Closes#2949
This fixes a special case of the behavior described in #2501 - the
special case is that WHERE conditions that were selected as seek
predicates for the left join table were not properly evaluated when the
right-hand-side table returned no match.
The test in commit 12d72d115588a9e744bdb22382998ba1bf9031ab should
demonstrate this adequately - this should return no rows, but on `main`
it returns `1|NULL`.
Closes#2955
Closes#2946
currently we always evaluate the binary expression, then coerce it to
zero/null with the `ZeroOrNull` instruction, and then emit a separate
jump.
this is fine for non-conditional expressions where we are using the
value itself (e.g. in a SELECT result column), but in conditionals we
don't care about that at all and just want to either jump or not jump.
so: try to keep the spirit of code reuse, but still have distinct
implementations for conditionals and non-conditionals.
Reviewed-by: Preston Thorpe <preston@turso.tech>
Closes#2947
This adds the [`json_each`](https://sqlite.org/json1.html#the_json_each_
and_json_tree_table_valued_functions) TVF. Only the 1-arg version is
supported for now.
As suggested in the comments on this PR, I've also extended the virtual
table system to support internal TVF's, as opposed to extensions and
pragma TVF's.
Reviewed-by: Preston Thorpe <preston@turso.tech>
Closes#2691
currently we always evaluate the binary expression, then coerce it
to zero/null with the `ZeroOrNull` instruction, and then emit a separate
jump.
this is fine for non-conditional expressions where we are using the value
itself (e.g. in a SELECT result column), but in conditionals we don't care
about that at all and just want to jump.
so: try to keep the spirit of code reuse, but still have distinct implementations
for conditionals and non-conditionals.
On the syscall IO backend, on TPC-H query 12, the _dominating_ part
of the stack trace is trying to construct affinities from a character,
failing, allocating an error&string, and then immediately falling back to
Blob affinity and dropping the error&string.
Since I'm on vacation I won't spend cycles on figuring out why we are passing
an incorrect affinity in `flags.get_affinity()` and instead make this lazy PR
just to improve performance and stop doing silly things :]
Add handling malformed inputs to function `read_varint` and test cases.
```
# 9 byte truncated to 8
read_varint(&[0x81, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80])
before -> panic index out of bounds: the len is 8 but the index is 8
after -> LimboError
# bits set without end
read_varint(&[0x80; 9])
before -> Ok((128, 9))
after -> LimboError
```
Reviewed-by: Pere Diaz Bou <pere-altea@homail.com>
Closes#2904
This fairly long commit implements persistence for materialized view.
It is hard to split because of all the interdependencies between components,
so it is a one big thing. This commit message will at least try to go into
details about the basic architecture.
Materialized Views as tables
============================
Materialized views are now a normal table - whereas before they were a virtual
table. By making a materialized view a table, we can reuse all the
infrastructure for dealing with tables (cursors, etc).
One of the advantages of doing this is that we can create indexes on view
columns. Later, we should also be able to write those views to separate files
with ATTACH write.
Materialized Views as Zsets
===========================
The contents of the table are a ZSet: rowid, values, weight. Readers will
notice that because of this, the usage of the ZSet data structure dwindles
throughout the codebase. The main difference between our materialized ZSet and
the standard DBSP ZSet, is that obviously ours is backed by a BTree, not a Hash
(since SQLite tables are BTrees)
Aggregator State
================
In DBSP, the aggregator nodes also have state. To store that state, there is a
second table. The table holds all aggregators in the view, and there is one
table per view. That is __turso_internal_dbsp_state_{view_name}. The format of
that table is similar to a ZSet: rowid, serialized_values, weight. We serialize
the values because there will be many aggregators in the table. We can't rely
on a particular format for the values.
The Materialized View Cursor
============================
Reading from a Materialized View essentially means reading from the persisted
ZSet, and enhancing that with data that exists within the transaction.
Transaction data is ephemeral, so we do not materialize this anywhere: we have
a carefully crafted implementation of seek that takes care of merging weights
and stitching the two sets together.
