state_store.set("last_run_summary", {"rows_loaded": len(records), "completed_at": now})

    load()

Reading the store from a consumer DAG:

with DAG(dag_id="consumer", schedule=[ORDERS], start_date=datetime(2026, 1, 1), catchup=False):

    @task(inlets=[ORDERS])
    def consume(asset_state_store=None):
        asset_state_store = asset_state_store[ORDERS]
        summary = asset_state_store.get("last_run_summary") or {}
        print(f"Processing {summary.get('rows_loaded')} rows up to {asset_state_store.get('watermark')}")

    consume()

Key rules:

• asset_state_store is injected by Airflow as a named kwarg — declare it as def my_task(asset_state_store=None). Do NOT combine with **context; Airflow injects it separately.
• Use datetime.now(tz=timezone.utc).isoformat() for timestamps — never datetime.utcnow() (not timezone-aware).
• Same JSON-serializable value constraint as task_state_store.
• No per-key expiry — asset state store entries have no TTL (the asset outlives any single run).
• Readable by any DAG that declares the asset as an inlet or outlet.

Mapped tasks — last writer wins:

asset_state_store is scoped to the asset, not the map index. If multiple mapped indices write the same key concurrently, the last write wins. Use distinct keys per index or ensure only one index writes to a given key.

@task(outlets=[my_asset])
def load_partition(partition_id, asset_state_store=None):
    asset_state_store = asset_state_store[my_asset]
    # Distinct key per index — no race condition
    asset_state_store.set(f"offset_{partition_id}", new_offset)

Before (anti-pattern):

Variable.set(f"watermark_{asset_name}", new_offset)   # global, not scoped to asset

After:

@task(inlets=[my_asset], outlets=[my_asset])
def load(asset_state_store=None):
    asset_state_store = asset_state_store[my_asset]
    asset_state_store.set("watermark", new_offset)

Section 5 — ResumableJobMixin: crash-safe external job submission

Use whenever a task submits a job to an external system (Spark, Databricks, dbt Cloud, AWS Batch, etc.) and could resubmit a duplicate on retry — whether that same task also polls for completion inside one execute() call, or hands the job id to a separate downstream poll/sensor task. Without this mixin (or a provider operator that already builds it in, like DatabricksSubmitRunOperator's durable=True default), a worker crash after submission means the next retry of the submit task resubmits a duplicate job — that risk exists regardless of whether polling happens in that same task or a separate one.

Scope check before recommending it: if submit and poll are already two separate tasks (e.g. a submit task handing a job id to a downstream sensor/poll task via XCom), the poll/sensor task doesn't need this mixin — it never submits anything, so retrying it is already safe. The submit task still does; splitting off the poll step doesn't make the submit task's own duplicate-submission risk go away. See the table row below.

When NOT to use ResumableJobMixin:

ResumableJobMixin holds the worker slot for the full polling duration — the same as a standard synchronous operator. The benefit is crash safety and job continuity, not resource efficiency.

Opting out of crash recovery:

The mixin ships with durable=True by default. Set durable=False to skip all task_state_store interaction and run a plain submit/poll/result cycle — useful in test environments or when the external system has its own dedup:

MyBatchOperator(task_id="job", durable=False)

# Or via default_args to disable for all tasks in a DAG:
with DAG("my_dag", default_args={"durable": False}):
    ...

Implementing the mixin

from airflow.sdk import BaseOperator, ResumableJobMixin
from pydantic import JsonValue


class MyBatchOperator(BaseOperator, ResumableJobMixin):

    external_id_key = "batch_job_id"   # key used in task_state_store; set once, never rename

    def execute(self, context):
        return self.execute_resumable(context)  # never call self.execute() — call this

    def submit_job(self, context) -> JsonValue:
        # Submit and return the job identifier. This value is persisted to task_state_store
        # before polling starts. Return None only if the system has no trackable ID
        # (in that case crash-safety is disabled and the job resubmits on every retry).
        return self.hook.submit_batch(...)

    def get_job_status(self, external_id: JsonValue, context) -> str:
        # Query the external system. Return a raw status string.
        return self.hook.get_status(external_id)

    def is_job_active(self, status: str) -> bool:
        # Return True if the job is still running and should be reconnected to.
        return status in ("RUNNING", "PENDING", "QUEUED")

    def is_job_succeeded(self, status: str) -> bool:
        return status == "SUCCEEDED"

    def poll_until_complete(self, external_id: JsonValue, context) -> None:
        # Block until the job reaches a terminal state. Raise on failure.
        self.hook.wait(external_id)

    def get_job_result(self, external_id: JsonValue, context):
        # Return the job result after success. Return None if not applicable.
        return None

What happens on retry

external_id_key warning

Never rename external_id_key on an operator that is already deployed with in-flight task instances. The old key is stored in task_state_store under the previous name. A rename makes the mixin treat every active retry as a fresh submission, defeating the crash-safety guarantee.

Before (anti-pattern):

def execute(self, context):
    job_id = Variable.get("spark_job_id", default_var=None)
    if job_id and self._is_running(job_id):
        self._wait(job_id)
    else:
        job_id = self.hook.submit(...)
        Variable.set("spark_job_id", job_id)   # global, race-prone
        self._wait(job_id)

After:

class MySparkOperator(BaseOperator, ResumableJobMixin):
    external_id_key = "spark_job_id"
    def execute(self, context): return self.execute_resumable(context)
    def submit_job(self, context): return self.hook.submit(...)
    # ... implement the 5 other methods ...

For the "submit + separate sensor task" DAG shape specifically — whether to collapse it to one task — see Section 6, "Submit-and-poll DAGs: one task or two?". That's an architecture call driven by Triggerer availability and job duration. It's a different question from whether the submit task itself needs durable=True for crash-safety (usually yes, whether or not the split stays) — see the table row above.

Section 6 — Submit-and-poll DAGs: one task or two?

A common DAG shape for Databricks/Snowflake/BigQuery/Redshift/Spark jobs: a submit task that fires the job with wait_for_termination=False (or equivalent) and returns immediately, followed by a hand-rolled sensor task that polls the same job to completion. The submit task going green the instant the external system accepts the job (not when it finishes) is always worth pointing out — a retry, alert, or SLA on the submit task alone tells you nothing about real job success, only the sensor task's outcome does. But whether the split itself should be removed depends on Triggerer availability:

If a Triggerer is deployed — collapse to one task with deferrable=True and set wait_for_termination=True explicitly. deferrable=True does not by itself imply wait_for_termination=True — on DatabricksSubmitRunOperator, the deferral helper only defers if wait_for_termination is also True; carry wait_for_termination=False over from an old submit task and it silently skips deferral and polling entirely, reproducing the exact fire-and-forget gap this section exists to remove.

This still frees the worker during polling and the task's own outcome reflects the real job result — but it isn't a strictly-better upgrade with zero tradeoff: the deferred path submits the job directly and never goes through the mixin's checkpointing step, so durable=True protects nothing here (unlike the synchronous path below). A worker crash between submission and the trigger taking over still resubmits on retry.

run_job = DatabricksSubmitRunOperator(
    task_id="run_job",
    tasks=[{"task_key": "job", "notebook_task": {"notebook_path": NOTEBOOK_PATH}}],
    deferrable=True,
    wait_for_termination=True,   # required — deferrable=True alone does not imply this
)

If no Triggerer is deployed — this is a genuine tradeoff, not an automatic call either way. It splits into two cases depending on whether holding a worker for the job's duration is acceptable:

• Job is OK to run synchronously (short/moderate runtime, or worker capacity isn't scarce) and the operator supports durable execution (inherits ResumableJobMixin from Section 5, exposes durable=True) — recommend replacing the two-task pattern with one task in the operator's durable mode: wait_for_termination=True, durable=True (both already the default). This is not just fewer moving parts — the operator's built-in poll loop is covered by ResumableJobMixin end-to-end (submit and poll), so a worker crash mid-poll reconnects instead of resubmitting. A hand-rolled sensor task is a separate, non-mixin code path with its own retry semantics — often weaker (check whether it has its own retries set; many don't). Collapsing here removes an entire hand-maintained file, not just a task:

  run_job = DatabricksSubmitRunOperator(
      task_id="run_job",
      tasks=[{"task_key": "job", "notebook_task": {"notebook_path": NOTEBOOK_PATH}}],
      wait_for_termination=True,   # synchronous — holds the worker for the run, acceptable here
      durable=True,                # default; ResumableJobMixin checkpoints the run id end-to-end
  )
  

• Job is genuinely long-running and worker slots are scarce enough that holding one for the full duration is the real cost to avoid — keep the submit + mode="reschedule" sensor split. That is the legitimate, worker-efficient design in this case, not an anti-pattern to remove on sight.

Either way:

• Verify downstream tasks (and any alerting/SLA) depend on the sensor task, not submit, if the split stays. If something downstream keys off submit succeeding, that's the real bug — fix the dependency, not the architecture.
• Name the tradeoff explicitly in the review (worker-slot cost and split outcome vs. single-task correctness and built-in crash-safety) rather than asserting one side is simply "the right pattern."

Crash-safety and the collapse decision are related, but they're not the same call. Whether to remove the split is decided by worker-slot cost (above) — that's independent of durable=True, which the submit task should generally keep on regardless of whether you collapse (see the "When NOT to use ResumableJobMixin" table in Section 5: the poll/sensor task needs nothing, but the submit task's own duplicate-submission risk doesn't disappear just because it's split from the poll task). When you do collapse, prefer the operator's own durable=True mode over keeping the hand-rolled sensor bolted on — the built-in path already carries ResumableJobMixin's crash-safety for both submit and poll in one place, which a hand-rolled sensor doesn't replicate.

Section 7 — Configuration reference

[state_store]
# Full dotted path to the storage backend. Default writes to the Airflow metadata DB.
backend = airflow.state.metastore.MetastoreStateStoreBackend

# Days to retain task state store entries after their last update. 0 = disable time-based cleanup.
# Does NOT affect asset_state_store rows — asset state store has no TTL.
default_retention_days = 30

# Rows deleted per batch during cleanup. 0 = no batching (single unbounded delete).
# Tune on large deployments to reduce lock contention.
state_cleanup_batch_size = 0

# Auto-delete all task state store keys when a task succeeds. Default: False.
# Does NOT affect asset_state_store — asset state store persists across runs and must be cleared explicitly.
clear_on_success = False

Worker-side backend (optional, [workers] section) — routes task state store writes through a local backend before they reach the API server. Useful when large payloads or credentialed storage should stay on the worker:

[workers]
state_store_backend = mypackage.store.WorkerSideBackend

Section 8 — Safety checklist

• Airflow version ≥ 3.3 (af config version)
• Values are JSON-serializable (str, int, float, bool, list, dict — no datetime, no custom objects)
• task_state_store keys are short, descriptive strings (avoid dots and slashes)
• Mapped tasks writing to asset_state_store: use distinct keys per index or accept last-writer-wins semantics
• Mapped tasks: fleet-wide state clear uses CLI/core API from a downstream task, not clear() inside the task body
• ResumableJobMixin: external_id_key is set and will not be renamed after deployment
• ResumableJobMixin: execute() calls self.execute_resumable(context), not custom logic
• ResumableJobMixin: durable=False is intentional if crash recovery is disabled
• Large payloads (> configured max_value_storage_bytes) use a custom [state_store] backend or a worker side backend configured via: [workers] state_store_backend

Related skills

• authoring-dags — general DAG writing patterns and conventions.
• airflow-hitl — pausing a DAG for human approval (Airflow 3.1+).
• airflow — af config, af registry, and general Airflow CLI reference.