6 ms·

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 core

by titzer 8y 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__ 8y ago
Isn't Ruby known for these kind of issues?
- Karunamon 8y ago
  Sadly yes. It’s an absolute joy to code in, but it isn’t even kind of light.
- nwmcsween 8y ago
  Ruby as a language no, most of the implementations yes.
- vidarh 8y 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
  - jashmatthews 8y ago
    It was actually Ruby 2.2 from 2014 that made this redundant. 2.2 introduced incremental marking, removing the last long GC phase in MRI Ruby.
twic 8y 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 8y 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.
  - puzzle 8y ago
    Not 100%, but running them at least 50% is not a bad idea. In these days of containers and QoS scheduling, it's easier to make good use of slack resources. Letting memory sit unused is an even bigger crime! Someone told me that the team at his previous job with a "large cloud provider" based in Seattle was told not to go above 30% CPU usage, at which point they'd buy more hardware. I'm sure not all teams there do things that way, but, coming from Google, 30% (95th percentile over a week) is very low.
    - merb 8y ago
      well if you use k8s you need to have some spare capacity for updates, etc. consider a deployment (deployment app) update it will first create a new container and than kill the old one, which means depending how much memory you request at container creation is the least minimum you would have on spare. if you schedule java apps with at least 1 Gi heap you would at least need 1Gi spare capacity and that is just with one pod/container (it will be worse if you need to do blue/green deploys, since you need the same capacity as your live cluster)
      - puzzle 8y ago
        If you're running Kubernetes, you probably have multiple services running (or it's not really worth the complexity). Then whatever slack you have in the cluster can be amortized over all your services, if they all share the same resources (quota). It's also a good idea not to update too many deployments all at the same time. Even then, deployment updates don't necessarily need to surge above their replica count. You can also configure them to terminate X replicas at a time before bringing up new ones. At Google, all teams have Borg quotas, so it's not unusual to max those up by running as many replicas as possible. During updates, Borg does not allow an user to temporarily oversubscribe their quota (unless you're changing replica count and replica footprint at the same time, but that's another fun story), so it will always take down Y tasks first.
deleted 8y ago
[deleted]
vidarh 8y ago
Those 1000 cores seems to have been equivalent to 10% of their load. The 10% number isn't nearly as shocking.
stmw 8y 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 8y 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.
  - Gibbon1 8y ago
    > handle most request data by stack allocation/deallocation I just write crappy code for microcontrollers. Over 30 years I've seen the amount of ram available for stack grow. From maybe 20-30 bytes to a couple of k. That said it appears to me that stack allocation is severely under utilized for historical reasons. The idea that stack space is a precious resource. Which doesn't fly on machines with 10's of gigabytes of memory. Think programs that put vast quantities of ephemeral objects and short strings on the heap. Using call tree analysis would allow you to put a lot of that on the stack resulting is much better performance and bounded latency.
Sean1708 8y 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.

by titzer 8y 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__ 8y ago

Isn't Ruby known for these kind of issues?

Karunamon 8y ago
Sadly yes. It’s an absolute joy to code in, but it isn’t even kind of light.
nwmcsween 8y ago
Ruby as a language no, most of the implementations yes.
vidarh 8y 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
- jashmatthews 8y ago
  It was actually Ruby 2.2 from 2014 that made this redundant. 2.2 introduced incremental marking, removing the last long GC phase in MRI Ruby.

twic 8y 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 8y 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.
- puzzle 8y ago
  Not 100%, but running them at least 50% is not a bad idea. In these days of containers and QoS scheduling, it's easier to make good use of slack resources. Letting memory sit unused is an even bigger crime! Someone told me that the team at his previous job with a "large cloud provider" based in Seattle was told not to go above 30% CPU usage, at which point they'd buy more hardware. I'm sure not all teams there do things that way, but, coming from Google, 30% (95th percentile over a week) is very low.
  - merb 8y ago
    well if you use k8s you need to have some spare capacity for updates, etc. consider a deployment (deployment app) update it will first create a new container and than kill the old one, which means depending how much memory you request at container creation is the least minimum you would have on spare. if you schedule java apps with at least 1 Gi heap you would at least need 1Gi spare capacity and that is just with one pod/container (it will be worse if you need to do blue/green deploys, since you need the same capacity as your live cluster)
    - puzzle 8y ago
      If you're running Kubernetes, you probably have multiple services running (or it's not really worth the complexity). Then whatever slack you have in the cluster can be amortized over all your services, if they all share the same resources (quota). It's also a good idea not to update too many deployments all at the same time. Even then, deployment updates don't necessarily need to surge above their replica count. You can also configure them to terminate X replicas at a time before bringing up new ones. At Google, all teams have Borg quotas, so it's not unusual to max those up by running as many replicas as possible. During updates, Borg does not allow an user to temporarily oversubscribe their quota (unless you're changing replica count and replica footprint at the same time, but that's another fun story), so it will always take down Y tasks first.

deleted 8y ago

[deleted]

vidarh 8y ago

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

stmw 8y 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 8y 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.
- Gibbon1 8y ago
  > handle most request data by stack allocation/deallocation I just write crappy code for microcontrollers. Over 30 years I've seen the amount of ram available for stack grow. From maybe 20-30 bytes to a couple of k. That said it appears to me that stack allocation is severely under utilized for historical reasons. The idea that stack space is a precious resource. Which doesn't fly on machines with 10's of gigabytes of memory. Think programs that put vast quantities of ephemeral objects and short strings on the heap. Using call tree analysis would allow you to put a lot of that on the stack resulting is much better performance and bounded latency.

Sean1708 8y 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.