The advantages of the new schema are:
* overlapping recordings can be unambiguously described and viewed.
This is a significant problem right now; the clock on my cameras appears to
run faster than the (NTP-synchronized) clock on my NVR. Thus, if an
RTSP session drops and is quickly reconnected, there's likely to be
overlap.
* less I/O is required to view mp4s when there are multiple cameras.
This is a pretty dramatic difference in the number of database read
syscalls with pragma page_size = 1024 (605 -> 39 in one test),
although I'm not sure how much of that maps to actual I/O wait time.
That's probably as dramatic as it is due to overflow page chaining.
But even with larger page sizes, there's an improvement. It helps to
stop interleaving the video_index fields from different cameras.
There are changes to the JSON API to take advantage of this, described
in design/api.md.
There's an upgrade procedure, described in guide/schema.md.
The benchmarks now require "cargo bench --features=nightly". The
extra #[cfg(nightly)] switches in the code needed for it are a bit
annoying; I may move the benches to a separate directory to avoid this.
But for now, this works.
This is a significant milestone; now the Rust branch matches the C++ branch's
features.
In the process, I switched from using serde_derive (which requires nightly
Rust) to serde_codegen (which does not). It was easier than I thought it'd
be. I'm getting close to no longer requiring nightly Rust.
I found this while bringing db.rs's test coverage up to the old
moonfire-db-test.cc. I mistakenly thought that in SQLite, an ungrouped
aggregate on a relation with no rows would return a row with a null result of
the aggregate. Instead, it returns no rows. In hindsight, this makes more
sense; it matches what grouped aggregates (have to) do.
I should have submitted/pushed more incrementally but just played with it on
my computer as I was learning the language. The new Rust version more or less
matches the functionality of the current C++ version, although there are many
caveats listed below.
Upgrade notes: when moving from the C++ version, I recommend dropping and
recreating the "recording_cover" index in SQLite3 to pick up the addition of
the "video_sync_samples" column:
$ sudo systemctl stop moonfire-nvr
$ sudo -u moonfire-nvr sqlite3 /var/lib/moonfire-nvr/db/db
sqlite> drop index recording_cover;
sqlite3> create index ...rest of command as in schema.sql...;
sqlite3> ^D
Some known visible differences from the C++ version:
* .mp4 generation queries SQLite3 differently. Before it would just get all
video indexes in a single query. Now it leads with a query that should be
satisfiable by the covering index (assuming the index has been recreated as
noted above), then queries individual recording's indexes as needed to fill
a LRU cache. I believe this is roughly similar speed for the initial hit
(which generates the moov part of the file) and significantly faster when
seeking. I would have done it a while ago with the C++ version but didn't
want to track down a lru cache library. It was easier to find with Rust.
* On startup, the Rust version cleans up old reserved files. This is as in the
design; the C++ version was just missing this code.
* The .html recording list output is a little different. It's in ascending
order, with the most current segment shorten than an hour rather than the
oldest. This is less ergonomic, but it was easy. I could fix it or just wait
to obsolete it with some fancier JavaScript UI.
* commandline argument parsing and logging have changed formats due to
different underlying libraries.
* The JSON output isn't quite right (matching the spec / C++ implementation)
yet.
Additional caveats:
* I haven't done any proof-reading of prep.sh + install instructions.
* There's a lot of code quality work to do: adding (back) comments and test
coverage, developing a good Rust style.
* The ffmpeg foreign function interface is particularly sketchy. I'd
eventually like to switch to something based on autogenerated bindings.
I'd also like to use pure Rust code where practical, but once I do on-NVR
motion detection I'll need to existing C/C++ libraries for speed (H.264
decoding + OpenCL-based analysis).
Scott Lamb