chore(iobench-dash): Delete older revisions and rename to iobench-dash.py for clarity
All checks were successful
Run Check Script / check (pull_request) Successful in 1m3s
All checks were successful
Run Check Script / check (pull_request) Successful in 1m3s
This commit is contained in:
parent
9a610661c7
commit
5e7803d2ba
@ -1,109 +0,0 @@
|
|||||||
from dash import Dash, dcc, html, Input, Output
|
|
||||||
import plotly.graph_objects as go
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
# Load the CSV data
|
|
||||||
df = pd.read_csv("iobench.csv") # Replace with the actual file path
|
|
||||||
|
|
||||||
# Initialize Dash app
|
|
||||||
app = Dash(__name__)
|
|
||||||
|
|
||||||
# Layout
|
|
||||||
app.layout = html.Div(
|
|
||||||
[
|
|
||||||
html.H1("IOBench Results Viewer", style={"textAlign": "center"}),
|
|
||||||
|
|
||||||
# Filters
|
|
||||||
html.Div(
|
|
||||||
[
|
|
||||||
html.Label("Filter by Label:"),
|
|
||||||
dcc.Dropdown(
|
|
||||||
id="label-filter",
|
|
||||||
options=[{"label": label, "value": label} for label in df["label"].unique()],
|
|
||||||
value=df["label"].unique().tolist(),
|
|
||||||
multi=True,
|
|
||||||
),
|
|
||||||
html.Label("Filter by Test Name:"),
|
|
||||||
dcc.Dropdown(
|
|
||||||
id="test-filter",
|
|
||||||
options=[{"label": test, "value": test} for test in df["test_name"].unique()],
|
|
||||||
value=df["test_name"].unique().tolist(),
|
|
||||||
multi=True,
|
|
||||||
),
|
|
||||||
],
|
|
||||||
style={"width": "25%", "display": "inline-block", "verticalAlign": "top", "padding": "10px"},
|
|
||||||
),
|
|
||||||
|
|
||||||
# Graphs
|
|
||||||
html.Div(
|
|
||||||
[
|
|
||||||
dcc.Graph(id="throughput-graph"),
|
|
||||||
dcc.Graph(id="latency-graph"),
|
|
||||||
],
|
|
||||||
style={"width": "70%", "display": "inline-block", "padding": "10px"},
|
|
||||||
),
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
# Callbacks
|
|
||||||
@app.callback(
|
|
||||||
[Output("throughput-graph", "figure"), Output("latency-graph", "figure")],
|
|
||||||
[Input("label-filter", "value"), Input("test-filter", "value")],
|
|
||||||
)
|
|
||||||
def update_graphs(selected_labels, selected_tests):
|
|
||||||
# Filter data
|
|
||||||
filtered_df = df[df["label"].isin(selected_labels) & df["test_name"].isin(selected_tests)]
|
|
||||||
|
|
||||||
# Throughput Graph
|
|
||||||
throughput_fig = go.Figure()
|
|
||||||
for label in filtered_df["label"].unique():
|
|
||||||
subset = filtered_df[filtered_df["label"] == label]
|
|
||||||
throughput_fig.add_trace(
|
|
||||||
go.Bar(
|
|
||||||
x=subset["test_name"],
|
|
||||||
y=subset["iops"],
|
|
||||||
name=f"{label} - IOPS",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
throughput_fig.add_trace(
|
|
||||||
go.Bar(
|
|
||||||
x=subset["test_name"],
|
|
||||||
y=subset["bandwidth_kibps"],
|
|
||||||
name=f"{label} - Bandwidth (KiB/s)",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
throughput_fig.update_layout(
|
|
||||||
title="Throughput (IOPS and Bandwidth)",
|
|
||||||
xaxis_title="Test Name",
|
|
||||||
yaxis_title="Value",
|
|
||||||
barmode="group",
|
|
||||||
)
|
|
||||||
|
|
||||||
# Latency Graph
|
|
||||||
latency_fig = go.Figure()
|
|
||||||
for label in filtered_df["label"].unique():
|
|
||||||
subset = filtered_df[filtered_df["label"] == label]
|
|
||||||
latency_fig.add_trace(
|
|
||||||
go.Scatter(
|
|
||||||
x=subset["test_name"],
|
|
||||||
y=subset["latency_mean_ms"],
|
|
||||||
mode="markers+lines",
|
|
||||||
name=f"{label} - Latency Mean (ms)",
|
|
||||||
error_y=dict(
|
|
||||||
type="data",
|
|
||||||
array=subset["latency_stddev_ms"],
|
|
||||||
visible=True,
|
|
||||||
),
|
|
||||||
)
|
|
||||||
)
|
|
||||||
latency_fig.update_layout(
|
|
||||||
title="Latency with Standard Deviation",
|
|
||||||
xaxis_title="Test Name",
|
|
||||||
yaxis_title="Latency (ms)",
|
|
||||||
)
|
|
||||||
|
|
||||||
return throughput_fig, latency_fig
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
app.run_server(debug=True)
|
|
||||||
@ -1,149 +0,0 @@
|
|||||||
import dash
|
|
||||||
from dash import dcc, html, Input, Output
|
|
||||||
import plotly.express as px
|
|
||||||
import pandas as pd
|
|
||||||
import dash_bootstrap_components as dbc
|
|
||||||
import io
|
|
||||||
|
|
||||||
# --- Sample Data ---
|
|
||||||
# In a real-world scenario, you would load this from a CSV file.
|
|
||||||
# For this self-contained example, we define the data directly.
|
|
||||||
# Example: df = pd.read_csv('benchmark_data.csv')
|
|
||||||
csv_data = """
|
|
||||||
config,op_type,iops,latency_ms,throughput_mbs
|
|
||||||
All-HDD,4k_random_read,260,60,1.02
|
|
||||||
All-HDD,4k_random_write,100,150,0.39
|
|
||||||
All-HDD,64k_sequential_read,2100,30,131.25
|
|
||||||
All-HDD,64k_sequential_write,1500,42,93.75
|
|
||||||
HDD+SSD_WAL,4k_random_read,270,58,1.05
|
|
||||||
HDD+SSD_WAL,4k_random_write,160,100,0.62
|
|
||||||
HDD+SSD_WAL,64k_sequential_read,2150,29,134.37
|
|
||||||
HDD+SSD_WAL,64k_sequential_write,1800,35,112.5
|
|
||||||
HDD+SSD_WAL_DB,4k_random_read,1250,12,4.88
|
|
||||||
HDD+SSD_WAL_DB,4k_random_write,1550,10,6.05
|
|
||||||
HDD+SSD_WAL_DB,64k_sequential_read,2200,28,137.5
|
|
||||||
HDD+SSD_WAL_DB,64k_sequential_write,2000,32,125
|
|
||||||
All-NVMe,4k_random_read,400000,0.1,1562.5
|
|
||||||
All-NVMe,4k_random_write,350000,0.12,1367.18
|
|
||||||
All-NVMe,64k_sequential_read,16000,4,1000
|
|
||||||
All-NVMe,64k_sequential_write,12500,5,800
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Read the data using pandas
|
|
||||||
df = pd.read_csv(io.StringIO(csv_data))
|
|
||||||
|
|
||||||
# Initialize the Dash app with a Bootstrap theme
|
|
||||||
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.FLATLY])
|
|
||||||
|
|
||||||
# --- App Layout ---
|
|
||||||
app.layout = dbc.Container([
|
|
||||||
# Header
|
|
||||||
dbc.Row([
|
|
||||||
dbc.Col([
|
|
||||||
html.H1("Ceph Cluster Benchmark Visualizer", className="text-primary"),
|
|
||||||
html.P(
|
|
||||||
"An interactive tool to compare performance metrics across different Ceph storage configurations.",
|
|
||||||
className="lead"
|
|
||||||
)
|
|
||||||
])
|
|
||||||
], className="my-4"),
|
|
||||||
|
|
||||||
# Controls and Graphs Row
|
|
||||||
dbc.Row([
|
|
||||||
# Control Panel Column
|
|
||||||
dbc.Col([
|
|
||||||
dbc.Card([
|
|
||||||
dbc.CardBody([
|
|
||||||
html.H4("Control Panel", className="card-title"),
|
|
||||||
html.Hr(),
|
|
||||||
|
|
||||||
# Metric Selection Radio Buttons
|
|
||||||
dbc.Label("Select Metric to Display:", html_for="metric-selector"),
|
|
||||||
dcc.RadioItems(
|
|
||||||
id='metric-selector',
|
|
||||||
options=[
|
|
||||||
{'label': 'IOPS (Input/Output Operations Per Second)', 'value': 'iops'},
|
|
||||||
{'label': 'Latency (in Milliseconds)', 'value': 'latency_ms'},
|
|
||||||
{'label': 'Throughput (in MB/s)', 'value': 'throughput_mbs'}
|
|
||||||
],
|
|
||||||
value='iops', # Default value
|
|
||||||
labelClassName="d-block" # Display labels as blocks
|
|
||||||
),
|
|
||||||
html.Hr(),
|
|
||||||
|
|
||||||
# Configuration Selection Checklist
|
|
||||||
dbc.Label("Select Configurations to Compare:", html_for="config-checklist"),
|
|
||||||
dcc.Checklist(
|
|
||||||
id='config-checklist',
|
|
||||||
options=[{'label': config, 'value': config} for config in df['config'].unique()],
|
|
||||||
value=df['config'].unique(), # Select all by default
|
|
||||||
labelClassName="d-block"
|
|
||||||
),
|
|
||||||
])
|
|
||||||
], className="mb-4")
|
|
||||||
], width=12, lg=4), # Full width on small screens, 1/3 on large
|
|
||||||
|
|
||||||
# Graph Display Column
|
|
||||||
dbc.Col([
|
|
||||||
dcc.Graph(id='benchmark-graph')
|
|
||||||
], width=12, lg=8) # Full width on small screens, 2/3 on large
|
|
||||||
])
|
|
||||||
], fluid=True) # Use a fluid container for full-width layout
|
|
||||||
|
|
||||||
|
|
||||||
# --- Callback Function ---
|
|
||||||
# This function connects the controls to the graph
|
|
||||||
@app.callback(
|
|
||||||
Output('benchmark-graph', 'figure'),
|
|
||||||
[Input('metric-selector', 'value'),
|
|
||||||
Input('config-checklist', 'value')]
|
|
||||||
)
|
|
||||||
def update_graph(selected_metric, selected_configs):
|
|
||||||
"""
|
|
||||||
This function is triggered whenever a control's value changes.
|
|
||||||
It filters the dataframe and returns an updated bar chart figure.
|
|
||||||
"""
|
|
||||||
if not selected_configs:
|
|
||||||
# If no configs are selected, return an empty figure to avoid errors
|
|
||||||
return go.Figure().update_layout(
|
|
||||||
title="Please select a configuration to view data.",
|
|
||||||
xaxis_title="",
|
|
||||||
yaxis_title=""
|
|
||||||
)
|
|
||||||
|
|
||||||
# Filter the DataFrame based on the selected configurations
|
|
||||||
filtered_df = df[df['config'].isin(selected_configs)]
|
|
||||||
|
|
||||||
# Create the bar chart using Plotly Express
|
|
||||||
fig = px.bar(
|
|
||||||
filtered_df,
|
|
||||||
x='op_type',
|
|
||||||
y=selected_metric,
|
|
||||||
color='config',
|
|
||||||
barmode='group', # Group bars for different configs side-by-side
|
|
||||||
labels={
|
|
||||||
"op_type": "Benchmark Operation Type",
|
|
||||||
"iops": "IOPS (Higher is Better)",
|
|
||||||
"latency_ms": "Latency in ms (Lower is Better)",
|
|
||||||
"throughput_mbs": "Throughput in MB/s (Higher is Better)",
|
|
||||||
"config": "Storage Configuration"
|
|
||||||
},
|
|
||||||
title=f"Benchmark Comparison for: {selected_metric.replace('_', ' ').title()}",
|
|
||||||
height=600 # Set a fixed height for the graph
|
|
||||||
)
|
|
||||||
|
|
||||||
# Update layout for better readability
|
|
||||||
fig.update_layout(
|
|
||||||
xaxis_title="Operation Type",
|
|
||||||
yaxis_title=selected_metric.replace('_', ' ').title(),
|
|
||||||
legend_title="Configuration",
|
|
||||||
title_x=0.5, # Center the title
|
|
||||||
xaxis={'categoryorder':'total descending' if selected_metric != 'latency_ms' else 'total ascending'}
|
|
||||||
)
|
|
||||||
|
|
||||||
return fig
|
|
||||||
|
|
||||||
# --- Run the App ---
|
|
||||||
if __name__ == '__main__':
|
|
||||||
# Use debug=True for development, allowing hot-reloading
|
|
||||||
app.run(debug=True)
|
|
||||||
@ -1,175 +0,0 @@
|
|||||||
import dash
|
|
||||||
from dash import dcc, html, Input, Output
|
|
||||||
import plotly.express as px
|
|
||||||
import pandas as pd
|
|
||||||
import dash_bootstrap_components as dbc
|
|
||||||
import io
|
|
||||||
import plotly.graph_objects as go
|
|
||||||
|
|
||||||
# --- Data Loading and Preparation ---
|
|
||||||
# 1. Use the exact iobench csv output format provided.
|
|
||||||
csv_data = """label,test_name,iops,bandwidth_kibps,latency_mean_ms,latency_stddev_ms
|
|
||||||
Ceph HDD Only,read-4k-sync-test,1474.302,5897,0.673,0.591
|
|
||||||
Ceph HDD Only,write-4k-sync-test,14.126,56,27.074,7.046
|
|
||||||
Ceph HDD Only,randread-4k-sync-test,225.140,900,4.436,6.918
|
|
||||||
Ceph HDD Only,randwrite-4k-sync-test,13.129,52,34.891,10.859
|
|
||||||
Ceph HDD Only,multiread-4k-sync-test,6873.675,27494,0.578,0.764
|
|
||||||
Ceph HDD Only,multiwrite-4k-sync-test,57.135,228,38.660,11.293
|
|
||||||
Ceph HDD Only,multirandread-4k-sync-test,2451.376,9805,1.626,2.515
|
|
||||||
Ceph HDD Only,multirandwrite-4k-sync-test,54.642,218,33.492,13.111
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,read-4k-sync-test,1495.700,5982,0.664,1.701
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,write-4k-sync-test,16.990,67,17.502,9.908
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,randread-4k-sync-test,159.256,637,6.274,9.232
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,randwrite-4k-sync-test,16.693,66,24.094,16.099
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multiread-4k-sync-test,7305.559,29222,0.544,1.338
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multiwrite-4k-sync-test,52.260,209,34.891,17.576
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multirandread-4k-sync-test,700.606,2802,5.700,10.429
|
|
||||||
Ceph 2 Hosts WAL+DB SSD and 1 Host HDD,multirandwrite-4k-sync-test,52.723,210,29.709,25.829
|
|
||||||
Ceph 2 Hosts WAL+DB SSD Only,randwrite-4k-sync-test,90.037,360,3.617,8.321
|
|
||||||
Ceph WAL+DB SSD During Rebuild,randwrite-4k-sync-test,41.008,164,10.138,19.333
|
|
||||||
Ceph WAL+DB SSD OSD HDD,read-4k-sync-test,1520.299,6081,0.654,1.539
|
|
||||||
Ceph WAL+DB SSD OSD HDD,write-4k-sync-test,78.528,314,4.074,9.101
|
|
||||||
Ceph WAL+DB SSD OSD HDD,randread-4k-sync-test,153.303,613,6.518,9.036
|
|
||||||
Ceph WAL+DB SSD OSD HDD,randwrite-4k-sync-test,48.677,194,8.785,20.356
|
|
||||||
Ceph WAL+DB SSD OSD HDD,multiread-4k-sync-test,6804.880,27219,0.584,1.422
|
|
||||||
Ceph WAL+DB SSD OSD HDD,multiwrite-4k-sync-test,311.513,1246,4.978,9.458
|
|
||||||
Ceph WAL+DB SSD OSD HDD,multirandread-4k-sync-test,581.756,2327,6.869,10.204
|
|
||||||
Ceph WAL+DB SSD OSD HDD,multirandwrite-4k-sync-test,120.556,482,13.463,25.440
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Read the data and create a more user-friendly bandwidth column in MB/s
|
|
||||||
df = pd.read_csv(io.StringIO(csv_data))
|
|
||||||
df['bandwidth_mbps'] = df['bandwidth_kibps'] / 1024
|
|
||||||
|
|
||||||
# --- App Initialization and Global Settings ---
|
|
||||||
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.FLATLY])
|
|
||||||
|
|
||||||
# 3. Create a consistent color map for each unique label (cluster topology).
|
|
||||||
unique_labels = df['label'].unique()
|
|
||||||
color_map = {label: color for label, color in zip(unique_labels, px.colors.qualitative.Plotly)}
|
|
||||||
|
|
||||||
# --- App Layout ---
|
|
||||||
app.layout = dbc.Container([
|
|
||||||
# Header
|
|
||||||
dbc.Row([
|
|
||||||
dbc.Col([
|
|
||||||
html.H1("Ceph iobench Performance Dashboard", className="text-primary"),
|
|
||||||
html.P(
|
|
||||||
"Compare benchmark results across different Ceph cluster configurations and metrics.",
|
|
||||||
className="lead"
|
|
||||||
)
|
|
||||||
])
|
|
||||||
], className="my-4"),
|
|
||||||
|
|
||||||
# Controls and Graphs Row
|
|
||||||
dbc.Row([
|
|
||||||
# Control Panel Column
|
|
||||||
dbc.Col([
|
|
||||||
dbc.Card([
|
|
||||||
dbc.CardBody([
|
|
||||||
html.H4("Control Panel", className="card-title"),
|
|
||||||
html.Hr(),
|
|
||||||
|
|
||||||
# 2. Metric Selection Checklist to view multiple graphs
|
|
||||||
dbc.Label("Select Metrics to Display:", html_for="metric-checklist", className="fw-bold"),
|
|
||||||
dcc.Checklist(
|
|
||||||
id='metric-checklist',
|
|
||||||
options=[
|
|
||||||
{'label': 'IOPS', 'value': 'iops'},
|
|
||||||
{'label': 'Latency (ms)', 'value': 'latency_mean_ms'},
|
|
||||||
{'label': 'Bandwidth (MB/s)', 'value': 'bandwidth_mbps'}
|
|
||||||
],
|
|
||||||
value=['iops', 'latency_mean_ms'], # Default selection
|
|
||||||
labelClassName="d-block"
|
|
||||||
),
|
|
||||||
html.Hr(),
|
|
||||||
|
|
||||||
# Configuration Selection Checklist
|
|
||||||
dbc.Label("Select Configurations to Compare:", html_for="config-checklist", className="fw-bold"),
|
|
||||||
dcc.Checklist(
|
|
||||||
id='config-checklist',
|
|
||||||
options=[{'label': label, 'value': label} for label in unique_labels],
|
|
||||||
value=unique_labels, # Select all by default
|
|
||||||
labelClassName="d-block"
|
|
||||||
),
|
|
||||||
])
|
|
||||||
], className="mb-4")
|
|
||||||
], width=12, lg=4),
|
|
||||||
|
|
||||||
# Graph Display Column - This will be populated by the callback
|
|
||||||
dbc.Col(id='graph-container', width=12, lg=8)
|
|
||||||
])
|
|
||||||
], fluid=True)
|
|
||||||
|
|
||||||
|
|
||||||
# --- Callback Function ---
|
|
||||||
@app.callback(
|
|
||||||
Output('graph-container', 'children'),
|
|
||||||
[Input('metric-checklist', 'value'),
|
|
||||||
Input('config-checklist', 'value')]
|
|
||||||
)
|
|
||||||
def update_graphs(selected_metrics, selected_configs):
|
|
||||||
"""
|
|
||||||
This function is triggered when a control's value changes.
|
|
||||||
It generates and returns a list of graphs based on user selections.
|
|
||||||
"""
|
|
||||||
# Handle cases where no selection is made to prevent errors
|
|
||||||
if not selected_metrics or not selected_configs:
|
|
||||||
return dbc.Alert("Please select at least one metric and one configuration to view data.", color="info")
|
|
||||||
|
|
||||||
# Filter the DataFrame based on the selected configurations
|
|
||||||
filtered_df = df[df['label'].isin(selected_configs)]
|
|
||||||
|
|
||||||
# Create a list to hold all the graph components
|
|
||||||
graph_list = []
|
|
||||||
|
|
||||||
# Define user-friendly titles for graphs
|
|
||||||
metric_titles = {
|
|
||||||
'iops': 'IOPS Comparison (Higher is Better)',
|
|
||||||
'latency_mean_ms': 'Mean Latency (ms) Comparison (Lower is Better)',
|
|
||||||
'bandwidth_mbps': 'Bandwidth (MB/s) Comparison (Higher is Better)'
|
|
||||||
}
|
|
||||||
|
|
||||||
# Loop through each selected metric and create a graph for it
|
|
||||||
for metric in selected_metrics:
|
|
||||||
# Determine if sorting should be ascending (for latency) or descending
|
|
||||||
sort_order = 'total ascending' if metric == 'latency_mean_ms' else 'total descending'
|
|
||||||
|
|
||||||
# Special handling for latency to include error bars for standard deviation
|
|
||||||
error_y_param = 'latency_stddev_ms' if metric == 'latency_mean_ms' else None
|
|
||||||
|
|
||||||
fig = px.bar(
|
|
||||||
filtered_df,
|
|
||||||
x='test_name',
|
|
||||||
y=metric,
|
|
||||||
color='label',
|
|
||||||
barmode='group',
|
|
||||||
color_discrete_map=color_map, # 3. Apply the consistent color map
|
|
||||||
error_y=error_y_param, # Adds error bars for latency stddev
|
|
||||||
title=metric_titles.get(metric, metric),
|
|
||||||
labels={
|
|
||||||
"test_name": "Benchmark Test Name",
|
|
||||||
"iops": "IOPS",
|
|
||||||
"latency_mean_ms": "Mean Latency (ms)",
|
|
||||||
"bandwidth_mbps": "Bandwidth (MB/s)",
|
|
||||||
"label": "Cluster Configuration"
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
fig.update_layout(
|
|
||||||
height=500,
|
|
||||||
xaxis_title=None, # Clean up x-axis title
|
|
||||||
legend_title="Configuration",
|
|
||||||
title_x=0.5, # Center the title
|
|
||||||
xaxis={'categoryorder': sort_order},
|
|
||||||
xaxis_tickangle=-45 # Angle labels to prevent overlap
|
|
||||||
)
|
|
||||||
|
|
||||||
# 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