ClickHouse/tests/ci/autoscale_runners_lambda/app.py

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#!/usr/bin/env python3
"""The lambda to decrease/increase ASG desired capacity based on current queue"""
import logging
from dataclasses import dataclass
from pprint import pformat
from typing import Any, List, Literal, Optional, Tuple
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import boto3 # type: ignore
from lambda_shared import (
CHException,
ClickHouseHelper,
RUNNER_TYPE_LABELS,
get_parameter_from_ssm,
)
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### Update comment on the change ###
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# 4 HOUR - is a balance to get the most precise values
# - Our longest possible running check is around 5h on the worst scenario
# - The long queue won't be wiped out and replaced, so the measurmenet is fine
# - If the data is spoiled by something, we are from the bills perspective
# Changed it to 3 HOUR: in average we have 1h tasks, but p90 is around 2h.
# With 4h we have too much wasted computing time in case of issues with DB
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QUEUE_QUERY = f"""SELECT
last_status AS status,
toUInt32(count()) AS length,
labels
FROM
(
SELECT
arraySort(groupArray(status))[-1] AS last_status,
labels,
id,
html_url
FROM default.workflow_jobs
WHERE has(labels, 'self-hosted')
AND hasAny({RUNNER_TYPE_LABELS}, labels)
AND started_at > now() - INTERVAL 3 HOUR
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GROUP BY ALL
HAVING last_status IN ('in_progress', 'queued')
)
GROUP BY ALL
ORDER BY labels, last_status"""
@dataclass
class Queue:
status: Literal["in_progress", "queued"]
lentgh: int
label: str
def get_scales(runner_type: str) -> Tuple[int, int]:
"returns the multipliers for scaling down and up ASG by types"
# Scaling down is quicker on the lack of running jobs than scaling up on
# queue
scale_down = 2
scale_up = 5
if runner_type == "style-checker":
# the style checkers have so many noise, so it scales up too quickly
scale_down = 1
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# The 5 was too quick, there are complainings regarding too slow with
# 10. I am trying 7 now.
# UPDATE THE COMMENT ON CHANGES
scale_up = 7
elif runner_type == "limited-tester":
# The limited runners should inflate and deflate faster
scale_down = 1
scale_up = 2
return scale_down, scale_up
CH_CLIENT = None # type: Optional[ClickHouseHelper]
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def set_capacity(
runner_type: str, queues: List[Queue], client: Any, dry_run: bool = True
) -> None:
assert len(queues) in (1, 2)
assert all(q.label == runner_type for q in queues)
as_groups = client.describe_auto_scaling_groups(
Filters=[
{"Name": "tag-key", "Values": ["github:runner-type"]},
{"Name": "tag-value", "Values": [runner_type]},
]
)["AutoScalingGroups"]
assert len(as_groups) == 1
asg = as_groups[0]
running = 0
queued = 0
for q in queues:
if q.status == "in_progress":
running = q.lentgh
continue
if q.status == "queued":
queued = q.lentgh
continue
raise ValueError("Queue status is not in ['in_progress', 'queued']")
scale_down, scale_up = get_scales(runner_type)
# How much nodes are free (positive) or need to be added (negative)
capacity_reserve = asg["DesiredCapacity"] - running - queued
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stop = False
if capacity_reserve < 0:
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# This part is about scaling up
capacity_deficit = -capacity_reserve
# It looks that we are still OK, since no queued jobs exist
stop = stop or queued == 0
# Are we already at the capacity limits
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stop = stop or asg["MaxSize"] <= asg["DesiredCapacity"]
# Let's calculate a new desired capacity
desired_capacity = asg["DesiredCapacity"] + (capacity_deficit // scale_up)
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desired_capacity = max(desired_capacity, asg["MinSize"])
desired_capacity = min(desired_capacity, asg["MaxSize"])
# Finally, should the capacity be even changed
stop = stop or asg["DesiredCapacity"] == desired_capacity
if stop:
logging.info(
"Do not increase ASG %s capacity, current capacity=%s, "
"maximum capacity=%s, running jobs=%s, queue size=%s",
asg["AutoScalingGroupName"],
desired_capacity,
asg["MaxSize"],
running,
queued,
)
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return
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logging.info(
"The ASG %s capacity will be increased to %s, current capacity=%s, "
"maximum capacity=%s, running jobs=%s, queue size=%s",
asg["AutoScalingGroupName"],
desired_capacity,
asg["DesiredCapacity"],
asg["MaxSize"],
running,
queued,
)
if not dry_run:
client.set_desired_capacity(
AutoScalingGroupName=asg["AutoScalingGroupName"],
DesiredCapacity=desired_capacity,
)
return
# Now we will calculate if we need to scale down
stop = stop or asg["DesiredCapacity"] == asg["MinSize"]
desired_capacity = asg["DesiredCapacity"] - (capacity_reserve // scale_down)
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desired_capacity = max(desired_capacity, asg["MinSize"])
desired_capacity = min(desired_capacity, asg["MaxSize"])
stop = stop or asg["DesiredCapacity"] == desired_capacity
if stop:
logging.info(
"Do not decrease ASG %s capacity, current capacity=%s, "
"minimum capacity=%s, running jobs=%s, queue size=%s",
asg["AutoScalingGroupName"],
desired_capacity,
asg["MinSize"],
running,
queued,
)
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return
logging.info(
"The ASG %s capacity will be decreased to %s, current capacity=%s, "
"minimum capacity=%s, running jobs=%s, queue size=%s",
asg["AutoScalingGroupName"],
desired_capacity,
asg["DesiredCapacity"],
asg["MinSize"],
running,
queued,
)
if not dry_run:
client.set_desired_capacity(
AutoScalingGroupName=asg["AutoScalingGroupName"],
DesiredCapacity=desired_capacity,
)
def main(dry_run: bool = True) -> None:
logging.getLogger().setLevel(logging.INFO)
asg_client = boto3.client("autoscaling")
try:
global CH_CLIENT
CH_CLIENT = CH_CLIENT or ClickHouseHelper(
get_parameter_from_ssm("clickhouse-test-stat-url"), "play"
)
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queues = CH_CLIENT.select_json_each_row("default", QUEUE_QUERY)
except CHException as ex:
logging.exception(
"Got an exception on insert, tryuing to update the client "
"credentials and repeat",
exc_info=ex,
)
CH_CLIENT = ClickHouseHelper(
get_parameter_from_ssm("clickhouse-test-stat-url"), "play"
)
queues = CH_CLIENT.select_json_each_row("default", QUEUE_QUERY)
logging.info("Received queue data:\n%s", pformat(queues, width=120))
for runner_type in RUNNER_TYPE_LABELS:
runner_queues = [
Queue(queue["status"], queue["length"], runner_type)
for queue in queues
if runner_type in queue["labels"]
]
runner_queues = runner_queues or [Queue("in_progress", 0, runner_type)]
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set_capacity(runner_type, runner_queues, asg_client, dry_run)
def handler(event: dict, context: Any) -> None:
_ = event
_ = context
return main(False)