Question

我有一段代码简单地循环了两个数组，对于第一个数组的每个元素，它在第二个数组中找到相关的元素，仅更改第一个出现的元素并删除其余的元素。

 /**
     * The aggregation data structure:
     * "_id": {
     * "geometry": geometry,
     * "dups": [
     *    "5b3b25b4e54029249c459bfc", keep only the fisrt element in allDocs
     *    "5b3b25b4e54029249c459e65", delete it from allDocs
     *    "5b3b25b4e54029249c459d7d"   delete it from allDocs
     *   ],
     * "dupsProp": [  ], array of all properties of duplicatePoints
     * "count": 3
     */
var aggregationRes =[46,000 objects]
var allDocs =[345,000 objects]
aggregationRes.forEach(function (resElem, counter) {
        console.log(counter + "/" + aggregationRes.length)
        //Delete objects in allDocs based on dups array except the first one
        var foundIndex = allDocs.findIndex(x => x._id.toString() == resElem.dups[0]);
                //assign the mergedProperties
        allDocs[foundIndex].properties = resElem.dupsProp;
        //delete the remaining ids in Docs from dups array 
        resElem.dups.forEach(function (dupElem, index) {
            var tmpFoundIndex = allDocs.findIndex(x => x._id.toString() == resElem.dups[index + 1]);
            if (tmpFoundIndex !== -1) {
                allDocs.splice(tmpFoundIndex, 1)
            }
        })
    })

此脚本运行将近4个小时。如您所见，计算确实很简单，但是由于allDocs数组很大，因此需要花费很长时间。如果有人给我提示如何减少计算时间，那就太好了。预先感谢

Answer 1

根据Bergi的想法，我们通过id为文档编制索引，以避免不得不查找昂贵的索引：

var allDocs =[345,000 objects]
var aggregationRes =[46,000 objects]
var allDocsIndexed = {};

allDocs.forEach(function(doc){
    allDocsIndexed[doc._id.toString()] = doc;
});

aggregationRes.forEach(function (resElem, counter) {
    allDocsIndexed[resElem.dups[0]].properties = resElem.dupsProp;
    for (var i = 1; i < resElem.dupsProp.length; i++) {
        delete allDocsIndexed[resElem.dupsProp[i]];
    }
});

var allUndeletedDocs = allDocs.filter(doc => allDocsIndexed.hasOwnProperty(doc_id.toString()));

请注意，对于javascript，这是一种有效的解决方案，但提供了更多详细信息，使用mongodb功能可能会存在更好的解决方案。

如何在node.js中的array.forEach中优化计算速度

1 个答案: