Indexing Pipeline

ElasticSearch provides endpoints like /_doc to index documents. ReactiveSearch API exposes these endpoints for external use directly (given you have proper credentials).

However, let's say we want to build an indexing endpoint on steroids. Something that lets the users send a request body to be indexed and then, some fields are added to this body before it reaches ElasticSearch.

Deploy this pipeline with one-click

How?

ReactiveSearch Pipelines makes it very easy to override any endpoint. We just need to add a pipeline that gets invoked by the endpoint that we want to override and voila, we have a custom endpoint that works exactly how we want it to work.

Example Scenario: Add location details based on passed IP

Let's take an example. We want to index data of an user. This users data also includes an ip field that indicates the IP address from where they signed up. We want to extract their location details and save it to the index as well.

So our pipeline will basically intercept the document to be indexed, add a new field to it and then send it to ElasticSearch.

Pre Setup

Before we start with the pipeline stages, let's setup the routes and other prerequisites of a pipeline. We want this pipeline to be invoked whenever data is to be indexed to the user-info index. So we can define it in the following way

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enabled: true
description: Index data into user-info with a location field

routes:
  - path: /user-info/_doc
    method: POST
      classify:
        category: elasticsearch
        acl: index

We are setting the category as elasticsearch because this will eventually lead to an elasticsearch call. Think of acl like a nested category that specifies exactly what the operation is. Here index as acl works perfectly as we are creating an indexing pipeline.

Stages

We will have a few stages in this pipeline. We will start with the most important stage: authorization. Following stages will be executed:

  1. authorise user
  2. get location details
  3. add details to body
  4. index data

Authorise User

This is one of the most important stages while defining a pipeline. We want to use the pre-built authorization stage which will make sure that the request contains credentials to access the endpoint being invoked.

This is important to make sure only the proper credentials can index data into the pipeline.

We can use this stage in the following way:

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- id: authorise user
  uses: authorization

Yes, as simple as that, we will take care of the rest.

Get Location Details

Now that we know the user has access to index the data, let's use the ip field to get the location from it.

Since, this is a custom stage, we will pass a JavaScript file to the pipeline. We will also pass the async stage set to true so that this stage is run asynchronously.

This stage will be defined in the followin way:

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- id: get location details
  scriptRef: "getLocation.js"
  async: true

In the above, we are referencing a getLocation.js in the scriptRef field.

scriptRef is a path to a JS script that contains the code to be executed for this stage.

The file will be defined in the following way:

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async function getLocationFromIP(ip) {
    /* Get location from the passed IP address */
    const response = await fetch(`http://ip-api.com/json/${ip}`);

    if (response.status != 200) return "";

    /* Parse the response */
    const jsonResponse = JSON.parse(response);
    
    if (jsonResponse.status != "success") return "";

    /* Return the string in `city, state, country` format */
    return [jsonResponse.city, jsonResponse.regionName, jsonResponse.country].join(", ");
}

async function handleRequest() {
    const requestBody = JSON.parse(context.request.body);
    const passedIP = requestBody.ip;

    if (passedIP == undefined || passedIP == null || passedIP == "") return {};

    /* Get the location from IP */
    const location = await getLocationFromIP(passedIP);
    return {
        extractedLocation: location
    };
}

In the above script, we are adding an extractedLocation field in the context with the extracted location.

Things to Note

  • For the location we will use the ip-api.
  • This stage will have to be run asynchronously because we will be making an external call.

Why not update the request body directly?

The question might arise, why are we not updating the request body directly in the above script?

This is because stages that are running asynchronously, or in other words, that have the async: true set are not allowed to update the context values but they are allowed to add new fields to the context.

Add Details to Body

We now have the location added to the context, we can update the request body with the extracted location. This stage is important because it will run synchronously and update the request body in context.

This stage will be simple and defined in the following way

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- id: add details to body
  scriptRef: "addDetails.js"
  needs:
    - get location details

Note that we have a needs stage here. The needs makes sure this stage is executed only after all the stages that it needs are completed.

In the above stage, we are refering an addDetails.js file. This file can be defined in the following way

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function handleRequest() {
    const requestBody = JSON.parse(context.request.body)
    return {
        request: {
            ...context.request,
            body: JSON.stringify({
                ...requestBody,
                location: context.extractedLocation
            })
        }
    }
}

In the above script, we are adding the context.extractedLocation field to the request body. Here, the request body is a stringified JSON. This is why we are parsing it and unparsing it back to make it properly readable in former stages.

Index Data

We are at the final stage now. We just need to pass the request body to ElasticSearch and we are done. We can do that by using the pre-built stage elasticsearchQuery.

We can define this stage in the following way

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- id: index data
  uses: elasticsearchQuery
  needs:
    - add details to body

We are using the needs property to make sure this stage gets executed after the location is added to the body.

Complete Pipeline

Let's now take a look at the completed pipeline to see how it looks like in one file.

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enabled: true
description: Index data into user-info with a location field

routes:
  - path: /user-info/_doc
    method: POST
      classify:
        category: elasticsearch
        acl: index

stages:
  - id: authorise user
    uses: authorization
  - id: get location details
    scriptRef: "getLocation.js"
    async: true
  - id: add details to body
    scriptRef: "addDetails.js"
    needs:
      - get location details
  - id: index data
    uses: elasticsearchQuery
    needs:
      - add details to body

In just a few lines of code, we have the pipeline ready. This pipeline can be tested through the dashboard at dash.appbase.io.

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 "addDetails.js=addDetails.js" --form "getLocation.js=getLocation.js"