Cube

Cube is middleware that connects your data sources and your data applications. Cube provides an API-first semantic layer for consolidating, caching, and securing connections. Instead of having independent lines between data stores and analytics, business or AI tools, Cube consolidates the complexity of overall data modelling and cross-source data exchange into a cleaner interface.

As a high performance time-series database, QuestDB and Cube are a strong pair. Together, efficiently bridge your QuestDB data to one of the many applications and libraries which integrate with Cube.

This document is a quick start designed to get both applications running together. For a deeper tutorial, see Time Series Data Analytics with QuestDB and Cube.

Prerequisites​

Start Cube & QuestDB​

Run QuestDB and Cube through Docker.

First, create and enter an example directory:

mkdir questdb-cube && cd $_

Dockerfile​

Next, create a dockerfile within the project directory:

version: "2.2"

services:
  cube:
    environment:
      - CUBEJS_DEV_MODE=true
    image: "cubejs/cube:latest"
    ports:
      - "4000:4000"
    env_file: "cube.env"
    volumes:
      - ".:/cube/conf"
  questdb:
    container_name: questdb
    hostname: questdb
    image: "questdb/questdb:latest"
    ports:
      - "9000:9000"
      - "8812:8812"

Within your project directory, create an cube.env file.

These variables will allow Cube to connect to your QuestDB deployment.

Remember: default passwords are dangerous! We recommend altering them.

CUBEJS_DB_HOST=questdb
CUBEJS_DB_PORT=8812
CUBEJS_DB_NAME=qdb
CUBEJS_DB_USER=admin
CUBEJS_DB_PASS=quest
CUBEJS_DB_TYPE=questdb

Create model directory to be used by Cube:

mkdir model

Finally, bring it all up with Docker:

docker-compose up -d

Access QuestDB & Cube​

Both applications are now up and ready.

Not sure what to do next? Check out our tutorial for inspiration.