Appbase.io uses OpenFaas under the hood to create and deploy functions. We use OpenFaas because:

  • It lets you write function in any language
  • It is open source and actively maintained
  • Can be easily deployed and maintained with kubernetes orchestration

To create an build functions using OpenFaas, you need to install faas-cli, a command line utility to bootstrap, build and deploy functions. Here are the steps which you can follow to install faas-cli


For Linux users

curl -sSL https://cli.openfaas.com | sudo -E sh

For Mac users

brew install faas-cli

Once the faas-cli is installed, we can create functions using OpenFaas templates. OpenFaas have templates for creating functions in various programming languages.

In this documentation we will be using NodeJS template to create and publish function. For more templates you can check the template store by OpenFaas

Quick Start

Let us create a function to promote a result with each search query.

Step 1: Get the template

mkdir promote-result && cd promote-result

faas template pull https://github.com/openfaas-incubator/node10-express-template

Step 2: Create a function

faas new --lang node10-express promote-result

Step 3: Edit Business Logic in ./promote-result/handler.js

Functions business logic can be developed based on when you would like to trigger them and some other environment variables. Example if you would like to trigger function before it hits Elasticsearch and modify request body or if you would like to trigger a function after Elasticsearch request is completed and modify response body before it is sent to the end user.

To simplify the development process we have created a body structure which you can access while adding business logic to the function. Please refer the docs here for more information on function data.

In the example below we are trying to update response of Elasticsearch and add a promoted result in the response.

'use strict';

module.exports = (event, context) => {
    // check if it is a _search or _msearch
    // request then only update response.
    if (event.body.env.acl === 'search' && event.body.response.status === 200) {
        if (event.body.response.body.hits) {
            event.body.response.body = promoteResult(event.body.response.body);
    } else if (event.body.env.acl === 'msearch' && event.body.response.status === 200) {
        if (
            event.body.response.body.responses &&
            event.body.response.body.responses[0] &&
        ) {
            event.body.response.body.responses[0] = promoteResult(

function promoteResult(responses) {
    if (responses && responses.hits) {
        // object that we want to promote
        // with each search request
        const promotedRes = {
            _index: 'phones',
            _type: '_doc',
            _id: 'promoted-res',
            _socre: 1.0,
            _source: {
                name: 'iphone',
        // update the total status
        responses.hits.total.value = responses.hits.total.value + 1;
        // prepend the object in result list

    return Object.assign(responses);

Step 4: Update image name in ./promote-result.yml

You need to update the image name with DOCKER_USERNAME/image-name: VERSION.

version: 1.0
    name: openfaas
        lang: node10-express
        handler: ./promote-result
        image: DOCKER_USERNAME/promote-result:0.1.0
        read_timeout: '30s' # default is 5s, Maximum time to read HTTP request
        write_timeout: '30s' # default is 5s, Maximum time to write HTTP response
        upstream_timeout: '30s' # Maximum duration of upstream function call

Step 5: Build function

faas-cli build -f promote-result.yml

Step 6: Publish function on docker hub

faas-cli push -f promote-result.yml

Once your function is published as docker image, you can also make it private from your registry / docker hub.

Note: If you are using Self Hosted version of Appbase.io and want to deploy private image of function, you will have to add OPENFAAS_KUBE_CONFIG env with the value where your kubernetes config file exists.

Event Body structure

With each function definition, you get access to following data, which can help you build the business logic for the function and enhance the search experience.

Here is the list of parameters that you can get access in your functions event.body

Parameter Description
extraRequestPayload JSON object to pass extra information to function.
env JSON object to get information about various trigger related environment variables.
env.acl String to do granular classification of the category of the incoming request. You can see the full list of values over here
env.category String value to classify type of incoming request. It can be one of docs, search, indices, cat, clusters, misc.
env.query String value to know the keyword being queried.
env.index Array of strings to know the indexes on which the function will be executed
env.filter String value to set filter data based on trigger logic. Accepts the string expression based on expr library of Golang.
request [Optional] parameter available when trigger is set to before search. It is a JSON object which contains meta information about the request.
request.url String value to know the exact URL using which Elasticsearch cluster is accessed.
request.method String value to know HTTP method of request
request.headers JSON object to know header values
request.body JSON object to get access to the request payload.
response [Optional] parameter available when trigger is set to after search. It is a JSON object which is obtained after execution HTTP request.
response.body JSON object obtained after execution HTTP request.
response.headers JSON object to know header values
response.status HTTP Status value.


  // extra information to be passed with functions, example env variables.
  "extraRequestPayload": {},
  // in case of before search request execution
  "request": {
    "url": "http://foo:bar@localhost:8000/phones/_search",
    "method": "GET",
    "headers": {
      "Content-Type": "application/json"
    "body": {
      "query": {
        "match": {
          "title": {
            "query": "iphone"
  // in case of after search request execution
  "response": {
    "body": {
      "_shards": {
        "failed": 0,
        "skipped": 0,
        "successful": 1,
        "total": 1
      "hits": {
        "hits": [
            "_id": "9E41hG8B-WWLBcH3Zqmb",
            "_index": "phones",
            "_score": 1,
            "_source": {
              "name": "Samsung M1"
            "_type": "_doc"
        "max_score": 1,
        "total": {
          "relation": "eq",
          "value": 1
      "timed_out": false,
      "took": 1058
    "headers": {
      "Access-Control-Allow-Credentials": true,
      "Content-Type": "application/json"
    "status": 200
  "env": {
    "acl": "msearch",
    "category": "search",
    "index": [
    "query": "phones",
    "now": 1578485425

Now let us see how we can deploy this function using Appbase.io Dashboard.