A distributed queue in a single JSON file on object storage

https://turbopuffer.com/blog/object-storage-queue

by Sirupsen • 5 months ago

171 54 5 months ago

soletta2• 5 months ago

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.

jrjeksjd8d• 5 months ago

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.

loevborg4• 5 months ago

Failover happens by missing a compare-and-set so there's probably a second of latency to become leader?

Conceptually that makes sense. How complicated is it to implement this failover logic in a safe way? If there are two processes, competing for CAS wins, is there not a risk that both will think they're non-leaders and terminate themselves?

danhhz• 5 months ago

(author here)

It seems like this is an approach that trades off scale and performance for operational simplicity.

Yes, this is exactly it. Given that turbopuffer itself is built on the idea of object storage + stateless cache, we're all very comfortable dealing with it operationally. This design is enough for our needs and is much easier to be oncall for than adding an entirely new dependency would have been.

formerly_proven• 5 months ago

Write amplification >9000 mostly

jamescun• 5 months ago

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.

dewey3• 5 months ago

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.

Sirupsen3• 5 months ago

(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.

dewey3• 5 months ago

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,...).

loevborg4• 5 months ago

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.

Love this approach

Normal_gaussian2• 5 months ago

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.

pjc50• 5 months ago

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?

candiddevmike• 5 months ago

AFAIK you can kinda "seek" reads in S3 using a range header, WCGW? =D

staticassertion12• 5 months ago

You can, and it's actually great if you store little "headers" etc to tell you those offsets. Their design doesn't seem super amenable to it because it appears to be one file, but this is why a system that actually intends to scale would break things up. You then cache these headers and, on cache hit, you know "the thing I want is in that chunk of the file, grab it". Throw in bloom filters and now you have a query engine.

Works great for Parquet.

UltraSane4• 5 months ago

Amazon S3 Select enables SQL queries directly on CSV, JSON, or Apache Parquet objects, allowing retrieval of filtered data subsets to reduce latency and costs

Sirupsen3• 5 months ago

Yep! Other than random reads (~p99=200ms on larger ranges), it's essential to get good download performance of a single file. A single (range) request can "only" drive ~500 MB/s, so you need multiple offsets.

https://github.com/sirupsen/napkin-math

jitl2• 5 months ago

The broker runs a single group commit loop on behalf of all clients, so no one contends for the object. Critically, it doesn't acknowledge a write until the group commit has landed in object storage. No client moves on until its data is durably committed.

aduffy• 5 months ago

Yea, the group commit is the real insight here.

I read this blog post and to help wrap my head around it I put together a simple TCP-based KV store with group commit, helped make it click for me.

https://github.com/a10y/group-commit/

isoprophlex• 5 months ago

Is this reinventing a few redis features with an object storage for persistence?

dewey3• 5 months ago

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.

jitl2• 5 months ago

it’s got some more 9s of durability compared to redis (redis did not invent “queue”)

jstrong• 5 months ago

that's A choice.

loevborg4• 5 months ago

Love this writeup. There's so much interesting stuff you can build on top of Object Storage + compare-and-swap. You learn a lot about distributed systems this way.

I'd love to see a full sample implementation based on s3 + ecs - just to study how it works.

staticassertion12• 5 months ago

Yeah, I mean, I think we're all basically doing this now, right? I wouldn't choose this design, but I think something similar to DeltaLake can be simplified down for tons of use cases. Manifest with CAS + buffered objects to S3, maybe compaction if you intend to do lots of reads. It's not hard to put it together.

You can achieve stupidly fast read/write operations if you do this right with a system that is shocking simple to reason about.

Step 4: queue.json with an HA brokered group commit The broker is stateless, so it's easy and inexpensive to move. And if we end up with more than one broker at a time? That's fine: CAS ensures correctness even with two brokers.

TBH this is the part that I think is tricky. Just resolving this in a way that doesn't end up with tons of clients wasting time talking to a broker that buffers their writes, pushes them, then always fails. I solved this at one point with token fencing and then decided it wasn't worth it and I just use a single instance to manage all writes. I'd again point to DeltaLake for the "good" design here, which is to have multiple manifests and only serialize compaction, which also unlocks parallel writers.

The other hard part is data deletion. For the queue it looks deadly simple since it's one file, but if you want to ramp up your scale and get multiple writers or manage indexes (also in S3) then deletion becomes something you have to slip into compaction. Again, I had it at one point and backed it out because it was painful.

But I have 40k writes per second working just fine for my setup, so I'm not worrying. I'd suggest others basically punt as hard as possible on this. If you need more writes, start up a separate index with its own partition for its own separate set of data, or do naive sharding.

zbentley• 5 months ago

I solved this at one point with token fencing

Could you expand on that? Even if it wasn't the approach you stuck with, I'm curious.

staticassertion12• 5 months ago

Oof, I probably misspoke there just slightly. I attempted to solve this with token fencing, I honestly don't know if it worked under failure conditions. This was also a while ago. But the idea was basically that there were two tiers - one was a ring based approach where a single file determined which writer was allocated a 'space' in the ring. Then every write was prepended with that token. Even if a node dropped/ joined and others didn't know about it (because they hadn't re-read the ring file), every write had this token.

Writes were not visible until compaction in this system. At compaction time, tokens would be checked and writes for older tokens would be rejected, so even if two nodes thought that they owned a 'place' in the ring, only writes for the higher value would be accepted. Soooomething like that. I ended up disliking this because it had undesirable failure modes like lots of stale/ wasted writes, and the code sucked.

thomas_fa• 5 months ago

A lot of good insights here. I am also wandering if they can just simply put different jobs (unclaimed, in-progress, deleted/done) into different directory/prefix, and rely on atomic object rename primitive [1][2][3] to solve the problem more gracefully (group commit can still be used if needed).

[1] https://docs.cloud.google.com/storage/docs/samples/storage-m... [2] https://docs.aws.amazon.com/AmazonS3/latest/API/API_RenameOb... [3] https://fractalbits.com/blog/why-we-built-another-object-sto...

staticassertion12• 5 months ago

I didn't know about atomic object rename... it's going to take me a long time to think through the options here.

RenameObject is only supported for objects stored in the S3 Express One Zone storage class.

Ah interesting, I don't use this but I bet in a year+ AWS will have this everywhere lol S3 is just too good.

allknowingfrog• 5 months ago

This is news to me. What motivates you to reach for an S3-backed queue versus SQS?

staticassertion12• 5 months ago

I'm not building a queue, but a lot of things on s3 end up being queue-shaped (more like 'log shaped') because it's very easy to compose many powerful systems out of CAS + "buffer, then push". Basically, you start with "build an immutable log" with those operations and the rest of your system becomes a matter of what you do with that log. A queue needs to support a "pop", but I am supporting other operations. Still, the architecture overlap all begins with CAS + buffer.

That said, I suspect that you can probably beat SQS for a number of use cases, and definitely if you want to hold onto the data long term or search over it then S3 has huge options there.

Performance will be extremely solid unless you need your worst case latency for "push -> pop" to be very tight in your p90.

tomnicholas1• 5 months ago

What you describe is very similar to how Icechunk[1] works. It works beautifully for transactional writes to "repos" containing PBs of scientific array data in object storage.

[1] https://icechunk.io/en/latest/

motoboi2• 5 months ago

By typography alone I can now turbopuffer is written in zig.

soletta2• 5 months ago

It is by the juice of Zig that binaries acquire speed, the allocators acquire ownership, the ownership becomes a warning. It is by typography alone I can now turbopuffer is written in zig.

motoboi2• 5 months ago

thanks for that!

salil999• 5 months ago

Reminds me of WarpStream: https://www.warpstream.com

Similar idea but you have the power of S3 scale (if you really need it). For context, I do not work at WS. My company switched to it recently and we've seen great improvements over traditional Kafka.

talentedtumor• 5 months ago

Does this suffer from ABA problem, or does object storage solve that for you by e.g. refusing to accept writes where content has changed between the read and write?

staticassertion12• 5 months ago

refusing to accept writes where content has changed between the read and write?

Right. You can issue a write that will only be accepted if a condition is matched, like the etag of the object matching your expectation. If it doesn't match, your object was invalidated.

up2isomorphism• 5 months ago

The windows has passed already for this kind of opportunities since there are dozen of people all doing the same thing. Also abusing object storage is not very fun.

hinkley• 5 months ago

For performance reasons we needed a set of assets on all copies of a service. We were using consul for the task management, which is effectively a tree of data that’s tantamount to a json file (in fact we usually pull trees of data as a json file).

Among other problems I knew the next thing we were going to have to do was autoscaling and the system we had for call and response was a mess from that respect. Unanswered questions were: How do you know when all agents have succeeded, how do you avoid overwriting your peers’ data, and what do you do with agents that existed yesterday and don’t today?

I ended up rewriting all of the state management data so that each field had one writer and one or more readers. It also allowed me to move the last live service call for another service and decommission it. Instead of having a admin service you just called one of the peers at random and elected it leader for the duration of that operation. I also arranged the data so the leader could watch the parent key for the roll call and avoid needing to poll.

Each time a task was created the leader would do a service discovery call to get a headcount and then wait for everyone to set a suggests or failure state. Some of these state transitions were idempotent, so if you reissued a task you didn’t need to delete the old results. Everyone who already completed it would noop, and the ones that failed or the new servers that joined the cluster would finish up. If there was a delete operation later then the data would be purged from the data set and the agents, a subsequent call would be considered new.

Long story short, your CS program should have distributed computing classes because this shit is hard to work out from first principles when you don’t know what the principles even are.

ozgrakkurt• 5 months ago

This is really cool but feels like an attack on the pride of software development at the same time.

Just slap some garbage on it and it is better

A distributed queue in a single JSON file on object storage

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