ReactiveSearch supports MongoDB as a valid backend. This means, instead of using ElasticSearch, Solr can also be used and all the features of ReactiveSearch can be used along with that.

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Pre Setup

Let's define the basics of the pipeline. It will be in the following way:

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enabled: true
description: Pipeline to use mongo db as search backend
routes:
- path: mongo-db-example/_reactivesearch
  method: POST
  classify:
    category: reactivesearch

envs:
  category: reactivesearch
  MONGO_DB: ""
  MONGO_COLLECTION: ""
  MONGO_HOST: ""
  MONGO_CREDS: ""

We are setting some of the sensitive input data as environment variables so that they can be reused in the inputs part of MongoDB Query. The values are:

  • MONGO_HOST: The host address of the mongoDB instance. This can be an IP address or a domain.
  • MONGO_DB: The name of the database to connect to in the MongoDB instance.
  • MONGO_COLLECTION: The collection to use after connecting to MongoDB.
  • MONGO_CREDS: Credentials in order to access the mongoDB instance. This should be in the format of <username>:<password> without the <, >.

We are setting the envs.category as reactivesearch for reference.

Stages

Now that we have the pre setup out of the way, let's define the stages for the pipeline.

Authorization

We need to make sure that the requests made to this endpoint are authenticated. To do this, we can use the pre-built stage authorization. We can define it in the following way:

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- id: authorization
  use: authorization
  continueOnError: false

It's as simple as that, we don't need to do anything else, rest will be taken care of by the pipeline.

ReactiveSearch Query

We can start with reactivesearch query to convert the request body into it's MongoDB equivalent. The only difference here is that we will need to pass the inputs.backend value as mongodb.

This can be done in the following way:

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- use: reactivesearchQuery
  inputs:
    backend: mongodb
  continueOnError: false

Extract Envs

Since there are a few dynamic fields, these will need to be extracted from the environment directly. This can be done with a script. The script can be passed in the pipeline using scriptRef functionality.

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- id: extract envs to context
  scriptRef: "extractEnvs.js"

The file should be saved as extractEnvs.js and can contain some code like this:

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function handleRequest() {
    return {
        mongoHost: context.envs.MONGO_HOST,
        mongoCreds: context.envs.MONGO_CREDS,
        mongoDb: context.envs.MONGO_DB,
        mongoCollection: context.envs.MONGO_COLLECTION
    }
}

MongoDB Query

Now that we have the required values extracted and set in the context, we can use the mongoDBQuery stage to execute the converted request and get the response. This can be done in the following way:

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- use: mongoDBQuery
  inputs:
    host: "{{mongoHost}}"
    credentials: "{{mongoCreds}}"
    db: "{{mongoDb}}"
    collection: "{{mongoCollection}}"
    connectionOptions: authSource=admin&readPreference=primary&ssl=true
  continueOnError: false

Complete Pipeline

Now that all the stages are defined, let's take a look at the whole pipeline at once:

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enabled: true
description: Pipeline to use mongo db as search backend
routes:
- path: mongo-db-example/_reactivesearch
  method: POST
  classify:
    category: reactivesearch

envs:
  category: reactivesearch
  MONGO_DB: ""
  MONGO_COLLECTION: ""
  MONGO_HOST: ""
  MONGO_CREDS: ""

stages:
- use: authorization
  continueOnError: false
- use: reactivesearchQuery
  inputs:
    backend: mongodb
  continueOnError: false
- id: extract envs to context
  scriptRef: "extractEnvs.js"
- use: mongoDBQuery
  inputs:
    host: "{{mongoHost}}"
    credentials: "{{mongoCreds}}"
    db: "{{mongoDb}}"
    collection: "{{mongoCollection}}"
    connectionOptions: authSource=admin&readPreference=primary&ssl=true
  continueOnError: false

Create the pipeline

Now that we have the whole pipeline defined, we can create the pipeline by hitting the ReactiveSearch instance.

The URL we will hit is: /_pipeline with a POST request.

The above endpoint expects a multipart/form-data body with the pipeline key containing the path to the pipeline file. All the scriptRef files can be passed as a separate key in the form data and will be parsed by the API automatically. Read more about this endpoint here

We can create the pipeline in the following request:

Below request assumes all the files mentioned in this guide are present in the current directory

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curl -X POST 'CLUSTER_ID/_pipeline' -H "Content-Type: multipart/form-data" --form "pipeline=pipeline.yaml" --form "extractEnvs.js=extractEnvs.js"

Testing the Pipeline

We can hit the pipeline endpoint now to see MongoDB working live. Use the following request to hit MongoDB and get the response in the ReactiveSearch format.

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curl -X POST CLUSTER_URL/good-books-ds-pipeline/_reactivesearch -H "Content-Type: application/json" -d '{"query": [{"id": "some ID", "value": "sudoku", "dataField": ["name_s"]}]}'

Above request should return a response with search results (considering there are results matching the search term) in the ReactiveSearch format.