Sqlite has a crazy easter egg where a 1 Gib file offset, it creates a
`PENDING_BYTE_PAGE` that is used only by the VFS layer, and is never
read or written into.
To properly test this, I took inspiration from SQLITE testing framework,
and defined a helper method, that is conditionally compiled with the
`test_helper` feature enabled.
https://github.com/sqlite/sqlite/blob/7e38287da43ea3b661da3d8c1f431aa907
d648c9/src/main.c#L4327
As the `PENDING_BYTE` is normally at the 1 Gib mark, I created a
function that modifies the static `PENDING_BYTE` atomic to whatever
value we want. This means we can test this unusual behaviours at any DB
file size we want.
`fuzz_pending_byte_database` is the test that fuzzes different pending
byte offsets and does an integrity check at the end to confirm, we are
compatible with SQLITE
Closes#2749
<img width="1100" height="740" alt="image" src="https://github.com/user-
attachments/assets/06eb258f-b4b4-47bf-85f9-df1cf411e1df" />
Reviewed-by: Jussi Saurio <jussi.saurio@gmail.com>
Closes#3431
We caught a pretty bad bug quite late because this fuzz test only
ran on btree changes - let's run it on every CI run but with less
iterations than before.
Our current UPDATE/DELETE fuzz tests are coupled to the btree and do
not exercise the VDBE code paths at all, so a separate one makes sense.
This test repeats the following:
- Creates one table with n columns
- Creates (0..=n) indexes
- Executes UPDATE/DELETE statements
- Asserts that both sqlite and tursodb have the same DB state after each stmt
We had a bug where we were checking for duplicates in the DISTINCT
index based on both the result column count plus any ORDER BY columns
not present in the DISTINCT clause.
This is wrong, so fix it by only using the result columns for the
dedupe check.
WAL insert API introduced in the #2231 works incorrectly as it never
mark inserted pages as dirty.
This PR fixes this issue and also add simple fuzz test which fails
without fixes.
Reviewed-by: Jussi Saurio <jussi.saurio@gmail.com>
Closes#2245
# Fix SUM aggregate function for mixed types
Fixes#2133
The SUM aggregate function was returning incorrect results when
processing tables with mixed numeric and non-numeric values. According
to SQLite documentation:
> "If any input to sum() is neither an integer nor a NULL, then sum()
returns a floating point value"
[*](https://sqlite.org/lang_aggfunc.html)
Now both SQLite and Turso yield the same output of 44.0.
--
I modified `Sum` to increment only for numeric values, skipping non-
numeric values. However, if we have mixed numeric values or non-numeric
values, we return a float output. Added a flag to keep track of it.
as pointed out by @FHaggs , If there are no non-NULL input rows then
sum() returns NULL but total() returns 0.0. I decided to include it in
this PR as well. Empty was such a natural test case.
Reviewed-by: Jussi Saurio <jussi.saurio@gmail.com>
Closes#2182
`BTreeTable::to_sql` makes us incompatible with SQLite by losing e.g. the original whitespace provided during the CREATE TABLE command.
For now let's fix our tests by regex-replacing every CREATE TABLE in
the entire repo to have exactly 1 space after the table name in the
CREATE TABLE statement.
The original `rowid_seek_fuzz` test had a design flaw: it inserted contiguous
integers, which prevented issues such as the one fixed in #2065 from being
discovered.
Further, the test has at some point also been neutered a bit by only inserting
100 values which makes the btree very small, hiding interactions between interior
pages and neighboring leaf pages.
This should not be merged until #2065 is merged.
Makes it easier to test the feature:
```
$ cargo run -- --experimental-indexes
Limbo v0.0.22
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database
limbo> CREATE TABLE t(x);
limbo> CREATE INDEX t_idx ON t(x);
limbo> DROP INDEX t_idx;
```
It is easy to chalk this fuzzer issue to erratic floating point
behaviour, but this is not the case here.
Currently, `exec_math_log` calculates log with arbitrary bases by using
the following formula: `log_a(b) ~= ln(b) / ln(a)`. This calculation is
an approximation with lots of its floating point precision lost to
dividing the results of natural logarithms.
By using the specialized versions of the log functions (`log2` &
`log10`), we can avoid this loss of precision.
SQLite also uses these specialized log functions when possible, so it
doesn't hurt to do the same thing when aiming for parity.
