1046 lines
25 KiB
Plaintext
1046 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7f1f5a4a-ae34-4a27-9efa-399edc0e384a",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Benchmark: ClickHouse Vs. InfluxDB Vs. Postgresql Vs. Parquet \n",
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"\n",
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"-----\n",
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"\n",
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"#### How to use:\n",
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"* Rename the file \"properties-model.ini\" to \"properties.ini\"\n",
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"* Fill with your own credentials\n",
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"----\n",
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"\n",
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"The proposal of this work is to compare the speed in read/writing a midle level of data ( a dataset with 9 columns and 50.000 lines) to four diferent databases:\n",
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"* ClickHouse\n",
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"* InfluxDB\n",
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"* Postgresql\n",
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"* Parquet (in a S3 Minio Storage) <br>\n",
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"ToDo: <br>\n",
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"* DuckDB with Polars\n",
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"* MongoDB\n",
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"* Kdb+\n",
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"\n",
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" \n",
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"Deve-se relevar:\n",
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"é uma \"cold-storage\" ou \"frezze-storage\"? <br>\n",
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"influxdb: alta leitura e possui a vantagem da indexaçõa para vizualização de dados em gráficos.\n",
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"\n",
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"notas: \n",
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"* comparar tamanho do csv com parquet"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6bb26ce7-1e84-4665-accd-916bb977f95d",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Imports "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"False"
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]
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},
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"execution_count": 68,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import configparser\n",
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"from datetime import datetime\n",
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"\n",
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"import duckdb\n",
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"import influxdb_client\n",
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"import pandas as pd\n",
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"\n",
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"# import pymongo\n",
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"from clickhouse_driver import Client\n",
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"from dotenv import load_dotenv\n",
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"from minio import Minio\n",
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"from pymongo import MongoClient\n",
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"from pytz import timezone\n",
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"from sqlalchemy import create_engine\n",
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"\n",
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"load_dotenv()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "55c3cd57-0996-4723-beb5-8f3196c96009",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Variables\n",
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"dbname = \"EURUSDtest\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "968403e3-2e5e-4834-b969-be4600e2963a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"arq = configparser.RawConfigParser()\n",
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"arq.read(\"properties.ini\")\n",
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"ClickHouseUser = arq.get(\"CLICKHOUSE\", \"user\")\n",
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"ClickHouseKey = arq.get(\"CLICKHOUSE\", \"key\")\n",
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"ClickHouseUrl = arq.get(\"CLICKHOUSE\", \"url\")\n",
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"\n",
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"InfluxDBUser = arq.get(\"INFLUXDB\", \"user\")\n",
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"InfluxDBKey = arq.get(\"INFLUXDB\", \"key\")\n",
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"InfluxDBUrl = arq.get(\"INFLUXDB\", \"url\")\n",
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"InfluxDBBucket = arq.get(\"INFLUXDB\", \"bucket\")\n",
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"\n",
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"PostgresqlUser = arq.get(\"POSTGRESQL\", \"user\")\n",
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"PostgresqlKey = arq.get(\"POSTGRESQL\", \"key\")\n",
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"PostgresqlUrl = arq.get(\"POSTGRESQL\", \"url\")\n",
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"PostgresqlDB = arq.get(\"POSTGRESQL\", \"database\")\n",
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"\n",
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"S3MinioUser = arq.get(\"S3MINIO\", \"user\")\n",
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"S3MinioKey = arq.get(\"S3MINIO\", \"key\")\n",
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"S3MinioUrl = arq.get(\"S3MINIO\", \"url\")\n",
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"S3MinioRegion = arq.get(\"S3MINIO\", \"region\")\n",
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"\n",
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"MongoUser = arq.get(\"MONGODB\", \"user\")\n",
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"MongoKey = arq.get(\"MONGODB\", \"key\")\n",
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"MongoUrl = arq.get(\"MONGODB\", \"url\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3634a4ec-04c2-4f1e-8659-5d22eb17a254",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Load Dataset\n",
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"df = pd.read_csv(\"out.csv\", index_col=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e7c46b6-90ee-4ca3-8b5a-553b09ece913",
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"metadata": {},
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"outputs": [],
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"source": [
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"# df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "76199f91-31d6-416b-9f15-5d435b3792c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"df[\"from\"] = pd.to_datetime(df[\"from\"], unit=\"s\")\n",
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"df[\"to\"] = pd.to_datetime(df[\"to\"], unit=\"s\")\n",
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"# Optional use when not transoformed yet\n",
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"# Transform Datetime"
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]
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},
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{
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"cell_type": "markdown",
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"id": "274cc026-2f48-4e38-b80f-b1a9ff982060",
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"metadata": {
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"tags": []
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},
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"source": [
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"#### Funçoes\n",
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"\n",
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"-> Class"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "27de1ec8-4de1-440a-b555-b4a46c5ef7ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"def timestamp2dataHora(x, timezone_=\"America/Sao_Paulo\"):\n",
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" d = datetime.fromtimestamp(x, tz=timezone(timezone_))\n",
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" return d"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4a8d5703-9bc9-4d38-83ff-457159304d58",
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"metadata": {
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"tags": []
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},
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"source": [
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"### ClickHouse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9cf86669-7722-4a2c-895c-51f0a5eebefc",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# !! O client oficial usa um driver http, nesse exemplo vamos usar a biblioteca\n",
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"# de terceirtos clickhouse_driver recomendada, por sua vez que usa tcp.\n",
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"client = Client(\n",
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" host=ClickHouseUrl,\n",
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" user=ClickHouseUser,\n",
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" password=ClickHouseKey,\n",
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" settings={\"use_numpy\": True},\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a0a1f67b-2e63-462e-be66-d322d99837ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create Tables in ClickHouse\n",
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"# !! ALTERAR TIPOS !!\n",
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"# ENGINE: 'Memory' desaparece quando server é reiniciado\n",
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"client.execute(\n",
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" \"CREATE TABLE IF NOT EXISTS {} (id UInt32,\"\n",
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" \"from DateTime, at UInt64, to DateTime, open Float64,\"\n",
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" \"close Float64, min Float64, max Float64, volume UInt32)\"\n",
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" \"ENGINE MergeTree ORDER BY to\".format(dbname)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3a029a09-46f4-43c3-b3df-cfbed33fb0dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Write dataframe to db\n",
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"client.insert_dataframe(\"INSERT INTO {} VALUES\".format(dbname), df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "17251288-2442-43ee-98f2-ca680c3c4f13",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"client.query_dataframe(\"SELECT * FROM default.{}\".format(dbname)) # LIMIT 10000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51497522-bd6c-44a8-aaea-ec5dda30b95b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# %%time\n",
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"# df = pd.DataFrame(client.query_dataframe(\"SELECT * FROM default.{}\".format(dbname)))"
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]
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},
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{
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"cell_type": "markdown",
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|
"id": "1d389546-911f-43f7-aad1-49f7bcc83503",
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"metadata": {
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"tags": []
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},
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"source": [
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"### InfluxDB\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af",
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"metadata": {},
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"outputs": [],
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"source": [
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"client = influxdb_client.InfluxDBClient(\n",
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" url=InfluxDBUrl, token=InfluxDBKey, org=InfluxDBUser\n",
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")"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": null,
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"id": "cbf61f12-830b-4c57-804a-2257d8b3599a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
|
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"# Read data from CSV without index and parse 'TimeStamp' as date.\n",
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"df = pd.read_csv(\"out.csv\", sep=\",\", index_col=False, parse_dates=[\"from\"])\n",
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"# Set 'TimeStamp' field as index of dataframe # test another indexs\n",
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"df.set_index(\"from\", inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "54342a28-ba2b-4ade-a692-00566b53a639",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f861fab2-f1b1-49dd-b758-12d10aef3462",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# gravando... demorou... mas deu certo\n",
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"with client.write_api() as writer:\n",
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" writer.write(\n",
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" bucket=InfluxDBBucket,\n",
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" record=df,\n",
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" data_frame_measurement_name=\"id\",\n",
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" data_frame_tag_columns=[\"volume\"],\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0bb2563d-68e2-4ff4-8842-70ac730dc6b1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# data\n",
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"# |> pivot(\n",
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"# rowKey:[\"_time\"],\n",
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"# columnKey: [\"_field\"],\n",
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"# valueColumn: \"_value\"\n",
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"# )"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": null,
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"id": "bb1596f9-4cee-4642-803a-ee61c9dddf64",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b9ddfdc6-c899-4f6c-9b4e-8ec6ab6d7e05",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Postgresql"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "16cd8eb7-333d-43fd-88e0-ee983645d3fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Connect / Create Tables\n",
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"engine = create_engine(\n",
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" \"postgresql+psycopg2://{}:{}@{}:5432/{}\".format(\n",
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" PostgresqlUser, PostgresqlKey, PostgresqlUrl, PostgresqlDB\n",
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" )\n",
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")"
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]
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
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"id": "be31f3a0-b7ed-48e6-9b65-dc16319fb8d1",
|
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"metadata": {},
|
|
"outputs": [],
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"source": [
|
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"# Drop old table and create new empty table\n",
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"df.head(0).to_sql(\"comparedbs\", engine, if_exists=\"replace\", index=False)"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a7883c4d-4609-4380-8a45-246b7ca2f9c5",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
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"source": [
|
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"%%time\n",
|
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"# Write\n",
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"conn = engine.raw_connection()\n",
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"cur = conn.cursor()\n",
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"output = io.StringIO()\n",
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"df.to_csv(output, sep=\"\\t\", header=False, index=False)\n",
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"output.seek(0)\n",
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"contents = output.getvalue()\n",
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"\n",
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"cur.copy_from(output, \"comparedbs\") # , null=\"\") # null values become ''\n",
|
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"conn.commit()\n",
|
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"cur.close()\n",
|
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"conn.close()"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e37a93e1-fc0e-4d27-9e16-dca6c8aea324",
|
|
"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
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"# Read"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f9e0393d-7d1d-406a-a068-9dbf4968e977",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"### S3 Parquet"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "60a990e2-4607-4654-84ec-17d4985adae2",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fazer sem funçao para ver se melhora\n",
|
|
"# verifique se esta no ssd os arquivos da pasta git\n",
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"def main():\n",
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" client = Minio(\n",
|
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" S3MinioUrl,\n",
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" secure=False,\n",
|
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" region=S3MinioRegion,\n",
|
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" access_key=\"MatMPA7NyHltz7DQ\",\n",
|
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" secret_key=\"SO1IG5iBPSjNPZanYUaHCLcoSbjphLCP\",\n",
|
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" )\n",
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"\n",
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" # Make bucket if not exist.\n",
|
|
" found = client.bucket_exists(\"data\")\n",
|
|
" if not found:\n",
|
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" client.make_bucket(\"data\")\n",
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" else:\n",
|
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" print(\"Bucket 'data' already exists\")\n",
|
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"\n",
|
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" # Upload\n",
|
|
" client.fput_object(\n",
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" \"data\",\n",
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" \"data.parquet\",\n",
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" \"data/data.parquet\",\n",
|
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" )\n",
|
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" # print(\n",
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" # \"'data/data.parquet' is successfully uploaded as \"\n",
|
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" # \"object 'data.parquet' to bucket 'data'.\"\n",
|
|
" # )"
|
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "390918c8-c88f-404a-96c4-685d578fdad0",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"df.to_parquet(\"data/data.parquet\")\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" try:\n",
|
|
" main()\n",
|
|
" except S3Error as exc:\n",
|
|
" print(\"error occurred.\", exc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a9e07143-8c11-4b68-a869-c3922cda9092",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pq = pd.read_parquet(\"data/data.parquet\", engine=\"pyarrow\")\n",
|
|
"pq.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "50d1fc58-89a7-4507-aff0-6e943656cfe0",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"### MongoDB"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d104d9af-fa34-4261-8478-329a28ee4f2e",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load csv dataset\n",
|
|
"data = pd.read_csv(\"out.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "0af8f72c-5b58-4dfc-af36-c5b4bc79f127",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Connect to MongoDB\n",
|
|
"client = MongoClient(\n",
|
|
" # \"mongodb://192.168.1.133:27017\"\n",
|
|
" \"mongodb://{}:{}@{}/EURUSDtest?retryWrites=true&w=majority\".format(\n",
|
|
" MongoUser, MongoKey, MongoUrl\n",
|
|
" ),\n",
|
|
" authSource=\"admin\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f1b20d15-f5af-463c-813f-ffae61119de1",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"db = client[\"EUROUSDtest\"]\n",
|
|
"collection = db[\"finance\"]\n",
|
|
"# data.reset_index(inplace=True)\n",
|
|
"data_dict = data.to_dict(\"records\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "70674d23-f375-4659-87ec-c745dec96d54",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"# Insert collection\n",
|
|
"collection.insert_many(data_dict)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "81a4a33d-5914-45d8-af4e-2b0aabd2ac38",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# read"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "97405e42-61dc-42c7-8220-237a312c0ec7",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"### DuckDB"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bbcdb883-d6dc-46db-88db-4c90b84522ba",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cursor = duckdb.connect()\n",
|
|
"print(cursor.execute(\"SELECT 42\").fetchall())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "35025a6e-9dc7-46cf-a792-76b3d84f1ac0",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"conn = duckdb.connect()\n",
|
|
"data = pd.read_csv(\"out.csv\")\n",
|
|
"conn.register(\"EURUSDtest\", data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c6abdaaa-3ac2-425b-9208-d6cb79afe966",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"display(conn.execute(\"SHOW TABLES\").df())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2acce0f3-f0b2-47d0-8e0d-f9e9687efc18",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"df = conn.execute(\"SELECT * FROM EURUSDtest\").df()\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4409cc89-ed14-4313-ac89-65b826038533",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"### Kdb+"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"id": "14f63810-1943-4e28-9bce-2148be6be02d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"np.bool = np.bool_\n",
|
|
"from qpython import qconnection"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"id": "8ff6c090-7e02-435a-a179-f2aab81da972",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# read csv\n",
|
|
"data = pd.read_csv(\"out.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"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": 75,
|
|
"id": "97cb6b5b-65a5-46a0-a4ee-e5c535a716ab",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 925 ms, sys: 40 ms, total: 965 ms\n",
|
|
"Wall time: 1.43 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"# send df to kd+ in memory bank\n",
|
|
"q.sendSync(\"{t::x}\", data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"id": "c2ed2d51-bc8e-4207-892a-35fc55d43570",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"b':/home/sandman/q/tab1'"
|
|
]
|
|
},
|
|
"execution_count": 76,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# write to on disk table\n",
|
|
"q.sendSync(\"`:/home/sandman/q/tab1 set t\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 77,
|
|
"id": "9c055a95-f73f-43a3-8fbd-61e42235117e",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 1.94 ms, sys: 1 µs, total: 1.94 ms\n",
|
|
"Wall time: 426 ms\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"# read from on disk table\n",
|
|
"df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"id": "9760de38-9f04-4322-bfff-c7ee12d5dee5",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# print(df2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"id": "c06c9222-c69d-4872-9d21-052281a013e2",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 1.08 s, sys: 116 ms, total: 1.2 s\n",
|
|
"Wall time: 1.27 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"# load to variable df2\n",
|
|
"df2 = q.sendSync(\"tab2\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 80,
|
|
"id": "8815f01c-fd0a-4f94-ab7f-f8ede84ba4e7",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# df2(type)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 82,
|
|
"id": "e6ed3927-4395-45cd-9a28-88c5db01f2e5",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 1.25 s, sys: 132 ms, total: 1.39 s\n",
|
|
"Wall time: 1.46 s\n"
|
|
]
|
|
},
|
|
{
|
|
"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>Unnamed: 0</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>0</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>7730801</td>\n",
|
|
" <td>b'2023-01-02 15:58:45'</td>\n",
|
|
" <td>1672675140000000000</td>\n",
|
|
" <td>b'2023-01-02 15:59:00'</td>\n",
|
|
" <td>1.065995</td>\n",
|
|
" <td>1.066035</td>\n",
|
|
" <td>1.065930</td>\n",
|
|
" <td>1.066070</td>\n",
|
|
" <td>57</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>7730802</td>\n",
|
|
" <td>b'2023-01-02 15:59:00'</td>\n",
|
|
" <td>1672675155000000000</td>\n",
|
|
" <td>b'2023-01-02 15:59:15'</td>\n",
|
|
" <td>1.066055</td>\n",
|
|
" <td>1.066085</td>\n",
|
|
" <td>1.066005</td>\n",
|
|
" <td>1.066115</td>\n",
|
|
" <td>52</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>7730803</td>\n",
|
|
" <td>b'2023-01-02 15:59:15'</td>\n",
|
|
" <td>1672675170000000000</td>\n",
|
|
" <td>b'2023-01-02 15:59:30'</td>\n",
|
|
" <td>1.066080</td>\n",
|
|
" <td>1.066025</td>\n",
|
|
" <td>1.066025</td>\n",
|
|
" <td>1.066110</td>\n",
|
|
" <td>57</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>7730804</td>\n",
|
|
" <td>b'2023-01-02 15:59:30'</td>\n",
|
|
" <td>1672675185000000000</td>\n",
|
|
" <td>b'2023-01-02 15:59:45'</td>\n",
|
|
" <td>1.065980</td>\n",
|
|
" <td>1.065985</td>\n",
|
|
" <td>1.065885</td>\n",
|
|
" <td>1.066045</td>\n",
|
|
" <td>64</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>7730805</td>\n",
|
|
" <td>b'2023-01-02 15:59:45'</td>\n",
|
|
" <td>1672675200000000000</td>\n",
|
|
" <td>b'2023-01-02 16:00:00'</td>\n",
|
|
" <td>1.065975</td>\n",
|
|
" <td>1.066055</td>\n",
|
|
" <td>1.065830</td>\n",
|
|
" <td>1.066055</td>\n",
|
|
" <td>50</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Unnamed: 0 id from at \n",
|
|
"0 0 7730801 b'2023-01-02 15:58:45' 1672675140000000000 \\\n",
|
|
"1 1 7730802 b'2023-01-02 15:59:00' 1672675155000000000 \n",
|
|
"2 2 7730803 b'2023-01-02 15:59:15' 1672675170000000000 \n",
|
|
"3 3 7730804 b'2023-01-02 15:59:30' 1672675185000000000 \n",
|
|
"4 4 7730805 b'2023-01-02 15:59:45' 1672675200000000000 \n",
|
|
"\n",
|
|
" to open close min max volume \n",
|
|
"0 b'2023-01-02 15:59:00' 1.065995 1.066035 1.065930 1.066070 57 \n",
|
|
"1 b'2023-01-02 15:59:15' 1.066055 1.066085 1.066005 1.066115 52 \n",
|
|
"2 b'2023-01-02 15:59:30' 1.066080 1.066025 1.066025 1.066110 57 \n",
|
|
"3 b'2023-01-02 15:59:45' 1.065980 1.065985 1.065885 1.066045 64 \n",
|
|
"4 b'2023-01-02 16:00:00' 1.065975 1.066055 1.065830 1.066055 50 "
|
|
]
|
|
},
|
|
"execution_count": 82,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"# converto to dataframe\n",
|
|
"df = pd.DataFrame(q(\"t\")) # , pandas=True))\n",
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 83,
|
|
"id": "0fc7f16b-6c39-4ebe-88d2-ff857e30ab62",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 1.11 s, sys: 116 ms, total: 1.23 s\n",
|
|
"Wall time: 1.3 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"# select\n",
|
|
"df3 = q.sendSync(\"select from t\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 84,
|
|
"id": "c88646ca-3d25-4a85-80b5-f9e559f568dd",
|
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{
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