Because JSON doesn't support 64 bit numbers, we need to either convert the 64
bit numbers to a string during transport, or store the ids as a string. At
base 91 a 64 bit number only takes an extra two bytes, and it means we can
dump DB results directly into JSON without having to apply any transformation.
Syncing and shared decks have conflicting priorities:
- For syncing, we need to ensure that the deck remains in a consistent state.
In the past, Anki allowed deletions to be overriden by a more recently
modified object, but this could lead to a broken deck in certain
circumstances. For example, if a user deletes a fact (and its cards) on one
side, but does something to bump a card's mod time on another side, then
when syncing the card would be brought back to life without its fact. Short
of complex code to check all the relations, we're limited to two options:
forcing a full sync when things are deleted, or ensuring objects can't come
back to life.
- When facts are shared between people, we need a way to identify if two facts
arose from the same source. We can't compare based on content, as the
content may have changed partially or completely. And we can't use the
timestamp ids because of the above restriction on bringing objects back to
life. If we did that, people could download a shared deck, decide they don't
want it, and delete it. When they later decide to add it again, it wouldn't
be possible: either nothing would be imported because of the old graves, or
the ids would have to be rewritten. If we do the latter, the facts are no
longer associated with each other, and we lose the ability to update the
deck.
So we need to give facts two IDs: one used as the primary key and for syncing,
and another 'global id' for importing/sharing. I used a 64 bit random number,
because a) it's what Anki's used in the past, so by reusing the old IDs we
don't break existing associations on upgrade, and b) it's a decent compromise
between the possibility of conflicts and performance.
Also re-added a flags column to the facts. The 'data' column is intended to
store JSON in the future for extra features without changing the schema, but
that's slow for simple state checks. Flags will be used as a bitmask.
I removed the media database in an earlier commit, but it's now necessary
again as I decided to add native media syncing to AnkiWeb.
This time, the DB is stored in the media folder rather than with the deck.
This means we avoid sending it in a full sync, and makes deck backups faster.
The DB is a cache of file modtimes and checksums. When findChanges() is
called, the code checks to see which files were added, changed or deleted
since the last time, and updates the log of changes. Because the scanning step
and log retrieval is separate, it's possible to do the scanning in the
background if the need arises.
If the DB is deleted by the user, Anki will forget any deletions, and add all
the files back to the DB the next time it's accessed.
File changes are recorded as a delete + add.
media.addFile() could be optimized in the future to log media added manually
by the user, allowing us to skip the full directory scan in cases where the
only changes were manually added media.
- moved tags into json like previous changes, and dropped the unnecessary id
- added tags.py for a tag manager
- moved the tag utilities from utils into tags.py
Like the previous change, models have been moved from a separate DB table to
an entry in the deck. We need them for many operations including reviewing,
and it's easier to keep them in memory than half on disk with a cache that
gets cleared every time we .reset(). This means they are easily serialized as
well - previously they were part Python and part JSON, which made access
confusing.
Because the data is all pulled from JSON now, the instance methods have been
moved to the model registry. Eg:
model.addField(...) -> deck.models.addField(model, ...).
- IDs are now timestamped as with groups et al.
- The data field for plugins was also removed. Config info can be added to
deck.conf; larger data should be stored externally.
- Upgrading needs to be updated for the new model structure.
- HexifyID() now accepts strings as well, as our IDs get converted to strings
in the serialization process.
The approach of using incrementing id numbers works for syncing if we assume
the server is canonical and all other clients rewrite their ids as necessary,
but upon reflection it is not sufficient for merging decks in general, as we
have no way of knowing whether objects with the same id are actually the same
or not. So we need some way of uniquely identifying the object.
One approach would be to go back to Anki 1.0's random 64bit numbers, but as
outlined in a previous commit such large numbers can't be handled easy in some
languages like Javascript, and they tend to be fragmented on disk which
impacts performance. It's much better if we can keep content added at the same
time in the same place on disk, so that operations like syncing which are mainly
interested in newly added content can run faster.
Another approach is to add a separate column containing the unique id, which
is what Mnemosyne 2.0 will be doing. Unfortunately it means adding an index
for that column, leading to slower inserts and larger deck files. And if the
current sequential ids are kept, a bunch of code needs to be kept to ensure ids
don't conflict when merging.
To address the above, the plan is to use a millisecond timestamp as the id.
This ensures disk order reflects creation order, allows us to merge the id and
crt columns, avoids the need for a separate index, and saves us from worrying
about rewriting ids. There is of course a small chance that the objects to be
merged were created at exactly the same time, but this is extremely unlikely.
This commit changes models. Other objects will follow.
- convert checksums to int
- add bulk update & update on upgrade
- add indices pending performance testing. The fsum table & indices add about
2MB to a deck with 50k unique fields
the field cache (fsums table) also needs to store the model id to preserve the
old behaviour of limiting duplicate checks to a given model, and to ensure
we're actually comparing against the same fields
removed the dingsbums and wcu importers; will accept them back if the authors
port them to the new codebase.
Anki used random 64bit IDs for cards, facts and fields. This had some nice
properties:
- merging data in syncs and imports was simply a matter of copying each way,
as conflicts were astronomically unlikely
- it made it easy to identify identical cards and prevent them from being
reimported
But there were some negatives too:
- they're more expensive to store
- javascript can't handle numbers > 2**53, which means AnkiMobile, iAnki and
so on have to treat the ids as strings, which is slow
- simply copying data in a sync or import can lead to corruption, as while a
duplicate id indicates the data was originally the same, it may have
diverged. A more intelligent approach is necessary.
- sqlite was sorting the fields table based on the id, which meant the fields
were spread across the table, and costly to fetch
So instead, we'll move to incremental ids. In the case of model changes we'll
declare that a schema change and force a full sync to avoid having to deal
with conflicts, and in the case of cards and facts, we'll need to update the
ids on one end to merge. Identical cards can be detected by checking to see if
their id is the same and their creation time is the same.
Creation time has been added back to cards and facts because it's necessary
for sync conflict merging. That means facts.pos is not required.
The graves table has been removed. It's not necessary for schema related
changes, and dead cards/facts can be represented as a card with queue=-4 and
created=0. Because we will record schema modification time and can ensure a
full sync propagates to all endpoints, it means we can remove the dead
cards/facts on schema change.
Tags have been removed from the facts table and are represented as a field
with ord=-1 and fmid=0. Combined with the locality improvement for fields, it
means that fetching fields is not much more expensive than using the q/a
cache.
Because of the above, removing the q/a cache is a possibility now. The q and a
columns on cards has been dropped. It will still be necessary to render the
q/a on fact add/edit, since we need to record media references. It would be
nice to avoid this in the future. Perhaps one way would be the ability to
assign a type to fields, like "image", "audio", or "latex". LaTeX needs
special consider anyway, as it was being rendered into the q/a cache.
SQLAlchemy is a great tool, but it wasn't a great fit for Anki:
- We often had to drop down to raw SQL for performance reasons.
- The DB cursors and results were wrapped, which incurred a
sizable performance hit due to introspection. Operations like fetching 50k
records from a hot cache were taking more than twice as long to complete.
- We take advantage of sqlite-specific features, so SQL language abstraction
is useless to us.
- The anki schema is quite small, so manually saving and loading objects is
not a big burden.
In the process of porting to DBAPI, I've refactored the database schema:
- App configuration data that we don't need in joins or bulk updates has been
moved into JSON objects. This simplifies serializing, and means we won't
need DB schema changes to store extra options in the future. This change
obsoletes the deckVars table.
- Renamed tables:
-- fieldModels -> fields
-- cardModels -> templates
-- fields -> fdata
- a number of attribute names have been shortened
Classes like Card, Fact & Model remain. They maintain a reference to the deck.
To write their state to the DB, call .flush().
Objects no longer have their modification time manually updated. Instead, the
modification time is updated when they are flushed. This also applies to the
deck.
Decks will now save on close, because various operations that were done at
deck load will be moved into deck close instead. Operations like undoing
buried card are cheap on a hot cache, but expensive on startup.
Programmatically you can call .close(save=False) to avoid a save and a
modification bump. This will be useful for generating due counts.
Because of the new saving behaviour, the save and save as options will be
removed from the GUI in the future.
The q/a cache and field cache generating has been centralized. Facts will
automatically rebuild the cache on flush; models can do so with
model.updateCache().
Media handling has also been reworked. It has moved into a MediaRegistry
object, which the deck holds. Refcounting has been dropped - it meant we had
to compare old and new value every time facts or models were changed, and
existed for the sole purpose of not showing errors on a missing media
download. Instead we just media.registerText(q+a) when it's updated. The
download function will be expanded to ask the user if they want to continue
after a certain number of files have failed to download, which should be an
adequate alternative. And we now add the file into the media DB when it's
copied to th emedia directory, not when the card is commited. This fixes
duplicates a user would get if they added the same media to a card twice
without adding the card.
The old DeckStorage object had its upgrade code split in a previous commit;
the opening and upgrading code has been merged back together, and put in a
separate storage.py file. The correct way to open a deck now is import anki; d
= anki.Deck(path).
deck.getCard() -> deck.sched.getCard()
same with answerCard
deck.getCard(id) returns a Card object now.
And the DB wrapper has had a few changes:
- sql statements are a more standard DBAPI:
- statement() -> execute()
- statements() -> executemany()
- called like execute(sql, 1, 2, 3) or execute(sql, a=1, b=2, c=3)
- column0 -> list
The tags tables were initially added to speed up the loading of the browser by
speeding up two operations: gathering a list of all tags to show in the
dropdown box, and finding cards with a given tag. The former functionality is
provided by the tags table, and the latter functionality by the cardTags
table.
Selective study is handled by groups now, which perform better since they
don't require a join or subselect, and can be embedded in the index. So the
only remaining benefit of cardTags is for the browser.
Performance testing indicates that cardTags is not saving us a large amount.
It only takes us 30ms to search a 50k card table for matches with a hot cache.
On a cold cache it means the facts table has to be loaded into memory, which
roughly doubles the load time with the default settings (we need to load the
cards table too, as we're sorting the cards), but that startup time was
necessary with certain settings in the past too (sorting by fact created for
example). With groups implemented, the cost of maintaining a cache just for
initial browser load time is hard to justify.
Other changes:
- the tags table has any missing tags added to it when facts are added/edited.
This means old tags will stick around even when no cards reference them, but
is much cheaper than reference counting or a separate table, and simplifies
updates and syncing.
- the tags table has a modified field now so we can can sync it instead of
having to scan all facts coming across in a sync
- priority field removed
- we no longer put model names or card templates into the tags table. There
were two reasons we did this in the past: so we could cram/selective study
them, and for plugins. Selective study uses groups now, and plugins can
check the model's name instead (and most already do). This also does away
with the somewhat confusing behaviour of names also being tags.
- facts have their tags as _tags now. You can get a list with tags(), but
editing operations should use add/deleteTags() instead of manually editing
the string.
- removed 'created' column from various tables. We don't care when things like
models are created, and card creation time didn't reflect the actual time a
card was created
- facts were previously ordered by their creation date. The code would
manually set the creation time for subsequent facts on import by 0.0001
seconds, and then card due times were set by adding the fact time to the
ordinal number*0.000001. This was prone to error, and the number of zeros used
was actually different in different parts of the code. Instead of this, we
replace it with a 'pos' column on facts, which increments for each new fact.
- importing should add new facts with a higher pos, but concurrent updates in
a synced deck can have multiple facts with the same pos
- due times are completely different now, and depend on the card type
- new cards have due=fact.pos or random(0, 10000)
- reviews have due set to an integer representing days since deck
creation/download
- cards in the learn queue use an integer timestamp in seconds
- many columns like modified, lastSync, factor, interval, etc have been converted to
integer columns. They are cheaper to store (large decks can save 10s of
megabytes) and faster to search for.
- cards have their group assigned on fact creation. In the future we'll add a
per-template option for a default group.
- switch to due/random order for the review queue on upgrade. Users can still
switch to the old behaviour if they want, but many people don't care what
it's set to, and due is considerably faster, which may result in a better
user experience
Previously we had an index on the value field, which was very expensive for
long fields. Instead we use a separate column and take the first 8 characters
of the field value's md5sum, and index that. In decks with lots of text in
fields, it can cut the deck size by 30% or more, and many decks improve by
10-20%. Decks with only a few characters in fields may increase in size
slightly, but this is offset by the fact that we only generate a checksum for
fields that have uniqueness checking on.
Also, fixed import->update reporting the total # of available facts instead of
the number of facts that were imported.
