Great video! Love the concepts discussed, and the tigerstyle mentioned by adityaathealye.
For Halo 4, I architected a system for our telemetry
that that was a many-reader/many-writer circular lockless queue with performance measured in the microseconds. We had to support subsecond latencies for cloud-driven per-user aggregations, with up to 400ms one-way latencies. So we had 200ms client & server time to manage lifetime counts.
I was so proud of our team for pulling off the vision. When we launched, there was a bug (someone left in telemetry in a tight render loop during a cutsceen, so our 20k/s client-side throttles kicked in). We hit an average of 700k/s on launch day, with peak ingestion throughput of 831k/s (2 weeks to hit a trillion). And we didn't break a sweat.
Partner teams that were trying to provide our reporting capabilities started slowing down, though, haha, and brought it to our attention ("We, uh, can't scale anymore, there's no more servers on the eastcoast data center we can allocate")... so we hit the killswitch on that category of event (another piece I was proud of: each instrumentation point did a quick binary mask check for 64 categories and 64 subcategories to see if it should emit... one reason why the instrumentation times were so blazing fast, we had minimal branching based of a hotly-cached variable that would hang around L1 because it was touched so frequently).
Querying that day for insights, though... queries touching the launch day would 30x query duration. XD
I think I need a deeper-dive into the "diagonal scaling" presented. From my understanding, this is actually no different from "industry decoupling" he disparages earlier in the presentation. There are even off-the-shelf libraries for LSMs backed by object storage like SlateDB.
The author of SlateDB, Chris Riccomini, is an angel investor in TigerBeetle.
However, TB here is also providing a Replicated State Machine with consensus and strict serializability, in front of object storage, to provide remote object storage capacity and recovery, but with local NVMe latency and without sacrificing consistency or durability.
TB navigates the entire design space, specializing for both hot and cold (transactional) data.
The more you zoom, it’s a stronger set of guarantees in terms of safety and performance.
I feel the Expression Problem neatly frames the "diagonal scaling" proposition; what system design choices will allow the architecture to scale vertically in what fashion, while also being able to scale what horizontally, without losing strict serialisability.
If we add a "vertical" capability, it cannot be at the cost of any existing "horizontal" capability, nor should doing so forfend any future "horizontal" capability. And vice-versa (adding horizontal capability should not mess with vertical ones). The point at which one will break the other is the theoretical design limit of the system.
in general these aren't in conflict. in particular once I have a system which can distribute work among faulty nodes and maintain serializability, exploiting parallelism _within_ a fault domain just falls out.
This was a team effort: the object storage connector, the scale test, the visualization, the slides, even provisioning the hardware had its challenges!
Oh most certainly; I am remiss to have not included the group effort in my comment, particularly as a person surrounded by theatre and film making friends.
Still, for the same reason, I have some idea of why their productions turn out well (or not). Where "well" is "a story well told", not "successful" as in "did well at the box office". The why is usually one person who keeps asking the questions and making the decisions that take the story from imagination to imagination via screen or floor.
Something tells me your doubtlessly excellent "production team" (in film terms) will agree with my original comment :)
This looks like yet another basic key value store.
Benchmarking is a complicated problem, but FoundationDB claims 300,000 reads per second on a single core. TigerBeetle claims 100k-500k TPS on... Some kind of hardware?
I have tried to use tiger beetle in production. haven't been successful yet.
nice stuff, multi master replication.
user API, super small.
doubts about how to do streaming backup.
after studying the API and doing some spike architectures I come to the conclusion (I may be wrong):
tiger beetle is awesome to keep the account balance. that's it.
because you pretty much get the transactions affecting and account and IIRC there was not a lot you can do about how to query them or use them.
also I was thinking it would be nice to have something like an account grouping other accounts to answer something like: how much money out user accounts have in this microsecond?
I think that was more or less about itm they have some special fields u128 to store ids to the transaction they represent into your actual system
and IIRC handle multi currency in different books
my conclusion was: I think I don't get it yet. I think I'm missing something. had to write a ruby client for it and build an UI to play with the API and do some transactions and see how it behaved. yet that was my conclusion
To a first approximation, yes. But, why? And for up to how many hundred terabytes of data can you get away with the single beefy server? Provided you make what design choices?
To keep things simple. My current company is running multiple instances of back-end services for absolutely no fucking reason, and I had to fix numerous race condition bugs for them. I had an interview with a startup where, after I asked why they were using distributed DynamoDB locks in a monolith app with only a single instance running, the person said "it works for us" and got defensive. Later they told me I wasn't experienced enough. I am so frustrated that there appears to be zero basic engineering rigor anywhere I can find nowadays.
> And for up to how many hundred terabytes of data can you get away with the single beefy server?
Do you even need to store many hundred terabytes of data? I have never encountered a scenario in my career (admittedly not very long so far) where there was a need to store even one terabyte of data. But in case of TigerBeetle, from skimming through the video, it appears they offload the main bulk of data to a "remote storage."
Boot it up again. You'll still have higher availability than AWS, GitHub, OpenAI, Anthropic, and many others.
> Where do you think those object storage live exactly?
On a RAID5 array with hot-swappable disks, of course.
(Edit to add: this is just a comment on Kubernetes being invoked whenever someone talks about scalability; I have massive respect for what the TigerBeetle folks are doing)
Great video! Love the concepts discussed, and the tigerstyle mentioned by adityaathealye.
For Halo 4, I architected a system for our telemetry that that was a many-reader/many-writer circular lockless queue with performance measured in the microseconds. We had to support subsecond latencies for cloud-driven per-user aggregations, with up to 400ms one-way latencies. So we had 200ms client & server time to manage lifetime counts.
I was so proud of our team for pulling off the vision. When we launched, there was a bug (someone left in telemetry in a tight render loop during a cutsceen, so our 20k/s client-side throttles kicked in). We hit an average of 700k/s on launch day, with peak ingestion throughput of 831k/s (2 weeks to hit a trillion). And we didn't break a sweat.
Partner teams that were trying to provide our reporting capabilities started slowing down, though, haha, and brought it to our attention ("We, uh, can't scale anymore, there's no more servers on the eastcoast data center we can allocate")... so we hit the killswitch on that category of event (another piece I was proud of: each instrumentation point did a quick binary mask check for 64 categories and 64 subcategories to see if it should emit... one reason why the instrumentation times were so blazing fast, we had minimal branching based of a hotly-cached variable that would hang around L1 because it was touched so frequently).
Querying that day for insights, though... queries touching the launch day would 30x query duration. XD
I think I need a deeper-dive into the "diagonal scaling" presented. From my understanding, this is actually no different from "industry decoupling" he disparages earlier in the presentation. There are even off-the-shelf libraries for LSMs backed by object storage like SlateDB.
The author of SlateDB, Chris Riccomini, is an angel investor in TigerBeetle.
However, TB here is also providing a Replicated State Machine with consensus and strict serializability, in front of object storage, to provide remote object storage capacity and recovery, but with local NVMe latency and without sacrificing consistency or durability.
TB navigates the entire design space, specializing for both hot and cold (transactional) data.
The more you zoom, it’s a stronger set of guarantees in terms of safety and performance.
I feel the Expression Problem neatly frames the "diagonal scaling" proposition; what system design choices will allow the architecture to scale vertically in what fashion, while also being able to scale what horizontally, without losing strict serialisability.
If we add a "vertical" capability, it cannot be at the cost of any existing "horizontal" capability, nor should doing so forfend any future "horizontal" capability. And vice-versa (adding horizontal capability should not mess with vertical ones). The point at which one will break the other is the theoretical design limit of the system.
in general these aren't in conflict. in particular once I have a system which can distribute work among faulty nodes and maintain serializability, exploiting parallelism _within_ a fault domain just falls out.
TigerBeetle's Joran Greef is a teacher / explainer par excellence.
Ah I appreciate your kind words, Aditya!
This was a team effort: the object storage connector, the scale test, the visualization, the slides, even provisioning the hardware had its challenges!
Oh most certainly; I am remiss to have not included the group effort in my comment, particularly as a person surrounded by theatre and film making friends.
Still, for the same reason, I have some idea of why their productions turn out well (or not). Where "well" is "a story well told", not "successful" as in "did well at the box office". The why is usually one person who keeps asking the questions and making the decisions that take the story from imagination to imagination via screen or floor.
Something tells me your doubtlessly excellent "production team" (in film terms) will agree with my original comment :)
This looks like yet another basic key value store.
Benchmarking is a complicated problem, but FoundationDB claims 300,000 reads per second on a single core. TigerBeetle claims 100k-500k TPS on... Some kind of hardware?
https://apple.github.io/foundationdb/benchmarking.html
I have tried to use tiger beetle in production. haven't been successful yet.
nice stuff, multi master replication.
user API, super small.
doubts about how to do streaming backup.
after studying the API and doing some spike architectures I come to the conclusion (I may be wrong):
tiger beetle is awesome to keep the account balance. that's it.
because you pretty much get the transactions affecting and account and IIRC there was not a lot you can do about how to query them or use them.
also I was thinking it would be nice to have something like an account grouping other accounts to answer something like: how much money out user accounts have in this microsecond?
I think that was more or less about itm they have some special fields u128 to store ids to the transaction they represent into your actual system
and IIRC handle multi currency in different books
my conclusion was: I think I don't get it yet. I think I'm missing something. had to write a ruby client for it and build an UI to play with the API and do some transactions and see how it behaved. yet that was my conclusion
would be great to have an official UI client
On the streaming side, are you looking for Change Data Capture?
https://docs.tigerbeetle.com/operating/cdc/
sorry, i meant something like an external continuous backup. just in case the system get compromised, a constant off-site non operational backup
The takeaway is that you can just buy a single beefy server instead of using kubernetes or whatever.
To a first approximation, yes. But, why? And for up to how many hundred terabytes of data can you get away with the single beefy server? Provided you make what design choices?
Which leads to the real takeaway which is "Tiger Style": https://tigerstyle.dev/ which I am partial to, along with Rich Hickey's "Hammock Driven Development" https://www.youtube.com/watch?v=f84n5oFoZBc
"Tiger on Hammock" will absolutely smoke the competition.
(edit: add links)
> But, why?
To keep things simple. My current company is running multiple instances of back-end services for absolutely no fucking reason, and I had to fix numerous race condition bugs for them. I had an interview with a startup where, after I asked why they were using distributed DynamoDB locks in a monolith app with only a single instance running, the person said "it works for us" and got defensive. Later they told me I wasn't experienced enough. I am so frustrated that there appears to be zero basic engineering rigor anywhere I can find nowadays.
> And for up to how many hundred terabytes of data can you get away with the single beefy server?
Do you even need to store many hundred terabytes of data? I have never encountered a scenario in my career (admittedly not very long so far) where there was a need to store even one terabyte of data. But in case of TigerBeetle, from skimming through the video, it appears they offload the main bulk of data to a "remote storage."
If your opinion was worth listening to, you'd be working somewhere that had multiple back-end instances with a very clear reason, designed with rigor.
You'd still be fixing race conditions though.
And your beefy server goes down what do you do? Where do you think those object storage live exactly?
Kubernetes is not just for scaling, it's a way to standardize all ops.
> And your beefy server goes down what do you do?
Boot it up again. You'll still have higher availability than AWS, GitHub, OpenAI, Anthropic, and many others.
> Where do you think those object storage live exactly?
On a RAID5 array with hot-swappable disks, of course.
(Edit to add: this is just a comment on Kubernetes being invoked whenever someone talks about scalability; I have massive respect for what the TigerBeetle folks are doing)
"Always has been"