Let's say, I have P processes running some business logic on N physical machines. These processes call some web service S, say. I want to ensure that not more than X calls are made to the service S per second by all the P processes combined.
How can such a solution be implemented?
Google Guava's Rate Limiter works well for processes running on single box, but not in distributed setup.
Are there any standard, ready to use, solutions available for JAVA? [may be based on zookeeper]
Thanks!
Bucket4j is java implementation of "token-bucket" rate limiting algorithm. It works both locally and distributed(on top of JCache). For distributed use case you are free to choose any JCache implementation like Hazelcast or Apache Ignite. See this example of using Bucket4j in cluster.
I have been working on an opensource solution for these kind of problems.
Limitd is a "server" for limits. The limits are implemented using the Token Bucket Algorithm.
Basically you define limits in the service configuration like this:
buckets:
"request to service a":
per_minute: 10
"request to service b":
per_minute: 5
The service is run as a daemon listening on a TCP/IP port.
Then your application does something along these lines:
var limitd = new Limitd('limitd://my-limitd-address');
limitd.take('request to service a', 'app1' 1, function (err, result) {
if (result.conformant) {
console.log('everything is okay - this should be allowed');
} else {
console.error('too many calls to this thing');
}
});
We are currently using this for rate-limiting and debouncing some application events.
The server is on:
https://github.com/auth0/limitd
We are planning to work on several SDKs but for now we only have node.js and partially implemented go:
https://github.com/jdwyah/ratelimit-java provides distributed rate limits that should do just this. You can configure your limit as S per second / minute etc and choose burst size / refill rate of the leaky bucket that is under the covers.
Simple rate limiting in java where you want to achieve a concurrency of 3 transactions every 3 seconds. If you want to centralize this then either store the tokens array in elasticache or any database. And in place of synchronized block you will have to implement a lock flag as well.
import java.util.Date;
public class RateLimiter implements Runnable {
private long[] tokens = new long[3];
public static void main(String[] args) {
// TODO Auto-generated method stub
RateLimiter rateLimiter = new RateLimiter();
for (int i=0; i<20; i++) {
Thread thread = new Thread(rateLimiter,"Thread-"+i );
thread.start();
}
}
@Override
public void run() {
// TODO Auto-generated method stub
long currentStartTime = System.currentTimeMillis();
while(true) {
if(System.currentTimeMillis() - currentStartTime > 100000 ) {
throw new RuntimeException("timed out");
}else {
if(getToken()) {
System.out.println(Thread.currentThread().getName() +
" at " +
new Date(System.currentTimeMillis()) + " says hello");
break;
}
}
}
}
synchronized private boolean getToken() {
// TODO Auto-generated method stub
for (int i = 0; i< 3; i++) {
if(tokens[i] == 0 || System.currentTimeMillis() - tokens[i] > 3000) {
tokens[i] = System.currentTimeMillis();
return true;
}
}
return false;
}
}
So with all distributed rate limiting architecture, you'll need a single backend store that acts as single source of true to track the number of requests. You can always use zookeeper as a in memory datastore for this out of convenience, although there are better choices such as Redis.
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