I’m getting lots of 504 Timeout errors, please help!

HTTP 504 error means a request to Splash took more than timeout seconds to complete (30s by default) - Splash aborts script execution after the timeout. To override the timeout value pass ‘timeout’ argument to the Splash endpoint you’re using.

Note that the maximum allowed timeout value is limited by the maximum timeout setting, which is by default 60 seconds. In other words, by default you can’t pass ?timeout=300 to run a long script - an error will be returned.

Maximum allowed timeout can be increased by passing --max-timeout option to Splash server on startup:

$ python -m splash.server --max-timeout 3600

For Docker the command would be something like this (see Passing Custom Options):

$ docker run -it -p 8050:8050 scrapinghub/splash --max-timeout 3600

The next question is why a request can need 10 minutes to render. There are 3 common reasons:

1. Slow website

A website can be really slow, or it can try to get some remote resources which are really slow.

There is no way around increasing timeouts and reducing request rate if the website itself is slow. However, often the problem lays in unreliable remote resources like third-party trackers or advertisments. By default Splash waits for all remote resources to load, but in most cases it is better not to wait for them forever.

To abort resource loading after a timeout and give the whole page a chance to render use resource timeouts. For render.*** endpoints use ‘resource_timeout’ argument; for execute use either splash.resource_timeout or request:set_timeout (see splash:on_request).

It is a good practive to always set resource_timeout; something similar to resource_timeout=20 often works well.

2. Splash Lua script does too many things

When a script fetches many pages or uses large delays then timeouts are inevitable. Sometimes you have to run such scripts; in this case increase --max-timeout Splash option and use larger timeout values.

But before increasing the timeouts consider splitting your script into smaller steps and sending them to Splash individually. For example, if you need to fetch 100 websites, don’t write a Splash Lua script which takes a list of 100 URLs and fetches them - write a Splash Lua script that takes 1 URL and fetches it, and send 100 requests to Splash. This approach has a number of benefits: it makes scripts more simple and robust and enables parallel processing.

3. Splash instance is overloaded

When Splash is overloaded it may start producing 504 errors.

Splash renders requests in parallel, but it doesn’t render them all at the same time - concurrency is limited to a value set at startup using --slots option. When all slots are used a request is put into a queue. The thing is that a timeout starts to tick once Splash receives a request, not when Splash starts to render it. If a request stays in an internal queue for a long time it can timeout even if a website is fast and splash is capable of rendering the website.

To increase rendering speed and fix an issue with a queue it is recommended to start several Splash instances and use a load balancer capable of maintaining its own request queue. HAProxy has all necessary features; check an example config here. A shared request queue in a load balancer also helps with reliability: you won’t be loosing requests if a Splash instance needs to be restarted.


Nginx (which is another popular load balancer) provides an internal queue only in its commercial version, Nginx Plus.

How to run Splash in production?

Easy Way

If you want to get started quickly take a look at Aquarium (which is a Splash setup without many of the pitfalls) or use a hosted solution like ScrapingHub’s.

Don’t forget to use resource timeous in your client code (see 1. Slow website). It also makes sense to retry a couple of times if Splash returns 5xx error response.

Hard Way

If you want to create your own production setup, here is a small non-exhaustive checklist:

  • Splash should be daemonized and started on boot;
  • in case of failures or segfaults Splash must be restarted;
  • memory usage should be limited;
  • several Splash instances should be started to use all CPU cores and/or multiple servers;
  • requests queue should be moved to the load balancer to make rendering more robust (see 3. Splash instance is overloaded).

Of course, it is also good to setup monitoring, configuration management, etc. - all the usual stuff.

To daemonize Splash, start it on boot and restart on failures one can use Docker: since Docker 1.2 there are --restart and -d options which can be used together. Another way to do that is to use standard tools like upstart, systemd or supervisor.


Docker --restart option won’t work without -d.

Splash uses an unbound in-memory cache and so it will eventually consume all RAM. A workaround is to restart the process when it uses too much memory; there is Splash --maxrss option for that. You can also add Docker --memory option to the mix.

In production it is a good idea to pin Splash version - instead of scrapinghub/splash it is usually better to use something like scrapinghub/splash:2.0.

A command for starting a long-running Splash server which uses up to 4GB RAM and daemonizes & restarts itself could look like this:

$ docker run -d -p 8050:8050 --memory=4.5G --restart=always scrapinghub/splash:2.0 --maxrss 4000

You also need a load balancer; for example configs check Aquarium or an HAProxy config in Splash repository.