fix benchmark

ploomber · Jul 12, 2023 · e3bc5de · e3bc5de
1 parent a08a356
commit e3bc5de
Showing 1 changed file with 4 additions and 165 deletions.
diff --git a/benchmarks/duckdb.ipynb b/benchmarks/duckdb.ipynb
@@ -80,6 +80,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "892bc403-4f35-4489-889e-92eb76bda725",
    "metadata": {
@@ -106,100 +107,6 @@
     "%config SqlMagic.displaylimit = 1"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c7755eae-1943-4f57-9eb6-cf1354397bde",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %timeit %sql select * from df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f1434280-93f5-4693-a106-67ac9ae03fab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sql.run import run\n",
-    "from sql.connection import Connection\n",
-    "from unittest.mock import Mock\n",
-    "\n",
-    "config = Mock()\n",
-    "config.autocommit = True\n",
-    "config.autolimit = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "8d904cc9-975e-4475-bd52-b4cfa1a303a0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sql.magic import SqlMagic\n",
-    "from IPython import start_ipython, get_ipython\n",
-    "import duckdb\n",
-    "from pandas import DataFrame\n",
-    "import numpy as np\n",
-    "\n",
-    "num_rows = 1_000_000\n",
-    "\n",
-    "df = DataFrame(np.random.randn(num_rows, 20))\n",
-    "\n",
-    "magic = SqlMagic(get_ipython())\n",
-    "\n",
-    "conn = duckdb.connect()\n",
-    "# magic.execute(line=\"conn --alias duckdb\")\n",
-    "magic.execute(line=\"duckdb:// --alias duckdb\")\n",
-    "magic.autopandas = True\n",
-    "magic.displaycon = False\n",
-    "\n",
-    "\n",
-    "def run_magic():\n",
-    "    return magic.execute(\"SELECT * FROM df\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ed06bc68-223a-4657-8dfe-4f07d6277b2c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.61 s ± 18.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "run_magic()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "066fe987-cdd4-4b34-9203-cf58881f34e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "82.5 ms ± 1.38 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "run(Connection.current, \"SELECT * FROM df\", config)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 18,
@@ -218,80 +125,11 @@
    ],
    "source": [
     "%%timeit\n",
-    "df_ = %sql select * from df\n",
-    "df_.DataFrame()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d1a0e680-6f2f-4ace-a388-dd14e59dba02",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%config SqlMagic.autopandas = False\n",
-    "df_ = %sql select * from df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2705fec6-27b8-44c0-bf2a-420b35138038",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "86.5 ms ± 1.81 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "df_.DataFrame()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "af181428-4586-4c5c-bf01-10ed4be5cff6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "85.7 ms ± 659 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "df_.sqlaproxy.execute(df_.statement)\n",
-    "df_.sqlaproxy.df()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9321e0a8-6f83-4dd4-abff-8dffe958e35b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "87.1 ms ± 651 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "df_.sqlaproxy.execute(df_.statement).df()"
+    "%sql select * from df"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "1abcf8ab-0542-43fc-9cac-5ea9363e0827",
    "metadata": {
@@ -331,6 +169,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e7fb905f-4a13-4ee4-8150-30ee079e4ec4",
    "metadata": {