Go Debugging and Analyzing Tools
The Go language has evolved fairly quickly in recent years, making it quite an attractive choice for many start-ups as well as big companies. Many companies are re-writing the performance-demanding architecture and business modules in Go. It is also a wonderful alternative if you want to lower your cost, with its very limited memory footprint and efficient multi-threading model, compared to, say, Java. A recent project done in my team, as an example, can replace 8 - 10 heavily provisioned Java batch servers with just one server that runs a Go app. The app only needs 2 cores and a minimum of 500 MB memory.
With all the powerful features, Go programs, however, can be difficult to write and to debug. It lacks some of the handy tools like Java's JMX or C's gdb. The language's ability to achieve extremly high concurrency (a Go app can easily scale up to hundreds or thousands of threads/goroutines) makes it even harder to debug multi-thread related issues, such as data race. Data race is almost impossible to avoid as the code grows bigger, there was race condition even in Go's standard library.
Here I'd like to share 3 very handy Go debugging and analyzing tools.
The first 2 are used for code analysis, and the 3rd one is a debugger very much like gdb. I use the first 2 tools before almost every major code check-in. And I will feed the executable into Delve if I see some weird behavior that might be related to bad logics. Let's take a look at them one by one.
Go Race Detector
The race detector is part of the Go tool chain and as its name indicates, used for locating "racy" codes in your program. The general introduction is in the official Go blog and here I will only focus on using it and code examples.
How to use
The race detector works at runtime. You need the compile flag "-race" to enable it since the Go compiler needs insert its own code into your source code. You can simply do:
Then the compiled binary will have the code necessary to detect race conditions.
The next step is to actually run the executable with real-world data. It's very important to simulate the real work load because it can only locate the "racy" code if race conditions are actually triggered by the code. I would use the data that can touch as many branches in your code as possible. Running a load test would be a great choice.
As the program is running, it will print warning messages to stderr in real-time whenever it detects a data race. The warning log has the code location and race type. Here is an example from the documentation:
The above log indicates there's a read-write race. If it detects a write-write race, then it will print something like
Write by goroutine 5, Previous write by goroutine 1.
A failure case
As I used it, I found out that it cannot detect every piece of "racy" code, especially the ones that seem to be well protected. Let's look at an example.
Suppose we're using the expvar package to expose some internal stats to an http port. The other threads can modify those stats at the same time.
The race detector failed to find the race condition in the above code. There is an obvious race there because I'm returning a pointer in
getStats() function. Although both the write and the read in
getStats() are protected, at the time of dereferencing the pointer, the object may not be protected.
The reason why it didn't detect the race is unknown to me. Probably because expvar's use of reflection (to dereference) masked the issue.
Do not enable "-race" compile flag in production, it will cause huge performance issues and sometimes can make your program 10 - 20 times slower.
Command vet is a tool used to run against your Go source code. You don't need to compile your code. It uses heuristics on source files to find errors not caught by compilers.
An essential difference between errors detected by vet and erros thrown by compiler is that compile errors are truly errors that will fail the build, but complains from vet may be a developer's intention or mistakes that won't fail the build. For example, copying a lock is a trigger for vet. It might be a programer's intention to do so, although not very possible, not recommended and very hacky in almost every case.
Mistakes that won't fail the build are much more often, such as violations of the
passing rules (which will only surface at runtime), unreachable code and failture to call the cancelation function from the context (its consequences can only happen at runtime as well) etc. A complete list of situations that will cause vet to complain can be found in the official Command vet documentation.
Before every code check-in, you can just run
go vet against any package, file or directory and then look at the report. It will help to improve your code quality a lot.
Go had been struggling for a long time to have its own debugger with quality. Some of its unique characteristics (like the userspace scheduler and "defer" syntax) make traditional debuggers unfeasible. Before Delve, Go programers can only borrow tools like gdb from the C family. But gdb is not designed for Go and it has many problems running Go code (for e.g, it crashes very easily). Later there is godebug, which inserts breakpoints and displays the program's current state in a very talented way. There is a detailed explaination on how the tool achieved this on the project's Github page. But basically, in order to use it, you need to change your source code to "guide" the tool, which is very bothering and not cool at all. Its features are very limited as well.
At last we have Delve. It works by parsing the various information out of a Go binary, cooperating with the OS (like the use of ptrace) and CPU to manipulate the program as it runs. If you're familiar with C, you can just regard it as Go's gdb.
How to use
The documentation has very detailed guides. Here I'm only going to introduce the using of it in Docker, since you should really use Docker and debug your app in a production-like environment, right?
First, build with the debug flag turned on (
-N -l). This is to prevent any compiler optimizations that may confuse the debugger:
Second, give container the ptrace privilege so that Delve can take control of your program. You probably also want to overwrite the image's entrypoint to a shell so that you can easily get into it and launch your app with Delve:
Third, launch Delve and debug your program in the container:
At last, after the 3rd step, Delve has attached a process to your program and taken control of it. Now you can set the breakpoints and run your program.
The first thing is not to enable flag
-N -l for any production build. It disables compiler optimization which will cause performance issues.
Another caveat is that although Delve is extremly powerful, don't rely on it for multi-threading related issues, like race conditions. There is nothing wrong with the tool itself, it's just when you're debugging a program, you just cannot simulate the high concurrency and very fast switches of goroutines in real world. And the weird bugs are usually related to high concurrency.
Recently we found a bug in one of our core libraries. None of the tools worked because of its high concurrency nature. How did we find it then? By reading the code with critical eyes.comments powered by Disqus social sharing buttons