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<template>
  <div>
    <label>User Query:</label>
    <input v-model="query" :disabled="loading" ></input> 
    <button @click="getData({ query })" :busy="loading" />
    <User v-if="data != null || error != null" :data="data" :error="error" />
  </div>
</template>

<script>

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import { Ref, ref } from "vue";

export function useFetcher(fetcher) {
  const data = ref(null);
  const loading = ref(false);
  const error = ref(null);

  const getData = async (...params) => {
    loading.value = true;
    data.value = null;

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// Example usage:
// userFetcher({ query: this.query });
async function userFetcher(params) {
  const data = await axios.get("/api/user", { params });
  return data;
}

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<template>
  <div>
    <label>User Query:</label>
    <input v-model="query" :disabled="loading" ></input> 
    <button @click="getData" :busy="loading" />
    <User v-if="data != null || error != null" :data="data" :error="error" />
  </div>
</template>

<script>

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// Load Environment variables from dotenv
require('dotenv').config();

const express = require("express");
const bodyParser = require("body-parser");
// Import Botkit's core features
const { Botkit } = require("botkit");
// Import a platform-specific adapter for slack.
const { SlackAdapter, SlackEventMiddleware } = require('botbuilder-adapter-slack');

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## Accompanying code for - Processing large S3 files with AWS Lambda
## https://medium.com/swlh/processing-large-s3-files-with-aws-lambda-2c5840ae5c91

import csv
import json
import os

import boto3
import botocore.response

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new_offset = offset + bodylines.offset
if new_offset < s3_object.content_length:
    new_event = {
        **event,
        "offset": new_offset,
        "fieldnames": fieldnames
    }
    invoke_lambda(context.function_name, new_event)

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MINIMUN_REMAINING_TIME_MS = 10000
  
csv_reader = csv.DictReader(bodylines.iter_lines(), fieldnames=fieldnames)
for row in csv_reader:
    ## process records here
    ## or alternatively forward records to kinesis or SQS to be processed seperately
    if context.get_remaining_time_in_millis() < MINIMUN_REMAINING_TIME_MS:
        fieldnames = fieldnames or csv_reader.fieldnames
        break

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def get_object_bodylines(s3_object, offset):
    resp = s3_object.get(Range=f'bytes={offset}-')
    body: botocore.response.StreamingBody = resp['Body']
    return BodyLines(body)


class BodyLines:
    def __init__(self, body: botocore.response.StreamingBody, initial_offset=0):
        self.body = body
        self.offset = initial_offset

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def handler(event, context):
    bucket_name = event['bucket']
    object_key = event['object_key']
    offset = event.get('offset', 0)
    fieldnames = event.get('fieldnames', None)