355 lines
14 KiB
Python
355 lines
14 KiB
Python
import time
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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 timestamp_minutes_ago(minutes):
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current_time = time.time()
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minutes_in_seconds = minutes * 60
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time_in_past = current_time - minutes_in_seconds
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timestamp_in_ms = int(time_in_past * 1000)
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return timestamp_in_ms
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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': 'DAMPdbiIz',
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},
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'expr': 'rate(python_gc_time_sum{instance="matrix.synapse",job=~".*",index=~".*"}[2m])/rate(python_gc_time_count[2m])',
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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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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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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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if not response['results'].get('A', {}).get('frames'):
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return []
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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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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz'
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},
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'editorMode': 'code',
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'expr': 'rate(synapse_http_client_requests_total{job=~".*",index=~".*",instance="matrix.synapse"}[2m])',
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'range': True,
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'refId': 'A',
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'interval': '',
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'exemplar': False,
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'utcOffsetSec': 0,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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}, {
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz'
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},
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'editorMode': 'code',
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'expr': 'rate(synapse_http_matrixfederationclient_requests_total{job=~".*",index=~".*",instance="matrix.synapse"}[2m])',
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'range': True,
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'refId': 'B',
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'interval': '',
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'exemplar': False,
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'utcOffsetSec': 0,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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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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if len(result['frames']):
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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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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': 'DAMPdbiIz',
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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="matrix.synapse",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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'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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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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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="matrix.synapse",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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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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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="matrix.synapse",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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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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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="matrix.synapse",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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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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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="matrix.synapse",code=~"2.."}[2m])) by (le))',
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'refId': 'F',
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'interval': '',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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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="matrix.synapse",code=~"2.."}[2m])) by (le))',
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'refId': 'G',
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'interval': '',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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},
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'expr': 'sum(rate(synapse_http_server_response_time_seconds_sum{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="matrix.synapse",code=~"2.."}[2m])) / sum(rate(synapse_http_server_response_time_seconds_count{servlet=\'RoomSendEventRestServlet\',index=~".*",instance="matrix.synapse",code=~"2.."}[2m]))',
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'refId': 'H',
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'interval': '',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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},
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'expr': 'sum(rate(synapse_storage_events_persisted_events_total{instance="matrix.synapse"}[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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'editorMode': 'code',
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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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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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if not response['results'].get('E', {}).get('frames'):
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return None
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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': 'DAMPdbiIz',
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},
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'expr': 'rate(synapse_storage_schedule_time_sum{instance="matrix.synapse",job=~".*",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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'queryType': 'timeSeriesQuery',
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'exemplar': False,
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': interval * 1000,
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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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if not len(response['results']['A']['frames']):
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return None, None, None
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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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else:
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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).
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"""
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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': 'DAMPdbiIz',
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},
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'exemplar': False,
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'expr': 'sum(rate(synapse_state_res_db_for_biggest_room_seconds_total{instance="matrix.synapse"}[1m]))',
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'format': 'time_series',
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'hide': False,
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'instant': False,
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'interval': '',
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'refId': 'B',
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'queryType': 'timeSeriesQuery',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': 15000,
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'maxDataPoints': 1863,
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},
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{
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'datasource': {
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'type': 'prometheus',
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'uid': 'DAMPdbiIz',
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},
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'exemplar': False,
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'expr': 'sum(rate(synapse_state_res_cpu_for_biggest_room_seconds_total{instance="matrix.synapse"}[1m]))',
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'format': 'time_series',
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'hide': False,
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'instant': False,
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'interval': '',
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'refId': 'C',
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'queryType': 'timeSeriesQuery',
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'utcOffsetSec': -25200,
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'legendFormat': '',
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'datasourceId': 8,
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'intervalMs': 15000,
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'maxDataPoints': 1863,
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},
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],
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'range': {
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'from': '2023-02-23T04:36:12.870Z',
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'to': '2023-02-23T07:36:12.870Z',
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'raw': {
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'from': 'now-3h',
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'to': 'now',
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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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# AVerage CPU time per block
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