anilktechie

import sqlite3 as sql
from contextlib import closing

class dal:
    
    def __init__(self, dbname):
        self.dbname = dbname
    
    @staticmethod
    def __connection(dbname):

anilktechie

import psycopg2

dsn = dict(
	dbname='dbname',
	host='hostname',
	password='password',
	port=5432,
	user='username'
)
conn = psycopg2.connect(**dsn)

anilktechie

import functools

def excuteImmediately(fn):
	@functools.wraps(fn)
	def wrapper(*args, **kwargs):
		return fn(*args, **kwargs)
	return wrapper()
	
	
example:

anilktechie

###########################################################################
#
## @file postgres.py
#
###########################################################################

import psycopg2

###########################################################################
#

anilktechie

import MySQLdb
import MySQLdb.cursors
from config import config


class Cursor(object):
    def __init__(self, mysql_cursor):
        self.cursor = mysql_cursor

    def __iter__(self):

anilktechie

from psycopg2 import sql
import queries

pg_session = queries.Session(
    queries.uri(
        host='localhost',
        port='5439',
        dbname='postgres',
        user='postgres',
        password='password'

anilktechie

{
  "paragraphs": [
    {
      "text": "%md\n### Run simple queries to check the count of both the iotdata and iotrollup tables.\nClick the play button within each of the paragraphs to rerun/update the content\n",
      "user": "admin",
      "dateUpdated": "2018-10-05T19:03:40-0400",
      "config": {
        "tableHide": false,
        "editorSetting": {
          "language": "markdown",

anilktechie

--set up your environment
create database my_test_db;
create schema my_test_db.witsml;
use schema my_test_db.witsml;

             
--create the staging table where all WITSML files are loaded to by Snowpipe    
create table witsml_temp (col1 variant);

anilktechie

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<template encoding-version="1.2">
    <description>Workflow to monitor Snowflake credit consumption and report to Slack. Full article can be found here https://snowflakecomputing.atlassian.net/wiki/spaces/RT/pages/771200336/Creating+a+Snowflake+usage+report+using+Apache+Nifi

Requires existing tables and apps defined in your Snowflake account and your Slack account</description>
    <groupId>be279da7-0167-1000-6a67-4d0cb36decc6</groupId>
    <name>SnowflakeCreditMonitoring</name>
    <snippet>
        <connections>
            <id>1d50d3ac-3446-3e09-0000-000000000000</id>

anilktechie

--create a table to hold the metrics you wish to monitor and their threshold values
CREATE TABLE "MY_TEST_DB"."PUBLIC"."THRESHOLDS" ("SERVICE_TYPE" STRING NOT NULL, "CREDITS_BILLED" DOUBLE NOT NULL) COMMENT = 'Used for the Nifi alerting demo';

--insert some sample records into the thresholds table
insert into "MY_TEST_DB"."PUBLIC"."THRESHOLDS" values ('AUTO_CLUSTERING', 10),('PIPE', 10),('MATERIALIZED_VIEW', 10),('WAREHOUSE_METERING', 10);

--query to compare current metrics to threshold values
--This is used for the HOURLY report
select a.*, iff(b.credits_billed is null, 0, b.credits_billed)::string as credits_billed from
"MY_TEST_DB"."PUBLIC"."THRESHOLDS" a left join