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LearnHowTos & TutorialsBuilding an Analytics dashboard app using Redis

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Building an Analytics dashboard app using Redis

Ajeet Raina
Author
Ajeet Raina, Former Developer Growth Manager at Redis

Interactive analytics dashboards serve several purposes. They allow you to share data and provide you with all those vital information to make game-changing decisions at a faster pace. Building a real-time dynamic dashboard using a traditional relational database might require a complex set of queries. By using a NoSQL database like Redis, you can build a powerful interactive and dynamic dashboard with a small number of Redis commands.

Redis is an open source, in-memory, key-value data store most commonly used as a primary database, cache, message broker, and queue. Redis cache delivers sub-millisecond response times, enabling fast and powerful real-time applications in industries such as gaming, fintech, ad-tech, social media, healthcare, and IoT. This tutorial shows a basic analytics dashboard app writen in NodeJS (JavaScript) that uses the Redis Bitmap data type.

  • •Install Node - v12.19.0
  • •Install NPM - v6.14.8
  • •Install Docker - v19.03.13 (optional)

First we will be setting up some environment variables.

Go to /server folder (cd ./server) and then execute the following command:

You may need to preface the docker command with sudo. If you don't want to use sudo, create a Unix group called docker and add users to it. When the Docker daemon starts, it creates a Unix socket accessible by members of the docker group. Once the Redis database is up and running, you can connect to it using the redis-cli command.

Go to /client folder (cd ./client) and then:

The event data is stored in various keys and various data types.

For each of time spans:

  • •year: like 2021
  • •month: like 2021-03 (means March of 2021)
  • •day: like 2021-03-03 (means 3rd March of 2021)
  • •weekOfMonth: like 2021-03/4 (means 4th week of March 2021)
  • •anytime

and for each of scopes:

  • •source
  • •action
  • •source + action
  • •action + page
  • •userId + action
  • •global

and for each of data types (types):

  • •count (Integer stored as String)
  • •bitmap
  • •set

Is generated key like:

where values in [] are optional.

  • •For each generated key like rab:count:*, data is stored like: INCR {key} Example:

For each generated key like: rab:set:*, data is stored like: SADD {key} {userId} Example:

For each generated key like rab:bitmap:*, data is stored like: SETBIT {key} {userId} 1. Example:

  • •We store users who register and then bought some products (action order matters).
  • •For each buy action in December we check if user performed register action before (register counter must be greater than zero).
  • •If so, we set user bit to 1 like: SETBIT rab:bitmap:custom:cohort-buy:timeSpan:{timeSpan} {userId} 1
  • •E.g User Id 2 bought 2 products on 2015-12-17. It won't be stored.
  • •E.g User Id 10 bought 1 product on 2015-12-17 and registered on 2015-12-16. It will be stored like: SETBIT rab:bitmap:custom:cohort-buy:timeSpan:2015-12 10 1.
  • •We assume that user cannot buy without register.
  • •Retention means users who bought on two different dates
  • •For each buy action we check if user bought more products anytime than bought on particular day (current purchase not included).
  • •If so, we add user id to set like: SADD rab:set:custom:retention-buy:timeSpan:{timeSpan} {userId}
  • •E.g User Id 5 bought 3 products on 2015-12-15. His retention won't be stored (products bought on particular day: 2, products bought anytime: 0).
  • •E.g User Id 3 bought 1 product on 2015-12-15 and before - 1 product on 2015-12-13. His retention will be stored (products bought on particular day: 0, products bought anytime: 1) like: SADD rab:set:custom:retention-buy:timeSpan:2015-12 3.
  • •Total Traffic:

December: BITCOUNT rab:bitmap:custom:global:timeSpan:2015-12 X week of December: BITCOUNT rab:bitmap:custom:global:timeSpan:2015-12/{X} Example:

  • •Traffic per Page ({page} is one of: homepage, product1, product2, product3):

Example:

  • •X week of December:

Example:

  • •Traffic per Source ({source} is one of: google, Facebook, email, direct, referral, none):

December:

Example:

  • •X week of December: BITCOUNT rab:bitmap:source:{source}:timeSpan:2015-12/{X}

Example:

  • •Trend traffic ({page} is one of: homepage, product1, product2, product3):
  • •December: from BITCOUNT rab:bitmap:action:visit:{page}:timeSpan:2015-12-01 to BITCOUNT rab:bitmap:action:visit:{page}:timeSpan:2015-12-31
  • •1 Week of December: Similar as above, but from 2015-12-01 to 2015-12-07
  • •2 Week of December: Similar as above, but from 2015-12-08 to 2015-12-14
  • •3 Week of December: Similar as above, but from 2015-12-15 to 2015-12-21
  • •4 Week of December: Similar as above, but from 2015-12-22 to 2015-12-28
  • •5 Week of December: Similar as above, but from 2015-12-29 to 2015-12-31
  • •Example:
  • •Total products bought:
  • •December: GET rab:count:action:buy:timeSpan:2015-12
  • •X week of December: GET rab:count:action:buy:timeSpan:2015-12/{X} Example:
  • •Total products added to cart:

December: GET rab:count:action:addToCart:timeSpan:2015-12 X week of December: GET rab:count:action:addToCart:timeSpan:2015-12/{X} Example:

  • •Shares of products bought ({productPage} is on of product1, product2, product3):

December: GET rab:count:action:buy:page:{productPage}:timeSpan:2015-12 Example:

X week of December: GET rab:count:action:buy:page:{productPage}:timeSpan:2015-12/{X} Example:

  • •People who registered: BITCOUNT rab:bitmap:action:register:timeSpan:2015-12
  • •People who register then bought (order matters): BITCOUNT rab:bitmap:custom:cohort-buy:timeSpan:2015-12
  • •Dropoff: (People who register then bought / People who register) * 100 [%]
  • •Customers who bought only specified product ({productPage} is one of: product1, product2, product3):

Example:

  • •Customers who bought Product1 and Product2:
  • •Customer Retention (customers who bought on the different dates): SMEMBERS rab:set:custom:retention-buy:timeSpan:anytime
Last updated on Jan 31, 2025
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