Otherwise, we end up propagating those dependencies into targets that
link against that library, which creates unnecessary link-time
dependencies.
Also included are changes to readd now missing dependencies to tools
that actually need them.
Also do this for Shell.
This greatly simplifies the CMakeLists in Lagom, replacing many glob
patterns with a big list of libraries. There are still a few special
libraries that need some help to conform to the pattern, like LibELF and
LibWebView.
It also lets us remove essentially all of the Serenity or Lagom binary
directory detection logic from code generators, as now both projects
directories enter the generator logic from the same place.
This statement (for now) outputs the name and types of the different
attributes in a table. It's not standard SQL but all DBMSs that I know
of implement a sort of statement for such functionality.
Since the output of DESCRIBE TABLE is just a relation, an internal
schema, `master` was created and a table definition for DESCRIBE into
it. The table definition and the master schema are not accessible by the
user.
Ordering is done by replacing the straight Vector holding the query
result in the SQLResult object with a dedicated Vector subclass that
inserts result rows according to their sort key using a binary search.
This is done in the ResultSet class.
There are limitations:
- "SELECT ... ORDER BY 1" (or 2 or 3 etc) is supposed to sort by the
n-th result column. This doesn't work yet
- "SELECT ... column-expression alias ... ORDER BY alias" is supposed to
sort by the column with the given alias. This doesn't work yet
What does work however is something like
```SELECT foo FROM bar SORT BY quux```
i.e. sorted by a column not in the result set. Once functions are
supported it should be possible to sort by random functions.
Because SQL is the craptastic language that it is, sometimes expressions
need to know details about the calling statement. For example the tables
in the 'FROM' clause may be needed to determine which columns are
referenced in 'WHERE' expressions. So the current statement is added
to the ExecutionContext and a new 'execute' overload on Statement is
created which takes the Database and the Statement and builds an
ExecutionContaxt from those.
Classes reading and writing to the data heap would communicate directly
with the Heap object, and transfer ByteBuffers back and forth with it.
This makes things like caching and locking hard. Therefore all data
persistence activity will be funneled through a Serializer object which
in turn submits it to the Heap.
Introducing this unfortunately resulted in a huge amount of churn, in
which a number of smaller refactorings got caught up as well.
This patch provides very basic, bare bones implementations of the
INSERT and SELECT statements. They are *very* limited:
- The only variant of the INSERT statement that currently works is
SELECT INTO schema.table (column1, column2, ....) VALUES
(value11, value21, ...), (value12, value22, ...), ...
where the values are literals.
- The SELECT statement is even more limited, and is only provided to
allow verification of the INSERT statement. The only form implemented
is: SELECT * FROM schema.table
These statements required a bit of change in the Statement::execute
API. Originally execute only received a Database object as parameter.
This is not enough; we now pass an ExecutionContext object which
contains the Database, the current result set, and the last Tuple read
from the database. This object will undoubtedly evolve over time.
This API change dragged SQLServer::SQLStatement into the patch.
Another API addition is Expression::evaluate. This method is,
unsurprisingly, used to evaluate expressions, like the values in the
INSERT statement.
Finally, a new test file is added: TestSqlStatementExecution, which
tests the currently implemented statements. As the number and flavour of
implemented statements grows, this test file will probably have to be
restructured.
This patch introduces the SQLServer system server. This service is
supposed to be the only process/application talking to database storage.
This makes things like locking and caching more reliable, easier to
implement, and more efficient.
In LibSQL we added a client component that does the ugly IPC nitty-
gritty for you. All that's needed is setting a number of event handler
lambdas and you can connect to databases and execute statements on them.
Applications that wish to use this SQLClient class obviously need to
link LibSQL and LibIPC.
This patch introduces the ability execute parsed SQL statements. The
abstract AST Statement node now has a virtual 'execute' method. This
method takes a Database object as parameter and returns a SQLResult
object.
Also introduced here is the CREATE SCHEMA statement. Tables live in a
schema, and if no schema is present in a table reference the 'default'
schema is implied. This schema is created if it doesn't yet exist when
a Database object is created.
Finally, as a proof of concept, the CREATE SCHEMA and CREATE TABLE
statements received an 'execute' implementation. The CREATE TABLE
method is not able to create tables created from SQL queries yet.
Unfortunately this patch is quite large.
The main functionality included are a BTree index implementation and
the Heap class which manages persistent storage.
Also included are a Key subclass of the Tuple class, which is a
specialization for index key tuples. This "dragged in" the Meta layer,
which has classes defining SQL objects like tables and indexes.
This patch adds the basic dynamic value classes used by the SQL Storage
layer. The most elementary class is Value, which holds a typed Value
which can be converted to standard C++ types. A Tuple is a collection
of Values described by a TupleDescriptor, which specifies the names,
types, and ordering of the elements in the Tuple.
Tuples and Values can be serialized and deserialized to and from
ByteBuffers. This is mechanism which is used to save them to disk.
Tuples are used as keys in SQL indexes and rows in SQL tables.
Also included is a test file.
LibSQL aims to be a SQLite clone for SerenityOS. Step 1 is creating a
tokenizer to lex SQL tokens. This lexer is heavily influenced by the
LibJS lexer.
