Mongo sharding

Demonstration of a distributed Personally Identifiable Information (PPI) storing using MongoDB sharding

Table of Contents

• Problem
  • Requirements
  • Features
  • Task
• Solution
  • Theory
  • Implementation
  • API
  • How-to-run
  • Info

Problem

All end-user data is stored in the booking. Booking belongs to a distributor and the separation is made by the ID of the distributor. All bookings and related PII are stored in the EU. There are a lot of problems with this approach:

• A simple typo in code may expose PII to other distributors (by leaving out the ID of the distributor from queries)
• Data is not actually separated between distributors.
• US customer data is stored in the EU which is not allowed.

Requirements

• PII is not stored in the booking
• The location of PII storage can be defined by customer residence country
• Customer data is separated by a distributor
• Customer data lookup is fast

Features

• As a distributor, I am able to store, modify, retrieve, delete customer data
• As a distributor, I am able to search customer data by knowing the name or e-mail address of the customer

Task

How to solve the problem? What would be the architecture of such a solution? What technologies would be suitable?
Create a proof of concept

Solution

Theory

• First of all, it’s an architecture disaster to store PII inside the Booking table/doc. PII should be stored separately from the Bookings. Bookings should have only refs(foreign keys) to related PPI.
• Horizontal sharding can be used for storing PPI physically at the permitted location. The residence country of the user will define to which shard his PPI will belong.
• It's possible to encrypt PPI (to make it not identified) and store in forbidden regions for more optimal aggregation and statistics queries.
• It's possible to separate all PPI by a particular distributor on each shard using separated collections/tables on each shard, but it has its own cons.

Implementation

Imagine, we have several MongoDB shards located in different continents. Then PPI will be stored at sharded collection db.profiles. Let's separate PPI by two shards:

• shard1
  ZONE NA (North America)
  country: "US", distributorId: from 1 to 100
  country: "CA", distributorId: from 101 to 200

• shard2
  ZONE EU (European Union)
  country: "EE", distributorId: from 201 to 300
  country: "FI", distributorId: from 301 to 400

API

• Create a new profile:

    POST /api/profiles/  {country, distributorId, email, fullName}
    Code: 201
    Response: {
                  "data": {
                      "type": "profile",
                      "profile": {
                          "country": "EE",
                          "distributorId": 224,
                          "email": "Gregory82@gmail.com",
                          "fullName": "Gregory Shields",
                          "id": "5e4b4dd7f45aac001ed3718e"
                      }
                  },
                  "status": "OK"
              }

• Get profile by Id:

    GET /api/profiles/:id
    Code: 200
    Response: {
                  "data": {
                      "type": "profile",
                      "profile": {
                          "country": "EE",
                          "distributorId": 224,
                          "email": "Gregory82@gmail.com",
                          "fullName": "Gregory Shields",
                          "id": "5e4b4dd7f45aac001ed3718e"
                      }
                  },
                  "status": "OK"
              }

• Search profile by:
  • county code
  • distributor id
  • fullName (partial text match)
  • email (partial text match)

    GET /api/profiles/search?country=&distributorId=&email=&fullName=&
    Code: 200
    Response: {"data":{"profiles":[],"searchArgs":{}},"status":"OK"}

(!) If to pass explain query param, instead of search results the query explaination will be returned.

    GET /api/profiles/search?explain=true&...
    Code: 200
    Response: {"data":{"explain":{"queryPlanner":{"mongosPlannerVersi.....}, "searchArgs":{}},"status":"OK"}

• Delete by Id:

    DELETE /api/profiles/:id
    Code: 200
    Response: {
                  "data": {
                      "type": "profile",
                      "result": true,
                      "message": "Instance with id = 5e4b4dd7f45aac001ed3718e is  deleted"
                  },
                  "status": "OK"
              }

How-to-run

It is possible to run all inside Docker containers. All steps are introduced on the video:

Steps (inside containers):

1. Build and up all containers:

   docker-compose up --build -d
   

2. Wait until all containers get up.
3. Run tests:

   cd server
   npm run test:in_container
   

Steps (local):

1. Build and up only MongoDB services:

   docker-compose up --build -d router1
   

2. Wait until all containers get up.
3. Install all dependencies

   cd server
   npm i
   

4. Start a server

   npm run dev
   

5. Run tests:

   npm run test
   

Info

General info:

• Node.js version: v13.6.0. Using ESM
• All db data from each MongoDB instance will be mapped to ./mongo/data/ folder
• Output from sh.status({}) command can bu found at ./info/shardStatus.txt

Used ports:

• 27017 - MongoDB Mongos server
• 3000 - HTTP web server

Config files:

• .env
• ./server/config/index.js

MongoDB connection URL:

mongodb://admin:admin@localhost:27017/db