A lot of the old checks in fixIntegrity() are no longer relevant, and some of
the others may no longer be required. They can be added back in as the need
arises.
We want to ensure that we never recycle ids from deleted cards. We could do
this with an autoincrement column in sqlite, but it's cheaper for us to handle
the ids ourselves, as the deck object is always in memory.
When adding facts, you can now pass in a group id which the GUI should support
editing. Templates will have an optional group id which overrides the provided
id, so users can automatically put certain card types in a different group (or
all of them, if desired). Greying out the group box in the GUI in that case
would be a good idea.
This means that the default learn queue sort order doesn't need another column
in the index, but it also means that generated cards will have a higher id,
and would appear later even if they have a lower ordinal. This is probably an
infrequent issue, and a plugin which rewrites ids would probably be an
adequate solution.
Our goal is to allow decks created on one platform to use similar fonts (even
if named differently) when moving to another platform. The old solution wasn't
useful for the web version or mobile versions. Instead, we store a mapping in
the deck, and when generating the CSS, we list all possible fonts. An option
in the interface for the user to add extra fonts might be nice.
The model now has a css column, and when it's flushed, it generates the css
for the fields and templates. This means we don't have to generate the CSS on
deck load anymore.
The hex cache has also been removed. Javascript couldn't handle big ints, but
since ints are small numbers now, we no longer need a cache to efficiently
convert an id to hex.
- 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.
Since Anki first moved to an SQL backend, it has stored fields in a fields
table, with one field per line. This is a natural layout in a relational
database, and it had some nice properties. It meant we could retrieve an
individual field of a fact, which we used for limiting searches to a
particular field, for sorting, and for determining if a field was unique, by
adding an index on the field value.
The index was very expensive, so as part of the early work towards 2.0 I added
a checksum field instead, and added an index to that. This was a lot cheaper
than storing the entire value twice for the purpose of fast searches, but it
only partly solved the problem. We still needed an index on factId so that we
could retrieve a given fact's fields quickly. For simple models this was
fairly cheap, but as the number of fields grows the table grows very big. 25k
facts with 30 fields each and the fields table has grown to 750k entries. This
makes the factId index and checksum index really expensive - with the q/a
cache removed, about 30% of the deck in such a situation.
Equally problematic was sorting on those fields. Short of adding another
expensive index, a sort involves a table scan of the entire table.
We solve these problems by moving all fields into the facts table. For this to
work, we need to address some issues:
Sorting: we'll add an option to the model to specify the sort field. When
facts are modified, that field is written to a separate sort column. It can be
HTML stripped, and possibly truncated to a maximum number of letters. This
means that switching sort to a different field involves an expensive rewrite
of the sort column, but people tend to leave their sort field set to the same
value, and we don't need to clear the field if the user switches temporarily
to a non-field sort like due order. And it has the nice properties of allowing
different models to be sorted on different columns at the same time, and
makes it impossible for models to be hidden because the user has sorted on a
field which doesn't appear in some models.
Searching for words with embedded HTML: 1.2 introduced a HTML-stripped cache
of the fields content, which both sped up searches (since we didn't have to
search the possibly large fields table), and meant we could find "bob" in
"b<b>ob</b>" quickly. The ability to quickly search for words peppered with
HTML was nice, but it meant doubling the cost of storing text in many cases,
and meant after any edit more data has to be written to the DB. Instead, we'll
do it on the fly. On this i7 computer, stripping HTML from all fields takes
1-2.6 seconds on 25-50k decks. We could possibly skip the stripping for people
who don't require it - the number of people who bold parts of words is
actually pretty small.
Duplicate detection: one option would be to fetch all fields when the add
cards dialog or editor are opened. But this will be expensive on mobile
devices. Instead, we'll create a separate table of (fid, csum), with an index
on both columns. When we edit a fact, we delete all the existing checksums for
that fact, and add checksums for any fields that must be checked as unique. We
could optionally skip the index on csum - some benchmarking is required.
As for the new table layout, creating separate columns for each field won't
scale. Instead, we store the fields in a single column, separated by an ascii
record separator. We split on that character when extracting from
the database, and join on it when writing to the DB.
Searching on a particular field in the browser will be accomplished by finding
all facts that match, and then unpacking to see if the relevant field matched.
Tags have been moved back to a separate column. Now that fields are on the
facts table, there is no need to pack them in as a field simply to avoid
another table hit.
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.
- make sure we're actually stripping text in the field cache
- make sure a default group is added on upgrade
- make sure old style field references are upgrade
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
- instead of the old 4 settings, we move to just two, as there's no point
having separate include and exclude options for a non-overlapping set of
cards
- revGroups and newGroups are a list of groupIds to include in the queue. If
all groups are enabled, the UI should set it to an empty list rather than a
list of every available group, and groupLimit() will leave off the
constraint completely
- skip updating buried cards on startup; it's expensive and we'll do that on
deck close in the future
- add an index for groupId. Initial profiling indicates that groupId-based
selective study is considerably faster in certain scenarios
The 50k element deck I'm testing with now opens and builds the queue in 40ms
on a cold cache, of which 34ms is the initial deck startup and 6ms the queue
build. Adding back the undo log and backups will of course increase this, but
this is a big improvement for checking due times in the deck browser.
Users who want to study small subsections at one time (eg, "lesson 14") are
currently best served by creating lots of little decks. This is because:
- selective study is a bit cumbersome to switch between
- the graphs and statitics are for the entire deck
- selective study can be slow on mobile devices - when the list of cards to
hide/show is big, or when there are many due cards, performance can suffer
- scheduling can only be configured per deck
Groups are intended to address the above problems. All cards start off in the
same group, but they can have their group changed. Unlike tags, cards can only
be a member of a single group at once time. This allows us to divide the deck
up into a non-overlapping set of cards, which will make things like showing
due counts for a single category considerably cheaper. The user interface
might want to show something like a deck browser for decks that have more than
one group, showing due counts and allowing people to study each group
individually, or to study all at once.
Instead of storing the scheduling config in the deck or the model, we move the
scheduling into a separate config table, and link that to the groups table.
That way a user can have multiple groups that all share the same scheduling
information if they want.
And deletion tracking is now in a single table.
- limits are stored separately so we can access them quickly when checking
deck counts
- data is used to store cssCache and hexCache; these may be refactored or go
away in the future
- model config is now stored as a json-serialized dict, which allows us to
quickly gather the info and allows for adding extra options more easily in
the future
- denormalize modelId into the cards table, so we can get the model scheduling
information without having to hit the facts table
- remove position - since we will handle spacing differently we don't need a
separate variable to due to define sort order
- remove lastInterval from cards; the new cram mode and review early shouldn't
need it
- successive->streak
- add new columns for learn mode
- move cram mode into new file; learn more and review early need more thought
- initial work on learn mode
- initial unit tests
- move most scheduling parameters from deck to models
- remove obsolete fields in deck and models
- decks->deck
- remove deck id reference in models
- move some deckVars into the deck table
- simplify deckstorage
- lock sessionhelper by default
- add models/currentModel as properties instead of ORM mappings
- remove models.tags
- remove remaining support for memory-backed databases
- use a blank string for syncName instead of null
- remove backup code; will handle in gui
- bump version to 100
- update unit tests
- tags.tag -> tags.name
- priority reset to 0 for now; will be used differently in the future
- cardTags.id removed; (tagId, cardId) is the primary key now
- cardTags.src -> cardTags.type
Cards had developed quite a lot of cruft from incremental changes, and a
number of important attributes were stored in names that had no bearing to
their actual use.
Added:
- position, which new cards will be sorted on in the future
- flags, which is reserved for future use
Renamed:
- type to queue
- relativeDelay to type
- noCount to lapses
Removed:
- all new/young/matureEase counts; the information is in the revlog
- firstAnswered, lastDue, lastFactor, averageTime and totalTime for the same
reason
- isDue, spaceUntil and combinedDue, because they are no longer used. Spaced
cards will be implemented differently in a coming commit.
- priority
- yesCount, because it can be inferred from reps & lapses
- tags; they've been stored in facts for a long time now
Also compatibility with deck versions less than 65 has been dropped, so decks
will need to be upgraded to 1.2 before they can be upgraded by the dev code.
All shared decks are on 1.2, so this should hopefully not be a problem.
- rename to revlog
- change the pk to time, as we want an index on time, and the old multi-column
index was expensive and not useful
- remove yes/no count; they can be inferred from the ease
- remove lastFactor, as it's in the previous entry
- remove delay, it can be inferred from last entry
- remove 'next' from nextInterval and nextFactor
- rename 'thinkingTime' to 'userTime'
- rename reps to rep
- migrate old data to new table, and fix some problems in the process: ease0
-> ease1, and limit thinking time to 60 seconds as it should have been
previously
The stats table was how the early non-SQL versions of Anki kept track of
statistics, before there was a revision log. It is being removed because:
- it's not possible to show the statistics for a subset of the deck
- it can't meaningfully be copied on import/export
- it makes it harder to implement sync merging
Implications:
- graphs and deck stats roughly 1.5-3x longer than before, but we'll have the
ability to generate stats for subsections of the deck, and it's not time
critical code
- people who've been using anki since the very early days may notice a drop in
statistics, as early repetitions were recorded in the stats table but the
revlog didn't exist at that point.
- due bugs in old syncs and imports/exports, the stats and revlog may not
match numbers exactly
To remove it, the following changes have been made:
- the graphs and deck stats now generate their data entirely from the revlog
- there are no stats to keep track of how many cards we've answered, so we
pull that information from the revlog in reset()
- we remove _globalStats and _dailyStats from the deck
- we check if a day rollover has occurred using failedCutoff instead
- we remove the getStats() routine
- the ETA code is currently disabled
- timeboxing routines use repsToday instead of stats
- remove stats delete from export
- remove stats table and index in upgrade
- remove stats syncing and globalStats refresh pre-sync
- remove stats count check in fullSync check, which was redundant anyway
- update unit tests
Also:
- newCountToday -> newCount, to bring it in line with revCount&failedCount
which also reflect the currently due count
- newCount -> newAvail
- timeboxing routines renamed since the old names were confusingly similar to
refreshSession() which does something different
Todo:
- update newSeenToday & repsToday when answering a card
- reimplement eta
