rate-limiting

How to build a rate-limiting API with Observables?

一世执手 提交于 2019-12-10 23:10:38
问题 I would like to create a simple Calculator service that has a single method to add numbers. This Add method should be async and has to limit the number of concurrent calls being made at a given time. For instance, no more than 5 concurrent calls per second. If the rate limit is exceeded, the call should throw an exception. The class should be like: public class RateLimitingCalculator { public async Task<int> Add(int a, int b) { //... } } Any ideas? I would like implement it with Reactive

Protect my public oauth API from abuse, but allow anonymous access from my app?

佐手、 提交于 2019-12-10 10:20:53
问题 I have a website and an API. The website allows anonymous people to browse the catalogue, but you must be logged in to post stuff. I have built an API that exposes the same functionality. The API is used by a mobile app we are developing, but we are also going to allow other developers to use the API (i.e. it's publicly documented). The entire API is currently requires OAuth (2.0) authentication. To prevent abuse we use rate-limiting per OAuth client-id/user-id combination. Now a new

Rate limiting to prevent malicious behavior in ExpressJS

拈花ヽ惹草 提交于 2019-12-09 03:07:42
问题 Someone made me aware of some flaws in an application I'm working on (mostly within my JavaScript on the front-end), that leaves open the possibility of, say, clicking a ton of buttons at once and sending out a ton of transactional emails. This is clearly not good. I think one way to handle this in ExpressJS is by using app.all() to count the number of requests that happen within a certain timeframe. I'd store this in the session metadata with timestamps, and if more than X requests happen in

Twitter Api : Rate limit - know remaining Tweets I can do [closed]

余生颓废 提交于 2019-12-08 05:28:23
问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last year . I'm using Twitter API and there is something I don't undestand. I can ask how many remaining calls I can do on a lot of things with the " rate_limit_status " call. (https://dev.twitter.com/rest/reference/get/application/rate_limit_status) But it doesn't tell me how many tweets I can do or how many favorites I can

How to use ChannelTrafficShapingHandler in Netty 4+?

99封情书 提交于 2019-12-08 02:28:30
问题 I need to push a big file to client, but I want to limit the speed(such as 100Kb/s), how to use ChannelTrafficShapingHandler? ServerBootstrap b = new ServerBootstrap(); b.group(bossGroup, workerGroup) .channel(NioServerSocketChannel.class) .option(ChannelOption.SO_BACKLOG, 100) .handler(new LoggingHandler(LogLevel.INFO)) .childHandler(new ChannelInitializer<SocketChannel>() { @Override public void initChannel(SocketChannel ch) throws Exception { ChannelPipeline p = ch.pipeline(); p.addLast(

How to do akka-http request-level backpressure?

徘徊边缘 提交于 2019-12-07 19:59:04
问题 In akka-http, you can: Set akka.http.server.max-connections, which will prevent more than that number of connections. Exceeding this limit means clients will get connection timeouts. Set akka.http.server.pipelining-limit, which prevents a given connection from having more than this number of requests outstanding at once. Exceeding this means clients will get socket timeouts. These are both forms of backpressure from the http server to the client, but both are very low level, and only

How to do akka-http request-level backpressure?

岁酱吖の 提交于 2019-12-06 12:09:48
In akka-http, you can: Set akka.http.server.max-connections, which will prevent more than that number of connections. Exceeding this limit means clients will get connection timeouts. Set akka.http.server.pipelining-limit, which prevents a given connection from having more than this number of requests outstanding at once. Exceeding this means clients will get socket timeouts. These are both forms of backpressure from the http server to the client, but both are very low level, and only indirectly related to your server's performance. What seems better would be to backpressure at the http level,

rate limiting and throttling in java

孤街醉人 提交于 2019-12-06 09:44:58
I need to implement ratelimiter/throttling in one of my microservice. For example I have one User microservice that handles login and get user data based on role like Admin or normal user that is implemented using JWT token and annotation @Secured, So, My ask is to throttle based on these what api is being called.And, I should be able to modify the throttle limit at runtime too. I don't want to re-invent the wheel, so, any ideas please? technology stack:- java, spring boot Answer to this surely depends on what you relate throttling to. If you are thinking to throttle data returned by api based

Twitter API - Get number of followers of followers

筅森魡賤 提交于 2019-12-05 11:31:29
I'm trying to get the number of followers of each follower for a specific account (with the goal of finding the most influencial followers). I'm using Tweepy in Python but I am running into the API rate limits and I can only get the number of followers for 5 followers before I am cut off. The account I'm looking at has about 2000 followers. Is there any way to get around this? my code snippet is ids = api.followers_ids(account_name) for id in ids: more = api.followers_ids(id) print len(more) Thanks You don't need to get all user followers in order to count them. Use followers_count property. E

Why do Flask rate limiting solutions use Redis?

不打扰是莪最后的温柔 提交于 2019-12-04 14:45:21
I want to rate limit my Flask API. I found 2 solutions. The Flask-Limiter extension. A snippet from the Flask website using Redis: http://flask.pocoo.org/snippets/70/ What is the significance of Redis when Flask-Limiter is able to rate limit the request on the basis of remote address without Redis? Redis allows you to store the rate-limiting state in a persistent store. This means you can: Restart your web server or web application and still have the rate-limitation work. You won't lose the records of the last requests made because of the working process being destroyed and a new one being