diff --git a/compareDBs.ipynb b/compareDBs.ipynb
index 6d2d5a0..e1c6322 100644
--- a/compareDBs.ipynb
+++ b/compareDBs.ipynb
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a",
    "metadata": {
     "tags": []
@@ -59,7 +59,7 @@
        "False"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -71,14 +71,17 @@
     "from datetime import datetime\n",
     "\n",
     "import duckdb\n",
+    "\n",
+    "# from influxdb_client import InfluxDBClient\n",
+    "import influxdb_client\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pdmongo as pdm\n",
     "from clickhouse_driver import Client\n",
     "from dotenv import load_dotenv\n",
-    "from influxdb_client import InfluxDBClient\n",
-    "from influxdb_client.client.write_api import SYNCHRONOUS\n",
+    "\n",
+    "# from influxdb_client.client.write_api import SYNCHRONOUS\n",
     "from minio import Minio\n",
     "from pymongo import MongoClient\n",
     "from pytz import timezone\n",
@@ -139,10 +142,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3634a4ec-04c2-4f1e-8659-5d22eb17a254",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>from</th>\n",
+       "      <th>at</th>\n",
+       "      <th>to</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>999995</th>\n",
+       "      <td>7984748</td>\n",
+       "      <td>2023-03-03 18:13:30</td>\n",
+       "      <td>1677867225000000000</td>\n",
+       "      <td>2023-03-03 18:13:45</td>\n",
+       "      <td>1.062695</td>\n",
+       "      <td>1.062635</td>\n",
+       "      <td>1.062630</td>\n",
+       "      <td>1.062700</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999996</th>\n",
+       "      <td>7984749</td>\n",
+       "      <td>2023-03-03 18:13:45</td>\n",
+       "      <td>1677867240000000000</td>\n",
+       "      <td>2023-03-03 18:14:00</td>\n",
+       "      <td>1.062645</td>\n",
+       "      <td>1.062650</td>\n",
+       "      <td>1.062625</td>\n",
+       "      <td>1.062650</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999997</th>\n",
+       "      <td>7984750</td>\n",
+       "      <td>2023-03-03 18:14:00</td>\n",
+       "      <td>1677867255000000000</td>\n",
+       "      <td>2023-03-03 18:14:15</td>\n",
+       "      <td>1.062640</td>\n",
+       "      <td>1.062625</td>\n",
+       "      <td>1.062620</td>\n",
+       "      <td>1.062665</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999998</th>\n",
+       "      <td>7984751</td>\n",
+       "      <td>2023-03-03 18:14:15</td>\n",
+       "      <td>1677867270000000000</td>\n",
+       "      <td>2023-03-03 18:14:30</td>\n",
+       "      <td>1.062625</td>\n",
+       "      <td>1.062535</td>\n",
+       "      <td>1.062535</td>\n",
+       "      <td>1.062645</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999999</th>\n",
+       "      <td>7984752</td>\n",
+       "      <td>2023-03-03 18:14:30</td>\n",
+       "      <td>1677867285000000000</td>\n",
+       "      <td>2023-03-03 18:14:45</td>\n",
+       "      <td>1.062535</td>\n",
+       "      <td>1.062520</td>\n",
+       "      <td>1.062520</td>\n",
+       "      <td>1.062580</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             id                 from                   at  \\\n",
+       "999995  7984748  2023-03-03 18:13:30  1677867225000000000   \n",
+       "999996  7984749  2023-03-03 18:13:45  1677867240000000000   \n",
+       "999997  7984750  2023-03-03 18:14:00  1677867255000000000   \n",
+       "999998  7984751  2023-03-03 18:14:15  1677867270000000000   \n",
+       "999999  7984752  2023-03-03 18:14:30  1677867285000000000   \n",
+       "\n",
+       "                         to      open     close       min       max  volume  \n",
+       "999995  2023-03-03 18:13:45  1.062695  1.062635  1.062630  1.062700      64  \n",
+       "999996  2023-03-03 18:14:00  1.062645  1.062650  1.062625  1.062650      43  \n",
+       "999997  2023-03-03 18:14:15  1.062640  1.062625  1.062620  1.062665      47  \n",
+       "999998  2023-03-03 18:14:30  1.062625  1.062535  1.062535  1.062645      43  \n",
+       "999999  2023-03-03 18:14:45  1.062535  1.062520  1.062520  1.062580      59  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# %%time\n",
     "# Load Dataset\n",
@@ -486,7 +607,6 @@
    "cell_type": "markdown",
    "id": "1d389546-911f-43f7-aad1-49f7bcc83503",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -495,7 +615,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 33,
+   "id": "ecd217ab-0e16-40a6-9b92-9212b9bb20e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "from(bucket: \"EURUSDtest\")\n",
+    "|> range(start:2023-03-03T18:14:30Z, stop: now())\n",
+    "|> filter(fn: (r) => r._measurement == \"id\")\n",
+    "|> pivot(rowKey:[\"_time\"], columnKey: [\"_field\"], valueColumn: \"_value\")\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af",
    "metadata": {},
    "outputs": [],
@@ -582,22 +718,6 @@
     "print(influxdb_write_execution_time)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "ecd217ab-0e16-40a6-9b92-9212b9bb20e9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "query = \"\"\"\n",
-    "from(bucket: \"EURUSDtest\")\n",
-    "|> range(start:2023-03-03T18:14:30Z, stop: now())\n",
-    "|> filter(fn: (r) => r._measurement == \"id\")\n",
-    "|> pivot(rowKey:[\"_time\"], columnKey: [\"_field\"], valueColumn: \"_value\")\"\"\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 120,
@@ -1507,7 +1627,6 @@
    "cell_type": "markdown",
    "id": "97405e42-61dc-42c7-8220-237a312c0ec7",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -1521,6 +1640,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# https://duckdb.org/2022/07/27/art-storage.html\n",
+    "\n",
+    "\n",
     "def duckdbConnect():\n",
     "    cursor = duckdb.connect()\n",
     "    return cursor\n",
@@ -1763,12 +1885,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "bbd217e3-695f-4fa6-ae42-83db1dde8311",
    "metadata": {},
    "outputs": [],
    "source": [
     "# functions\n",
+    "# cd ~ && q/l64/q -p 5001\n",
     "\n",
     "\n",
     "def kdbConnect():\n",
@@ -1793,52 +1916,60 @@
     "\n",
     "def kdbRead():\n",
     "    q = kdbConnect()\n",
-    "    df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")\n",
-    "    df2 = q.sendSync(\"tab2\")\n",
+    "    # df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")\n",
+    "    # df2 = q.sendSync(\"tab2\")\n",
     "    df = pd.DataFrame(q(\"t\"))  # , pandas=True))\n",
-    "    df3 = q.sendSync(\"select from t\")\n",
+    "    # df3 = q.sendSync(\"select from t\")\n",
     "    # ver todos esses loads\n",
-    "    q.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc239236-bb47-4bcb-8e50-ac900852cd47",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load"
+    "    q.close()\n",
+    "    return 0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "67f0c26e-44fb-40b0-a147-5d97bfbbded2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# write"
+    "# write\n",
+    "start = timeit.default_timer()\n",
+    "dfKdb = kdbWrite()\n",
+    "stop = timeit.default_timer()\n",
+    "kdb_write_execution_time = stop - start"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "dcb200be-ffc9-4bcc-8554-5740fb420ab5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8739770100000896\n"
+     ]
+    }
+   ],
    "source": [
-    "# print write time"
+    "# print write time\n",
+    "print(kdb_write_execution_time)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "d4ce0203-b0c7-440b-a3ca-d7b2a7682474",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# read"
+    "# read\n",
+    "start = timeit.default_timer()\n",
+    "dfKdb = kdbRead()\n",
+    "stop = timeit.default_timer()\n",
+    "kdb_read_execution_time = stop - start"
    ]
   },
   {
@@ -1846,150 +1977,107 @@
    "execution_count": null,
    "id": "1a16fd76-2158-40fe-9285-c53791f8ed51",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print read time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "8ff6c090-7e02-435a-a179-f2aab81da972",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read csv\n",
-    "data = pd.read_csv(\"out.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4eb8ab9-81e8-4732-8cf7-51f0981d3d57",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# open connection\n",
-    "q = qconnection.QConnection(host=\"localhost\", port=5001)\n",
-    "q.open()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "97cb6b5b-65a5-46a0-a4ee-e5c535a716ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "# send df to kbd+ in memory bank\n",
-    "q.sendSync(\"{t::x}\", data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2ed2d51-bc8e-4207-892a-35fc55d43570",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# write to on disk table\n",
-    "q.sendSync(\"`:/home/sandman/q/tab1 set t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c055a95-f73f-43a3-8fbd-61e42235117e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "# read from on disk table\n",
-    "df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9760de38-9f04-4322-bfff-c7ee12d5dee5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# print(df2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c06c9222-c69d-4872-9d21-052281a013e2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "# load to variable df2\n",
-    "df2 = q.sendSync(\"tab2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8815f01c-fd0a-4f94-ab7f-f8ede84ba4e7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# df2(type)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e6ed3927-4395-45cd-9a28-88c5db01f2e5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "# converto to dataframe\n",
-    "df = pd.DataFrame(q(\"t\"))  # , pandas=True))\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0fc7f16b-6c39-4ebe-88d2-ff857e30ab62",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.153738381999574\n"
+     ]
+    }
+   ],
    "source": [
-    "%%time\n",
-    "# select\n",
-    "df3 = q.sendSync(\"select from t\")"
+    "# print read time\n",
+    "print(kdb_read_execution_time)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c88646ca-3d25-4a85-80b5-f9e559f568dd",
+   "execution_count": 46,
+   "id": "3a09558c-73e6-4324-9fc5-782fcd0d12e5",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Write Time</th>\n",
+       "      <th>Read Time</th>\n",
+       "      <th>Total Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Kdb+</th>\n",
+       "      <td>2.87 sec</td>\n",
+       "      <td>4.15 sec</td>\n",
+       "      <td>7.03 sec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r2</th>\n",
+       "      <td>fill</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r3</th>\n",
+       "      <td>fill</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Write Time Read Time Total Time\n",
+       "Kdb+   2.87 sec  4.15 sec   7.03 sec\n",
+       "r2         fill        15          0\n",
+       "r3         fill        14          0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "q.close()"
+    "s = \" sec\"\n",
+    "data = [\n",
+    "    [\n",
+    "        \"{:.2f}\".format(kdb_write_execution_time) + s,\n",
+    "        \"{:.2f}\".format(kdb_read_execution_time) + s,\n",
+    "        \"{:.2f}\".format(kdb_write_execution_time + kdb_read_execution_time) + s,\n",
+    "    ],\n",
+    "    [\"fill\", 15, 0],\n",
+    "    [\"fill\", 14, 0],\n",
+    "]\n",
+    "\n",
+    "index_labels=['Kdb+','r2','r3']\n",
+    "# Create the pandas DataFrame\n",
+    "df = pd.DataFrame(data, columns=[\"Write Time\", \"Read Time\", \"Total Time\"],index=index_labels)\n",
+    "\n",
+    "# print dataframe.\n",
+    "df"
    ]
   },
   {