Processing a large dataset in less than 100 lines of Node.js with async.queue

If you’re more of a skip to the code person, check out the gist here.

caolan’s async.queue to the rescue

To fix the call stack issue needed to manage API calls by pushing them into a queue where they could be processed in parallel. 

Pushing items to the queue

Image IDs are in a newline delimited JSON file. First convert this file into a JSON object using readFileSync. The object contains a list of image IDs and in queue want to send each image to the Vision API. The queue takes a task (in this case my object of image IDs) and a callback function, called when the worker is finished processing:

q.push(imageIds, function (err) {
 if (err) {
  console.log(err)
 }
});

Defining the queue

The queue takes a function and a concurrency number as parameters. Let’s start with the function: we pass it a task (our image ID from above) and a callback, which will be called when the worker completes a task. Inside the function is where do image processing.

This function should return some JSON about the image which want to write to a local JSON file. Will define that in the next step.

                        Concurrency tells Node.js the maximum number of workers to process our task in parallel. Playing with the number until  found a balance of something that wasn’t too slow, but also didn’t result in API limits or call stack errors. The number will vary depending on what you’re doing, so it’s definitely ok to fine tune it by hand until you find your “magic number.” Here’s queue:

let q = async.queue(callVision, 20);

Processing images

Last, it’s time to write the callVision() function referenced above. This part isn’t exactly async.queue specific, but it’s still important because it’s the meat of my queue task. 

Here using Google’s Cloud Vision API for image analysis, and use the Google Cloud Node.js module to call it. Once get a JSON response for each image, create a JSON string of the response to write to a newline delimited JSON file (using this format because it’s  what BigQuery expects, which is where will be storing the data eventually). Once this function completes, the data is sent back to the queue where it is written to local JSON file. You can find all of the callVision() code in the gist.

That’s it!  you’ve done something interesting with async.queue 

                                                                                        *Sara Robinson