Data Explorer lets you explore a dataset. The code (in this repo and data-explorer-indexers repo) is dataset-agnostic. All dataset configuration happens in config files.

Examples:

• Data Explorer for the 1000 Genomes dataset. Config files here and here.
• Data Explorer for the Framingham Heart Study Teaching Dataset. This Data Explorer demonstrates time-series visualizations. Config files here and here.

Run local Data Explorer with the 1000 Genomes dataset:

• If ~/.config/gcloud/application_default_credentials.json doesn't exist, create it by running gcloud auth application-default login.
• docker-compose up --build
• Navigate to localhost:4400
• If you want to use the Save in Terra feature, do this one-time setup.

• Index your dataset into Elasticsearch.
  Before you can run the servers in this repo to display a Data Explorer UI, your dataset must be indexed into Elasticsearch. Use an indexer from https://github.com/DataBiosphere/data-explorer-indexers.

• Create dataset_config/<my dataset>

  • If you used https://github.com/DataBiosphere/data-explorer-indexers, copy the config directory from there.
  • Copy and fill out ui.json. (ui.json is not in data-explorer-indexers repo.)
  • If you used your own indexer, copy the config files from here and here. All files except gcs.json must be filled out.

• If you want to use the Save in Terra feature, do this one-time setup.

• If ~/.config/gcloud/application_default_credentials.json doesn't exist, create it by running gcloud auth application-default login.

• DATASET_CONFIG_DIR=dataset_config/<my dataset> docker-compose up --build -t 0

  • The -t 0 makes Kibana stop more quickly after Ctrl-C
  • If you get an error like ui_1 | Module not found: Can't resolve 'superagent' in '/ui/src/api/src', add -V: DATASET_CONFIG_DIR=dataset_config/<my dataset> docker-compose up --build -t 0 -V. -V is only needed for the next invocation of docker-compose, not all future invocations.
  • If ES crashes due to OOM, you can increase heap size:

    ES_JAVA_OPTS="-Xms10g -Xmx10g" docker-compose up --build -t 0
    

• Navigate to localhost:4400

The basic flow:

1. Index dataset into Elasticsearch using an indexer from https://github.com/DataBiosphere/data-explorer-indexers
2. Run the servers in this repo to display Data Explorer UI

GCP deployment:

For local development, an nginx reverse proxy is used to get around CORS:

Here's one possible flow.

• Run local Data Explorer with public 1000 Genomes dataset.
  This makes sure docker and git are installed correctly. (A JSON cache of the 1000 Genomes indices is imported into Elasticsearch; no indexer is run.)
• Run local BigQuery indexer with 1000 Genomes dataset
• Run locally with your dataset
  • Run BigQuery indexer on your dataset
  • Run UI and API servers for your dataset
• Deploy on GCP for your dataset
  • Deploy indexer for your dataset
  • Deploy UI and API servers for your dataset

If your dataset includes sample files (VCF, BAM, etc), then Data Explorer will have:

• A Samples Overview facet, which gives an overview of your sample files:

  

• Sample file facets will display number of sample files instead of number of participants. For example, if your dataset has 100 participant and each participant has 5 files, and there is a facet for "Raw coverage", the number on the upper right of the facet can be 0-500, and represents how many sample files are in the current selection.

If your dataset has longitudinal data, then Data Explorer will show time-series visualizations:

We use swagger-codegen to automatically implement the API, as defined in api/api.yaml, for the API server and the UI. Whenever the API is updated, follow these steps to update the server implementations:

• Clear out existing generated models:

  rm ui/src/api/src/model/*
  rm api/data_explorer/models/*
  

• Regenerate Javascript and Python definitions.
  • From the .jar (Linux):

    java -jar ~/swagger-codegen-cli.jar generate -i api/api.yaml -l python-flask -o api -DsupportPython2=true,packageName=data_explorer
    java -jar ~/swagger-codegen-cli.jar generate -i api/api.yaml -l javascript -o ui/src/api -DuseES6=true
    yapf -ir . --exclude ui/node_modules --exclude api/.tox
    

  • From the global script (macOS or other):

    swagger-codegen generate -i api/api.yaml -l python-flask -o api -DsupportPython2=true,packageName=data_explorer
    swagger-codegen generate -i api/api.yaml -l javascript -o ui/src/api -DuseES6=true
    yapf -ir . --exclude ui/node_modules
    

• Update API and UI servers.
• Don't forget to fix JS warnings. (Otherwise CircleCI will fail.)

• docker-compose should be at least 1.21.0. The data-explorer-indexer repo refers to the network created by docker-compose in this repo. Prior to 1.21.0, the network name was dataexplorer_default. Starting with 1.21.0, the network name is data-explorer_default.

• Install swagger-codegen-cli.jar. This is only needed if you modify api.yaml

  # Linux
  wget http://central.maven.org/maven2/io/swagger/swagger-codegen-cli/2.3.1/swagger-codegen-cli-2.3.1.jar -O ~/swagger-codegen-cli.jar
  # macOS
  brew install swagger-codegen
  

• In ui/ run npm install. This will install tools used during git precommit, such as formatting tools.

• Set up git secrets.

The Save in Terra feature temporarily stores data in a GCS bucket.

• If you haven't already, fill out deploy.json for your dataset.
  • Even if you don't plan on deploying Data Explorer to GCP, deploy.json will still need to be filled out. A temporary file will be written to a GCS bucket in the project in deploy.json, even for local deployment of Data Explorer. Choose a project where you have at least Project Editor permissions.
• Create export bucket. This only needs to be done once per deploy project. Run deploy/create-export-url-bucket.sh DATASET from the root of the repo, where DATASET is the name of the directory in dataset_config.
• The Save in Terra feature requires a service account private key. Follow these instructions to download a key. This needs to be done once per person per deploy project. If three people run Data Explorer with the same deploy project, then all three need to download a key for the deploy project.
  • Go to the Service Accounts page for your deploy project.
  • Click on the three-dot Actions menu for the App Engine default service account -> Create Key -> CREATE.
  • Move the downloaded file to dataset_config/DATASET/private-key.json

Every commit on a remote branch kicks off all tests on CircleCI.

API server unit tests use pytest and tox. To run locally:

virtualenv ~/virtualenv/tox
source ~/virtualenv/tox/bin/activate
pip install tox
cd api && tox -e py35

End-to-end tests use Puppeteer and jest-puppeteer. To run locally:

# Optional: ensure the elasticsearch index is clean
docker-compose up --build -d elasticsearch
curl -XDELETE localhost:9200/_all
# Start the rest of the services
docker-compose up --build
cd ui && npm test

Troubleshooting tips for end-to-end tests:

• Uncomment headless to see the browser during test run.
• Run a single test: npm test -- -t Participant
• More tips here

ui/ is formatted with Prettier. husky is used to automatically format files upon commit. To fix formatting, in ui/ run npm run fix.

Python files are formatted with YAPF.