A superb CPU profiler is price its weight in gold. Measuring efficiency in-situ normally means utilizing a sampling profile. They supply a variety of info whereas having very low overhead. In a concurrent system, nonetheless, it’s onerous to make use of the ensuing knowledge 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 info (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, nonetheless, a number of queries can execute on the identical time. Queries are executed in a distributed trend throughout the entire nodes, so which means that 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 particularly vital for prime QPS workloads and quick queries, which have extra coordination relative to their basic work. Concurrent execution can be vital for lowering head-of-line blocking and latency outliers; it prevents an occasional heavy question from blocking completion of the queries that comply with it.
We handle concurrency by breaking work into micro-tasks which might be run by a set set of thread swimming pools. This considerably reduces the necessity for locks, as a result of we will handle synchronization through job dependencies, and it additionally minimizes context switching overheads. Sadly, this micro-task structure makes it troublesome to profile particular person queries. Callchain samples (stack backtraces) may need come from any lively question, so the ensuing profile exhibits solely the sum of the CPU work.
Profiles that mix the entire lively queries are higher than nothing, however a variety of handbook experience is required to interpret the noisy outcomes. Trampoline histories allow us to assign a lot 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 instrument 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 could be very easy:
class DynamicLabel {
public:
DynamicLabel(std::string key, std::string worth);
~DynamicLabel();
template
std::invoke_result_t apply(Func&& func) const;
};
DynamicLabel::apply invokes func. Profile samples taken throughout that invocation can 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 vital 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 job whatever the sampling fee. In some circumstances our micro-tasks may be fairly micro, so it is crucial 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 often.
Let’s work by means of some methods we would 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 bounce straight to the vacation spot, go to the “Trampoline Histories” part.
Implementation Concepts
Thought #1: Resolve dynamic labels at pattern assortment time
The obvious technique 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 instrument. perf_event has many benefits over signal-based profilers (akin to gperftools). It has decrease bias, decrease skew, decrease overhead, entry to {hardware} efficiency counters, visibility into each userspace and kernel callchains, and the power to measure interference from different processes. These benefits come from its structure, by which system-wide profile samples are taken by the kernel and asynchronously handed to userspace by means of a lock-free ring buffer.
Though perf_event has a variety of benefits, we will’t use it for concept #1 as a result of it could actually’t learn arbitrary userspace knowledge at sampling time. eBPF profilers have an identical limitation.
Thought #2: File a perf pattern when the metadata modifications
If it’s not doable to tug 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 method 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 gradual to make use of on this trend for us. Thread pool overhead for us is about 110 nanoseconds per job. 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 resolution, the place we replace an eBPF map in apply after which modify an eBPF profiler like BCC to fetch the labels when sampling.
edit: eBPF can be utilized to tug from userspace when amassing a pattern, studying fsbase after which utilizing bpfprobelearnconsumer() to stroll a userspace knowledge construction that’s hooked up to a threadnative. When you have BPF permissions enabled in your manufacturing surroundings and are utilizing a BPF-based profiler then this different is usually a good one. The engineering and deployment points are extra complicated however the consequence doesn’t require in-process profile processing. Because of Jason Rahman for pointing this out.
Thought #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 seem strictly monotonic (a minimum of on the x86_64 or arm64 cases we would use), so the be part of could be actual. A name to clock_gettime utilizing the VDSO mechanism is quite a bit sooner 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 repeatedly 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.
Thought #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 write down the histories to disk in any respect.
The most typical approach to make use of Linux’s perf_event subsystem is through the perf command line instrument, however the entire deep kernel magic is on the market to any course of through the perf_event_open syscall. There are a variety of 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 competition, we might keep 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’d search the corresponding historical past utilizing the pattern’s timestamp.
This strategy has possible efficiency, however can we do higher?
Thought #5: Use the callchains to optimize the historical past of calls to `apply`
We are able to use the truth that apply exhibits up within the recorded callchains to scale back the historical past measurement. If we block inlining in order that we will discover DynamicLabel::apply within the name stacks, then we will use the backtrace to detect exit. Which means apply solely wants to write down the entry information, which file the time that an affiliation was created. Halving the variety of information halves the CPU and knowledge footprint (of the a part of the work that isn’t sampled).
This technique is one of the best one but, however we will do even higher! The historical past entry information 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, fairly than per-invocation. This optimization may be very efficient if we now have a number of variations of apply to search for within the name stack. This leads us to trampoline histories, the design that we now have applied and deployed.
Trampoline Histories
If the stack has sufficient info to search out the best 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 seem like this:
__attribute__((__noinline__))
void trampoline(std::move_only_function func) {
func();
asm unstable (""); // forestall tailcall optimization
}
Observe that we have to forestall compiler optimizations that might 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 scrub up and return. Together with padding that is 32 bytes of code.
C++ templates allow us to simply generate an entire household of trampolines, every of which has a novel handle.
utilizing Trampoline = __attribute__((__noinline__)) void (*)(
std::move_only_function);
constexpr size_t kNumTrampolines = ...;
template
__attribute__((__noinline__))
void trampoline(std::move_only_function func) {
func();
asm unstable (""); // forestall tailcall optimization
}
template
constexpr std::array makeTrampolines(
std::index_sequence) {
return {&trampoline...};
}
Trampoline getTrampoline(unsigned idx) {
static constexpr auto kTrampolines =
makeTrampolines(std::make_index_sequence{});
return kTrampolines.at(idx);
}
We’ve now received the entire low-level items we have to implement DynamicLabel:
- DynamicLabel building → discover a trampoline that isn’t presently 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, lookup the label within the trampoline’s historical past
Efficiency Impression
Our purpose is to make DynamicLabel::apply quick, in order that we will use it to wrap even small items of labor. We measured it by extending our present 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 exhibits 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 appears to be like like a digital methodology name to the CPU. That is extraordinarily widespread, so processor distributors have put a variety of 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 additional directions to be executed per loop. This is able to gradual 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 truly improve this system’s latency. Rockset is usually reminiscence certain when executing queries; the zero-latency consequence holds in our manufacturing surroundings as properly.
A Few Implementation Particulars
There are some things we have performed to enhance the ergonomics of our profile ecosystem:
- The perf.knowledge format emitted by
perfis optimized for CPU-efficient writing, not for simplicity or ease of use. Despite the fact that Rockset’sperf_event_open-based profiler pulls knowledge fromperf_event_open, we now have 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 power to filter on these tags, so it was straightforward 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 will likely be run after returning. That is particularly vital when utilizing inline frames, since neighboring directions are sometimes not from the identical supply perform.
- We rewrite
trampolinetotrampoline<0>in order that we now have the choice of ignoring the tags and rendering a daily flame graph. - When simplifying demangled constructor names, we use one thing like
Foo::copy_constructandFoo::move_constructfairly than simplifying each toFoo::Foo. Differentiating constructor sorts makes it a lot simpler to seek for pointless copies. (In the event you implement this be sure to can deal with demangled names with unbalanced<and>, akin tostd::enable_if.)4, void>::sort - We compile with
-fno-omit-frame-pointerand use body tips to construct our callchains, however some vital glibc capabilities likememcpyare written in meeting and don’t contact the stack in any respect. For these capabilities, 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 type of capabilities. That is a lot much less overhead than making an attempt to seize the whole 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 capacity 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 approach of encoding the lively work within the callchain, the place the present profiling infrastructure can simply seize it. By making the DynamicLabel ↔ trampoline binding comparatively long-lived (milliseconds, fairly than microseconds), the overhead of including the labels is saved extraordinarily low. The approach applies to any system that wishes to enhance sampled callchains with software state.
Rockset is hiring engineers in its Boston, San Mateo, London and Madrid places of work. Apply to open engineering positions at present.