Updated benchmark to reflect deprecation of Table.dtypes

benchmarks.ipynb

@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -60,15 +60,14 @@
       "Table 1,000,000 rows is 240 Mb on disk\n",
       "Table 2,000,000 rows is 480 Mb on disk\n",
       "Table 5,000,000 rows is 1,200 Mb on disk\n",
-      "Table 20,000,000 rows is 4,800 Mb on disk\n",
-      "Table 50,000,000 rows is 12,000 Mb on disk\n"
+      "Table 10,000,000 rows is 2,400 Mb on disk\n"
      ]
     }
    ],
    "source": [
     "process = psutil.Process(os.getpid())\n",
     "\n",
-    "def make_tables(sizes=[1,2,5,20,50]):\n",
+    "def make_tables(sizes=[1,2,5,10,20,50]):\n",
     "    # The last tables are too big for RAM (~24Gb), so I create subtables of 1M rows and append them.\n",
     "    t = synthetic_order_data(Config.PAGE_SIZE)\n",
     "    real, flat = t.nbytes()\n",
@@ -790,14 +789,47 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Table.dtypes()\n",
+    "### Table.types()\n",
+    "\n",
+    "Table.types() is implemented for near constant speed lookup.\n",
     "\n",
-    "Table.dtypes() is implemented for near constant speed lookup."
+    "Here is an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'#': {int: 10000000},\n",
+       " '1': {int: 10000000},\n",
+       " '2': {str: 10000000},\n",
+       " '3': {int: 10000000},\n",
+       " '4': {int: 10000000},\n",
+       " '5': {int: 10000000},\n",
+       " '6': {str: 10000000},\n",
+       " '7': {str: 10000000},\n",
+       " '8': {str: 10000000},\n",
+       " '9': {str: 10000000},\n",
+       " '10': {float: 10000000},\n",
+       " '11': {str: 10000000}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tables[-1].types()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -807,7 +839,7 @@
     "\n",
     "    for table in tables:\n",
     "        start_time = perf_counter()\n",
-    "        dt = table.dtypes()\n",
+    "        dt = table.types()\n",
     "        end_time = perf_counter()\n",
     "        assert isinstance(dt, dict) and len(dt) != 0\n",
     "        dtypes_results.add_rows( len(table), round(end_time-start_time, 3) )\n",
@@ -817,19 +849,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div><table border=1><tr><th>#</th><th>rows</th><th>time (s)</th></tr><tr><th> 0</th><th>1000000</th><th>0.0</th></tr><tr><th> 1</th><th>2000000</th><th>0.0</th></tr><tr><th> 2</th><th>5000000</th><th>0.0</th></tr><tr><th> 3</th><th>20000000</th><th>0.0</th></tr><tr><th> 4</th><th>50000000</th><th>0.0</th></tr></table></div>"
+       "<div><table border=1><tr><th>#</th><th>rows</th><th>time (s)</th></tr><tr><th> 0</th><th>1000000</th><th>0.0</th></tr><tr><th> 1</th><th>2000000</th><th>0.0</th></tr><tr><th> 2</th><th>5000000</th><th>0.0</th></tr><tr><th> 3</th><th>10000000</th><th>0.0</th></tr></table></div>"
       ],
       "text/plain": [
-       "Table(2 columns, 5 rows)"
+       "Table(2 columns, 4 rows)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }