It feels like you didn't read the design rationale, because the use cases and issues are listed therein. Maybe you don't see the value, but that doesn't mean it's not there for others. I certainly find its query features useful.
It feels like you didn't read the design rationale, because the use cases and issues are listed therein. Maybe you don't see the value, but that doesn't mean it's not there for others. I certainly find its query features useful.
I did, and I still don't get why you would want this monstrosity over a structured append-only log file. If you want to index the data, you can do that when you roll over the file. That way you get the exact same robustness guarantees, without the insane architecture.
Like ultimately it isn't even fast, journalctl is so bad at rendering text that it's approximately still as slow as seeking in a 400 MB .log-file using less.
Anyone with any sort of scale where you actually need indexing immediately drops journald and uses loki or elasticsearch instead. Journald is not even remotely a contender in that space.
> Anyone with any sort of scale where you actually need indexing immediately drops journald and uses loki or elasticsearch instead. Journald is not even remotely a contender in that space.
That I agree with. I don't personally use journalctl much these days, particularly now that practically everything's a container and all their logs are getting shipped off-host for indexing. But I get why, 14 years ago, it was considered a good idea.
The point is that ripgrep will give you basically all the same query features just by being fast. A more structured format makes sense, but the indexing is not obviously adding value in most cases.
The difference between grep/ripgrep and querying by field is the difference between a full table scan and an index query. Query performance is a very good reason to have databases. ripgrep is certainly fast, but it's still O(N). Doing complete file scans also trashes the OS's buffer cache.
I really want to love journald (it sounds like it's aiming for a good system) but I share the other commenter(s)' frustration here about journald being slower than just pulling out ripgrep on regular files.
We have some services at work that log to text files and some to journald.
The log volume to file is >> the log volume to journald. Yet `rg query myservice.2026-08-01.log` seems to always wind up being faster and better than something like `journalctl -u myservice.service --since '2026-08-01' --until '2026-08-02' -g 'query'`. (The tab completion and discoverability is also better, I guess)
What do the metrics look like in practice? seems is a bit too handwavy. I get that impressions matter, but data is actionable.
Sure, I'll try my best to put some concrete numbers on it:
On a file of ~1M lines (350 MB), ripgrep returns a query matching 22k lines in 1.9s.
(Separately: We have gzip log rotation for old logs. A log with 1.9M lines (106 MB gzipped) on disk can be searched from cold in 1.3s by ripgrep, whilst still being ergonomic. Maybe a different compressor is better?)
On a service unit filter over ~218k lines, `journalctl -u service --since <...> -g query` returns 4.8k matching lines in 4.127s (and this is after having warmed the cache by returning the unfiltered query cold, in 11.9s. I don't have root to clear the disk cache
I should have ran the filtered query first but ah well, since the difference is already so large it doesn't matter that the filtered journalctl query gets an unfair advantage)
Comparing input rates:
I don't have a great deal of understanding of journald's internals, so perhaps there is some variable here that is unreasonably unfavourable to journald.Transcript
Probably more painful on the day to day is how `journalctl -fu myservice` seems to stall for on the order of 5 to 10s, sometimes. I can't reproduce at the moment and maybe it only happens on some machines, but if you were interested in 'real-world anecdata' it's something to maybe note.I have been complaining about journald abysmal performances for almost as long as I can remember. Here is my latest documented benchmark from 2023, which was slightly better than the one I ran in 2020.
$ time journalctl > /tmp/all.log
real 1m11.364s user 0m52.299s sys 0m6.540s
$ time wc -l /tmp/all.log 3659597 /tmp/all.log
real 0m0.152s user 0m0.056s sys 0m0.096s
$ time journalctl | grep sshd | wc -l
12944
real 0m53.973s user 0m49.535s sys 0m5.210s
$ time grep sshd /tmp/all.log | wc -l 12944
real 0m0.429s user 0m0.332s sys 0m0.100s
https://github.com/systemd/systemd/issues/2460#issuecomment-...
I'm no fan of journald, but I have some methodological issues with this test.
The reads from /tmp/all.log are almost certainly cached since you just wrote the file, and will basically boil down to a memcpy call, rather than actual disk I/O. Speed difference isn't as big as you would think on a modern SSD, but it isn't nothing either.
Running this between calls should flush the changes to disk and then drop the page cache, making for a fairer test.
$ sudo sync
$ echo 3 | sudo tee /proc/sys/vm/drop_caches
I tried similar. Each command looped 5 times, ignore the best and worst times, and average the mean three.
You didn't use the query feature of journalctl, so you basically had it do an entire table conversion. Of course that's not going to show its performance value.
This is what you should be running for a proper comparison:
1. `echo 3 | sudo tee /proc/sys/vm/drop_caches`
2. `time journalctl -u ssh.service >/dev/null`
3. `journalctl >/tmp/all.log`
4. `echo 3 | sudo tee /proc/sys/vm/drop_caches`
5. `time grep -q sshd /tmp/all.log`
Writing multiple pages for a few hundred actual bytes also thrashes the OS cache...
Not if you use the correct cache hint flags on the write.
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