moonfire-nvr/design/schema.md

Moonfire NVR Storage Schema

Status: current. This is largely implemented; there is optimization and testing work left to do.

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

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:

• the recording samples 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 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:

1. recording table rows have sample files on disk (named by the given UUID) with the indicated size and SHA-1 hash.
2. There are no sample files without a corresponding recording or reserved_sample_files table row referencing their UUID.
3. After an orderly shutdown of Moonfire NVR, there are no reserved_sample_files rows, 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 avoids an accidental data loss scenario. On startup, as part of normal crash recovery, Moonfire NVR should delete sample files which are half-written (and useless without their indices) and ones which were already in the process of being deleted (for exceeding their retention time). The absence of a recording table row could be taken to indicate one of these conditions. But consider another possibility: the SQLite database might not match the sample directory. This could happen if the wrong disk is mounted at a given path or after a botched restore from backup. Moonfire NVR would delete everything in this case! It's far safer to require a specific mention of each file to be deleted, requiring human intervention before touching unexpected files.

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

Sample files are named by UUID. Imagine if files were named by autoincrement instead. One file could be mistaken for another on database vs directory mismatch. With UUIDs, this is impossible: by design they can be assumed to be universally unique, so two distinct recordings will never share a UUID.

These invariants are updated through the following procedure:

Create a recording:

1. Insert a reserved_sample_files row, in state WRITING.
2. Write the sample file, aborting if open(..., O\_WRONLY|O\_CREATE|O\_EXCL) fails with EEXIST. (This would indicate a non-unique UUID, a serious defect.)
3. fsync() the sample file.
4. fsync() the sample file directory.
5. Replace the reserved_sample_files row with a recording row, marking its size and SHA-1 hash in the process.

Delete a recording:

1. Replace the recording row with a reserved_sample_files row in state DELETED.
2. unlink() the sample file, warning on ENOENT. (This would indicate invariant #2 is false.)
3. fsync() the sample file directory.
4. Delete the reserved_sample_files row.

Startup (crash recovery):

1. 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.
2. Query reserved_sample_files table.
3. unlink() all the sample files associated with rows returned by #2, ignoring ENOENT.
4. fsync() the samples directory.
5. Delete the rows returned by #2 from the reserved_sample_files 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.

There could be another procedure for moving a sample file from one filesystem to another. This might be used when splitting cameras across hard drives. New states could be introduced indicating that a recording is "is moving from A to B" (thus, A is complete, and B is in an undefined state) or "has just moved from A to B" (thus, B is complete, and A may be present or not). Alternatively, a camera might have a search path specified for its recordings, such that the first directory in which a recording is found must have a complete copy (and subsequent directories' copies may be partial/corrupt).

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:

1. Compare presence of sample files.
2. Compare size of sample files.
3. 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()	~3 sec
size	fstat()	~3 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 size check is fast enough that it seems reasonable to simply always perform it on startup. Hash checks are too expensive to wait for in normal operation; they will either be a rare offline data recovery mechanism or done 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,
  camera_id integer references camera (id) not null,

  sample_file_uuid blob unique not null,
  sample_file_sha1 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 (
  -- A SHA-1 hash of |bytes|.
  sha1 blob primary 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:

1. the duration (in 90kHz units) of each sample
2. the byte size of each sample
3. 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)

See also the example below:

	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.

Copyright

This file is part of Moonfire NVR, a security camera network video recorder. Copyright (C) 2016 Scott Lamb slamb@slamb.org

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

In addition, as a special exception, the copyright holders give permission to link the code of portions of this program with the OpenSSL library under certain conditions as described in each individual source file, and distribute linked combinations including the two.

You must obey the GNU General Public License in all respects for all of the code used other than OpenSSL. If you modify file(s) with this exception, you may extend this exception to your version of the file(s), but you are not obligated to do so. If you do not wish to do so, delete this exception statement from your version. If you delete this exception statement from all source files in the program, then also delete it here.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.