A great CPU profiler is price its weight in gold. Measuring efficiency in-situ normally means utilizing a sampling profile. They supply numerous data whereas having very low overhead. In a concurrent system, nevertheless, it’s arduous to make use of the ensuing information to extract high-level insights. Samples don’t embody context like question IDs and application-level statistics; they present you what code was run, however not why.
This weblog introduces trampoline histories, a method Rockset has developed to effectively connect application-level data (question IDs) to the samples of a CPU profile. This lets us use profiles to grasp the efficiency of particular person queries, even when a number of queries are executing concurrently throughout the identical set of employee threads.
Primer on Rockset
Rockset is a cloud-native search and analytics database. SQL queries from a buyer are executed in a distributed trend throughout a set of servers within the cloud. We use inverted indexes, approximate vector indexes, and columnar layouts to effectively execute queries, whereas additionally processing streaming updates. The vast majority of Rockset’s performance-critical code is C++.
Most Rockset prospects have their very own devoted compute assets known as digital cases. Inside that devoted set of compute assets, nevertheless, a number of queries can execute on the identical time. Queries are executed in a distributed trend throughout all the nodes, so because of this a number of queries are lively on the identical time in the identical course of. This concurrent question execution poses a problem when making an attempt to measure efficiency.
Concurrent question processing improves utilization by permitting computation, I/O, and communication to be overlapped. This overlapping is very necessary for prime QPS workloads and quick queries, which have extra coordination relative to their basic work. Concurrent execution can also be necessary for lowering head-of-line blocking and latency outliers; it prevents an occasional heavy question from blocking completion of the queries that observe it.
We handle concurrency by breaking work into micro-tasks which might be run by a hard and fast set of thread swimming pools. This considerably reduces the necessity for locks, as a result of we are able to handle synchronization through activity dependencies, and it additionally minimizes context switching overheads. Sadly, this micro-task structure makes it tough to profile particular person queries. Callchain samples (stack backtraces) may need come from any lively question, so the ensuing profile reveals solely the sum of the CPU work.
Profiles that mix all the lively queries are higher than nothing, however numerous guide experience is required to interpret the noisy outcomes. Trampoline histories allow us to assign many of the CPU work in our execution engine to particular person question IDs, each for steady profiles and on-demand profiles. It is a very highly effective software when tuning queries or debugging anomalies.
DynamicLabel
The API we’ve constructed for including application-level metadata to the CPU samples known as DynamicLabel. Its public interface may be very easy:
class DynamicLabel {
public:
DynamicLabel(std::string key, std::string worth);
~DynamicLabel();
template <typename Func>
std::invoke_result_t<Func> apply(Func&& func) const;
};
DynamicLabel::apply invokes func. Profile samples taken throughout that invocation could have the label hooked up.
Every question wants just one DynamicLabel. Every time a micro-task from the question is run it’s invoked through DynamicLabel::apply.
One of the crucial necessary properties of sampling profilers is that their overhead is proportional to their sampling fee; that is what lets their overhead be made arbitrarily small. In distinction, DynamicLabel::apply should do some work for each activity whatever the sampling fee. In some circumstances our micro-tasks may be fairly micro, so it will be important that apply has very low overhead.
apply‘s efficiency is the first design constraint. DynamicLabel‘s different operations (building, destruction, and label lookup throughout sampling) occur orders of magnitude much less continuously.
Let’s work by some methods we’d attempt to implement the DynamicLabel performance. We’ll consider and refine them with the purpose of constructing apply as quick as doable. If you wish to skip the journey and leap straight to the vacation spot, go to the “Trampoline Histories” part.
Implementation Concepts
Concept #1: Resolve dynamic labels at pattern assortment time
The obvious strategy to affiliate software metadata with a pattern is to place it there from the start. The profiler would search for dynamic labels on the identical time that it’s capturing the stack backtrace, bundling a duplicate of them with the callchain.
Rockset’s profiling makes use of Linux’s perf_event, the subsystem that powers the perf command line software. perf_event has many benefits over signal-based profilers (equivalent to gperftools). It has decrease bias, decrease skew, decrease overhead, entry to {hardware} efficiency counters, visibility into each userspace and kernel callchains, and the flexibility to measure interference from different processes. These benefits come from its structure, through which system-wide profile samples are taken by the kernel and asynchronously handed to userspace by a lock-free ring buffer.
Though perf_event has numerous benefits, we are able to’t use it for concept #1 as a result of it will possibly’t learn arbitrary userspace information at sampling time. eBPF profilers have an analogous limitation.
Concept #2: Report a perf pattern when the metadata modifications
If it’s not doable to drag dynamic labels from userspace to the kernel at sampling time, then what about push? We might add an occasion to the profile each time that the thread→label mapping modifications, then post-process the profiles to match up the labels.
A technique to do that could be to make use of perf uprobes. Userspace probes can file perform invocations, together with perform arguments. Sadly, uprobes are too sluggish to make use of on this trend for us. Thread pool overhead for us is about 110 nanoseconds per activity. Even a single crossing from the userspace into the kernel (uprobe or syscall) would multiply this overhead.
Avoiding syscalls throughout DynamicLabel::apply additionally prevents an eBPF answer, the place we replace an eBPF map in apply after which modify an eBPF profiler like BCC to fetch the labels when sampling.
Concept #3: Merge profiles with a userspace label historical past
If it is too costly to file modifications to the thread→label mapping within the kernel, what if we do it within the userspace? We might file a historical past of calls to DynamicLabel::apply, then be part of it to the profile samples throughout post-processing. perf_event samples can embody timestamps and Linux’s CLOCK_MONOTONIC clock has sufficient precision to look strictly monotonic (not less than on the x86_64 or arm64 cases we’d use), so the be part of could be actual. A name to clock_gettime utilizing the VDSO mechanism is quite a bit quicker than a kernel transition, so the overhead could be a lot decrease than that for concept #2.
The problem with this strategy is the info footprint. DynamicLabel histories could be a number of orders of magnitude bigger than the profiles themselves, even after making use of some easy compression. Profiling is enabled constantly on all of our servers at a low sampling fee, so making an attempt to persist a historical past of each micro-task invocation would shortly overload our monitoring infrastructure.
Concept #4: In-memory historical past merging
The sooner we be part of samples and label histories, the much less historical past we have to retailer. If we might be part of the samples and the historical past in near-realtime (maybe each second) then we wouldn’t want to jot down the histories to disk in any respect.
The commonest method to make use of Linux’s perf_event subsystem is through the perf command line software, however all the deep kernel magic is on the market to any course of through the perf_event_open syscall. There are numerous configuration choices (perf_event_open(2) is the longest manpage of any system name), however when you get it arrange you’ll be able to learn profile samples from a lock-free ring buffer as quickly as they’re gathered by the kernel.
To keep away from rivalry, we might preserve the historical past as a set of thread-local queues that file the timestamp of each DynamicLabel::apply entry and exit. For every pattern we might search the corresponding historical past utilizing the pattern’s timestamp.
This strategy has possible efficiency, however can we do higher?
Concept #5: Use the callchains to optimize the historical past of calls to `apply`
We are able to use the truth that apply reveals up within the recorded callchains to scale back the historical past measurement. If we block inlining in order that we are able to discover DynamicLabel::apply within the name stacks, then we are able to use the backtrace to detect exit. Which means apply solely wants to jot down the entry data, which file the time that an affiliation was created. Halving the variety of data halves the CPU and information footprint (of the a part of the work that isn’t sampled).
This technique is the very best one but, however we are able to do even higher! The historical past entry data a variety of time for which apply was certain to a selected label, so we solely have to make a file when the binding modifications, somewhat than per-invocation. This optimization may be very efficient if we’ve a number of variations of apply to search for within the name stack. This leads us to trampoline histories, the design that we’ve carried out and deployed.
Trampoline Histories
If the stack has sufficient data to search out the precise DynamicLabel , then the one factor that apply must do is depart a body on the stack. Since there are a number of lively labels, we’ll want a number of addresses.
A perform that instantly invokes one other perform is a trampoline. In C++ it would appear to be this:
__attribute__((__noinline__))
void trampoline(std::move_only_function<void()> func) {
func();
asm risky (""); // stop tailcall optimization
}
Word that we have to stop compiler optimizations that may trigger the perform to not be current within the stack, particularly inlining and tailcall elimination.
The trampoline compiles to solely 5 directions, 2 to arrange the body pointer, 1 to invoke func(), and a pair of to wash up and return. Together with padding that is 32 bytes of code.
C++ templates allow us to simply generate a complete household of trampolines, every of which has a novel handle.
utilizing Trampoline = __attribute__((__noinline__)) void (*)(
std::move_only_function<void()>);
constexpr size_t kNumTrampolines = ...;
template <size_t N>
__attribute__((__noinline__))
void trampoline(std::move_only_function<void()> func) {
func();
asm risky (""); // stop tailcall optimization
}
template <size_t... Is>
constexpr std::array<Trampoline, sizeof...(Is)> makeTrampolines(
std::index_sequence<Is...>) {
return {&trampoline<Is>...};
}
Trampoline getTrampoline(unsigned idx) {
static constexpr auto kTrampolines =
makeTrampolines(std::make_index_sequence<kNumTrampolines>{});
return kTrampolines.at(idx);
}
We’ve now received all the low-level items we have to implement DynamicLabel:
- DynamicLabel building → discover a trampoline that isn’t at present in use, append the label and present timestamp to that trampoline’s historical past
- DynamicLabel::apply → invoke the code utilizing the trampoline
- DynamicLabel destruction → return the trampoline to a pool of unused trampolines
- Stack body symbolization → if the trampoline’s handle is present in a callchain, search for the label within the trampoline’s historical past
Efficiency Affect
Our purpose is to make DynamicLabel::apply quick, in order that we are able to use it to wrap even small items of labor. We measured it by extending our current dynamic thread pool microbenchmark, including a layer of indirection through apply.
{
DynamicThreadPool executor({.maxThreads = 1});
for (size_t i = 0; i < kNumTasks; ++i) {
executor.add([&]() {
label.apply([&] { ++depend; }); });
}
// ~DynamicThreadPool waits for all duties
}
EXPECT_EQ(kNumTasks, depend);
Maybe surprisingly, this benchmark reveals zero efficiency influence from the additional stage of indirection, when measured utilizing both wall clock time or cycle counts. How can this be?
It seems we’re benefiting from a few years of analysis into department prediction for oblique jumps. The within of our trampoline seems like a digital technique name to the CPU. That is extraordinarily widespread, so processor distributors have put numerous effort into optimizing it.
If we use perf to measure the variety of directions within the benchmark we observe that including label.apply causes about three dozen further directions to be executed per loop. This might sluggish issues down if the CPU was front-end certain or if the vacation spot was unpredictable, however on this case we’re reminiscence certain. There are many execution assets for the additional directions, so that they don’t really enhance this system’s latency. Rockset is mostly reminiscence certain when executing queries; the zero-latency end result holds in our manufacturing surroundings as properly.
A Few Implementation Particulars
There are some things we have achieved to enhance the ergonomics of our profile ecosystem:
- The perf.information format emitted by
perfis optimized for CPU-efficient writing, not for simplicity or ease of use. Though Rockset’sperf_event_open-based profiler pulls information fromperf_event_open, we’ve chosen to emit the identical protobuf-based pprof format utilized by gperftools. Importantly, the pprof format helps arbitrary labels on samples and thepprofvisualizer already has the flexibility to filter on these tags, so it was simple so as to add and use the data fromDynamicLabel. - We subtract one from most callchain addresses earlier than symbolizing, as a result of the return handle is definitely the primary instruction that shall be run after returning. That is particularly necessary when utilizing inline frames, since neighboring directions are sometimes not from the identical supply perform.
- We rewrite
trampoline<i>totrampoline<0>in order that we’ve the choice of ignoring the tags and rendering an everyday flame graph. - When simplifying demangled constructor names, we use one thing like
Foo::copy_constructandFoo::move_constructsomewhat than simplifying each toFoo::Foo. Differentiating constructor varieties makes it a lot simpler to seek for pointless copies. (In case you implement this ensure you can deal with demangled names with unbalanced<and>, equivalent tostd::enable_if<sizeof(Foo) > 4, void>::kind.) - We compile with
-fno-omit-frame-pointerand use body tips to construct our callchains, however some necessary glibc features likememcpyare written in meeting and don’t contact the stack in any respect. For these features, the backtrace captured byperf_event_open‘sPERF_SAMPLE_CALLCHAINmode omits the perform that calls the meeting perform. We discover it through the use ofPERF_SAMPLE_STACK_USERto file the highest 8 bytes of the stack, splicing it into the callchain when the leaf is in a kind of features. That is a lot much less overhead than making an attempt to seize all the backtrace withPERF_SAMPLE_STACK_USER.
Conclusion
Dynamic labels let Rockset tag CPU profile samples with the question whose work was lively at that second. This means lets us use profiles to get insights about particular person queries, though Rockset makes use of concurrent question execution to enhance CPU utilization.
Trampoline histories are a method of encoding the lively work within the callchain, the place the prevailing profiling infrastructure can simply seize it. By making the DynamicLabel ↔ trampoline binding comparatively long-lived (milliseconds, somewhat than microseconds), the overhead of including the labels is stored extraordinarily low. The method applies to any system that desires to enhance sampled callchains with software state.
Rockset is hiring engineers in its Boston, San Mateo, London and Madrid workplaces. Apply to open engineering positions immediately.