Skip to main content

RateLimiting based on load on nodes

We are a cloud based file storage company and we allow many access point to the cloud. One of the access point is Webdav api and people can use any webdav client to access the cloud. But some of the webdav client especially on mac OS are really abusive. Though the user is not doing any abusive action, the webdav client does aggressive caching so even when you are navigating at a top directory it does PROPFINDs for depth at 5 or 6 level to make the user experience seamless as if he is navigating local drive. This makes life miserable on the server because from some clients we get more than 1000 requests in a minute. If there are 5-10 clients do the webdav activity then it causes 100 or more propfinds per sec. Luckily the server is able to process these but it hurts other activities. So we needed to rate limit this. Now as the user is really not doing any abusive action it would be bad to slow down on penalize the user in normal circumstance, however if the server is under load then it would be worth while to throttle the user for some time.

Luckily Jdk1.6 has a light weight api to get system load average.

 public double getSystemLoadAverage() {
  OperatingSystemMXBean osStats = ManagementFactory.getOperatingSystemMXBean();
  double loadAverage = osStats.getSystemLoadAverage();
  return loadAverage;
 }

And now I can write code like

  if(throttlePropFinds(req, user)) {
   logger.info("Throttling due to excessive webdav requests from user {}", user.getId());
   if(throttlingHelper.isBlockUsers()) {
    double loadAverage = SpringBeanLocator.getJvmStatsLogger().getSystemLoadAverage();
    if (loadAverage > 4) {
     logger.info("Sending 503 as loadAverage>4 and excessive webdav requests from user {}", user.getId());
           resp.sendError(WebdavStatus.SC_SERVICE_UNAVAILABLE);
           return;
    }
   }
  }

The logic I used for propfind rate limiting is simple where I keep track of all users who made>1000 requests in last 5 min and only if loadAvg is >4, I send 503. For brevity I am not putting the code to count no of request made by users in last 5 min but I am using memcache to maintain the counters in one minute buckets.

Comments

Post a Comment

Popular posts from this blog

Haproxy and tomcat JSESSIONID

One of the biggest problems I have been trying to solve at our startup is to put our tomcat nodes in HA mode. Right now if a customer comes, he lands on to a node and remains there forever. This has two major issues: 1) We have to overprovision each node with ability to handle worse case capacity. 2) If two or three high profile customers lands on to same node then we need to move them manually. 3) We need to cut over new nodes and we already have over 100+ nodes.  Its a pain managing these nodes and I waste lot of my time in chasing node specific issues. I loath when I know I have to chase this env issue. I really hate human intervention as if it were up to me I would just automate thing and just enjoy the fruits of automation and spend quality time on major issues rather than mundane task,call me lazy but thats a good quality. So Finally now I am at a stage where I can put nodes behing HAProxy in QA env. today we were testing the HA config and first problem I immediately
Post a Comment
Read more

Adding Jitter to cache layer

Thundering herd is an issue common to webapp that rely on heavy caching where if lots of items expire at the same time due to a server restart or temporal event, then suddenly lots of calls will go to database at same time. This can even bring down the database in extreme cases. I wont go into much detail but the app need to do two things solve this issue. 1) Add consistent hashing to cache layer : This way when a memcache server is added/removed from the pool, entire cache is not invalidated.  We use memcahe from both python and Java layer and I still have to find a consistent caching solution that is portable across both languages. hash_ring and spymemcached both use different points for server so need to read/test more. 2) Add a jitter to cache or randomise the expiry time: We expire long term cache  records every 8 hours after that key was added and short term cache expiry is 2 hours. As our customers usually comes to work in morning and access the cloud file server it can happe
Post a Comment
Read more

Spring 3.2 quartz 2.1 Jobs added with no trigger must be durable.

I am trying to enable HA on nodes and in that process I found that in a two test node setup a job that has a frequency of 10 sec was running into deadlock. So I tried upgrading from Quartz 1.8 to 2.1 by following the migration guide but I ran into an exception that says "Jobs added with no trigger must be durable.". After looking into spring and Quartz code I figured out that now Quartz is more strict and earlier the scheduler.addJob had a replace parameter which if passed to true would skip the durable check, in latest quartz this is fixed but spring hasnt caught up to this. So what do you do, well I jsut inherited the factory and set durability to true and use that public class DurableJobDetailFactoryBean extends JobDetailFactoryBean {     public DurableJobDetailFactoryBean() {         setDurability(true);     } } and used this instead of JobDetailFactoryBean in the spring bean definition     <bean id="restoreJob" class="com.xxx.infrastructure.quar
7 comments
Read more