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design/schema.md
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@@ -1,4 +1,4 @@
# Moonfire NVR Storage Schema
# Moonfire NVR Storage Schema <!-- omit in toc -->
Status: **current**.
@@ -6,42 +6,56 @@ This is the initial design for the most fundamental parts of the Moonfire NVR
storage schema. See also [guide/schema.md](../guide/schema.md) for more
administrator-focused documentation.
* [Objective](#objective)
* [Cameras](#cameras)
* [Hard drives](#hard-drives)
* [Overview](#overview)
* [Detailed design](#detailed-design)
* [SQLite3](#sqlite3)
* [Duration of recordings](#duration-of-recordings)
* [Lifecycle of a sample file directory](#lifecycle-of-a-sample-file-directory)
* [Lifecycle of a recording](#lifecycle-of-a-recording)
* [Verifying invariants](#verifying-invariants)
* [Recording table](#recording-table)
* [`video_index`](#video_index)
* [<a href="on-demand"></a>On-demand `.mp4` construction](#on-demand-mp4-construction)
## 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][pi2]
* support high- and low-bandwidth playback
* support near-live playback (~second old), including "trick play"
* allow verifying database consistency with an `fsck` tool
* 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][pi2]
* 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
* 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][xandem] tomography data).
* record audio and/or other types of timestamped samples (such as
[Xandem][xandem] tomography data).
### Cameras
@@ -51,12 +65,12 @@ 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 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
@@ -73,13 +87,17 @@ different quality settings as well.
Decode:
$ time ffmpeg -y -threads 1 -i input.mp4 \
-f null /dev/null
```
$ 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
```
$ 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 |
@@ -115,8 +133,10 @@ 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)
```
disk time fraction <= (seek rate) / (50 seeks/sec) +
(bandwidth) / (100 MB/sec)
```
## Overview
@@ -127,19 +147,20 @@ 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][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][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).
* 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][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][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
@@ -167,17 +188,18 @@ All metadata, including the `recording` table and others, will be stored in
the SQLite3 database using [write-ahead logging][sqlite3-wal]. 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][file-consistency].)
* 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.
* 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][file-consistency].)
* 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
@@ -189,40 +211,42 @@ to be a performance bottleneck.
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](#on-demand),
`.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.
* 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](#on-demand),
`.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.
* 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
@@ -230,19 +254,20 @@ 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.
* 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
@@ -323,74 +348,74 @@ 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.
1. Open the metadata file.
2. Rewrite the fixed-length data atomically.
3. `fdatasync` the file.
1. Open the metadata file.
2. Rewrite the fixed-length data atomically.
3. `fdatasync` the file.
*Open the database as read-only*
1. Lock the database directory with `LOCK_SH` (shared).
2. Open the SQLite database with `SQLITE_OPEN_READ_ONLY`.
1. Lock the database directory with `LOCK_SH` (shared).
2. Open the SQLite database with `SQLITE_OPEN_READ_ONLY`.
*Open the database as read-write*
1. Lock the database directory with `LOCK_EX` (exclusive).
2. Open the SQLite database with `SQLITE_OPEN_READ_WRITE`.
3. Insert a new `open` table row with the new sequence number and uuid.
1. Lock the database directory with `LOCK_EX` (exclusive).
2. Open the SQLite database with `SQLITE_OPEN_READ_WRITE`.
3. Insert a new `open` table row with the new sequence number and uuid.
*Create a sample file directory*
Precondition: database open read-write.
1. Lock the sample file directory with `LOCK_EX` (exclusive).
2. Verify there is no metadata file or `last_complete_open` is unset.
3. Write new metadata file with a fresh `dir_uuid` and a `in_progress_open`
matching the database's current open.
4. Add a matching row to the database with `last_complete_open_id` matching
the current open.
5. Update the metadata file to move `in_progress_open` to
`last_complete_open`.
1. Lock the sample file directory with `LOCK_EX` (exclusive).
2. Verify there is no metadata file or `last_complete_open` is unset.
3. Write new metadata file with a fresh `dir_uuid` and a `in_progress_open`
matching the database's current open.
4. Add a matching row to the database with `last_complete_open_id` matching
the current open.
5. 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).
1. Lock the sample file directory with `LOCK_SH` (shared).
2. 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.
1. Lock the sample file directory with `LOCK_SH` (shared).
2. 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.
1. Lock the sample file directory with `LOCK_EX` (exclusive).
2. Verify the metadata file matches the database (as above).
3. Update the metadata file with `in_progress_open` matching the current
open.
3. Update the database row with `last_complete_open_id` matching the current
open.
4. Update the metadata file with `last_complete_open` rather than
`in_progress_open`.
5. Run the recording startup procedure for this directory.
1. Lock the sample file directory with `LOCK_EX` (exclusive).
2. Verify the metadata file matches the database (as above).
3. Update the metadata file with `in_progress_open` matching the current
open.
4. Update the database row with `last_complete_open_id` matching the current
open.
5. Update the metadata file with `last_complete_open` rather than
`in_progress_open`.
6. Run the recording startup procedure for this directory.
*Close a sample file directory*
1. Drop the sample file directory lock.
1. Drop the sample file directory lock.
*Delete a sample file directory*
1. 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.
2. Rewrite the directory metadata with `in_progress_open` set to the current open,
`last_complete_open` cleared.
3. Delete the directory's row from the database.
1. 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.
2. Rewrite the directory metadata with `in_progress_open` set to the current open,
`last_complete_open` cleared.
3. Delete the directory's row from the database.
### Lifecycle of a recording
@@ -398,15 +423,15 @@ 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 with the indicated size
and SHA-1 hash.
2. 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.
3. After an orderly shutdown of Moonfire NVR, there is a `recording` table row
for every sample file, even if there have been previous crashes.
1. `recording` table rows have sample files on disk with the indicated size
and SHA-1 hash.
2. 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.
3. 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
@@ -423,31 +448,31 @@ These invariants are updated through the following procedure:
*Create a recording:*
1. Write the sample file, aborting if `open(..., O\_WRONLY|O\_CREATE|O\_EXCL)`
fails with `EEXIST`.
3. `fsync()` the sample file.
4. `fsync()` the sample file directory.
5. Insert the `recording` row, marking its size and SHA-1 hash in the process.
1. Write the sample file, aborting if `open(..., O\_WRONLY|O\_CREATE|O\_EXCL)`
fails with `EEXIST`.
3. `fsync()` the sample file.
4. `fsync()` the sample file directory.
5. Insert the `recording` row, marking its size and SHA-1 hash in the process.
*Delete a recording:*
1. Replace the `recording` row with a `garbage` row.
2. `unlink()` the sample file, warning on `ENOENT`. (This would indicate
invariant #2 is false.)
3. `fsync()` the sample file directory.
4. Delete the `garbage` row.
1. Replace the `recording` row with a `garbage` row.
2. `unlink()` the sample file, warning on `ENOENT`. (This would indicate
invariant #2 is false.)
3. `fsync()` the sample file directory.
4. Delete the `garbage` 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 `garbage` table and `cum_recordings` field in the `stream` table.
3. `unlink()` all the sample files associated with garbage rows, ignoring
`ENOENT`.
4. For each stream, `unlink()` all the existing files with recording ids >=
`cum_recordings`.
4. `fsync()` the sample file directory.
5. Delete all rows from the `garbage` table.
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 `garbage` table and `cum_recordings` field in the `stream` table.
3. `unlink()` all the sample files associated with garbage rows, ignoring
`ENOENT`.
4. For each stream, `unlink()` all the existing files with recording ids >=
`cum_recordings`.
4. `fsync()` the sample file directory.
5. 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
@@ -471,9 +496,9 @@ problem.
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.
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.
@@ -487,15 +512,17 @@ The times are roughly:
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
```
$ 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 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
@@ -515,42 +542,44 @@ the background at low priority.
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,
```sql
-- 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,
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,
-- 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,
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,
-- 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,
-- 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
);
-- 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
@@ -579,21 +608,21 @@ only with certain firmware versions (see [thread][hikvision-sr]). 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
* 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)
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][iso-14496-12] `stts` (TimeToSampleBox,
section 8.6.1.2), `stsz` (SampleSizeBox, section 8.7.3), and `stss`
@@ -614,16 +643,18 @@ 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)
```
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:
@@ -643,10 +674,10 @@ See also the example below:
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][media-bmff]), for
a browser-based user interface.
* 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][media-bmff]), 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
@@ -654,38 +685,6 @@ 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 The Moonfire NVR Authors
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/>.
[pi2]: https://www.raspberrypi.org/products/raspberry-pi-2-model-b/
[xandem]: http://www.xandemhome.com/
[hikcam]: http://overseas.hikvision.com/us/Products_accessries_10533_i7696.html