chore(iobench-dash): Delete older revisions and rename to iobench-dash.py for clarity
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from dash import Dash, dcc, html, Input, Output
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import plotly.graph_objects as go
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import pandas as pd
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# Load the CSV data
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df = pd.read_csv("iobench.csv") # Replace with the actual file path
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# Initialize Dash app
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app = Dash(__name__)
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# Layout
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app.layout = html.Div(
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[
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html.H1("IOBench Results Viewer", style={"textAlign": "center"}),
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# Filters
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html.Div(
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[
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html.Label("Filter by Label:"),
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dcc.Dropdown(
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id="label-filter",
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options=[{"label": label, "value": label} for label in df["label"].unique()],
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value=df["label"].unique().tolist(),
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multi=True,
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),
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html.Label("Filter by Test Name:"),
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dcc.Dropdown(
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id="test-filter",
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options=[{"label": test, "value": test} for test in df["test_name"].unique()],
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value=df["test_name"].unique().tolist(),
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multi=True,
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),
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],
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style={"width": "25%", "display": "inline-block", "verticalAlign": "top", "padding": "10px"},
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),
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# Graphs
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html.Div(
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[
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dcc.Graph(id="throughput-graph"),
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dcc.Graph(id="latency-graph"),
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],
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style={"width": "70%", "display": "inline-block", "padding": "10px"},
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),
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]
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)
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# Callbacks
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@app.callback(
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[Output("throughput-graph", "figure"), Output("latency-graph", "figure")],
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[Input("label-filter", "value"), Input("test-filter", "value")],
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)
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def update_graphs(selected_labels, selected_tests):
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# Filter data
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filtered_df = df[df["label"].isin(selected_labels) & df["test_name"].isin(selected_tests)]
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# Throughput Graph
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throughput_fig = go.Figure()
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for label in filtered_df["label"].unique():
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subset = filtered_df[filtered_df["label"] == label]
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throughput_fig.add_trace(
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go.Bar(
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x=subset["test_name"],
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y=subset["iops"],
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name=f"{label} - IOPS",
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)
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)
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throughput_fig.add_trace(
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go.Bar(
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x=subset["test_name"],
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y=subset["bandwidth_kibps"],
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name=f"{label} - Bandwidth (KiB/s)",
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)
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)
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throughput_fig.update_layout(
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title="Throughput (IOPS and Bandwidth)",
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xaxis_title="Test Name",
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yaxis_title="Value",
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barmode="group",
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)
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# Latency Graph
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latency_fig = go.Figure()
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for label in filtered_df["label"].unique():
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subset = filtered_df[filtered_df["label"] == label]
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latency_fig.add_trace(
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go.Scatter(
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x=subset["test_name"],
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y=subset["latency_mean_ms"],
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mode="markers+lines",
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name=f"{label} - Latency Mean (ms)",
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error_y=dict(
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type="data",
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array=subset["latency_stddev_ms"],
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visible=True,
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),
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)
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)
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latency_fig.update_layout(
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title="Latency with Standard Deviation",
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xaxis_title="Test Name",
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yaxis_title="Latency (ms)",
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)
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return throughput_fig, latency_fig
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if __name__ == "__main__":
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app.run_server(debug=True)
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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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# --- Sample Data ---
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# In a real-world scenario, you would load this from a CSV file.
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# For this self-contained example, we define the data directly.
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# Example: df = pd.read_csv('benchmark_data.csv')
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csv_data = """
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config,op_type,iops,latency_ms,throughput_mbs
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All-HDD,4k_random_read,260,60,1.02
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All-HDD,4k_random_write,100,150,0.39
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All-HDD,64k_sequential_read,2100,30,131.25
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All-HDD,64k_sequential_write,1500,42,93.75
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HDD+SSD_WAL,4k_random_read,270,58,1.05
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HDD+SSD_WAL,4k_random_write,160,100,0.62
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HDD+SSD_WAL,64k_sequential_read,2150,29,134.37
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HDD+SSD_WAL,64k_sequential_write,1800,35,112.5
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HDD+SSD_WAL_DB,4k_random_read,1250,12,4.88
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HDD+SSD_WAL_DB,4k_random_write,1550,10,6.05
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HDD+SSD_WAL_DB,64k_sequential_read,2200,28,137.5
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HDD+SSD_WAL_DB,64k_sequential_write,2000,32,125
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All-NVMe,4k_random_read,400000,0.1,1562.5
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All-NVMe,4k_random_write,350000,0.12,1367.18
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All-NVMe,64k_sequential_read,16000,4,1000
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All-NVMe,64k_sequential_write,12500,5,800
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"""
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# Read the data using pandas
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df = pd.read_csv(io.StringIO(csv_data))
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# Initialize the Dash app with a Bootstrap theme
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.FLATLY])
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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 Cluster Benchmark Visualizer", className="text-primary"),
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html.P(
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"An interactive tool to compare performance metrics across different Ceph storage configurations.",
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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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# Metric Selection Radio Buttons
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dbc.Label("Select Metric to Display:", html_for="metric-selector"),
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dcc.RadioItems(
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id='metric-selector',
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options=[
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{'label': 'IOPS (Input/Output Operations Per Second)', 'value': 'iops'},
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{'label': 'Latency (in Milliseconds)', 'value': 'latency_ms'},
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{'label': 'Throughput (in MB/s)', 'value': 'throughput_mbs'}
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],
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value='iops', # Default value
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labelClassName="d-block" # Display labels as blocks
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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"),
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dcc.Checklist(
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id='config-checklist',
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options=[{'label': config, 'value': config} for config in df['config'].unique()],
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value=df['config'].unique(), # 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), # Full width on small screens, 1/3 on large
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# Graph Display Column
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dbc.Col([
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dcc.Graph(id='benchmark-graph')
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], width=12, lg=8) # Full width on small screens, 2/3 on large
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])
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], fluid=True) # Use a fluid container for full-width layout
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# --- Callback Function ---
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# This function connects the controls to the graph
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@app.callback(
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Output('benchmark-graph', 'figure'),
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[Input('metric-selector', 'value'),
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Input('config-checklist', 'value')]
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)
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def update_graph(selected_metric, selected_configs):
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"""
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This function is triggered whenever a control's value changes.
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It filters the dataframe and returns an updated bar chart figure.
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"""
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if not selected_configs:
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# If no configs are selected, return an empty figure to avoid errors
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return go.Figure().update_layout(
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title="Please select a configuration to view data.",
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xaxis_title="",
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yaxis_title=""
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)
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# Filter the DataFrame based on the selected configurations
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filtered_df = df[df['config'].isin(selected_configs)]
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# Create the bar chart using Plotly Express
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fig = px.bar(
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filtered_df,
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x='op_type',
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y=selected_metric,
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color='config',
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barmode='group', # Group bars for different configs side-by-side
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labels={
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"op_type": "Benchmark Operation Type",
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"iops": "IOPS (Higher is Better)",
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"latency_ms": "Latency in ms (Lower is Better)",
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"throughput_mbs": "Throughput in MB/s (Higher is Better)",
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"config": "Storage Configuration"
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},
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title=f"Benchmark Comparison for: {selected_metric.replace('_', ' ').title()}",
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height=600 # Set a fixed height for the graph
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)
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# Update layout for better readability
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fig.update_layout(
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xaxis_title="Operation Type",
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yaxis_title=selected_metric.replace('_', ' ').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':'total descending' if selected_metric != 'latency_ms' else 'total ascending'}
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)
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return fig
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# --- Run the App ---
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if __name__ == '__main__':
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# Use debug=True for development, allowing hot-reloading
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app.run(debug=True)
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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
|
||||
graph_list.append(dbc.Row(dbc.Col(dcc.Graph(figure=fig)), className="mb-4"))
|
||||
|
||||
return graph_list
|
||||
|
||||
# --- Run the App ---
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True)
|
||||
Loading…
Reference in New Issue
Block a user