Jean-Gabriel Gill-Couture
fd8f643a8f
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176 lines
7.5 KiB
Python
176 lines
7.5 KiB
Python
import dash
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from dash import dcc, html, Input, Output
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import plotly.express as px
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import pandas as pd
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import dash_bootstrap_components as dbc
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import io
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import plotly.graph_objects as go
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# --- Data Loading and Preparation ---
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# 1. Use the exact iobench csv output format provided.
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csv_data = """label,test_name,iops,bandwidth_kibps,latency_mean_ms,latency_stddev_ms
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Ceph HDD Only,read-4k-sync-test,1474.302,5897,0.673,0.591
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Ceph HDD Only,write-4k-sync-test,14.126,56,27.074,7.046
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Ceph HDD Only,randread-4k-sync-test,225.140,900,4.436,6.918
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Ceph HDD Only,randwrite-4k-sync-test,13.129,52,34.891,10.859
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Ceph HDD Only,multiread-4k-sync-test,6873.675,27494,0.578,0.764
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Ceph HDD Only,multiwrite-4k-sync-test,57.135,228,38.660,11.293
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Ceph HDD Only,multirandread-4k-sync-test,2451.376,9805,1.626,2.515
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Ceph HDD Only,multirandwrite-4k-sync-test,54.642,218,33.492,13.111
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,read-4k-sync-test,1495.700,5982,0.664,1.701
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,write-4k-sync-test,16.990,67,17.502,9.908
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,randread-4k-sync-test,159.256,637,6.274,9.232
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,randwrite-4k-sync-test,16.693,66,24.094,16.099
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multiread-4k-sync-test,7305.559,29222,0.544,1.338
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multiwrite-4k-sync-test,52.260,209,34.891,17.576
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multirandread-4k-sync-test,700.606,2802,5.700,10.429
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Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multirandwrite-4k-sync-test,52.723,210,29.709,25.829
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Ceph 2 Hosts WAL+DB SSD Only,randwrite-4k-sync-test,90.037,360,3.617,8.321
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Ceph WAL+DB SSD During Rebuild,randwrite-4k-sync-test,41.008,164,10.138,19.333
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Ceph WAL+DB SSD OSD HDD,read-4k-sync-test,1520.299,6081,0.654,1.539
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Ceph WAL+DB SSD OSD HDD,write-4k-sync-test,78.528,314,4.074,9.101
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Ceph WAL+DB SSD OSD HDD,randread-4k-sync-test,153.303,613,6.518,9.036
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Ceph WAL+DB SSD OSD HDD,randwrite-4k-sync-test,48.677,194,8.785,20.356
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Ceph WAL+DB SSD OSD HDD,multiread-4k-sync-test,6804.880,27219,0.584,1.422
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Ceph WAL+DB SSD OSD HDD,multiwrite-4k-sync-test,311.513,1246,4.978,9.458
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Ceph WAL+DB SSD OSD HDD,multirandread-4k-sync-test,581.756,2327,6.869,10.204
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Ceph WAL+DB SSD OSD HDD,multirandwrite-4k-sync-test,120.556,482,13.463,25.440
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"""
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# Read the data and create a more user-friendly bandwidth column in MB/s
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df = pd.read_csv(io.StringIO(csv_data))
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df['bandwidth_mbps'] = df['bandwidth_kibps'] / 1024
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# --- App Initialization and Global Settings ---
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.FLATLY])
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# 3. Create a consistent color map for each unique label (cluster topology).
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unique_labels = df['label'].unique()
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color_map = {label: color for label, color in zip(unique_labels, px.colors.qualitative.Plotly)}
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# --- App Layout ---
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app.layout = dbc.Container([
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# Header
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dbc.Row([
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dbc.Col([
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html.H1("Ceph iobench Performance Dashboard", className="text-primary"),
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html.P(
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"Compare benchmark results across different Ceph cluster configurations and metrics.",
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className="lead"
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)
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])
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], className="my-4"),
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# Controls and Graphs Row
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dbc.Row([
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# Control Panel Column
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dbc.Col([
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dbc.Card([
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dbc.CardBody([
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html.H4("Control Panel", className="card-title"),
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html.Hr(),
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# 2. Metric Selection Checklist to view multiple graphs
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dbc.Label("Select Metrics to Display:", html_for="metric-checklist", className="fw-bold"),
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dcc.Checklist(
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id='metric-checklist',
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options=[
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{'label': 'IOPS', 'value': 'iops'},
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{'label': 'Latency (ms)', 'value': 'latency_mean_ms'},
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{'label': 'Bandwidth (MB/s)', 'value': 'bandwidth_mbps'}
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],
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value=['iops', 'latency_mean_ms'], # Default selection
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labelClassName="d-block"
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),
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html.Hr(),
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# Configuration Selection Checklist
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dbc.Label("Select Configurations to Compare:", html_for="config-checklist", className="fw-bold"),
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dcc.Checklist(
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id='config-checklist',
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options=[{'label': label, 'value': label} for label in unique_labels],
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value=unique_labels, # Select all by default
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labelClassName="d-block"
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),
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])
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], className="mb-4")
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], width=12, lg=4),
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# Graph Display Column - This will be populated by the callback
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dbc.Col(id='graph-container', width=12, lg=8)
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])
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], fluid=True)
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# --- Callback Function ---
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@app.callback(
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Output('graph-container', 'children'),
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[Input('metric-checklist', 'value'),
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Input('config-checklist', 'value')]
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)
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def update_graphs(selected_metrics, selected_configs):
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"""
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This function is triggered when a control's value changes.
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It generates and returns a list of graphs based on user selections.
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"""
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# Handle cases where no selection is made to prevent errors
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if not selected_metrics or not selected_configs:
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return dbc.Alert("Please select at least one metric and one configuration to view data.", color="info")
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# Filter the DataFrame based on the selected configurations
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filtered_df = df[df['label'].isin(selected_configs)]
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# Create a list to hold all the graph components
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graph_list = []
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# Define user-friendly titles for graphs
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metric_titles = {
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'iops': 'IOPS Comparison (Higher is Better)',
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'latency_mean_ms': 'Mean Latency (ms) Comparison (Lower is Better)',
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'bandwidth_mbps': 'Bandwidth (MB/s) Comparison (Higher is Better)'
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}
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# Loop through each selected metric and create a graph for it
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for metric in selected_metrics:
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# Determine if sorting should be ascending (for latency) or descending
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sort_order = 'total ascending' if metric == 'latency_mean_ms' else 'total descending'
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# Special handling for latency to include error bars for standard deviation
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error_y_param = 'latency_stddev_ms' if metric == 'latency_mean_ms' else None
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fig = px.bar(
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filtered_df,
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x='test_name',
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y=metric,
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color='label',
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barmode='group',
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color_discrete_map=color_map, # 3. Apply the consistent color map
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error_y=error_y_param, # Adds error bars for latency stddev
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title=metric_titles.get(metric, metric),
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labels={
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"test_name": "Benchmark Test Name",
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"iops": "IOPS",
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"latency_mean_ms": "Mean Latency (ms)",
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"bandwidth_mbps": "Bandwidth (MB/s)",
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"label": "Cluster Configuration"
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}
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)
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fig.update_layout(
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height=500,
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xaxis_title=None, # Clean up x-axis title
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legend_title="Configuration",
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title_x=0.5, # Center the title
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xaxis={'categoryorder': sort_order},
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xaxis_tickangle=-45 # Angle labels to prevent overlap
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)
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# Add the generated graph to our list, wrapped in a column for layout
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graph_list.append(dbc.Row(dbc.Col(dcc.Graph(figure=fig)), className="mb-4"))
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return graph_list
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# --- Run the App ---
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if __name__ == '__main__':
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app.run(debug=True)
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