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write influxdb read function

master
flashlan 3 years ago
parent
09e1b8e6bb
commit
84a2bbd8c4
1 changed files with 369 additions and 104 deletions
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  1. 467
      compareDBs.ipynb

467
compareDBs.ipynb
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@ -47,7 +47,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 34,
"id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a", "id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a",
"metadata": { "metadata": {
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@ -59,7 +59,7 @@
"False" "False"
] ]
}, },
"execution_count": 1, "execution_count": 34,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -71,12 +71,13 @@
"from datetime import datetime\n", "from datetime import datetime\n",
"\n", "\n",
"import duckdb\n", "import duckdb\n",
"import influxdb_client\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"from clickhouse_driver import Client\n", "from clickhouse_driver import Client\n",
"from dotenv import load_dotenv\n", "from dotenv import load_dotenv\n",
"from influxdb_client import InfluxDBClient\n",
"from influxdb_client.client.write_api import SYNCHRONOUS\n",
"from minio import Minio\n", "from minio import Minio\n",
"from pymongo import MongoClient\n", "from pymongo import MongoClient\n",
"from pytz import timezone\n", "from pytz import timezone\n",
@ -100,7 +101,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 42,
"id": "968403e3-2e5e-4834-b969-be4600e2963a", "id": "968403e3-2e5e-4834-b969-be4600e2963a",
"metadata": { "metadata": {
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@ -135,7 +136,7 @@
}, },
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"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -252,7 +253,7 @@
"999999 2023-03-03 18:14:45 1.062535 1.062520 1.062520 1.062580 59 " "999999 2023-03-03 18:14:45 1.062535 1.062520 1.062520 1.062580 59 "
] ]
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"execution_count": 5, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -291,7 +292,7 @@
}, },
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"id": "27de1ec8-4de1-440a-b555-b4a46c5ef7ce", "id": "27de1ec8-4de1-440a-b555-b4a46c5ef7ce",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -305,6 +306,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "4a8d5703-9bc9-4d38-83ff-457159304d58", "id": "4a8d5703-9bc9-4d38-83ff-457159304d58",
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@ -313,7 +315,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 8,
"id": "c3202bbb-2655-45b2-b166-9f45a3ef854c", "id": "c3202bbb-2655-45b2-b166-9f45a3ef854c",
"metadata": { "metadata": {
"tags": [] "tags": []
@ -325,7 +327,7 @@
"'Database created'" "'Database created'"
] ]
}, },
"execution_count": 20, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -379,7 +381,7 @@
}, },
{ {
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"execution_count": 21, "execution_count": 9,
"id": "cc4865b3-a1bc-4a35-9624-15334754b3a1", "id": "cc4865b3-a1bc-4a35-9624-15334754b3a1",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -393,7 +395,7 @@
}, },
{ {
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"id": "1fac82c1-2d04-44ef-893a-dc13b755e6d8", "id": "1fac82c1-2d04-44ef-893a-dc13b755e6d8",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -407,7 +409,7 @@
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@ -447,64 +449,64 @@
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>2999995</th>\n", " <th>999995</th>\n",
" <td>8230798</td>\n", " <td>7857774</td>\n",
" <td>2023-05-03 10:35:00</td>\n", " <td>2023-02-01 17:06:00</td>\n",
" <td>1683110115000000000</td>\n", " <td>1675271175000000000</td>\n",
" <td>2023-05-03 10:35:15</td>\n", " <td>2023-02-01 17:06:15</td>\n",
" <td>1.103340</td>\n", " <td>1.091725</td>\n",
" <td>1.10330</td>\n", " <td>1.091670</td>\n",
" <td>1.103275</td>\n", " <td>1.09166</td>\n",
" <td>1.103340</td>\n", " <td>1.09176</td>\n",
" <td>61</td>\n", " <td>84</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2999996</th>\n", " <th>999996</th>\n",
" <td>8230799</td>\n", " <td>7857775</td>\n",
" <td>2023-05-03 10:35:15</td>\n", " <td>2023-02-01 17:06:15</td>\n",
" <td>1683110130000000000</td>\n", " <td>1675271190000000000</td>\n",
" <td>2023-05-03 10:35:30</td>\n", " <td>2023-02-01 17:06:30</td>\n",
" <td>1.103300</td>\n", " <td>1.091680</td>\n",
" <td>1.10341</td>\n", " <td>1.091660</td>\n",
" <td>1.103300</td>\n", " <td>1.09165</td>\n",
" <td>1.103410</td>\n", " <td>1.09168</td>\n",
" <td>44</td>\n", " <td>51</td>\n",
" </tr>\n", " </tr>\n",
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" <th>2999997</th>\n", " <th>999997</th>\n",
" <td>8230799</td>\n", " <td>7857775</td>\n",
" <td>2023-05-03 10:35:15</td>\n", " <td>2023-02-01 17:06:15</td>\n",
" <td>1683110130000000000</td>\n", " <td>1675271190000000000</td>\n",
" <td>2023-05-03 10:35:30</td>\n", " <td>2023-02-01 17:06:30</td>\n",
" <td>1.103300</td>\n", " <td>1.091680</td>\n",
" <td>1.10341</td>\n", " <td>1.091660</td>\n",
" <td>1.103300</td>\n", " <td>1.09165</td>\n",
" <td>1.103410</td>\n", " <td>1.09168</td>\n",
" <td>44</td>\n", " <td>51</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2999998</th>\n", " <th>999998</th>\n",
" <td>8230800</td>\n", " <td>7857776</td>\n",
" <td>2023-05-03 10:35:30</td>\n", " <td>2023-02-01 17:06:30</td>\n",
" <td>1683110145000000000</td>\n", " <td>1675271205000000000</td>\n",
" <td>2023-05-03 10:35:45</td>\n", " <td>2023-02-01 17:06:45</td>\n",
" <td>1.103415</td>\n", " <td>1.091660</td>\n",
" <td>1.10351</td>\n", " <td>1.091655</td>\n",
" <td>1.103385</td>\n", " <td>1.09164</td>\n",
" <td>1.103515</td>\n", " <td>1.09168</td>\n",
" <td>51</td>\n", " <td>63</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2999999</th>\n", " <th>999999</th>\n",
" <td>8230800</td>\n", " <td>7857776</td>\n",
" <td>2023-05-03 10:35:30</td>\n", " <td>2023-02-01 17:06:30</td>\n",
" <td>1683110145000000000</td>\n", " <td>1675271205000000000</td>\n",
" <td>2023-05-03 10:35:45</td>\n", " <td>2023-02-01 17:06:45</td>\n",
" <td>1.103415</td>\n", " <td>1.091660</td>\n",
" <td>1.10351</td>\n", " <td>1.091655</td>\n",
" <td>1.103385</td>\n", " <td>1.09164</td>\n",
" <td>1.103515</td>\n", " <td>1.09168</td>\n",
" <td>51</td>\n", " <td>63</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
@ -512,21 +514,21 @@
], ],
"text/plain": [ "text/plain": [
" id from at to \\\n", " id from at to \\\n",
"2999995 8230798 2023-05-03 10:35:00 1683110115000000000 2023-05-03 10:35:15 \n", "999995 7857774 2023-02-01 17:06:00 1675271175000000000 2023-02-01 17:06:15 \n",
"2999996 8230799 2023-05-03 10:35:15 1683110130000000000 2023-05-03 10:35:30 \n", "999996 7857775 2023-02-01 17:06:15 1675271190000000000 2023-02-01 17:06:30 \n",
"2999997 8230799 2023-05-03 10:35:15 1683110130000000000 2023-05-03 10:35:30 \n", "999997 7857775 2023-02-01 17:06:15 1675271190000000000 2023-02-01 17:06:30 \n",
"2999998 8230800 2023-05-03 10:35:30 1683110145000000000 2023-05-03 10:35:45 \n", "999998 7857776 2023-02-01 17:06:30 1675271205000000000 2023-02-01 17:06:45 \n",
"2999999 8230800 2023-05-03 10:35:30 1683110145000000000 2023-05-03 10:35:45 \n", "999999 7857776 2023-02-01 17:06:30 1675271205000000000 2023-02-01 17:06:45 \n",
"\n", "\n",
" open close min max volume \n", " open close min max volume \n",
"2999995 1.103340 1.10330 1.103275 1.103340 61 \n", "999995 1.091725 1.091670 1.09166 1.09176 84 \n",
"2999996 1.103300 1.10341 1.103300 1.103410 44 \n", "999996 1.091680 1.091660 1.09165 1.09168 51 \n",
"2999997 1.103300 1.10341 1.103300 1.103410 44 \n", "999997 1.091680 1.091660 1.09165 1.09168 51 \n",
"2999998 1.103415 1.10351 1.103385 1.103515 51 \n", "999998 1.091660 1.091655 1.09164 1.09168 63 \n",
"2999999 1.103415 1.10351 1.103385 1.103515 51 " "999999 1.091660 1.091655 1.09164 1.09168 63 "
] ]
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} }
@ -537,7 +539,7 @@
}, },
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@ -547,7 +549,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"10.64297915700081\n" "5.7032926019999195\n"
] ]
} }
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@ -557,7 +559,7 @@
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"id": "e7926062-8e84-4d3f-90a9-32807ce4f3d4", "id": "e7926062-8e84-4d3f-90a9-32807ce4f3d4",
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@ -567,7 +569,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"6.190685558998666\n" "6.242225094000105\n"
] ]
} }
], ],
@ -593,15 +595,14 @@
} }
], ],
"source": [ "source": [
"%%time\n", "# %%time\n",
"dfCh = cHouseQueryDf(dbname)" "# dfCh = cHouseQueryDf(dbname)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1d389546-911f-43f7-aad1-49f7bcc83503", "id": "1d389546-911f-43f7-aad1-49f7bcc83503",
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@ -610,61 +611,325 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 54,
"id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af", "id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"client = influxdb_client.InfluxDBClient(\n", "def influxdbConnect():\n",
" client = influxdb_client.InfluxDBClient(\n",
" url=InfluxDBUrl, token=InfluxDBKey, org=InfluxDBUser\n", " url=InfluxDBUrl, token=InfluxDBKey, org=InfluxDBUser\n",
")" " )\n",
" return client\n",
"\n",
"\n",
"def influxdbLoadCsv(csv=\"out.csv\", dictDates=[\"from\", \"to\"], index=\"from\"):\n",
" # Read data from CSV without index and parse 'TimeStamp' as date.\n",
" df = pd.read_csv(csv, sep=\",\", index_col=False, parse_dates=dictDates)\n",
" # Set 'TimeStamp' field as index of dataframe # test another indexs\n",
" df.set_index(index, inplace=True)\n",
" return df\n",
"\n",
"\n",
"def influxdbWriteCsv(dataFrame, bucket, measurement=\"id\", tag=\"volume\"):\n",
" client = influxdbConnect()\n",
" # write_options=SYNCHRONOUS\n",
" with client.write_api() as writer:\n",
" writer.write(\n",
" bucket=bucket,\n",
" record=dataFrame,\n",
" data_frame_measurement_name=measurement,\n",
" data_frame_tag_columns=[tag],\n",
" )\n",
" writer.__del__()\n",
" client.__del__()\n",
" return 0"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 55,
"id": "cbf61f12-830b-4c57-804a-2257d8b3599a", "id": "e05266b8-ff32-462c-b059-325a40a53d25",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"client = influxdbLoadCsv()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "71300e86-2e39-4c71-8b0c-6d6e21728e48",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# Read data from CSV without index and parse 'TimeStamp' as date.\n", "# write from db and benchmark time\n",
"df = pd.read_csv(\"out.csv\", sep=\",\", index_col=False, parse_dates=[\"from\"])\n", "start = timeit.default_timer()\n",
"# Set 'TimeStamp' field as index of dataframe # test another indexs\n", "influxdbWriteCsv(df, InfluxDBBucket)\n",
"df.set_index(\"from\", inplace=True)" "# dfIdw = cHouseQueryDf(dbname)\n",
"stop = timeit.default_timer()\n",
"influxdb_write_execution_time = stop - start"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "c8515d8e-2f34-4a2f-b9be-3faefef2d0ca",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"89.17334011999992\n"
]
}
],
"source": [
"print(influxdb_write_execution_time)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "ecd217ab-0e16-40a6-9b92-9212b9bb20e9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"query = \"\"\"\n",
"from(bucket: \"system\")\n",
"|> range(start:2023-03-03T18:14:30Z, stop: now())\n",
"|> filter(fn: (r) => r._measurement == \"id\")\n",
"|> filter(fn: (r) => r._field == \"volume\")\n",
"|> pivot(rowKey:[\"_time\"], columnKey: [\"_field\"], valueColumn: \"_value\")\"\"\"\n",
"# |> filter(fn: (r) => r._field == \"volume\")\n",
"# |> filter(fn: (r) => r.cpu == \"cpu-total\")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "09028588-e450-491c-8b84-246807dad9c3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# 2021-01-01T00:00:00Z\n",
"# 2023-03-03T18:14:30Z\n",
"\n",
"\n",
"def influxdRead(org=InfluxDBUrl, query=query):\n",
" client = influxdbConnect()\n",
" InfluxDf = client.query_api().query_data_frame(org=org, query=query)\n",
" display(InfluxDf.head())\n",
" return InfluxDf"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "54342a28-ba2b-4ade-a692-00566b53a639", "id": "8f28e32f-59d3-427b-8fe9-3d8bdf0ab675",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"df.head()" "dfIdb = influxdRead()\n",
"dfIdb.head()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "f861fab2-f1b1-49dd-b758-12d10aef3462", "id": "86a950d2-1c8e-46d5-aafe-444beb88bb88",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%time\n", "# read from db and benchmark time\n",
"# gravando... demorou... mas deu certo\n", "start = timeit.default_timer()\n",
"with client.write_api() as writer:\n", "dfIdr = cHouseQueryDf(dbname)\n",
" writer.write(\n", "stop = timeit.default_timer()\n",
" bucket=InfluxDBBucket,\n", "influxdb_read_execution_time = stop - start"
" record=df,\n", ]
" data_frame_measurement_name=\"id\",\n", },
" data_frame_tag_columns=[\"volume\"],\n", {
" )" "cell_type": "code",
"execution_count": 19,
"id": "54342a28-ba2b-4ade-a692-00566b53a639",
"metadata": {
"tags": []
},
"outputs": [
{
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" <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>2023-03-03 18:14:00</th>\n",
" <td>999997</td>\n",
" <td>7984750</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>2023-03-03 18:14:15</th>\n",
" <td>999998</td>\n",
" <td>7984751</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>2023-03-03 18:14:30</th>\n",
" <td>999999</td>\n",
" <td>7984752</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": [
" Unnamed: 0 id at \\\n",
"from \n",
"2023-03-03 18:13:30 999995 7984748 1677867225000000000 \n",
"2023-03-03 18:13:45 999996 7984749 1677867240000000000 \n",
"2023-03-03 18:14:00 999997 7984750 1677867255000000000 \n",
"2023-03-03 18:14:15 999998 7984751 1677867270000000000 \n",
"2023-03-03 18:14:30 999999 7984752 1677867285000000000 \n",
"\n",
" to open close min \\\n",
"from \n",
"2023-03-03 18:13:30 2023-03-03 18:13:45 1.062695 1.062635 1.062630 \n",
"2023-03-03 18:13:45 2023-03-03 18:14:00 1.062645 1.062650 1.062625 \n",
"2023-03-03 18:14:00 2023-03-03 18:14:15 1.062640 1.062625 1.062620 \n",
"2023-03-03 18:14:15 2023-03-03 18:14:30 1.062625 1.062535 1.062535 \n",
"2023-03-03 18:14:30 2023-03-03 18:14:45 1.062535 1.062520 1.062520 \n",
"\n",
" max volume \n",
"from \n",
"2023-03-03 18:13:30 1.062700 64 \n",
"2023-03-03 18:13:45 1.062650 43 \n",
"2023-03-03 18:14:00 1.062665 47 \n",
"2023-03-03 18:14:15 1.062645 43 \n",
"2023-03-03 18:14:30 1.062580 59 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
] ]
}, },
{
"cell_type": "code",
"execution_count": 20,
"id": "f861fab2-f1b1-49dd-b758-12d10aef3462",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 25.8 s, sys: 408 ms, total: 26.2 s\n",
"Wall time: 1min 36s\n"
]
}
],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,

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