As mentioned above, there are two main memory analysis strategies with pprof.
One is around looking at the current allocations (bytes or object count), called inuse.
The other is looking at all the allocated bytes or object count throughout the run-time of the program, called alloc.
This means regardless if it was gc-ed, a summation of everything sampled.
This is a good place to reiterate that the heap profile is a sampling of memory allocations.
pprof behind the scenes is using the runtime.
MemProfile function, which by default collects allocation information on each 512KB of allocated bytes.
It is possible to change MemProfile to collect information on all objects.
Note that most likely, this will slow down your application.
This means that by default, there is some chance that a problem may happen with smaller objects that will slip under pprof’s radar.
For a large codebase / long-running program, this is not an issue.
Once we collected the profile file, it is time to load it into the interactive console pprof offers.
Do so by running:> go tool pprof heap.
outLet's observe the information displayedType: inuse_spaceTime: Jan 22, 2019 at 1:08pm (IST)Entering interactive mode (type "help" for commands, "o" for options)(pprof)The important thing to note here is the Type: inuse_space .
This means we are looking at allocation data of a specific moment (when we captured the profile).
The type is the configuration value of sample_index, and the possible values are:inuse_space — amount of memory allocated and not released yetinuse_object s— amount of objects allocated and not released yetalloc_space — total amount of memory allocated (regardless of released)alloc_objects — total amount of objects allocated (regardless of released)Now type top in the interactive, the output will be the top memory consumersWe can see a line telling us about Dropped Nodes, this means they are filtered out.
A node is an object entry, or a ‘node’ in the tree.
Dropping nodes is a good idea to reduce some noise, but sometimes it may hide the root cause of a memory issue.
We will see an example of that as we continue our investigation.
If you want to include all data of the profile, add the -nodefraction=0 option when running pprof or type nodefraction=0 in the interactive.
In the outputted list we can see two values, flat and cum.
flat means that the memory allocated by this function and is held by that functioncum means that the memory was allocated by this function or function that it called down the stackThis information alone can sometimes help us understand if there is a problem.
Take for example a case where a function is responsible of allocating a lot of memory but is not holding it.
This would mean that some other object is pointing to that memory and keeping it allocated, meaning we may have a system design issue or a bug.
Another neat trick about top in the interactive window is that it is actually running top10.
The top command supports topN format where N is the number of entries you want to see.
In the case pasted above, typing top70 for example, would output all nodes.
VisualizationsWhile the topN gives a textual list, there are several very useful visualization options that come with pprof.
It is possible to type png or gif and many more (see go tool pprof -help for a full list).
On our system, the default visual output looks something like:This may be intimidating at first, but it is the visualization of memory allocation flows (according to stack traces) in a program.
Reading the graph is not as complicated as it looks.
A white square with a number shows allocated space (and the cumulative of how much memory it’s taking right now on the edge of the graph), and each wider rectangle shows the allocating function.
Note that in the above image, I took a png off a inuse_space execution mode.
Many times you should also take a look at inuse_objects as well, as it can assist with finding allocation issues.
Digging deeper, finding a root causeSo far, we were able to understand what is allocating memory in our application during runtime.
This helps us get a feeling of how our program behaves (or misbehaves).
In our case, we could see that memory is retained by membuffers, which is our data serialization library.
This does not mean that we have a memory leak at that code segment, it means that the memory is being retained by that function.
It is important to understand how to read the graph, and the pprof output in general .
In this case, understanding that when we serialize data, meaning that we allocate memory to structs and primitive objects (int, string), it is never released.
Jumping to conclusions or misinterpreting the graph, we could have assumed that one of the nodes on the path to serialization is responsible to retaining the memory, for example:subset of the graphSomewhere in the chain we can see our logging library, responsible for >50MB of allocated memory.
This is memory which is allocated by functions called by our logger.
Thinking it through, this is actually expected.
The logger causes memory allocations as it needs to serialize data for outputting it to the log and thus it is causing memory allocations in the process.
We can also see that down the allocation path, the memory is only retained by serialization and nothing else.
Additionally, the amount of memory retained by the logger is about 30% of the total.
The above tells us that, most likely, the problem is not with the logger.
If it was 100%, or something close to it, then we should have been looking there — but it’s not.
What it could mean is that something is being logged that shouldn’t be, but it is not a memory leak by the logger.
This is a good time to introduce another pprof command called list.
It accepts a regular expression which will be a filter of what to list.
The ‘list’ is in actual the annotated source code related to the allocation.
In the context of the logger which we are looking into, we will execute list RequestNew as we would like to see the calls made to the logger.
These calls are coming from two functions which happen to begin with the same prefix.
We can see that the allocations made are sitting in the cum column, meaning the memory allocated is retained down the call stack.
This correlates to what the graph also shows.
At that point it is easy to see that the reason the logger was allocating the memory is because we sent it the entire ‘block’ object.
It needed to serialize some parts of it at the very least (our objects are membuffer objects, which always implement some String() function).
Is it a useful log message, or good practice?.Probably not, but it is not a memory leak, not at the logger end or the code which called the logger.
list can find the source code when searching for it under your GOPATH environment.
In cases where the root it is searching for does not match, which depends on your build machine, you can use the -trim_path option.
This will assist with fixing it and letting you see the annotated source code.
Remember to set your git to the right commit which was running when the heap profile was captured.
So why is memory retained?The background to this investigation was the suspicion that we have a problem — a memory leak.
We came to that notion as we saw memory consumption was higher than what we would expect the system to need.
On top of that, we saw it ever increasing, which was another strong indicator for ‘there is a problem here’.
At this point, in the case of Java or .
Net, we would open some ‘gc roots’ analysis or profiler and get to the actual object which is referencing to that data, and is creating the leak.
As explained, this is not exactly possible with Go, both because of a tooling issue but also because of Go’s low level memory representation.
Without going into details, we do not think Go retains which object is stored at which address (except for pointers maybe).
This means that in actual, understanding which memory address represents which member of your object (struct) will require some sort of mapping to the output of a heap profile.
Talking theory, this could mean that before taking a full core dump, one should also take a heap profile so the addresses can be mapped to the allocating line and file and thus the object represented in the memory.
At this point, because we are familiar with our system, it was easy to understand this is not a bug anymore.
It was (almost) by design.
But let’s continue to explore how to get the information from the tools (pprof) to find the root cause.
When setting nodefraction=0 we will get to see the entire map of the allocated objects, including the smaller ones.
Let’s look at the output:memory visualization with nodefraction=0We have two new subtrees.
Reminding again, pprof heap profile is sampling memory allocations.
For our system that works — we are not missing any important information.
The longer new tree, in green, which is completely disconnected from the rest of the system is the test runner, it is not interesting.
system was configured to “leak” ????The shorter one, in blue, which has an edge connecting it to the entire system is inMemoryBlockPersistance .
That name also explains the ‘leak’ we imagined we have.
This is the data backend, which is storing all data in memory and not persisting to disk.
Whats nice to note is that we could see immediately that it is holding two large objects.
Why two?.Because we can see the object is of size 1.
28MB and the function is retaining 2.
57MB, meaning two of them.
The problem is well understood at this point.
We could have used delve (the debugger) to see that this is the array holding all blocks for the in-memory persistence driver we have.
So what could we fix?Well, that sucked, it was a human error.
While the process was educating (and sharing is caring), we did not get any better, or did we?There was one thing that still ‘smelled’ about this heap information.
The deserialized data was taking up too much memory, why 142MB for something that should be taking substantially less? .
pprof can answer that — actually, it exists to answer such questions exactly.
To look into the annotated source code of the function, we will run list lazy.
We use lazy, as the function name we are looking for is lazyCalcOffsets() and we know no other functions in our code begin with lazy.
Typing list lazyCalcOffsets would work as well of course.
We can see two interesting pieces of information.
Again, remember that pprof heap profile samples information about allocations.
We can see that both the flat and the cum numbers are the same.
This indicates that the memory allocated is also retained by these allocation points.
Next, we can see that the make() is taking some memory.
That makes sense, it is the pointer to the data structure.
Yet we also see the assignment at line 43 is taking up memory, meaning it creates an allocation.
This educated us about maps, where an assignment to a map is not a straightforward variable assignment.
This article goes into great detail on how map works.
In short a map has an overhead, and the more elements the bigger this overhead is going to ‘cost’ when comparing to a slice.
The following should be taken with a grain of salt: It would be okay to say that using a map[int]T, when the data is not sparse or can be converted to sequential indices, should usually be attempted with a slice implementation if memory consumption is a relevant consideration.
Yet, a large slice, when expanded, might slow down an operation, where in a map this slowdown will be negligible.
There is no magic formula for optimizations.
In the code above, after checking how we used that map, we realized that while we imagined it to be a sparse array, it came out as not so sparse.
This matches the above argument and we could immediately see that a small refactor of changing the map to a slice is actually possible, and might make that code more memory efficient.
So we changed it to:line 10/34 from above is 5 hereAs simple as that, instead of using a map we are now using a slice.
Because of the way we receive the data which is lazy loaded into it, and how we later access that data, other than these two lines and the struct holding that data, no other code change was required.
What did it do to the memory consumption?Let’s look at the benchcmp for just a couple of testsThe read tests initialize the data structure, which creates the allocations.
We can see that runtime improved by ~30%, allocations are down by 50% and memory consumption by >90% (!)Since the map, now-slice, was never filled with a lot of items, the numbers pretty much show what we will see in production.
It depends on the data entropy, but there may be cases where both allocations and memory consumption improvements would have been even greater.
Looking at pprof again, and taking a heap profile from the same test we will see that now the memory consumption is in fact down by ~90%.
The takeaway will be that for smaller data sets, you shouldn’t use maps where slices would suffice, as maps have a large overhead.
Full core dumpAs mentioned, this is where we see the biggest limitation with tooling right now.
When we were investigating this issue we got obsessed with being able to get to the root object, without much success.
Go evolves over time at a great pace, but that evolution comes with a price in the case of the full dump or memory representation.
The full heap dump format, as it changes, is not backwards compatible.
The latest version described here and to write a full heap dump, you can use debug.
Albeit right now we do not find ourselves ‘stuck’ because there is no good solution for exploring full dumps.
pprof answered all our questions until now.
Do note, the internet remembers a lot of information which is no longer relevant.
Here are some things you should ignore if you are going to try and open a full dump yourself, as of go1.
11:There is no way to open and debug a full core dump on MacOS, only Linux.
The tools at https://github.
com/randall77/hprof are for Go1.
3, there exists a fork for 1.
7+ but it does not work properly either (incomplete).
viewcore at https://github.
com/golang/debug/tree/master/cmd/viewcore does not really compile.
It is easy enough to fix (packages in the internal are pointing to golang.
org and not github.
com), but, it does not work either, not on MacOS, maybe on Linux.
com/randall77/corelib fails on MacOSpprof UIOne last detail to be aware of when it comes to pprof, is its UI feature.
It can save a lot of time when beginning an investigation into any issue relating to a profile taken with pprof.
go tool pprof -http=:8080 heap.
outAt that point it should open the web browser.
If it does not then browse to the port you set it to.
It enables you to change the options and get the visual feedback much faster than you can from the command line.
A very useful way to consume the information.
The UI actually got me familiar with the flame graphs, which expose culprit areas of the code very quickly.
ConclusionGo is an exciting language with a very rich toolset, there is a lot more you can do with pprof.
This post does not touch CPU profiling at all, for example.
Some other good reads:https://rakyll.
org/archive/ — I believe this to be one of the go contributors around performance monitoring, a lot of good posts at her bloghttps://github.
com/google/gops — written by JBD (who runs rakyll.
org), this tool warrants its own blog post.
com/@cep21/using-go-1-10-new-trace-features-to-debug-an-integration-test-1dc39e4e812d — go tool trace which is around CPU profiling, this is a great post about that profiling feature.