Performance problem under high load

Hi, firstly thanks for this cool library.

If the number of jobs being created is not too much, this library is perfect. I wanted to use this library in a heavy load scenario. Unfortunately results of my experiments were not so bright.

I'm working on > 100.000 keys of sample data. In Redis:

debug populate 100000 my:namespace

Here is my job creator:

var kue = require('kue');
var ioredis = require('ioredis');
var redis = ioredis();
var jobs = kue.createQueue();

var stream = redis.scanStream({
        match: 'my:namespace:*',
        count: 100
    });

    var keys = [];
    stream.on('data', function(resultKeys) {
        resultKeys.forEach(function(item, i) {
            console.log(i, item);
            keys.push(item);
        })

    });
    stream.on('end', function() {
        console.log("FINISHED!");
        keys.forEach(function(item) {
            jobs.create('ad_fetch', {
                item: item
            }).save();
        })
    });

    jobs.on('error', function(err) {
        console.log("There was an error!:");
        console.log(err);
    })

And here is my worker code:

var kue = require('kue');
var jobs = kue.createQueue();

jobs.process('ad_fetch', 100, function(job, done) {
  var data = job.data.item
  console.log(data + " is BEING PROCESSED!");
  done();

});

In this scenario, it takes 2 seconds to fetch 100.000 records from Redis using .scan stream. After the streaming ends and I have all keys inside an array and I iterate over it to create jobs for per key returned from Redis. Even though stream is complete, it takes almost 13 seconds for jobs to be created. I know that it's the time elapsed during necessary key/value creation in Redis. But, frankly, I think there should be some stuff to be optimized. For example why does the queue should wait for all jobs to be saved to start executing?

Am I doing something wrong? If there is a better way to do achieve what I'm trying to do, I'd be glad if you share it. Thank you!

Update: By the way, I thought maybe the reason could be limitations of a single Redis instance and I installed a second Redis instance. I seperated them in my sample app. I mean data is received from Redis1 and jobs data is saved to Redis2... Nothing changed. Same performance.