That's a very rigorously written article.
Let's also note the 4x speed increase on windows 10, once again underlining just how slow windows filesystem calls are, when compared to direct access, and other (kernel, filesystem) combinations.
TLDR; don't do it.
I've used SQLite blob fields for storing files extensively.
Note that there is a 2GB blob maximum: https://www.sqlite.org/limits.html
To read/write blobs, you have to serialize/deserialize your objects to bytes. This process is not only tedious, but also varies for different objects and it's not a first-class citizen in other tools, so serialization kept breaking as my dependencies upgraded.
As my app matured, I found that I often wanted hierarchical folder-like functionality. Rather than recreating this mess in db relationships, it was easier to store the path and other folder-level metadata in sqlite so that I could work with it in Python. E.g. `os.listdir(my_folder)`.
Also, if you want to interact with other systems/services, then you need files. sqlite can't be read over NFS (e.g. AWS EFS) and by design it has no server for requests. so i found myself caching files to disk for export/import.
SQLite has some settings for handling parallel requests from multiple services, but when I experimented with them I always wound up with a locked db due to competing requests.
For one reason or another, you will end up with hybrid (blob/file) ways of persisting data.
I've used SQLite blob fields for storing files extensively. Note that there is a 2GB blob maximum: https://www.sqlite.org/limits.html
Also note that SQLite does have an incremental blob I/O API (sqlite3_blob_xxx), so unlike most other RDBMS there is no need to read/write blobs as a contiguous piece of memory - handling large blobs is more reasonable than in those. Though the blob API is still separate from normal querying.
Do you have or know of a clear example of how to do this? I have to ask because I spent half of yesterday trying to make it work. The blob_open command wouldn't work until I set a default value on the blob column and then the blob_write command wouldn't work because you can't resize a blob. It was very weird but I'm pretty confident it's because I'm missing something stupid.
I’m pretty sure you can’t resize blobs using this API. The intended usage is to insert/update a row using bind_zeroblob and then update that in place (sans journaling) using the incremental API. It’s a major limitation especially for writing compressed data.
Great. I had not seen bind_zeroblob when reading yesterday. Maybe that’s what I needed to get it moving. Thanks.
Most RDBMS's have streaming blob apis:
MS SQL Server: READTEXT, WRITETEXT, substring, UPDATE.WRITE
Oracle: DBMS_LOB.READ, DBMS_LOB.WRITE
PG: Large Objects
Most of my experience is with SQL server and it can stream large objects incrementally through a web app to browser without loading the whole thing into memory at 100's Mbytes/sec on normal hardware.
I wasn’t aware of PG Large Objects, though I do know about MSSQL Filestream, which iirc are actual files on disk and remotely transparently accessed via SMB - which in this context would be the moral equivalent of storing just the path in a SQLite db. The functionality you quote seems to be deprecated?
SQL server has image, text, varbinary(max) and varchar(max) none of those except varbinary(max) are filestream enabled, and varbinary is only file stream if that is setup and the column is specified file stream when created otherwise its in the DB like the others.
image and text with READTEXT and WRITETEXT are deprecated but still work fine, varbinary(max) and substring UPDATE.WRITE are the modern equivalent and use the same implementations underneath.
Filestream allows larger than 2 gigs, stores the blob data in the filesystem but otherwise is accessed like the other blobs along with some special capability like getting a SMB file pointer from the client for direct access. It is also backed up and replicated like normal, definitely not just like storing a path in the DB.
Filestream performs worse the in db for blobs under about 100kb I believe where it would be recommended to keep them in db for max perf.
I have used MSSQL blobs long before filestream existed and it works well except for the 2 gig limit and once the db gets large backup and log management get more unwieldy than if you just stored them outside the db but it does keep them transactional consistent, which filestream also does.
That sounds nice for chunking, but what if you need contiguous memory? E.g. viewing an image or running an AI model
You can always use a chunked API to read data into a contiguous buffer. Just allocate a large enough contiguous block of memory, then copy chunk by chunk into that contiguous memory until you've read everything.
I wish the API was compatible with iovec though. As that's what all the c standard lib APIs use for non-contiguous memory
As my app matured, I found that I often wanted hierarchical folder-like functionality. Rather than recreating this mess in db relationships, it was easier to store the path and other folder-level metadata in sqlite so that I could work with it in Python. E.g. `os.listdir(my_folder)`.
This is a silly argument, there's no reason to recreate the full hierarchy. If you have something like this:
CREATE TABLE files (path TEXT UNIQUE COLLATE NOCASE);
SELECT path FROM files WHERE path LIKE "./some/path/%";
The reason it's fast is because the table has a UNIQUE index, and LIKE uses it if you turn off case-sensitivity. No need to faff about with hierarchies.
EDIT: btw, I am using SQLite for this purpose in a production application, couldn't be happier with it.
cool. i don't want to recreate a filesystem in my app logic
If that SELECT query is too much for you, I agree, SQLite is maybe not meant for you. Not a very solid argument against SQLite, though.
stop making personal attacks.
i'm sure your suggestion exhibits creative step-one thinking, but i've described several reasons why it doesn't make sense to recreate a filesystem in sqlite, the combination of which should make it clear why doing so is naive
The postgres ltree extension does this beautifully. I'm not sure if similar things exist for sqlite or other rdbmses.
To read/write blobs, you have to serialize/deserialize your objects to bytes. This process is not only tedious, but also varies for different objects and it's not a first-class citizen in other tools, so things kept breaking as my dependencies upgraded.
I'm confused what you mean by this. Files also only contain bytes, so that serialization/deserialization has to happen anyway?
Maybe you need to encode bytes as text for sql?
Writing and reading bytes in sqlite is very easy: https://www.sqlite.org/c3ref/blob_open.html
so i found myself caching files to disk for export/import
Could use a named pipe.
I’m reminded of what I often do at the shell with psub in fish. psub -f creates and returns the path to a fifo/named pipe in $TMPDIR and writes stdin to that; you’ve got a path but aren’t writing to the filesystem.
e.g. you want to feed some output to something that takes file paths as arguments. We want to compare cmd1 | grep foo and cmd2 | grep foo. We pipe each to psub in command substitutions:
diff -u $(cmd1 | grep foo | psub -f) $(cmd2 | grep foo | psub -f)
diff -u /tmp/fish0K5fd.psub /tmp/fish0hE1c.psub
I’m reminded of what I often do at the shell with psub in fish.
ksh and bash too have this as <(…) and >(…) under Process Substitution.
An example from ksh(1) man page:
paste <(cut -f1 file1) <(cut -f3 file2) | tee >(process1) >(process2)bash (at least) has a built-in mechanism to do that
diff <(cmd1 | grep foo) <(cmd2 | grep foo)
Can you describe how you stored the paths in sqlite? I'm not entirely getting it.
just a string field that points to the file path
As my app matured, I found that I often wanted hierarchical folder-like functionality. Rather than recreating this mess in db relationships, it was easier to store the path in sqlite and work with it in Python. E.g. `os.listdir(my_folder)`
This makes total sense and it is also "frowned upon" by people who take a too purist view of databases
(Until it comes a time to backup, or extract files, or grow a hard drive etc and then you figure out how you shot yourself in the foot)
To make it more queryable, you can have different classes for dataset types with metadata like: file_format, num_files, sizes
The idea to emulate hierarchical folder-like functionality ala filepaths is quite brilliant - I might try it out.
Storing Hierarchical Data in Relational Databases
https://medium.com/@rishabhdevmanu/from-trees-to-tables-stor...
As my app matured, I found that I often wanted hierarchical folder-like functionality.
In the process of prototyping some "remote" collaborating file systems, I always wonder whether it is a good idea maintaining a flat map from path concatenated with "/" like an S3 to the file content, in term of efficiency or elegancy.
As my app matured, I found that I often wanted hierarchical folder-like functionality
(1) Slim table "items"
- id / parent_id / kind (0/1 file folder) integer
- name text
- Maybe metadata.
(2) Separate table "content"
- id integer
- data blob
There you have file-system-like structure and fast access times (don't mix content in the first table)
Or, if you wish for deduplication or compression, add item_content (3)
Why hasn’t someone made sqlitefs yet?
I remember reading someone's comments about how instead of databases using their own data serialisation formats for persistence and then optimizing writes and read over that they should just utilize the FS directly and let all of the optimisations built by FS authors be taken advantage of.
I wish I could find that comment, because my explanation doesn't do it justice. Very interesting idea, someone's probably going to explain how it's already been tried in some old IBM database a long time ago and failed due to whatever reason.
I still think it should be tried with newer technologies though, sounds like a very interesting idea.
As far as I can remember MongoDB did not have any dedicated block caching mechanism in its earlier releases.
They basically mmap’ed the database file and argued that OS cache should do its job. Which makes sense but I guess it did not perform as well as any fune tuned caching mechanism.
Early MongoDB design choices are probably not great to call out for anything other than ignorance. mmap is a very naive view on how to easily work with data but it falls over pretty hard for any situation where ensuring your data doesn't get corrupted is important. MongoDB has come a long way, but its early technical decisions were not based on technical insight.
ReiserFS was built on the premise that doing a filesystem right could get us to a point where the filesystem is the database for a lot of use cases.
It's now "somewhat possible" in that modern filesystem are overall mostly less broken about handling large number of small (or at least moderately small) files than they used to be.
But databases are still far more optimized for handling small pieces of data in the ways we want to handle data we put into databases, which typically also includes a need to index etc.
instead of databases using their own data serialisation formats for persistence and then optimizing writes and read over that they should just utilize the FS directly and let all of the optimisations built by FS authors be taken advantage of.
The original article effectively argues the opposite: if your use case matches a database, then that will be way faster. Because the filesystem is both fully general, multi-process and multi-user, it's got to be pessimistic about its concurrency.
This is why e.g. games distribute their assets as massive blobs which are effectively filesystems - better, more predictable seek performance. Starting from the Doom WAD onwards.
For an example of databases that use the file system, both the mbox and maildir systems for email probably count?
Because SQLite not being an FS is apparently the reason why it’s fast:
The performance difference arises (we believe) because when working from an SQLite database, the open() and close() system calls are invoked only once, whereas open() and close() are invoked once for each blob when using blobs stored in individual files.
Additionally, it may also be that accesses to a single file allows the OS to efficiently retrieve (and IIRC in the case of Windows, predict) the working set allowing the reduction of access times; which is not the case if you open multiple files.
Not so much working set, as you only have to check the access control once. Windows does a lot of stuff when you open a file, and creating a process is even worse.
No mention of how it performs when you need random access (seek) into files. Perhaps it underperforms the file system at that?
SQLite does have low-level C APIs for that:
https://www.sqlite.org/c3ref/blob_open.html
https://www.sqlite.org/c3ref/blob_read.html
I've not seen performance numbers for those. Could make for an interesting micro-benchmark.
Probably because you wouldn't seek into a database row?
I guess querying by PK has some similarities but it is not as unstructured and random as a seek.
Also side effects such as sparse files do not mean much from a database interface standpoint.
Would you put sqlite in the kernel? Or using something like FUSE?
It seems to me that all the extra indirection from using FUSE would lead to more than a 35% performance hit.
Statically linking an sqlite into a kernel module and providing it with filesystem access seems like something non trivial to me.
Could we expect performance gains from Sqlite being in the kernel?
just point it at your block device
I don't think SQLite can run on a block device out of the box, it needs locking primitives and a second file for the journal or WAL plus a shared memory file in WAL mode
Proxmox puts the VM configuration information in a SQLite database and exposes it through a FUSE file system. It even gets replicated across the cluster using their replication algorithm. It’s a bespoke implementation, but it’s a SQLite-backed filesystem.
Here you go:)
https://github.com/narumatt/sqlitefs
And it seems quite interesting:
"sqlite-fs allows Linux and MacOS to mount a sqlite database file as a normal filesystem."
"If a database file name isn't specified, sqlite-fs use in-memory-db instead of a file. All data will be deleted when the filesystem is closed."
There's quite a number of sqlite FUSE implementations around, if you want to head in that direction.
There are at least two for macOS. But they run into trouble nowadays because FUSE wants kernel extensions.
Why hasn’t someone made sqlitefs yet?
What do you expect the value proposition of something loosely described as a sqlitefs to be?
One of the main selling points of SQLite is that you can statically link it into a binary and no one needs to maintain anything between the OS and the client application. I'm talking about things like versioning.
What benefit would there be to replace a library with a full blown file system?
POSIX interfaces (open, read, write, seek, close, etc) are very challenging to implement in an efficient/reliable way.
Using SQLite let's you tailor your data access patterns in a much more rigorous way and side step the POSIX tarpit.
This is precisely why I'm considering appending to a sqlite DB in WAL2 mode instead of plain text log files. Almost no performance penalty for writes but huge advantages for reading/analysis. No more Grafana needed.
Careful, some people will be along any second pointing out your approach limits your ability to use "grep" and "cat" on your log after recovering it to your pdp-11 running in your basement. Also something about the "Unix philosophy" :p
Seriously though, I think this is a great idea, and would be interested in how easy it is to write sqlite output adaptors for the various logging libraries out there.
some people will be along any second pointing out your approach limits your ability to use "grep" and "cat" on your log
And they won’t be wrong.
Unix philosophy still applies
sqlite3 logs.db "select log from logs" | grep whatever
by the same argument the systemd binary logs also follow the unix philosophy
As per the example above, for all practical purposes it does.
How would you tail or watch a sqlite log? (on a pdp-11 if necessary :)
Write a program using
https://www.sqlite.org/c3ref/update_hook.html
On a PDP-11, run this program via telnet, rsh, or rexec.
If you're more ambitious, porting SQLite to 2.11BSD would be a fun exercise.
Update hook doesn't trigger if the write happened from a different process.
`watch -n 5 sqlite3 logs.db "select log from logs" | grep whatever`
Careful, some people will be along any second pointing out your approach limits your ability to use "grep" and "cat" on your log after recovering it
I wish Splunk and friends would have an interface like that. Sure it does basic grep, and it is a much more powerful language, but sometimes you just needed some command line magic to find what you wanted.
I've learned so much about Splunk this month. I hate it. The UX is hot garbage. Why are settings scattered everywhere? Why does a simple word search not return any results? Why is there no obvious way to confirm data is being forwarded; like actual packets, not just what connections are configured.
Is there a way to mount the sqlite tables as a filesystem?
SQLite doesn't look like a good fit for large logs - nothing can beat liner write at least on HDD and with plain text logs you will have it (though linear write of compressed data even better but rare software supports it out of the box). With SQLite I would expect more write requests for the same stream of logs (may be not much more). Reading analysis will be faster than using grep over plain text log only if you'll create indices which add write cost (and space overhead).
ClikcHouse works really well when you need to store and analyze large logs but compare to SQLite it would require to maintain a server(s). There is DuckDB which is embedded like SQLite and it could be a better than SQLite fit for logs but I have no experience with DuckDB.
SQLite is meant for transactional data, DuckDB for analytical data.
I am not sure which one would be better for logs, I would need to play around with it. But i am not sure if SQLite wouldn’t be a better fit.
Logs can be different but most of the time it is close to analytics than to transaction processing. Also the way to get a fast SELECT in SQLite (and most traditional relational databases) is to use indexes and indexes IMHO don't work well with logs - you may need to use any column in a WHERE condition and having all columns indexed requires a lot of space and reduces write speed. Additionally many columns in logs have low cardinality and an index doesn't significantly reduces query time compare to a full scan. So with logs one often have to use full-scan queries and full-scan is where columnar storage works much better than a row-oriented storage. Additionally with a columnar storage you are getting a better compression ratio which allows to save space (often important for logs) and increase read speed.
Some people have thought of this before. Here is one implementation
https://git.sr.ht/~martijnbraam/logbookd
Although I'm not sure it uses WAL2 mode, but that should be a trivial change.
Are you using the wal2 branch of SQLite?
I didn’t know what WAL/WAL2 mode was, so I looked it up. For anyone else interested: https://www.sqlite.org/wal.html
I've been doing this for years. I keep SQLite log databases at varying grains depending on the solution. One for global logs, one per user/session/workflow, etc. I've also done things like in-memory SQLite trace databases that only get written to disk if an exception occurs.
It could probably work. For a peculiar application I even used sqlite to record key frame-only video. (There was a reason)
One could flip it around and store logs in a multimedia container, but then you won't have nice indices like with sqlite, just the one big time index
The performance difference arises (we believe) because when working from an SQLite database, the open() and close() system calls are invoked only once, whereas open() and close() are invoked once for each blob when using blobs stored in individual files. It appears that the overhead of calling open() and close() is greater than the overhead of using the database
I wonder how io_uring compares.
Yeah but imagine a Beowulf cluster^H^H io_uring of SQLites
It seems like nobody has suggested putting SQLite databases inside SQLite blobs yet... you could have SQLite all the way down.
RecordIO would be a good choice for this use case
Reading is about an order of magnitude faster than writing
not a native speaker, what does it mean?
An order of magnitude is for example from 10 to 100, or from 1,000 to 10,000, so typically an increase by 10x or similar.
Reading is about 10 times faster than writing
(Very) approximately 10 times faster
Order of magnitude usually refers to a 10x difference. Two orders of magnitude would be 100x difference.
(Sometimes the phrase is casually used to just mean "a lot", but here I think they mean 10x).
Depends, depends.. but just of logic:
All fs/drive access is managed by the OS. No DB systems have raw access to sectors or direct raw access to files.
Having a database file on the disc, offers a "cluster" of successive blocks on the hard drive (if it's not fragmented), resulting in relatively short moving distances of the drive head to seek the necessary sectors. There will still be the same sectors occupied, even after vast insert/write/del operations. Absolutely no change of DB file's position on hard drive. It's not a problem with SSDs, though.
So, the following apply:
client -> DB -> OS -> Filesystem
I think, you already can see the DB part is an extra layer. So, if one wouldn't have this, it would be "faster" in terms of execution time. Always.
If it's slower, then you use the not-optimal settings for your use case/filesystem.
My father did this once. He took H2 and made it even more faster :) incredible fast on Windows in direct comparison of H2/h2-modificated with same data.
So having a DBMS is convenient and made in decisions to serve certain domains and their problems. Having it is convenient, but that doesn't mean it's the most optimized way of doing it.
No DB systems have raw access to sectors or direct raw access to files.
Oracle can use raw disks without having a filesystem on them, though it's more common to use ASM (Automatic Storage Management) which is Oracle's alternative to raw or OS managed disks.
MySQL also supported this a million years ago, I'm not sure if it still does
MySQL can work off a block device without a file system.
I was looking at self hosted RSS readers recently. The application is single user. I don't expect it to be doing a lot of DB intensive stuff.
It surprised me that almost all required PostgreSQL, and most of those that didn't opted for something otherwise complex such as Mongo or MySQL.
SQLite, with no dependencies, would have simplified the process no end.
Last I checked, FreshRSS[1] can use a SQLite database.
No file system attributes or metadata on records which also means no (xattrs/fattrs) being written or updated, no checks to see if it's a physical file or a pipe/symlink, no permission checks, no block size alignment mismatches, single open command.
Makes sense when you consider you're throwing out functionality and disregarding general purpose design.
If you use a fuse mapping to SQLite, mount that directory and access it, you'd probably be very similar performance (perhaps even slower) and storage use as you'd need to add additional columns in the table to track these attributes.
I have no doubt that you could create a custom tuned file system on a dedicated mount with attributes disabled, minimized file table and correct/optimized block size and get very near to this perf.
Let's not forget the simplicity of being able to use shell commands (like rsync) to browse and manipulate those files without running the application or an SQL client to debug.
Makes sense for developers to use SQLite for this use case though for an appliance-type application or for packaged static assets (this is already commonplace in game development - a cab file is essentially the same concept)
If you use a fuse mapping to SQLite, mount that directory and access it
Related ongoing discussion, if someone cares to test this:
Let's assume that filesystems are fairly optimized pieces of software. Let's assume that the people building them heard of databases and at some point along the way considered things like the costs of open/close calls.
What is SQLite not doing that filesystems are?
A discussion in the comments of the cost of opening a file on Windows: https://stackoverflow.com/questions/21309852/what-is-the-mem...
Access control is a big cost. Some AV systems (like everyone's favourite Crowdstrike) also hook every open/close.
I recently had the idea to record every note coming out of my digital piano in real-time. That way if I come up with a good idea when noodling around I don’t have to hope I can remember it later.
I was debating what storage layer to use and decided to try SQLite because of its speed claims — essentially a single table where each row is a MIDI event from the piano (note on, note off, control pedal, velocity, timestamp). No transactions, just raw inserts on every possible event. It so far has worked beautifully: it’s performant AND I can do fun analysis later on, e.g. to see what keys I hit more than others or what my average note velocity is.
I wouldn't expect performance of pretty much any plausible approach to matter much. The notes just aren't going to be coming very quickly.
How much more performance could you get by bypassing the filesystem and writing directly to the block device? Of course, you'd need to effectively implement your own filesystem, but you'd be able to optimise it more for the specific workload.
Oracle, some other databases did this back in a day in 00s by wrong with block devices directly.
I am not sure if this is done anymore, because the performance gains were modest compared to the hassle of a custom formatted partition.
Reminds me of this talk: https://www.destroyallsoftware.com/talks/the-birth-and-death...
i.e. opening and closing many files from disk is slower than opening and closing one file and using memory.
It's important. But understandable.
I did some research in a database research lab, and we had a lot of colleagues working on OS research. It was always interesting to compare the constraints and assumptions across the two systems. I remember one of the big differences was the scale of individual records we expected to be working with, which in turn affected how memory and disk was managed. Most relational databases are very much optimized for small individual records and eventual consistency, which allows them to cache a lot more in memory. On the other hand, performance often drops sharply with the size of your rows.
Slight OT: does this apply to SQLite on OpenBSD?
Because with OpenBSD introduction of pinning all syscalls to libc, doesn’t this block SQLite from making syscall direct.
When something built on top of the filesystem is "faster" than the filesystem, it just means "when you use the filesystem in a less-than-optimal manner, it will be slower than an app that uses it in a sophisticated manner." An interesting point, but perhaps obvious...
* for certain operations.
Which is a bit d'oh, since being faster for some things is one of the main motivations for a database in the first place.
numbers are from 2017, update would be cool
SQLite can be faster than FileSystem for small files. For big files, it can do more than 1 GB/s. On Pack [1], I benchmarked these speeds, and you can go very fast. It can be even 2X faster than tar [2].
In my opinion, SQLite can be faster in big reads and writes too, but the team didn't optimise it as much (like loading the whole content into memory) as maybe it was not the main use of the project. My hope is that we will see even faster speeds in the future.
[1] https://pack.ac [2] https://forum.lazarus.freepascal.org/index.php/topic,66281.m...
maybe the malware detection program adds to the performance as well
NTFS is really horrible handling many small files. When compiling/watching node modules (easily 10-100k files), we've seen a 10x size difference internally (same hardware, just different OSes). At some point that meant a compile time difference of 10-30 sec vs 6-10 min. Not fun.
To pile onto NTFS, it's performance is so notoriously bad that there are developer teams working on Windows projects that configure their build farm to do cross builds from Linux to Windows just to avoid the performance penalty.
As an anecdote, we had a really long build time for our pipeline (going back prob 15 years).
I argued for a Linux laptop, and the boss said, "OK, prove it. Here's two equivalent laptops, time it.".
Turns out there was zero difference, or negligible (Windows won), between compilation times. That has always annoyed me.
I think there was something seriously flawed in your test. If you Google for a minute, you find multiple posts on how moving the same builds to Linux led to performance improvements in the range of 40% drops in build times.
Some anecdotes even compare doing the same builds in Ubunto with NTFS to see double-digit gains.
NTFS is notoriously awful in scenarios involving reading/writing many small projects. This is the bottleneck in Windows builds. There is a myriad of benchmarks documenting this problem.
Nowadays there are plenty of cross-platform projects to serve as benchmarks. Checking this can be as easy as checking out a project, start a full rebuild, and check how long it takes.
That's survivor bias. Fewer people blog about unsuccessful initiatives.
Java/dotnet pipelines don't have Windows penalty. Both have dependencies as compiled modules zipped into large jar/dll. While even simple node project have 10s of thousands of files inside node_modules folder.
It seems possible that their pipeline just had large files, or something like that.
NTFS can be especially awful if you're used to Linux behaviour, port code over to Windows and expect the same performance.
Here's a talk on porting rustup to Windows: https://www.youtube.com/watch?v=qbKGw8MQ0i8
To begin with, it takes rustup 3m30s to install on Windows. After rejigging the code again and again away from its naive implementation which works fine on Linux, to perform sympathetically towards NTFS, it takes the same rustup 14s to install. That's quite a performance gain! But it needed a lot of changes to rustup, and to Windows itself.
Love that your boss was open to the idea but also wanted data to back it up. (imagine a slow clap)
A single datapoint from an experiment done by only one person you call it data? What's not to love...
It was the only data point which mattered for the decision that needed to be made.
Yep, was a good experience. I'm sure with fine tuning it (Linux) could've been better, but I ate that humble pie.
You can't have windows laptop without running a ton of Antivirus and other crapware .e.g MS store, HP/Dell diagnostics and MS Teams. If you factor that in, Linux will win everytime.
Not the case unfortunately; you can. I'm not defending Windows here (bitter hatred of Microsoft here, since they started charging 2k for the compilation tools back in the 90s).
In your particular corporate environment that might be the case, but not in this case, I had free run of a fresh install and no offensive AV there, and detuned to remove the crap.
Other posters have said certain optimizations (which I'm not sure would help, it was pure compilation, no large files that I'm aware of). Just saying, always good to keep an open mind.
If your company has windows and Linux but only guards against threats on windows, then you may have a different problem.
I believe you can lock a Linux box down tighter than a windows box, but then you're trading compile times for other costs.
Yeah sorry, I left out a lot of details for a quick anecdote. It was Java compilation, so the different JDK's may've made a difference (almost certainly). But I figured the better file system handling etc etc would've gone in favour of Linux, versus Windows.
In any case it wasn't much of a fun codebase. But I think a good lesson was, always test it, always measure. Not casting shade on either OS.
Do you remember which file system you used on Linux?
For the most part the two are pretty equivalent in performance, it's particularly constant blocking on small file IO where Windows falls way behind. Also a bit in the "tons of cores" and "tons of networking" categories but not as nearly as bad as small file IO.
Were all your tests pure unit tests? Then sure—the OS isn’t getting in the way (and if your Linux distribution didn’t set the correct cpu scheduler, windows could be faster!)
If, however, your tests make any filesystem calls or fork a child process, there’s slim chances that Linux doesn’t absolutely trounce Windows.
To throw in a morsel of anecdata: same laptop with a dual boot runs the PhotoStructure core test suite (some 11,000 tests which have a pleasing melange of system and integration tests, not just unit tests) in 8m30s on Windows 11. Almost all the same tests complete in 3m30s on Linux.
Why wouldn't they work on improving NTFS performance instead?
Much of the cost is imposed by semantics and the security model; there's no silver bullet solution that can be imposed without turning it into a different filesystem with different semantics and/or on disk representation.
At one point they planned to replace it with a database filesystem, but that was too complicated and abandoned. That was probably the end of replacement work on NTFS. https://en.wikipedia.org/wiki/WinFS
WinFS was never designed as a replacement as it rode on top of NTFS. It was a fancy SQL Server database that exposed files as .NET Objects for API interaction.
Sans the API piece, think of it like storing blobs in SQL Server, just like SharePoint does.
I was lucky enough to play around with beta 1. Not much you could do with it, though.
Because likely they aren’t a. allowed to, and/or b. have no file system code experience or understanding.
There are other old-school techniques which are far easier to implement and maintain, such as using RAM drives/partitions. Expensing 32GB of RAM is simpler than maintaining weird NTFS configurations.
Splitting your project into submodules/subpackages also helps amortize the impact of long build times. You can run multiple builds in parallel and then have a last build task to aggregate the all. Everyone can live with builds that take 5 minutes instead of 3.
Even Microsoft doesn't touch NTFS code (if they haven't lost it). All new file system features like new compression methods are implemented on layers above ntfs.
Didn't they introduced ReFS ("dev drives") to alleviate this?
NTFS is perfectly fine at handling small files and performs on-par with other modern file systems.
The issue is Defender in sync mode/other AV/other file system filters.
DevDrive as noted by default uses an async scanning technique as well as ReFS. ReFS will suffer the exact same performance issues with Defender (or other AV/other file system filters) doing its thing when running in sync mode, which it does by default for ReFS-formatted drives in Windows Server.
https://gregoryszorc.com/blog/2021/04/06/surprisingly-slow/
https://news.ycombinator.com/item?id=26737521
This doesn't take away from your point that _it is slow_, but the reasons are not due to the file system in use.
I've had folders take a full minute to open on an SSD.
It got to the point where I went to open the folder, it started loading. I needed the file quickly, so I searched for it online, found it, and opened it before windows finished loading that folder for me.
After exempting that folder from Windows Defender the folder loads instantly. For the life of me I cannot understand why Defender blocks Explorer.
I suppose if you wanted to find out, you could use dtrace/ETW.
Explorer has other things going on, though, including other apps that hook into it (shell extensions, like Adobe Reader, TortiseGit/SVN, and so on) which can certainly cause performance issues.
Probably because Explorer hosts shell hooks which can potentially execute arbitrary code.
Just one example: File icons or thumbnails can be dynamically generated by shell extensions based on the file contents. A maliciously crafted file could potentially exploit a vulnerability in such a shell extension.
Technically, they're because of the filesystem in use: it's providing the APIs these garbage-ware utilize... which causes the performance issues ( ◠ ‿ ・ ) —
File system filter drives apply to all (RW) file systems on Windows. It's not exclusive to NTFS or ReFS.
Windows has an extensible model. It's a different approach from most (all?) other OSes. It offers a different set of features.
Sure, AV could perhaps be done in a different manner that would be more effective/faster, I can't comment on that as I lack the insight required -- only MSFTies that work on kernel code could respond in any authoritative way.
Must be why windows 11 added that dev drive feature
That, and continuous scanning of all file IO corporations love so much can 2x-10x that time still. Dev drive is excluded from Defender scrutiny by default because of that.
Not excluded, but the scanning in a dev drive is asynchronous rather than synchronous.
So we should put node_modules into SQLite database?
Honestly, maybe? Would be a fun project to look at at least.
That may be due to a combination of Malware detection + most unix programs not really written to take advantage of the features NTFS has to offer
This is a great talk on the topic https://youtu.be/qbKGw8MQ0i8?si=rh6WJ3DV0jDZLddn
It’s somewhat more complex than “NTFS is slow”. Here’s a good explanation: https://github.com/Microsoft/WSL/issues/873#issuecomment-425...
I’ve benchmarked deleting files (around ~65,000 small node_modules sort of files) and it takes 40 seconds through Explorer, 20 seconds with rd, and roughly a second inside WSL2 (cloned to the VM’s ext4 virtual hard drive).
Why not use https://github.com/bobranten/Ext4Fsd ??
Their "windows dev drive" setup addresses this. I haven't tested it myself but I saw a couple of inexpertly executed tests showing significant performance gains. I honestly have no idea if my compile times are quicker.
You can get benches from the horses mouth.
https://devblogs.microsoft.com/visualstudio/devdrive/
But the primary improvement comes from async A/V, not the file system.
ReFS offers other improvements such as CoW and instant file initialization which can benefit developers. From this standpoint, it is the correct choice over NTFS.
https://devblogs.microsoft.com/engineering-at-microsoft/dev-...
But is it faster than accessing the filesystem with io_uring as well? I feel like this article should be updated
io_uring/IOCP won't change how long it takes to access a file.
Some of it is slow implementations I’m sure, but most of it is the designed-in overhead on NTFS.
https://news.ycombinator.com/item?id=18783525 has previous discussion on why windows filesystem is slow
With the right code, NTFS is not much slower than ext4, for example. Nearly 3% for tar.gz and more than 30% for a heavily multi-threaded use like Pack.
https://forum.lazarus.freepascal.org/index.php/topic,66281.m...