Profiling Particular person Queries in a Concurrent System

An excellent CPU profiler is price its weight in gold. Measuring efficiency in-situ often means utilizing a sampling profile. They supply a whole lot of data whereas having very low overhead. In a concurrent system, nevertheless, it’s onerous to make use of the ensuing information 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 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 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. Nearly all 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 vogue 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 attempting 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 decreasing 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 troublesome to profile particular person queries. Callchain samples (stack backtraces) might need come from any lively question, so the ensuing profile reveals solely the sum of the CPU work.

Profiles that mix the entire lively queries are higher than nothing, however a whole 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. This can be 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 can have the label hooked up.

Every question wants just one DynamicLabel. Each 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 activity whatever the sampling fee. In some circumstances our micro-tasks may 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 (building, destruction, and label lookup throughout sampling) occur orders of magnitude much less ceaselessly.

Let’s work by some methods we’d attempt to implement the DynamicLabel performance. We’ll consider and refine them with the objective of creating apply as quick as potential. If you wish to skip the journey and soar straight to the vacation spot, go to the “Trampoline Histories” part.

Implementation Concepts

Concept #1: Resolve dynamic labels at pattern assortment time

The obvious approach to affiliate utility 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 device. perf_event has many benefits over signal-based profilers (resembling 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, during 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 a whole lot of benefits, we are able to’t use it for concept #1 as a result of it could’t learn arbitrary userspace information at sampling time. eBPF profilers have an analogous limitation.

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

If it’s not potential to drag 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 technique to do that can be to make use of perf uprobes. Userspace probes can file operate invocations, together with operate arguments. Sadly, uprobes are too sluggish to make use of on this vogue 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.

edit: eBPF can be utilized to drag from userspace when gathering a pattern, studying fsbase after which utilizing bpfprobelearnperson() to stroll a userspace information construction that’s hooked up to a threadnative. In case you have BPF permissions enabled in your manufacturing atmosphere 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 end result doesn’t require in-process profile processing. Due to Jason Rahman for pointing this out.

Concept #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 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 (no less than on the x86_64 or arm64 cases we’d use), so the be part of can be actual. A name to clock_gettime utilizing the VDSO mechanism is loads quicker than a kernel transition, so the overhead can be a lot decrease than that for concept #2.

The problem with this strategy is the information footprint. DynamicLabel histories can 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 attempting to persist a historical past of each micro-task invocation would rapidly 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 may be part of the samples and the historical past in near-realtime (maybe each second) then we wouldn’t want to put in writing 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 offered to any course of through the perf_event_open syscall. There are a whole lot of configuration choices (perf_event_open(2) is the longest manpage of any system name), however when you get it arrange you possibly can learn profile samples from a lock-free ring buffer as quickly as they’re gathered by the kernel.

To keep away from competition, we may 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 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 will 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 that apply solely wants to put in writing the entry information, which file the time that an affiliation was created. Halving the variety of information halves the CPU and information footprint (of the a part of the work that’s not sampled).

This technique is one of the best one but, however we are able to 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, relatively 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 carried out and deployed.

Trampoline Histories

If the stack has sufficient data to seek out the proper 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 operate that instantly invokes one other operate is a trampoline. In C++ it would appear to be this:

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

Be aware that we have to forestall compiler optimizations that may trigger the operate to not be current within the stack, specifically inlining and tailcall elimination.

The trampoline compiles to solely 5 directions, 2 to arrange the body pointer, 1 to invoke func(), and a couple 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 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 risky (""); // forestall 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 obtained the entire low-level items we have to implement DynamicLabel:

• DynamicLabel building → 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, search for the label within the trampoline’s historical past

Efficiency Impression

Our objective 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 impression from the additional degree 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 frequent, so processor distributors have put a whole 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 is able to 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 improve this system’s latency. Rockset is usually reminiscence certain when executing queries; the zero-latency end result holds in our manufacturing atmosphere as nicely.

A Few Implementation Particulars

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

• The perf.information 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 information from perf_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 the pprof visualizer already has the power to filter on these tags, so it was straightforward 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 will probably 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 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_construct and Foo::move_construct relatively than simplifying each to Foo::Foo. Differentiating constructor varieties makes it a lot simpler to seek for pointless copies. (In the event you implement this ensure you can deal with demangled names with unbalanced < and >, resembling std::enable_if<sizeof(Foo) > 4, void>::sort.)
• We compile with -fno-omit-frame-pointer and use body tips to construct our callchains, however some vital glibc capabilities like memcpy are written in meeting and don’t contact the stack in any respect. For these capabilities, 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 capabilities. That is a lot much less overhead than attempting to seize the complete backtrace with PERF_SAMPLE_STACK_USER.

Conclusion

Dynamic labels let Rockset tag CPU profile samples with the question whose work was lively at that second. This capability lets us use profiles to get insights about particular person queries, although 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, relatively than microseconds), the overhead of including the labels is stored extraordinarily low. The method applies to any system that wishes 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 as we speak.