- .. but object storage "solves" most of that for you, handing you a set of semantics which work reliably
- single file throughput sucks hilariously badly
- .. because 1Gb is ridiculously large for an atomic unit
- (this whole thing resembles a project I did a decade ago for transactional consistency on TFAT on Flash, except that somehow managed faster commit times despite running on a 400Mhz MIPS CPU. Edit: maybe I should try to remember how that worked and write it up for HN)
- therefore, all of the actual work is shifted to the broker. The broker is just periodically committing its state in case it crashes
- it's not clear whether the broker ACKs requests before they're in durable storage? Is it possible to lose requests in flight anyway?
- there's a great design for a message queue system between multiple nodes that aims for at least once delivery, and has existed for decades, while maintaining high throughput: SMTP. Actually, there's a whole bunch of message queue systems?
The original graph appears to simply show the blocking issue of their previous synchronisation mechanism; 10 min to process an item down to 6 min. Any central system would seem to resolve this for them.
In any organisation its good to make choices for simplicity rather than small optimisations - you're optimising maintenance, incident resolution, and development.
Typically I have a small pg server for these things. It'll work out slightly more expensive than this setup for one action, yet it will cope with so much more - extending to all kinds of other queues and config management - with simple management, off the shelf diagnostics etc.
While the object store is neat, there is a confluence of factors which make it great and simple for this workload, that may not extend to others. 200ms latency is a lot for other workloads, 5GB/s doesn't leave a lot of headroom, etc. And I don't want to be asked to diagnose transient issues with this.
So I'm torn. It's simple to deploy and configure from a fresh deployment PoV. Yet it wouldn't be accepted into any deployment I have worked on.
The usual path an engineer takes is to take a complex and slow system and reengineer it into something simple, fast, and wrong. But as far as I can tell from the description in the blog though, it actually works at scale! This feels like a free lunch and I’m wondering what the tradeoff is.
It seems like this is an approach that trades off scale and performance for operational simplicity. They say they only have 1GB of records and they can use a single committer to handle all requests. Failover happens by missing a compare-and-set so there's probably a second of latency to become leader?
This is not to say it's a bad system, but it's very precisely tailored for their needs. If you look at the original Kafka implementation, for instance, it was also very simple and targeted. As you bolt on more use cases and features you lose the simplicity to try and become all things to all people.
This post touches on a realisation I made a while ago, just how far you can get with the guarantees and trade-offs of object storage.
What actually _needs_ to be in the database? I've never gone as far as building a job queue on top of object storage, but have been involved in building surprisingly consistent and reliable systems with object storage.
Depending on who hosts your object storage this seems like it could get much more expensive than using a queue table in your database? But I'm also aware that this is a blog post of an object storage company.
We don't have a relational database, otherwise that would work great for a queue! You can imagine us continuing to iterate here to Step 5, Step 6, ... Step N over time. The tradeoff of each step is complexity, and complexity has to be deserved. This is working exceptionally well currently.
Makes total sense for your use case! I have got bitten by using object storage as a database before (and churning through "update" ops) so this will depend on the pricing (and busy-ness of the queue of course) of the provider anyway. Using whatever you have available instead of introducing complexity is the way. Sqlite / Postgres goes a long way for use cases you wouldn't originally think would go well with a relational database too (full text search, using as queue,...).
Assuming you already using object storage in your project, but don't use Redis yet it wouldn't be re-inventing but just avoiding an extra dependency that would only be used by a single feature.
Several things going on here:
- concurrency is very hard
- .. but object storage "solves" most of that for you, handing you a set of semantics which work reliably
- single file throughput sucks hilariously badly
- .. because 1Gb is ridiculously large for an atomic unit
- (this whole thing resembles a project I did a decade ago for transactional consistency on TFAT on Flash, except that somehow managed faster commit times despite running on a 400Mhz MIPS CPU. Edit: maybe I should try to remember how that worked and write it up for HN)
- therefore, all of the actual work is shifted to the broker. The broker is just periodically committing its state in case it crashes
- it's not clear whether the broker ACKs requests before they're in durable storage? Is it possible to lose requests in flight anyway?
- there's a great design for a message queue system between multiple nodes that aims for at least once delivery, and has existed for decades, while maintaining high throughput: SMTP. Actually, there's a whole bunch of message queue systems?
AFAIK you can kinda "seek" reads in S3 using a range header, WCGW? =D
The original graph appears to simply show the blocking issue of their previous synchronisation mechanism; 10 min to process an item down to 6 min. Any central system would seem to resolve this for them.
In any organisation its good to make choices for simplicity rather than small optimisations - you're optimising maintenance, incident resolution, and development.
Typically I have a small pg server for these things. It'll work out slightly more expensive than this setup for one action, yet it will cope with so much more - extending to all kinds of other queues and config management - with simple management, off the shelf diagnostics etc.
While the object store is neat, there is a confluence of factors which make it great and simple for this workload, that may not extend to others. 200ms latency is a lot for other workloads, 5GB/s doesn't leave a lot of headroom, etc. And I don't want to be asked to diagnose transient issues with this.
So I'm torn. It's simple to deploy and configure from a fresh deployment PoV. Yet it wouldn't be accepted into any deployment I have worked on.
The usual path an engineer takes is to take a complex and slow system and reengineer it into something simple, fast, and wrong. But as far as I can tell from the description in the blog though, it actually works at scale! This feels like a free lunch and I’m wondering what the tradeoff is.
It seems like this is an approach that trades off scale and performance for operational simplicity. They say they only have 1GB of records and they can use a single committer to handle all requests. Failover happens by missing a compare-and-set so there's probably a second of latency to become leader?
This is not to say it's a bad system, but it's very precisely tailored for their needs. If you look at the original Kafka implementation, for instance, it was also very simple and targeted. As you bolt on more use cases and features you lose the simplicity to try and become all things to all people.
Write amplification >9000 mostly
This post touches on a realisation I made a while ago, just how far you can get with the guarantees and trade-offs of object storage.
What actually _needs_ to be in the database? I've never gone as far as building a job queue on top of object storage, but have been involved in building surprisingly consistent and reliable systems with object storage.
Depending on who hosts your object storage this seems like it could get much more expensive than using a queue table in your database? But I'm also aware that this is a blog post of an object storage company.
(cofounder of tpuf here)
We don't have a relational database, otherwise that would work great for a queue! You can imagine us continuing to iterate here to Step 5, Step 6, ... Step N over time. The tradeoff of each step is complexity, and complexity has to be deserved. This is working exceptionally well currently.
Makes total sense for your use case! I have got bitten by using object storage as a database before (and churning through "update" ops) so this will depend on the pricing (and busy-ness of the queue of course) of the provider anyway. Using whatever you have available instead of introducing complexity is the way. Sqlite / Postgres goes a long way for use cases you wouldn't originally think would go well with a relational database too (full text search, using as queue,...).
Due to the batching, this will only consume a few million class B per month. They are $5/million
Is this reinventing a few redis features with an object storage for persistence?
Assuming you already using object storage in your project, but don't use Redis yet it wouldn't be re-inventing but just avoiding an extra dependency that would only be used by a single feature.
that's A choice.