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20220602_fine_grained_cpu_attribution.md

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  • Feature Name: Fine-grained CPU attribution
  • Status: accepted
  • Start Date: 2022-06-02
  • Authors: Irfan Sharif, Austen McClernon
  • RFC PR: #82356
  • Cockroach Issue: N/A

Summary

We propose using a patched Go runtime to track CPU use at the level of individual goroutines, using the primitive to derive an accurate resource consumption signal in various CRDB subsystems. The compiler, language, and tooling will remain the same.

NOTE: This RFC started off as an internal gdoc (grep for "Fine-grained CPU attribution" with valuable commentary and additional links).

Motivation

CRDB lacks primitives to attribute CPU usage to specific scopes (ranges, tenants, sessions, statements) or processes (pebble compactions, snapshot generation). For subsystems that rely on such attribution, we use proxy signals like '# of batch requests' or 'size of read/write request' as observed by a range/replica or a given tenant. With respect to modeling CPU usage, this is:

  • Inaccurate: two proportions of the signal can translate to different proportions of actual CPU usage;
  • Imprecise: repeated equal measurements of the signal can correspond to varied actual CPU usage.

The inaccuracy and imprecision are due to:

  • Different batch requests having different compositions with respect to the underlying requests;
  • Different request types varying in how much CPU activity they incur;
  • Requests being variadic in their size and costing accordingly.

This can lead to suboptimal decisions (placement and forecasting in the allocator, resource control in the per-store tenant rate limiter), and is difficult to reason about. The latter also becomes important as we build visualizations for CRDB internal state for end-users.

Our proxy signals don’t generalize to other subsystems, some of which consequently are CPU unaware. Changefeed processor placement for e.g. is agnostic to the CPU usage driven by the processors themselves, and can lead to CPU hotspots and poor cluster-wide resource utilization. It's difficult today to answer how much cluster-wide CPU usage a single statement execution drove, or what % of CPU on a given node is due to activity on a specific index, or driven by a specific tenant.

Design

The time spent per-goroutine in the running state (henceforth "CPU time") is an accurate and precise measure for CPU usage. This is not currently tracked by the Go runtime (though there's an issue upstream); we propose doing so with the following patch:

diff --git a/src/runtime/runtime2.go b/src/runtime/runtime2.go
index 1e4f872726..ced56dc4f6 100644
--- a/src/runtime/runtime2.go
+++ b/src/runtime/runtime2.go
@@ -488,6 +487,9 @@ type g struct {
     labels         unsafe.Pointer // profiler labels
     timer          *timer         // cached timer for time.Sleep
     selectDone     uint32         // are we participating in a select and did someone win the race?
+    lastsched      int64          // timestamp when the G last started running
+    runningnanos   int64          // wall time spent in the running state


diff --git a/src/runtime/proc.go b/src/runtime/proc.go
index f5e528e8e9..9f938311b8 100644
--- a/src/runtime/proc.go
+++ b/src/runtime/proc.go
@@ -994,8 +994,18 @@ func casgstatus(gp *g, oldval, newval uint32) {
         }
     }

-    // Handle tracking for scheduling latencies.
+    // Handle tracking for scheduling and running latencies.
+    now := nanotime()
+    if newval == _Grunning {
+        // We're transitioning into the running state, record the timestamp for
+        // subsequent use.
+        gp.lastsched = now
+    }
     if oldval == _Grunning {
+        // We're transitioning out of running, record how long we were in the
+        // state.
+        gp.runningnanos += now - gp.lastsched
+
         // Track every 8th time a goroutine transitions out of running.
         if gp.trackingSeq%gTrackingPeriod == 0 {
             gp.tracking = true
@@ -1007,14 +1017,12 @@ func casgstatus(gp *g, oldval, newval uint32) {
             // We transitioned out of runnable, so measure how much
             // time we spent in this state and add it to
             // runnableTime.
-            now := nanotime()
             gp.runnableTime += now - gp.runnableStamp
             gp.runnableStamp = 0
         }
         if newval == _Grunnable {
             // We just transitioned into runnable, so record what
             // time that happened.
-            now := nanotime()
             gp.runnableStamp = now
         } else if newval == _Grunning {
             // We're transitioning into running, so turn off
@@ -3258,6 +3266,14 @@ func dropg() {
     setGNoWB(&_g_.m.curg, nil)
 }

+// grunningnanos returns the wall time spent by current g in the running state.
+// A goroutine may be running on an OS thread that's descheduled by the OS
+// scheduler, this time still counts towards the metric.
+func grunningnanos() int64 {
+    gp := getg()
+    return gp.runningnanos + nanotime() - gp.lastsched
+}
+
 // checkTimers runs any timers for the P that are ready.
 // If now is not 0 it is the current time.
 // It returns the passed time or the current time if now was passed as 0.
@@ -3491,6 +3507,8 @@ func goexit0(gp *g) {
     gp.param = nil
     gp.labels = nil
     gp.timer = nil
+    gp.lastsched = 0
+    gp.runningnanos = 0

     if gcBlackenEnabled != 0 && gp.gcAssistBytes > 0 {
         // Flush assist credit to the global pool. This gives
diff --git a/src/runtime/sizeof_test.go b/src/runtime/sizeof_test.go
index 9ce0a3afcd..71de8052bd 100644
--- a/src/runtime/sizeof_test.go
+++ b/src/runtime/sizeof_test.go
@@ -21,7 +21,7 @@ func TestSizeof(t *testing.T) {
         _32bit uintptr // size on 32bit platforms
         _64bit uintptr // size on 64bit platforms
     }{
-        {runtime.G{}, 240, 392},   // g, but exported for testing
+        {runtime.G{}, 256, 408},   // g, but exported for testing
         {runtime.Sudog{}, 56, 88}, // sudog, but exported for testing
     }

Background: The runtime maintains a type g struct for every goroutine, and the scheduler is responsible for transitioning each one through various states. _Grunning is the one we're interested in, which indicates that the goroutine may execute user (i.e. CRDB) code. The goroutine is also assigned to an OS thread (type m struct) that is in turn assigned to a CPU core (type p struct). In addition to the src/runtime package docs, Kavya Joshi's and Dmitry Vyukov's presentations on the runtime internals can serve as helpful reading material.

At the point where a g transitions in and out of the _Grunning state, we maintain per-g counters that capture the wall time spent in that state. It's possible for an OS thread (m) that a goroutine (g) was running on to be descheduled by the OS scheduler in favor of non-CRDB processes running on the same node. This is invisible to the Go runtime, and as such, the patch above will count this off-CPU time towards the per-g total (this is evaluated below).

Comparison to RUs and '# of batch requests'

In multi-tenant environments we currently model CPU usage using a linear model of the '# of read/write requests' and their corresponding sizes. We use this linear model for cost attribution and resource control, models that are more or less accurate depending on how the running workload compares to the workloads we've trained the linear model using. Improved signal accuracy for CPU translates to:

  • Better accounting independent of how we present consumption (RUs, rows read/written), we should know what patterns we're subsidizing;
  • Performance predictability in multi-tenant environments.

Derived signals like '# of batch requests' are hard to map to the underlying hardware capacities or utilization. Base signals like 'time-spent on CPU', in contrast, are easier to normalize to capacity and infer utilization from. We imagine introducing similar base signals for other resources of interest (disk IOPs and bandwidth) and tooling rebalancing algorithms to directly consider independent hardware dimensions separately instead of using signals that combine dimensions.[disk-util]. We can try calibrating derived signal models using experimental data, an expensive process even without varying for saturation, hardware, contention, etc. Even perfect models would need recalibration as the implementation changes, placing practical limits on how accurate they can be.

Short-term use cases

We propose the following features to build experience with using a modified runtime, and to further evaluate signal accuracy:

  • Observability: A per-store CPU usage breakdown by ranges and tenants, powered by measured CPU time. We'd make this accessible through vtables and include results in debug zips, serving as a CPU-only version of today's hot-ranges report. This was prototyped, results below.
  • Observability: Surfacing per-statement cluster-wide CPU usage as part of EXPLAIN ANALYZE.
  • (stretch goal) Tenant isolation: Integrating measured CPU time in the tenant rate limiter to improve performance predictability observed by tenants sharing KV nodes.
    • We foresee integrating the tenant rate limiter into admission control which would help evaluate how effective this is as a signal for CPU resource control.

From an earlier prototype exposing per-store replica/tenant CPU time:

Usage across machines, goroutines, and code paths

It's common to spawn multiple goroutines for the same operation and to also do so across RPC boundaries. For the latter (say, if looking to accumulate cluster-wide CPU usage for a given statement) we can propagate per-node request-scoped total CPU time using tracing events; this is plumbing that already exists. For request-scoped total CPU time across cooperating goroutines, we can atomically maintain a counter stashed in the surrounding context. To retrieve per-goroutine running time, we imagine doing so in libraries we already use to manage goroutine lifetimes (Stopper, ctxgroup), lending more weight to banning naked gos altogether.

For work done per-store that's attributable to an individual range or tenant, we don’t go through a single code path (the Sender stack). Consider KV queues that step through individual replicas at a time and do some work on behalf of each one, work we’d perhaps want to attribute to the specific replicas/tenants. In prototypes, we captured CPU profiles from KV under stock workloads to get a broad sense of what stack traces are attributable to individual ranges/tenants and opted into tracking at those points (purple segments in the figure below) to maintain a store-level view of how much CPU activity is driven by a specific range/tenant.

Development and release

We point to mirrored artifacts of the Go runtime that engineers use when building/running tests through Bazel. The official CRDB release binaries use Bazel, using the same mirrored runtime. Pointing to a modified runtime[release-go] is a matter of hosting it in publicly maintained buckets for things to "just work" (prototype). As for deprecated Make based workflows, we can ensure that CRDB library components/tests that make use of the runtime patch are gated behind a bazel-only build tag. This would also provide IDE integration.

Future runtime changes

We argue that this proposal should be considered more along the lines of a patchset (and one that we intend to upstream) than a hard fork. A parallel is whenever we patch a 3rd-party library and point to CRDB forks, we still send out PRs and point back to upstream SHAs once they’re merged. In this lens, Bazel only provides machinery to do the same for the Go runtime. The review discussions around a new public API for the counter is an important consideration for the Go team but less so for us; we’d happily go:linkname against tracked fields or private helpers to get what we need (we do similar things for goroutine IDs and runnable goroutines). Independent of if/when it’s upstreamed, we can avoid coupling using precise attribution to the major Go release the change possibly appears; the diff is conservative in size to make it easy to patch to minor/major Go releases going forward. We expect future proposals for runtime changes, if any, to be evaluated on their own terms. Some guidelines to consider: patch size, review expertise, upstream-ability, fallback behavior without runtime changes, proximity to crypto ("core" functionality in general), and performance implications.

Evaluation

Under stock TPC-C runs we verified that measured CPU time was:

  • Stable over time, under idle conditions and sustained load;
  • Trends with actual CPU usage (baseline: CRDB process CPU %), can be normalized to capacity and used to forecast effect of range/tenant movement.

Smoke tests running kv --init --read-percent 50 --concurrency 1000 --ramp 1m --duration 4m on a multi-node cluster showed no discernable throughput or latency impact when built with the proposed Go changes. Retrieving the goroutine running time (included is the vDSO call to nanotime()) is in the order of nanoseconds:

goos: linux
goarch: amd64
cpu: Intel(R) Xeon(R) CPU @ 2.20GHz
BenchmarkGRunningNanos
BenchmarkGRunningNanos-24       195321096               30.67 ns/op
BenchmarkGRunningNanos-24       195100147               30.77 ns/op
BenchmarkGRunningNanos-24       195415414               30.71 ns/op
BenchmarkGRunningNanos-24       195564742               30.70 ns/op
BenchmarkGRunningNanos-24       195472393               30.70 ns/op
PASS

Microbenchmarks from the Go runtime that evaluate scheduler behavior show little to no impact:

goos: linux
goarch: amd64
cpu: Intel(R) Xeon(R) CPU @ 2.20GHz
name                             old time/op    new time/op    delta
PingPongHog-24                      517ns ± 8%     513ns ±13%    ~     (p=0.690 n=5+5)
CreateGoroutines-24                 302ns ± 1%     304ns ± 1%    ~     (p=0.310 n=5+5)
CreateGoroutinesParallel-24        33.6ns ± 1%    34.0ns ± 1%  +1.30%  (p=0.032 n=5+5)
CreateGoroutinesCapture-24         2.86µs ± 2%    2.89µs ± 1%    ~     (p=0.310 n=5+5)
CreateGoroutinesSingle-24           406ns ± 0%     407ns ± 1%    ~     (p=0.421 n=5+5)
WakeupParallelSpinning/0s-24       14.7µs ± 1%    14.7µs ± 2%    ~     (p=0.548 n=5+5)
WakeupParallelSpinning/1µs-24      19.0µs ± 3%    18.9µs ± 3%    ~     (p=1.000 n=5+5)
WakeupParallelSpinning/2µs-24      24.4µs ± 4%    24.7µs ± 2%    ~     (p=0.421 n=5+5)
WakeupParallelSpinning/5µs-24      36.7µs ± 3%    37.0µs ± 1%    ~     (p=0.548 n=5+5)
WakeupParallelSpinning/10µs-24     54.0µs ± 0%    54.0µs ± 1%    ~     (p=0.802 n=5+5)
WakeupParallelSpinning/20µs-24     96.3µs ± 0%    96.1µs ± 0%    ~     (p=0.222 n=5+5)
WakeupParallelSpinning/50µs-24      222µs ± 0%     222µs ± 0%    ~     (p=0.690 n=5+5)
WakeupParallelSpinning/100µs-24     386µs ± 2%     391µs ± 3%    ~     (p=0.310 n=5+5)
WakeupParallelSyscall/0s-24         171µs ± 1%     170µs ± 1%    ~     (p=0.095 n=5+5)
WakeupParallelSyscall/1µs-24        173µs ± 1%     172µs ± 1%    ~     (p=0.222 n=5+5)
WakeupParallelSyscall/2µs-24        176µs ± 2%     174µs ± 1%    ~     (p=0.421 n=5+5)
WakeupParallelSyscall/5µs-24        183µs ± 1%     184µs ± 1%    ~     (p=0.095 n=5+5)
WakeupParallelSyscall/10µs-24       190µs ± 0%     193µs ± 1%  +1.49%  (p=0.008 n=5+5)
WakeupParallelSyscall/20µs-24       213µs ± 1%     213µs ± 1%    ~     (p=0.548 n=5+5)
WakeupParallelSyscall/50µs-24       274µs ± 1%     275µs ± 3%    ~     (p=0.690 n=5+5)
WakeupParallelSyscall/100µs-24      377µs ± 1%     380µs ± 3%    ~     (p=0.151 n=5+5)
Matmult-24                         0.97ns ± 1%    0.97ns ± 2%    ~     (p=0.841 n=5+5)

name                             old alloc/op   new alloc/op   delta
CreateGoroutinesCapture-24           144B ± 0%      144B ± 0%    ~     (all equal)

name                             old allocs/op  new allocs/op  delta
CreateGoroutinesCapture-24           5.00 ± 0%      5.00 ± 0%    ~     (all equal)

The metric is also accurate (matches actual on-CPU proportions) and precise (repeated measurements have low variability). To verify, we used a form of the tests proposed in go#36821: TestEquivalentGoroutines and TestProportionalGoroutines.

=== RUN   TestEquivalentGoroutines             # want ~10% for each
    0's got  9.98% of total time
    1's got  9.53% of total time
    2's got  9.22% of total time
    3's got 10.42% of total time
    4's got  9.84% of total time
    5's got 10.43% of total time
    6's got 10.50% of total time
    7's got 10.21% of total time
    8's got 10.03% of total time
    9's got  9.86% of total time

=== RUN   TestProportionalGoroutines           # want incrementing multipliers
    0's got  1.87% of total time (1.000000x)
    1's got  3.60% of total time (1.931999x)
    2's got  5.41% of total time (2.899312x)
    3's got  7.21% of total time (3.864451x)
    4's got  9.11% of total time (4.880925x)
    5's got 10.94% of total time (5.864723x)
    6's got 12.77% of total time (6.842004x)
    7's got 14.34% of total time (7.685840x)
    8's got 16.58% of total time (8.885060x)
    9's got 18.18% of total time (9.741030x)

TODO: Evaluate inaccuracy given time spent descheduled by the OS is also counted here. The Go scheduler tries its best to hog the processors it’s been allotted, and given we’re running goroutines much higher than the number of processors, the observed inaccuracy across all goroutines (only the ones pinned to a descheduled OS thread is overcounted) should be diminishingly small.

Alternatives

There are a few options to get CPU attribution with varying degrees of accuracy, granularity and convenience. It’s possible we incorporate one or more of the listed alternatives in the future.

Sampling profiler

We can get coarse-grained, non-instantaneous CPU attribution using pprof and profiler labels. A sketch of what this would look like was prototyped here, and has roughly the following idea.

  • We'd make use of profiler labels at points of interest (code paths where requests to a single range/tenant feed through for e.g.)
  • Go automatically propagates profiler labels across goroutine boundaries with shared contexts. gRPC also propagates labels across RPC boundaries.
  • We'd periodically capture profiling data and use it to compute the %es of CPU time attributable to specific scopes.

Comparison:

  • Does not necessitate runtime changes.
  • Provides coarse-grained attribution, which is likely sufficient for things like the allocator or the tenant rate limiter, but harder to apply to per-statement/request accounting.
    • Sampling rates have a maximum frequency of 100hz with implications on fidelity.
    • Sampling-backed signals are accurate over a larger number of samples (i.e. non-instantaneous), which is less-than-usable when we care about capturing CPU data for executions in the tail (think statements that don't take that long, don't happen frequently).
  • CPU profiling with labels is allocation inefficient and has a higher performance overhead (though bounded to <1%; TODO: more numbers). Profiles capture the entire stack trace; for CPU attribution we only need to identify the running goroutine.
  • CPU attribution from sampling data can be inaccurate and imprecise, as observed in go#36821; measured CPU time does better as shown in the evaluation above.

Task groups

Another proposal for CPU attribution is introducing two new abstractions in the Go runtime: taskgroups and inheritable goroutine IDs. The proposal observes the need to accumulate timings (and other statistics, like memory usage) across a set of cooperating goroutines, and having the ability to retrieve goroutine timings from possibly another goroutine. It also segues into precise tracking of memory allocations and possibility of resource control within the runtime (we don’t), and helpfully outlines a few other options for resource attribution and control from counting CPU ticks, using separate processes, and manual instrumentation.

Comparison:

  • Among the alternatives listed here this is the closest one in spirit, differing in the specific runtime changes proposed (with library code implications).
    • Maintaining a modified runtime is logistically easier now compared to when the proposal was authored given our Bazel investments.
  • Task groups push the cross-goroutine tracking logic into the runtime itself which makes it a larger patch set to maintain, and less likely to be upstreamed. This also has implications for reviewability. This proposal is a more targeted form, lifting as much as possible out of the runtime and into CRDB libraries.
  • Task groups let you read off counters before an "inherited" goroutine is done, which could matter for very long-lived goroutines. With this proposal, if we’re interested in accumulating counters for long-lived goroutines, the goroutines would themselves have to retrieve their running time and maintain external counters. Possible, but perhaps more awkward.
  • Task groups make use of atomics to maintain running counters as opposed to uncontended per-g counters. This could be slower due to cache coherency protocols across processors but we suspect the effects would be negligible.
  • At a high level, task groups are an opt-out form of timing tracking (a "unrelated" goroutine spawned off would have to be excluded from accumulating time into the parent task group). This proposal is more opt-in.

eBPF probes

Linux has the ability to define probes at specific callsites in user-level code (which for us would include the Go runtime). This document is a good primer on uprobes specifically. We can run eBPF programs every time the callsite is executed and maintain state across invocations. We could then, in theory, instantiate maps keyed by the goroutine ID and maintain running counters (last ran, total ran) in the same way we’re proposing above (lastsched, runningnanos) but without modifying the runtime itself. We’d probe explicit callsites, probably the same as above, where g’s change state and where they’re created and destroyed.

An unrelated example of what eBPF could look like with CRDB (probing the Go scheduler to figure out how often it’s invoked, how often it sleeps, and a distribution of how long it does in so in nanoseconds):

sudo bpftrace -p 9593 -e '
uprobe:/home/ubuntu/cockroach:runtime.schedule {
    @in[tid] = 1;
    @steal[tid] = 0;
    @parkstart[tid] = 0;
    @parkns[tid] = 0
} 
uretprobe:/home/ubuntu/cockroach:runtime.runqsteal {
    if (@in[tid] == 1 && @steal[tid] == 0 && retval != 0) { 
        @steal[tid] = 1
    }
}
uprobe:/home/ubuntu/cockroach:runtime.notesleep {
    if (@in[tid] == 1) { 
        @parkstart[tid] = nsecs
    }
}
uretprobe:/home/ubuntu/cockroach:runtime.notesleep {
    if (@in[tid] == 1) {
        @parkns[tid] += (nsecs - @parkstart[tid]);
        @parkstart[tid] = 0
    }
}
uprobe:/home/ubuntu/cockroach:runtime.execute {
    if (@in[tid] == 1) {
       if (@steal[tid] == 1) { @steals += 1 }
       if (@parkns[tid] > 1) {
           @parks += 1;
           @park_dur = hist(@parkns[tid]);
       }
       @schedules += 1;
       @in[tid] = 0;
    }
}
interval:s:1 { 
    printf("schedules/s %d, steals/s %d, parks/s %d \n", @schedules, @steals, @parks);
    print(@park_dur);
    clear(@steals);    
    clear(@schedules);
    clear(@parks);
    clear(@park_dur);
}'

schedules/s 3796, steals/s 18, parks/s 3450 
@park_dur: 
[4K, 8K)               4 |                                                    |
[8K, 16K)              0 |                                                    |
[16K, 32K)             4 |                                                    |
[32K, 64K)            27 |@                                                   |
[64K, 128K)           98 |@@@@@                                               |
[128K, 256K)         304 |@@@@@@@@@@@@@@@@                                    |
[256K, 512K)         975 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ |
[512K, 1M)           590 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@                     |
[1M, 2M)             984 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@|
[2M, 4M)             351 |@@@@@@@@@@@@@@@@@@                                  |
[4M, 8M)              50 |@@                                                  |
[8M, 16M)             25 |@                                                   |
[16M, 32M)            27 |@                                                   |
[32M, 64M)            11 |                                                    |
[64M, 128M)            4 |

Comparison:

  • Linux-only; processes accessing eBPF probes also need to run as root.
  • Does not necessitate runtime changes but does effectively need an understanding of runtime internals.
  • Can be used to probe OS scheduling events, something the Go runtime itself has no visibility into.
  • uretprobes don’t work in Go (see bcc#1320 and go#22008) because of stack rewriting probes do to trampoline off to eBPF programs – something the Go runtime does not expect when growing/shrinking stacks. This presents as panics (TODO: memory corruption claims).
    • There are ways to simulate uretprobes through uprobes but it’s more annoying to do; we’d need to decode architecture-specific instructions and ensure coverage for all kinds of return instructions on each architecture.
  • eBPF probes have more overhead than tracking running nanos in the runtime itself (instrumented function calls take in the order of µs as opposed to ns). Also, we’d still have the vDSO call in our eBPF program.
  • Recent developments in the eBPF + Go ecosystem might make things progressively less painful (libraries to read arguments from the Go stack, decoding Go structs).
    • The out-of-the-box bpftrace use of thread IDs (tid in the snippet above) don’t apply to Go, we need glue work to access goroutine IDs.

Admission control slot times

CPU bound work (for e.g. KV-level request and SQL statement processing) gets queued through per-node admission queues to provide overload protection and fairness. Queued work proceeds once granted a slot, and we have visibility into when work for a given slot starts and ends. These wall time readings could serve as a proxy for CPU processing the work entailed; timings we could then use for per-store attribution to specific ranges or tenants. The inaccuracies here stem from wait times in:

  • the Go scheduler after work is slotted (though this is bounded by admission control, and affects all range/tenant-scoped work uniformly);
  • txn contention handling (something we can instrument and subtract by timing things within our concurrency control libraries);
  • I/O wait times.

Considerations:

  • Does not necessitate runtime changes.
  • Proxy for actual CPU time, but perhaps good enough for use cases we imagine needing accurate/precise CPU modeling for.
  • Usable for work that makes sense to enqueue through admission control (unclear if it applies to all subsystems where CPU attribution could be used).