GoGCTuner brought CPU utilisation down ~50%

As share of Uber engineering’s extensive efforts to reach profitability, just recently our crew became once centered on cutting back fee of compute capacity by making improvements to efficiency. A pair of of basically the most impactful work became once spherical GOGC optimization. On this blog we’re searching to allotment our expertise with a highly efficient, low-grief, natty-scale, semi-automatic Lunge GC tuning mechanism.

Uber’s tech stack is level-headed of hundreds of microservices, backed by a cloud-native, scheduler-based fully mostly infrastructure. These carry out of companies and products are written in Lunge. Our crew, Maps Manufacturing Engineering, has previously performed an instrumental feature in seriously making improvements to the efficiency of loads of Java companies and products by tuning GC. On the starting up of 2021, we explored the potentialities of having a an identical impact on Lunge-based fully mostly companies and products. We ran several CPU profiles to assess the present scenario and we realized that GC became once the cease CPU consumer for an infinite majority of mission-excessive companies and products. Here’s a illustration of some CPU profiles where GC (known by the runtime.scanobject formula) is ingesting a indispensable fragment of allocated compute resources.

Provider #1

Figure 1: GC CPU fee of Instance Provider #1

Provider #2

Figure 2: GC CPU fee of Instance Provider #1

Emboldened by this discovering, we commenced to tune GC for the relevant companies and products. To our satisfaction, Lunge’s GC implementation and the simplicity of tuning allowed us to automate the majority of the detection and tuning mechanism. We detail our ability and its impact in the next sections.

GOGC Tuner 

Lunge runtime invokes a concurrent garbage collector at periodic intervals except there is a triggering match before it. The triggering events are in step with memory encourage strain. Due to this, GC-impacted Lunge companies and products reduction from more memory, because it reduces the instances GC has to bustle. As smartly as, we realized that our host-stage CPU to memory ratio is 1:5 (1 core : 5 GB RAM), whereas most Golang companies and products were configured with a 1:1 to 1:2 ratio. Therefore, we were assured that we might well well additionally leverage utilizing more memory to cut GC CPU impact. Here’s a carrier-agnostic mechanism that might well yield a natty impact when utilized judiciously.

Delving deep into Lunge’s garbage collection is previous the scope of this article, but right here are the relevant bits for this work: Garbage collection in Lunge is concurrent and entails analyzing all objects to identify which of them are peaceable reachable. We would name the reachable objects the “stay dataset.” Lunge offers finest one knob, GOGC, expressed in percentage of stay dataset, to manipulate garbage collection. The GOGC trace acts as a multiplier for the dataset. The default trace of GOGC is 100%, meaning Lunge runtime will reserve the an identical quantity of memory for unusual allocations because the stay dataset. To illustrate:

hard_target = live_dataset + live_dataset (GOGC / 100).

Then the pacer is to blame of predicting when it’s miles the accurate time to recount off GC to preserve a ways from hitting the exhausting aim (soft aim).

Figure 3: Instance heap with default configuration.

Dynamic and Various: One Dimension Does No longer Match All

We known that a group GOGC trace-based fully mostly tuning is now now not accurate for companies and products at Uber. A pair of of the challenges are:

  • It is now now not privy to the maximum memory assigned to the container and might reason out of memory components.
  • Our microservices like a seriously various memory utilization portfolio. To illustrate, a sharded draw can like very various stay datasets. We skilled this in one in every of our companies and products where the p99 utilization became once 1G but the p1 became once 100MB, therefore the 100MB instances were having a large GC impact.

A Case for Automation

The worry components previously offered are the explanation of the idea of GOGCTuner. GOGCTuner is a library which simplifies the approach of tuning garbage collection for carrier house owners and adds a reliability layer on top of it.

GOGCTuner dynamically computes the simply GOGC trace in accordance with the container’s memory restrict (or the greater restrict from the carrier owner) and items it utilizing Lunge’s runtime API. Following are the specifics of the GOGCTuner library’s formula:

  • Simplified configuration for more straightforward reasoning and deterministic calculations. GOGC at 100% is now now not decided for beginner Lunge developers and it’s now now not deterministic, because it peaceable is depending on the stay dataset. On the unreal hand a 70% restrict ensures that the carrier is typically going to make exercise of 70% of the heap rental.
  • Security against OOMs (Out Of Memory): The library reads the memory restrict from the cgroup and uses a default exhausting restrict of 70%, a stable trace from our expertise.
        • It is excessive to expose that there is a restrict to this security. The tuner can finest modify the buffer allocation, so in case your carrier stay objects are greater than the restrict the tuner would recount a default lower restrict of 1.25X your stay objects utilization.
  • Allow greater GOGC values for nook instances admire:
        • As we talked about above, manual GOGC is now now not deterministic. We are peaceable counting on the scale of the stay dataset. What if live_dataset doubles our closing peak trace? GOGCTuner would set up in power the an identical memory restrict at the worth of more CPU. Handbook tuning as every other might well well additionally reason OOMs. Therefore, carrier house owners broken-the total arrangement down to present hundreds of buffer for these carry out of eventualities. Glance the instance below:

Usual visitors (stay dataset is 150M)

Figure 4: Usual operation. Default configuration on the left, manually tuned on the handsome.

Traffic elevated 2X (stay dataset is 300M)

Figure 5: Double the burden. Default configuration on the left, manually tuned on the handsome.

Traffic elevated 2X with GOGCTuner at 70% (stay dataset is 300M)

Figure 6: Double the burden, but utilizing the tuner. Default configuration on the left, GOGCTuner tuned on the handsome.
  • Services utilizing MADV_FREE memory policy that ends up in obscene memory metrics. To illustrate, our observability metrics were showing 50% memory utilization (when it genuinely had already launched 20% of that 50%). Then carrier house owners were finest tuning GOGC utilizing this “inaccurate” metric.


We realized that we lacked some excessive metrics which might give us more insights into garbage collection of each carrier.

  • Intervals between garbage collections: helpful to know if we will peaceable tune. To illustrate, Lunge forces a garbage collection every 2 minutes. In case your carrier is peaceable having high GC impact, but you already gape 120s for this graph, it formula which that it’s doubtless you’ll now now now not tune utilizing GOGC. On this case that it’s doubtless you will have to optimize your allocations.
Figure 7: Graph for intervals between GCs.
  • GC CPU impact: lets in us to know which companies and products are basically the most tormented by GC.
Figure 8: Graph for p99 GC CPU fee.
  • Are living dataset size: helps us to identify memory leaks. The anxiousness smartly-known by carrier house owners became once that they saw an carry in memory utilization. In uncover to expose them there became once no memory leak we added the “stay utilization” metric, which showed an everyday utilization.
Figure 9: Graph for estimated p99 stay dataset.
  • GOGC trace: helpful to know how the tuner is reacting.
Figure 10: Graph for min, p50, p99 GOGC trace assigned to the software by the tuner.


Our preliminary ability became once to love a ticker to bustle every 2d to visual show unit the heap metrics, and then modify GOGC trace accordingly. The scheme back of this means is that the overhead begins to became grand, because in uncover to study heap metrics Lunge needs to set up a STW (ReadMemStats) and it’s slightly of inaccurate, because we might well like a few garbage collection per 2d.

Fortunately we were ready to uncover a correct substitute. Lunge has finalizers (SetFinalizer), which are functions that bustle when the article goes to be garbage collected. They are mainly helpful for cleansing memory in C code or every other resources. We were ready to exercise a self-referencing finalizer that resets itself on every GC invocation. This lets in us to cut any CPU overhead. To illustrate:

Figure 11: Instance code for GC triggered events.

Calling runtime.SetFinalizer(f, finalizerHandler) internal of finalizerHandler is what lets in the handler to bustle on every GC; it’s in general now now not letting the reference die, because it’s now now not a costly resource to preserve alive (it’s handsome a pointer).


After deploying GOGCTuner across a few dozen of our companies and products, we dove deep on a few that showed indispensable, double-digit enchancment of their CPU utilization. Gathered fee financial savings from these companies and products on my own are spherical 70Okay cores. Following are 2 such examples:

Figure 12: Observability carrier that operates on hundreds of compute cores with high current deviation for live_dataset (max trace became once 10X of the bottom trace), showed ~65% discount in p99 CPU utilization.
Figure 13: Mission excessive Uber eats carrier that operates on hundreds of compute cores, showed ~30% discount in p99 CPU utilization.

The resulting CPU utilization discount improves p99 latency (and associated SLA, consumer expertise) tactically, and fee of capacity strategically (since companies and products are scaled in step with their utilization). 

Garbage collection is one in every of basically the most elusive and underestimated efficiency influencers of an software. Lunge’s sturdy GC mechanism and simplified tuning, our various, natty-scale Lunge companies and products footprint, and a sturdy interior platform (Lunge, compute, observability) collectively allowed us to carry out such a natty-scale impact. We request to continue making improvements to how we tune GC because the explain rental itself is evolving, in consequence of modifications in the tech and our competency.

To reiterate what we talked about at the introduction: there is no one size fits all solution. We feel GC efficiency will stay variable in cloud-native setup in consequence of the highly variable efficiency of both public clouds and containerized workloads that bustle within. Coupled with the truth that an infinite majority of CNCF panorama initiatives that we exercise are written in Golang (Kubernetes, Prometheus, Jaeger, etc.), this kind any natty-scale deployment outside might well well additionally additionally reduction from such effort.

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