blocks|key|159878|text|您似乎需要使用基于流的库，例如fast-csv，它也包含验证支持。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|159879|entityMap|0|LINK|mutability|MUTABLE|url|https://www.npmjs.org/package/fast-csv^0|F|8|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Seems like you need to use a stream-based library such as <a href="https://www.npmjs.org/package/fast-csv" rel="noreferrer">fast-csv</a>, which also includes validation support.

blocks|key|2959394|text|您正在引用的node-csv项目对于转换大部分CSV数据的每一行的任务是完全足够的，这些数据来自位于：http://csv.adaltas.com/transform/的文档|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2959395|csv()
++.from('82,Preisner,Zbigniew\n94,Gainsbourg,Serge')
++.to(console.log)
++.transform(function(row,+index,+callback){
++++process.nextTick(function(){
++++++callback(null,+row.reverse());
++++});
});|code-block|syntax|javascript|2959396|根据我的经验，我可以说它也是一个相当快的实现，我已经在具有近10k条记录的数据集上使用它，整个数据集的处理时间处于合理的数十毫秒水平。|2959397|Rearding的基于流的解决方案建议:+node-csv是基于流的，并遵循Node.js的流应用编程接口。|2959398|entityMap|0|LINK|mutability|MUTABLE|url|http://csv.adaltas.com/transform/^0|1F|X|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|C|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

The node-csv project that you are referencing is completely sufficient for the task of transforming each row of a large portion of CSV data, from the docs at: <a href="http://csv.adaltas.com/transform/" rel="noreferrer">http://csv.adaltas.com/transform/</a>:

<pre><code>csv()
 .from('82,Preisner,Zbigniew\n94,Gainsbourg,Serge')
 .to(console.log)
 .transform(function(row, index, callback){
 process.nextTick(function(){
 callback(null, row.reverse());
 });
});
</code></pre>

From my experience, I can say that it is also a rather fast implementation, I have been working with it on data sets with near 10k records and the processing times were at a reasonable tens-of-milliseconds level for the whole set.

Rearding jurka's stream based solution suggestion: node-csv IS stream based and follows the Node.js' streaming API.

blocks|key|2959419|text|我目前的解决方案使用异步模块来串行执行：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2959420|var+fs+=+require('fs');
var+parse+=+require('csv-parse');
var+async+=+require('async');

var+inputFile='myfile.csv';

var+parser+=+parse({delimiter:+','},+function+(err,+data)+{
++async.eachSeries(data,+function+(line,+callback)+{
++++//+do+something+with+the+line
++++doSomething(line).then(function()+{
++++++//+when+processing+finishes+invoke+the+callback+to+move+to+the+next+one
++++++callback();
++++});
++})
});
fs.createReadStream(inputFile).pipe(parser);|code-block|syntax|javascript|2959421|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

My current solution uses the async module to execute in series: 

<pre><code>var fs = require('fs');
var parse = require('csv-parse');
var async = require('async');

var inputFile='myfile.csv';

var parser = parse({delimiter: ','}, function (err, data) {
 async.eachSeries(data, function (line, callback) {
 // do something with the line
 doSomething(line).then(function() {
 // when processing finishes invoke the callback to move to the next one
 callback();
 });
 })
});
fs.createReadStream(inputFile).pipe(parser);
</code></pre>

blocks|key|2746809|text|尝试使用逐行npm插件。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2746810|npm+install+line-by-line+--save|code-block|syntax|javascript|2746811|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Try the line by line npm plugin.

<pre><code>npm install line-by-line --save
</code></pre>

blocks|key|157454|text|我是这样使用的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|157455|var+fs+=+require('fs');+
var+parse+=+require('csv-parse');

var+csvData=[];
fs.createReadStream(req.file.path)
++++.pipe(parse({delimiter:+':'}))
++++.on('data',+function(csvrow)+{
++++++++console.log(csvrow);
++++++++//do+something+with+csvrow
++++++++csvData.push(csvrow);++++++++
++++})
++++.on('end',function()+{
++++++//do+something+with+csvData
++++++console.log(csvData);
++++});|code-block|syntax|javascript|157456|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I used this way:-

<pre><code>var fs = require('fs'); 
var parse = require('csv-parse');

var csvData=[];
fs.createReadStream(req.file.path)
 .pipe(parse({delimiter: ':'}))
 .on('data', function(csvrow) {
 console.log(csvrow);
 //do something with csvrow
 csvData.push(csvrow); 
 })
 .on('end',function() {
 //do something with csvData
 console.log(csvData);
 });
</code></pre>

blocks|key|156294|text|为了在fast-csv中暂停流，您可以执行以下操作：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|156295|let+csvstream+=+csv.fromPath(filePath,+{+headers:+true+})
++++.on("data",+function+(row)+{
++++++++csvstream.pause();
++++++++//+do+some+heavy+work
++++++++//+when+done+resume+the+stream
++++++++csvstream.resume();
++++})
++++.on("end",+function+()+{
++++++++console.log("We+are+done!")
++++})
++++.on("error",+function+(error)+{
++++++++console.log(error)
++++});|code-block|syntax|javascript|156296|entityMap|0|LINK|mutability|MUTABLE|url|https://www.npmjs.org/package/fast-csv^0|3|8|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

In order to pause the streaming in <a href="https://www.npmjs.org/package/fast-csv" rel="noreferrer">fast-csv</a> you can do the following:

<pre><code>let csvstream = csv.fromPath(filePath, { headers: true })
 .on("data", function (row) {
 csvstream.pause();
 // do some heavy work
 // when done resume the stream
 csvstream.resume();
 })
 .on("end", function () {
 console.log("We are done!")
 })
 .on("error", function (error) {
 console.log(error)
 });
</code></pre>

blocks|key|156311|text|npm安装csv|type|unstyled|depth|inlineStyleRanges|entityRanges|data|156312|示例CSV文件您将需要一个CSV文件来解析，因此您要么已经有一个CSV文件，要么可以复制下面的文本并将其粘贴到一个新文件中，并将该文件命名为"mycsv.csv“。|156313|ABC,+123,+Fudge
532,+CWE,+ICECREAM
8023,+POOP,+DOGS
441,+CHEESE,+CARMEL
221,+ABC,+HOUSE
1
ABC,+123,+Fudge
2
532,+CWE,+ICECREAM
3
8023,+POOP,+DOGS
4
441,+CHEESE,+CARMEL
5
221,+ABC,+HOUSE|code-block|syntax|javascript|156314|读取和解析CSV文件的示例代码|156315|创建一个新文件，并在其中插入以下代码。一定要通读一下幕后发生的事情。|156316|++++var+csv+=+require('csv');+
++++//+loads+the+csv+module+referenced+above.

++++var+obj+=+csv();+
++++//+gets+the+csv+module+to+access+the+required+functionality

++++function+MyCSV(Fone,+Ftwo,+Fthree)+{
++++++++this.FieldOne+=+Fone;
++++++++this.FieldTwo+=+Ftwo;
++++++++this.FieldThree+=+Fthree;
++++};+
++++//+Define+the+MyCSV+object+with+parameterized+constructor,+this+will+be+used+for+storing+the+data+read+from+the+csv+into+an+array+of+MyCSV.+You+will+need+to+define+each+field+as+shown+above.

++++var+MyData+=+[];+
++++//+MyData+array+will+contain+the+data+from+the+CSV+file+and+it+will+be+sent+to+the+clients+request+over+HTTP.+

++++obj.from.path('../THEPATHINYOURPROJECT/TOTHE/csv_FILE_YOU_WANT_TO_LOAD.csv').to.array(function+(data)+{
++++++++for+(var+index+=+0;+index+<+data.length;+index%2B%2B)+{
++++++++++++MyData.push(new+MyCSV(data[index][0],+data[index][1],+data[index][2]));
++++++++}
++++++++console.log(MyData);
++++});
++++//Reads+the+CSV+file+from+the+path+you+specify,+and+the+data+is+stored+in+the+array+we+specified+using+callback+function.++This+function+iterates+through+an+array+and+each+line+from+the+CSV+file+will+be+pushed+as+a+record+to+another+array+called+MyData+,+and+logs+the+data+into+the+console+to+ensure+it+worked.

var+http+=+require('http');
//Load+the+http+module.

var+server+=+http.createServer(function+(req,+resp)+{
++++resp.writeHead(200,+{+'content-type':+'application/json'+});
++++resp.end(JSON.stringify(MyData));
});
//+Create+a+webserver+with+a+request+listener+callback.++This+will+write+the+response+header+with+the+content+type+as+json,+and+end+the+response+by+sending+the+MyData+array+in+JSON+format.

server.listen(8080);
//+Tells+the+webserver+to+listen+on+port+8080(obviously+this+may+be+whatever+port+you+want.)
1
var+csv+=+require('csv');+
2
//+loads+the+csv+module+referenced+above.
3
​
4
var+obj+=+csv();+
5
//+gets+the+csv+module+to+access+the+required+functionality
6
​
7
function+MyCSV(Fone,+Ftwo,+Fthree)+{
8
++++this.FieldOne+=+Fone;
9
++++this.FieldTwo+=+Ftwo;
10
++++this.FieldThree+=+Fthree;
11
};+
12
//+Define+the+MyCSV+object+with+parameterized+constructor,+this+will+be+used+for+storing+the+data+read+from+the+csv+into+an+array+of+MyCSV.+You+will+need+to+define+each+field+as+shown+above.
13
​
14
var+MyData+=+[];+
15
//+MyData+array+will+contain+the+data+from+the+CSV+file+and+it+will+be+sent+to+the+clients+request+over+HTTP.+
16
​
17
obj.from.path('../THEPATHINYOURPROJECT/TOTHE/csv_FILE_YOU_WANT_TO_LOAD.csv').to.array(function+(data)+{
18
++++for+(var+index+=+0;+index+<+data.length;+index%2B%2B)+{
19
++++++++MyData.push(new+MyCSV(data[index][0],+data[index][1],+data[index][2]));
20
++++}
21
++++console.log(MyData);
22
});
23
//Reads+the+CSV+file+from+the+path+you+specify,+and+the+data+is+stored+in+the+array+we+specified+using+callback+function.++This+function+iterates+through+an+array+and+each+line+from+the+CSV+file+will+be+pushed+as+a+record+to+another+array+called+MyData+,+and+logs+the+data+into+the+console+to+ensure+it+worked.
24
​
25
var+http+=+require('http');
26
//Load+the+http+module.
27
​
28
var+server+=+http.createServer(function+(req,+resp)+{
29
++++resp.writeHead(200,+{+'content-type':+'application/json'+});
30
++++resp.end(JSON.stringify(MyData));
31
});
32
//+Create+a+webserver+with+a+request+listener+callback.++This+will+write+the+response+header+with+the+content+type+as+json,+and+end+the+response+by+sending+the+MyData+array+in+JSON+format.
33
​
34
server.listen(8080);
35
//+Tells+the+webserver+to+listen+on+port+8080(obviously+this+may+be+whatever+port+you+want.)
Things+to+be+aware+of+in+your+app.js+code
In+lines+7+through+11,+we+define+the+function+called+'MyCSV'+and+the+field+names.

If+your+CSV+file+has+multiple+columns+make+sure+you+define+this+correctly+to+match+your+file.

On+line+17+we+define+the+location+of+the+CSV+file+of+which+we+are+loading.++Make+sure+you+use+the+correct+path+here.|156317|启动应用程序并验证功能打开控制台并键入以下命令：|156318|节点应用程序1节点应用程序您应该会在控制台中看到以下输出：|156319|[++MYCSV+{+Fieldone:+'ABC',+Fieldtwo:+'123',+Fieldthree:+'Fudge'+},
+++MYCSV+{+Fieldone:+'532',+Fieldtwo:+'CWE',+Fieldthree:+'ICECREAM'+},
+++MYCSV+{+Fieldone:+'8023',+Fieldtwo:+'POOP',+Fieldthree:+'DOGS'+},
+++MYCSV+{+Fieldone:+'441',+Fieldtwo:+'CHEESE',+Fieldthree:+'CARMEL'+},
+++MYCSV+{+Fieldone:+'221',+Fieldtwo:+'ABC',+Fieldthree:+'HOUSE'+},+]|156320|1+MYCSV+{+Fieldone：'ABC'，Fieldtwo：'123'，Fieldthree：'Fudge‘}，2+MYCSV+{+Fieldone：'532'，Fieldtwo：'CWE'，Fieldthree：'ICECREAM’}，3+MYCSV+{+Fieldone：'8023'，Fieldtwo：'POOP'，Fieldthree：‘狗’}，4+MYCSV+{+Fieldone：'441'，Fieldtwo：‘奶酪’，Fieldone：'CARMEL‘}，5+MYCSV+{Fieldone：'221'，’CARMEL‘}Fieldtwo：'ABC'，Fieldthree：'HOUSE‘}，现在您应该打开web浏览器并导航到您的服务器。您应该会看到它以JSON格式输出数据。|156321|结论通过使用node.js及其CSV模块，我们可以快速、方便地读取和使用存储在服务器上的数据，并根据请求将其提供给客户端|156322|entityMap^0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|14|8|@]|9|@]|A|$]]|$1|M|3|N|5|F|7|15|8|@]|9|@]|A|$G|H]]|$1|O|3|P|5|6|7|16|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|17|8|@]|9|@]|A|$]]|$1|S|3|T|5|F|7|18|8|@]|9|@]|A|$G|H]]|$1|U|3|V|5|6|7|19|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|1A|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|Z|$]]

npm install csv

Sample CSV file
You're going to need a CSV file to parse, so either you have one already, or you can copy the text below and paste it into a new file and call that file "mycsv.csv"

<pre><code>ABC, 123, Fudge
532, CWE, ICECREAM
8023, POOP, DOGS
441, CHEESE, CARMEL
221, ABC, HOUSE
1
ABC, 123, Fudge
2
532, CWE, ICECREAM
3
8023, POOP, DOGS
4
441, CHEESE, CARMEL
5
221, ABC, HOUSE
</code></pre>

Sample Code Reading and Parsing the CSV file

Create a new file, and insert the following code into it. Make sure to read through what is going on behind the scenes.

<pre><code> var csv = require('csv'); 
 // loads the csv module referenced above.

 var obj = csv(); 
 // gets the csv module to access the required functionality

 function MyCSV(Fone, Ftwo, Fthree) {
 this.FieldOne = Fone;
 this.FieldTwo = Ftwo;
 this.FieldThree = Fthree;
 }; 
 // Define the MyCSV object with parameterized constructor, this will be used for storing the data read from the csv into an array of MyCSV. You will need to define each field as shown above.

 var MyData = []; 
 // MyData array will contain the data from the CSV file and it will be sent to the clients request over HTTP. 

 obj.from.path('../THEPATHINYOURPROJECT/TOTHE/csv_FILE_YOU_WANT_TO_LOAD.csv').to.array(function (data) {
 for (var index = 0; index &lt; data.length; index++) {
 MyData.push(new MyCSV(data[index][0], data[index][1], data[index][2]));
 }
 console.log(MyData);
 });
 //Reads the CSV file from the path you specify, and the data is stored in the array we specified using callback function. This function iterates through an array and each line from the CSV file will be pushed as a record to another array called MyData , and logs the data into the console to ensure it worked.

var http = require('http');
//Load the http module.

var server = http.createServer(function (req, resp) {
 resp.writeHead(200, { 'content-type': 'application/json' });
 resp.end(JSON.stringify(MyData));
});
// Create a webserver with a request listener callback. This will write the response header with the content type as json, and end the response by sending the MyData array in JSON format.

server.listen(8080);
// Tells the webserver to listen on port 8080(obviously this may be whatever port you want.)
1
var csv = require('csv'); 
2
// loads the csv module referenced above.
3
​
4
var obj = csv(); 
5
// gets the csv module to access the required functionality
6
​
7
function MyCSV(Fone, Ftwo, Fthree) {
8
 this.FieldOne = Fone;
9
 this.FieldTwo = Ftwo;
10
 this.FieldThree = Fthree;
11
}; 
12
// Define the MyCSV object with parameterized constructor, this will be used for storing the data read from the csv into an array of MyCSV. You will need to define each field as shown above.
13
​
14
var MyData = []; 
15
// MyData array will contain the data from the CSV file and it will be sent to the clients request over HTTP. 
16
​
17
obj.from.path('../THEPATHINYOURPROJECT/TOTHE/csv_FILE_YOU_WANT_TO_LOAD.csv').to.array(function (data) {
18
 for (var index = 0; index &lt; data.length; index++) {
19
 MyData.push(new MyCSV(data[index][0], data[index][1], data[index][2]));
20
 }
21
 console.log(MyData);
22
});
23
//Reads the CSV file from the path you specify, and the data is stored in the array we specified using callback function. This function iterates through an array and each line from the CSV file will be pushed as a record to another array called MyData , and logs the data into the console to ensure it worked.
24
​
25
var http = require('http');
26
//Load the http module.
27
​
28
var server = http.createServer(function (req, resp) {
29
 resp.writeHead(200, { 'content-type': 'application/json' });
30
 resp.end(JSON.stringify(MyData));
31
});
32
// Create a webserver with a request listener callback. This will write the response header with the content type as json, and end the response by sending the MyData array in JSON format.
33
​
34
server.listen(8080);
35
// Tells the webserver to listen on port 8080(obviously this may be whatever port you want.)
Things to be aware of in your app.js code
In lines 7 through 11, we define the function called 'MyCSV' and the field names.

If your CSV file has multiple columns make sure you define this correctly to match your file.

On line 17 we define the location of the CSV file of which we are loading. Make sure you use the correct path here.
</code></pre>

Start your App and Verify Functionality
Open a console and type the following Command:

Node app
1
Node app
You should see the following output in your console:

<pre><code>[ MYCSV { Fieldone: 'ABC', Fieldtwo: '123', Fieldthree: 'Fudge' },
 MYCSV { Fieldone: '532', Fieldtwo: 'CWE', Fieldthree: 'ICECREAM' },
 MYCSV { Fieldone: '8023', Fieldtwo: 'POOP', Fieldthree: 'DOGS' },
 MYCSV { Fieldone: '441', Fieldtwo: 'CHEESE', Fieldthree: 'CARMEL' },
 MYCSV { Fieldone: '221', Fieldtwo: 'ABC', Fieldthree: 'HOUSE' }, ]
</code></pre>

1
 [ MYCSV { Fieldone: 'ABC', Fieldtwo: '123', Fieldthree: 'Fudge' },
2
 MYCSV { Fieldone: '532', Fieldtwo: 'CWE', Fieldthree: 'ICECREAM' },
3
 MYCSV { Fieldone: '8023', Fieldtwo: 'POOP', Fieldthree: 'DOGS' },
4
 MYCSV { Fieldone: '441', Fieldtwo: 'CHEESE', Fieldthree: 'CARMEL' },
5
 MYCSV { Fieldone: '221', Fieldtwo: 'ABC', Fieldthree: 'HOUSE' }, ]
Now you should open a web-browser and navigate to your server. You should see it output the data in JSON format.

Conclusion
Using node.js and it's CSV module we can quickly and easily read and use data stored on the server and make it available to the client upon request

blocks|key|2746976|text|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2746977|此解决方案使用csv-parser，而不是上述某些答案中使用的csv-parse。在csv-parse之后2年左右，出现了|unordered-list-item|offset|length|style|CODE|2746978|csv-parser。|2746979|两者解决了相同的目的，但就我个人而言，我发现csv-parser更好，因为通过它很容易处理报头。|2746980|2746981|首先安装csv-parser：|2746982|npm+install+csv-parser|code-block|syntax|javascript|2746983|因此，假设您有一个csv文件，如下所示：|2746984|NAME,+AGE
Lionel+Messi,+31
Andres+Iniesta,+34|2746985|您可以执行所需的操作，如下所示：|2746986|const+fs+=+require('fs');+
const+csv+=+require('csv-parser');

fs.createReadStream(inputFilePath)
.pipe(csv())
.on('data',+function(data){
++++try+{
++++++++console.log("Name+is:+"%2Bdata.NAME);
++++++++console.log("Age+is:+"%2Bdata.AGE);

++++++++//perform+the+operation
++++}
++++catch(err)+{
++++++++//error+handler
++++}
})
.on('end',function(){
++++//some+final+operation
});++|2746987|有关进一步阅读refer的信息|2746988|entityMap|0|LINK|mutability|MUTABLE|url|https://www.npmjs.com/package/csv-parser^0|0|7|A|V|9|16|9|0|0|A|0|M|A|0|0|0|0|0|0|0|0|7|5|0|0^^$0|@$1|2|3|-4|4|5|6|1B|7|@]|8|@]|9|$]]|$1|A|3|B|4|C|6|1C|7|@$D|1D|E|1E|F|G]|$D|1F|E|1G|F|G]|$D|1H|E|1I|F|G]]|8|@]|9|$]]|$1|H|3|I|4|C|6|1J|7|@$D|1K|E|1L|F|G]]|8|@]|9|$]]|$1|J|3|K|4|C|6|1M|7|@$D|1N|E|1O|F|G]]|8|@]|9|$]]|$1|L|3|-4|4|5|6|1P|7|@]|8|@]|9|$]]|$1|M|3|N|4|5|6|1Q|7|@]|8|@]|9|$]]|$1|O|3|P|4|Q|6|1R|7|@]|8|@]|9|$R|S]]|$1|T|3|U|4|5|6|1S|7|@]|8|@]|9|$]]|$1|V|3|W|4|Q|6|1T|7|@]|8|@]|9|$R|S]]|$1|X|3|Y|4|5|6|1U|7|@]|8|@]|9|$]]|$1|Z|3|10|4|Q|6|1V|7|@]|8|@]|9|$R|S]]|$1|11|3|12|4|5|6|1W|7|@]|8|@$D|1X|E|1Y|1|1Z]]|9|$]]|$1|13|3|-4|4|5|6|20|7|@]|8|@]|9|$]]]|14|$15|$4|16|17|18|9|$19|1A]]]]

<ul>
<li>This solution uses <code>csv-parser</code> instead of <code>csv-parse</code> used in some
of the answers above. </li>
<li><code>csv-parser</code> came around 2 years after
<code>csv-parse</code>. </li>
<li>Both of them solve the same purpose, but personally I have found
<code>csv-parser</code> better, as it is easy to handle headers through it.</li>
</ul>

Install the csv-parser first:

<pre><code>npm install csv-parser
</code></pre>

So suppose you have a csv-file like this:

<pre><code>NAME, AGE
Lionel Messi, 31
Andres Iniesta, 34
</code></pre>

You can perform the required operation as:

<pre><code>const fs = require('fs'); 
const csv = require('csv-parser');

fs.createReadStream(inputFilePath)
.pipe(csv())
.on('data', function(data){
 try {
 console.log("Name is: "+data.NAME);
 console.log("Age is: "+data.AGE);

 //perform the operation
 }
 catch(err) {
 //error handler
 }
})
.on('end',function(){
 //some final operation
}); 
</code></pre>

For further reading <a href="https://www.npmjs.com/package/csv-parser" rel="noreferrer">refer</a>

blocks|key|156352|text|我需要一个异步csv阅读器，最初尝试了@Pransh+Tiwari的答案，但无法在await和util.promisify()上使用。最终我遇到了node-csvtojson，它的功能与csv-parser几乎相同，但有一些承诺。下面是csvtojson的实际使用示例：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|156353|const+csvToJson+=+require('csvtojson');

const+processRecipients+=+async+()+=>+{
++++const+recipients+=+await+csvToJson({
++++++++trim:true
++++}).fromFile('./recipients.csv');

++++//+Code+executes+after+recipients+are+fully+loaded.
++++recipients.forEach((recipient)+=>+{
++++++++console.log(recipient.name,+recipient.email);
++++});
};|code-block|syntax|javascript|156354|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/Keyang/node-csvtojson^0|15|5|1B|G|21|E|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|H|7|10|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|11|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

I needed an async csv reader and originally tried @Pransh Tiwari's answer but couldn't get it working with <code>await</code> and <code>util.promisify()</code>. Eventually I came across <a href="https://github.com/Keyang/node-csvtojson" rel="noreferrer">node-csvtojson</a>, which pretty much does the same as csv-parser, but with promises. Here is an example usage of csvtojson in action:

<pre><code>const csvToJson = require('csvtojson');

const processRecipients = async () =&gt; {
 const recipients = await csvToJson({
 trim:true
 }).fromFile('./recipients.csv');

 // Code executes after recipients are fully loaded.
 recipients.forEach((recipient) =&gt; {
 console.log(recipient.name, recipient.email);
 });
};
</code></pre>

blocks|key|160016|text|fs+=+require('fs');
fs.readFile('FILENAME+WITH+PATH','utf8',+function(err,content){
if(err){
++++console.log('error+occured+'+%2BJSON.stringify(err));
+}
+console.log('Fileconetent+are+'+%2B+JSON.stringify(content));
})|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|160017|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>fs = require('fs');
fs.readFile('FILENAME WITH PATH','utf8', function(err,content){
if(err){
 console.log('error occured ' +JSON.stringify(err));
 }
 console.log('Fileconetent are ' + JSON.stringify(content));
})
</code></pre>

blocks|key|2747054|text|csv+npm模块可以从fast-csv文件中逐行读取数据。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2747055|下面是一个示例：|2747056|let+csv=+require('fast-csv');

var+stream+=+fs.createReadStream("my.csv");

csv
+.parseStream(stream,+{headers+:+true})
+.on("data",+function(data){
+++++console.log('I+am+one+line+of+data',+data);
+})
+.on("end",+function(){
+++++console.log("done");
+});|code-block|syntax|javascript|2747057|entityMap|0|LINK|mutability|MUTABLE|url|https://www.npmjs.com/package/fast-csv^0|C|8|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|X|8|@]|9|@]|C|$I|J]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]]]

The <a href="https://www.npmjs.com/package/fast-csv" rel="nofollow noreferrer">fast-csv</a> npm module can read data line-by-line from csv file.

Here is an example:

<pre><code>let csv= require('fast-csv');

var stream = fs.createReadStream("my.csv");

csv
 .parseStream(stream, {headers : true})
 .on("data", function(data){
 console.log('I am one line of data', data);
 })
 .on("end", function(){
 console.log("done");
 });
</code></pre>

blocks|key|157563|text|您可以使用csv-+to+-json模块将csv转换为json格式，然后您可以轻松地在程序中使用json文件。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|157564|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

You can convert csv to json format using csv-to-json module and then you can easily use json file in your program

blocks|key|156380|text|这是我从外部url获取csv文件的解决方案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|156381|const+parse+=+require(+'csv-parse/lib/sync'+);
const+axios+=+require(+'axios'+);
const+readCSV+=+(+module.exports.readCSV+=+async+(+path+)+=>+{
try+{
+++const+res+=+await+axios(+{+url:+path,+method:+'GET',+responseType:+'blob'+}+);
+++let+records+=+parse(+res.data,+{
++++++columns:+true,
++++++skip_empty_lines:+true
++++}+);

++++return+records;
+}+catch+(+e+)+{
+++console.log(+'err'+);
+}

}+);
readCSV('https://urltofilecsv');|code-block|syntax|javascript|156382|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

this is my solution to get csv file from external url

<pre><code>const parse = require( 'csv-parse/lib/sync' );
const axios = require( 'axios' );
const readCSV = ( module.exports.readCSV = async ( path ) =&gt; {
try {
 const res = await axios( { url: path, method: 'GET', responseType: 'blob' } );
 let records = parse( res.data, {
 columns: true,
 skip_empty_lines: true
 } );

 return records;
 } catch ( e ) {
 console.log( 'err' );
 }

} );
readCSV('https://urltofilecsv');
</code></pre>

blocks|key|160041|text|我使用这个简单的方法：https://www.npmjs.com/package/csv-parser|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|160042|使用起来非常简单：|160043|const+csv+=+require('csv-parser')
const+fs+=+require('fs')
const+results+=+[];

fs.createReadStream('./CSVs/Update+20191103C.csv')
++.pipe(csv())
++.on('data',+(data)+=>+results.push(data))
++.on('end',+()+=>+{
++++console.log(results);
++++console.log(results[0]['Lowest+Selling+Price'])
++});|code-block|syntax|javascript|160044|entityMap|0|LINK|mutability|MUTABLE|url|https://www.npmjs.com/package/csv-parser^0|B|14|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|X|8|@]|9|@]|C|$I|J]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]]]

I use this simple one: <a href="https://www.npmjs.com/package/csv-parser" rel="nofollow noreferrer">https://www.npmjs.com/package/csv-parser</a>

Very simple to use:

<pre><code>const csv = require('csv-parser')
const fs = require('fs')
const results = [];

fs.createReadStream('./CSVs/Update 20191103C.csv')
 .pipe(csv())
 .on('data', (data) =&gt; results.push(data))
 .on('end', () =&gt; {
 console.log(results);
 console.log(results[0]['Lowest Selling Price'])
 });
</code></pre>

blocks|key|160056|text|我使用的是csv-parse，但对于较大的文件遇到了性能问题，我发现Papa+Parse是一个更好的库，文档很好，很好的支持，轻量级，没有依赖。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|160057|安装papaparse|160058|npm+install+papaparse|code-block|syntax|javascript|160059|用法：|160060|160061|async+/+await|unordered-list-item|BOLD|160062|160063|const+fs+=+require('fs');
const+Papa+=+require('papaparse');

const+csvFilePath+=+'data/test.csv'

//+Function+to+read+csv+which+returns+a+promise+so+you+can+do+async+/+await.

const+readCSV+=+async+(filePath)+=>+{
++const+csvFile+=+fs.readFileSync(filePath)
++const+csvData+=+csvFile.toString()++
++return+new+Promise(resolve+=>+{
++++Papa.parse(csvData,+{
++++++header:+true,
++++++transformHeader:+header+=>+header.trim(),
++++++complete:+results+=>+{
++++++++console.log('Complete',+results.data.length,+'records.');+
++++++++resolve(results.data);
++++++}
++++});
++});
};

const+test+=+async+()+=>+{
++let+parsedData+=+await+readCSV(csvFilePath);+
}

test()|160064|160065|callback|160066|160067|const+fs+=+require('fs');
const+Papa+=+require('papaparse');

const+csvFilePath+=+'data/test.csv'

const+file+=+fs.createReadStream(csvFilePath);

var+csvData=[];
Papa.parse(file,+{
++header:+true,
++transformHeader:+header+=>+header.trim(),
++step:+function(result)+{
++++csvData.push(result.data)
++},
++complete:+function(results,+file)+{
++++console.log('Complete',+csvData.length,+'records.');+
++}
});|160068|160069|注意：header:+true是配置中的一个选项，有关其他选项，请参阅文档|160070|entityMap|0|LINK|mutability|MUTABLE|url|https://www.papaparse.com/^0|5|9|Y|A|0|0|2|9|0|0|0|0|0|D|0|0|0|0|0|8|0|0|0|0|3|C|0^^$0|@$1|2|3|4|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@$9|1G|A|1H|1|1I]]|E|$]]|$1|F|3|G|5|6|7|1J|8|@$9|1K|A|1L|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|1M|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1N|8|@]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1O|8|@]|D|@]|E|$]]|$1|P|3|Q|5|R|7|1P|8|@$9|1Q|A|1R|B|S]]|D|@]|E|$]]|$1|T|3|-4|5|6|7|1S|8|@]|D|@]|E|$]]|$1|U|3|V|5|J|7|1T|8|@]|D|@]|E|$K|L]]|$1|W|3|-4|5|6|7|1U|8|@]|D|@]|E|$]]|$1|X|3|Y|5|R|7|1V|8|@$9|1W|A|1X|B|S]]|D|@]|E|$]]|$1|Z|3|-4|5|6|7|1Y|8|@]|D|@]|E|$]]|$1|10|3|11|5|J|7|1Z|8|@]|D|@]|E|$K|L]]|$1|12|3|-4|5|6|7|20|8|@]|D|@]|E|$]]|$1|13|3|14|5|6|7|21|8|@$9|22|A|23|B|C]]|D|@]|E|$]]|$1|15|3|-4|5|6|7|24|8|@]|D|@]|E|$]]]|16|$17|$5|18|19|1A|E|$1B|1C]]]]

I was using <code>csv-parse</code> but for larger files was running into performance issues one of the better libraries I have found is <a href="https://www.papaparse.com/" rel="nofollow noreferrer">Papa Parse</a>, docs are good, good support, lightweight, no dependencies.

Install <code>papaparse</code>

<pre><code>npm install papaparse
</code></pre>

Usage:

<ul>
<li>async / await</li>
</ul>

<pre><code>const fs = require('fs');
const Papa = require('papaparse');

const csvFilePath = 'data/test.csv'

// Function to read csv which returns a promise so you can do async / await.

const readCSV = async (filePath) =&gt; {
 const csvFile = fs.readFileSync(filePath)
 const csvData = csvFile.toString() 
 return new Promise(resolve =&gt; {
 Papa.parse(csvData, {
 header: true,
 transformHeader: header =&gt; header.trim(),
 complete: results =&gt; {
 console.log('Complete', results.data.length, 'records.'); 
 resolve(results.data);
 }
 });
 });
};

const test = async () =&gt; {
 let parsedData = await readCSV(csvFilePath); 
}

test()
</code></pre>

<ul>
<li>callback</li>
</ul>

<pre><code>const fs = require('fs');
const Papa = require('papaparse');

const csvFilePath = 'data/test.csv'

const file = fs.createReadStream(csvFilePath);

var csvData=[];
Papa.parse(file, {
 header: true,
 transformHeader: header =&gt; header.trim(),
 step: function(result) {
 csvData.push(result.data)
 },
 complete: function(results, file) {
 console.log('Complete', csvData.length, 'records.'); 
 }
});
</code></pre>

<hr>

Note <code>header: true</code> is an option on the config, see docs for other options

blocks|key|160098|text|好的，这里有很多答案，我不认为他们回答了你的问题，我认为这和我的相似。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|160099|你需要做一个像联系数据库或第三方api这样的操作，这需要时间，而且是异步的。由于太大或其他原因，您不希望将整个文档加载到内存中，因此您需要逐行读取以进行处理。|160100|我读了文件系统的文件，它可以在读的时候暂停，但是使用.on('data')调用会使它继续使用这些答案中的大多数，并导致问题。|160101|160102|更新:我知道的关于流的信息比我想要的要多|160103|最好的方法是创建一个可写的流。这会将csv数据输送到您可以管理异步调用的可写流中。管道将一直管理缓冲区，直到读取器，因此不会占用大量内存|160104|简单版本|160105|const+parser+=+require('csv-parser');
const+stripBom+=+require('strip-bom-stream');
const+stream+=+require('stream')

const+mySimpleWritable+=+new+stream.Writable({
++objectMode:+true,+//+Because+input+is+object+from+csv-parser
++write(chunk,+encoding,+done)+{+//+Required
++++//+chunk+is+object+with+data+from+a+line+in+the+csv
++++console.log('chunk',+chunk)
++++done();
++},
++final(done)+{+//+Optional
++++//+last+place+to+clean+up+when+done
++++done();
++}
});
fs.createReadStream(fileNameFull).pipe(stripBom()).pipe(parser()).pipe(mySimpleWritable)|code-block|syntax|javascript|160106|类版本|160107|const+parser+=+require('csv-parser');
const+stripBom+=+require('strip-bom-stream');
const+stream+=+require('stream')
//+Create+writable+class
class+MyWritable+extends+stream.Writable+{
++//+Used+to+set+object+mode+because+we+get+an+object+piped+in+from+csv-parser
++constructor(another_variable,+options)+{
++++//+Calls+the+stream.Writable()+constructor.
++++super({+...options,+objectMode:+true+});
++++//+additional+information+if+you+want
++++this.another_variable+=+another_variable
++}
++//+The+write+method
++//+Called+over+and+over,+for+each+line+in+the+csv
++async+_write(chunk,+encoding,+done)+{
++++//+The+chunk+will+be+a+line+of+your+csv+as+an+object
++++console.log('Chunk+Data',+this.another_variable,+chunk)

++++//+demonstrate+await+call
++++//+This+will+pause+the+process+until+it+is+finished
++++await+new+Promise(resolve+=>+setTimeout(resolve,+2000));

++++//+Very+important+to+add.++Keeps+the+pipe+buffers+correct.++Will+load+the+next+line+of+data
++++done();
++};
++//+Gets+called+when+all+lines+have+been+read
++async+_final(done)+{
++++//+Can+do+more+calls+here+with+left+over+information+in+the+class
++++console.log('clean+up')
++++//+lets+pipe+know+its+done+and+the+.on('final')+will+be+called
++++done()
++}
}

//+Instantiate+the+new+writable+class
myWritable+=+new+MyWritable(somevariable)
//+Pipe+the+read+stream+to+csv-parser,+then+to+your+write+class
//+stripBom+is+due+to+Excel+saving+csv+files+with+UTF8+-+BOM+format
fs.createReadStream(fileNameFull).pipe(stripBom()).pipe(parser()).pipe(myWritable)

//+optional
.on('finish',+()+=>+{
++//+will+be+called+after+the+wriables+internal+_final
++console.log('Called+very+last')
})|160108|160109|旧方法：|160110|可读性问题|160111|const+csv+=+require('csv-parser');
const+fs+=+require('fs');

const+processFileByLine+=+async(fileNameFull)+=>+{

++let+reading+=+false

++const+rr+=+fs.createReadStream(fileNameFull)
++.pipe(csv())

++//+Magic+happens+here
++rr.on('readable',+async+function(){
++++//+Called+once+when+data+starts+flowing
++++console.log('starting+readable')

++++//+Found+this+might+be+called+a+second+time+for+some+reason
++++//+This+will+stop+that+event+from+happening
++++if+(reading)+{
++++++console.log('ignoring+reading')
++++++return
++++}
++++reading+=+true
++++
++++while+(null+!==+(data+=+rr.read()))+{
++++++//+data+variable+will+be+an+object+with+information+from+the+line+it+read
++++++//+PROCESS+DATA+HERE
++++++console.log('new+line+of+data',+data)
++++}

++++//+All+lines+have+been+read+and+file+is+done.
++++//+End+event+will+be+called+about+now+so+that+code+will+run+before+below+code

++++console.log('Finished+readable')
++})


++rr.on("end",+function+()+{
++++//+File+has+finished+being+read
++++console.log('closing+file')
++});

++rr.on("error",+err+=>+{
++++//+Some+basic+error+handling+for+fs+error+events
++++console.log('error',+err);
++});
}|160112|您将注意到一个reading标志。我注意到，由于某些原因，在接近文件末尾的时候，小文件和大文件都会再次调用.on('readable')。我不知道为什么，但这阻止了第二个进程读取相同的行项目。|offset|length|style|CODE|160113|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|7|7|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1B|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|1C|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|1E|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1F|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1G|8|@]|9|@]|A|$]]|$1|M|3|N|5|O|7|1H|8|@]|9|@]|A|$P|Q]]|$1|R|3|S|5|6|7|1I|8|@]|9|@]|A|$]]|$1|T|3|U|5|O|7|1J|8|@]|9|@]|A|$P|Q]]|$1|V|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|1L|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|1M|8|@]|9|@]|A|$]]|$1|10|3|11|5|O|7|1N|8|@]|9|@]|A|$P|Q]]|$1|12|3|13|5|6|7|1O|8|@$14|1P|15|1Q|16|17]]|9|@]|A|$]]|$1|18|3|-4|5|6|7|1R|8|@]|9|@]|A|$]]]|19|$]]

Ok so there are many answers here and I dont think they answer your question which I think is similar to mine.
You need to do an operation like contacting a database or third part api that will take time and is asyncronus. You do not want to load the entire document into memory due to being to large or some other reason so you need to read line by line to process.
I have read into the fs documents and it can pause on reading but using .on('data') call will make it continous which most of these answer use and cause the problem.
<hr />
UPDATE: I know more info about Streams than I ever wanted
The best way to do this is to create a writable stream. This will pipe the csv data into your writable stream which you can manage asyncronus calls. The pipe will manage the buffer all the way back to the reader so you will not wind up with heavy memory usage
Simple Version
<pre><code>const parser = require('csv-parser');
const stripBom = require('strip-bom-stream');
const stream = require('stream')

const mySimpleWritable = new stream.Writable({
 objectMode: true, // Because input is object from csv-parser
 write(chunk, encoding, done) { // Required
 // chunk is object with data from a line in the csv
 console.log('chunk', chunk)
 done();
 },
 final(done) { // Optional
 // last place to clean up when done
 done();
 }
});
fs.createReadStream(fileNameFull).pipe(stripBom()).pipe(parser()).pipe(mySimpleWritable)

</code></pre>
Class Version
<pre><code>const parser = require('csv-parser');
const stripBom = require('strip-bom-stream');
const stream = require('stream')
// Create writable class
class MyWritable extends stream.Writable {
 // Used to set object mode because we get an object piped in from csv-parser
 constructor(another_variable, options) {
 // Calls the stream.Writable() constructor.
 super({ ...options, objectMode: true });
 // additional information if you want
 this.another_variable = another_variable
 }
 // The write method
 // Called over and over, for each line in the csv
 async _write(chunk, encoding, done) {
 // The chunk will be a line of your csv as an object
 console.log('Chunk Data', this.another_variable, chunk)

 // demonstrate await call
 // This will pause the process until it is finished
 await new Promise(resolve =&gt; setTimeout(resolve, 2000));

 // Very important to add. Keeps the pipe buffers correct. Will load the next line of data
 done();
 };
 // Gets called when all lines have been read
 async _final(done) {
 // Can do more calls here with left over information in the class
 console.log('clean up')
 // lets pipe know its done and the .on('final') will be called
 done()
 }
}

// Instantiate the new writable class
myWritable = new MyWritable(somevariable)
// Pipe the read stream to csv-parser, then to your write class
// stripBom is due to Excel saving csv files with UTF8 - BOM format
fs.createReadStream(fileNameFull).pipe(stripBom()).pipe(parser()).pipe(myWritable)

// optional
.on('finish', () =&gt; {
 // will be called after the wriables internal _final
 console.log('Called very last')
})
</code></pre>
<hr />
OLD METHOD:
PROBLEM WITH readable
<pre><code>const csv = require('csv-parser');
const fs = require('fs');

const processFileByLine = async(fileNameFull) =&gt; {

 let reading = false

 const rr = fs.createReadStream(fileNameFull)
 .pipe(csv())

 // Magic happens here
 rr.on('readable', async function(){
 // Called once when data starts flowing
 console.log('starting readable')

 // Found this might be called a second time for some reason
 // This will stop that event from happening
 if (reading) {
 console.log('ignoring reading')
 return
 }
 reading = true
 
 while (null !== (data = rr.read())) {
 // data variable will be an object with information from the line it read
 // PROCESS DATA HERE
 console.log('new line of data', data)
 }

 // All lines have been read and file is done.
 // End event will be called about now so that code will run before below code

 console.log('Finished readable')
 })


 rr.on(&quot;end&quot;, function () {
 // File has finished being read
 console.log('closing file')
 });

 rr.on(&quot;error&quot;, err =&gt; {
 // Some basic error handling for fs error events
 console.log('error', err);
 });
}
</code></pre>
You will notice a <code>reading</code> flag. I have noticed that for some reason right near the end of the file the .on('readable') gets called a second time on small and large files. I am unsure why but this blocks that from a second process reading the same line items.

blocks|key|156423|text|var+fs+=+require("fs");
//+READ+CSV+INTO+STRING
var+data+=+fs.readFileSync("your.csv").toLocaleString();

//+STRING+TO+ARRAY
var+rows+=+data.split("\n");+//+SPLIT+ROWS
rows.forEach((row)+=>+{
++++columns+=+row.split(",");+//SPLIT+COLUMNS
++++console.log(columns);
})|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|156424|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>var fs = require(&quot;fs&quot;);
// READ CSV INTO STRING
var data = fs.readFileSync(&quot;your.csv&quot;).toLocaleString();

// STRING TO ARRAY
var rows = data.split(&quot;\n&quot;); // SPLIT ROWS
rows.forEach((row) =&gt; {
 columns = row.split(&quot;,&quot;); //SPLIT COLUMNS
 console.log(columns);
})
</code></pre>

blocks|key|2959756|text|我已经使用promise方法做到了这一点|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2959757|const+fs+=+require('fs')
const+{parse}+=+require('csv-parse')
function+readFile(path){
++++return+new+Promise((resolve,reject)=>{
++++++++fs.readFile(path,+function+(err,+fileData)+{
++++++++++++parse(fileData,+{columns:+false,+trim:+true},+async+function(err,+rows)+{
++++++++++++++++if(err){
++++++++++++++++++++reject(err)
++++++++++++++++}
++++++++++++++++resolve(rows)
++++++++++++})
++++++++++})
++++})
}|code-block|syntax|javascript|2959758|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I've done this using promise approach
<pre><code>const fs = require('fs')
const {parse} = require('csv-parse')
function readFile(path){
 return new Promise((resolve,reject)=&gt;{
 fs.readFile(path, function (err, fileData) {
 parse(fileData, {columns: false, trim: true}, async function(err, rows) {
 if(err){
 reject(err)
 }
 resolve(rows)
 })
 })
 })
}
</code></pre>

blocks|key|2747188|text|csv-parse目前支持Async+Iterators，这应该非常适合您的用例|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2747189|entityMap|0|LINK|mutability|MUTABLE|url|https://csv.js.org/parse/api/async_iterator/^0|D|F|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

csv-parse currently supports <a href="https://csv.js.org/parse/api/async_iterator/" rel="nofollow noreferrer">Async Iterators</a> which should fit your use case nicely

With nodejs I want to parse a .csv file of 10000 records and do some operation on each row. I tried using <a href="http://www.adaltas.com/projects/node-csv" rel="noreferrer">http://www.adaltas.com/projects/node-csv</a>. I couldnt get this to pause at each row. This just reads through all the 10000 records. I need to do the following:
<ol>
<li>read csv line by line</li>
<li>perform time consuming operation on each line</li>
<li>go to the next line</li>
</ol>
Can anyone please suggest any alternative ideas here?

Parsing a CSV file using NodeJS

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使用nodejs，我想解析一个包含10000条记录的.csv文件，并对每一行执行一些操作。我试过使用。我不能让它在每一行都暂停。这只会读取所有的10000条记录。我需要做以下工作：逐行读取csv 在每行上执行耗时的操作转到下一行有没有人能在这里提出其他的想法呢？

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