monstrado

I think the point of ACP being an open protocol is so that other editors (e.g. VSCode, Neovim) can implement it as a receiver and integration with ClaudeCode/GeminiCLI/... would just work.

No write up, but the main reason was reusing the existing database we were comfortable deploying at the time. We were already using FDB for an online aggregation / mutation store for ad-hoc time-series analytics...albeit, a custom layer that we wrote (not RecordLayer).

When RecordLayer launched, I tested it out by building a catalog system that we could evolve and add new services with a single repository of protobuf schemas.

I leveraged FoundationDB and RecordLayer to build a transactional catalog system for all our data services at a previous company, and it was honestly just an amazing piece of software. Adding gRPC into the mix for the serving layer felt so natural since schemas / records are defined using Protobuf with RecordLayer.

The only real downside is that the onramp for running FoundationDB at scale is quite a bit higher than a traditional distributed database.

One reason would be if you're already fluent in ClickHouse's SQL dialect. Although they maintain great standard SQL compatibility, they also have a great deal of special functions/aggregates/etc that are ClickHouse specific.

Other reasons include their wide range of input formats, special table functions (e.g. query a URL).

I built an online / mutable time-series database using FDB a few years back at a previous company. Not only was it rock solid, but it scaled linearly pretty effortlessly. It truly is one of novel modern pieces of technologies out there, and I wish there were more layers built on top of it.

As an engineer who admires the work done by DuckDB, I'm disappointed that the co-founder of its evolution is spreading FUD about competitors before its even in the competitive conversation.

> Stability. It OOMS, your CTO mentioned that last week.

I ran ClickHouse clusters for years with zero stability issues (even as a beginner at the time) at an extremely large volume video game studio with real-time needs. Using online materialized views, I was able to construct rollups of vital KPIs at millisecond level while maintaining multi-thousand QPS. Stability was never a concern of ours, and quite frankly, we were kind of blown away.

> Scale. The distributed plan is broken and I'm not sure Clickhouse even has shuffle.

First, I hate the word "broken" with zero explanation what you mean by this. Based on your language, I'm assuming you're just suggesting the distributed plans aren't as efficient as possible, a limitation that the engineers are not shy to admit.

> SQL. It is very non-standard.

I would argue the language is more a superset than "non-standard". Most everything for us just worked, and often I found areas of SQL that I could reduce significantly due to the "non-standard" extras they've added. For example: Did you know they have built-in aggregate functions for computing retention?!

> Knobs. Lots of knobs that are poorly documented. It's unclear which are mandatory. You have to restart for most.

Yes, there are a lot of knobs. ClickHouse works wonderfully out of the box with the default knobs, but you're free to tinker because that's how flexible the technology is.

You worked at Google for over a decade? You should know. Google's tech is notorious for having a TON of knobs for their internal technology (e.g. BigTable). Just because the knobs are there doesn't mean they must be tuned, it just means the engineers thought ahead. Also, the vast majority of configuration changes I've made never required a restart...I'm not even sure why you pointed this out.

(Disclaimer: I have been using ClickHouse successfully for several years)

Not sure if I hit those limits, we were at around 100 nodes and over 170-180 processes. The biggest thing we recognized was tuning the number of recruited proxies and other stateless roles. We were doing up to around 400k tps once we tuned those.

Running it locally is as easy as downloading and installing. Scaling FDB is a bit more of a challenge partially due to their process-per-core design decision, which coincidently helps make FDB as bullet proof as it is.

This project (mvSQLite) appears to have found a way around the 5s transaction limit as well as the size, so that's really promising. That being said, I believe the new RedWood storage engine in FDB 7.0+ is making inroads in eliminating some of these limitations, and this project should also benefit from that new storage engine...(prefix compression is a big one).

Is this the project you guys referenced using Apache Arrow for?

The introduction of parallelized Parquet reads coupled with s3Cluster is really awesome. I feel ClickHouse is one step closer to unlocking the ephemeral SQL compute cluster (e.g. Presto, Hive) use case. I could imagine one day it having a HiveMetaStore read-only database option for querying existing data in companies...very fast, I might add.

I've had a really positive experience using ClickHouse as an InfluxDB replacement. Initially I used the BUFFERED table type to overcome the "tiny inserts" problem, but ultimately just batch up writes in my custom line-protocol TCP server which translates line-protocol to JDBC inserts (RowBinary).

Last time I checked I have a few hundred billion rows in the table with a significant compression ratio (not sure off hand). Most importantly, the table is ordered efficiently enough to allow me to query years of metrics (Grafana plugin) at millisecond speed.

Side note, I recall ClickHouse developers mentioning they are currently working on an implementation change which will allow many tiny inserts to be much more performant and realistic to use in the real-world.

Hope this helps!