383 lines
18 KiB
Python
383 lines
18 KiB
Python
import numpy as np
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import requests
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from urllib3.exceptions import InsecureRequestWarning
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requests.packages.urllib3.disable_warnings(category=InsecureRequestWarning)
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def get_avg_python_gc_time(api_key, interval, data_range, endpoint):
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json_data = {
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'queries': [
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'rate(python_gc_time_sum{instance="10.0.0.34:9000",job=~"(federation-receiver|federation-sender|initialsync|synapse|synchrotron)",index=~".*"}[30s])/rate(python_gc_time_count[30s])',
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'format': 'time_series',
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'intervalFactor': 2,
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'refId': 'A',
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'step': 20,
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'target': '',
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'interval': '',
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# 'key': 'Q-7edaea76-89bd-4b29-8412-a68bf4646712-0',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-7edaea76-89bd-4b29-8412-a68bf4646712-0A',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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],
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'from': f'now-{data_range}m',
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'to': 'now',
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}
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response = requests.post(f'{endpoint}/api/ds/query', headers={'Authorization': f'Bearer {api_key}'}, json=json_data, verify=False).json()
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good = []
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for i in response['results']['A']['frames']:
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# This one can sometimes be null
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new = []
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for x in range(len(i['data']['values'][1])):
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if i['data']['values'][1][x] is not None:
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new.append(i['data']['values'][1][x])
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good.append(new)
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# Remove empty arrays
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results = []
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for x in good:
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if len(x) > 0:
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results.append(x)
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return [np.round(np.average(i), 5) for i in results]
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def get_outgoing_http_request_rate(api_key, interval, data_range, endpoint):
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json_data = {
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'queries': [
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'editorMode': 'code',
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'expr': 'rate(synapse_http_client_requests_total{job=~"(federation-receiver|federation-sender|initialsync|synapse|synchrotron)",index=~".*",instance="10.0.0.34:9000"}[2m])',
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'range': True,
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'refId': 'A',
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'interval': '',
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# 'key': 'Q-8b3dabd7-358e-45ed-a9ba-7be3f5fcf274-0',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-8b3dabd7-358e-45ed-a9ba-7be3f5fcf274-0Q-c5c08c6b-7591-424c-8eac-53837fa51e89-1A',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 10,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'editorMode': 'code',
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'expr': 'rate(synapse_http_matrixfederationclient_requests_total{job=~"(federation-receiver|federation-sender|initialsync|synapse|synchrotron)",index=~".*",instance="10.0.0.34:9000"}[2m])',
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'range': True,
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'refId': 'B',
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'interval': '',
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# 'key': 'Q-c5c08c6b-7591-424c-8eac-53837fa51e89-1',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-8b3dabd7-358e-45ed-a9ba-7be3f5fcf274-0Q-c5c08c6b-7591-424c-8eac-53837fa51e89-1B',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 10,
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},
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],
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'from': f'now-{data_range}m',
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'to': 'now',
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}
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response = requests.post(f'{endpoint}/api/ds/query', headers={'Authorization': f'Bearer {api_key}'}, json=json_data, verify=False).json()
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output = {}
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for letter, result in response['results'].items():
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name = result['frames'][0]['schema']['name'].split('=')[-1].strip('}').strip('"')
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output[name] = np.round(np.average(result['frames'][0]['data']['values'][1]), 2)
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return output
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# return {
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# 'GET': np.round(np.average(response['results']['A']['frames'][0]['data']['values'][1]), 2),
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# 'POST': np.round(np.average(response['results']['A']['frames'][1]['data']['values'][1]), 2),
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# 'PUT': np.round(np.average(response['results']['A']['frames'][2]['data']['values'][1]), 2),
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# 'fedr_GET': np.round(np.average(response['results']['B']['frames'][0]['data']['values'][1]), 2)
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# }
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def get_event_send_time(api_key, interval, data_range, endpoint):
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json_data = {
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'queries': [
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'histogram_quantile(0.99, sum(rate(synapse_http_server_response_time_seconds_bucket{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) by (le))',
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'format': 'time_series',
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'intervalFactor': 1,
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'refId': 'D',
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'interval': '',
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# 'key': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0',
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'editorMode': 'builder',
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'range': True,
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'instant': True,
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7D',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'histogram_quantile(0.9, sum(rate(synapse_http_server_response_time_seconds_bucket{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) by (le))',
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'format': 'time_series',
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'interval': '',
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'intervalFactor': 1,
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'refId': 'A',
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# 'key': 'Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7A',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'histogram_quantile(0.75, sum(rate(synapse_http_server_response_time_seconds_bucket{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) by (le))',
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'format': 'time_series',
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'intervalFactor': 1,
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'refId': 'C',
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'interval': '',
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# 'key': 'Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7C',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'histogram_quantile(0.5, sum(rate(synapse_http_server_response_time_seconds_bucket{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) by (le))',
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'format': 'time_series',
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'intervalFactor': 1,
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'refId': 'B',
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'interval': '',
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# 'key': 'Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7B',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'histogram_quantile(0.25, sum(rate(synapse_http_server_response_time_seconds_bucket{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) by (le))',
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'refId': 'F',
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'interval': '',
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# 'key': 'Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7F',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'histogram_quantile(0.05, sum(rate(synapse_http_server_response_time_seconds_bucket{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) by (le))',
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'refId': 'G',
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'interval': '',
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# 'key': 'Q-502b8ed5-4050-461c-befc-76f6796dce68-5',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7G',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'sum(rate(synapse_http_server_response_time_seconds_sum{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m])) / sum(rate(synapse_http_server_response_time_seconds_count{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="10.0.0.34:9000",code=~"2.."}[2m]))',
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'refId': 'H',
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'interval': '',
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# 'key': 'Q-364dc896-c399-4e58-8930-cba2e3d1d579-6',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7H',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'sum(rate(synapse_storage_events_persisted_events_total{instance="10.0.0.34:9000"}[2m]))',
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'hide': False,
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'instant': False,
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'refId': 'E',
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'interval': '',
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# 'key': 'Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7',
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'editorMode': 'code',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-d8eb3572-9aea-4a73-92f2-e08b33c21ecb-0Q-a9222e59-18ff-4b3b-80ae-27bea8f149a9-1Q-0378a458-1ade-410e-a4b3-ae4aaa91d709-2Q-da4c00b6-61c1-49f5-8a0a-9f19990acfb7-3Q-21254889-3cf6-4d97-8dc5-ddf68360847e-4Q-502b8ed5-4050-461c-befc-76f6796dce68-5Q-364dc896-c399-4e58-8930-cba2e3d1d579-6Q-9072e904-da8d-4b00-b454-dac45b7c38f0-7E',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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],
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'from': f'now-{data_range}m',
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'to': 'now',
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}
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response = requests.post(f'{endpoint}/api/ds/query', headers={'Authorization': f'Bearer {api_key}'}, json=json_data, verify=False).json()
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return np.round(np.average(response['results']['E']['frames'][0]['data']['values'][1]), 2)
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def get_waiting_for_db(api_key, interval, data_range, endpoint):
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json_data = {
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'queries': [
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'AbuT5CJ4z',
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},
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'expr': 'rate(synapse_storage_schedule_time_sum{instance="10.0.0.34:9000",job=~"(federation-receiver|federation-sender|initialsync|synapse|synchrotron)",index=~".*"}[30s])/rate(synapse_storage_schedule_time_count[30s])',
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'format': 'time_series',
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'intervalFactor': 2,
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'refId': 'A',
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'step': 20,
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'interval': '',
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# 'key': 'Q-459af7f4-0427-4832-9353-46086b3f5c27-0',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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# 'requestId': 'Q-459af7f4-0427-4832-9353-46086b3f5c27-0A',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 1,
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'intervalMs': interval * 1000,
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# 'maxDataPoints': 1383,
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},
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],
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'from': f'now-{data_range}m',
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'to': 'now',
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}
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response = requests.post(f'{endpoint}/api/ds/query', headers={'Authorization': f'Bearer {api_key}'}, json=json_data, verify=False).json()
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raw_data = response['results']['A']['frames'][0]['data']['values'][1]
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data = []
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null_present = False
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for i in range(len(raw_data)):
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if raw_data[i] is not None:
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data.append(raw_data[i])
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null_present = True
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return np.round(np.average(data), 5), null_present, raw_data
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def get_stateres_worst_case(api_key, interval, data_range, endpoint):
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"""
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CPU and DB time spent on most expensive state resolution in a room, summed over all workers.
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|
This is a very rough proxy for "how fast is state res", but it doesn't accurately represent the system load (e.g. it completely ignores cheap state resolutions).
|
|
"""
|
|
json_data = {
|
|
'queries': [
|
|
{
|
|
'datasource': {
|
|
'type': 'prometheus',
|
|
'uid': 'AbuT5CJ4z',
|
|
},
|
|
'exemplar': False,
|
|
'expr': 'sum(rate(synapse_state_res_db_for_biggest_room_seconds_total{instance="10.0.0.34:9000"}[1m]))',
|
|
'format': 'time_series',
|
|
'hide': False,
|
|
'instant': False,
|
|
'interval': '',
|
|
'refId': 'B',
|
|
'queryType': 'timeSeriesQuery',
|
|
'utcOffsetSec': -25200,
|
|
'legendFormat': '',
|
|
'datasourceId': 1,
|
|
'intervalMs': 15000,
|
|
'maxDataPoints': 1863,
|
|
},
|
|
{
|
|
'datasource': {
|
|
'type': 'prometheus',
|
|
'uid': 'AbuT5CJ4z',
|
|
},
|
|
'exemplar': False,
|
|
'expr': 'sum(rate(synapse_state_res_cpu_for_biggest_room_seconds_total{instance="10.0.0.34:9000"}[1m]))',
|
|
'format': 'time_series',
|
|
'hide': False,
|
|
'instant': False,
|
|
'interval': '',
|
|
'refId': 'C',
|
|
'queryType': 'timeSeriesQuery',
|
|
'utcOffsetSec': -25200,
|
|
'legendFormat': '',
|
|
'datasourceId': 1,
|
|
'intervalMs': 15000,
|
|
'maxDataPoints': 1863,
|
|
},
|
|
],
|
|
'range': {
|
|
'from': '2023-02-23T04:36:12.870Z',
|
|
'to': '2023-02-23T07:36:12.870Z',
|
|
'raw': {
|
|
'from': 'now-3h',
|
|
'to': 'now',
|
|
},
|
|
},
|
|
'from': f'now-{data_range}m',
|
|
'to': 'now',
|
|
}
|
|
response = requests.post(f'{endpoint}/api/ds/query', headers={'Authorization': f'Bearer {api_key}'}, json=json_data, verify=False).json()
|
|
|
|
# AVerage CPU time per block
|