i want to build an api using python and host it on google cloud. api will basically read some data in bucket and then do some processing on it and return the data back. i am hoping that i can read the data in memory and when request comes, just process it and send response back to serve it with low latency. assume i will read few thousand records from some database/storage and when request comes process and send 10 back based on request parameters. i dont want to make connection/read storage when the request comes as it will take time and i want to serve as fast as possible.
will google cloud function work for this need? or shoudl i go with app engine. (basically i want to be able to read the data once and hold it for incoming requests). data mostly will be less than 1-2 gb (max)
thanks,
Manish
You have to have the static data with your function code. Increase the Cloud Functions memory to allow it to load in memory the data to keep them warm and for having a very fast access to it.
Then, you have 2 way to achieve it:
Load the data at startup. You load them only once, the first call has a high latency to download (from GCS for instance) and load the data in memory. The advantage is: in case of data update, you don't have to redeploy your function, only update the data in their location. At the next function start, the new data will be loaded
Deploy the function with the static data in the deployment. This time the startup time is much faster (no download), only the data to load in memory. But when you want to update the data, you have to redeploy your function.
A final word if you have 2 set of static data, you must have 2 functions. The responsibility is different, so the deployment is different.
Related
I am re-designing a project I built a year ago when I was just starting to learn how to code. I used MEAN stack, back then and want to convert it to a PERN stack now. My AWS knowledge has also grown a bit and I'd like to expand on these new skills.
The application receives real-time data from an api which I clean up to write to a database as well as broadcast that data to connected clients.
To better conceptualize this question I will refer to the following items:
api-m1 : this receives the incoming data and passes it to my schema I then send it to my socket-server.
socket-server: handles the WSS connection to the application's front-end clients. It also will write this data to a postgres database which it gets from Scraper and api-m1. I would like to turn this into clusters eventually as I am using nodejs and will incorporate Redis. Then I will run it behind an ALB using sticky-sessions etc.. for multiple EC2 instances.
RDS: postgres table which socket-server writes incoming scraper and api-m1 data to. RDS is used to fetch the most recent data stored along with user profile config data. NOTE: RDS main data table will have max 120-150 UID records with 6-7 columns
To help better visualize this see img below.
From a database perspective, what would be the quickest way to write my data to RDS.
Assuming we have during peak times 20-40 records/s from the api-m1 + another 20-40 records/s from the scraper? After each day I tear down the database using a lambda function and start again (as the data is only temporary and does not need to be saved for any prolonged period of time).
1.Should I INSERT each record using a SERIAL id, then from the frontend fetch the most recent rows based off of the uid?
2.a Should I UPDATE each UID so i'd have a fixed N rows of data which I just search and update? (I can see this bottlenecking with my Postgres client.
2.b Still use UPDATE but do BATCHED updates (what issues will I run into if I make multiple clusters i.e will I run into concurrency problems where table record XYZ will have an older value overwrite a more recent value because i'm using BATCH UPDATE with Node Clusters?
My concern is UPDATES are slower than INSERTS and I don't want to make it as fast as possible. This section of the application isn't CPU heavy, and the rt-data isn't that intensive.
To make my comments an answer:
You don't seem to need SQL semantics for anything here, so I'd just toss RDS and use e.g. Redis (or DynamoDB, I guess) for that data store.
We're running our Node backend on Firebase Functions, and have to frequently hit a third-party API (HubSpot), which is rate-limited to 100 requests / 10 seconds.
We're making these requests to HubSpot from our cloud functions, and often find ourselves exceeding HubSpot's rate-limit during campaigns or other website usage spikes. Also, since they are all write requests to update data on HubSpot, these requests cannot be made out of order.
Is there a way to throttle our requests to HubSpot, so as to not exceed their rate limit? Open to suggestions that may not necessarily involve cloud functions, although that would be preferred.
Note: When I say "throttle", I mean that all requests to HubSpot need to go through. I'm trying to achieve something similar to what Lodash's throttle method does, if that makes sense.
What we usually do in this case is store the data into a database, and then pass it over to HubSpot in a tempered way (e.g. without exceeding their rate limit) using a cron that runs every minute. For every data item that we pass to HubSpot successfully, we mark it as "success" in the database.
Cloud Functions can not be rate limited. It will always attempt to service requests and events as fast as they arrive. But you can use Cloud Tasks to create an task queue to spread out the load of some work over time using a configured rate limit. A task queue can target another HTTP function. This effectively makes your processing asynchronous, but is really the only mechanism that Google Cloud gives you to smooth out load.
I have an Azure function app triggered by an HttpRequest. The function app reads the request, tosses one copy of it into a storage table for safekeeping and sends another copy to a queue for further processing by another element of the system. I have a client running an ApacheBench test that reports approximately 148 requests per second processed. That rate of processing will not be enough for our expected load.
My understanding of function apps is that it should spawn as many instances as is needed to handle the load sent to it. But this function app might not be scaling out quickly enough as it’s only handling that 148 requests per second. I need it to handle at least 200 requests per second.
I’m not 100% sure the problem is on my end, though. In analyzing the performance of my function app I found a LOT of 429 errors. What I found online, particularly https://learn.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-request-limits, suggests that these errors could be due to too many requests being sent from a single IP. Would several ApacheBench 10K and 20K request load tests within a given day cause the 429 error?
However, if that’s not it, if the problem is with my function app, how can I force my function app to spawn more instances more quickly? I assume this is the way to get more throughput per second. But I’m still very new at working with function apps so if there is a different way, I would more than welcome your input.
Maybe the Premium app service plan that’s in public preview would handle more throughput? I’ve thought about switching over to that and running a quick test but am unsure if I’d be able to switch back?
Maybe EventHub is something I need to investigate? Is that something that might increase my apparent throughput by catching more requests and holding on to them until the function app could accept and process them?
Thanks in advance for any assistance you can give.
You dont provide much context of you app but this is few steps how you can improve
If you want more control you need to use App Service plan with always on to avoid cold start, also you will need to configure auto scaling since you are responsible in this plan and auto scale is not enabled by default in app service plan.
Your azure function must be fully async as you have external dependencies so you dont want to block thread while you are calling them.
Look on the limits. Using host.json you can tweek it.
429 error means that function is busy to process your request, so probably when you writing to table you are not using async and blocking thread
Function apps work very well and scale as it says. It could be because request coming from Single IP and Azure could be considering it DDOS. You can do the following
AzureDevOps Load Test
You can load test using one of the azure service . I am very sure they have better criteria of handling IPs. Azure DeveOps Load Test
Provision VM in Azure
The way i normally do is provision the VM (windows 10 pro) in azure and use JMeter to Load test. I have use this method to test and it works fine. You can provision couple of them and subdivide the load.
Use professional Load testing services
If possible you may use services like Loader.io . They use sophisticated algos to run the load test and provision bunch of VMs to run the same test.
Use Application Insights
If not already you must be using application insights to have a better look from server perspective. Go to live stream and see how many instance it would provision to handle the load test . You can easily look into events and error logs that may be arising and investigate. You can deep dive into each associated dependency and investigate the problem.
I am serving my users with data fetched from an external API. Now, I don't know when this API will have new data, how would be the best approach to do that using Node, for example?
I have tried setInterval's and node-schedule to do that and got it working, but isn't it expensive for the CPU? For example, over a day I would hit this endpoint to check for new data every minute, but it could have new data every five minutes or more.
The thing is, this external API isn't ran by me. Would the only way to check for updates hitting it every minute? Is there any module that can do that in Node or any approach that fits better?
Use case 1 : Call a weather API for every city of the country and just save data to my db when it is going to rain in a given city.
Use case 2 : Send notification to the user when a given Philips Hue lamp is turned on at the time it is turned on without having to hit the endpoint to check if it is on or not.
I appreciate the time to discuss this.
If this external API has no means of notifying you when there's new data, then the only thing you can do is to "poll" it to check for new data.
You will have to decide what an "efficient design" for polling is in your specific application and given the type of data and the needs of the client (what is an acceptable latency for new data).
You also need to be sure that your service is not violating any terms of service with your polling scheme or running afoul of rate limiting that may deny you access to the server if you use it "too much".
Would the only way to check for updates hitting it every minute?
Unless the API offers some notification feature, there is no other scheme other than polling at some interval. Polling every minute is fairly quick. Do your clients really need information that is less than a minute old? Or would it really make no difference if the information was as much as 5 minutes old.
For example, in your example of weather, a client wouldn't really need temperature updates more often than probably every 10-15 minutes.
Is there any module that can do that in Node or any approach that fits better?
No. Not really. You'll probably just use some sort of timer (either repeated setTimeout() or setInterval() in a node.js app to repeatedly carry out your API operations.
Use case: Call a weather API for every city of the country and just save data to my db when it is going to rain in a given city.
Trying to pre-save every possible piece of data from an external API is probably a losing proposition. You're essentially trying to "scrape" all the data from the external API. That is likely against the terms of service and will likely also run afoul of rate limits. And, it's just not very practical.
Instead, you will probably want to fetch data upon demand (when a client requests data for Phoenix, then, and only then, do you start collecting data for Phoenix) and then once a demand for a certain type of data (temperatures in a particular city) is established, then you might want to pre-cache that data more regularly so you can notify clients of changes. If, after awhile, no clients are asking for data from Phoenix, you stop requesting updates for Phoenix any more until a client establishes demand again.
I have tried setInterval's and node-schedule to do that and got it working, but isn't it expensive for the CPU? For example, over a day I would hit this endpoint to check for new data every minute, but it could have new data every five minutes or more.
Making a remote network request is not a CPU intensive operation, even if you're doing it every minute. node.js uses non-blocking networking so most of the time during a network request, node.js isn't doing anything and isn't using the CPU at all. The only time the CPU would be briefly used is when you first send the API request and then when you receive back the result from the API call and need to process it.
Whether you really need to "poll" every minute depends upon the data and the needs of the client. I'd ask yourself if your app will work just fine if you check for new data every 5 minutes.
The method I would use to update would be contained outside of the code in a scheduled batch/powershell/bash file. In windows you can schedule tasks based upon time of day or duration since last run, so what you could do is run a simple command that will kill your application for five minutes, run npm update, and then restart your application before closing the shell.
That way you're staying out of your API and keeping code to a minimum, and if your code is inside that Node package in the update, it'll be there and ready once you make serious application changes or you need to take the server down for maintenance and updates to the low-level code.
This is a light-weight solution for you and it's a method I've used once or twice at my workplace. There are lots of options out there, and if this isn't what you're looking for I can keep looking out for you.
I have an app where I need to check people's posts constantly. I am trying to make sure that the -server- handles more than 100,000 posts. I tried to explain the program and specify the issues I am worried about by numbers.
I am running a simple node.js program on my terminal that runs as firebase admin controlling the Firebase Database. The program has no connectivity with clients(users), it just keeps the database locally to check users' posts every 2-3 seconds. I am keeping the posts in local hash variables by using on('child_added') to simply push the post to a posts hash and so on for on('child_removed') and on('child_changed').
Are these functions able to handle more than 5 requests per second?
Is this the proper way of keeping data locally for faster processing(and not abusing firebase limits)? I need to check every post on the platform every 2-3 seconds, so I am trying to keep a local copy of the -posts data.
That local copy of the posts are looped through every 2-3 seconds.
If there are thousands of posts, will a simple array variable handle that load?
Second part of the program:
I run a for loop to loop through the posts in a function. I run the function every 2-3 seconds using setInterval(). The program needs not only to check new added posts but it constantly needs to check all posts on the database.
If(specific condition for a post) => the program changes the state of the post
.on(child_changed) function => sends an API request to a website after that state change
Can this function run asynchronously ? When it is called, the function should not wait for the previous call to finish because the old call is sending an API request and it might not complete fast. How can I make sure that .on(child_changed) doesn't miss a single change on the -posts data?
Listen for Value Events documentation shows how to observe changes, namely one uses the .on method.
In terms of backing up your Realtime Database, you simply export the data manually, or if you have the paid plan you can automate it.
I don't understand why you would want to recreate the wheel, so to speak, and have your server ping firebase for updates. Simply use firebase observers.