chaotic-good

I don't see any mentions of p99 latency in the benchmark results. Pushing gigabytes per second is not that difficult on modern hardware. Doing so with reasonable latency is what's challenging. Also, instead of using custom benchmarks it's better to just use the OMB (open-messaging benchmark).

I tend to agree with this.

The code tend to be loaded with primitives that express ownership semantics or error handling. Every time something changes (for instance, you want not just read but also modify values referenced by the iterator) you have to change code in many places (you will have to invoke 'as_mut' explicitly even if you're accessing your iterator through mutable ref). This could be attributed (partially) to the lack of function overload. People believe that overload is often abused so it shouldn't be present in the "modern" language. But in languages like C++ overload also helps with const correctness and move semantics. In C++ I don't have to invoke 'as_mut' to modify value referenced by the non-const iterator because dereferencing operator has const and non-const overloads.

Async Rust is on another level of complexity compared to anything I used. The lifetimes are often necessary and everything is warpped into mutliple layers, everything is Arc<Mutex<Box<*>>>.

I'm not asking anything on behalf of any company and just genuinely curious (and I don't think that we're competitors, both systems are designed for totally different niches). I'm working on tiered-storage implementation btw. Looks like the approach here is the total opposite of what everyone else is doing. I see some advantages but also disadvantages to this. Hence the question.

> Why replicate data at the VM/disk level when those disks are already provided as a fully redundant system?

That's easy. EBS and similar solutions comes with the price. They're very expensive. Especially, when you need a lot of IOPs. You may be saving on cross-AZ traffic but you will pay ridiculous amount of money on storage. If you have replication you can use attached storage which is way cheaper.

It could be easy to operate when everything is fine but what's about incidents? If I understand correctly, there is a metadata database (BTW, is it multi-AZ as well?). But what if there is a data loss incident and some metadata was lost? Is it possible to recover from S3? If this is possible, then I guess that can't be very simple and should require a lot of time because S3 is not that easy to scan to find all the artefacts needed for recovery.

Also, this metadata database looks like a bottleneck. All writes and reads should go through it so it could be a point of failure. It's probably distributed and in this case it has its own complex failure modes and it has to be operated somehow.

Also, putting things from different partitions into one object is also something I'm not very keen about. You're introducing a lot of read amplification and S3 bills for egress. So if the object/file has data from 10 partitions and I only need 1, I'm paying for 10x more egress than I need to. The doc mentions fanout reads from multiple agents to satisfy a fetch request. I guess this is the price to pay for this. This is also affects the metadata database. If every object stores data from one partition the metadata can be easily partitioned. But if the object could have data from many partitions it's probably difficult to partition. One reason why Kafka/Redpanda/Pulsar scale very well is that the data and metadata can be easily partitioned and these systems do not have to handle as much metadata as I think WarpStream have to.

The disk thrashes because of fsyncs (Kafka doesn't perform any fsync's). But you can provision more disk space to mitigate this problem. And it looks like the test was set up this way to make Redpanda look worse.

You have to provision you disk space accordingly. NVMe needs some free space to have good performance. In this case I think that in Redpanda benchmarks the disk space was available and in case of benchmarks done by Confluent guy the system was provisioned to use all disk space.

With page cache it's OK, because the FTL layer of the drive will work with 32MiB blocks but in case of Redpanda the drive will struggle because FTL mappings are complex and GC has more work. If Kafka would be doing fsync's the behaviour would be the same.

Overall, this looks like a smearing campaign against Redpanda. The guy who wrote this article works for Confluent and he published it on his own domain to look more neutral. The benchmarks are not fair because one of the systems is doing fsyncs and the other does not. Most differences could be explained by this fact alone.

Modern TSDB is expected to support tags. This means that every series will have a unique set of tag-value pairs associated with it. E.g. 'host=Foo OS=CentOS arch=amd64 ssdmodel=Intel545 ...'. And in the query you could pinpoint relevant series by this tags thus the tags should be searchable. For instance, I may want to see how specific SSD model performs on specific OS or specific app. If the set of tags is stored as a json in one field such queries wouldn't work efficiently.

About that 1M writes thing. You have two options. 1) Organize data by metric name first, or 2) by timestamp. In case of 2) the updates will be linear but reads will have huge amplification. In case of 1) updates will be random, but reads will be fast.

90% compression on time series is not viable unless you have some very specific dataset.

'metric_name text' is actually a tag-value list. Many TSDB's allows you to match data by tag. Each tag should be represented by a column in your example.

Single table design will be prone to high read/write amplification due to data alignment. Usually, you need to read many series at once so your query will turn into full table scan. Or it will read a lot of unneeded data which happened to be located near the data you need. Writes will be slow since your key starts with metrics name. Imagine that you have 1M series and each series gets new data point every second. In your scema it will result in 1M random writes.

Cardinality of the table will go through the roof, BTW. Every data point will add the key. Good luck dealing with this.

They can't handle high cardinality. Imagine having millions of columns in the column-oriented database (70% of those columns are updated every second). Imagine that you have to add new columns all the time.

The main misconception about TSDB's is that it's just a data with timestamp. TSDB's has multi-dimentional data model, time is only one of the dimensions.