Performance Impact of Removing an Out-of-Band Garbage Collector

https://githubengineering.com/removing-oobgc/

by jbredeche • 8 years ago

94 60 8 years ago

githubengineering.com

titzer• 8 years ago

TLDR: they saved 400 to 1000 cores by switching off the switching off of the GC during requests.

The fact that they are running Ruby and are spending 1000 cores on GC is o_O.

k__• 8 years ago

Isn't Ruby known for these kind of issues?

Karunamon• 8 years ago

Sadly yes. It’s an absolute joy to code in, but it isn’t even kind of light.

nwmcsween• 8 years ago

Ruby as a language no, most of the implementations yes.

vidarh5• 8 years ago

In this case it was Github's hack to work around no-longer-existing issues with Ruby's GC that was the issue.... Note that the speedup came from removing their hack in favour of relying on the default behaviour of Ruby 2.4

twic5• 8 years ago

Moreover, "removing OOBGC reduced average response times by about 25%". CPU utilisation is a pretty terrible metric on its own; once you've paid for a CPU, you might as well run it at 100%!

majewsky• 8 years ago

Not really. Running CPUs at 100% load means you have no breathing room for high-load situations. It also makes them hotter, which may lead to worse performance because of throttling.

LukaAl• 8 years ago

Also average response time is a bad metrics. For one, the average is a bad measure if the variance is high. So I'll probably use 95% time instead of average.

But as a PM I have a different question. Which is the impact of the response time of Github on the user experience? 25% reduction on an average of 2 sec improves greatly the user experience, on a 200ms for a service that has basically no competition is just a marginal improvement, on a 50 ms average is not noticeable.

How is it distributed across multiple operations? Improving the latency of loading a diff is probably more important than the latency of approving a PR. In one case I'm trying to work, in the second I'm done with the work.

Now, my experience with GitHub is that it is already reasonably fast unless I'm doing something stupid. So the CPU utilization is a good metrics because 10% less CPU means 10% less server I need to pay and that goes to their bottom line (I don't know their economics to decide if it is substantial or not). The 25% reduction in latency is just icing on the cake...

vidarh5• 8 years ago

Those 1000 cores seems to have been equivalent to 10% of their load. The 10% number isn't nearly as shocking.

stmw• 8 years ago

Impressive and interesting, but it is really a great example of how in larger production systems, Garbage Collection turns into "manual memory management" akin to that required to manage malloc() and free() correctly -- little performance traps, ever-changing library behaviors, tuning parameters, etc. (This is not specific to Ruby, the JVM has the same kind of "here is a whole blog post about person-weeks or person-months that went into getting the GC to behave better).

tetha• 8 years ago

The thing is, the more I work with network based reactive systems, the more a GC seems like an inefficient hack in an application, arising from the fact that we had nothing better at the time it was invented and grew big.

I mean a reactive application needs three things:

- a fast network connection to persistence. If you can't talk quickly to your database, latency eats your response time no matter what you do.

- Maybe some LRU caches across requests, but caches inside an application tend to become a maintenance nightmare quickly. Stales reads are fun, and stalled cache writes are too.

- And beyond that, it should be possible to handle most request data by stack allocation/deallocation with references to a cache version if you have a cache with little overhead, depending on your security requirements.

I understand the ease of use and simplicity of a GC'd language, especially given the time GC'd languages came around big time. But I've always been wondering if you really need a GC if you think properly about request and object lifetimes.

Sean1708• 8 years ago

TLDR: they saved 400 to 1000 cores by switching off the switching off of the GC during requests.

Isn't that exactly the opposite of what the article is saying?

An OOBGC is not really a Garbage Collector, but more of a technique to use when deciding when to collect garbage in your program. Instead of allowing the GC to run normally, the GC is stopped before processing a web request, then restarted after the response has been sent to the client. Meaning that garbage collection occurs “out of band” of request and response processing.
This graph shows the difference in core utilization before and after OOBGC removal. In other words “number of cores used yesterday” minus “number of cores used today” ... We saw a savings of between 400 and around 1000 cores depending on usage at that point in the day.

So it sounds to me that by switching the GC on during requests they saved that many cores.

twic5• 8 years ago

I find this an interesting contrast to Go's move towards a "request-oriented collector":

https://docs.google.com/document/d/1gCsFxXamW8RRvOe5hECz98Ft...

Perhaps a key difference is this point from the Github document:

"Since the OOBGC runs the GC after the response is finished, it can cause the web worker to take longer in order to be ready to process the next incoming request. This means that clients can suffer from latency due to queuing wait times."

It seems like a little bit more intelligence in the load balancer that sits in front of the application servers would go a long way here. Could it only route requests to servers which have finished garbage collecting and are ready to serve again?

vidarh5• 8 years ago

One option is a reversal: Make the worker request a request. Slow, unavailable or failed workers will in effect automatically back off.

jashmatthews5• 8 years ago

Phusion Passenger has some intelligent routing to balance requests so you don't end up with one process idle and one process serving two requests using 2 threads.

tybit• 8 years ago

I’ve been wondering whether this approach would be viable for a while now. Are there any systems that do this? I know solo’s Gloo reverses the flow using NATS but that’s all I’ve seen use this approach.

puzzle3• 8 years ago

Now you have another problem: the load balancer needs to know accurately when the backend is ready. This can be prone to race conditions, too, especially for services that have very short response times, unless you manage to keep the LB/RPC protocol in lockstep with the garbage collector's behaviour. E.g. you might have to extend the LB/RPC protocol for responses in-band, to include a bit that says "those were the results for the request and, by the way, don't send me new work until I signal you again", which also assumes a permanent connection between the two ends, etc.

It is doable, but it benefits greatly if you have complete control over the whole stack. It gets harder and harder as more languages are thrown into the mix.

ASalazarMX• 8 years ago

Come on, we can solve that by adding a message queue manager in between. Whenever in doubt, just throw more layers at the problem.

zokier• 8 years ago

If the OOBGC pauses are anywhere near predictable, then maybe even a super stupid simple solution would work: just have the load balancer assume that backend is busy a fixed amount of time after each request. Even if you massively underestimate the busy time, it shouldn't hurt the latencies. Overestimating on the other hand leads to under-utilization, so it would make sense to set the busy time value to some small percentile of observed oobgc times.

twic5• 8 years ago

You absolutely have to extend the protocol - or rather, at least one protocol in the stack - to communicate the status in-band. It would be a nightmare otherwise.

With HTTP/2 you could have a separate control channel carrying a text/event-stream payload, with messages indicating when the server was ready.

With HTTP/1.x, you could abuse HTTP trailers to indicate readiness - the load balancer could read the response until the end of the body, send that to the client, then wait until the server sends a trailer to say it's ready for more work. Or the load balancer could follow every real request with a request to /pleaseBlockUntilYouAreReadyForMoreWork, and only send another real request once it got a reponse to that.

With RPC protocols that aren't HTTP, and don't have a flexible presentation layer where this would fit, then you would probably have to add explicit messages to the protocol, which would be a shame.

jashmatthews5• 8 years ago

Go's "request oriented collector" proposal is a generational GC, and Ruby has had a generational GC since 2.1. Generational GC is about minimizing GC overhead by avoiding re-marking the entire heap.

OOBC is very different. It's trading an increased overhead from collecting more often than necessary for a lower latency experienced by users.

twic5• 8 years ago

The request-oriented collector is not just a generational GC by another name. The key point:

This write barrier design allows ROC to efficiently reclaim unpublished objects when a goroutine exits simply by resetting the current sweep pointer back to the initial sweep pointer.

If you use a disposable goroutine for a task, then when it exits, any memory it allocated but did not publish to other goroutines can be reclaimed without doing any further work. It's very much based on the idea that if you can put off collection to the end of a task, you can do it more efficiently, like OOBGC, but approached from a different angle.

I have no idea how well it actually works in practice. I've wanted something similar in Java for years, though!

smials• 8 years ago

And contrast again with Erlang's approach, where each process (actor) keeps its own heap, which can just be thrown away once the request handling process is done. Simple and clean, and requires no proofs, barriers or other juju... :)

jashmatthews5• 8 years ago

It does have a lot of overhead in copying, though. Erlang has lower throughput than Ruby in a lot of cases.

It's somewhat mitigated by the reference counted global heap for passing large messages by reference but multi-threaded reference counting is extremely hard to do fast and AFAIK Erlang doesn't really try and do anything too complex.

TheSwordsman• 8 years ago

I thought this Request-Oriented Collector idea was abandoned? I seem to remember it being talked about in the Go community, where this hypothesis was tested and the results were sub-par.

zik• 8 years ago

Yes, and the current GC has sub millisecond pauses so it's not necessary in any case.

twic5• 8 years ago

Ah, i stand corrected!

jonhohle• 8 years ago

A Least Connections strategy effectively does this without needing to know why slow servers are slow (be it GC, lemon host, networking issues, etc) or needing the server to communicate anything back to the load balancer. Hosts which are more performant will naturally get more traffic.

compsciphd• 8 years ago

I was thinking of this, if the backend handler would say "take me out of rotation for Xms as I'm about to do a garbage collection" and the load balancer would see this and act on it.

for http I'd imagine it can be a header. For https, I can imagine it might be harder.

nneonneo• 8 years ago

Interesting work, thanks for sharing.

Nitpick:

The blue line is CPU utilization for the day the patch went out. You can see a great drop around 15:20.

I see a dip at 18:20, not at 15:20 - if there is a dip at 15:20, maybe it would be worth highlighting it.

irishsultan• 8 years ago

I'd assume that it's a timezone issue. The author probably didn't check to see if the labels on the x-axis matched his own timezone and just pointed out where he knew the drop happened, instead of where it actually happened (on his graph).

bluesnowmonkey• 8 years ago

Of course removing it reduced your CPU consumption. What did it do to p99 request latency? That was the trade-off being made by OOBGC.

Does Github use SOA? The more backend services that are involved in handling a user facing request, the more the latency of frontend requests will be dominated by the tail latency of backend services. So in distributed systems it makes much more sense to focus on those tail latencies. Who cares about average latency?

hinkley3• 8 years ago

Who cares about average latency?

People who are bad at statistics. So most of us programmers.

Seriously though, I don’t think many of us get around to acknowledging that microservices are distributed computing and then consider all of the limitations that go with it.

Latency is dominated by the slowest thing you can’t start earlier. So in real distributed computing you see a degree of duplicate effort in order to improve latency. It reduces throughout but increases the utility of the system.

When I took CS in college there was a proper class on distributed computing. When I got out I discovered not everybody had one of these (and I think in my program it was an elective. IIRC I chose it instead of kernel design). I had hoped that situation improved but I got tired of asking people disappointing questions about their education so I stopped asking.

srean3• 8 years ago

People who are bad at statistics.

Heh! sadly very true.

Many of those who do care about the p95s of an internal service often do it with a sense of chivalry that they have that 5% of the users back. The end user latency percentiles would be quite different from the service side latency, more so if its composed by a "Last_of" or a Max operator.

At times I feel tempted to write a library that offers primitives to glue microservice calls such as fan_out, retry, timeout etc but one that can reason about the percentiles of the output from the percentiles of the input.

BTW are statistical properties of latencies covered in distributed system courses ? I thought they were mostly about different kinds of guarantees, such as consensus, atleast-once, atmost-once etc etc.

taf2• 8 years ago

There is definitely a trade off here... I noticed without OOBGC we did increase response times for the p99 and p95 requests however it was a few ms. So, for us the trade off of more CPU capacity and slightly slower in the p99 was worth the trade off. The cool thing about seeing this github post for me was to be reminded about OOBGC being a thing and that we should re-evaluate it after upgrading ruby.

tenderlove• 8 years ago

Hi. Sorry, I should have included p99 and p95 info in the post (I wrote the post and shipped the patches). We didn't see any change on p99 and p95 requests, which is why we decided to keep it. I will include that info next time I make a post like this, sorry.

chris_wot• 8 years ago

That's a remarkable author profile on that article.

ksec• 8 years ago

I am actually surprised GitHub still has OOBGC, which I thought was made redundant in 2014/2015 when Ruby 2.2 had incremental marking, and further improvement in GC 2.3, 2.4 and now 2.5.

In 2.6 they are introducing something similar called Sleepy GC

https://bugs.ruby-lang.org/issues/14723

There are even better memory default for 2.6 using less memory. ( Or better switch to Jemalloc )

https://bugs.ruby-lang.org/issues/14718

I cant find the thread, but there also a test showing how on Alpine Linux memory and performance were much better for Ruby Apps.

Meanwhile we are waiting for Truffle Ruby as a true drop in replacement, that should take lots of Ruby Pain point away. ( Native Image deployment, Better Memory handling and GC, Much faster JIT )

Performance Impact of Removing an Out-of-Band Garbage Collector

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