One example among many:
https://github.com/DiceDB/dice/blob/0e241a9ca253f17b4d364cdf... defines func ExpandID, which reads from cycleMap without locking the package-global mutex; and func NextID, which writes to cycleMap under a lock of the package-global mutex. So writes are synchronized, but only between each other, and not with reads, so concurrent calls to ExpandID and NextID would race.
This is all fine as a hobby project or whatever, but very far from any kind of production-capable system.
This PR attempted to fix the memory model violation I mentioned in the parent comment, but also added in an extra change that swapped the sync.Mutex to a sync.RWMutex. The PR description claimed 2 benefits: "Eliminates the data race, ensuring thread safety" -- correct! at least to some level; but also "Improves performance by allowing concurrent ExpandID calls, which is likely a common operation" -- which is totally unsubstantiated, and very likely false, as RWMutex is only faster than a regular Mutex under very narrowly-defined load patterns.
In any case, the PR had no kind of test or benchmark to validate either of these claims, so not a great start by the author. But then a maintainer chimed in with a comment that expressed concerns about edge-condition performance details, without any kind of data or evidence, and apparently didn't care about (or know about?) the much more important fixes that the PR made re: data races.
https://github.com/DiceDB/dice/pull/1588#issuecomment-274521...
> I tried changing this, but I did not see any benefit in benchmark numbers.
No apparent understanding of the bugs in this code, nor how changes may or may not fix those bugs, nor really how performance is defined or can be meaningfully evaluated.
Again, hobby project or whatever, all good. But the authors and maintainers of this project are clearly, demonstrably, in over their heads on this one.
I understand the need for correct lock-free impls: Given OP's description, simply avoiding read mutexes can't be the way to go about it?
I could be wrong but the primary in-memory storage appears to be a standard Go map with locking. Is this a temporary choice for iterative development, and is there a longer-term plan to adopt a more optimized or custom data structure ?
I find the DiceDB's reactivity mechanism very intriguing, particularly the "re-execution" of the entire watch command (i.e re-running GET.WATCH mykey on key modification), it's an intriguing design choice.
From what I understand is the Eval func executes client side commands this seem to be laying foundation for more complex watch command that can be evaluated before sending notifications to clients.
But I have the following question.
What is the primary motivation behind re-executing the entire command, as opposed to simply notifying clients of a key change (as in Redis Pub/Sub or streams)? Is the intent to simplify client-side logic by handling complex key dependencies on the server?
Given that re-execution seems computationally expensive, especially with multiple watchers or more complex (hypothetical) watch commands, how are potential performance bottlenecks addressed?
How does this "re-execution" approach compare in terms of scalability and consistency to more established methods like server-side logic (e.g., Lua scripts in Redis) or change data capture (CDC) ?
Are there plans to support more complex watch commands beyond GET.WATCH (e.g. JSON.GET.WATCH), and how would re-execution scale in those cases?
I'm curious about the trade-offs considered in choosing this design and how it aligns with the project's overall goals. Any insights into these design decisions would help me understand its use-cases.
Thanks
I'm skeptical that the re-execution approach can scale for complex queries, the latency and throughput improvements would be offseted by the computational cost and bottlenecks introduced for achieving it via its reactivity mechanism (query subscription), this might not work at scale and serve niche use cases.
There are various ways throughput and latency for kv stores can be improved, so bar is really high here.
The messaging with Dice seems unclear and confusing to describe its purpose/use-cases over alternatives, or how it achieves them, which could just be how it's marketed. But it seems to be a collection of ideas and a WIP project.
I think reducing data fetching complexity and complex key dependencies for end clients could be appealing, and it would be great to have it at the KV store level, but there is no reason this type of reactivity can't be implemented on top of various clients for existing KV stores (like Redis). And basic WATCH with transactions are even offered out of the box in them.
Deno kv seem nice but its vendor locked, also there are many others like dragonfly, valkey etc, redis could still work, even something over sqlite can work, deno has a selfhosted kv on top of sqlite - https://github.com/denoland/denokv
Also with dice its creator had made this talk
https://hasgeek.com/rootconf/2024/sub/how-we-made-dicedb-a-t...
From that and the thread so far it seems, they want to make some super cache by building a realtime multi-threaded kv store, improving latency and reducing its read load via its reactivity mechanism. Solving the problem of cache invalidation.
Not sure how this will be achieved but there is no harm in trying. From what is said and shared, rationale behind this design and its tradeoffs are not clear, code could be fixed/improved but providing clarity on this is essential for adoption.
As if nobody ever uses anything else.
Description from GitHub:
> DiceDB is an open-source, fast, reactive, in-memory database optimized for modern hardware. Commonly used as a cache, it offers a familiar interface while enabling real-time data updates through query subscriptions. It delivers higher throughput and lower median latencies, making it ideal for modern workloads.
DiceDB is an in-memory database that is also reactive. So, instead of polling the database for changes, the database pushes the resultset if you subscribe to it.
We have a similar set of commands as Redis, but are not Redis-compliant.
This application may be very capable, but I agree with the person saying that its use-case isn't clear on the home page, you have to go deeper into the docs. "Smarter than a database" also seems kind of debatable.
Feels arrogant. "Of course you already know what this is, how could you not?"
"What is DiceDB? DiceDB is an open-source, fast, reactive, in-memory database optimized for modern hardware. Commonly used as a cache, it offers a familiar interface while enabling real-time data updates through query subscriptions. It delivers higher throughput and lower median latencies, making it ideal for modern workloads."
> DiceDB is an open-source, fast, reactive, in-memory database optimized for modern hardware.
A Redis-like database with a Redis-like interface. No info about drop-in compatibility, I assume no.
Should be the first sentence on their website and repo.
Kinda refreshing to see someone own it and run with it
Reactive looks promising, doesn't look much useful in realworld for a cache. For example, a client subscribes for something and the machines goes down, what happens to reactivity?
| Metric | DiceDB | Redis |
| -------------------- | -------- | -------- |
| Throughput (ops/sec) | 15655 | 12267 |
| GET p50 (ms) | 0.227327 | 0.270335 |
| GET p90 (ms) | 0.337919 | 0.329727 |
| SET p50 (ms) | 0.230399 | 0.272383 |
| SET p90 (ms) | 0.339967 | 0.331775 |
Something I still fail to understand is where you can actually spend 20ms while answering a GET request in a RAM keyvalue storage (unless you implement it in Java).
I never gained much experience with existing opensource implementations, but when I was building proprietary solutions at my previous workplace, the in-memory response time was measured in tens-hundreds of microseconds. The lower bound of latency is mostly defined by syscalls so using io_uring should in theory result in even better timings, even though I never got to try it in production.
If you read from nvme AND also do the erasure-recovery across 6 nodes (lrc-12-2-2) then yes, you got into tens of milliseconds. But seeing these numbers for a single node RAM DB just doesn't make sense and I'm surprised everyone treats them as normal.
Does anyone has experience with low-latency high-throughput opensource keyvalue storages? Any specific implementation to recommend?
Aren’t these numbers .2 ms, ie 200 microseconds?
If it is built into your language/framework, you can completely ignore the problem of updating the client, as it happens automatically.
Hope that makes sense.
Is there a plan to commercialise this product? (Offer commercial support, features, etc.) I could not find anything obvious from the home page.
I was typing that out and felt like something was wrong but couldn’t put my finger on what.
Would be great to disclose details of this one. I'm interested in using what DiceDB achieves higher throughput.
FYI this is a misspelling of "higher"
Anyway to persist data in case of reboots?
That's the only thing missing here.
Is Go the only SDK ?
But certainly you could build something to handle these and most other needs in this realm with mostly just redis, using streams for what needs to be more robust, in tandem with pub/sub, keyspace notifs, etc. in the areas they are suited to.
It’s written in Go.
These are the real numbers - https://dzone.com/articles/performance-and-scalability-analy...
Does not match with your benchmarks.
We had to write a small benchmark utility (membench) ourselves because the long-term metrics that we are optimizing need to be evaluated in a different way.
Also, the scripts, utilities, and infra configurations are mentioned. Feel free to run it.