Mongodb - $match operation of aggregation pipeline

One 、$ match Overview

• $ match Used to filter documents , After that, we can aggregate the document subsets ,$ match You can use all the regular query operators except geospatial , In practical application, try to $ match Put it in front of the pipe . This has two advantages ： First, it can quickly filter out unnecessary documents , To reduce the workload of the pipeline ; The second is if it is executed before projection and grouping $match, Query can use index .
• When screening pipeline operation and other pipeline operations cooperate , Try to put it at the beginning , This can reduce the number of documents to be operated by subsequent pipeline operators , Improve efficiency .

Two 、 Data preparation

• Prepare the dataset , Execute the script

  var tags = ["nosql","mongodb","document","developer","popular"];
  var types = ["technology","sociality","travel","novel","literature"];
  var books=[];
  for(var i=0;i<50;i++){
        
  	var typeIdx = Math.floor(Math.random()*types.length);
  	var tagIdx = Math.floor(Math.random()*tags.length);
  	var tagIdx2 = Math.floor(Math.random()*tags.length);
  	var favCount = Math.floor(Math.random()*100);
  	var username = "xx00"+Math.floor(Math.random()*10);
  	var age = 20 + Math.floor(Math.random()*15);
  	var book = {
        
  		title: "book-"+i, 
  		type: types[typeIdx],
  		tag: [tags[tagIdx],tags[tagIdx2]],
  		favCount: favCount, 
  		author: {
        name:username,age:age}
  	};
  	books.push(book)
  }
  db.books1.insertMany(books);
  

3、 ... and 、 Of polymerization pipeline $ match The operation sample

• Inquire about books1 Data in the collection

  > db.books1.find()
  

  

• Inquire about books1 Collection type Field is technology All data for

  db.books1.aggregate([{
        $match:{
        type:"technology"}}])