python-bpf/BCC-Examples/vfsreadlat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d1a31e8-35d7-4ee6-8c90-f3715eaa2501",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pythonbpf import bpf, map, struct, section, bpfglobal, BPF\n",
    "from pythonbpf.helper import ktime, pid\n",
    "from pythonbpf.maps import HashMap, PerfEventArray\n",
    "from ctypes import c_void_p, c_uint64\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0198957-42a3-4e1b-89ba-f08d3b9d3736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading BPF program...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'do_entry': <pylibbpf.pylibbpf.BpfProgram at 0xffff7c9e2c70>,\n",
       " 'do_return': <pylibbpf.pylibbpf.BpfProgram at 0xffff7c6bb330>}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@bpf\n",
    "@struct\n",
    "class latency_event:\n",
    "    pid: c_uint64\n",
    "    delta_us: c_uint64  # Latency in microseconds\n",
    "\n",
    "\n",
    "@bpf\n",
    "@map\n",
    "def start() -> HashMap:\n",
    "    return HashMap(key=c_uint64, value=c_uint64, max_entries=10240)\n",
    "\n",
    "\n",
    "@bpf\n",
    "@map\n",
    "def events() -> PerfEventArray:\n",
    "    return PerfEventArray(key_size=c_uint64, value_size=c_uint64)\n",
    "\n",
    "\n",
    "@bpf\n",
    "@section(\"kprobe/vfs_read\")\n",
    "def do_entry(ctx: c_void_p) -> c_uint64:\n",
    "    p, ts = pid(), ktime()\n",
    "    start.update(p, ts)\n",
    "    return 0\n",
    "\n",
    "\n",
    "@bpf\n",
    "@section(\"kretprobe/vfs_read\")\n",
    "def do_return(ctx: c_void_p) -> c_uint64:\n",
    "    p = pid()\n",
    "    tsp = start.lookup(p)\n",
    "\n",
    "    if tsp:\n",
    "        delta_ns = ktime() - tsp\n",
    "\n",
    "        # Only track if latency > 1 microsecond\n",
    "        if delta_ns > 1000:\n",
    "            evt = latency_event()\n",
    "            evt.pid, evt.delta_us = p, delta_ns // 1000\n",
    "            events.output(evt)\n",
    "\n",
    "        start.delete(p)\n",
    "\n",
    "    return 0  # type: ignore [return-value]\n",
    "\n",
    "\n",
    "@bpf\n",
    "@bpfglobal\n",
    "def LICENSE() -> str:\n",
    "    return \"GPL\"\n",
    "\n",
    "\n",
    "# Load BPF\n",
    "print(\"Loading BPF program...\")\n",
    "b = BPF()\n",
    "b.load()\n",
    "b.attach_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d84bba81-2923-4244-ad86-98925b9cc7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing vfs_read latency... Hit Ctrl-C to end.\n",
      "Collected 1000 samples...\n",
      "Collected 2000 samples...\n",
      "Collected 3000 samples...\n",
      "Collected 4000 samples...\n",
      "Collected 5000 samples...\n",
      "Collected 6000 samples...\n",
      "Collected 6050 samples. Generating histogram...\n"
     ]
    }
   ],
   "source": [
    "# Collect latencies\n",
    "latencies = []\n",
    "\n",
    "\n",
    "def callback(cpu, event):\n",
    "    latencies.append(event.delta_us)\n",
    "\n",
    "\n",
    "b[\"events\"].open_perf_buffer(callback, struct_name=\"latency_event\")\n",
    "\n",
    "print(\"Tracing vfs_read latency... Hit Ctrl-C to end.\")\n",
    "\n",
    "try:\n",
    "    while True:\n",
    "        b[\"events\"].poll(1000)\n",
    "        if len(latencies) > 0 and len(latencies) % 1000 == 0:\n",
    "            print(f\"Collected {len(latencies)} samples...\")\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    print(f\"Collected {len(latencies)} samples. Generating histogram...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "939ffdc8-1806-4e6c-84ce-be109681178c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics:\n",
      "  Count:  6050\n",
      "  Min:    1 µs\n",
      "  Max:    1086 µs\n",
      "  Mean:   9.80 µs\n",
      "  Median: 3.00 µs\n",
      "  P95:    47.00 µs\n",
      "  P99:    158.00 µs\n",
      "Histogram saved to vfs_read_latency.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram with matplotlib\n",
    "if latencies:\n",
    "    # Use log scale for better visualization\n",
    "    log_latencies = np.log2(latencies)\n",
    "\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    # Plot 1: Linear histogram\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.hist(latencies, bins=50, edgecolor=\"black\", alpha=0.7)\n",
    "    plt.xlabel(\"Latency (microseconds)\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.title(\"VFS Read Latency Distribution (Linear)\")\n",
    "    plt.grid(True, alpha=0.3)\n",
    "\n",
    "    # Plot 2: Log2 histogram (like BCC)\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.hist(log_latencies, bins=50, edgecolor=\"black\", alpha=0.7, color=\"orange\")\n",
    "    plt.xlabel(\"log2(Latency in µs)\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.title(\"VFS Read Latency Distribution (Log2)\")\n",
    "    plt.grid(True, alpha=0.3)\n",
    "\n",
    "    # Add statistics\n",
    "    print(\"Statistics:\")\n",
    "    print(f\"  Count:  {len(latencies)}\")\n",
    "    print(f\"  Min:    {min(latencies)} µs\")\n",
    "    print(f\"  Max:    {max(latencies)} µs\")\n",
    "    print(f\"  Mean:   {np.mean(latencies):.2f} µs\")\n",
    "    print(f\"  Median: {np.median(latencies):.2f} µs\")\n",
    "    print(f\"  P95:    {np.percentile(latencies, 95):.2f} µs\")\n",
    "    print(f\"  P99:    {np.percentile(latencies, 99):.2f} µs\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"vfs_read_latency.png\", dpi=150)\n",
    "    print(\"Histogram saved to vfs_read_latency.png\")\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No samples collected!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "348abd5a-0d94-4cbc-866c-45455c2eb953",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}