Export Checkly result stats to table format

checkly_stats.py

#!/usr/bin/python3

# Checkly API reference: https://www.checklyhq.com/docs/api

import datetime
import dateutil.parser as dp
import time

import numpy as np
import requests
from tabulate import tabulate


BASE_URL = "https://api.checklyhq.com/v1"
headers = {
    'Authorization': 'Bearer <API KEY>',
    'X-Checkly-Account': '<ACCOUNT ID>',
}

today = datetime.date.today()
weekago = today - datetime.timedelta(7)

r = requests.get(
    f"{BASE_URL}/check-groups/<GROUP ID>/checks",
    headers=headers,
)

checks = {}
for check in r.json():
    checks[check['id']] = check['name']

check_results = []

query_interval = 43200
for check_id, check_name in checks.items():
    print(f"retrieving check results for {check_name}")

    start_epoch = int(weekago.strftime("%s"))
    while True:
        print(f"Start date: {start_epoch}")
        r = requests.get(
            f"{BASE_URL}/check-results/{check_id}",
            headers=headers,
            params={
                "hasFailures": False,
                "from": start_epoch,
                "to": start_epoch + query_interval,
                "limit": 100,
            }
        )

        crs = [chk for chk in r.json() if isinstance(chk, dict)]
        check_results.extend(crs)

        start_epoch = int(start_epoch) + query_interval + 1
        if start_epoch >= int(time.time()):
            print(f"no more check results for {check_name}, break.")
            break

response_times = {}
computed_stats = {}
for result in check_results:
    if result['hasFailures']:
        continue

    name = checks[result['checkId']]
    region = result['runLocation']
    response_times.setdefault(name, dict())
    response_times[name].setdefault(region, {
        "response_time": [],
        "fcp": [],
        "ttfb": [],
    })

    computed_stats.setdefault(region, dict())
    computed_stats[region].setdefault(name, dict())

    page = result['browserCheckResult']['pages'][0]
    web_vitals = page['webVitals']
    response_times[name][region]["response_time"].append(int(result['responseTime']))
    response_times[name][region]["fcp"].append(int(web_vitals['FCP']['value']))
    response_times[name][region]["ttfb"].append(int(web_vitals['TTFB']['value']))

    check_count = len(response_times[name][region]["response_time"])
    computed_stats[region][name]["count"] = check_count

    for stat in ["response_time", "fcp", "ttfb"]:
        average = round(np.average(response_times[name][region][stat]) / 1000.0, 4)
        computed_stats[region][name][f"{stat}_average"] = average

        for perct in [75, 90, 95]:
            percentile = round(np.percentile(response_times[name][region][stat], perct) / 1000.0, 4)
            computed_stats[region][name][f"{stat}_p{perct}"] = percentile

for region, data in computed_stats.items():
    print(f"Region: {region}")
    headers = [["Name"] + list(v.keys()) for k, v in data.items()][0]
    print(tabulate([[k] + list(v.values()) for k, v in data.items()], headers=headers))

example_output.txt

Region: ca-central-1
Name                     count    response_time_average    response_time_p75    response_time_p90    response_time_p95    fcp_average    fcp_p75    fcp_p90    fcp_p95    ttfb_average    ttfb_p75    ttfb_p90    ttfb_p95
---------------------  -------  -----------------------  -------------------  -------------------  -------------------  -------------  ---------  ---------  ---------  --------------  ----------  ----------  ----------
my_check_name #1            94                   5.2564               5.208                5.36                 5.8505         0.6177     0.6302     0.7077      0.761          0.4453       0.455      0.5079      0.5492
my_check_name #2           216                   5.2233               5.3108               5.731                6.1382         0.8465     0.836      1.068       1.651          0.6946       0.683      0.923       1.533
my_check_name #3           218                   5.1883               5.3048               5.5646               5.7523         0.5233     0.5498     0.6101      0.654          0.3858       0.409      0.468       0.524