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SMILE Dashboard

Run locally

This section describes how to run the dashboard backend and frontend locally without docker.

Pre-requisites

Make sure you have installed the following:

  • Node.js: 16.10.0
  • Yarn: 1.22.22

Tip: We recommend that you use nvm: Node Version Manager and yvm: Yarn Version Manager to switch between versions more easily.

Windows Tip: If you are developing on Windows, we recommend that you use Ubuntu / Windows Subsystem for Linux.

Pre-build Steps

If starting from a fresh build, pull dependencies by running yarn from the project root directory.

If running into build issues, try purging existing contents from all /node_modules directories in project.

rm -rf ./node_modules frontend/node_modules graphql-server/node_modules

Download the Oracle Instant Client

Note: Skip this step if you are using a MacBook machine with an M1 chip or newer. Visit this link for the latest supported systems.

The Oracle Instant Client allows us to connect to an Oracle database (CRDB) for MRN-CMO-DMP data. This enables the searching of PHI data on the Patients page of the dashboard.

Download the Oracle Instant Client from here.

Select the version corresponding to your operating system and download the latest version's Basic Package zip file.

Unzip and copy the entire directory to /graphql-server/opt/oracle or your preferred location.

Why can't we just use the Nodejs' oracledb package?

By default, node-oracledb runs in Thin mode (vs. Thick mode). The CRDB uses a password verifier type (0x939) that is not supported by Thin mode. Pairing the Oracle Instant Client with node-oracledb allows us enables Thick mode and allows us to connect to the CRDB.

Dashboard Backend

The backend for the dashboard is under ./graphql-server.

Set up your ./graphql-server/src/env/application.properties.EXAMPLE with all of the application properties needed for running the dashboard backend.

Add the following variable to your environment:

export NODE_TLS_REJECT_UNAUTHORIZED=0
export SMILE_CONFIG_HOME=[path to the dashboard configuration directory]
export SMILE_DATA_HOME=[path to the dashboard data directory]

# Skip if you skipped the Oracle Instant Client step
export LD_LIBRARY_PATH=[path to the Oracle Instant Client directory]

# Skip if you're not deploying the dashboard backend with Docker
export NODE_OPTIONS="--max-old-space-size=8192"

Build and launch the node app from the project root directory with:

yarn build:backend
yarn dev:backend

If successful, the graphql client should be available at https://localhost:4000/graphql.

Dashboard App

Set the following environment variables to point the web app to the React frontend and the Express backend, respectively.

Example:

export REACT_APP_REACT_SERVER_ORIGIN=https://localhost:3006
export REACT_APP_EXPRESS_SERVER_ORIGIN=https://localhost:4000

To run the frontend:

yarn dev:frontend

Example pages:

Tip: This page should open automatically through your default web browser. If not then please navigate to the web app manually.

Building the Docker Images

To build the frontend docker image:

docker build -f Dockerfile . -t [org]/smile-dashboard:[build version]

To build the backend docker image:

docker build -f graphql-server/Dockerfile . -t [org]/graphql-client:[build version]

Running with docker-compose

Running the dashboard backend and frontend with docker-compose.

Requirements:

  • ${SMILE_CONFIG_HOME}: path to the dashboard configuration directory
  • ${SMILE_CONFIG_HOME}/resources/smile-dashboard: path to application.properties and SSL cert files for the dashboard backend
  • ${SMILE_CONFIG_HOME}/nats: path to NATS config, must contain rootCA.pem

See ./server/env/application.properties.EXAMPLE for all of the application properties needed for running the dashboard.

Example docker-compose file contents:

version: '3'

networks:
  default:
    name: smile-network
    external: true

services:
  graphql-client:
    container_name: graphql-client
    image: cmometadb/graphql-client:[build version]
    restart: unless-stopped
    environment:
      - SMILE_CONFIG_HOME=${SMILE_CONFIG_HOME}
      - NODE_TLS_REJECT_UNAUTHORIZED=${NODE_TLS_REJECT_UNAUTHORIZED}
      - LD_LIBRARY_PATH=${LD_LIBRARY_PATH}
      - REACT_APP_REACT_SERVER_ORIGIN=${REACT_APP_REACT_SERVER_ORIGIN}
      - REACT_APP_EXPRESS_SERVER_ORIGIN=${REACT_APP_EXPRESS_SERVER_ORIGIN}
      - SMILE_DATA_HOME=${SMILE_DATA_HOME}
      - NODE_OPTIONS="--max-old-space-size=8192"
    volumes:
      - type: bind
        source: ${SMILE_CONFIG_HOME}/resources/smile-dashboard
        target: /server/graphql-server/dist/env
      - type: bind
        source: ${SMILE_CONFIG_HOME}/nats
        target: /server/nats
      - type: bind
        source: ${SMILE_DATA_HOME}/logs/smile-dashboard
        target: ${SMILE_DATA_HOME}/logs/smile-dashboard
      - type: bind
        source: ${LD_LIBRARY_PATH}
        target: ${LD_LIBRARY_PATH}
    external_links:
      - nats-jetstream
    links:
      - neo4j
    depends_on:
      neo4j:
        condition: service_healthy
      keycloak:
        condition: service_started
    ports:
      - 4000:4000
    healthcheck:
      test: ["CMD-SHELL", "wget --no-check-certificate --no-verbose --spider https://localhost:4000 || exit 1"]
      interval: 30s
      timeout: 10s
      retries: 5

  smile-dashboard:
    container_name: smile-dashboard
    image: cmometadb/smile-dashboard:[build version]
    restart: unless-stopped
    environment:
      - SMILE_CONFIG_HOME=${SMILE_CONFIG_HOME}
      - REACT_APP_EXPRESS_SERVER_ORIGIN=${REACT_APP_EXPRESS_SERVER_ORIGIN}
      - REACT_APP_REACT_SERVER_ORIGIN=${REACT_APP_REACT_SERVER_ORIGIN}
    volumes:
      - type: bind
        source: ${SMILE_CONFIG_HOME}/resources/smile-dashboard
        target: ${SMILE_CONFIG_HOME}/resources/smile-dashboard
    links:
      - graphql-client
    ports:
      - 3006:3006
    depends_on:
      graphql-client:
        condition: service_healthy

Command:

docker-compose up -d

Custom Schema

The SMILE dashboard displays data in a tabular format, but the underlying data is stored in a graph database (Neo4j). When querying with GraphQL, the result is an object-like structure with nested objects that represents the relationships between nodes in the graph.

For example, below is a query that retrieves data from nodeA and its child node nodeB. field1 is a field of nodeA, and field2 and field3 are field of nodeB.

{
  nodeA {
    field1
    hasChildNodeB {
      field2
      field3
    }
  }
}

To simplify the process of transforming and processing nested graph data into a table format, we "flatten" the data schema via GraphQL custom resolvers so that nested fields (field2 and field3 in this example) are also represented as top-level fields in the queried result.

The above query is transformed into the following:

Before After
{
  nodeA {
    field1
    hasChildNodeB {
      field2
      field3
    }
  }
}

{
  nodeA {
    field1        
    field2        
    field3        
    hasChildNodeB {
      field2
      field3
    } 
  }
}

Notes

field2 and field3 are now top-level fields in the queried result, but they are not "true" fields of nodeA in the database. For clarity on which fields are flattened, refer to graphql-server/src/utils/flattening.ts. Specifically, the nestedValueGetters object contains the fields that are flattened for each node type and how these flattened fields are accessed/resolved.

We can't remove the nested fields from the query because they are needed to resolve the flattened fields. When this query is called, GraphQL will automatically resolve the nested fields first, then use our custom resolvers to resolve the flattened fields.

How to flatten a new field

  1. Add the new field to the corresponding query in operations.graphql.
  2. Add the new field to the extended schema in the extendedTypeDefs configurations in graphql-server/src/neo4j.ts.
  3. Add the new field to the corresponding flattened field array in graphql-server/src/utils/flattening.ts.
  4. Write logic to access/resolve the field in the nestedValueGetters object in graphql-server/src/utils/flattening.ts.
  5. Generate the typescript types by running yarn run codegen.