Throttles and Rate Limiting

With the way Sentry works you may find yourself in a situation where you’ll see too much inbound traffic without a good way to drop excess messages. There’s a few solutions to this, and you’ll likely want to employ them all if you are faced with this problem.

Event Quotas

One of the primary mechanisms for throttling workloads in Sentry involves setting up event quotas. These can be configured per project as well as system wide and will allow you to limit the maximum number of events accepted within a 60 second period of time.


The primary implementation uses Redis, and simply requires you to configure the connection information:

SENTRY_QUOTAS = 'sentry.quotas.redis.RedisQuota'

By default, this will use the default named Redis cluster. To use a different cluster, provide the cluster option, as such:

    'cluster': 'quota',

If you have additional needs, you’re freely available to extend the base Quota class just as the Redis implementation does.

System-wide Rate Limiting

You can configure the system-wide maximum per-minute rate limit:

system.rate-limit: 500

For example, in your project’s, you can do something like this:

from sentry.conf.server import SENTRY_OPTIONS

SENTRY_OPTIONS['system.rate-limit'] = 500

Alternatively, if you navigate to /manage/settings/ you will find an admin panel with an option for setting Rate Limit, which gets stored in your quota implementation described above.

Project-based Rate Limiting

For doing project-based rate limiting, click on a project’s Settings. Under the Client Keys (DSN) tab, find the key that you’d like to rate-limit, and click the corresponding Configure button. That should bring up key/project-specific rate-limiting settings.

Notification Rate Limits

In some cases there may be concerns about limiting things such as outbound email notifications. To address this Sentry provides a rate limits subsystem which supports arbitrary rate limits.


Like event quotas, the primary implementation uses Redis:

SENTRY_RATELIMITER = 'sentry.ratelimits.redis.RedisRateLimiter'

By default, this will use the default named Redis cluster. To use a different cluster, provide the cluster option, as such:

    'cluster': 'ratelimiter',

Rate Limiting with IPTables

One of your most effective options is to rate limit with your system’s firewall, in our case, IPTables. If you’re not sure how IPTables works, take a look at Ubuntu’s IPTables How-to.

A sample configuration, which will limit a single IP from bursting more than 5 messages in a 10 second period might look like this:

# create a new chain for rate limiting

# rate limit individual ips to prevent stupidity
-I INPUT -p tcp --dport 80 -m state --state NEW -m recent --set
-I INPUT -p tcp --dport 443 -m state --state NEW -m recent --set
-I INPUT -p tcp --dport 80 -m state --state NEW -m recent --update --seconds 10 --hitcount 5 -j LIMITED
-I INPUT -p tcp --dport 443 -m state --state NEW -m recent --update --seconds 10 --hitcount 5 -j LIMITED

# log rejected ips
-A LIMITED -p tcp -m limit --limit 5/min -j LOG --log-prefix "Rejected TCP: " --log-level 7

Rate Limiting with Nginx

While IPTables will help prevent DDOS they don’t effectively communicate to the client that it’s being rate limited. This can be important depending on how the client chooses to respond to the situation.

An alternative (or rather, an addition) is to use something like ngx_http_limit_conn_module.

An example configuration looks something like this:

limit_req_zone  $binary_remote_addr  zone=one:100m   rate=3r/s;
limit_req_zone  $projectid  zone=two:100m   rate=6r/s;
limit_req_status 429;
limit_req_log_level warn;

server {
  listen   80;

  location / {
    proxy_pass        http://internal;

  location ~* /api/(?P<projectid>\d+/)?store/ {
    proxy_pass        http://internal;

    limit_req   zone=one  burst=3  nodelay;
    limit_req   zone=two  burst=10  nodelay;

Using Cyclops (Client Proxy)

An additional option for rate limiting is to do it on the client side. Cyclops is a third-party proxy written in Python (using Tornado) which aims to solve this.

It’s not officially supported, however it is used in production by several large users.