Rare project - Data discovery

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Contribute

If you want to install the program locally, it's cool, just keep reading at Setup section and beyond.

Data

You might probably want to know how to contribute to the federation of data. That's great, let's have a look at the WheatIS/Plant guide or the RARe guide to know how to.

Code

If you do want to contribute to code, that's great also, have a look at CONTRIBUTING.md.

Setup

Requirements

The application itself is running under a Java OpenJDK 17+: https://openjdk.java.net/install/

For getting the data, you need to install Git LFS: https://git-lfs.github.com/

The indexing process depends on the following tools, you need to have them installed and available in your PATH variable:

• JQ 1.6+: https://stedolan.github.io/jq/
• GNU Parallel (recent enough version): https://www.gnu.org/software/parallel/
• GNU coreutils (sed, date...): https://www.gnu.org/software/coreutils/
• GNU GZIP: https://git.savannah.gnu.org/cgit/gzip.git
• GNU Bash v4+: https://www.gnu.org/software/bash/

Data handling

At the moment, all data is located next to the code in the data directory. If you want to have a look at the code only, you can ignore this directory at git clone step by setting the variable GIT_LFS_SKIP_SMUDGE=1, ie.:

GIT_LFS_SKIP_SMUDGE=1 git clone git@forgemia.inra.fr:urgi-is/data-discovery.git

After clone done, if you want to fetch some of the data (for instance for RARe only), let's run:

$ git lfs pull -I data/rare/
Downloading LFS objects: 100% (16/16), 8.8 MB | 0 B/s

Git might request you to enable additional parameters, which is acceptable:

git config lfs.https://forgemia.inra.fr/urgi-is/data-discovery.git/info/lfs.locksverify true

Backend

The project uses Spring Boot for the backend.

You need to install:

• a recent enough JDK17

The docker images need quite a bit of resources, so make sure you have at least 4g of RAM configured (Docker Desktop / Resources / Memory).

The application expects to connect on an Elasticsearch instance running on http://127.0.0.1:9200. To have such an instance, simply run:

docker compose up

And this will start Elasticsearch and a Kibana instance (allowing to explore the data on http://localhost:5601).

Then at the root of the application, run ./gradlew build to download the dependencies. Then run ./gradlew bootRun to start the app.

You can stop the Elasticsearch and Kibana instances by running:

docker compose stop

or run the following command to also remove the stopped containers as well as any networks that were created:

docker compose down

Frontend

The project uses Angular for the frontend, with the Angular CLI.

You need to install:

• a recent enough NodeJS (ie. v18 LTS) is required for Angular.
• Yarn as a package manager (see here to install)

Then in the frontend directory, run yarn to download the dependencies. Then run yarn start to start the app, using the proxy conf to reroute calls to /api to the backend.

The application will be available on:

• http://localhost:4000/rare-dev for RARe (runs with: yarn start:rare or simply yarn start)
• http://localhost:4000/brc4env-dev for RARe with basket (runs with: yarn start:brc4env)
• http://localhost:4100/wheatis-dev for WheatIS (runs with: yarn start:wheatis)
• http://localhost:4200/faidare-dev for Faidare (runs with: yarn start:faidare)

See ./frontend/package.json (scripts section) for other yarn commands.

Build

To build the app, just run:

./gradlew assemble

or

./gradlew assemble -Papp=wheatis

or

./gradlew assemble -Papp=brc4env

This will build a standalone jar at backend/build/libs/, that you can run with either:

java -jar backend/build/libs/rare.jar
java -jar backend/build/libs/brc4env.jar
java -jar backend/build/libs/wheatis.jar
java -jar backend/build/libs/faidare.jar

And the full app running on:

• http://localhost:8080/rare-dev
• http://localhost:8180/wheatis-dev
• http://localhost:8280/faidare-dev
• http://localhost:8580/brc4env-dev

CI

The .gitlab-ci.yml file describes how Gitlab is running the CI jobs.

It uses a base docker image named urgi/docker-browsers available on DockerHub and INRA-MIA Gitlab. The image is based on openjdk:8 and adds all stuff needed to run the tests (ie. a Chrome binary with a headless Chrome in --no-sandbox mode).

We install node and yarn in /tmp (this is not the case for local builds) to avoid symbolic links issues on Docker.

You can approximate what runs on CI by executing:

docker run --rm -v "$PWD":/home/rare -w /home/rare urgi/docker-browsers ./gradlew build

Or also run a gitlab-runner as Gitlab-CI would do (minus the environment variables and caching system):

gitlab-runner exec docker test

Documentation

An API documentation describing most of the webservices can be generated using the build task asciidoctor, which executes tests and generates documentation based on snippets generated by these tests. The documentation is generated in the folder backend/build/docs/asciidoc/index.html

./gradlew asciidoctor

Harvest

Before all, if you have cloned the repository without fetching the data (see Data handling section), take care to get it before running any indexing script.

TL;DR

Data (from master branch) indexing to your local Elasticsearch is done using the following command. Note that your local Elasticsearch instance should be already runing using docker-compose up:

docker run -t --volume $(pwd)/data:/opt/data/ --volume /tmp/bulk:/tmp/bulk/ --network=container:elasticsearch registry.forgemia.inra.fr/urgi-is/docker-rare/data-discovery-loader:latest --help

Example for indexing RARe data:

docker run -t --volume $(pwd)/data:/opt/data/ --volume /tmp/bulk:/tmp/bulk/ --network=container:elasticsearch registry.forgemia.inra.fr/urgi-is/docker-rare/data-discovery-loader:latest -host elasticsearch -app rare -env dev

If you need to spread the load on several CPUs, duplicate the value of host argument to simulate several Elasticsearch nodes, ie. below to use 4 CPUs:

docker run -t --volume $(pwd)/data:/opt/data/ --volume /tmp/bulk:/tmp/bulk/ --network=container:elasticsearch registry.forgemia.inra.fr/urgi-is/docker-rare/data-discovery-loader:latest -host "elasticsearch elasticsearch elasticsearch elasticsearch" -app rare -env dev

Take care to use your branch's CI_COMMIT_REF_SLUG instead of latest docker tag if you have modified the indexing scripts, Elasticsearch mappings or settings, see https://docs.gitlab.com/ee/ci/variables/predefined_variables.html. Example for branch story/faidare-fusion, use docker tag story-faidare-fusion:

docker run -t --volume $(pwd)/data:/opt/data/ --volume /tmp/bulk:/tmp/bulk/ --network=container:elasticsearch registry.forgemia.inra.fr/urgi-is/docker-rare/data-discovery-loader:story-faidare-fusion -host "elasticsearch elasticsearch elasticsearch elasticsearch" -app rare -env dev

If you need to test the docker loader with your local changes, look at job named build-loader-docker-image in the .gitlab-ci.yml at the root of the project to see how to build the image with your custom docker tag.

Output logs should be available in directory /tmp/bulk/rare-dev.

Portability

Docker

TL;DR section above expects to have an available docker image on the forgemia docker registry. You can update or push such an image using the following:

# build the image
docker build -t registry.forgemia.inra.fr/urgi-is/docker-rare/data-discovery-loader:latest .

# Login before pushing the image
docker login registry.forgemia.inra.fr/urgi-is/docker-rare -u <your ForgeMIA username>

# push the built image
docker push registry.forgemia.inra.fr/urgi-is/docker-rare/data-discovery-loader:latest

That should ease the indexing of data without having to craft a dedicated environment, which is explained below.

UNIX/BSD

Feedback related to portability on MacOS and other GNU/Linux distro is really welcomed.

For MacOS, care to use latest GNU Parallel and Bash v4 versions, not the version provided by default via Brew. Don't use zsh!

Install the following packages to be able to run the scripts:

brew install gnu-sed coreutils parallel

Harvesting (i.e. importing JSON documents into Elasticsearch) consists in creating the necessary index and aliases and Elasticsearch templates.

To create the index and its aliases execute the script below for local dev environment:

./scripts/index.sh -app rare|brc4env|wheatis|data-discovery --local

The -app parameter will trigger a harvest of the resources stored in the Git LFS subdirectories data/rare and data/faidare filtered or not (wheatis and brc4env rely on faidare and rare data respectively).

Indices and aliases

The application uses several physical indices:

• one to store physical resources, containing the main content
• one to store suggestions, used for the search type-ahead feature only

Both indices must be created explicitly before using the application. If not, requests to the web services will return errors.

Each index and alias below refers to rare application in dev environment, the equivalent shall be created for wheatis and data-discovery app in dev environment as same as in beta or staging or prod environments. For brevity, only rare-dev is explained here. {: .alert .alert-info}

The application doesn't use the physical resources index directly. Instead, it uses two aliases, that must be created before using the application:

• rare-dev-resource-alias is the alias used by the application to store and search for documents
• rare-dev-suggestions-alias is the alias used by the application to store and search for suggestions, only used for completion service.

In normal operations, these two aliases should refer to physical indices having a timestamp such as rare-dev-tmstp1579877133-suggestions and rare-dev-tmstp1579877133-resource-index. Those timestamps allow for reindexing data without breaking another runnning application having another timestamp. The alias switch being done atomicly, we always see data in the web interface.

Using two aliases is useful when deleting obsolete documents. This is actually done by removing everything and then harvesting the new JSON files again, to re-populate the index from scratch.

Spring Cloud config

On bootstrap, the application will try to connect to a remote Spring Cloud config server to fetch its configuration. The details of this remote server are filled in the application.yml file. By default, it tries to connect to the local server on http://localhost:8888 but it can of course be changed, or even configured via the SPRING_CONFIG_URI environment variable.

It will try to fetch the configuration for the application name specified in the profile-specific spring.cloud.config.name property. If such a configuration is not found, it will then fallback to the local application.yml properties.

To avoid running the Spring Cloud config server every time when developing the application, all the properties are still available in application.yml even if they are configured on the remote Spring Cloud server as well.

If you want to use the Spring Cloud config app locally, see https://forgemia.inra.fr/urgi-is/data-discovery-config

The configuration is currently only read on startup, meaning the application has to be reboot if the configuration is changed on the Spring Cloud server. For a dynamic reload without restarting the application, see http://cloud.spring.io/spring-cloud-static/Finchley.SR1/single/spring-cloud.html#refresh-scope to check what has to be changed.

In case of testing configuration from the config server, one may use a dedicated branch on data-discovery-config project and append the --spring.cloud.config.label=<branch name to test> parameter when starting the application's executable jar, or use the corresponding Spring env variable (ie. SPRING_CLOUD_CONFIG_LABEL). More info on how to pass a parameter to a Spring Boot app.

Building other apps

By default, the built application is RARe (without basket, i.e. without the possibility to add accessions to a basket and create an accession order on the rare-basket application). But this project actually allows building other applications (RARe with basket and WheatIS, for the moment, but more could come).

To build a different app, specify an app property when building. For example, to assemble the WheatIS app, run the following command

./gradlew assemble -Papp=wheatis

You can also run the backend WheatIS application using

./gradlew bootRun -Papp=wheatis

To assemble the RARe app with support for adding accessions to a basket, run the following command

./gradlew assemble -Papp=brc4env

You can also run the backend RARe application with basket support using

./gradlew bootRun -Papp=brc4env

Adding this property has the following consequences:

• the generated jar file (in backend/build/libs) is named wheatis.jar (resp. brc4env.jar instead of rare.jar;
• the Spring active profile is wheatis-app (resp. brc4env-app) instead of rare-app;
• the frontend application built and embedded inside the jar file is the WheatIS frontend application (resp. the RARe application with basket support) instead of the RARe frontend application, i.e. the frontend command yarn build:wheatis (resp. yarn build:brc4env) is executed instead of the command yarn:rare.

Since the active Spring profile is different, all the properties specific to this profile are applied. In particular:

• the context path of the application is /wheatis-dev instead of /rare-dev;
• the Elasticsearch prefix used for the index aliases is different.

See the backend/src/main/resources/application.yml file for details.

You can adapt the elasticsearch index used with the following parameter

java -jar backend/build/libs/faidare.jar --data-discovery.elasticsearch-prefix="faidare-staging-"

For debuging:

java -jar backend/build/libs/faidare.jar --debug

Configuration

The RARe and RARe with basket applications can be configured to apply an implicit filtering on the searches, aggregations, and pillar list. There is currently only one implicit filter that can be added, which is a filter on the pillar name.

To activate it, add the following YAML configuration under the appropriate profile:

rare:
  implicit-terms:
    PILLAR:
      - Pilier Forêt
      - Pilier Micro-organisme