A **program profile** is a snapshot of a program's use of resources at runtime. Profiles can be extremely helpful to identify problem areas, troubleshoot bugs and crashes, and optimize code.
Profiles report on consumers of CPU and memory, show stack traces of goroutines, and help track down deadlocks or high-contention synchronization primitives.
When reporting certain bugs in Caddy, we may ask for a profile. This article can help. It describes both how to obtain profiles with Caddy, and how to use and interpret the resulting pprof profiles in general.
1.**Caddy profiles are NOT security-sensitive.** They contain benign technical readouts, not the contents of memory. They do not grant access to systems. They are safe to share.
2.**Profiles are lightweight and can be collected in production.** In fact, this is a recommended best practice for many users; see later in this article.
## Obtaining profiles
Profiles are available via the [admin interface](/docs/api) at `/debug/pprof/`. On a machine running Caddy, open it in your browser:
```
http://localhost:2019/debug/pprof/
```
<asideclass="tip">
By default, the admin API is only accessible locally. If running remotely, in VMs, or in containers, see the next section for how to access this endpoint.
The counts are a handy way to quickly identify leaks. If you suspect a leak, refresh the page repeatedly and you'll see one or more of those counts constantly increasing. If the heap count grows, it's a possible memory leak; if the goroutine count grows, it's a possible goroutine leak.
Click through the profiles and see what they look like. Some may be empty and that's normal a lot of the time. The most commonly-used ones are <b>goroutine</b> (function stacks), <b>heap</b> (memory), and <b>profile</b> (CPU). Other profiles are useful for troubleshooting mutex contention or deadlocks.
At the bottom, there's a simple description of each profile:
- **allocs:** A sampling of all past memory allocations
- **block:** Stack traces that led to blocking on synchronization primitives
- **cmdline:** The command line invocation of the current program
- **goroutine:** Stack traces of all current goroutines. Use debug=2 as a query parameter to export in the same format as an unrecovered panic.
- **heap:** A sampling of memory allocations of live objects. You can specify the gc GET parameter to run GC before taking the heap sample.
- **mutex:** Stack traces of holders of contended mutexes
- **profile:** CPU profile. You can specify the duration in the seconds GET parameter. After you get the profile file, use the go tool pprof command to investigate the profile.
- **threadcreate:** Stack traces that led to the creation of new OS threads
- **trace:** A trace of execution of the current program. You can specify the duration in the seconds GET parameter. After you get the trace file, use the go tool trace command to investigate the trace.
The difference between "goroutine" and "full goroutine stack dump" is the `?debug=2` parameter: the full stack dump is like output you'd see after a panic; it's more verbose and, notably, does not collapse identical goroutines.
Clicking the links on the pprof index page above will give you profiles in text format. This is useful for debugging, and it's what we on the Caddy team prefer because we can scan it to look for obvious clues without needing extra tooling.
But binary is actually the default format. The HTML links append the `?debug=` query string parameter to format them as text, except for the (CPU) "profile" link, which does not have a textual representation.
- **`seconds=N` (cpu, trace profiles):** profile for the given duration
Because these are HTTP endpoints, you can also use any HTTP client like curl or wget to download profiles.
Once your profiles are downloaded, you can upload them to a GitHub issue comment or use a site like [pprof.me](https://pprof.me/). For CPU profiles specifically, [flamegraph.com](https://flamegraph.com/) is another option.
## Accessing remotely
_If you're already able to access the admin API locally, skip this section._
By default, Caddy's admin API is only accessible over the loopback socket. However, there are at least 3 ways you can access Caddy's `/debug/pprof` endpoint remotely:
This will, of course, make profiles available to who can connect to your site. If that's not desired, you can add some authentication using an HTTP auth module of your choice.
(Don't forget the `/debug/pprof/*` matcher, otherwise you'll proxy the entire admin API!)
Another way is to use an SSH tunnel. This is an encrypted connection using the SSH protocol between your computer and your server. Run a command like this on your computer:
This tunnels `localhost:8123` (on your local machine) to `localhost:2019` on `example.com`. Make sure to replace `username`, `example.com`, and ports as necessary.
<asideclass="tip">
This command will run in the foreground. Keep in mind that if you try to background the process with <kbd>Ctrl</kbd>+<kbd>Z</kbd>, it will pause the tunnel, and connections using the tunnel will fail to connect.
You can avoid the need for `-H "Host: ..."` by using port `2019` on both sides of the tunnel (but this requires that port `2019` is not already taken on your own computer, i.e. not having Caddy running locally).
Running a tunnel with the above command requires that you keep the terminal open. If you want to run the tunnel in the background, you can start the tunnel like this:
This will start in the background and create a control socket at `/tmp/caddy-tunnel.sock`. You can then use the control socket to close the tunnel when you're done with it:
You can also configure the admin API to accept remote connections to authorized clients.
(TODO: Write article about this.)
## Goroutine profiles
The goroutine dump is useful for knowing what goroutines exist and what their call stacks are. In other words, it give us an idea of code that is either currently executing or is blocking/waiting.
If you click "goroutines" or go to `/debug/pprof/goroutine?debug=1`, you'll see a list of goroutines and their call stacks. For example:
The first line, `goroutine profile: total 88`, tells us what we're looking at and how many goroutines there are.
The list of goroutines follows. They are grouped by their call stacks in descending order of frequency.
A goroutine line has this syntax: `<count> @ <addresses...>`
The line starts with a count of the goroutines that have the associated call stack. The `@` symbol indicates the start of the call instruction addresses, i.e. the function pointers, that originated the goroutine. Each pointer is a function call, or call frame.
You may notice that many of your goroutines share the same first call address. This is your program's main, or entry point. Some goroutines won't originate there because programs have various `init()` functions and the Go runtime may also spawn goroutines.
The lines that follow start with `#` and are actually just comments for the benefit of the reader. They contain the current stack trace of the goroutine. The top represents the top of the stack, i.e. the current line of code being executed. The bottom represents the bottom of the stack, or the code that the goroutine initially started running.
The stack trace has this format:
```
<address><package/func>+<offset><filename>:<line>
```
The address is the function pointer, then you'll see the Go package and function name (with the associated type name if it's a method), and the instruction offset within the function. Then perhaps the most useful piece of info, the file and line number, are at the end.
### Full goroutine stack dump
If we change the query string parameter to `?debug=2`, we get a full dump. This includes a verbose stack trace of every goroutine, and identical goroutines are not collapsed. This output can be very large on busy servers, but it's interesting information!
Let's look at one that corresponds to the first call stack above (truncated):
Despite its verbosity, the most useful information uniquely provided by this dump are the first and last lines for every goroutine.
The first line contains the goroutine's number (61961905), state ("IO wait"), and duration ("1 minutes"):
- **Goroutine number:** Yes, goroutines have numbers! But they are not exposed to our code. These numbers are especially helpful in a stack trace, however, because we can see which goroutine spawned this one (see at the end: "created by ... in goroutine 61961902"). Tooling shown below helps us draw visual graphs of this.
- **State:** This tells us what the goroutine is currently doing. Here are some possible states you may see:
-`running`: Executing code - awesome!
-`IO wait`: Waiting for network. Does not consume an OS thread because it is parked on a non-blocking network poller.
-`sleep`: We all need more of it.
-`select`: Blocked on a select; waiting for a case to become available.
-`select (no cases):` Blocked on an empty select `select {}` specifically. Caddy uses one in its main to keep running because shutdowns are initiated from other goroutines.
-`chan receive`: Blocked on a channel receive (`<-ch`).
-`semacquire`: Waiting to acquire a semaphore (low-level synchronization primitive).
-`syscall`: Executing a system call. Consumes an OS thread.
- **Duration:** How long the goroutine has existed. Useful for finding bugs like goroutine leaks. For example, if we expect all network connections to be closed after a few minutes, what does it mean when we find a lot of netconn goroutines alive for hours?
Without looking at code, what can we learn about the above goroutine?
It was created only about a minute ago, is waiting for data over a network socket, and its goroutine number is quite large (61961905).
From the first dump (debug=1), we know its call stack is executed relatively frequently, and the large goroutine number combined with the short duration suggests that there have been tens of millions of these relatively short-lived goroutines. It's in a function called `pollWait` and its call history includes reading HTTP/2 frames from an encrypted network connection that uses TLS.
So, we can deduce that this goroutine is serving an HTTP/2 request! It's waiting on data from the client. What's more, we know that the goroutine that spawned it is not one of the first goroutines of the process because it also has a high number; finding that goroutine in the dump reveals that it was spawned to handle a new HTTP/2 stream during an existing request. By contrast, other goroutines with high numbers may be spawned by a low-numbered goroutine (such as 32), indicating a brand new connection fresh off an `Accept()` call from the socket.
Every program is different, but when debugging Caddy, these patterns tend to hold true.
Memory (or heap) profiles track heap allocations, which are the major consumers of memory on a system. Allocations are also a usual suspect for performance problems because allocating memory requires system calls, which can be slow.
In the example above, we have a single allocation made by `bufio.NewWriterSize()` but currently no live objects from this call stack.
Interestingly, we can infer from that call stack that the http2 package used a pooled 4 KB to write HTTP/2 frame(s) to the client. You'll often see pooled objects in Go memory profiles if hot paths have been optimized to reuse allocations. This reduces new allocations, and the heap profile can help you know if the pool is being used properly!
## CPU profiles
CPU profiles help you understand where the Go program is spending most of its scheduled time on the processor.
To download a CPU profile, make a request to `/debug/pprof/profile?seconds=N`, where N is the number of seconds over which you want to collect the profile. During CPU profile collection, program performance may be mildly impacted. (Other profiles have virtually no performance impact.)
You can use this command to examine any type of profile, not just CPU profiles. The principles are the same for other profiles and the concepts carry over.
This is something you can explore. Entering `help` gives you a list of commands and `o` will show you current options. And if you type `help <command>` you can get information about a specific command.
The top 10 consumers of the CPU were all in the Go runtime -- in particular, lots of garbage collection (remember that syscalls are used to free and allocate memory). This is a hint that we could reduce allocations to improve performance, and a heap profile would be worthwhile.
Well, it's clear that Prometheus metrics are another top consumer, but you'll notice that cumulatively, they amount to orders of magnitude less than GC above. The stark difference suggests that we should focus on reducing GC.
<asideclass="tip">
It's important to note that CPU profiles get their measurements from intermittent sampling, and samples will never be captured more frequently than the sampling rate, which is 10ms by default. That's why you won't see any cumulative time durations that are less than 10ms (they are likely less, but rounded up). For more specific timings, you can do an execution trace, which does not use sampling. (TODO: Add section about tracing.)
</aside>
Let's use `q` to quit this profile and use the same command on the heap profile:
```
(pprof) top
Showing nodes accounting for 22259.07kB, 81.30% of 27380.04kB total
Bingo. Nearly half of memory is allocated strictly for read and write buffers from our use of the bufio package. Thus, we can infer that optimizing our code to reduce buffering would be very beneficial. (The [associated patch in Caddy](https://github.com/caddyserver/caddy/pull/4978) does just that).
