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# Rare project - Data discovery

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## Contribute

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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](./HOW-TO-JOIN-WHEATIS-AND-PLANT-FEDERATIONS.md) or the [RARe guide](./HOW-TO-JOIN-RARe-FEDERATION.md) to know how to.
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If you do want to contribute to code or even only install the program on-premise it's great also, just keep reading below.
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## Setup

### Backend

The project uses Spring (5.x) for the backend,
with Spring Boot.

You need to install:

- a recent enough JDK8

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

    docker-compose up

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And this will start Elasticsearch and a Kibana instance (allowing to explore the data on http://localhost:5601).
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Then at the root of the application, run `./gradlew build` to download the dependencies.
Then run `./gradlew bootRun` to start the app.

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You can stop the Elastic Search and Kibana instances by running:

    docker-compose stop

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### Frontend

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The project uses Angular (7.x) for the frontend, with the Angular CLI.
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You need to install:

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- a recent enough NodeJS (8.11+). Node 10 is recommended for Angular 7.
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- Yarn as a package manager (see [here to install](https://yarnpkg.com/en/docs/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.

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The application will be available on:
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- http://localhost:4000/rare-dev for RARe (runs with: `yarn start:rare` or simply `yarn start`)
- http://localhost:4100/wheatis-dev for WheatIS (runs with: `yarn start:wheatis`)
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## Build

To build the app, just run:

    ./gradlew assemble
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or 
    ./gradlew assemble -Papp=wheatis
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This will build a standalone jar at `backend/build/libs/rare.jar` or  `backend/build/libs/wheatis.jar`, that you can run with:
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    java -jar backend/build/libs/rare.jar
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    java -jar backend/build/libs/wheatis.jar

And the full app run on:
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- http://localhost:8080/rare-dev
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- http://localhost:8180/wheatis-dev
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## CI

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

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

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    docker run --rm -v "$PWD":/home/rare -w /home/rare urgi/docker-browsers ./gradlew build
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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/asciidoc/html5/index.html`/

    ./gradlew asciidoctor

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## Harvest

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Harvesting (i.e. importing documents stored in JSON files into Elasticsearch) consists in
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creating the necessary index and aliases and Elasticsearch templates.
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To create the index and its aliases execute the script below for local dev environment:
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    ./scripts/createIndexAndAliases.sh

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This script is a wrapper for the `./scripts/createIndexAndAliases4CI.sh` which handle some parameters to create
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indices, aliases and so on, on another (possible remote) Elasticsearch for fitting to a specific environment:
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    ./scripts/createIndexAndAliases4CI.sh -host localhost -app rare -env dev
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You can run the scripts:
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    ./scripts/harvestRare.sh
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    ./scripts/harvestWheatis.sh
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to trigger a harvest of the resources stored in the Git LFS directories `data/rare` and `data/wheatis` respectively.
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## Indices and aliases

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The application uses several physical indices, which (at least the resources index) can be rolled over automatically based on the policies defined in the
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`./backend/src/test/resources/fr/inra/urgi/datadiscovery/dao/*_policy.json` files. This is based on the
[Index Lifecyle Management](https://www.elastic.co/guide/en/elasticsearch/reference/6.6/index-lifecycle-management.html)
provided by Elasticsearch.
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 * one to store physical resources, containing the main content
 * one to store suggestions, use 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.
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Each index and alias below refers to `rare` application in `dev` environment, the equivalent shall be created for `wheatis` 
app in `dev` environment as same as in `beta` or `prod` environments. For brevity, only `rare-dev` is explained here.
{: .alert .alert-info}

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

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 * `rare-dev-resource-index` is the alias used by the application to search for documents
 * `rare-dev-resource-harvest-index` is the alias used by the application to store documents when the harvest is triggered.
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In normal operations, these two aliases should refer to the same physical resource index. The script
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`createIndexAndAliases.sh` creates a physical index (named `rare-dev-resource-physical-index`) and creates these two aliases 
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referring to this physical index.

Once the index and the aliases have been created, a harvest can be triggered. The first operation that a harvest
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does is to create or update (put) the mapping for the document entity into the index aliased by `rare-dev-resource-harvest-index`. 
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Then it parses the JSON files and stores them into this same index. Since the `rare-dev-resource-index` alias 
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normally refers to the same physical index, searches will find the resources stored by the harvester.

### Why two aliases

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.

Two scenarios are possible:

#### Deleting with some downtime

The harvest duration depends on the performance of Elasticsearch, of the performance of the harvester, and 
of course, of the number of documents to harvest. If you don't mind about having a period of time 
where the documents are not available, you can simply 

 - delete the physical index;
 - re-create it with its aliases;
 - trigger a new harvest.
 
Keep in mind that, with the current known set of documents (17172), on a development machine where everything is running
concurrently, when both the Elasticsearch server and the application are hot, a harvest only takes 12 seconds.
So, even if you have 10 times that number of documents (170K documents), it should only take around 2 minutes of downtime.
If you have 100 times that number of documents (1.7M documents), it should take around 20 minutes, which is still not a 
very long time.

(Your mileage may vary: I assumed a linear complexity here).

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Here are curl commands illustrating the above scenario:
```
# delete the physical index and its aliases
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curl -X DELETE "localhost:9200/rare-dev-resource-physical-index"
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# recreate the physical index and its aliases
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curl -X PUT "localhost:9200/rare-dev-resource-physical-index" -H 'Content-Type: application/json' -d'
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{
    "aliases" : {
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        "rare-dev-resource-index" : {},
        "rare-dev-resource-harvest-index" : {}
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    }
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    "settings": ...
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}
'
```

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**NOTE**: Every time a physical index is created, the settings must be specified, the same ay as in the 
`createIndexAndAliases.sh` script. The exact content of the settings is omitted here for brevity and readability.
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{: .alert .alert-info}
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## Spring Cloud config

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On bootstrap, the application will try to connect to a remote Spring Cloud config server to fetch its configuration.
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The details of this remote server are filled in the `bootstrap.yml` file.
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By default, it tries to connect to the local server on http://localhost:8888
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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 `rare`, and the default profile.
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.

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If you want to use the Spring Cloud config app locally, see https://forgemia.inra.fr/urgi-is/data-discovery-config
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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.
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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.
More info on how pass a parameter to a Spring Boot app: 
https://docs.spring.io/spring-boot/docs/current/reference/html/boot-features-external-config.html#boot-features-external-config

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## Building other apps

By default, the built application is RARe. But this project actually allows building other
applications (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
    
Adding this property has the following consequences:

 - the generated jar file (in `backend/build/libs`) is named `wheatis.jar` instead of `rare.jar`;
 - the Spring active profile in `bootstrap.yml` is `wheatis-app` instead of `rare-app`;
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 - the frontend application built and embedded inside the jar file is the WheatIS frontend application instead of the
 RARe frontend application, i.e. the frontend command `yarn build:wheatis` is executed instead of the command `yarn:rare`.
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Since the active Spring profile is different, all the properties specific to this profile
are applies. In particular:
 
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 - the context path of the application is `/wheatis-dev` instead of `/rare-dev`; 
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 - the Elasticsearch prefix used for the index aliases is different.

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See the `backend/src/main/resources/application.yml` file for details.
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You can adapt the elasticsearch index used with the following parameter
java -jar backend/build/libs/data-discovery.jar --data-discovery.elasticsearch-prefix="data-discovery-staging-"