Go makes it very easy to write highly-concurrent applications – so easy, in fact, that it exposes you to Operating System limits fairly quickly.
Imagine building a web crawler. You have a long list of URLs, and you want to fetch the contents of each page. In many languages, you might spawn up a number of worker threads, each looping, pulling a URL off the queue and fetching it, until the queue is empty. This is a reasonable model, and it’s the most obvious choice in ruby and languages like it.
In Go, on the other hand, we have goroutines. Goroutines are cheaper to create and to schedule than real threads, and more of them can run at the same time without losing performance to thread overhead. It’s not uncommon to have many hundreds or thousands of goroutines running concurrently, without incurring much performance overhead.
In fact, Go is quite capable of handling thousands of HTTP requests at the same time. However, most operating systems won’t let you do that by default. OS X has a default maximum of 256 open files per process (an HTTP request uses a File Descriptor). If you’re coming to Go from a slow scripting language, this isn’t something you’ve had to think about on a daily basis, but it will be with Go.
One obvious approach to building this web crawler in Go would be to just spawn a goroutine for each URL in the queue and let the scheduler figure it out. For example:
This works really well if the channel never hands off urls above a certain rate. If it produces too fast – if you already have 256 open files and you try to spawn a new HTTP request, one of two things will happen, depending in part on which version of Go you’re using:
In Go 1.0, DNS lookup will fail cryptically, claiming “no such host”. This is because DNS lookup consumes a file descriptor temporarily, and the failed allocation is interpreted as NXDOMAIN.
In Go 1.1, or if you didn’t trigger the case above in 1.0,
Dial will simply return an error rather than a connection.
The first case in particular is rather misleading, but it’s almost always an indicator that you are out of File Descriptors.
So how can you moderate your FD consumption in the above example? There are two simple ways:
The Worker Pool strategy is reminiscent of the ruby example above:
The outer loop runs
nWorkers times, and spawns
nWorkers goroutines. Each loops indefinitely, calling fetch synchronously with a URL from the input channel. This limits the concurrency to
nWorkers, meaning that you will never have more than 100 HTTP requests pending, and never be consuming more than 100 FDs as a result of this function.
Using a semaphore is a simliar strategy, but I feel it’s cleaner in certain circumstances:
You first pre-fill the semaphore with 100 tokens. Each time you want to start an HTTP request, you must first withdraw a token. If all 100 tokens are currently out, you must wait until one has been returned.
The semaphore strategy accomplishes largely the same performance characteristics as the worker pool strategy, but one or the other can feel more appropriate depending on the problem. It’s good to be familiar with both. Both will prevent you from exceeding the file descriptor limit (which, incidentally, you can adjust using