UNION queries, while useful on their own, are a cornerstone of recursive
CTEs.
This PR implements:
* the merge operator, required to merge both sides of a union query.
* the circuitry necessary to issue the Merge operator.
* extraction of tables mentioned in union and CTE expressions, so we can
correctly populate tables that contain them.
Closes#3234
We currently only support column / literal comparisons in the filter
operator. But with JOINs, comparisons are usually against two columns.
Do the work to support it.
Unlike the other operators, project works just fine with ambiguous
columsn, because it works with compiled expressions. We don't need to
patch it, but let's make sure it keeps working by writing a test.
We already did similarly for the AggregateOperator: for joins
you can have the same column name in many tables. And passing schema
information to the operator is a layering violation (the operator may be
operating on the result of a previous node, and at that point there is
no more "schema"). Therefore we pass indexes into the column set the
operator has.
The FilterOperator has a complication: we are using it to generate the
SQL for the populate statement, and that needs column names. However,
we should *not* be using the FilterOperator for that, and that is a
relic from the time where we had operator information directly inside
the IncrementalView.
To enable moving the FilterOperator to index-based, we rework that code.
For joins, we'll need to populate many tables anyway, so we take the
time to do that work here.
This PR improves the DBSP circuit so that it handles the JOIN operator.
The JOIN operator exposes a weakness of our current model: we usually
pass a list of columns between operators, and find the right column by
name when needed.
But with JOINs, many tables can have the same columns. The operators
will then find the wrong column (same name, different table), and
produce incorrect results.
To fix this, we must do two things:
1) Change the Logical Plan. It needs to track table provenance.
2) Fix the aggregators: it needs to operate on indexes, not names.
For the aggregators, note that table provenance is the wrong
abstraction. The aggregator is likely working with a logical table that
is the result of previous nodes in the circuit. So we just need to be
able to tell it which index in the column array it should use.
We have not implemented them before because they require the raw
elements to be kept. It is easy to see why in the following example:
current_min = 3;
insert(2) => current_min = 2 // can be done without state
delete(2) => needs to look at the state to determine new min!
The aggregator state was a very simple key-value structure. To
accomodate for min/max, we will make it into a more complex table, where
we can encode a more complex structure.
The key insight is that we can use a primary key composed of:
1) storage_id
2) zset_id,
3) element
The storage_id and zset_id are our previous key, except they are now
exploded to support a larger range of storage_id. With more bits
available in the storage_id, we can encode information about which
column we are storing. For aggregations in multiple columns, we will
need to keep a different list of values for min/max!
The element is just the values of the columns.
Because this is a primary key, the data will be sorted in the btree.
We can then just do a prefix search in the first two components of
the key and easily find the min/max when needed.
This new format is also adequate for joins. Joins will just have
a new storage_id which encodes two "columns" (left side, right side).
And also change the schema of the main table. I have come to see the
current key-value schema as inadequate for non-aggregate operators.
Calculating Min/Max, for example, doesn't feat in this schema because
we have to be able to track existing values and index them.
Another alternative is to keep one table per operator type, but this
quickly leads to an explosion of tables.
This is a collection of fixes for materialized views ahead of adding
support for JOINs.
It is mostly issues with how we assume there is a single table, with a
single delta, but we have to send more than one.
Those are things that are just objectively wrong, so I am sending it
separately to make the JOIN PR smaller.
Reviewed-by: Preston Thorpe <preston@turso.tech>
Closes#3009
A DeltaSet is a collection of Deltas, one per table.
We'll need that for joins. The populate step for now will still generate
a single set. That will be our next step to fix.
We have code written for BTree (ZSet) persistence in both compiler.rs
and operator.rs, because there are minor differences between them. With
joins coming, it is time to unify this code.
indexes with the naming scheme "sqlite_autoindex_<tblname>_<number>"
are automatically created when a table is created with UNIQUE or
PRIMARY KEY definitions.
these indexes must map to the table definition SQL in definition order,
i.e. sqlite_autoindex_foo_1 must be the first instance of UNIQUE or
PRIMARY KEY and so on.
this commit fixes our autoindex creation / parsing so that this invariant
is upheld.
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.
