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Moonfire NVR Storage Schema

Status: current.

This is the initial design for the most fundamental parts of the Moonfire NVR storage schema. See also guide/schema.md for more administrator-focused documentation.

Objective
- Cameras
- Hard drives
Overview
Detailed design

Objective

Goals:

record streams from modern ONVIF/PSIA IP security cameras
support several cameras
maintain full fidelity of incoming compressed video streams
record continuously
support on-demand serving in different file formats / protocols (such as standard .mp4 files for arbitrary timespans, fragmented .mp4 files for MPEG-DASH or HTML5 Video Source Extensions, MPEG-TS files for HTTP Live Streaming, and "trick play" RTSP)
annotate camera timelines with metadata (such as motion detection, security alarm events, etc)
retain video segments with ~1-minute granularity based on metadata (e.g., extend retention of motion events)
take advantage of compact, inexpensive, low-power, commonly-available hardware such as the $35 Raspberry Pi 2 Model B
support high- and low-bandwidth playback
support near-live playback (~second old), including "trick play"
allow verifying database consistency with an fsck tool

Non-goals:

record streams from older cameras: JPEG/MJPEG USB "webcams" and analog security cameras/capture cards
allow users to directly access or manipulate the stored data with standard video or filesystem tools
support H.264 features not used by common IP camera encoders, such as B-frames and Periodic Infra Refresh.
support recovering the last ~minute of video after a crash or power loss

Possible future goals:

record audio and/or other types of timestamped samples (such as Xandem tomography data).

Cameras

Inexpensive modern ONVIF/PSIA IP security cameras, such as the $100 Hikvision DS-2CD2032-I, support two H.264-encoded RTSP streams. They have many customizable settings, such as resolution, frame rate, compression quality, maximum bitrate, I-frame interval. A typical setup might be as follows:

the high-quality "main" stream as 1080p/30fps, 3000 kbps. This stream is well-suited to local viewing or forensics.
the low-bandwidth "sub" stream as 704x480/10fps, 100 kbps. This stream may be preferred for mobile/remote viewing, when viewing several streams side-by-side, and for real-time computer vision (such as salient motion detection).

The dual pre-encoded H.264 video streams provide a tremendous advantage over older camera models (which provided raw video or JPEG-encoded frames) because the encoding is prohibitively expensive in multi-camera setups. libx264 supports "encoding 4 or more 1080p streams in realtime on a single consumer-level computer", but this does not apply to the low-cost devices Moonfire NVR targets. In fact, even decoding can be expensive on the full-quality streams, enough to challenge the feasibility of on-NVR motion detection. It's valuable to have the "sub" stream for this purpose.

The table below shows cost of processing a single stream, as a percentage of the whole processor ((user+sys) time / video duration / CPU cores). TODO: try different quality settings as well.

Decode:

$ time ffmpeg -y -threads 1 -i input.mp4 \
              -f null /dev/null

Combo (Decode + encode with libx264):

$ time ffmpeg -y -threads 1 -i input.mp4 \
              -c:v libx264 -preset ultrafast -threads 1 -f mp4 /dev/null

Processor	1080p30 decode	1080p30 combo	704x480p10 decode	704x480p10 combo
Intel i7-2635QM	6.0%	23.7%	0.2%	1.0%
Intel Atom C2538	16.7%	58.1%	0.7%	3.0%
Raspberry Pi 2 Model B	68.4%	230.1%	2.9%	11.7%

Hardware-accelerated decoding/encoding is possible in some cases (VAAPI on the Intel processors, or OpenMAX on the Raspberry Pi), but similarly it would not be possible to have several high-quality streams without using the camera's encoding. TODO: get numbers.

Hard drives

With current hard drives prices (see WD Purple prices below), it's cost-effective to store a month or more of high-quality video, at roughly 1 camera-month per TB.

Capacity	Price
1 TB	$61
2 TB	$82
3 TB	$107
4 TB	$157
6 TB	$240

Typical sequential bandwidth is >100 MB/sec, more than that required by over a hundred streams at 3 Mbps. The concern is seek times: a WD20EURS appears to require 20 ms per sequential random access (across the full range of the disk), as measured with seeker. Put another way, the drive is only capable of 50 random accesses per second, and each one takes time that otherwise could be used to transfer 2+ MB. The constrained resource, disk time fraction, can be bounded as follows:

disk time fraction <= (seek rate) / (50 seeks/sec) +
                      (bandwidth) / (100 MB/sec)

Overview

Moonfire NVR divides video streams into 1-minute recordings. These boundaries are invisible to the user. On playback, the UI moves from one recording to another seamlessly. When exporting video, recordings are automatically spliced together.

Each recording is stored in two places:

a sample file directory, intended to be stored on spinning disk. Each file in this directory is simply a concatenation of the compressed, timestamped video samples (also called "packets" or encoded frames), as received from the camera. In MPEG-4 terminology (see ISO 14496-12), this is the contents of a mdat box for a .mp4 file representing the segment. These files do not contain framing data (start and end byte offsets of samples) and thus are not meant to be decoded on their own.
the recording table in a SQLite3 database, intended to be stored on flash if possible. A row in this table contains all the metadata associated with the segment, including the sample-by-sample contents of the MPEG-4 stbl box. At 30 fps, a row is expected to require roughly 4 KB of storage (2 bytes per sample, plus some fixed overhead).

Putting the metadata on flash means metadata operations can be fast (sub-millisecond random access, with parallelism) and do not take precious disk time fraction away from accessing sample data. Disk time can be saved for long sequential accesses. Assuming filesystem metadata is cached, Moonfire NVR can seek directly to the correct sample.

To avoid a burst of seeks every minute, rotation times will be staggered. For example, if there are two cameras (A and B), camera A's main stream might switch to a new recording at :00 seconds past the minute, B's main stream at :15 seconds past the minute, and likewise the sub streams, as shown below.

camera	stream	switchover
A	main	xx:xx:00
B	main	xx:xx:15
A	sub	xx:xx:30
B	sub	xx:xx:45

Detailed design

SQLite3

All metadata, including the recording table and others, will be stored in the SQLite3 database using write-ahead logging. There are several reasons for this decision:

No user administration required. SQLite3, unlike its heavier-weight friends MySQL and PostgreSQL, can be completely internal to the application. In many applications, end users are unaware of the existence of a RDBMS, and Moonfire NVR should be no exception.
Correctness. It's relatively easy to make guarantees about the state of an ACID database, and SQLite3 in particular has a robust implementation. (See Files Are Hard.)
Developer ease and familiarity. SQL-based RDBMSs are quite common and provide a lot of high-level constructs that ease development. SQLite3 in particular is ubiquitous. Contributors are likely to come with some understanding of the database, and there are many resources to learn more.

Total database size is expected to be roughly 4 KB per minute at 30 fps, or 1 GB for six camera-months of video. This will easily fit on a modest flash device. Given the fast storage and modest size, the database is not expected to be a performance bottleneck.

Duration of recordings

There are many constraints that influenced the choice of 1 minute as the duration of recordings.

Per-recording metadata size. There is a fixed component to the size of each row, including the starting/ending timestamps, sample file UUID, etc. This should not cause the database to be too large to fit on low-cost flash devices. As described in the previous section, with 1 minute recordings the size is quite modest.
Disk seeks. Sample files should be large enough that even during simultaneous recording and playback of several streams, the disk seeks incurred when switching from one file to another should not be significant. At the extreme, a sample file per frame could cause an unacceptable 240 seeks per second just to record 8 30 fps streams. At one minute recording time, 16 recording streams (2 per each of 8 cameras) and 4 playback streams would cause on average 20 seeks per minute, or under 1% disk time.
Internal fragmentation. Common Linux filesystems have a block size of 4 KiB (see statvfs.f_frsize). Up to this much space per file will be wasted at the end of each file. At the bitrates described in "Background", this is an insignicant .02% waste for main streams and .5% waste for sub streams.
Number of "slices" in .mp4 files. As described below, .mp4 files will be constructed on-demand for export. It should be possible to export an hours-long segment without too much overhead. In particular, it must be possible to iterate through all the recordings, assemble the list of slices, and calculate offsets and total size. One minute seems acceptable; though we will watch this as work proceeds.
Crashes. On program crash or power loss, ideally it's acceptable to simply discard any recordings in progress rather than add a checkpointing scheme.
Granularity of retention. It should be possible to extend retention time around motion events without forcing retention of too much additional data or copying bytes around on disk.

The design avoids the need for the following constraints:

Dealing with events crossing segment boundaries. This is meant to be invisible.
Serving close to live. It's possible to serve a recording as it is being written.

Lifecycle of a sample file directory

One major disadvantage to splitting the state in two (the SQLite3 database in flash and the sample file directories on spinning disk) is the possibility of inconsistency. There are many ways this could arise:

a sample file directory's disk is unexpectedly not mounted due to hardware failure or misconfiguration.
the administrator mixing up the mount points of two filesystems holding different sample file directories.
the administrator renaming a sample file directory without updating the database.
the administrator restoring the database from backup but not the sample file directory, or vice versa.
the administrator providing two sample file directory paths pointed at the same inode via symlinks or non-canonical paths. (Note that flock(2) has a design flaw in which multiple file descriptors can share a lock, so the current locking scheme is not sufficient to detect this otherwise.)
database and sample file directories forked from the same version, opened the same number of times, then crossed.

To combat this, each sample file directory has some metadata its database row and stored file called meta. These track uuids associated with the database and directory to avoid mixups. They also track sequence numbers and uuids associated with "opens": each time the database has been opened in read/write mode.

create table open (
  id integer primary key,
  uuid blob unique not null check (length(uuid) = 16)
);

create table sample_file_dir (
  id integer primary key,
  path text unique not null,
  uuid blob unique not null check (length(uuid) = 16),

  -- The last (read/write) open of this directory which fully completed.
  -- See schema.proto:DirMeta for a more complete description.
  last_complete_open_id integer references open (id)
);

// Metadata stored in sample file dirs as "<dir>/meta". This is checked
// against the metadata stored within the database to detect inconsistencies
// between the directory and database, such as those described in
// design/schema.md.
//
// As of schema version 4, the overall file format is as follows: a
// varint-encoded length, followed by a serialized DirMeta message, followed
// by NUL bytes padding to a total length of 512 bytes. This message never
// exceeds that length.
//
// The goal of this format is to allow atomically rewriting a meta file
// in-place. I hope that on modern OSs and hardware, a single-sector
// rewrite is atomic, though POSIX frustratingly doesn't seem to guarantee
// this. There's some discussion of that here:
// <https://stackoverflow.com/a/2068608/23584>. At worst, there's a short
// window during which the meta file can be corrupted. As the file's purpose
// is to check for inconsistencies, it can be reconstructed if you assume no
// inconsistency exists.
message DirMeta {
  // A uuid associated with the database, in binary form. dir_uuid is strictly
  // more powerful, but it improves diagnostics to know if the directory
  // belongs to the expected database at all or not.
  bytes db_uuid = 1;

  // A uuid associated with the directory itself.
  bytes dir_uuid = 2;

  // Corresponds to an entry in the `open` database table.
  message Open {
    uint32 id = 1;
    bytes uuid = 2;
  }

  // The last open that was known to be recorded in the database as completed.
  // Absent if this has never happened. Note this can backtrack in exactly one
  // scenario: when deleting the directory, after all associated files have
  // been deleted, last_complete_open can be moved to in_progress_open.
  Open last_complete_open = 3;

  // The last run which is in progress, if different from last_complete_open.
  // This may or may not have been recorded in the database, but it's
  // guaranteed that no data has yet been written by this open.
  Open in_progress_open = 4;
}

These are updated through procedures below:

Write the metadata file

This is a sub-procedure used in several places below.

Precondition: the directory's lock is held with LOCK_EX (exclusive) and there is an existing metadata file.

Open the metadata file.
Rewrite the fixed-length data atomically.
fdatasync the file.

Open the database as read-only

Lock the database directory with LOCK_SH (shared).
Open the SQLite database with SQLITE_OPEN_READ_ONLY.

Open the database as read-write

Lock the database directory with LOCK_EX (exclusive).
Open the SQLite database with SQLITE_OPEN_READ_WRITE.
Insert a new open table row with the new sequence number and uuid.

Create a sample file directory

Precondition: database open read-write.

Lock the sample file directory with LOCK_EX (exclusive).
Verify there is no metadata file or last_complete_open is unset.
Write new metadata file with a fresh dir_uuid and a in_progress_open matching the database's current open.
Add a matching row to the database with last_complete_open_id matching the current open.
Update the metadata file to move in_progress_open to last_complete_open.

Open a sample file directory read-only

Precondition: database open (read-only or read-write).

Lock the sample file directory with LOCK_SH (shared).
Verify the metadata file matches the database:
- database uuid matches.
- dir uuid matches.
- if the database's last_complete_open is set, it must match the directory's last_complete_open or in_progress_open.
- if the database's last_complete_open is absent, the directory's must be as well.

Open a sample file directory read-write

Precondition: database open read-write.

Lock the sample file directory with LOCK_EX (exclusive).
Verify the metadata file matches the database (as above).
Update the metadata file with in_progress_open matching the current open.
Update the database row with last_complete_open_id matching the current open.
Update the metadata file with last_complete_open rather than in_progress_open.
Run the recording startup procedure for this directory.

Close a sample file directory

Drop the sample file directory lock.

Delete a sample file directory

Remove all sample files (of all three categories described below: recording table rows, garbage table rows, and files with recording ids >= their stream's cum_recordings); see "delete a recording" procedure below.
Rewrite the directory metadata with in_progress_open set to the current open, last_complete_open cleared.
Delete the directory's row from the database.

Lifecycle of a recording

Because a major part of the recording state is outside the SQL database, care must be taken to guarantee consistency and durability. Moonfire NVR maintains three invariants about sample files:

recording table rows have sample files on disk with the indicated size and SHA-1 hash.
Exactly one of the following statements is true for every sample file:
- It has a recording table row.
- It has a garbage table row.
- Its recording id is greater than or equal to the cum_recordings for its stream.
After an orderly shutdown of Moonfire NVR, there is a recording table row for every sample file, even if there have been previous crashes.

The first invariant provides certainty that a recording is properly stored. It would be prohibitively expensive to verify hashes on demand (when listing or serving recordings), or in some cases even to verify the size of the files via stat() calls.

The second invariant improves auditability of the database and sample file directory.

The third invariant prevents accumulation of garbage files which could fill the drive and stop recording.

These invariants are updated through the following procedure:

Create a recording:

Write the sample file, aborting if open(..., O\_WRONLY|O\_CREATE|O\_EXCL) fails with EEXIST.
fsync() the sample file.
fsync() the sample file directory.
Insert the recording row, marking its size and SHA-1 hash in the process.

Delete a recording:

Replace the recording row with a garbage row.
unlink() the sample file, warning on ENOENT. (This would indicate invariant #2 is false.)
fsync() the sample file directory.
Delete the garbage row.

Startup (crash recovery):

Acquire a lock to guarantee this is the only Moonfire NVR process running against the given database. This lock is not released until program shutdown.
Query garbage table and cum_recordings field in the stream table.
unlink() all the sample files associated with garbage rows, ignoring ENOENT.
For each stream, unlink() all the existing files with recording ids >= cum_recordings.
fsync() the sample file directory.
Delete all rows from the garbage table.

The procedures can be batched: while for a given recording, the steps must be strictly ordered, multiple recordings can be proceeding through the steps simultaneously. In particular, there is no need to hurry syncing deletions to disk, so deletion steps #3 and #4 can be done opportunistically if it's desirable to avoid extra disk seeks or flash write cycles.

It'd also be possible to conserve some partial recordings. Moonfire NVR could, as a recording is written, record the latest sample tables, size, and hash fields without marking the recording as fully written. On startup, the file would be truncated to match and then the recording marked as fully written. The file would either have to be synced prior to each update (to guarantee it is at least as new as the row) or multiple checkpoints would be kept, using the last one with a correct hash (if any) on a best-effort basis. However, this may not be worth the complexity; it's simpler to just keep recording time short enough that losing partial recordings is not a problem.

Verifying invariants

There should be a means to verify the invariants above. There are three possible levels of verification:

Compare presence of sample files.
Compare size of sample files.
Compare hashes of sample files.

Consider a database with a 6 camera-months of recordings at 3.1 Mbps (for both main and sub streams). There would be 0.5 million files, taking 5.9 TB. The times are roughly:

level	operation	time
presence	`readdir()`	~19 sec
size	`fstat()`	~120 sec
hash	`read()`	~8 hours

The readdir() and fstat() times can be tested simply:

$ mkdir testdir
$ cd testdir
$ seq 1 $[60*24*365*6/12*2] | xargs touch
$ sudo sh -c 'echo 1 > /proc/sys/vm/drop_caches'
$ time ls -1 -f | wc -l
$ sudo sh -c 'echo 1 > /proc/sys/vm/drop_caches'
$ time ls -1 -f --size | wc -l

(The system calls used by ls can be verified through strace.)

The hash verification time is easiest to calculate: reading 5.9 TB at 100 MB/sec takes about 8 hours. On some systems, it will be even slower. On the Raspberry Pi 2, flash, network, and disk are all on the same USB 2.0 bus (see Raspberry Pi 2 NAS Experiment HOWTO). Disk throughput seems to be about 25 MB/sec on an idle system (~40% of the theoretical 480 Mbit/sec). Therefore the process will take over a day.

The presence check is fast enough that it seems reasonable to simply always perform it on startup. Size could be checked with a verification command used for more extensive verification, such as before and after schema upgrades. Hash checks could be performed in a rare offline data recovery mechanism or in the background at low priority.

Recording table

The snippet below is a illustrative excerpt of the SQLite schema; see schema.sql for the authoritative, up-to-date version.

-- A single, typically 60-second, recorded segment of video.
create table recording (
    id integer primary key,
    open_id integer references open (id),
    camera_id integer references camera (id) not null,

    sample_file_uuid blob unique not null,
    sample_file_blake3 blob,
    sample_file_size integer,

    -- The starting time and duration of the recording, in 90 kHz units since
    -- 1970-01-01 00:00:00 UTC.
    start_time_90k integer not null,
    duration_90k integer,

    video_samples integer,
    video_sample_entry_id blob references visual_sample_entry (id),
    video_index blob,

    ...
);

-- A concrete box derived from a ISO/IEC 14496-12 section 8.5.2
-- VisualSampleEntry box. Describes the codec, width, height, etc.
create table visual_sample_entry (
    id integerprimary key,

    -- The width and height in pixels; must match values within
    -- `sample_entry_bytes`.
    width integer,
    height integer,

    -- A serialized SampleEntry box, including the leading length and box
    -- type (avcC in the case of H.264).
    data blob
);

As mentioned by the start_time_90k field above, recordings use a 90 kHz time base. This matches the RTP timestamp frequency used for H.264 and other video encodings. See RFC 3551 section 5 for an explanation of this choice.

It's tempting to downscale to a coarser timebase, rounding as necessary, in the name of a more compact encoding of video_index. (By having timestamp deltas near zero and borrowing some of the timestamp varint to represent additional bits of the size deltas, it's possible to use barely more than 2 bytes per frame on a typical recording. TODO: recalculate database size estimates above, which were made using this technique.) But matching the input timebase is the most understandable approach and leaves the most flexibility available for handling timestamps encoded in RTCP Sender Report messages. In practice, a database size of two gigabytes rather than one is unlikely to cause problems.

One likely point of difficulty is reliably mapping recordings to wall clock time. (This may be the subject of a separate design doc later.) In an ideal world, the NVR and cameras would each be closely synced to a reliable NTP time reference, time would advance at a consistent rate, time would never jump forward or backward, each transmission would take bounded time, and cameras would reliably send RTCP Sender Reports. In reality, none of that is likely to be consistently true. For example, Hikvision cameras send RTCP Sender Reports only with certain firmware versions (see thread). Most likely it will be useful to have any available clock/timing information for diagnosing problems, such as the following:

the NVR's wall clock time
the NVR's NTP server sync status
the NVR's uptime
the camera's time as of the RTP play response
the camera's time as of any RTCP Sender Reports, and the corresponding RTP timestamps

`video_index`

The video_index field conceptually holds three pieces of information about the samples:

the duration (in 90kHz units) of each sample
the byte size of each sample
which samples are "sync samples" (aka key frames or I-frames)

These correspond to ISO/IEC 14496-12 stts (TimeToSampleBox, section 8.6.1.2), stsz (SampleSizeBox, section 8.7.3), and stss (SyncSampleBox, section 8.6.2) boxes, respectively.

Currently the stsc (SampleToChunkBox, section 8.7.4) information is implied: all samples are in a single chunk from the beginning of the file to the end. If in the future support for interleaved audio is added, there will be a new blob field with chunk information. TODO: can audio data really be sliced to fit the visual samples like this?

The index is structured as two varints per sample. The first varint represents the delta between this frame's duration and the previous frame's, in zigzag form. The low bit is borrowed to indicate if this frame is a key frame. The second varint represents the delta between this frame's duration and the duration of the last frame of the same type (key or non-key). This encoding is chosen so that values will be near zero, and thus the varints will be at their most compact possible form. An index might be written by the following pseudocode:

prev_duration = 0
prev_bytes_key = 0
prev_bytes_nonkey = 0
for each frame:
  duration_delta = duration - prev_duration
  bytes_delta = bytes - (is_key ? prev_bytes_key : prev_bytes_nonkey)
  prev_duration_ms = duration_ms
  if key: prev_bytes_key = bytes else: prev_bytes_nonkey = bytes
  PutVarint((Zigzag(duration_delta) << 1) | is_key)
  PutVarint(Zigzag(bytes_delta)

	frame 1	frame 2	frame 3	frame 4	frame 5
duration	10	9	11	10	10
is_key	1	0	0	0	1
bytes	1000	10	15	12	1050
duration_delta	10	-1	2	-1	0
bytes_delta	1000	10	5	-3	50
varint1	41	2	8	3	1
varint2	2000	20	10	5	100
encoded	`29 d0 0f`	`02 14`	`08 0a`	`02 05`	`01 64`

On-demand `.mp4` construction

A major goal of this format is to support on-demand serving in various formats, including two types of .mp4 files:

unfragmented .mp4 files, for traditional video players.
fragmented .mp4 files for MPEG-DASH or HTML5 Media Source Extensions (see Media Source ISO BMFF Byte Stream Format), for a browser-based user interface.

This does not require writing new .mp4 files to disk. In fact, HTTP range requests (for "pseudo-streaming") can be satisfied on .mp4 files aggregated from several segments. The implementation details are outside the scope of this document, but this is possible in part due to the use of an on-flash database to store metadata and the simple, consistent format of sample indexes.

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