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`deliver_scheduled_emails` tries to deliver the email synchronously, and if it fails, it retries after 10 seconds. Since it does not track retries, and always tries the earliest-scheduled-but-due message first, the worker will not make forward progress if there is a persistent failure with that message, and will retry indefinitely. This can result in excessive network or email delivery charges from the remote SMTP server. Switch to delivering emails via a new queue worker. The `deliver_scheduled_emails` job now serves only to pull deferred jobs out of the table once they are due, insert them into RabbitMQ, and then delete them. This limits the potential for head-of-queue failures to failures inserting into RabbitMQ, which is more reasonable than failures speaking to a complex external system we do not control. Retries and any connections to the SMTP server are left to the RabbitMQ consumer. We build a new RabbitMQ queue, rather than use the existing `email_senders` queue, because that queue is expected to be reasonably low-latency, for things like missed message notifications. The `send_future_email` codepath which inserts into ScheduledEmails is also (ab)used to digest emails, which are extremely bursty in their frequency -- and a large burst could significantly delay emails behind it in the queue. The new queue is explicitly only for messages which were not initiated by user actions (e.g., invitation reminders, digests, new account follow-ups) which are thus not latency-sensitive. Fixes: #32463.
217 lines
7.2 KiB
Python
217 lines
7.2 KiB
Python
import json
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import os
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import re
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import subprocess
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import sys
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import time
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from collections import defaultdict
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from typing import Any
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ZULIP_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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sys.path.append(ZULIP_PATH)
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from scripts.lib.zulip_tools import atomic_nagios_write, get_config, get_config_file
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normal_queues = [
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"deferred_work",
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"deferred_email_senders",
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"digest_emails",
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"email_mirror",
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"email_senders",
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"embed_links",
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"embedded_bots",
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"missedmessage_emails",
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"missedmessage_mobile_notifications",
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"outgoing_webhooks",
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"thumbnail",
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"user_activity",
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"user_activity_interval",
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]
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mobile_notification_shards = int(
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get_config(get_config_file(), "application_server", "mobile_notification_shards", "1")
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)
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OK = 0
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WARNING = 1
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CRITICAL = 2
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UNKNOWN = 3
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states = {
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0: "OK",
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1: "WARNING",
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2: "CRITICAL",
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3: "UNKNOWN",
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}
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MAX_SECONDS_TO_CLEAR: defaultdict[str, int] = defaultdict(
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lambda: 30,
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deferred_work=600,
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digest_emails=1200,
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missedmessage_mobile_notifications=120,
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embed_links=60,
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deferred_email_senders=240,
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)
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CRITICAL_SECONDS_TO_CLEAR: defaultdict[str, int] = defaultdict(
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lambda: 60,
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deferred_work=900,
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missedmessage_mobile_notifications=180,
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digest_emails=1800,
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embed_links=90,
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deferred_email_senders=300,
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)
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def analyze_queue_stats(
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queue_name: str, stats: dict[str, Any], queue_count_rabbitmqctl: int
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) -> dict[str, Any]:
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now = int(time.time())
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if stats == {}:
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return dict(status=UNKNOWN, name=queue_name, message="invalid or no stats data")
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if now - stats["update_time"] > 180 and queue_count_rabbitmqctl > 10:
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# Queue isn't updating the stats file and has some events in
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# the backlog, it's likely stuck.
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#
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# TODO: There's an unlikely race condition here - if the queue
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# was fully emptied and was idle due to no new events coming
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# for over 180 seconds, suddenly gets a burst of events and
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# this code runs exactly in the very small time window between
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# those events popping up and the queue beginning to process
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# the first one (which will refresh the stats file at the very
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# start), we'll incorrectly return the CRITICAL status. The
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# chance of that happening should be negligible because the queue
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# worker should wake up immediately and log statistics before
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# starting to process the first event.
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return dict(
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status=CRITICAL,
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name=queue_name,
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message="queue appears to be stuck, last update {}, queue size {}".format(
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stats["update_time"], queue_count_rabbitmqctl
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),
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)
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current_size = queue_count_rabbitmqctl
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average_consume_time = stats["recent_average_consume_time"]
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if average_consume_time is None:
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# Queue just started; we can't effectively estimate anything.
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#
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# If the queue is stuck in this state and not processing
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# anything, eventually the `update_time` rule above will fire.
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return dict(status=OK, name=queue_name, message="")
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expected_time_to_clear_backlog = current_size * average_consume_time
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if expected_time_to_clear_backlog > MAX_SECONDS_TO_CLEAR[queue_name]:
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if expected_time_to_clear_backlog > CRITICAL_SECONDS_TO_CLEAR[queue_name]:
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status = CRITICAL
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else:
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status = WARNING
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return dict(
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status=status,
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name=queue_name,
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message=f"clearing the backlog will take too long: {expected_time_to_clear_backlog}s, size: {current_size}",
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)
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return dict(status=OK, name=queue_name, message="")
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WARN_COUNT_THRESHOLD_DEFAULT = 10
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CRITICAL_COUNT_THRESHOLD_DEFAULT = 50
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def check_other_queues(queue_counts_dict: dict[str, int]) -> list[dict[str, Any]]:
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"""Do a simple queue size check for queues whose workers don't publish stats files."""
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results = []
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for queue, count in queue_counts_dict.items():
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if queue in normal_queues:
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continue
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if count > CRITICAL_COUNT_THRESHOLD_DEFAULT:
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results.append(dict(status=CRITICAL, name=queue, message=f"count critical: {count}"))
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elif count > WARN_COUNT_THRESHOLD_DEFAULT:
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results.append(dict(status=WARNING, name=queue, message=f"count warning: {count}"))
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else:
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results.append(dict(status=OK, name=queue, message=""))
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return results
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def check_rabbitmq_queues() -> None:
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pattern = re.compile(r"(\w+)\t(\d+)\t(\d+)")
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if "USER" in os.environ and os.environ["USER"] not in ["root", "rabbitmq"]:
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print("This script must be run as the root or rabbitmq user")
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list_queues_output = subprocess.check_output(
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["/usr/sbin/rabbitmqctl", "list_queues", "name", "messages", "consumers"],
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text=True,
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)
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queue_counts_rabbitmqctl = {}
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queues_with_consumers = []
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for line in list_queues_output.split("\n"):
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line = line.strip()
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m = pattern.match(line)
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if m:
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queue = m.group(1)
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count = int(m.group(2))
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consumers = int(m.group(3))
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queue_counts_rabbitmqctl[queue] = count
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if consumers > 0 and not queue.startswith("notify_tornado"):
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queues_with_consumers.append(queue)
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queue_stats_dir = subprocess.check_output(
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[os.path.join(ZULIP_PATH, "scripts/get-django-setting"), "QUEUE_STATS_DIR"],
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text=True,
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).strip()
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queue_stats: dict[str, dict[str, Any]] = {}
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check_queues = normal_queues
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if mobile_notification_shards > 1:
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# For sharded queue workers, where there's a separate queue
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# for each shard, we need to make sure none of those are
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# backlogged.
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check_queues += [
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f"missedmessage_mobile_notifications_shard{d}"
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for d in range(1, mobile_notification_shards + 1)
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]
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queues_to_check = set(check_queues).intersection(set(queues_with_consumers))
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for queue in queues_to_check:
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fn = queue + ".stats"
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file_path = os.path.join(queue_stats_dir, fn)
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if not os.path.exists(file_path):
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queue_stats[queue] = {}
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continue
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with open(file_path) as f:
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try:
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queue_stats[queue] = json.load(f)
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except json.decoder.JSONDecodeError:
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queue_stats[queue] = {}
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results = []
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for queue_name, stats in queue_stats.items():
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results.append(analyze_queue_stats(queue_name, stats, queue_counts_rabbitmqctl[queue_name]))
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results.extend(check_other_queues(queue_counts_rabbitmqctl))
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status = max(result["status"] for result in results)
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if status > 0:
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queue_error_template = "queue {} problem: {}:{}"
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error_message = "; ".join(
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queue_error_template.format(result["name"], states[result["status"]], result["message"])
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for result in results
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if result["status"] > 0
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)
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sys.exit(
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atomic_nagios_write(
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"check-rabbitmq-results",
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"critical" if status == CRITICAL else "warning",
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error_message,
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)
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)
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else:
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atomic_nagios_write("check-rabbitmq-results", "ok", "queues normal")
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