Mukan Erkin Törük
ef24c0b67e
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151 lines
13 KiB
Python
151 lines
13 KiB
Python
# pip install numpy pandas matplotlib requests
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import sys
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import os
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import matplotlib.dates as md
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import numpy as np
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import pandas as pd
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import requests
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from urllib.parse import urljoin
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from prometheus_pandas import query
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#release = 'v0.37.0-alpha.2'
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release = 'v0.38.0-alpha.2'
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path = os.path.join('imgs')
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prometheus = query.Prometheus('http://localhost:9090')
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# Time window
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#window_size = dict(seconds=150) #CMT 0.37.x-alpha3
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#window_size = dict(seconds=126) #TM v0.37 (200 nodes) baseline
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#window_size = dict(hours=1, minutes=28, seconds=25) #TM v0.37.0-alpha.2 (rotating)
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#window_size = dict(seconds=130) #homogeneous
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#window_size = dict(seconds=127) #baseline
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#window_size = dict(seconds=115) #CMT v0.38.0-alpha.2 (200 nodes)
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#window_size = dict(hours=1, minutes=46) #CMT v0.38.0-alpha.2 (rotating)
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window_size = dict(seconds=150) #CMT v0.38.0-alpha.2 (ve baseline)
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ext_window_size = dict(seconds=200)
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# Use the time provided by latency_plotter for the selected experiment.
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#left_end = '2023-02-08T13:12:20Z' #cmt2 tm1
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#left_end = '2023-02-08T10:31:50Z' #cmt1 tm2
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#left_end = '2023-02-14T15:18:00Z' #cmt1 tm1
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#left_end = '2023-02-07T18:07:00Z' #homogeneous
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#left_end = '2022-10-13T19:41:23Z' #baseline
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#left_end = '2023-02-22T18:56:29Z' #CMT v0.37.x-alpha3
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#left_end = '2022-10-13T15:57:50Z' #TM v0.37 (200 nodes) baseline
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#left_end = '2023-03-20T19:45:35Z' #feature/abci++vef merged with main (7d8c9d426)
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#left_end = '2023-05-22T09:39:20Z' #CMT v0.38.0-alpha.2 - 200 nodes
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#left_end = '2022-10-10T15:47:15Z' #TM v0.37.0-alpha.2 - rotating
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#left_end = '2023-05-23T08:09:50Z' #CMT v0.38.0-alpha.2 - rotating
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#left_end = '2023-05-25T18:18:04Z' #CMT v0.38.0-alpha.2 - ve baseline
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#left_end = '2023-05-30T19:05:32Z' #CMT v0.38.0-alpha.2 - ve 2k
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left_end = '2023-05-30T20:44:46Z' #CMT v0.38.0-alpha.2 - ve 4k
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#left_end = '2023-05-25T19:42:08Z' #CMT v0.38.0-alpha.2 - ve 8k
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#left_end = '2023-05-26T00:28:12Z' #CMT v0.38.0-alpha.2 - ve 16k
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#left_end = '2023-05-26T02:12:27Z' #CMT v0.38.0-alpha.2 - ve 32k
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useManualrightEnd = False
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if useManualrightEnd:
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#right_end = '2023-05-25T18:54:04Z' #CMT v0.38.0-alpha.2 - ve baseline
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#right_end = '2023-05-30T19:40:41Z' #CMT v0.38.0-alpha.2 - ve 2k
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right_end = '2023-05-30T21:15:37Z' #CMT v0.38.0-alpha.2 - ve 4k
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#right_end = '2023-05-25T20:16:00Z' #CMT v0.38.0-alpha.2 - ve 8k
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#right_end = '2023-05-26T01:01:57Z' #CMT v0.38.0-alpha.2 - ve 16k
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#right_end = '2023-05-26T02:46:19Z' #CMT v0.38.0-alpha.2 - ve 32k
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time_window = (left_end, right_end)
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else:
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right_end = pd.to_datetime(left_end) + pd.Timedelta(**window_size)
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time_window = (left_end, right_end.strftime('%Y-%m-%dT%H:%M:%SZ'))
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ext_right_end = pd.to_datetime(left_end) + pd.Timedelta(**ext_window_size)
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ext_time_window = (left_end, ext_right_end.strftime('%Y-%m-%dT%H:%M:%SZ'))
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fork='cometbft'
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#fork='tendermint'
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# Do prometheus queries, depending on the test case
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queries200Nodes = [
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(( fork + '_mempool_size', time_window[0], time_window[1], '1s'), 'mempool_size', dict(ylabel='TXs', xlabel='time (s)', title='Mempool Size', legend=False, figsize=(10,6), grid=True, kind='area',stacked=True), False),
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(( fork + '_p2p_peers', time_window[0], time_window[1], '1s'), 'peers', dict(ylabel='# Peers', xlabel='time (s)', title='Peers', legend=False, figsize=(10,6), grid=True), True),
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(( 'avg(' + fork + '_mempool_size)', time_window[0], time_window[1], '1s'), 'avg_mempool_size', dict(ylabel='TXs', xlabel='time (s)', title='Average Mempool Size', legend=False, figsize=(10,6), grid=True), False),
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#(( 'cometbft_consensus_height', time_window[0], time_window[1], '1s'), 'blocks_regular', dict(ylabel='# Blocks', xlabel='time (s)', title='Blocks in time', legend=False, figsize=(10,6), grid=True), False),
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(( fork + '_consensus_rounds', time_window[0], time_window[1], '1s'), 'rounds', dict(ylabel='# Rounds', xlabel='time (s)', title='Rounds per block', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_height[20s])*60', time_window[0], time_window[1], '1s'), 'block_rate_regular', dict(ylabel='Blocks/min', xlabel='time (s)', title='Rate of block creation', legend=False, figsize=(10,6), grid=True), True),
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#(( 'avg(rate(cometbft_consensus_height[20s])*60)', time_window[0], time_window[1], '1s'), 'block_rate_avg_reg', dict(ylabel='Blocks/min', xlabel='time (s)', title='Rate of block creation', legend=False, figsize=(10,6), grid=True), False),
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#(( 'cometbft_consensus_total_txs', time_window[0], time_window[1], '1s'), 'total_txs_regular', dict(ylabel='# TXs', xlabel='time (s)', title='Transactions in time', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_total_txs[20s])*60', time_window[0], time_window[1], '1s'), 'total_txs_rate_regular', dict(ylabel='TXs/min', xlabel='time (s)', title='Rate of transaction processing', legend=False, figsize=(10,6), grid=True), True),
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#(( 'avg(rate(cometbft_consensus_total_txs[20s])*60)', time_window[0], time_window[1], '1s'), 'total_txs_rate_avg_reg', dict(ylabel='TXs/min', xlabel='time (s)', title='Rate of transaction processing', legend=False, figsize=(10,6), grid=True), False),
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(( 'process_resident_memory_bytes', time_window[0], time_window[1], '1s'), 'memory', dict(ylabel='Memory (bytes)', xlabel='time (s)', title='Memory usage', legend=False, figsize=(10,6), grid=True), False),
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(( 'avg(process_resident_memory_bytes)', time_window[0], time_window[1], '1s'), 'avg_memory', dict(ylabel='Memory (bytes)', xlabel='time (s)', title='Average Memory usage', legend=False, figsize=(10,6), grid=True), False),
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(( 'node_load1', time_window[0], time_window[1], '1s'), 'cpu', dict(ylabel='Load', xlabel='time (s)', title='Node load', legend=False, figsize=(10,6), grid=True), False),
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(( 'avg(node_load1)', time_window[0], time_window[1], '1s'), 'avg_cpu', dict(ylabel='Load', xlabel='time (s)', title='Average Node load', legend=False, figsize=(10,6), grid=True), False),
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#extended window metrics
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(( fork + '_consensus_height', ext_time_window[0], ext_time_window[1], '1s'), 'blocks', dict(ylabel='# Blocks', xlabel='time (s)', title='Blocks in time', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_height[20s])*60', ext_time_window[0], ext_time_window[1], '1s'), 'block_rate', dict(ylabel='Blocks/min', xlabel='time (s)', title='Rate of block creation', legend=False, figsize=(10,6), grid=True), True),
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(( fork + '_consensus_total_txs', ext_time_window[0], ext_time_window[1], '1s'), 'total_txs', dict(ylabel='# TXs', xlabel='time (s)', title='Transactions in time', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_total_txs[20s])*60', ext_time_window[0], ext_time_window[1], '1s'), 'total_txs_rate', dict(ylabel='TXs/min', xlabel='time (s)', title='Rate of transaction processing', legend=False, figsize=(10,6), grid=True), True),
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]
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queriesRotating = [
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(( 'rate(' + fork + '_consensus_height[20s])*60', time_window[0], time_window[1], '1s'), 'rotating_block_rate', dict(ylabel='blocks/min', xlabel='time', title='Rate of Block Creation', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_total_txs[20s])*60', time_window[0], time_window[1], '1s'), 'rotating_txs_rate', dict(ylabel='TXs/min', xlabel='time', title='Rate of Transaction processing', legend=False, figsize=(10,6), grid=True), False),
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(( fork + '_consensus_height{job=~"ephemeral.*"} or ' + fork + '_blocksync_latest_block_height{job=~"ephemeral.*"}',
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time_window[0], time_window[1], '1s'), 'rotating_eph_heights', dict(ylabel='height', xlabel='time', title='Heights of Ephemeral Nodes', legend=False, figsize=(10,6), grid=True), False),
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(( fork + '_p2p_peers', time_window[0], time_window[1], '1s'), 'rotating_peers', dict(ylabel='# peers', xlabel='time', title='Peers', legend=False, figsize=(10,6), grid=True), False),
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(( 'avg(process_resident_memory_bytes)', time_window[0], time_window[1], '1s'), 'rotating_avg_memory', dict(ylabel='memory (bytes)', xlabel='time', title='Average Memory Usage', legend=False, figsize=(10,6), grid=True), False),
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(( 'node_load1', time_window[0], time_window[1], '1s'), 'rotating_cpu', dict(ylabel='load', xlabel='time', title='Node Load', legend=False, figsize=(10,6), grid=True), False),
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]
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queriesVExtension= [
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(( fork + '_mempool_size', time_window[0], time_window[1], '1s'), 'mempool_size', dict(ylabel='TXs', xlabel='time (s)', title='Mempool Size', legend=False, figsize=(10,6), grid=True, kind='area',stacked=True), False),
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(( fork + '_mempool_size', time_window[0], time_window[1], '1s'), 'mempool_size_not_stacked', dict(ylabel='TXs', xlabel='time (s)', title='Mempool Size', legend=False, figsize=(10,6), grid=True, stacked=False), False),
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(( fork + '_p2p_peers', time_window[0], time_window[1], '1s'), 'peers', dict(ylabel='# Peers', xlabel='time (s)', title='Peers', legend=False, figsize=(10,6), grid=True), True),
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(( 'avg(' + fork + '_mempool_size)', time_window[0], time_window[1], '1s'), 'avg_mempool_size', dict(ylabel='TXs', xlabel='time (s)', title='Average Mempool Size', legend=False, figsize=(10,6), grid=True), False),
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(( fork + '_consensus_rounds', time_window[0], time_window[1], '1s'), 'rounds', dict(ylabel='# Rounds', xlabel='time (s)', title='Rounds per block', legend=False, figsize=(10,6), grid=True), False),
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(( 'process_resident_memory_bytes', time_window[0], time_window[1], '1s'), 'memory', dict(ylabel='Memory (bytes)', xlabel='time (s)', title='Memory usage', legend=False, figsize=(10,6), grid=True), False),
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(( 'avg(process_resident_memory_bytes)', time_window[0], time_window[1], '1s'), 'avg_memory', dict(ylabel='Memory (bytes)', xlabel='time (s)', title='Average Memory usage', legend=False, figsize=(10,6), grid=True), False),
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(( 'node_load1', time_window[0], time_window[1], '1s'), 'cpu', dict(ylabel='Load', xlabel='time (s)', title='Node load', legend=False, figsize=(10,6), grid=True), False),
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(( 'avg(node_load1)', time_window[0], time_window[1], '1s'), 'avg_cpu', dict(ylabel='Load', xlabel='time (s)', title='Average Node load', legend=False, figsize=(10,6), grid=True), False),
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(( fork + '_consensus_height', time_window[0], time_window[1], '1s'), 'blocks', dict(ylabel='# Blocks', xlabel='time (s)', title='Blocks in time', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_height[20s])*60', time_window[0], time_window[1], '1s'), 'block_rate', dict(ylabel='Blocks/min', xlabel='time (s)', title='Rate of block creation', legend=False, figsize=(10,6), grid=True), True),
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(( fork + '_consensus_total_txs', time_window[0], time_window[1], '1s'), 'total_txs', dict(ylabel='# TXs', xlabel='time (s)', title='Transactions in time', legend=False, figsize=(10,6), grid=True), False),
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(( 'rate(' + fork + '_consensus_total_txs[20s])*60', time_window[0], time_window[1], '1s'), 'total_txs_rate', dict(ylabel='TXs/min', xlabel='time (s)', title='Rate of transaction processing', legend=False, figsize=(10,6), grid=True), True),
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]
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#queries = queries200Nodes
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#queries = queriesRotating
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queries = queriesVExtension
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for (query, file_name, pandas_params, plot_average) in queries:
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print(query)
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data_frame = prometheus.query_range(*query)
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#Tweak the x ticks
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data_frame = data_frame.set_index(md.date2num(data_frame.index))
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pandas_params["title"] += " - " + release
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ax = data_frame.plot(**pandas_params)
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if plot_average:
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average = data_frame.mean(axis=1)
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data_frame['__average__'] = average
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pandas_params['lw'] = 8
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pandas_params['style'] = ['--']
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pandas_params['color'] = ['red']
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ax = data_frame['__average__'].plot(**pandas_params)
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ax.xaxis.set_major_formatter(md.DateFormatter('%H:%M:%S'))
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plt.savefig(os.path.join(path, file_name + '.png'))
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plt.plot()
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plt.show()
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