Related
I need to query my database for users based on an array of emails and then execute a function for each result, I do this with eachAsync:
mongoose.model('User')
.find({email: {$in: ['foo#bar.com', 'bar#foo.com']}})
/* -- Run side effects before continuing -- */
.cursor()
.eachAsync((doc) => {
// do stuff
});
The problem I'm having is that I need to return a 404 status if any of the users with the given emails do not exist.
I've been looking through the mongoose docs but I can't seem to find a way of running "side effects" when working with queries. Simply "resolving" the DocumentQuery with .then doesn't work since you can't turn it into a cursor afterwards.
How can I achieve this?
You could try implementing it as shown below. I hope it helps.
// Function using async/await
getCursor: async (_, res) => {
try {
const result = []; // To hold result of cursor
const searchArray = ['foo#bar.com', 'bar#foo.com'];
let hasError = false; // to track error when email from find isn't in the array
const cursor = await mongoose.model('User').find({ email: { $in: searchArray } }).cursor();
// NOTE: Use cursor.on('data') to read the stream of data passed
cursor.on('data', (cursorChunk) => {
// NOTE: Run your side effect before continuing
if (searchArray.indexOf(cursorChunk.email) === -1) {
hasError = true;
res.status(404).json({ message: 'Resource not found!' });
} else {
// Note: Push chunk to result array if you need it
result.push(cursorChunk);
}
});
// NOTE: listen to the cursor.on('end')
cursor.on('end', () => {
// Do stuff or return result to client
if (!hasError) {
res.status(200).json({ result, success: true });
}
});
} catch (error) {
// Do error log and/or return to client
res.status(404).json({ error, message: 'Resource not found!' });
}
}
I've been using node-oracledb for a few months and I've managed to achieve what I have needed to so far.
I'm currently working on a search app that could potentially return about 2m rows of data from a single call. To ensure I don't get a disconnect from the browser and the server, I thought I would try queryStream so that there is a constant flow of data back to the client.
I implemented the queryStream example as-is, and this worked fine for a few hundred thousand rows. However, when the returned rows is greater than one million, Node runs out of memory. By logging and watching both client and server log events, I can see that client is way behind the server in terms of rows sent and received. So, it looks like Node is falling over because it's buffering so much data.
It's worth noting that at this point, my selectstream implementation is within a req/res function called via Express.
To return the data, I do something like....
stream.on('data', function (data) {
rowcount++;
let obj = new myObjectConstructor(data);
res.write(JSON.stringify(obj.getJson());
});
I've been reading about how streams and pipe can help with flow, so what I'd like to be able to do is to be able to pipe the results from the query to a) help with flow and b) to be able to pipe the results to other functions before sending back to the client.
E.g.
function getData(req, res){
var stream = myQueryStream(connection, query);
stream
.pipe(toSomeOtherFunction)
.pipe(yetAnotherFunction)
.pipe(res);
}
I'm spent a few hours trying to find a solution or example that allows me to pipe results, but I'm stuck and need some help.
Apologies if I'm missing something obvious, but I'm still getting to grips with Node and especially streams.
Thanks in advance.
There's a bit of an impedance mismatch here. The queryStream API emits rows of JavaScript objects, but what you want to stream to the client is a JSON array. You basically have to add an open bracket to the beginning, a comma after each row, and a close bracket to the end.
I'll show you how to do this in a controller that uses the driver directly as you have done, instead of using separate database modules as I advocate in this series.
const oracledb = require('oracledb');
async function get(req, res, next) {
try {
const conn = await oracledb.getConnection();
const stream = await conn.queryStream('select * from employees', [], {outFormat: oracledb.OBJECT});
res.writeHead(200, {'Content-Type': 'application/json'});
res.write('[');
stream.on('data', (row) => {
res.write(JSON.stringify(row));
res.write(',');
});
stream.on('end', () => {
res.end(']');
});
stream.on('close', async () => {
try {
await conn.close();
} catch (err) {
console.log(err);
}
});
stream.on('error', async (err) => {
next(err);
try {
await conn.close();
} catch (err) {
console.log(err);
}
});
} catch (err) {
next(err);
}
}
module.exports.get = get;
Once you get the concepts, you can simplify things a bit with a reusable Transform class which allows you to use pipe in the controller logic:
const oracledb = require('oracledb');
const { Transform } = require('stream');
class ToJSONArray extends Transform {
constructor() {
super({objectMode: true});
this.push('[');
}
_transform (row, encoding, callback) {
if (this._prevRow) {
this.push(JSON.stringify(this._prevRow));
this.push(',');
}
this._prevRow = row;
callback(null);
}
_flush (done) {
if (this._prevRow) {
this.push(JSON.stringify(this._prevRow));
}
this.push(']');
delete this._prevRow;
done();
}
}
async function get(req, res, next) {
try {
const toJSONArray = new ToJSONArray();
const conn = await oracledb.getConnection();
const stream = await conn.queryStream('select * from employees', [], {outFormat: oracledb.OBJECT});
res.writeHead(200, {'Content-Type': 'application/json'});
stream.pipe(toJSONArray).pipe(res);
stream.on('close', async () => {
try {
await conn.close();
} catch (err) {
console.log(err);
}
});
stream.on('error', async (err) => {
next(err);
try {
await conn.close();
} catch (err) {
console.log(err);
}
});
} catch (err) {
next(err);
}
}
module.exports.get = get;
Rather than writing your own logic to create a JSON stream, you can use JSONStream to convert an object stream to (stringified) JSON, before piping it to its destination (res, process.stdout etc) This saves the need to muck around with .on('data',...) events.
In the example below, I've used pipeline from node's stream module rather than the .pipe method: the effect is similar (with better error handling I think). To get objects from oracledb.queryStream, you can specify option {outFormat: oracledb.OUT_FORMAT_OBJECT} (docs). Then you can make arbitrary modifications to the stream of objects produced. This can be done using a transform stream, made perhaps using through2-map, or if you need to drop or split rows, through2. Below the stream is sent to process.stdout after being stringified as JSON, but you could equally send to it express's res.
require('dotenv').config() // config from .env file
const JSONStream = require('JSONStream')
const oracledb = require('oracledb')
const { pipeline } = require('stream')
const map = require('through2-map') // see https://www.npmjs.com/package/through2-map
oracledb.getConnection({
user: process.env.DB_USER,
password: process.env.DB_PASSWORD,
connectString: process.env.CONNECT_STRING
}).then(connection => {
pipeline(
connection.queryStream(`
select dual.*,'test' as col1 from dual
union select dual.*, :someboundvalue as col1 from dual
`
,{"someboundvalue":"test5"} // binds
,{
prefetchRows: 150, // for tuning
fetchArraySize: 150, // for tuning
outFormat: oracledb.OUT_FORMAT_OBJECT
}
)
,map.obj((row,index) => {
row.arbitraryModification = index
return row
})
,JSONStream.stringify() // false gives ndjson
,process.stdout // or send to express's res
,(err) => { if(err) console.error(err) }
)
})
// [
// {"DUMMY":"X","COL1":"test","arbitraryModification":0}
// ,
// {"DUMMY":"X","COL1":"test5","arbitraryModification":1}
// ]
I have Node App that collects vote submissions and stores them in Cassandra. The votes are stored as base64 encoded encrypted strings. The API has an endpoint called /export that should get all of these votes strings (possibly > 1 million), convert them to binary and append them one after the other in a votes.egd file. That file should then be zipped and sent to the client. My idea is to stream the rows from Cassandra, converting each vote string to binary and writing to a WriteStream.
I want to wrap this functionality in a Promise for easy use. I have the following:
streamVotesToFile(query, validVotesFileBasename) {
return new Promise((resolve, reject) => {
const writeStream = fs.createWriteStream(`${validVotesFileBasename}.egd`);
writeStream.on('error', (err) => {
logger.error(`Writestream ${validVotesFileBasename}.egd error`);
reject(err);
});
writeStream.on('drain', () => {
logger.info(`Writestream ${validVotesFileBasename}.egd error`);
})
db.client.stream(query)
.on('readable', function() {
let row = this.read();
while (row) {
const envelope = new Buffer(row.vote, 'base64');
if(!writeStream.write(envelope + '\n')) {
logger.error(`Couldn't write vote`);
}
row = this.read()
}
})
.on('end', () => { // No more rows from Cassandra
writeStream.end();
writeStream.on('finish', () => {
logger.info(`Stream done writing`);
resolve();
});
})
.on('error', (err) => { // err is a response error from Cassandra
reject(err);
});
});
}
When I run this it is appending all the votes to a file and downloading fine. But there are a bunch of problems/questions I have:
If I make a req to the /export endpoint and this function runs, while it's running all other requests to the app are extremely slow or just don't finish before the export request is done. I'm guessing because the event loop being hogged by all of these events from the Cassandra stream (thousands per second) ?
All the votes seem to write to the file fine yet I get false for almost every writeStream.write() call and see the corresponding logged message (see code) ?
I understand that I need to consider backpressure and the 'drain' event for the WritableStream so ideally I would use pipe() and pipe the votes to a file because that has built in backpressure support (right?) but since I need to process each row (convert to binary and possible add other data from other row fields in the future), how would I do that with pipe?
This the perfect use case for a TransformStream:
const myTransform = new Transform({
readableObjectMode: true,
transform(row, encoding, callback) {
// Transform the row into something else
const item = new Buffer(row['vote'], 'base64');
callback(null, item);
}
});
client.stream(query, params, { prepare: true })
.pipe(myTransform)
.pipe(fileStream);
See more information on how to implement a TransformStream in the Node.js API Docs.
I would like to know if it's possible to run a series of SQL statements and have them all committed in a single transaction.
The scenario I am looking at is where an array has a series of values that I wish to insert into a table, not individually but as a unit.
I was looking at the following item which provides a framework for transactions in node using pg. The individual transactions appear to be nested within one another so I am unsure of how this would work with an array containing a variable number of elements.
https://github.com/brianc/node-postgres/wiki/Transactions
var pg = require('pg');
var rollback = function(client, done) {
client.query('ROLLBACK', function(err) {
//if there was a problem rolling back the query
//something is seriously messed up. Return the error
//to the done function to close & remove this client from
//the pool. If you leave a client in the pool with an unaborted
//transaction weird, hard to diagnose problems might happen.
return done(err);
});
};
pg.connect(function(err, client, done) {
if(err) throw err;
client.query('BEGIN', function(err) {
if(err) return rollback(client, done);
//as long as we do not call the `done` callback we can do
//whatever we want...the client is ours until we call `done`
//on the flip side, if you do call `done` before either COMMIT or ROLLBACK
//what you are doing is returning a client back to the pool while it
//is in the middle of a transaction.
//Returning a client while its in the middle of a transaction
//will lead to weird & hard to diagnose errors.
process.nextTick(function() {
var text = 'INSERT INTO account(money) VALUES($1) WHERE id = $2';
client.query(text, [100, 1], function(err) {
if(err) return rollback(client, done);
client.query(text, [-100, 2], function(err) {
if(err) return rollback(client, done);
client.query('COMMIT', done);
});
});
});
});
});
My array logic is:
banking.forEach(function(batch){
client.query(text, [batch.amount, batch.id], function(err, result);
}
pg-promise offers a very flexible support for transactions. See Transactions.
It also supports partial nested transactions, aka savepoints.
The library implements transactions automatically, which is what should be used these days, because too many things can go wrong, if you try organizing a transaction manually as you do in your example.
See a related question: Optional INSERT statement in a transaction
Here's a simple TypeScript solution to avoid pg-promise
import { PoolClient } from "pg"
import { pool } from "../database"
const tx = async (callback: (client: PoolClient) => void) => {
const client = await pool.connect();
try {
await client.query('BEGIN')
try {
await callback(client)
await client.query('COMMIT')
} catch (e) {
await client.query('ROLLBACK')
}
} finally {
client.release()
}
}
export { tx }
Usage:
...
let result;
await tx(async client => {
const { rows } = await client.query<{ cnt: string }>('SELECT COUNT(*) AS cnt FROM users WHERE username = $1', [username]);
result = parseInt(rows[0].cnt) > 0;
});
return result;
I have a huge collection of documents in my DB and I'm wondering how can I run through all the documents and update them, each document with a different value.
The answer depends on the driver you're using. All MongoDB drivers I know have cursor.forEach() implemented one way or another.
Here are some examples:
node-mongodb-native
collection.find(query).forEach(function(doc) {
// handle
}, function(err) {
// done or error
});
mongojs
db.collection.find(query).forEach(function(err, doc) {
// handle
});
monk
collection.find(query, { stream: true })
.each(function(doc){
// handle doc
})
.error(function(err){
// handle error
})
.success(function(){
// final callback
});
mongoose
collection.find(query).stream()
.on('data', function(doc){
// handle doc
})
.on('error', function(err){
// handle error
})
.on('end', function(){
// final callback
});
Updating documents inside of .forEach callback
The only problem with updating documents inside of .forEach callback is that you have no idea when all documents are updated.
To solve this problem you should use some asynchronous control flow solution. Here are some options:
async
promises (when.js, bluebird)
Here is an example of using async, using its queue feature:
var q = async.queue(function (doc, callback) {
// code for your update
collection.update({
_id: doc._id
}, {
$set: {hi: 'there'}
}, {
w: 1
}, callback);
}, Infinity);
var cursor = collection.find(query);
cursor.each(function(err, doc) {
if (err) throw err;
if (doc) q.push(doc); // dispatching doc to async.queue
});
q.drain = function() {
if (cursor.isClosed()) {
console.log('all items have been processed');
db.close();
}
}
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
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.
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.
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);
}
}