go_metrics.py

#

Licensed 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.

#

Script to retrieve memory and GC statistic from the standard Go metrics.

Memory and GC statistic is being exported into CSV file to be further processed.

#

Link to generated documentation for this script: https://redhatinsights.github.io/insights-results-aggregator-utils/packages/go_metrics.html

import csv
import requests
import time

from argparse import ArgumentParser
from prometheus_client.parser import text_string_to_metric_families

#

Tuple containing all exported metrics.

exported_metrics = (
        "go_gc_duration_seconds_sum",
        "go_gc_duration_seconds_count",
        "go_memstats_alloc_bytes",
        "go_memstats_sys_bytes",
        "go_memstats_mallocs_total",
        "go_memstats_frees_total",
        )

#

Parse metrics from payload returned from the monitored service.

def parse_metrics(exported_metrics, payload):

#

    metrics = []

#

Iterate over all families in retrieved payload.

    for family in text_string_to_metric_families(str(payload)):

#

Each family consists of set of samples.

        for sample in family.samples:
            name, labels, value = sample

#

Try to find all metrics that need to be exported.

            for exported_metric in exported_metrics:
                if exported_metric == name:

#

Construct the list to be exported from this function.

                    metrics.append(value)
    return metrics

#

Monitor selected service and export retrieved metrics into CSV file.

def monitor_service(exported_metric, url, csv_filename, sleep_amount, max_records):

#

Try to open new file for writing.

    with open(csv_filename, 'w') as csvfile:

#

Initialize CSV writer.

        writer = csv.writer(csvfile, quoting=csv.QUOTE_NONNUMERIC)

#

First row with header.

        writer.writerow(exported_metrics)

        n = 0

#

We are gonna to retrieve the metrics with specified frequency and process it.

        while True:

#

TODO: make configurable

            payload = requests.get("http://localhost:8080/api/v1/metrics").text
            metrics = parse_metrics(exported_metrics, payload)
            writer.writerow(metrics)

#

Make sure the next Ctrl+C or kill won’t affect more that the last record.

            csvfile.flush()
            print("recorded")

#

Wait for the next metrics to be processed.

            time.sleep(sleep_amount)

#

It is possible to limit number of records.

            if max_records is not None:
                n += 1

#

We already acquired specified number of records, time to drop.

                if n >= max_records:
                    print("done")
                    break

#

Retrieve all CLI arguments.

def cli_arguments():

#

First of all, we need to specify all command line flags that are recognized by this tool.

    parser = ArgumentParser()
    parser.add_argument("-u", "--url", help="URL to get metrics",
                        default="http://localhost:8080/api/v1/metrics", type=str)
    parser.add_argument("-o", "--output", help="output file name", required=True)
    parser.add_argument("-d", "--delay", help="Delay in seconds between records",
                        default=5, type=int)
    parser.add_argument("-m", "--max_records", help="max records to export (default=all)",
                        default=None, type=int)

#

Now it is time to parse flags, check the actual content of command line and fill in the object named args.

    return parser.parse_args()

#

Entry point to this script.

def main():

#

Parse and process all command line arguments.

    args = cli_arguments()

#

Start the monitoring of service with the provided configuration.

    monitor_service(exported_metrics, args.url, args.output, args.delay, args.max_records)

#

If this script is started from command line, run the main function which is entry point to the processing.

if __name__ == "__main__":
    main()