johnlpage

#Start a machine use any method you like here, I'm doing it from the command line 40 cores, 120GB RAM, SSD with 8,500 IOPS (SAN) - Amazon linux (Centos)

ec2-run-instances ami-a10897d6 -t m4.10xlarge -g jlp -k john_page_demos -b "/dev/xvdb=:725:true:io1:8500"

#Log on

ssh -L 27017:localhost:27017 -i <your public key>.pem ec2-user@<your ip address>

#Set up disk

johnlpage

ENDPOINT
--------

https://data.mongodb-api.com/app/data-amzuu/endpoint/data/beta

API KEY
--------
0vaT8d5Vh9cgvm3KdIQJWkl5M8alZgnoOczmApFlWVTMqisg24QWrUfMS0wkQ5Sj

johnlpage

Quick Oracle 18 XE setup for AWS

These instructions are a quick and simple way to set up Oracle for experimenting - it is no no way fit for production use. This assumes familiarity with Amazon EC2 - you could also use RHEL 7.9 installed another way but I have not tested that.

Start an EC2 Instance in a region of your choice (Ideally the same one where you will be running MongoDB Atlas). Oracle XE has limitation on hardware and data size so it should be

• 2 CPU, 4GB RAM (t3.medium instance)
• OS: RHEL 7.9 64 bit (RHEL-7.9_HVM-20211005-x86_64-0-Hourly2-GP2 in COmmunity AMIs)
• 25GB gp3 root disk (Change on Add Storage screen from 10GB gp2)

johnlpage

//Convert fields to a string of digits and dots
var s={$reduce: {input: ["$x","$y","$s"],initialValue:"",in: {$concat:["$$value",{$toString:"$$this"},"."]}}}
var mx = {$pow:[227,{$mod:[{$add:["$$this",1]},6]}]}
//Iterate it doing iv=56781234 ; iv = (iv + 227^(i%6)*(s[i]+1))%1048576
//20 bit hash - can increase
var c={$reduce: {input: {$range : [0,{$strLenCP:"$$s"}]},initialValue:56781234,
in: {$mod: [{$add:["$$value",{$multiply:[mx,{$add:[1,{$convert:{to:"int",onError:10,
input:{$substrCP:["$$s","$$this",1]}}}]}]}]},1048576]}}}
var cksum = { $let : { vars: { s: s}, in:c }}
var validator = { $expr : { $eq : [ "$cksum", cksum]},$jsonSchema: { required: ["x","y","s","cksum"]}}

johnlpage

doc = {_id: "test", x:1,y:1,s:5.5}
try { db.readonlyfields.insertOne( doc) } catch(e) {print(e.errmsg)}
Document Failed validation

johnlpage

var sign = (doc,fields) => {
   let toSign=""
   fields.forEach(f => { toSign += parseFloat(doc[f]) + "." } )
   toSign = toSign.replace(/NaN/,'.')
   print(`Signing: ${toSign}`)
   iv = 56781234
   for(c=0;c<toSign.length;c++) {
      v = parseInt(toSign[c])
      if(isNaN(v)) v=10; //Dots etc
      v += 1; //No point multiplying 0 by anything

johnlpage

{
  "errInfo": {
    "failingDocumentId": "test",
    "details": {
      "operatorName": "$and",
      "clausesNotSatisfied": [
        {
          "index": 0,
          "details": {
            "operatorName": "$expr",

johnlpage

[{
    $match: {
      reading: {  $gt: 1000, $lt: 40000 }
    }
  }, {
    $setWindowFields: {
      partitionBy: "$type",
      sortBy: { date: 1 },
      output: {
        hourago: {

johnlpage

[{
  $group: {
    _id: {
      date: {
        $dateTrunc: {date: "$date", unit: "day"}
      },
      type: "$type"
    },
    max: { $max: "$reading" },
    min: { $min: "$reading" }

johnlpage

from bluepy.btle import Scanner, DefaultDelegate, Peripheral
from pprint import pprint
import time
import pymongo
import datetime

TEMPERATURE_SERVICE_UUID = "e95d6100-251d-470a-a062-fa1922dfa9a8"
TEMPERATURE_CHARACTERISTIC_UUID = "e95d9250-251d-470a-a062-fa1922dfa9a8"