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Description:

pythonyandexcloudmonitoring 0.0.4

Python Client for Yandex Cloud Monitoring
Installation
pip3 install python-yandex-cloud-monitoring

Getting started with Yandex Monitoring
Credentials
Service Account Keys only ...
Access management
Service Account Keys & Roles
For write metrics, add a folder role: monitoring.editor
import datetime
import random

from pyclm.monitoring import Monitoring

metrics = Monitoring(
credentials={
"service_account_key": {
"service_account_id": "....",
"id": "....",
"private_key": "<PEM>"
},
"cloudId": "<CLOUD_ID>",
"folderId": "<FOLDER_ID>"
},
group_id="default",
resource_type="....", resource_id="....",
elements=100, period=10, workers=1
)

for n in range(1000):
# Numeric value (decimal). It shows the metric value at a certain point in time.
# For example, the amount of used RAM
metrics.dgauge(
"temperature",
random.random(),
ts=datetime.datetime.now(datetime.timezone.utc),
labels={"building": "office", "room": "openspace"}
)
# Tag. It shows the metric value that increases over time.
# For example, the number of days of service continuous running.
metrics.counter("counter", n, labels={"building": "office", "room": "openspace"})
# Numeric value (integer). It shows the metric value at a certain point in time.
metrics.igauge("number", n, labels={"building": "office", "room": "openspace"})
# Derivative value. It shows the change in the metric value over time.
# For example, the number of requests per second.
metrics.rate("rate", random.random(), labels={"building": "office", "room": "openspace"})

credentials.cloudId - The ID of the cloud that the resource belongs to.
credentials.folderId - The ID of the folder that the resource belongs to.
resource_type - Resource type, serverless.function, hostname.
Value must match the regular expression ([a-zA-Z][-a-zA-Z0-9_.]{0,63})?.
resource_id - Resource ID, i.e., ID of the function producing metrics.
Value must match the regular expression ([a-zA-Z0-9][-a-zA-Z0-9_.]{0,63})?.
elements - The number of elements before writing, must be in the range 1-100.
period - Number of seconds to wait for new log entries before writing.
workers - Number of process ingestion.
from pyclm.monitoring import Monitoring, Chrono

metrics = Monitoring()

with Chrono(metrics, name="elapsed", labels={"measured": "calculation"}, mul=10**9):
# ... measured calculation ...

name - Name of the metric. The default value is elapsed. Additional metric process_{name} sum of the kernel and user-space CPU time.
mul - Process time for profiling default as seconds mul = 10^9 .. nanoseconds mul = 1
labels - Metric labels as key:value pairs.