Secure GCP infrastructure checklists

Initial setup

mieitza / crashes.sql

Created October 28, 2021 19:17 — forked from sbrissenden/crashes.sql

[BigQuery + Firebase Crashlytics] Crashes from May 9th

	#standardSQL
	SELECT
	event_id, issue_id, blame_frame.file, blame_frame.line
	FROM
	`projectId.firebase_crashlytics.package_name_ANDROID`
	WHERE
	FORMAT_TIMESTAMP("%Y%m%d", TIMESTAMP_TRUNC(event_timestamp, DAY)) = '20190509'

mieitza / predictions_churn.sql

Created October 28, 2021 19:17 — forked from sbrissenden/predictions_churn.sql

[BigQuery + Firebase Predictions] Number of Users Predicted to Churn

	SELECT COUNT(distinct p.user_id)
	FROM `friendly--pix.projectId.firebase_predictions.YOUR_PACKAGE_NAME_ANDROID` p CROSS JOIN UNNEST (predictions)

	WHERE
	prediction_name = "churn"
	AND score > 0.7
	AND TIMESTAMP_TRUNC(p.prediction_time, DAY) = TIMESTAMP('2018-10-04')

mieitza / firebase_performance_slow_screens.sql

Created October 28, 2021 19:16 — forked from sbrissenden/firebase_performance_slow_screens.sql

[BigQuery + Firebase Performance Monitoring] Find app screens with frozen frames impacting French users

	SELECT
	AVG(trace_info.duration_us / 1000000) AS seconds_on_screen,
	AVG(trace_info.screen_info.slow_frame_ratio) AS slow_frame_ratio,
	AVG(trace_info.screen_info.frozen_frame_ratio) AS frozen_frame_ratio,
	event_name as screen,
	country
	FROM `friendly--pix.projectId.firebase_performance.YOUR_PACKAGE_NAME_ANDROID`
	WHERE _PARTITIONTIME > TIMESTAMP("2019-05-09")
	AND event_type = "SCREEN_TRACE"
	AND country = "FR"

mieitza / user.py

Created January 3, 2018 15:20 — forked from ajoydas/user.py

From http://192.168.0.106:8888/notebooks/user.ipynb

	import pandas as pd
	from pandas.io.json import json_normalize
	from pymongo import MongoClient
	import matplotlib.pyplot as plt
	import re
	import time

	pd.set_option('display.expand_frame_repr', False)

	def _connect_mongo(host, port, db):

mieitza / clustering.py

Created January 3, 2018 15:20 — forked from shahradj/clustering.py

Perform clustering on mouse tracking data

	import pandas as pd
	from scipy.cluster.hierarchy import fclusterdata
	from pymongo import MongoClient
	import numpy as np
	from datetime import *
	import json

	def saveToRelational(jsonPacket):
	"""
	save the received json packet to a relational database

mieitza / logistic_regression_model (1).py

Created January 3, 2018 15:19 — forked from rohitr360/logistic_regression_model (1).py

	import pandas as pd
	import numpy as np
	import re, os, time, csv, random, pymongo, math, html, string, html2text
	from pymongo import MongoClient
	import xml.etree.ElementTree as ET
	import pymongo
	import sklearn

mieitza / mongo.py

Created January 3, 2018 15:19

parallel insert

	import json
	import pandas as pd
	from pymongo import MongoClient


	def mongo_to_pandas(client, database, collection):
	""" Reads mongo collection to pandas data frame."""
	cur = client[database][collection].find()
	df = pd.DataFrame(list(cur))
	df["_id"] = df["_id"].apply(lambda x: str(x)) #convert ids to strings

mieitza / comparing_two_fields.py

Created January 3, 2018 15:10 — forked from VITIMan/comparing_two_fields.py

PyMongo snippets


	def reaching_max_messages(current_hour_bitrange):
	"""
	from https://stackoverflow.com/a/29281409
	"""
	# https://stackoverflow.com/a/29281409
	# https://docs.mongodb.com/manual/reference/operator/query/bitsAllSet/

	pipeline = [
	{

mieitza / mongo_to_csv.py

Created January 3, 2018 15:09 — forked from wixb50/mongo_to_csv.py

python mongo to csv use pandas.

	# @Author: xiewenqian <int>
	# @Date: 2016-11-28T20:35:09+08:00
	# @Email: [email protected]
	# @Last modified by: int
	# @Last modified time: 2016-12-01T19:32:48+08:00


	import pandas as pd
	from pymongo import MongoClient