joao-timescale

import pydantic
import peewee
from playhouse.db_url import connect
from typing import List

import logging
logger = logging.getLogger('peewee')
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.DEBUG)

joao-timescale

SELECT
    CLIENT_INFO,
    STATUS,  -- Status da sessão (ex. ativa ou inativa)
    S.SID,   -- ID da sessão
    S.SERIAL#,  -- ID da operação
    TO_CHAR(S.LOGON_TIME,'dd/mm/yyyy hh24:mi') START_TIME,  -- Timestamp de início
    S.USERNAME,  -- Usuário da sessão
    S.PROGRAM,   -- Programa
    S.MACHINE,   -- Host de onde foi feita a conexão
    SQ.SQL_TEXT, -- Consulta SQL

joao-timescale

#!/usr/bin/env python

import argparse
import http.server
import sys
import socketserver
import os

class HTTPRequestHandler(http.server.SimpleHTTPRequestHandler):
    def __init__(self,req, client_addr, server):

joao-timescale

```shell

  [✓] Root user check

        .;;,.
        .ccccc:,.
         :cccclll:.      ..,,
          :ccccclll.   ;ooodc
           'ccll:;ll .oooodc
             .;cll.;;looo:.

joao-timescale

# Upstreams
upstream backend {
    server 127.0.0.1:3000;
}

# HTTPS Server
server {
    listen 443;
    server_name your_hostname.com;

joao-timescale

[Unit]
Description=Rocket.Chat Server
Requires=After=mongod.service       # Requires the mongod service to run first

[Service]
ExecStart=/usr/local/bin/node /opt/rocket.chat/main.js
WorkingDirectory=/opt/rocket.chat  # Set to rocket.chat directory
Restart=always
# Restart service after 10 seconds if node service crashes
RestartSec=10

joao-timescale

hits = np.asarray([cnf_matrix[i][i] for i in range(cnf_matrix.shape[0])])
expected = np.sum(cnf_matrix, axis=0)
num_hits = np.sum(hits)
total = test_data.shape[0]

precision = 100 * (num_hits / total)
recall_per_class = hits / expected
recall = 100 * (np.sum(recall_per_class) / recall_per_class.shape[0])

joao-timescale

from sklearn.metrics import confusion_matrix
cnf_matrix = confusion_matrix(test_data_labels, prediction)

joao-timescale

classifier = KNearestNeighbors(k=5)
# Train the classifier
classifier.fit(train_data, train_data_labels)
# Obtain the predictions based on the training data
prediction = classifier.predict(test_data)

joao-timescale

from sklearn.datasets import load_digits
import matplotlib.pyplot as plt

# Load data set and corresponding labels
digits_data = load_digits()
digits = digits_data.data
labels = digits_data.target
class_names = digits_data.target_names

# Partition data set into training and test data sets