I am now developing an app. and I want to send a message to all my users inbox. the code is like this in my cloud functions.
const query = db.collection(`users`)
.where("lastActivity","<=",now)
.where("lastActivity",">=",last30Days)
const usersQuerySnapshot = await query.get()
const promises = []
usersQuerySnapshot.docs.forEach( userSnapshot => {
const user = userSnapshot.data()
const userID = user.userID
// set promise to create data in user inbox
const p1 = db.doc(`users/${userID}/inbox/${notificationID}`).set(notificationData)
promises.push(p1)
})
return await Promise.all(promises)
there is a limit in Firebase:
Maximum writes per second per database 10,000 (up to 10 MiB per
second)
say if I send a message to 25k users (create a document to 25K users),
how long the operations of that await Promise.all(promises) will take place ? I am worried that operation will take below 1 second, I don't know if it will hit that limit or not using this code. I am not sure about the operation rate of this
if I hit that limit, how to spread it out over time ? could you please give a clue ? sorry I am a newbie.
If you want to throttle the rate at which document writes happen, you should probably not blindly kick off very large batches of writes in a loop. While there is no guarantee how fast they will occur, it's possible that you could exceed the 10K/second/database limit (depending on how good the client's network connection is, and how fast Firestore responds in general). Over a mobile or web client, I doubt that you'll exceed the limit, but on a backend that's in the same region as your Firestore database, who knows - you would have to benchmark it.
Your client code could simply throttle itself with some simple logic that measures its progress.
If you have a lot of documents to write as fast as possible, and you don't want to throttle your client code, consider throttling them as individual items of work using a Cloud Tasks queue. The queue can be configured to manage the rate at which the queue of tasks will be executed. This will drastically increase the amount of work you have to do to implement all these writes, but it should always stay in a safe range.
You could use e.g. p-limit to reduce promise concurrency in the general case, or preferably use batched writes.
Related
I have a service in NodeJS which fetches user details from DB and sends that to another application via http. There can be millions of user records, so processing this 1 by 1 is very slow. I have implemented concurrent processing for this like this:
const userIds = [1,2,3....];
const users$ = from(this.getUsersFromDB(userIds));
const concurrency = 150;
users$.pipe(
switchMap((users) =>
from(users).pipe(
mergeMap((user) => from(this.publishUser(user)), concurrency),
toArray()
)
)
).subscribe(
(partialResults: any) => {
// Do something with partial results.
},
(err: any) => {
// Error
},
() => {
// done.
}
);
This works perfectly fine for thousands of user records, it's processing 150 user records concurrently at a time, pretty faster than publishing users 1 by 1.
But problem occurs when processing millions of user records, getting those from database is pretty slow as result set size also goes to GBs(more memory usage also).
I am looking for a solution to get user records from DB in batches, while keep on publishing those records concurrently in parallel.
I thinking of a solution like, maintain a queue(of size N) of user records fetched from DB, whenever queue size is less than N, fetch next N results from DB and add to this queue.
Then the current solution which I have, will keep on getting records from this queue and keep on processing those concurrently with defined concurrency. But I am not quite able to put this in code. Is there are way we can do this using RxJS?
I think your solution is the right one, i.e. using the concurrent parameter of mergeMap.
The point that I do not understand is why you are adding toArray at the end of the pipe.
toArray buffers all the notifications coming from upstream and will emit only when the upstream completes.
This means that, in your case, the subscribe does not process partial results but processes all of the results you have obtained executing publishUser for all users.
On the contrary, if you remove toArray and leave mergeMap with its concurrent parameter, what you will see is a continuous flow of results into the subscribe due to the concurrency of the process.
This is for what rxjs is concerned. Then you can look at the specific DB you are using to see if it supports batch reads. In which case you can create buffers of user ids with the bufferCount operator and query the db with such buffers.
This question already has answers here:
How can I update more than 500 docs in Firestore using Batch?
(8 answers)
Closed 2 years ago.
I am trying to update more than 500 documents like 1000 to 2000 documents and i only know how to use batch for 500 documents i wanted to ask how can i update more than 500 documents using cloud firestore.here is how i am trying to update 500 documents. i am trying to update ms_timestamp for 1000 documents. can anyone tell me how can I do it using batch write
const batch = db.batch();
const campSnapshot = await db
.collection("camp")
.where("status", "in", ["PENDING", "CONFIRMED"])
.get();
await db.collection("camping").doc(getISO8601Date()).set({
trigger: campSnapshot.docs.length,
});
campSnapshot.forEach((docs) => {
const object = docs.data();
object.ms_timestamp = momentTz().tz("Asia/Kolkata").valueOf();
batch.set(
db.collection("camp").doc(docs.get("campId")),
object,
{ merge: true }
);
});
await Promise.all([batch.commit()]);
Cloud Firestore imposes a limit of 500 documents when performing a Transaction or Batched Write, and you can not change this, but a workaround may just work.
I am not an expert in web dev, so I am sharing a suggestion based on my viewpoint as a mobile app developer.
Create a collection that store a counter on how many documents that are contained within a specific collection. I update the counter through Cloud Functions/other approach, when an event (be it created, updated, or deleted) is fired within that specific collection. The counter should be atomic and consistent, and you can leverage Cloud Firestore Transaction here.
Fetch the counter value before performing batched writes. Here, I will know how many data/objects/documents that need to be updated.
Create an offset with initial value is 0. The offset is used to mark the data. A batched writes can only be performed for up to 500 documents, so if I want to perform a batched write again on document/data at 501-1000, then the offset will be 500, and so on.
Call a method that perform batched writes recursively using the defined offset, until it fully equals the counter - 1.
I do not test this since I don't have enough time right now, but I think it'll work.
You can comment if you still not understand, I'll be glad to help further.
What would be a good approach to running a repetitive task for each row in a large postgres db table on a different per row interval in Node.js.
To give you some more context, here's a quick description of the application:
It's a chat based customer support app.
It consists of teams, which can be either a client team or a support team. Teams have users, which can be either client users or support users.
Client users send messages to a support team and wait for one of that team's users to answer their question.
When there's an unanswered client message waiting for a response, every agent for the receiving support team will receive a notification every n seconds (n being set on a per-team basis by the team admin).
So this task needs to infinitely loop through the rows in the teams table and send notifications if:
The team has messages waiting to be answered.
N seconds have passed since the last notification was sent (N being the number of seconds set by the team admin).
There might be a better approach to this condition altogether.
So my questions are:
What is an efficient way to infinitely loop through a postgres table with no upper limit on the number rows?
Should I load 1 row at a time? Several at a time?
What would be a good way to do this in Node?
I'm using Knex. Does Knex provide a mechanism for lazy loading a table and iterating through the rows?
A) Running a repetitive task via node can be done via a the js built-in function 'setInterval'.
// run the intervalFnc() every 5 seconds
const timerId = setTimeout(intervalFnc, 5000);
function intervalFnc() { console.log("Hello"); }
// to quit running it:
clearTimeout(timerId);
Then your interval function can do the actual work. An alternative would be to use cron (linux), or some OS process scheduler to trigger the function. I would use this method if you want to do it every minute, and a cron job if you want to do it every hour (in between these times becomes more debatable).
B) An efficient way...
B-1) Retrieving a block of records from a DB will be more efficient than one at a time. Knex has .offset and .limit clauses to choose a group of records to retrieve. A sample from the knex doc:
knex.select('*').from('users').limit(10).offset(30)
B-2) Database indexed access is important for performance if your tables are very large. I would recommend including an status flag field in your table to note which records are 'in-process', and also include a "next-review-timestamp" field with both fields being both indexed. Retrieve the records that have status_flag='in-process' AND next_review_timestamp <= now(). Sample:
knex('users').where('status_flag', 'in-process').whereRaw('next_review_timestamp <= now()')
Hope this helps!
I have about ~300mb of data (~180k json objects) that gets updated once every 2-3 days.
This data is divided into three "collections", that I must keep up to date.
I decided to take the Node.JS way, but any solution in a language i know ( Java, Python) will be welcomed.
Whenever I perform a batch set using the node.JS firebase-admin client, not only it consumes an aberrant amount of ram ( about 4-6GB!), but it also tends to crash with errors that don't have a clear ( up to page 4 of google search without a meaningful answer ) reason.
My code is frankly simple, this is it:
var collection = db.collection("items");
var batch = db.batch();
array.forEach(item => {
var ref = collection.doc(item.id);
batch.set(ref, item);
});
batch.commit().then((res) => {
console.log("YAY",res);
});
I haven't found anywhere if there is a limit on the number of writes in a limited span of time (I understand doing 50-60k writes should be easy peasy with a backend the size of firebase), and also found that this can go up the ram train and have like 4-6GB of ram allocated.
I can confirm that when the errors are thrown, or the ram usage clogs my laptop, whatever happens first, I am still at less than 1-4% my daily usage quotas, so that is not the issue.
Background
I have a Node and React based application. I'm using Firebase for my storage and database. In my application users can fill out a form where they upload an image and select a time for the image to be added to their website. I save each image update as an object in my Firebase database like so. Images are arranged in order of ascending update time.
user-name: {
images: [
{
src: 'image-src-url',
updateTime: 1503953587727
}
{
src: 'image-src-url',
updateTime: 1503958424838
}
]
}
Scale
My applications db could potentially get very large with a lot of users and images. I'd like to ensure scalability.
Issue
How do I check when a specific image objects time has been met then execute a function? (I do not need assistance on the actual function that is being run just the checking of the db for a specific time.)
Attempts
I've thought about doing a cron job using node-cron that checks the entire database every 60s (users can only specify the minute the image will update, not the seconds.) Then if it finds a matching updateTime and executes my function. My concern is at a large scale that cron job will take a while to search the db and potentially miss a time.
I've also thought about when the user schedules a new update then dynamically create a specific cron job for that time. I'm unsure how to accomplish this.
Any other methods that may work? Are my concerns about node-cron not valid?
There are two approaches I can think of:
Keep track of the last timestamp you processed
Keep the "things to process" in a queue
Keep track of the last timestamp you processed
When you process items, you use the current timestamp as the cut-off point for your query. Something like:
var now = Date.now();
var query = ref.orderByChild("updateTime").endAt(now)
Now make sure to store this now somewhere (i.e. in your database) so that you can re-use it next time to retrieve the next batch of items:
var previous = ... previous value of now
var now = Date.now();
var query = ref.orderByChild("updateTime").startAt(previous).endAt(now);
With this you're only processing a single slice at a time. The only tricky bit is that somebody might insert a new node with an updateTime that you've already processed. If this is a concern for your use-case, you can prevent them from doing so with a validation rule on updateTime:
".validate": "newData.val() >= root.child('lastProcessed').val()"
As you add more items to the database, you will indeed be querying more items. So there is a scalability limit to this approach, but this approach should work well for anything up to a few hundreds of thousands of nodes (I haven't tested in a while so ymmv).
For a few previous questions on list size:
Firebase Performance: How many children per node?
Firebase Scalability Limit
How many records / rows / nodes is alot in firebase?
Keep the "things to process" in a queue
An alternative approach is to keep a queue of items that still need to be processed. So the clients add the items that they want processed to the queue with an updateTime of when they want to processed. And your server picks the items from the queue, performs the necessary updates, and removes the item from the queue:
var now = Date.now();
var query = ref.orderByChild("updateTime").endAt(now)
query.once("value").then(function(snapshot) {
snapshot.forEach(function(child) {
// TODO: process the child node
// remove the child node from the queue
child.ref.remove();
});
})
The difference with the earlier approach is that a queue's stable state is going to be empty (or at least quite small), so your queries will run against a much smaller list. That's also why you won't need to keep track of the last timestamp you processed: any item in the queue up to now is eligible for processing.