Isn't S3 Express not the same API? You have to use a "directory bucket" which isn't an object store anymore, as it has actual directories.
To be honest I'm not actually sure how different the API is. I've never used it. I just frequently trip over the existence of parallel APIs for directory buckets (when I'm doing something niche, mostly; I think GetObject/PutObject are the same.)
> By far the most important aspect is that we need to keep the new codebase as compatible as possible, both in terms of semantics and in terms of code structure. We expect to maintain both codebases for quite some time going forward. Languages that allow for a structurally similar codebase offer a significant boon for anyone making code changes because we can easily port changes between the two codebases. In contrast, languages that require fundamental rethinking of memory management, mutation, data structuring, polymorphism, laziness, etc., might be a better fit for a ground-up rewrite, but we're undertaking this more as a port that maintains the existing behavior and critical optimizations we've built into the language. Idiomatic Go strongly resembles the existing coding patterns of the TypeScript codebase, which makes this porting effort much more tractable.
I haven't looked at the tsc codebase. I do currently use Golang at my job and have used TypeScript at a previous job several years ago.
I'm surprised to hear that idiomatic Golang resembles the existing coding patterns of the tsc codebase. I've never felt that idiomatic code in Golang resembled idiomatic code in TypeScript. Notably, sum types are commonly called out as something especially useful in writing compilers, and when I've wanted them in Golang I've struggled to replace them.
Is there something special about the existing tsc codebase, or does the statement about idiomatic Golang resembling the existing codebase something you could say about most TypeScript codebases?
> As he points out: it would be terrible! We’d lose the intuitive understanding of how to use the gears to solve any situation we encounter. Which mode do you use for gravel + downhill?
I have no idea, actually, what gears I should use for gravel + downhill, which is surprising since about 6 years ago I biked from Pittsburgh to DC, which had a fairly long downhill section. I remember mostly coasting on that section. I might've stopped a few times to make sure my brakes could cool down. I don't think there is a wrong gear on a bicycle when coasting down on gravel?
Perhaps I am the user who does need a nightmare bicycle with a gravel + downhill button.
> In exchange for such favorable terms (i.e., small carrying cost, matures on death), the bank will receive a share of the collateral’s appreciation (essentially amounting to “stock appreciation rights"), and this obligation will be settled upon the borrower’s death.
I'm not following what you're saying. Why do we need a callback?
In this imaginary design, the syscalls you make would look something like:
- BeginChannelTx -> return ChannelTxID
- ReadZFSProperties(ChannelTxID, params) -> return data
- DestroySomeDatasets(ChannelTxID, params) -> ok
- CommitChannelTx(ChannelTxID)
Notably, DestroySomeDatasets doesn't actually do any work. It merely records which datasets you want to destroy. There are no callbacks as far as I can see: there's no kernel thread waiting on a user thread to do something. This way also lets you express branching.
The drawback of this approach is you need a lock on all mutating administrative commands when you call BeginChannelTX. I talked to a ZFS dev, and he said that with ZFS' design, that's actually the txg sync lock. This means that while reads will proceed, writes will only proceed for a short period of time, and nothing will make it to disk. The overhead of making all these syscalls was also judged to be problematic.
> Blocking within one, with those locks acquired, and thunking back down to userspace, would mean that 1. a CPU core, and 2. the filesystem itself, would both be tied up indefinitely until that callback thunk returns. If it ever does!
In this theoretical design, you could just block all other administrative modifications. I don't think you need to tie up an entire CPU core, and I'm fairly sure that these zfs operations don't block regular reads and writes.
I think you had it right in your initial comment. There's no good way express branching with an implementation which incrementally submits operations to be committed as a batch. You'd have to take an admin lock on an entire zpool.
EDIT: talked to a zfs dev, said this would take the txg sync lock.
I was really really excited when I saw the title because I've been having a lot of difficulties with other Go SQL libraries, but the caveats section gives me pause.
Needing to use arrays for the IN use case (see https://github.com/kyleconroy/sqlc/issues/216) and the bulk insert case feel like large divergences from what "idiomatic SQL" looks like. It means that you have to adjust how you write your queries. And that can be intimidating for new developers.
The conditional insert case also just doesn't look particularly elegant and the SQL query is pretty large.
sqlc also just doesn't look like it could help with very dynamic queries I need to generate - I work on a team that owns a little domain-specific search engine. The conditional approach could in theory with here, but it's not good for the query planner: https://use-the-index-luke.com/sql/where-clause/obfuscation/...
Can you point to a source that defines Levenstein distance as only referring to bitstreams?
A translation of the original article [1] that introduced the concept notes in a footnote that "the definitions given below are also meaningful if the code is taken to mean an arbitrary set of words (possibly of different lengths) in some alphabet containing r letters (r >= 2)".
And if you wish to strictly stick to how it was originally defined, you'd need to only use strings of the same length.
More recent sources [2] say instead "over some alphabet", and even in the first footnote, describe results for "arbitrarily large alphabets"!
This article prompted the following questions for me:
1. How well did the historical systems do, and why? Presumably non-negligible key travel is not a modern invention.
2. How much of the latency is key travel? (i.e. what korethr said)
3. How exactly were the keys pressed/how fast were they pressed? All the article says about the experimental setup is "The start-of-input was measured by pressing two keys at once – one key on the keyboard and a button that was also connected to the logic analyzer."
To be honest I'm not actually sure how different the API is. I've never used it. I just frequently trip over the existence of parallel APIs for directory buckets (when I'm doing something niche, mostly; I think GetObject/PutObject are the same.)