Profiling Particular person Queries in a Concurrent System -

An excellent CPU profiler is price its weight in gold. Measuring efficiency in-situ normally means utilizing a sampling profile. They supply quite a lot of info whereas having very low overhead. In a concurrent system, nevertheless, it’s arduous to make use of the ensuing knowledge to extract high-level insights. Samples don’t embrace context like question IDs and application-level statistics; they present you what code was run, however not why.

This weblog introduces trampoline histories, a way Rockset has developed to effectively connect application-level info (question IDs) to the samples of a CPU profile. This lets us use profiles to know 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 vogue 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 clients have their very own devoted compute assets known as digital situations. Inside that devoted set of compute assets, nevertheless, a number of queries can execute on the similar time. Queries are executed in a distributed vogue throughout the entire nodes, so which means a number of queries are energetic on the similar 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 elementary work. Concurrent execution can also be vital 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 set set of thread swimming pools. This considerably reduces the necessity for locks, as a result of we will handle synchronization through process 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) might need come from any energetic question, so the ensuing profile reveals solely the sum of the CPU work.

Profiles that mix the entire energetic queries are higher than nothing, however quite a lot of 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 device 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 may have the label connected.

Every question wants just one DynamicLabel. Every time a micro-task from the question is run it’s invoked through DynamicLabel::apply.

Some 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 process whatever the sampling fee. In some instances our micro-tasks might be fairly micro, so it can be crucial that apply has very low overhead.

apply‘s efficiency is the first design constraint. DynamicLabel‘s different operations (development, destruction, and label lookup throughout sampling) occur orders of magnitude much less steadily.

Let’s work by means of some methods we would attempt to implement the DynamicLabel performance. We’ll consider and refine them with the aim of constructing apply as quick as potential. 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 method to affiliate utility metadata with a pattern is to place it there from the start. The profiler would search for dynamic labels on the similar time that it’s capturing the stack backtrace, bundling a replica of them with the callchain.

Rockset’s profiling makes use of Linux’s perf_event, the subsystem that powers the perf command line device. perf_event has many benefits over signal-based profilers (reminiscent of 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 quite a lot of benefits, we will’t use it for concept #1 as a result of it may well’t learn arbitrary userspace knowledge at sampling time. eBPF profilers have the same limitation.

Thought #2: File a perf pattern when the metadata adjustments

If it’s not potential to tug dynamic labels from userspace to the kernel at sampling time, then what about push? We may add an occasion to the profile each time that the thread→label mapping adjustments, 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 operate invocations, together with operate arguments. Sadly, uprobes are too gradual to make use of on this vogue for us. Thread pool overhead for us is about 110 nanoseconds per process. 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 gathering a pattern, studying fsbase after which utilizing bpfprobelearnconsumer() to stroll a userspace knowledge construction that’s connected to a threadnative. You probably have BPF permissions enabled in your manufacturing surroundings and are utilizing a BPF-based profiler then this various could be a good one. The engineering and deployment points are extra complicated however the consequence doesn’t require in-process profile processing. Due to Jason Rahman for pointing this out.

Thought #3: Merge profiles with a userspace label historical past

If it is too costly to file adjustments to the thread→label mapping within the kernel, what if we do it within the userspace? We may file a historical past of calls to DynamicLabel::apply, then be a part of it to the profile samples throughout post-processing. perf_event samples can embrace timestamps and Linux’s CLOCK_MONOTONIC clock has sufficient precision to seem strictly monotonic (at the very least on the x86_64 or arm64 situations we would use), so the be a part of could be actual. A name to clock_gettime utilizing the VDSO mechanism is loads sooner than a kernel transition, so the overhead could be a lot decrease than that for concept #2.

The problem with this method 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 a part of samples and label histories, the much less historical past we have to retailer. If we may be a 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 device, however the entire deep kernel magic is out there to any course of through the perf_event_open syscall. There are quite a lot of configuration choices (perf_event_open(2) is the longest manpage of any system name), however when you get it arrange you may learn profile samples from a lock-free ring buffer as quickly as they’re gathered by the kernel.

To keep away from rivalry, we may 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’d search the corresponding historical past utilizing the pattern’s timestamp.

This method has possible efficiency, however can we do higher?

Thought #5: Use the callchains to optimize the historical past of calls to `apply`

We will use the truth that apply reveals up within the recorded callchains to cut 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 that apply solely wants to jot 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’s not 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 specific label, so we solely have to make a file when the binding adjustments, moderately than per-invocation. This optimization might 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 info to search out the suitable DynamicLabel , then the one factor that apply must do is depart a body on the stack. Since there are a number of energetic labels, we’ll want a number of addresses.

A operate that instantly invokes one other operate is a trampoline. In C++ it’d seem like this:

__attribute__((__noinline__))
void trampoline(std::move_only_function<void()> func) {
    func();
    asm unstable (""); // stop tailcall optimization
}

Notice that we have to stop compiler optimizations that will trigger the operate 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 singular tackle.

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 unstable (""); // 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 the entire low-level items we have to implement DynamicLabel:

DynamicLabel development → discover a trampoline that’s not 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 tackle is present in a callchain, lookup the label within the trampoline’s historical past

Efficiency Influence

Our aim 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([&] { ++rely; }); });
    }
    // ~DynamicThreadPool waits for all duties
}
EXPECT_EQ(kNumTasks, rely);

Maybe surprisingly, this benchmark reveals zero efficiency affect 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 to be like a digital methodology name to the CPU. That is extraordinarily widespread, so processor distributors have put quite a lot 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 further directions to be executed per loop. This might 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, in order that they don’t really enhance this system’s latency. Rockset is mostly reminiscence certain when executing queries; the zero-latency consequence holds in our manufacturing surroundings as effectively.

A Few Implementation Particulars

There are some things we have carried out to enhance the ergonomics of our profile ecosystem:

The perf.knowledge format emitted by perf is optimized for CPU-efficient writing, not for simplicity or ease of use. Regardless that Rockset’s perf_event_open-based profiler pulls knowledge from perf_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 the pprof visualizer already has the power to filter on these tags, so it was simple so as to add and use the data from DynamicLabel.
We subtract one from most callchain addresses earlier than symbolizing, as a result of the return tackle is definitely the primary instruction that might be run after returning. That is particularly vital when utilizing inline frames, since neighboring directions are sometimes not from the identical supply operate.
We rewrite trampoline<i> to trampoline<0> in order that we’ve the choice of ignoring the tags and rendering a daily flame graph.
When simplifying demangled constructor names, we use one thing like Foo::copy_construct and Foo::move_construct moderately than simplifying each to Foo::Foo. Differentiating constructor sorts makes it a lot simpler to seek for pointless copies. (If you happen to implement this be sure you can deal with demangled names with unbalanced < and >, reminiscent of std::enable_if<sizeof(Foo) > 4, void>::sort.)
We compile with -fno-omit-frame-pointer and use body tips that could construct our callchains, however some vital glibc features like memcpy are written in meeting and don’t contact the stack in any respect. For these features, the backtrace captured by perf_event_open‘s PERF_SAMPLE_CALLCHAIN mode omits the operate that calls the meeting operate. We discover it through the use of PERF_SAMPLE_STACK_USER to 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 the whole backtrace with PERF_SAMPLE_STACK_USER.

Conclusion

Dynamic labels let Rockset tag CPU profile samples with the question whose work was energetic 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 energetic work within the callchain, the place the prevailing profiling infrastructure can simply seize it. By making the DynamicLabel ↔ trampoline binding comparatively long-lived (milliseconds, moderately 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 utility state.

Rockset is hiring engineers in its Boston, San Mateo, London and Madrid workplaces. Apply to open engineering positions immediately.

Profiling Particular person Queries in a Concurrent System