diff --git a/bexhoma/evaluators.py b/bexhoma/evaluators.py
index 58b4d333..36a3fa77 100644
--- a/bexhoma/evaluators.py
+++ b/bexhoma/evaluators.py
@@ -1136,13 +1136,14 @@ def find_matching_files(directory, pattern):
if remove_last > 0:
df = df.iloc[:-remove_last]
#print(df)
- df['avg'] = df[column].mean()
+ #df['avg'] = df[column].mean()
#print(df)
#df_total = pd.concat([df_total, df], axis=1)
if not aggregate:
df_total.append(df.copy())
else:
if df_total.empty:
+ df['avg'] = df[column].mean()
df_total = df.copy()
else:
df_total = df_total.add(df, fill_value=0)
diff --git a/dev/Evaluation-YCSB-Timeseries-1737987228.ipynb b/dev/Evaluation-YCSB-Timeseries-1737987228.ipynb
new file mode 100644
index 00000000..6a7812aa
--- /dev/null
+++ b/dev/Evaluation-YCSB-Timeseries-1737987228.ipynb
@@ -0,0 +1,2971 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "085c102b-4c82-498e-a939-29c744c1e86a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip list\n",
+ "\n",
+ "\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "import re\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pickle\n",
+ "# Some nice output\n",
+ "pd.set_option(\"display.max_rows\", None)\n",
+ "pd.set_option('display.max_colwidth', None)\n",
+ "from IPython.display import display, Markdown\n",
+ "\n",
+ "import dbmsbenchmarker\n",
+ "import bexhoma\n",
+ "from bexhoma import evaluators\n",
+ "#import evaluator\n",
+ "\n",
+ "%matplotlib inline\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "737ca401-e48a-4302-b370-613d84226adb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cut_at = 1\n",
+ "def plot_measures(metric, smoothing=0):\n",
+ " display(Markdown(f'# Metric {metric}'))\n",
+ " list_configurations = list(df_benchmarks['configuration'].unique()) \n",
+ " for configuration in list_configurations:\n",
+ " #print(\"Configuration:\", configuration)\n",
+ " display(Markdown(f'## Configuration {configuration}'))\n",
+ " list_experiment_runs = sorted(list(df_benchmarks[df_benchmarks['configuration'] == configuration]['experiment_run'].unique()))\n",
+ " #print(list_experiment_runs)\n",
+ " for experiment_run in list_experiment_runs:\n",
+ " #print(\"Experiment run\", experiment_run)\n",
+ " display(Markdown(f'### Experiment run {experiment_run}'))\n",
+ " list_runs = sorted(list(df_benchmarks[df_benchmarks['configuration'] == configuration][df_benchmarks['experiment_run'] == experiment_run]['client'].unique()))\n",
+ " #print(list_runs)\n",
+ " #list_targets = sorted(list(df_benchmarks[df_benchmarks['configuration'] == configuration][df_benchmarks['experiment_run'] == experiment_run]['target'].unique()))\n",
+ " #print(list_targets)\n",
+ " num_runs = len(list_runs)\n",
+ " # Create a 2-column grid layout\n",
+ " n_cols = 2\n",
+ " n_rows = num_runs#6#int(np.ceil(len(dfs) / n_cols)) # Calculate rows needed\n",
+ " fig, axes = plt.subplots(n_rows, n_cols, figsize=(10, 5 * n_rows), sharex=True)\n",
+ " # Flatten axes for easy iteration (handles cases with n_rows == 1 or n_cols == 1)\n",
+ " axes = axes.flatten()\n",
+ " i = 0\n",
+ " for client in list_runs:\n",
+ " df_part = df_benchmarks[df_benchmarks['configuration'] == configuration]\n",
+ " df_part = df_part[df_part['experiment_run'] == (experiment_run)]#['threads']\n",
+ " df_part = df_part[df_part['client'] == (client)]#num_runs * int(client) -2)]\n",
+ " #print(df_part.T)\n",
+ " df_total = evaluation.get_benchmark_logs_timeseries_df_aggregated(metric=metric, configuration=configuration, client=client, experiment_run=experiment_run)\n",
+ " #print(df_total)\n",
+ " threads = df_part['threads'].sum()\n",
+ " pod_count = df_part['pod_count'].mean()\n",
+ " target = df_part['target'].sum()\n",
+ " avg = int(df_total['avg'].mean())\n",
+ " df_total = df_total.rename(columns={'avg': f'avg={avg}'})\n",
+ " #print(df_part['threads'])\n",
+ " title = \"{}/{} threads - target = {}\".format(threads, int(pod_count), target)\n",
+ " #plt.title(title)\n",
+ " ax = axes[i]\n",
+ " i = i + 1\n",
+ " #print(df_total)\n",
+ " df_total = df_total.fillna(0)\n",
+ " if smoothing > 0:\n",
+ " df_total[metric] = df_total[metric].rolling(window=smoothing, min_periods=1).mean()\n",
+ " #print(df_total)\n",
+ " df_total[:-cut_at].plot(title=title, ax=ax, ylim=(0,df_total[metric].max()), use_index=True)\n",
+ " #df_total[:-cut_at].plot(title=title, ylim=(0,df_total[metric].max()), use_index=True)\n",
+ " #print(df_total[:-cut_at].mean().mean())\n",
+ " df_total = evaluation.get_benchmark_logs_timeseries_df_single(metric=metric, configuration=configuration, client=client, experiment_run=experiment_run)\n",
+ " print(df_total)\n",
+ " df_combined = pd.DataFrame()\n",
+ " j=1\n",
+ " for df in df_total:\n",
+ " #df.plot(ylim=(0,df['current_ops_per_sec'].max()))\n",
+ " df_single = pd.DataFrame(df[metric][:-cut_at])\n",
+ " df_single.columns=[\"pod \"+str(j)]\n",
+ " df_combined = pd.merge(df_combined, df_single, how='outer', left_index=True, right_index=True)\n",
+ " j = j + 1\n",
+ " print(df_combined)\n",
+ " avg = df_combined.mean().mean()\n",
+ " df_combined[f'avg={avg}']=avg\n",
+ " df_combined = df_combined.fillna(0)\n",
+ " if smoothing > 0:\n",
+ " df_combined = df_combined.rolling(window=smoothing, min_periods=1).mean()\n",
+ " #plt.title(title)\n",
+ " ax = axes[i]\n",
+ " i = i + 1\n",
+ " df_combined.plot(title=title, ax=ax, ylim=(0,df[metric].max()), legend=False, use_index=True)\n",
+ " #print(df_total[:-cut_at].mean().mean())\n",
+ " # Hide any unused subplots\n",
+ " for j in range(num_runs*2, len(axes)):\n",
+ " fig.delaxes(axes[j])\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ " #return"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "27a0bf02-318b-4cb5-9645-9ddf744f015f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = \"1737987228\"\n",
+ "path = \"/home/perdelt/benchmarks\"\n",
+ "evaluation = evaluators.ycsb(code=code, path=path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "ffbda5df-45aa-4a98-8956-363bb93c4220",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\n",
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\n",
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\n",
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\n",
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+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
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+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
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\n",
+ " \n",
+ " [TOTAL_GC_TIME_MarkSweepCompact].Time(ms) | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
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+ " 0 | \n",
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\n",
+ " \n",
+ " [TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
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+ " 0.0 | \n",
+ " 0.0 | \n",
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\n",
+ " \n",
+ " [TOTAL_GCs].Count | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 8 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " [TOTAL_GC_TIME].Time(ms) | \n",
+ " 90 | \n",
+ " 166 | \n",
+ " 139 | \n",
+ " 164 | \n",
+ " 82 | \n",
+ " 81 | \n",
+ " 156 | \n",
+ " 87 | \n",
+ " 80 | \n",
+ " 65 | \n",
+ " ... | \n",
+ " 159 | \n",
+ " 88 | \n",
+ " 161 | \n",
+ " 81 | \n",
+ " 100 | \n",
+ " 83 | \n",
+ " 85 | \n",
+ " 152 | \n",
+ " 85 | \n",
+ " 79 | \n",
+ "
\n",
+ " \n",
+ " [TOTAL_GC_TIME_%].Time(%) | \n",
+ " 0.026213312285014435 | \n",
+ " 0.06078092504174112 | \n",
+ " 0.05095233207724227 | \n",
+ " 0.05595474489412952 | \n",
+ " 0.024197213189251717 | \n",
+ " 0.019860340126713873 | \n",
+ " 0.053658402355466275 | \n",
+ " 0.02565811592749665 | \n",
+ " 0.023427090501778994 | \n",
+ " 0.015524278777833241 | \n",
+ " ... | \n",
+ " 0.058577991622241955 | \n",
+ " 0.025884103924677253 | \n",
+ " 0.05978307284994448 | \n",
+ " 0.01942133997655053 | \n",
+ " 0.030052501720505727 | \n",
+ " 0.024758899034402936 | \n",
+ " 0.01996068918388961 | \n",
+ " 0.05220712489867696 | \n",
+ " 0.02470362910843149 | \n",
+ " 0.018694994710973016 | \n",
+ "
\n",
+ " \n",
+ " [READ].Operations | \n",
+ " 499474 | \n",
+ " 999163 | \n",
+ " 1000109 | \n",
+ " 999724 | \n",
+ " 499963 | \n",
+ " 499747 | \n",
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+ " 499884 | \n",
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+ " ... | \n",
+ " 999886 | \n",
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+ " 1000533 | \n",
+ " 500986 | \n",
+ " 499675 | \n",
+ " 498989 | \n",
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+ " 500085 | \n",
+ "
\n",
+ " \n",
+ " [READ].AverageLatency(us) | \n",
+ " 729.314361 | \n",
+ " 612.098972 | \n",
+ " 630.125819 | \n",
+ " 628.849606 | \n",
+ " 692.800687 | \n",
+ " 1027.217056 | \n",
+ " 636.585787 | \n",
+ " 652.219579 | \n",
+ " 680.361506 | \n",
+ " 630.014424 | \n",
+ " ... | \n",
+ " 609.032176 | \n",
+ " 739.949843 | \n",
+ " 603.079837 | \n",
+ " 629.824197 | \n",
+ " 718.244719 | \n",
+ " 643.202115 | \n",
+ " 632.084668 | \n",
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+ " 692.812257 | \n",
+ " 646.564164 | \n",
+ "
\n",
+ " \n",
+ " [READ].MinLatency(us) | \n",
+ " 161.0 | \n",
+ " 160.0 | \n",
+ " 165.0 | \n",
+ " 160.0 | \n",
+ " 156.0 | \n",
+ " 164.0 | \n",
+ " 162.0 | \n",
+ " 159.0 | \n",
+ " 159.0 | \n",
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+ " ... | \n",
+ " 163.0 | \n",
+ " 159.0 | \n",
+ " 162.0 | \n",
+ " 164.0 | \n",
+ " 158.0 | \n",
+ " 162.0 | \n",
+ " 157.0 | \n",
+ " 150.0 | \n",
+ " 161.0 | \n",
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+ "
\n",
+ " \n",
+ " [READ].MaxLatency(us) | \n",
+ " 16220159.0 | \n",
+ " 8273919.0 | \n",
+ " 8273919.0 | \n",
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+ "
\n",
+ " \n",
+ " [READ].95thPercentileLatency(us) | \n",
+ " 1114.0 | \n",
+ " 1138.0 | \n",
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+ " 1109.0 | \n",
+ " 1227.0 | \n",
+ " 1239.0 | \n",
+ " 1141.0 | \n",
+ " 1078.0 | \n",
+ " 1187.0 | \n",
+ " ... | \n",
+ " 1188.0 | \n",
+ " 1074.0 | \n",
+ " 1161.0 | \n",
+ " 1229.0 | \n",
+ " 1115.0 | \n",
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+ " 1261.0 | \n",
+ " 1257.0 | \n",
+ " 1079.0 | \n",
+ " 1182.0 | \n",
+ "
\n",
+ " \n",
+ " [READ].99thPercentileLatency(us) | \n",
+ " 6075.0 | \n",
+ " 5935.0 | \n",
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+ " 6691.0 | \n",
+ " 7243.0 | \n",
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+ " ... | \n",
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+ " 6983.0 | \n",
+ " 6599.0 | \n",
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+ " 6887.0 | \n",
+ " 6407.0 | \n",
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+ "
\n",
+ " \n",
+ " [READ].Return=OK | \n",
+ " 499474 | \n",
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\n",
+ " \n",
+ " [CLEANUP].Operations | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 8 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 8 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " [CLEANUP].AverageLatency(us) | \n",
+ " 623.75 | \n",
+ " 527.0 | \n",
+ " 379.0 | \n",
+ " 600.75 | \n",
+ " 742.25 | \n",
+ " 971.0 | \n",
+ " 565.0 | \n",
+ " 804.5 | \n",
+ " 877.75 | \n",
+ " 984.25 | \n",
+ " ... | \n",
+ " 358.0 | \n",
+ " 578.5 | \n",
+ " 689.75 | \n",
+ " 856.25 | \n",
+ " 474.0 | \n",
+ " 516.25 | \n",
+ " 506.5 | \n",
+ " 574.875 | \n",
+ " 758.75 | \n",
+ " 447.5 | \n",
+ "
\n",
+ " \n",
+ " [CLEANUP].MinLatency(us) | \n",
+ " 125.0 | \n",
+ " 131.0 | \n",
+ " 146.0 | \n",
+ " 144.0 | \n",
+ " 111.0 | \n",
+ " 135.0 | \n",
+ " 139.0 | \n",
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+ " ... | \n",
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+ " 196.0 | \n",
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+ " 152.0 | \n",
+ " 99.0 | \n",
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+ " 185.0 | \n",
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+ "
\n",
+ " \n",
+ " [CLEANUP].MaxLatency(us) | \n",
+ " 1778.0 | \n",
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+ " 2377.0 | \n",
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+ " ... | \n",
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+ " 3215.0 | \n",
+ " 2831.0 | \n",
+ " 1399.0 | \n",
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+ "
\n",
+ " \n",
+ " [CLEANUP].95thPercentileLatency(us) | \n",
+ " 1778.0 | \n",
+ " 1723.0 | \n",
+ " 1504.0 | \n",
+ " 2377.0 | \n",
+ " 2541.0 | \n",
+ " 3283.0 | \n",
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+ " 3225.0 | \n",
+ " ... | \n",
+ " 1473.0 | \n",
+ " 1229.0 | \n",
+ " 3215.0 | \n",
+ " 2831.0 | \n",
+ " 1399.0 | \n",
+ " 1482.0 | \n",
+ " 1262.0 | \n",
+ " 2333.0 | \n",
+ " 1719.0 | \n",
+ " 885.0 | \n",
+ "
\n",
+ " \n",
+ " [CLEANUP].99thPercentileLatency(us) | \n",
+ " 1778.0 | \n",
+ " 1723.0 | \n",
+ " 1504.0 | \n",
+ " 2377.0 | \n",
+ " 2541.0 | \n",
+ " 3283.0 | \n",
+ " 1399.0 | \n",
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+ " ... | \n",
+ " 1473.0 | \n",
+ " 1229.0 | \n",
+ " 3215.0 | \n",
+ " 2831.0 | \n",
+ " 1399.0 | \n",
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+ " 1262.0 | \n",
+ " 2333.0 | \n",
+ " 1719.0 | \n",
+ " 885.0 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].Operations | \n",
+ " 500526 | \n",
+ " 1000837 | \n",
+ " 999891 | \n",
+ " 1000276 | \n",
+ " 500037 | \n",
+ " 500253 | \n",
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+ " 1000114 | \n",
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+ " 501011 | \n",
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+ " 499336 | \n",
+ " 499915 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].AverageLatency(us) | \n",
+ " 1861.346468 | \n",
+ " 1460.016783 | \n",
+ " 1450.61078 | \n",
+ " 1602.078253 | \n",
+ " 1987.307765 | \n",
+ " 2211.868087 | \n",
+ " 1597.172607 | \n",
+ " 1915.094186 | \n",
+ " 2013.648054 | \n",
+ " 2668.637792 | \n",
+ " ... | \n",
+ " 1473.161051 | \n",
+ " 1950.94332 | \n",
+ " 1470.718883 | \n",
+ " 2681.724855 | \n",
+ " 1924.348901 | \n",
+ " 2012.476608 | \n",
+ " 2712.627565 | \n",
+ " 1610.247474 | \n",
+ " 1924.502109 | \n",
+ " 2670.547505 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].MinLatency(us) | \n",
+ " 174.0 | \n",
+ " 181.0 | \n",
+ " 179.0 | \n",
+ " 184.0 | \n",
+ " 177.0 | \n",
+ " 180.0 | \n",
+ " 182.0 | \n",
+ " 179.0 | \n",
+ " 177.0 | \n",
+ " 176.0 | \n",
+ " ... | \n",
+ " 177.0 | \n",
+ " 173.0 | \n",
+ " 171.0 | \n",
+ " 181.0 | \n",
+ " 175.0 | \n",
+ " 178.0 | \n",
+ " 178.0 | \n",
+ " 170.0 | \n",
+ " 177.0 | \n",
+ " 178.0 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].MaxLatency(us) | \n",
+ " 11878399.0 | \n",
+ " 13418495.0 | \n",
+ " 8478719.0 | \n",
+ " 9011199.0 | \n",
+ " 16596991.0 | \n",
+ " 68222975.0 | \n",
+ " 8855551.0 | \n",
+ " 11853823.0 | \n",
+ " 14966783.0 | \n",
+ " 75038719.0 | \n",
+ " ... | \n",
+ " 8454143.0 | \n",
+ " 15777791.0 | \n",
+ " 8413183.0 | \n",
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+ " 11894783.0 | \n",
+ " 14131199.0 | \n",
+ " 75038719.0 | \n",
+ " 8937471.0 | \n",
+ " 13189119.0 | \n",
+ " 75038719.0 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].95thPercentileLatency(us) | \n",
+ " 1925.0 | \n",
+ " 1980.0 | \n",
+ " 2026.0 | \n",
+ " 2063.0 | \n",
+ " 1985.0 | \n",
+ " 2195.0 | \n",
+ " 2139.0 | \n",
+ " 1992.0 | \n",
+ " 1920.0 | \n",
+ " 2137.0 | \n",
+ " ... | \n",
+ " 2032.0 | \n",
+ " 1938.0 | \n",
+ " 1995.0 | \n",
+ " 2163.0 | \n",
+ " 2011.0 | \n",
+ " 1978.0 | \n",
+ " 2149.0 | \n",
+ " 2117.0 | \n",
+ " 1972.0 | \n",
+ " 2079.0 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].99thPercentileLatency(us) | \n",
+ " 31695.0 | \n",
+ " 31279.0 | \n",
+ " 31519.0 | \n",
+ " 34047.0 | \n",
+ " 33215.0 | \n",
+ " 36927.0 | \n",
+ " 34399.0 | \n",
+ " 33183.0 | \n",
+ " 33247.0 | \n",
+ " 36735.0 | \n",
+ " ... | \n",
+ " 31407.0 | \n",
+ " 33375.0 | \n",
+ " 31391.0 | \n",
+ " 36543.0 | \n",
+ " 33407.0 | \n",
+ " 33247.0 | \n",
+ " 35775.0 | \n",
+ " 34847.0 | \n",
+ " 32591.0 | \n",
+ " 36607.0 | \n",
+ "
\n",
+ " \n",
+ " [UPDATE].Return=OK | \n",
+ " 500526 | \n",
+ " 1000837 | \n",
+ " 999891 | \n",
+ " 1000276 | \n",
+ " 500037 | \n",
+ " 500253 | \n",
+ " 1000328 | \n",
+ " 500454 | \n",
+ " 500116 | \n",
+ " 500006 | \n",
+ " ... | \n",
+ " 1000114 | \n",
+ " 500763 | \n",
+ " 999467 | \n",
+ " 499014 | \n",
+ " 500325 | \n",
+ " 501011 | \n",
+ " 499386 | \n",
+ " 1000039 | \n",
+ " 499336 | \n",
+ " 499915 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
44 rows × 50 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ "connection_pod PostgreSQL-64-8-65536-1-3-1 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod 5sv5v.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 343337.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2912.590254 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 90 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.026213312285014435 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 90 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.026213312285014435 \n",
+ "[READ].Operations 499474 \n",
+ "[READ].AverageLatency(us) 729.314361 \n",
+ "[READ].MinLatency(us) 161.0 \n",
+ "[READ].MaxLatency(us) 16220159.0 \n",
+ "[READ].95thPercentileLatency(us) 1114.0 \n",
+ "[READ].99thPercentileLatency(us) 6075.0 \n",
+ "[READ].Return=OK 499474 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 623.75 \n",
+ "[CLEANUP].MinLatency(us) 125.0 \n",
+ "[CLEANUP].MaxLatency(us) 1778.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1778.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1778.0 \n",
+ "[UPDATE].Operations 500526 \n",
+ "[UPDATE].AverageLatency(us) 1861.346468 \n",
+ "[UPDATE].MinLatency(us) 174.0 \n",
+ "[UPDATE].MaxLatency(us) 11878399.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1925.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 31695.0 \n",
+ "[UPDATE].Return=OK 500526 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-2-1 \\\n",
+ "connection PostgreSQL-64-8-65536-1-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 2 \n",
+ "pod n7j4p.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 273112.0 \n",
+ "[OVERALL].Throughput(ops/sec) 7323.003017 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 166 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.06078092504174112 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 166 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.06078092504174112 \n",
+ "[READ].Operations 999163 \n",
+ "[READ].AverageLatency(us) 612.098972 \n",
+ "[READ].MinLatency(us) 160.0 \n",
+ "[READ].MaxLatency(us) 8273919.0 \n",
+ "[READ].95thPercentileLatency(us) 1138.0 \n",
+ "[READ].99thPercentileLatency(us) 5935.0 \n",
+ "[READ].Return=OK 999163 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 527.0 \n",
+ "[CLEANUP].MinLatency(us) 131.0 \n",
+ "[CLEANUP].MaxLatency(us) 1723.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1723.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1723.0 \n",
+ "[UPDATE].Operations 1000837 \n",
+ "[UPDATE].AverageLatency(us) 1460.016783 \n",
+ "[UPDATE].MinLatency(us) 181.0 \n",
+ "[UPDATE].MaxLatency(us) 13418495.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1980.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 31279.0 \n",
+ "[UPDATE].Return=OK 1000837 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-2-2 \\\n",
+ "connection PostgreSQL-64-8-65536-1-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 2 \n",
+ "pod q9r7n.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 272804.0 \n",
+ "[OVERALL].Throughput(ops/sec) 7331.270802 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 139 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.05095233207724227 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 139 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.05095233207724227 \n",
+ "[READ].Operations 1000109 \n",
+ "[READ].AverageLatency(us) 630.125819 \n",
+ "[READ].MinLatency(us) 165.0 \n",
+ "[READ].MaxLatency(us) 8273919.0 \n",
+ "[READ].95thPercentileLatency(us) 1195.0 \n",
+ "[READ].99thPercentileLatency(us) 6335.0 \n",
+ "[READ].Return=OK 1000109 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 379.0 \n",
+ "[CLEANUP].MinLatency(us) 146.0 \n",
+ "[CLEANUP].MaxLatency(us) 1504.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1504.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1504.0 \n",
+ "[UPDATE].Operations 999891 \n",
+ "[UPDATE].AverageLatency(us) 1450.61078 \n",
+ "[UPDATE].MinLatency(us) 179.0 \n",
+ "[UPDATE].MaxLatency(us) 8478719.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2026.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 31519.0 \n",
+ "[UPDATE].Return=OK 999891 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-2-1 \\\n",
+ "connection PostgreSQL-64-8-65536-2-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 2 \n",
+ "pod fdn9r.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 293094.0 \n",
+ "[OVERALL].Throughput(ops/sec) 6823.749377 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 164 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.05595474489412952 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 164 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.05595474489412952 \n",
+ "[READ].Operations 999724 \n",
+ "[READ].AverageLatency(us) 628.849606 \n",
+ "[READ].MinLatency(us) 160.0 \n",
+ "[READ].MaxLatency(us) 15302655.0 \n",
+ "[READ].95thPercentileLatency(us) 1203.0 \n",
+ "[READ].99thPercentileLatency(us) 6223.0 \n",
+ "[READ].Return=OK 999724 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 600.75 \n",
+ "[CLEANUP].MinLatency(us) 144.0 \n",
+ "[CLEANUP].MaxLatency(us) 2377.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 2377.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 2377.0 \n",
+ "[UPDATE].Operations 1000276 \n",
+ "[UPDATE].AverageLatency(us) 1602.078253 \n",
+ "[UPDATE].MinLatency(us) 184.0 \n",
+ "[UPDATE].MaxLatency(us) 9011199.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2063.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 34047.0 \n",
+ "[UPDATE].Return=OK 1000276 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-2 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod 4z8ld.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 338882.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2950.879657 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 82 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.024197213189251717 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 82 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.024197213189251717 \n",
+ "[READ].Operations 499963 \n",
+ "[READ].AverageLatency(us) 692.800687 \n",
+ "[READ].MinLatency(us) 156.0 \n",
+ "[READ].MaxLatency(us) 16613375.0 \n",
+ "[READ].95thPercentileLatency(us) 1109.0 \n",
+ "[READ].99thPercentileLatency(us) 6691.0 \n",
+ "[READ].Return=OK 499963 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 742.25 \n",
+ "[CLEANUP].MinLatency(us) 111.0 \n",
+ "[CLEANUP].MaxLatency(us) 2541.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 2541.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 2541.0 \n",
+ "[UPDATE].Operations 500037 \n",
+ "[UPDATE].AverageLatency(us) 1987.307765 \n",
+ "[UPDATE].MinLatency(us) 177.0 \n",
+ "[UPDATE].MaxLatency(us) 16596991.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1985.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 33215.0 \n",
+ "[UPDATE].Return=OK 500037 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-3-1 \\\n",
+ "connection PostgreSQL-64-8-65536-2-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 3 \n",
+ "pod ghz2k.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 407848.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2451.893843 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 81 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.019860340126713873 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 81 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.019860340126713873 \n",
+ "[READ].Operations 499747 \n",
+ "[READ].AverageLatency(us) 1027.217056 \n",
+ "[READ].MinLatency(us) 164.0 \n",
+ "[READ].MaxLatency(us) 68222975.0 \n",
+ "[READ].95thPercentileLatency(us) 1227.0 \n",
+ "[READ].99thPercentileLatency(us) 7243.0 \n",
+ "[READ].Return=OK 499747 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 971.0 \n",
+ "[CLEANUP].MinLatency(us) 135.0 \n",
+ "[CLEANUP].MaxLatency(us) 3283.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 3283.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 3283.0 \n",
+ "[UPDATE].Operations 500253 \n",
+ "[UPDATE].AverageLatency(us) 2211.868087 \n",
+ "[UPDATE].MinLatency(us) 180.0 \n",
+ "[UPDATE].MaxLatency(us) 68222975.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2195.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 36927.0 \n",
+ "[UPDATE].Return=OK 500253 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-2-2 \\\n",
+ "connection PostgreSQL-64-8-65536-2-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 2 \n",
+ "pod hd5d6.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 290728.0 \n",
+ "[OVERALL].Throughput(ops/sec) 6879.282353 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 156 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.053658402355466275 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 156 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.053658402355466275 \n",
+ "[READ].Operations 999672 \n",
+ "[READ].AverageLatency(us) 636.585787 \n",
+ "[READ].MinLatency(us) 162.0 \n",
+ "[READ].MaxLatency(us) 9003007.0 \n",
+ "[READ].95thPercentileLatency(us) 1239.0 \n",
+ "[READ].99thPercentileLatency(us) 6391.0 \n",
+ "[READ].Return=OK 999672 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 565.0 \n",
+ "[CLEANUP].MinLatency(us) 139.0 \n",
+ "[CLEANUP].MaxLatency(us) 1399.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1399.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1399.0 \n",
+ "[UPDATE].Operations 1000328 \n",
+ "[UPDATE].AverageLatency(us) 1597.172607 \n",
+ "[UPDATE].MinLatency(us) 182.0 \n",
+ "[UPDATE].MaxLatency(us) 8855551.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2139.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 34399.0 \n",
+ "[UPDATE].Return=OK 1000328 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-3 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod whhrf.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 339074.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2949.208727 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 87 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.02565811592749665 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 87 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.02565811592749665 \n",
+ "[READ].Operations 499546 \n",
+ "[READ].AverageLatency(us) 652.219579 \n",
+ "[READ].MinLatency(us) 159.0 \n",
+ "[READ].MaxLatency(us) 13762559.0 \n",
+ "[READ].95thPercentileLatency(us) 1141.0 \n",
+ "[READ].99thPercentileLatency(us) 6179.0 \n",
+ "[READ].Return=OK 499546 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 804.5 \n",
+ "[CLEANUP].MinLatency(us) 200.0 \n",
+ "[CLEANUP].MaxLatency(us) 2087.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 2087.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 2087.0 \n",
+ "[UPDATE].Operations 500454 \n",
+ "[UPDATE].AverageLatency(us) 1915.094186 \n",
+ "[UPDATE].MinLatency(us) 179.0 \n",
+ "[UPDATE].MaxLatency(us) 11853823.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1992.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 33183.0 \n",
+ "[UPDATE].Return=OK 500454 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-4 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod zd79f.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 341485.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2928.386313 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 80 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.023427090501778994 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 80 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.023427090501778994 \n",
+ "[READ].Operations 499884 \n",
+ "[READ].AverageLatency(us) 680.361506 \n",
+ "[READ].MinLatency(us) 159.0 \n",
+ "[READ].MaxLatency(us) 14999551.0 \n",
+ "[READ].95thPercentileLatency(us) 1078.0 \n",
+ "[READ].99thPercentileLatency(us) 6143.0 \n",
+ "[READ].Return=OK 499884 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 877.75 \n",
+ "[CLEANUP].MinLatency(us) 175.0 \n",
+ "[CLEANUP].MaxLatency(us) 2389.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 2389.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 2389.0 \n",
+ "[UPDATE].Operations 500116 \n",
+ "[UPDATE].AverageLatency(us) 2013.648054 \n",
+ "[UPDATE].MinLatency(us) 177.0 \n",
+ "[UPDATE].MaxLatency(us) 14966783.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1920.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 33247.0 \n",
+ "[UPDATE].Return=OK 500116 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-3-2 ... \\\n",
+ "connection PostgreSQL-64-8-65536-2-3 ... \n",
+ "configuration PostgreSQL-64-8-65536 ... \n",
+ "experiment_run 2 ... \n",
+ "client 3 ... \n",
+ "pod p9k4r.dbmsbenchmarker ... \n",
+ "pod_count 16 ... \n",
+ "threads 4 ... \n",
+ "target 4096 ... \n",
+ "sf 16 ... \n",
+ "workload a ... \n",
+ "operations 1000000 ... \n",
+ "batchsize -1 ... \n",
+ "exceptions 0 ... \n",
+ "[OVERALL].RunTime(ms) 418699.0 ... \n",
+ "[OVERALL].Throughput(ops/sec) 2388.350581 ... \n",
+ "[TOTAL_GCS_Copy].Count 4 ... \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 65 ... \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.015524278777833241 ... \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 ... \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 ... \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 ... \n",
+ "[TOTAL_GCs].Count 4 ... \n",
+ "[TOTAL_GC_TIME].Time(ms) 65 ... \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.015524278777833241 ... \n",
+ "[READ].Operations 499994 ... \n",
+ "[READ].AverageLatency(us) 630.014424 ... \n",
+ "[READ].MinLatency(us) 157.0 ... \n",
+ "[READ].MaxLatency(us) 11337727.0 ... \n",
+ "[READ].95thPercentileLatency(us) 1187.0 ... \n",
+ "[READ].99thPercentileLatency(us) 6739.0 ... \n",
+ "[READ].Return=OK 499994 ... \n",
+ "[CLEANUP].Operations 4 ... \n",
+ "[CLEANUP].AverageLatency(us) 984.25 ... \n",
+ "[CLEANUP].MinLatency(us) 139.0 ... \n",
+ "[CLEANUP].MaxLatency(us) 3225.0 ... \n",
+ "[CLEANUP].95thPercentileLatency(us) 3225.0 ... \n",
+ "[CLEANUP].99thPercentileLatency(us) 3225.0 ... \n",
+ "[UPDATE].Operations 500006 ... \n",
+ "[UPDATE].AverageLatency(us) 2668.637792 ... \n",
+ "[UPDATE].MinLatency(us) 176.0 ... \n",
+ "[UPDATE].MaxLatency(us) 75038719.0 ... \n",
+ "[UPDATE].95thPercentileLatency(us) 2137.0 ... \n",
+ "[UPDATE].99thPercentileLatency(us) 36735.0 ... \n",
+ "[UPDATE].Return=OK 500006 ... \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-2-7 \\\n",
+ "connection PostgreSQL-64-8-65536-1-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 2 \n",
+ "pod 6t2hg.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 271433.0 \n",
+ "[OVERALL].Throughput(ops/sec) 7368.300833 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 159 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.058577991622241955 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 159 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.058577991622241955 \n",
+ "[READ].Operations 999886 \n",
+ "[READ].AverageLatency(us) 609.032176 \n",
+ "[READ].MinLatency(us) 163.0 \n",
+ "[READ].MaxLatency(us) 6635519.0 \n",
+ "[READ].95thPercentileLatency(us) 1188.0 \n",
+ "[READ].99thPercentileLatency(us) 6191.0 \n",
+ "[READ].Return=OK 999886 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 358.0 \n",
+ "[CLEANUP].MinLatency(us) 127.0 \n",
+ "[CLEANUP].MaxLatency(us) 1473.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1473.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1473.0 \n",
+ "[UPDATE].Operations 1000114 \n",
+ "[UPDATE].AverageLatency(us) 1473.161051 \n",
+ "[UPDATE].MinLatency(us) 177.0 \n",
+ "[UPDATE].MaxLatency(us) 8454143.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2032.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 31407.0 \n",
+ "[UPDATE].Return=OK 1000114 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-13 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod tlzfx.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 339977.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2941.375446 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 88 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.025884103924677253 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 88 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.025884103924677253 \n",
+ "[READ].Operations 499237 \n",
+ "[READ].AverageLatency(us) 739.949843 \n",
+ "[READ].MinLatency(us) 159.0 \n",
+ "[READ].MaxLatency(us) 15810559.0 \n",
+ "[READ].95thPercentileLatency(us) 1074.0 \n",
+ "[READ].99thPercentileLatency(us) 6195.0 \n",
+ "[READ].Return=OK 499237 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 578.5 \n",
+ "[CLEANUP].MinLatency(us) 196.0 \n",
+ "[CLEANUP].MaxLatency(us) 1229.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1229.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1229.0 \n",
+ "[UPDATE].Operations 500763 \n",
+ "[UPDATE].AverageLatency(us) 1950.94332 \n",
+ "[UPDATE].MinLatency(us) 173.0 \n",
+ "[UPDATE].MaxLatency(us) 15777791.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1938.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 33375.0 \n",
+ "[UPDATE].Return=OK 500763 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-2-8 \\\n",
+ "connection PostgreSQL-64-8-65536-1-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 2 \n",
+ "pod wp6gp.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 269307.0 \n",
+ "[OVERALL].Throughput(ops/sec) 7426.468677 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 161 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.05978307284994448 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 161 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.05978307284994448 \n",
+ "[READ].Operations 1000533 \n",
+ "[READ].AverageLatency(us) 603.079837 \n",
+ "[READ].MinLatency(us) 162.0 \n",
+ "[READ].MaxLatency(us) 8273919.0 \n",
+ "[READ].95thPercentileLatency(us) 1161.0 \n",
+ "[READ].99thPercentileLatency(us) 6095.0 \n",
+ "[READ].Return=OK 1000533 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 689.75 \n",
+ "[CLEANUP].MinLatency(us) 146.0 \n",
+ "[CLEANUP].MaxLatency(us) 3215.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 3215.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 3215.0 \n",
+ "[UPDATE].Operations 999467 \n",
+ "[UPDATE].AverageLatency(us) 1470.718883 \n",
+ "[UPDATE].MinLatency(us) 171.0 \n",
+ "[UPDATE].MaxLatency(us) 8413183.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1995.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 31391.0 \n",
+ "[UPDATE].Return=OK 999467 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-3-14 \\\n",
+ "connection PostgreSQL-64-8-65536-2-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 3 \n",
+ "pod dq2nh.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 417067.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2397.696293 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 81 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.01942133997655053 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 81 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.01942133997655053 \n",
+ "[READ].Operations 500986 \n",
+ "[READ].AverageLatency(us) 629.824197 \n",
+ "[READ].MinLatency(us) 164.0 \n",
+ "[READ].MaxLatency(us) 11010047.0 \n",
+ "[READ].95thPercentileLatency(us) 1229.0 \n",
+ "[READ].99thPercentileLatency(us) 6983.0 \n",
+ "[READ].Return=OK 500986 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 856.25 \n",
+ "[CLEANUP].MinLatency(us) 152.0 \n",
+ "[CLEANUP].MaxLatency(us) 2831.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 2831.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 2831.0 \n",
+ "[UPDATE].Operations 499014 \n",
+ "[UPDATE].AverageLatency(us) 2681.724855 \n",
+ "[UPDATE].MinLatency(us) 181.0 \n",
+ "[UPDATE].MaxLatency(us) 75038719.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2163.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 36543.0 \n",
+ "[UPDATE].Return=OK 499014 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-14 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod nq8jk.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 332751.0 \n",
+ "[OVERALL].Throughput(ops/sec) 3005.250172 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 100 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.030052501720505727 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 100 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.030052501720505727 \n",
+ "[READ].Operations 499675 \n",
+ "[READ].AverageLatency(us) 718.244719 \n",
+ "[READ].MinLatency(us) 158.0 \n",
+ "[READ].MaxLatency(us) 12574719.0 \n",
+ "[READ].95thPercentileLatency(us) 1115.0 \n",
+ "[READ].99thPercentileLatency(us) 6599.0 \n",
+ "[READ].Return=OK 499675 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 474.0 \n",
+ "[CLEANUP].MinLatency(us) 99.0 \n",
+ "[CLEANUP].MaxLatency(us) 1399.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1399.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1399.0 \n",
+ "[UPDATE].Operations 500325 \n",
+ "[UPDATE].AverageLatency(us) 1924.348901 \n",
+ "[UPDATE].MinLatency(us) 175.0 \n",
+ "[UPDATE].MaxLatency(us) 11894783.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2011.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 33407.0 \n",
+ "[UPDATE].Return=OK 500325 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-15 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod tfj25.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 335233.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2982.999884 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 83 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.024758899034402936 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 83 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.024758899034402936 \n",
+ "[READ].Operations 498989 \n",
+ "[READ].AverageLatency(us) 643.202115 \n",
+ "[READ].MinLatency(us) 162.0 \n",
+ "[READ].MaxLatency(us) 11788287.0 \n",
+ "[READ].95thPercentileLatency(us) 1104.0 \n",
+ "[READ].99thPercentileLatency(us) 6551.0 \n",
+ "[READ].Return=OK 498989 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 516.25 \n",
+ "[CLEANUP].MinLatency(us) 131.0 \n",
+ "[CLEANUP].MaxLatency(us) 1482.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1482.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1482.0 \n",
+ "[UPDATE].Operations 501011 \n",
+ "[UPDATE].AverageLatency(us) 2012.476608 \n",
+ "[UPDATE].MinLatency(us) 178.0 \n",
+ "[UPDATE].MaxLatency(us) 14131199.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1978.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 33247.0 \n",
+ "[UPDATE].Return=OK 501011 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-3-15 \\\n",
+ "connection PostgreSQL-64-8-65536-2-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 3 \n",
+ "pod lmklc.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 425837.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2348.316375 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 85 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.01996068918388961 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 85 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.01996068918388961 \n",
+ "[READ].Operations 500614 \n",
+ "[READ].AverageLatency(us) 632.084668 \n",
+ "[READ].MinLatency(us) 157.0 \n",
+ "[READ].MaxLatency(us) 10960895.0 \n",
+ "[READ].95thPercentileLatency(us) 1261.0 \n",
+ "[READ].99thPercentileLatency(us) 6887.0 \n",
+ "[READ].Return=OK 500614 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 506.5 \n",
+ "[CLEANUP].MinLatency(us) 185.0 \n",
+ "[CLEANUP].MaxLatency(us) 1262.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1262.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1262.0 \n",
+ "[UPDATE].Operations 499386 \n",
+ "[UPDATE].AverageLatency(us) 2712.627565 \n",
+ "[UPDATE].MinLatency(us) 178.0 \n",
+ "[UPDATE].MaxLatency(us) 75038719.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2149.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 35775.0 \n",
+ "[UPDATE].Return=OK 499386 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-2-8 \\\n",
+ "connection PostgreSQL-64-8-65536-2-2 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 2 \n",
+ "pod 9tg6c.dbmsbenchmarker \n",
+ "pod_count 8 \n",
+ "threads 8 \n",
+ "target 8192 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 2000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 291148.0 \n",
+ "[OVERALL].Throughput(ops/sec) 6869.358539 \n",
+ "[TOTAL_GCS_Copy].Count 8 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 152 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.05220712489867696 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 8 \n",
+ "[TOTAL_GC_TIME].Time(ms) 152 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.05220712489867696 \n",
+ "[READ].Operations 999961 \n",
+ "[READ].AverageLatency(us) 635.928938 \n",
+ "[READ].MinLatency(us) 150.0 \n",
+ "[READ].MaxLatency(us) 8708095.0 \n",
+ "[READ].95thPercentileLatency(us) 1257.0 \n",
+ "[READ].99thPercentileLatency(us) 6407.0 \n",
+ "[READ].Return=OK 999961 \n",
+ "[CLEANUP].Operations 8 \n",
+ "[CLEANUP].AverageLatency(us) 574.875 \n",
+ "[CLEANUP].MinLatency(us) 109.0 \n",
+ "[CLEANUP].MaxLatency(us) 2333.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 2333.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 2333.0 \n",
+ "[UPDATE].Operations 1000039 \n",
+ "[UPDATE].AverageLatency(us) 1610.247474 \n",
+ "[UPDATE].MinLatency(us) 170.0 \n",
+ "[UPDATE].MaxLatency(us) 8937471.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2117.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 34847.0 \n",
+ "[UPDATE].Return=OK 1000039 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-1-3-16 \\\n",
+ "connection PostgreSQL-64-8-65536-1-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 1 \n",
+ "client 3 \n",
+ "pod s8lhl.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 344079.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2906.309307 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 85 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.02470362910843149 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 85 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.02470362910843149 \n",
+ "[READ].Operations 500664 \n",
+ "[READ].AverageLatency(us) 692.812257 \n",
+ "[READ].MinLatency(us) 161.0 \n",
+ "[READ].MaxLatency(us) 17367039.0 \n",
+ "[READ].95thPercentileLatency(us) 1079.0 \n",
+ "[READ].99thPercentileLatency(us) 5999.0 \n",
+ "[READ].Return=OK 500664 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 758.75 \n",
+ "[CLEANUP].MinLatency(us) 129.0 \n",
+ "[CLEANUP].MaxLatency(us) 1719.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 1719.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 1719.0 \n",
+ "[UPDATE].Operations 499336 \n",
+ "[UPDATE].AverageLatency(us) 1924.502109 \n",
+ "[UPDATE].MinLatency(us) 177.0 \n",
+ "[UPDATE].MaxLatency(us) 13189119.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 1972.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 32591.0 \n",
+ "[UPDATE].Return=OK 499336 \n",
+ "\n",
+ "connection_pod PostgreSQL-64-8-65536-2-3-16 \n",
+ "connection PostgreSQL-64-8-65536-2-3 \n",
+ "configuration PostgreSQL-64-8-65536 \n",
+ "experiment_run 2 \n",
+ "client 3 \n",
+ "pod szgk6.dbmsbenchmarker \n",
+ "pod_count 16 \n",
+ "threads 4 \n",
+ "target 4096 \n",
+ "sf 16 \n",
+ "workload a \n",
+ "operations 1000000 \n",
+ "batchsize -1 \n",
+ "exceptions 0 \n",
+ "[OVERALL].RunTime(ms) 422573.0 \n",
+ "[OVERALL].Throughput(ops/sec) 2366.455027 \n",
+ "[TOTAL_GCS_Copy].Count 4 \n",
+ "[TOTAL_GC_TIME_Copy].Time(ms) 79 \n",
+ "[TOTAL_GC_TIME_%_Copy].Time(%) 0.018694994710973016 \n",
+ "[TOTAL_GCS_MarkSweepCompact].Count 0 \n",
+ "[TOTAL_GC_TIME_MarkSweepCompact].Time(ms) 0 \n",
+ "[TOTAL_GC_TIME_%_MarkSweepCompact].Time(%) 0.0 \n",
+ "[TOTAL_GCs].Count 4 \n",
+ "[TOTAL_GC_TIME].Time(ms) 79 \n",
+ "[TOTAL_GC_TIME_%].Time(%) 0.018694994710973016 \n",
+ "[READ].Operations 500085 \n",
+ "[READ].AverageLatency(us) 646.564164 \n",
+ "[READ].MinLatency(us) 154.0 \n",
+ "[READ].MaxLatency(us) 12550143.0 \n",
+ "[READ].95thPercentileLatency(us) 1182.0 \n",
+ "[READ].99thPercentileLatency(us) 6691.0 \n",
+ "[READ].Return=OK 500085 \n",
+ "[CLEANUP].Operations 4 \n",
+ "[CLEANUP].AverageLatency(us) 447.5 \n",
+ "[CLEANUP].MinLatency(us) 203.0 \n",
+ "[CLEANUP].MaxLatency(us) 885.0 \n",
+ "[CLEANUP].95thPercentileLatency(us) 885.0 \n",
+ "[CLEANUP].99thPercentileLatency(us) 885.0 \n",
+ "[UPDATE].Operations 499915 \n",
+ "[UPDATE].AverageLatency(us) 2670.547505 \n",
+ "[UPDATE].MinLatency(us) 178.0 \n",
+ "[UPDATE].MaxLatency(us) 75038719.0 \n",
+ "[UPDATE].95thPercentileLatency(us) 2079.0 \n",
+ "[UPDATE].99thPercentileLatency(us) 36607.0 \n",
+ "[UPDATE].Return=OK 499915 \n",
+ "\n",
+ "[44 rows x 50 columns]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_benchmarks = evaluation.get_df_benchmarking()\n",
+ "#df = df[df.columns.drop(list(df.filter(regex='FAILED')))]\n",
+ "df_benchmarks = evaluation.benchmarking_set_datatypes(df_benchmarks)\n",
+ "df_benchmarks.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "b100528f-41db-4197-a5fe-86c77277acd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "# Metric current_ops_per_sec"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "## Configuration PostgreSQL-64-8-65536"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "### Experiment run 1"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ current_ops_per_sec\n",
+ "sec \n",
+ "60 9947.72\n",
+ "120 18180.33\n",
+ "180 19878.62\n",
+ "240 25097.73\n",
+ "300 30567.88\n",
+ "360 35707.78\n",
+ "420 45317.85\n",
+ "480 52992.22\n",
+ "537 30254.62]\n",
+ " pod 1\n",
+ "sec \n",
+ "60 9947.72\n",
+ "120 18180.33\n",
+ "180 19878.62\n",
+ "240 25097.73\n",
+ "300 30567.88\n",
+ "360 35707.78\n",
+ "420 45317.85\n",
+ "480 52992.22\n",
+ "[ current_ops_per_sec\n",
+ "sec \n",
+ "60 5992.83\n",
+ "120 7123.25\n",
+ "180 9127.90\n",
+ "240 7298.63\n",
+ "273 6868.93, current_ops_per_sec\n",
+ "sec \n",
+ "60 6045.00\n",
+ "120 6924.42\n",
+ "180 9244.37\n",
+ "240 7185.42\n",
+ "272 7195.71, current_ops_per_sec\n",
+ "sec \n",
+ "60 6014.70\n",
+ "120 7285.53\n",
+ "180 9356.20\n",
+ "240 7092.35\n",
+ "270 7069.89, current_ops_per_sec\n",
+ "sec \n",
+ "60 5826.05\n",
+ "120 7393.18\n",
+ "180 9141.43\n",
+ "240 7383.82\n",
+ "268 7591.49, current_ops_per_sec\n",
+ "sec \n",
+ "60 5838.32\n",
+ "120 7132.32\n",
+ "180 9180.15\n",
+ "240 7299.38\n",
+ "269 7900.98, current_ops_per_sec\n",
+ "sec \n",
+ "60 6108.85\n",
+ "120 7009.00\n",
+ "180 9074.98\n",
+ "240 7106.25\n",
+ "275 6819.99, current_ops_per_sec\n",
+ "sec \n",
+ "60 5748.37\n",
+ "120 7097.48\n",
+ "180 9502.37\n",
+ "240 7036.27\n",
+ "271 7537.68, current_ops_per_sec\n",
+ "sec \n",
+ "60 5898.72\n",
+ "120 7142.75\n",
+ "180 9195.35\n",
+ "240 7534.46\n",
+ "269 7292.63]\n",
+ " pod 1 pod 2 pod 3 pod 4 pod 5 pod 6 pod 7 pod 8\n",
+ "sec \n",
+ "60 5992.83 6045.00 6014.70 5826.05 5838.32 6108.85 5748.37 5898.72\n",
+ "120 7123.25 6924.42 7285.53 7393.18 7132.32 7009.00 7097.48 7142.75\n",
+ "180 9127.90 9244.37 9356.20 9141.43 9180.15 9074.98 9502.37 9195.35\n",
+ "240 7298.63 7185.42 7092.35 7383.82 7299.38 7106.25 7036.27 7534.46\n",
+ "[ current_ops_per_sec\n",
+ "sec \n",
+ "60 2901.12\n",
+ "120 2347.62\n",
+ "180 3215.80\n",
+ "240 3534.18\n",
+ "300 2910.18\n",
+ "343 2433.62, current_ops_per_sec\n",
+ "sec \n",
+ "60 2086.68\n",
+ "120 2303.07\n",
+ "180 3529.20\n",
+ "240 3665.92\n",
+ "300 2841.73\n",
+ "338 3456.93, current_ops_per_sec\n",
+ "sec \n",
+ "60 2906.53\n",
+ "120 2213.91\n",
+ "180 3282.82\n",
+ "240 3715.88\n",
+ "300 3002.98\n",
+ "339 2371.71, current_ops_per_sec\n",
+ "sec \n",
+ "60 2015.67\n",
+ "120 2449.78\n",
+ "180 3435.20\n",
+ "240 3552.75\n",
+ "300 2727.27\n",
+ "341 3595.55, current_ops_per_sec\n",
+ "sec \n",
+ "60 2629.12\n",
+ "120 2307.52\n",
+ "180 3220.33\n",
+ "240 3478.73\n",
+ "300 2899.95\n",
+ "340 3133.29, current_ops_per_sec\n",
+ "sec \n",
+ "60 2374.43\n",
+ "120 2436.60\n",
+ "180 3473.35\n",
+ "240 3584.63\n",
+ "300 2938.55\n",
+ "332 3448.36, current_ops_per_sec\n",
+ "sec \n",
+ "60 2748.14\n",
+ "120 2252.15\n",
+ "180 3486.02\n",
+ "240 3549.37\n",
+ "300 2791.78\n",
+ "338 2888.82, current_ops_per_sec\n",
+ "sec \n",
+ "60 2834.49\n",
+ "120 2330.42\n",
+ "180 3412.23\n",
+ "240 3624.01\n",
+ "300 2892.35\n",
+ "336 2563.57, current_ops_per_sec\n",
+ "sec \n",
+ "60 2207.56\n",
+ "120 2379.08\n",
+ "180 3345.90\n",
+ "240 3571.53\n",
+ "300 2825.87\n",
+ "335 3946.21, current_ops_per_sec\n",
+ "sec \n",
+ "60 2485.08\n",
+ "120 2156.72\n",
+ "180 3383.08\n",
+ "240 3585.03\n",
+ "300 2900.10\n",
+ "340 3210.49, current_ops_per_sec\n",
+ "sec \n",
+ "60 3048.83\n",
+ "120 2143.85\n",
+ "180 2998.33\n",
+ "240 3565.63\n",
+ "300 2713.48\n",
+ "343 3026.59, current_ops_per_sec\n",
+ "sec \n",
+ "60 2202.42\n",
+ "120 2484.43\n",
+ "180 3470.15\n",
+ "240 3606.14\n",
+ "300 2907.47\n",
+ "333 3526.38, current_ops_per_sec\n",
+ "sec \n",
+ "60 2016.17\n",
+ "120 2265.30\n",
+ "180 3560.68\n",
+ "240 3546.85\n",
+ "300 2742.63\n",
+ "339 3804.78, current_ops_per_sec\n",
+ "sec \n",
+ "60 2266.11\n",
+ "120 2464.97\n",
+ "180 3416.92\n",
+ "240 3536.95\n",
+ "300 2994.20\n",
+ "332 3641.19, current_ops_per_sec\n",
+ "sec \n",
+ "60 2101.45\n",
+ "120 2497.32\n",
+ "180 3517.02\n",
+ "240 3571.03\n",
+ "300 2874.80\n",
+ "335 3584.84, current_ops_per_sec\n",
+ "sec \n",
+ "60 2818.93\n",
+ "120 2189.37\n",
+ "180 3075.78\n",
+ "240 3737.72\n",
+ "300 2893.50\n",
+ "344 2656.19]\n",
+ " pod 1 pod 2 pod 3 pod 4 pod 5 pod 6 pod 7 pod 8 \\\n",
+ "sec \n",
+ "60 2901.12 2086.68 2906.53 2015.67 2629.12 2374.43 2748.14 2834.49 \n",
+ "120 2347.62 2303.07 2213.91 2449.78 2307.52 2436.60 2252.15 2330.42 \n",
+ "180 3215.80 3529.20 3282.82 3435.20 3220.33 3473.35 3486.02 3412.23 \n",
+ "240 3534.18 3665.92 3715.88 3552.75 3478.73 3584.63 3549.37 3624.01 \n",
+ "300 2910.18 2841.73 3002.98 2727.27 2899.95 2938.55 2791.78 2892.35 \n",
+ "\n",
+ " pod 9 pod 10 pod 11 pod 12 pod 13 pod 14 pod 15 pod 16 \n",
+ "sec \n",
+ "60 2207.56 2485.08 3048.83 2202.42 2016.17 2266.11 2101.45 2818.93 \n",
+ "120 2379.08 2156.72 2143.85 2484.43 2265.30 2464.97 2497.32 2189.37 \n",
+ "180 3345.90 3383.08 2998.33 3470.15 3560.68 3416.92 3517.02 3075.78 \n",
+ "240 3571.53 3585.03 3565.63 3606.14 3546.85 3536.95 3571.03 3737.72 \n",
+ "300 2825.87 2900.10 2713.48 2907.47 2742.63 2994.20 2874.80 2893.50 \n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "