feat: Add iobench project and python dashboard

2025-08-14 10:37:30 -04:00
parent bd214f8fb8
commit fd8f643a8f
9 changed files with 1010 additions and 0 deletions
--- a/iobench/dash/iobench-dash-v1.py
+++ b/iobench/dash/iobench-dash-v1.py
@@ -0,0 +1,109 @@
+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)