When I was at Google, on an SRE team, here is the explanation that I was given.
Early on Google used dynamic libraries. But weird things happen at Google scale. For example Google has a dataset known, for fairly obvious reasons, as "the web". Basically any interesting computation with it takes years. Enough to be a multiple of the expected lifespan of a random computer. Therefore during that computation, you have to expect every random thing that tends to go wrong, to go wrong. Up to and including machines dying.
One of the weird things that becomes common at Google scale, are cosmic bit flips. With static binaries, you can figure out that something went wrong, kill the instance, launch a new one, and you're fine. That machine will later launch something else and also be fine.
But what happens if there was a cosmic bit flip in a dynamic library? Everything launched on that machine will be wrong. This has to get detected, then the processes killed and relaunched. Since this keeps happening, that machine is always there lightly loaded, ready for new stuff to launch. New stuff that...wind up broken for the same reason! Often the killed process will relaunch on the bad machine, failing again! This will continue until someone reboots the machine.
Static binaries are wasteful. But they aren't as problematic for the infrastructure as detecting and fixing this particular condition. And, according to SRE lore circa 2010, this was the actual reason for the switch to static binaries. And then they realized all sorts of other benefits. Like having a good upgrade path for what would normally be shared libraries.
> But what happens if there was a cosmic bit flip in a dynamic library?
I think there were more basic reasons we didn't ship shared libraries to production.
1. They wouldn't have been "shared", because every program was built from its own snapshot of the monorepo, and would naturally have slightly different library versions. Nobody worried about ABI compatibility when evolving C++ interfaces, so (in general) it wasn't possible to reuse a .so built at another time. Thus, it wouldn't actually save any disk space or memory to use dynamic linking.
2. When I arrived in 2005, the build system was embedding absolute paths to shared libraries into the final executable. So it wasn't possible to take a dynamically linked program, copy it to a different machine, and execute it there, unless you used a chroot or container. (And at that time we didn't even use mount namespaces on prod machines.) This was one of the things we had to fix to make it possible to run tests on Forge.
3. We did use shared libraries for tests, and this revealed that ld.so's algorithm for symbol resolution was quadratic in the number of shared objects. Andrew Chatham fixed some of this (https://sourceware.org/legacy-ml/libc-alpha/2006-01/msg00018...), and I got the rest of it eventually; but there was a time before GRTE, when we didn't have a straightforward way to patch the glibc in prod.
That said, I did hear a similar story from an SRE about fear of bitflips being the reason they wouldn't put the gws command line into a flagfile. So I can imagine it being a rationale for not even trying to fix the above problems in order to enable dynamic linking.
> Since this keeps happening, that machine is always there lightly loaded, ready for new stuff to launch. New stuff that...wind up broken for the same reason!
I did see this failure mode occur for similar reasons, such as corruption of the symlinks in /lib. (google3 executables were typically not totally static, but still linked libc itself dynamically.) But it always seemed to me that we had way more problems attributable to kernel, firmware, and CPU bugs than to SEUs.
Thanks. It is nice to hear another perspective on this.
But here is a question. How much of SEUs not being problems were because they weren't problems? Versus because there were solutions in place to mitigate the potential severity of that kind of problem? (The other problems that you name are harder to mitigate.)
Memory and disk corruption definitely were a problem in the early days. See https://news.ycombinator.com/item?id=14206811 for example. I also recall an anecdote about how the search index basically became unbuildable beyond a certain size due to the probability of corruption, which was what inspired RecordIO. I think ECC RAM and transport checksums largely fixed those problems.
It's pretty challenging for software to defend against SEUs corrupting memory, especially when retrofitting an existing design like Linux. While operating Forge, we saw plenty of machines miscompute stuff, and we definitely worried about garbage getting into our caches. But my recollection is that the main cause was individual bad CPUs. We would reuse files in tmpfs for days without reverifying their checksums, and while we considered adding a scrubber, we never saw evidence that it would have caught much.
Maybe the CPU failures were actually due to radiation damage, but as they tended to be fairly sticky, my guess is something more like electromigration.
In Azure - which I think is at Google scale - everything is dynamically linked. Actually a lot of Azure is built on C# which does not even support static linking...
Statically linking being necessary for scaling does not pass the smell test for me.
I never worked for Google, but have seen some strange things like bit flips at more modest scales. From the parent description, it looks like defaulting to static binaries is helping to speed up troubleshooting to remove the “this should never happen, but statistically will happen every so often” class of bugs.
As I see it, the issue isn’t requiring static compiling to scale. It’s requiring it to make troubleshooting or measuring performance at scale easier. Not required, per se, but very helpful.
Exactly. SRE is about monitoring and troubleshooting at scale.
Google runs on a microservices architecture. It's done that since before that was cool. You have to do a lot to make a microservices architecture work. Google did not advertise a lot of that. Today we have things like Data Dog that give you some of the basics. But for a long time, people who left Google faced a world of pain because of how far behind the rest of the world was.
Azure's devops record is not nearly as good as Google's was.
The biggest datasets that ChatGPT is aware of being processed in complex analytics jobs on Azure are roughly a thousand times smaller than an estimate of Google's regularly processed snapshot of the web. There is a reason why most of the fundamental advancements in how to parallelize data and computations - such as map-reduce and BigTable - all came from Google. Nobody else worked at their scale before they did. (Then Google published it, and people began to implement it. Then failed to understand what was operationally important to making it actually work at scale...)
So, despite how big it is, I don't think that Azure operates at Google scale.
For the record, back when I worked at Google, the public internet was only the third largest network that I knew of. Larger still was the network that Google uses for internal API calls. (Do you have any idea how many API calls it takes to serve a Google search page?) And larger still was the network that kept data synchronized between data centers. (So, for example, you don't lose your mail if a data center goes down.)
Does AWS have a good reputation in devops? Because large chunks of AWS are built on Java - which also does not offer static linking (bundling a bunch of *.jar files into one exe does not count as static linking). Still does not pass the smell test.
In AWS, only the very core Infra-as-a-Service that they dogfood can be considered "good", Everything else that's more Platform-as-a-Service can be considered a half baked leaky abstraction. Anything they release as "GA" especially around ReInvent should be avoided for a minimum of 6 months-1 year since it's more like a public Beta with some guaranteed bugs.
In AWS, only the very core Infra-as-a-Service that they dogfood can be considered "good" - large chunks of which are, by the way, written in Java. I think you are proving my point...
> But what happens if there was a cosmic bit flip in a dynamic library?
You'd need multiple of those, because you have ECC. Not impossible, but getting all those dice rolled the same way requires even bigger scale than Google's.
One reason is that using static binaries greatly simplifies the problem of establishing Binary Provenance, upon which security claims and many other important things rely. In environments like Google’s it's important to know that what you have deployed to production is exactly what you think it is.
> One reason is that using static binaries greatly simplifies the problem of establishing Binary Provenance, upon which security claims and many other important things rely.
It depends.
If it is a vulnerability stemming from libc, then every single binary has to be re-linked and redeployed, which can lead to a situation where something has been accidentally left out due to a unaccounted for artefact.
One solution could be bundling the binary or related multiple binaries with the operating system image but that would incur a multidimensional overhead that would be unacceptable for most people and then we would be talking about «an application binary statically linked into the operating system» so to speak.
> If it is a vulnerability stemming from libc, then every single binary has to be re-linked and redeployed, which can lead to a situation where something has been accidentally left out due to a unaccounted for artefact.
The whole point of Binary Provenance is that there are no unaccounted-for artifacts: Every build should produce binary provenance describing exactly how a given binary artifact was built: the inputs, the transformation, and the entity that performed the build. So, to use your example, you'll always know which artefacts were linked against that bad version of libc.
> […] which artefacts were linked against that bad version of libc.
There is one libc for the entire system (a physical server, a virtual one, etc.), including the application(s) that have/have been deployed into an operating environment.
In the case of the entire operating environment (the OS + applications) being statically linked against a libc, the entire operating environment has to be re-linked and redeployed as a single concerted effort.
In dynamically linked operating environments, only the libc needs to be updated.
The former is a substantially more laborious and inherently more risky effort unless the organisation has achieved a sufficiently large scale where such deployment artefacts are fully disposable and the deployment process is fully automated. Not many organisations practically operate at that level of maturity and scale, with FAANG or similar scale being a notable exception. It is often cited as an aspiration, yet the road to that level of maturity is windy and is fraught with many shortcuts in real life which result in the binary provenance being ignored or rendering it irrelevant. The expected aftermath is, of course, a security incident.
I claimed that Binary Provenance was important to organizations such as Google where it is important to know exactly what has gone into the artefacts that have been deployed into production. You then replied "it depends" but, when pressed, defended your claim by saying, in effect, that binary provenance doesn't work in organizations that have immaturate engineering practices where they don't actually follow the practice of enforcing Binary Provenance.
But I feel like we already knew that practices don't work unless organizations actually follow them.
My point is that static linking alone and by itself does not meaningfully improve binary provenance and is mostly expensive security theatre from a provenance standpoint due to a statically linked binary being more opaque from a component attribution perspective – unless an inseparable SBOM (which is cryptographically tied to the binary), plus signed build attestations are present.
Static linking actually destroys the boundaries that a provenance consumer would normally want due to erasure of the dependency identities rendering them irrecoverable in a trustworthy way from the binary by way of global code optimisation, inlining (sometimes heavy), LTO, dead code elimination and alike. It is harder to reason about and audit a single opaque blob than a set of separately versioned shared libraries.
Static linking, however, is very good at avoiding «shared/dynamic library dependency hell» which is a reliability and operability win. From a binary provenance standpoint, it is largely orthogonal.
Static linking can improve one narrow provenance-adjacent property: fewer moving parts at deploy and run time.
The «it depends» part of the comment concerned the FAANG-scale level of infrastructure and operational maturity where the organisation can reliably enforce hermetic builds and dependency pinning across teams, produce and retain attestations and SBOM's bound to release artefacts, rebuild the world quickly on demand and roll out safely with strong observability and rollback. Many organisations choose dynamic linking plus image sealing because it gives them similar provenance and incident response properties with less rebuild pressure at a substantially smaller cost.
So static linking mainly changes operational risk and deployment ergonomics, not evidentiary quality about where the code came from and how it was produced, whereas dynamic linking, on the other hand, may yield better provenance properties when the shared libraries themselves have strong identity and distribution provenance.
NB Please do note that the diatribe is not directed at you in any way, it is an off-hand remark and a reference to people who prescribe purported benefits to the static linking that it espouses because «Google does» it without taking into account the overall context, maturity and scale of the operating environment Google et al operate at.
Early on Google used dynamic libraries. But weird things happen at Google scale. For example Google has a dataset known, for fairly obvious reasons, as "the web". Basically any interesting computation with it takes years. Enough to be a multiple of the expected lifespan of a random computer. Therefore during that computation, you have to expect every random thing that tends to go wrong, to go wrong. Up to and including machines dying.
One of the weird things that becomes common at Google scale, are cosmic bit flips. With static binaries, you can figure out that something went wrong, kill the instance, launch a new one, and you're fine. That machine will later launch something else and also be fine.
But what happens if there was a cosmic bit flip in a dynamic library? Everything launched on that machine will be wrong. This has to get detected, then the processes killed and relaunched. Since this keeps happening, that machine is always there lightly loaded, ready for new stuff to launch. New stuff that...wind up broken for the same reason! Often the killed process will relaunch on the bad machine, failing again! This will continue until someone reboots the machine.
Static binaries are wasteful. But they aren't as problematic for the infrastructure as detecting and fixing this particular condition. And, according to SRE lore circa 2010, this was the actual reason for the switch to static binaries. And then they realized all sorts of other benefits. Like having a good upgrade path for what would normally be shared libraries.