node.js - 在亚马逊 lambda 中，并行异步调整多个缩略图大小会引发错误 : Stream yields empty buffer

Question

我已经改编了 resizing a photo in lambda 的亚马逊示例创建多个缩略图大小并并行运行。

我的代码在几秒钟内在本地运行良好，但在 lambda 云中，它不会并行运行，在调整第一个缩略图大小后抛出错误。如果我将它切换为串行而不是并行它连续运行大约需要 60 秒。

为什么在 lambda 中并行运行调整大小代码会导致流产生空缓冲区错误。我如何才能提高性能，以便在几秒钟内创建大小，同时在处理器成本方面仍然从 lambda 中获得良好的值(value)和效率？

// dependencies
var async = require('async');
var AWS = require('aws-sdk');
var gm = require('gm')
            .subClass({ imageMagick: true }); // Enable ImageMagick integration.
var util = require('util');

// constants
var SIZES = [100, 320, 640];

// get reference to S3 client 
var s3 = new AWS.S3();

exports.handler = function(event, context) {

    // Read options from the event.
    console.log("Reading options from event:\n", util.inspect(event, {depth: 5}));
    var srcBucket = event.Records[0].s3.bucket.name;
    var srcKey    = event.Records[0].s3.object.key;
    var dstBucket = srcBucket + "-resized";

    // Infer the image type.
    var typeMatch = srcKey.match(/\.([^.]*)$/);
    if (!typeMatch) {
        console.error('unable to infer image type for key ' + srcKey);
        return context.done();
    }
    var imageType = typeMatch[1];
    if (imageType != "jpg" && imageType != "png") {
        console.log('skipping non-image ' + srcKey);
        return context.done();
    }

    // Sanity check: validate that source and destination are different buckets.
    if (srcBucket == dstBucket) {
        console.error("Destination bucket must not match source bucket.");
        return context.done();
    }

    // Download the image from S3
    s3.getObject({
            Bucket: srcBucket,
            Key: srcKey
        },
        function(err, response){

            if (err)
                return console.error('unable to download image ' + err);

            var contentType = response.ContentType;

            var original =  gm(response.Body);
            original.size(function(err, size){

                if(err)
                    return console.error(err);

                //transform, and upload to a different S3 bucket.
                async.each(SIZES,
                    function (max_size,  callback) {
                        resize_photo(size, max_size, imageType, original, srcKey, dstBucket, contentType, callback);
                    },
                    function (err) {
                        if (err) {
                            console.error(
                                'Unable to resize ' + srcBucket +
                                ' due to an error: ' + err
                            );
                        } else {
                            console.log(
                                'Successfully resized ' + srcBucket
                            );
                        }

                        context.done();
                    });
            });


        });



};

//wrap up variables into an options object
var resize_photo = function(size, max_size, imageType, original, srcKey, dstBucket, contentType, done) {

    var dstKey = max_size +  "_" + srcKey;


    // transform, and upload to a different S3 bucket.
    async.waterfall([

        function transform(next) {


            // Infer the scaling factor to avoid stretching the image unnaturally.
            var scalingFactor = Math.min(
                max_size / size.width,
                max_size / size.height
            );
            var width  = scalingFactor * size.width;
            var height = scalingFactor * size.height;


            // Transform the image buffer in memory.
            original.resize(width, height)
                .toBuffer(imageType, function(err, buffer) {

                    if (err) {
                        next(err);
                    } else {
                        next(null, buffer);
                    }
                });

        },
        function upload(data, next) {
            // Stream the transformed image to a different S3 bucket.
            s3.putObject({
                    Bucket: dstBucket,
                    Key: dstKey,
                    Body: data,
                    ContentType: contentType
                },
                next);
            }
        ], function (err) {

            console.log('finished resizing ' + dstBucket + '/' + dstKey);

            if (err) {
                console.error(err)
                ;
            } else {
                console.log(
                    'Successfully resized ' + dstKey
                );
            }

            done(err);
        }
    );
};

Answer 1

我今晚刚遇到同样的问题。

虽然您可能还可以做其他事情，但我更新了 lambda 任务的内存，缓冲区问题就消失了。

我正在将大约 2.1mb 和 5000x3000 的图像调整为 3 个较小的尺寸。

Duration: 11619.86 ms Billed Duration: 11700 ms Memory Size: 1024 MB Max Memory Used: 582 MB

希望对你有帮助