# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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
#
# http://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.
from __future__ import annotations
import time
from collections.abc import Sequence
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any
from airflow.providers.anthropic.exceptions import AnthropicBatchJobError, AnthropicBatchTimeout
from airflow.providers.anthropic.hooks.anthropic import (
AnthropicHook,
evaluate_batch_counts,
validate_execute_complete_event,
)
from airflow.providers.anthropic.triggers.batch import AnthropicBatchTrigger
from airflow.providers.common.compat.sdk import BaseOperator, conf
if TYPE_CHECKING:
from airflow.providers.common.compat.sdk import Context
[docs]
class AnthropicBatchOperator(BaseOperator):
"""
Submit an Anthropic Message Batch and wait for it to complete.
Message Batches process many ``messages.create`` requests asynchronously at 50% of
standard cost; most complete within an hour (24h SLA). This operator submits the
batch and, in deferrable mode, releases the worker slot while a trigger polls for
completion.
The operator returns the **batch ID only** — never the results. Pull results with
:meth:`~airflow.providers.anthropic.hooks.anthropic.AnthropicHook.stream_batch_results`
and persist them to object storage; results can be very large and must not be pushed
to XCom. Results are retained for 29 days after the batch is created.
.. note::
A retry re-submits a brand-new batch. Prefer ``retries=0`` on this task (the
submitted ``batch_id`` is pushed to XCom under key ``batch_id`` immediately, so
a crashed run never loses track of an in-flight batch).
.. seealso::
For more information, take a look at the guide:
:ref:`howto/operator:AnthropicBatchOperator`
:param requests: A list of ``{"custom_id": str, "params": {...}}`` dicts, where
``params`` is a ``messages.create`` payload (``model``, ``max_tokens``, ``messages``, ...).
:param conn_id: The Anthropic connection ID to use.
:param deferrable: Run the operator in deferrable mode.
:param poll_interval: Seconds between status checks, in both the synchronous and
deferrable paths.
:param timeout: Seconds to wait for the batch to reach a terminal status. Defaults to
24 hours (the Message Batches SLA). In deferrable mode this also bounds the
deferral; set ``execution_timeout`` only if you want a shorter hard cap (note a
shorter ``execution_timeout`` preempts the graceful cancel-on-timeout path).
:param wait_for_completion: Whether to wait for the batch to complete. If ``False``,
the operator returns the batch ID immediately after submission.
:param fail_on_partial_error: If ``True``, fail the task when any request errored or
expired. Defaults to ``False`` (succeed and log a warning so the successful
results are not discarded).
"""
[docs]
template_fields: Sequence[str] = ("requests",)
def __init__(
self,
requests: list[dict[str, Any]],
conn_id: str = AnthropicHook.default_conn_name,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: float = 60,
timeout: float = 24 * 60 * 60,
wait_for_completion: bool = True,
fail_on_partial_error: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
[docs]
self.requests = requests
[docs]
self.deferrable = deferrable
[docs]
self.poll_interval = poll_interval
[docs]
self.wait_for_completion = wait_for_completion
[docs]
self.fail_on_partial_error = fail_on_partial_error
[docs]
self.batch_id: str | None = None
@cached_property
[docs]
def hook(self) -> AnthropicHook:
"""Return an instance of the AnthropicHook."""
return AnthropicHook(conn_id=self.conn_id)
[docs]
def execute(self, context: Context) -> str | None:
if not self.requests:
raise ValueError("AnthropicBatchOperator requires at least one request; got an empty list.")
batch = self.hook.create_batch(self.requests)
self.batch_id = batch.id
# Push immediately so a crash between submit and completion never loses the batch.
context["ti"].xcom_push(key="batch_id", value=batch.id)
self.log.info("Submitted Anthropic Message Batch %s (%d requests)", batch.id, len(self.requests))
if not self.wait_for_completion:
return self.batch_id
if self.deferrable:
self.defer(
# Backstop the deferral slightly beyond the trigger's own end_time so the
# trigger's clean "timeout" event (which cancels the batch) wins over a
# generic AirflowTaskTimeout. A user-set execution_timeout still applies
# as a shorter hard cap.
timeout=self.execution_timeout or timedelta(seconds=self.timeout + self.poll_interval + 60),
trigger=AnthropicBatchTrigger(
conn_id=self.conn_id,
batch_id=self.batch_id,
poll_interval=self.poll_interval,
end_time=time.time() + self.timeout,
),
method_name="execute_complete",
)
self.log.info("Waiting for batch %s to complete", self.batch_id)
try:
batch = self.hook.wait_for_batch(
self.batch_id, wait_seconds=self.poll_interval, timeout=self.timeout
)
except Exception:
# Any failure after submission (timeout, SDK 5xx, auth expiry) leaves the batch
# running and billing; cancel it best-effort before the task fails.
self.log.warning("Batch %s failed while waiting; requesting cancellation.", self.batch_id)
self._cancel_batch_quietly()
raise
counts = batch.request_counts
self._apply_policy(counts.canceled, counts.errored, counts.expired, counts.succeeded)
return self.batch_id
[docs]
def execute_complete(self, context: Context, event: Any = None) -> str:
"""
Resume after the trigger fires.
The deferred task is a fresh instance, so the batch ID is read from the event,
not ``self.batch_id``.
"""
event = validate_execute_complete_event(event)
self.batch_id = event["batch_id"]
status = event["status"]
if status == "timeout":
self.log.warning("Batch %s timed out; requesting cancellation.", self.batch_id)
self._cancel_batch_quietly()
raise AnthropicBatchTimeout(event["message"])
if status == "error":
# The trigger yields "error" when polling gives up (transient failures
# exhausted or the deadline passed mid-poll) while the batch may still be
# running; cancel it best-effort so it does not keep billing.
self.log.warning("Batch %s errored while polling; requesting cancellation.", self.batch_id)
self._cancel_batch_quietly()
raise AnthropicBatchJobError(event["message"])
counts = event.get("request_counts") or {}
self._apply_policy(
counts.get("canceled", 0),
counts.get("errored", 0),
counts.get("expired", 0),
counts.get("succeeded", 0),
)
self.log.info("%s completed successfully.", self.task_id)
return self.batch_id
def _apply_policy(self, canceled: int, errored: int, expired: int, succeeded: int) -> None:
evaluate_batch_counts(
batch_id=self.batch_id,
canceled=canceled,
errored=errored,
expired=expired,
succeeded=succeeded,
fail_on_partial_error=self.fail_on_partial_error,
)
[docs]
def on_kill(self) -> None:
"""
Cancel the batch if the (non-deferred) task is killed.
This only fires while the worker process is alive, i.e. the synchronous path
(``deferrable=False``). On Airflow 3.3+ a killed deferred task is cancelled by the
trigger's ``on_kill``. On older Airflow the batch of a killed deferred task is not
cancelled automatically; cancel it manually via the hook.
"""
if self.batch_id:
self.log.info("on_kill: cancelling Anthropic batch %s", self.batch_id)
self._cancel_batch_quietly()
def _cancel_batch_quietly(self) -> None:
"""Best-effort batch cancellation for the timeout and kill paths."""
if not self.batch_id:
return
try:
self.hook.cancel_batch(self.batch_id)
except Exception as e:
self.log.warning("Failed to cancel batch %s: %s", self.batch_id, e)