Using the mongodb driver, and modern NodeJS with async/await, a good solution is to use next():

const collection = db.collection('things')
const cursor = collection.find({
  bla: 42 // find all things where bla is 42
});
let document;
while ((document = await cursor.next())) {
  await collection.findOneAndUpdate({
    _id: document._id
  }, {
    $set: {
      blu: 43
    }
  });
}

This results in only one document at a time being required in memory, as opposed to e.g. the accepted answer, where many documents get sucked into memory, before processing of the documents starts. In cases of "huge collections" (as per the question) this may be important.

If documents are large, this can be improved further by using a projection, so that only those fields of documents that are required are fetched from the database.

var MongoClient = require('mongodb').MongoClient,
    assert = require('assert');

MongoClient.connect('mongodb://localhost:27017/crunchbase', function(err, db) {

    assert.equal(err, null);
    console.log("Successfully connected to MongoDB.");

    var query = {
        "category_code": "biotech"
    };

    db.collection('companies').find(query).toArray(function(err, docs) {

        assert.equal(err, null);
        assert.notEqual(docs.length, 0);

        docs.forEach(function(doc) {
            console.log(doc.name + " is a " + doc.category_code + " company.");
        });

        db.close();

    });

});

Notice that the call .toArray is making the application to fetch the entire dataset.

var MongoClient = require('mongodb').MongoClient,
    assert = require('assert');

MongoClient.connect('mongodb://localhost:27017/crunchbase', function(err, db) {

    assert.equal(err, null);
    console.log("Successfully connected to MongoDB.");

    var query = {
        "category_code": "biotech"
    };

    var cursor = db.collection('companies').find(query);

    function(doc) {
        cursor.forEach(
                console.log(doc.name + " is a " + doc.category_code + " company.");
            },
            function(err) {
                assert.equal(err, null);
                return db.close();
            }
    );
});

Notice that the cursor returned by the find() is assigned to var cursor. With this approach, instead of fetching all data in memory and consuming data at once, we're streaming the data to our application. find() can create a cursor immediately because it doesn't actually make a request to the database until we try to use some of the documents it will provide. The point of cursor is to describe our query. The 2nd parameter to cursor.forEach shows what to do when the driver gets exhausted or an error occurs.

In the initial version of the above code, it was toArray() which forced the database call. It meant we needed ALL the documents and wanted them to be in an array.

Also, MongoDB returns data in batch format. The image below shows, requests from cursors (from application) to MongoDB

forEach is better than toArray because we can process documents as they come in until we reach the end. Contrast it with toArray - where we wait for ALL the documents to be retrieved and the entire array is built. This means we're not getting any advantage from the fact that the driver and the database system are working together to batch results to your application. Batching is meant to provide efficiency in terms of memory overhead and the execution time. Take advantage of it, if you can in your application.

None of the previous answers mentions batching the updates. That makes them extremely slow 🐌 - tens or hundreds of times slower than a solution using bulkWrite.

Let's say you want to double the value of a field in each document. Here's how to do that fast 💨 and with fixed memory consumption:

// Double the value of the 'foo' field in all documents
let bulkWrites = [];
const bulkDocumentsSize = 100;  // how many documents to write at once
let i = 0;
db.collection.find({ ... }).forEach(doc => {
  i++;

  // Update the document...
  doc.foo = doc.foo * 2;

  // Add the update to an array of bulk operations to execute later
  bulkWrites.push({
    replaceOne: {
      filter: { _id: doc._id },
      replacement: doc,
    },
  });

  // Update the documents and log progress every `bulkDocumentsSize` documents
  if (i % bulkDocumentsSize === 0) {
    db.collection.bulkWrite(bulkWrites);
    bulkWrites = [];
    print(`Updated ${i} documents`);
  }
});
// Flush the last <100 bulk writes
db.collection.bulkWrite(bulkWrites);

And here is an example of using a Mongoose cursor async with promises:

new Promise(function (resolve, reject) {
  collection.find(query).cursor()
    .on('data', function(doc) {
      // ...
    })
    .on('error', reject)
    .on('end', resolve);
})
.then(function () {
  // ...
});

Reference:

• Mongoose cursors
• Streams and promises

You can now use (in an async function, of course):

for await (let doc of collection.find(query)) {
  await updateDoc(doc);
}

// all done

which nicely serializes all updates.

Leonid's answer is great, but I want to reinforce the importance of using async/promises and to give a different solution with a promises example.

The simplest solution to this problem is to loop forEach document and call an update. Usually, you don't need close the db connection after each request, but if you do need to close the connection, be careful. You must just close it if you are sure that all updates have finished executing.

A common mistake here is to call db.close() after all updates are dispatched without knowing if they have completed. If you do that, you'll get errors.

Wrong implementation:

collection.find(query).each(function(err, doc) {
  if (err) throw err;

  if (doc) {
    collection.update(query, update, function(err, updated) {
      // handle
    });
  } 
  else {
    db.close(); // if there is any pending update, it will throw an error there
  }
});

However, as db.close() is also an async operation (its signature have a callback option) you may be lucky and this code can finish without errors. It may work only when you need to update just a few docs in a small collection (so, don't try).

Correct solution:

As a solution with async was already proposed by Leonid, below follows a solution using Q promises.

var Q = require('q');
var client = require('mongodb').MongoClient;

var url = 'mongodb://localhost:27017/test';

client.connect(url, function(err, db) {
  if (err) throw err;

  var promises = [];
  var query = {}; // select all docs
  var collection = db.collection('demo');
  var cursor = collection.find(query);

  // read all docs
  cursor.each(function(err, doc) {
    if (err) throw err;

    if (doc) {

      // create a promise to update the doc
      var query = doc;
      var update = { $set: {hi: 'there'} };

      var promise = 
        Q.npost(collection, 'update', [query, update])
        .then(function(updated){ 
          console.log('Updated: ' + updated); 
        });

      promises.push(promise);
    } else {

      // close the connection after executing all promises
      Q.all(promises)
      .then(function() {
        if (cursor.isClosed()) {
          console.log('all items have been processed');
          db.close();
        }
      })
      .fail(console.error);
    }
  });
});

The node-mongodb-native now supports a endCallback parameter to cursor.forEach as for one to handle the event AFTER the whole iteration, refer to the official document for details http://mongodb.github.io/node-mongodb-native/2.2/api/Cursor.html#forEach.

Also note that .each is deprecated in the nodejs native driver now.

let's assume that we have the below MongoDB data in place.

Database name: users
Collection name: jobs
===========================
Documents
{ "_id" : ObjectId("1"), "job" : "Security", "name" : "Jack", "age" : 35 }
{ "_id" : ObjectId("2"), "job" : "Development", "name" : "Tito" }
{ "_id" : ObjectId("3"), "job" : "Design", "name" : "Ben", "age" : 45}
{ "_id" : ObjectId("4"), "job" : "Programming", "name" : "John", "age" : 25 }
{ "_id" : ObjectId("5"), "job" : "IT", "name" : "ricko", "age" : 45 }
==========================

This code:

var MongoClient = require('mongodb').MongoClient;
var dbURL = 'mongodb://localhost/users';

MongoClient.connect(dbURL, (err, db) => {
    if (err) {
        throw err;
    } else {
        console.log('Connection successful');
        var dataBase = db.db();
        // loop forEach
        dataBase.collection('jobs').find().forEach(function(myDoc){
        console.log('There is a job called :'+ myDoc.job +'in Database')})
});

I looked for a solution with good performance and I end up creating a mix of what I found which I think works good:

/**
 * This method will read the documents from the cursor in batches and invoke the callback
 * for each batch in parallel.
 * IT IS VERY RECOMMENDED TO CREATE THE CURSOR TO AN OPTION OF BATCH SIZE THAT WILL MATCH
 * THE VALUE OF batchSize. This way the performance benefits are maxed out since
 * the mongo instance will send into our process memory the same number of documents
 * that we handle in concurrent each time, so no memory space is wasted
 * and also the memory usage is limited.
 *
 * Example of usage:
 * const cursor = await collection.aggregate([
     {...}, ...],
     {
        cursor: {batchSize: BATCH_SIZE} // Limiting memory use
    });
 DbUtil.concurrentCursorBatchProcessing(cursor, BATCH_SIZE, async (doc) => ...)
 * @param cursor - A cursor to batch process on.
 * We can get this from our collection.js API by either using aggregateCursor/findCursor
 * @param batchSize - The batch size, should match the batchSize of the cursor option.
 * @param callback - Callback that should be async, will be called in parallel for each batch.
 * @return {Promise<void>}
 */
static async concurrentCursorBatchProcessing(cursor, batchSize, callback) {
    let doc;
    const docsBatch = [];

    while ((doc = await cursor.next())) {
        docsBatch.push(doc);

        if (docsBatch.length >= batchSize) {
            await PromiseUtils.concurrentPromiseAll(docsBatch, async (currDoc) => {
                return callback(currDoc);
            });

            // Emptying the batch array
            docsBatch.splice(0, docsBatch.length);
        }
    }

    // Checking if there is a last batch remaining since it was small than batchSize
    if (docsBatch.length > 0) {
        await PromiseUtils.concurrentPromiseAll(docsBatch, async (currDoc) => {
            return callback(currDoc);
        });
    }
}

An example of usage for reading many big documents and updating them:

        const cursor = await collection.aggregate([
        {
            ...
        }
    ], {
        cursor: {batchSize: BATCH_SIZE}, // Limiting memory use 
        allowDiskUse: true
    });

    const bulkUpdates = [];

    await DbUtil.concurrentCursorBatchProcessing(cursor, BATCH_SIZE, async (doc: any) => {
        const update: any = {
            updateOne: {
                filter: {
                    ...
                },
                update: {
                   ...
                }
            }
        };            

        bulkUpdates.push(update);

        // Updating if we read too many docs to clear space in memory
        await this.bulkWriteIfNeeded(bulkUpdates, collection);
    });

    // Making sure we updated everything
    await this.bulkWriteIfNeeded(bulkUpdates, collection, true);

...

    private async bulkWriteParametersIfNeeded(
    bulkUpdates: any[], collection: any,
    forceUpdate = false, flushBatchSize) {

    if (bulkUpdates.length >= flushBatchSize || forceUpdate) {
        // concurrentPromiseChunked is a method that loops over an array in a concurrent way using lodash.chunk and Promise.map
        await PromiseUtils.concurrentPromiseChunked(bulkUpsertParameters, (upsertChunk: any) => {
            return techniquesParametersCollection.bulkWrite(upsertChunk);
        });

        // Emptying the array
        bulkUpsertParameters.splice(0, bulkUpsertParameters.length);
    }
}