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Commit 8e457f4

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author
Bill Prin
committed
Add Custom Metric Examples
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‎monitoring/api/auth.py

Copy file name to clipboardExpand all lines: monitoring/api/auth.py
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"""Sample command-line program for retrieving Google Cloud Monitoring API data.
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Prerequisites: To run locally, download a Service Account JSON file from
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your project and point GOOGLE_APPLICATION_CREDENTIALS to the file.
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This sample is used on this page:
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https://cloud.google.com/monitoring/api/authentication
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from googleapiclient.discovery import build
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from oauth2client.client import GoogleCredentials
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METRIC = 'compute.googleapis.com/instance/disk/read_ops_count'
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YOUNGEST = '2015-01-01T00:00:00Z'
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‎monitoring/api/labeled_custom_metric.py

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#!/usr/bin/env python
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# Copyright 2015 Google Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Creates, writes, and reads a labeled custom metric.
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This is an example of how to use the Google Cloud Monitoring API to create,
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write, and read a labeled custom metric.
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The metric has two labels: color and size, and the data points represent
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the number of shirts of the given color and size in inventory.
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Prerequisites: To run locally, download a Service Account JSON file from
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your project and point GOOGLE_APPLICATION_CREDENTIALS to the file.
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From App Engine or a GCE instance with the correct scope, the Service
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Account step is not required.
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Typical usage: Run the following shell commands on the instance:
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python labeled_custom_metric.py --project_id <project_id> /
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--color yellow --size large --count 10
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"""
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import argparse
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import datetime
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import time
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from googleapiclient import discovery
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from oauth2client.client import GoogleCredentials
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CUSTOM_METRIC_DOMAIN = "custom.cloudmonitoring.googleapis.com"
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CUSTOM_METRIC_NAME = "{}/shirt_inventory".format(CUSTOM_METRIC_DOMAIN)
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def format_rfc3339(datetime_instance=None):
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"""Formats a datetime per RFC 3339.
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:param datetime_instance: Datetime instance to format, defaults to utcnow
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"""
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return datetime_instance.isoformat("T") + "Z"
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def get_now_rfc3339():
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return format_rfc3339(datetime.datetime.utcnow())
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def create_custom_metric(client, project_id):
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"""Create metric descriptor for the custom metric and send it to the
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API."""
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# You need to execute this operation only once. The operation is
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# idempotent, so, for simplicity, this sample code calls it each time
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# Create a label descriptor for each of the metric labels. The
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# "description" field should be more meaningful for your metrics.
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label_descriptors = []
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for label in ["color", "size", ]:
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label_descriptors.append({"key": "/{}".format(label),
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"description": "The {}.".format(label)})
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# Create the metric descriptor for the custom metric.
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metric_descriptor = {
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"name": CUSTOM_METRIC_NAME,
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"project": project_id,
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"typeDescriptor": {
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"metricType": "gauge",
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"valueType": "int64",
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},
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"labels": label_descriptors,
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"description": "The size of my shirt inventory.",
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}
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# Submit the custom metric creation request.
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try:
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request = client.metricDescriptors().create(
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project=project_id, body=metric_descriptor)
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request.execute() # ignore the response
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except Exception as e:
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print("Failed to create custom metric: exception={})".format(e))
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raise
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def write_custom_metric(client, project_id, now_rfc3339, color, size, count):
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"""Write a data point to a single time series of the custom metric."""
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# Identify the particular time series to which to write the data by
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# specifying the metric and values for each label.
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timeseries_descriptor = {
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"project": project_id,
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"metric": CUSTOM_METRIC_NAME,
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"labels": {
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"{}/color".format(CUSTOM_METRIC_DOMAIN): color,
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"{}/size".format(CUSTOM_METRIC_DOMAIN): size,
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}
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}
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# Specify a new data point for the time series.
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timeseries_data = {
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"timeseriesDesc": timeseries_descriptor,
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"point": {
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"start": now_rfc3339,
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"end": now_rfc3339,
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"int64Value": count,
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}
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}
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# Submit the write request.
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request = client.timeseries().write(
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project=project_id, body={"timeseries": [timeseries_data, ]})
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try:
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request.execute() # ignore the response
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except Exception as e:
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print("Failed to write data to custom metric: exception={}".format(e))
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raise
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def read_custom_metric(client, project_id, now_rfc3339, color, size):
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"""Read all the timeseries data points for a given set of label values."""
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# To identify a time series, specify values for in label as a list.
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labels = ["{}/color=={}".format(CUSTOM_METRIC_DOMAIN, color),
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"{}/size=={}".format(CUSTOM_METRIC_DOMAIN, size), ]
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# Submit the read request.
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request = client.timeseries().list(
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project=project_id,
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metric=CUSTOM_METRIC_NAME,
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youngest=now_rfc3339,
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labels=labels)
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# When a custom metric is created, it may take a few seconds
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# to propagate throughout the system. Retry a few times.
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start = time.time()
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while True:
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try:
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response = request.execute()
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for point in response["timeseries"][0]["points"]:
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print("{}: {}".format(point["end"], point["int64Value"]))
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break
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except Exception as e:
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if time.time() < start + 20:
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print("Failed to read custom metric data, retrying...")
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time.sleep(3)
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else:
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print("Failed to read custom metric data, aborting: "
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"exception={}".format(e))
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raise
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def get_client():
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"""Builds an http client authenticated with the application default
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credentials."""
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credentials = GoogleCredentials.get_application_default()
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client = discovery.build(
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'cloudmonitoring', 'v2beta2',
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credentials=credentials)
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return client
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def main(project_id, color, size, count):
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now_rfc3339 = get_now_rfc3339()
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client = get_client()
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print ("Labels: color: {}, size: {}.".format(color, size))
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print ("Creating custom metric...")
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create_custom_metric(client, project_id)
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time.sleep(2)
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print ("Writing new data to custom metric timeseries...")
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write_custom_metric(client, project_id, now_rfc3339,
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color, size, count)
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print ("Reading data from custom metric timeseries...")
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write_custom_metric(client, project_id, now_rfc3339, color,
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size, count)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter,
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)
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parser.add_argument(
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'--project_id', help='Project ID you want to access.', required=True)
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parser.add_argument("--color", required=True)
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parser.add_argument("--size", required=True)
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parser.add_argument("--count", required=True)
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args = parser.parse_args()
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main(args.project_id, args.color, args.size, args.count)

‎monitoring/api/labeled_custom_metric_test.py

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#!/usr/bin/env python
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# Copyright 2015 Google Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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import labeled_custom_metric
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def test_main(cloud_config, capsys):
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labeled_custom_metric.main(cloud_config.project, "yellow", "large", "10")
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output, _ = capsys.readouterr()
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assert re.search(
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re.compile(
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r'.*Creating.*'
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r'Writing.*'
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r'Reading.*', flags=re.DOTALL),
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output)

‎monitoring/api/lightweight_custom_metric.py

Copy file name to clipboard
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#!/usr/bin/env python
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# Copyright 2015 Google Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Writes and reads a lightweight custom metric.
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This is an example of how to use the Google Cloud Monitoring API to write
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and read a lightweight custom metric. Lightweight custom metrics have no
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labels and you do not need to create a metric descriptor for them.
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Prerequisites: To run locally, download a Service Account JSON file from
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your project and point GOOGLE_APPLICATION_CREDENTIALS to the file.
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From App Engine or a GCE instance with the correct scope, the Service
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Account step is not required.
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Typical usage: Run the following shell commands on the instance:
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python lightweight_custom_metric.py --project_id=<YOUR-PROJECT-ID>
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"""
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import argparse
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import datetime
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import os
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import time
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from googleapiclient import discovery
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from oauth2client.client import GoogleCredentials
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CUSTOM_METRIC_NAME = "custom.cloudmonitoring.googleapis.com/pid"
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def format_rfc3339(datetime_instance=None):
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"""Formats a datetime per RFC 3339.
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:param datetime_instance: Datetime instanec to format, defaults to utcnow
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"""
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return datetime_instance.isoformat("T") + "Z"
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def get_now_rfc3339():
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return format_rfc3339(datetime.datetime.utcnow())
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def get_client():
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"""Builds an http client authenticated with the service account
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credentials."""
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credentials = GoogleCredentials.get_application_default()
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client = discovery.build(
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'cloudmonitoring', 'v2beta2',
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credentials=credentials)
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return client
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def main(project_id):
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# Set up the write request.
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client = get_client()
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now = get_now_rfc3339()
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desc = {"project": project_id,
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"metric": CUSTOM_METRIC_NAME}
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point = {"start": now,
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"end": now,
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"doubleValue": os.getpid()}
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print("Writing {} at {}".format(point["doubleValue"], now))
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# Write a new data point.
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try:
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write_request = client.timeseries().write(
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project=project_id,
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body={"timeseries": [{"timeseriesDesc": desc, "point": point}]})
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write_request.execute() # Ignore the response.
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except Exception as e:
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print("Failed to write custom metric data: exception={}".format(e))
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raise
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# Read all data points from the time series.
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# When a custom metric is created, it may take a few seconds
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# to propagate throughout the system. Retry a few times.
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print("Reading data from custom metric timeseries...")
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read_request = client.timeseries().list(
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project=project_id,
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metric=CUSTOM_METRIC_NAME,
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youngest=now)
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start = time.time()
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while True:
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try:
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read_response = read_request.execute()
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for point in read_response["timeseries"][0]["points"]:
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print("Point: {}: {}".format(
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point["end"], point["doubleValue"]))
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break
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except Exception as e:
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if time.time() < start + 20:
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print("Failed to read custom metric data, retrying...")
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time.sleep(3)
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else:
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print("Failed to read custom metric data, aborting: "
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"exception={}".format(e))
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raise
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter
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)
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parser.add_argument(
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'--project_id', help='Project ID you want to access.', required=True)
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args = parser.parse_args()
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main(args.project_id)

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