We've long needed a way to see the most recent SQLite result code SQLite in the context of say a catch block (most other times you can assume SQLITE_OK was the last result code otherwise control flow would transfer elsewhere. Sometimes SQLITE_ROW or SQLITE_DONE might be the current result code.

Soon we'll provide a sample header that declares the most common error codes in an enum but for now you could do something like this:

The nested select statement is frequently misused, in particular if you get no rows back from the expression that's an error. So for instance:

This will throw (with a peculiar error, SQLITE_DONE) if there is no such row.

Sometimes people try to fix this problem with a nullcheck:

That doesn't help at all. It's not a null value situation, there's no row at all.

Is likewise unhelpful. To help with this situation we add two forms:

Both of these deal with the case where there is no row. The second lets you have a simple default for both no row or null value. That form is equivalent to:

i.e. both problem cases are handled.

Of course the -1 here could be any valid expression, even a second (select...)

Important change in CQL semantics.

Previously if you did an early return, or fall through the end, from a procedure that is supposed to return a result set but did not in fact provide one, you would get a fake SQLITE_ERROR. Now you get an empty result set for "free".

This interpretation seems much more natural and avoids a lot of really annoying stub selects to comply with the contract.

This also works for the out statement in the same fashion.

If you want to return an error, use throw. This is a lot more natural...

examples:

Further adding to the type calculus of the CQL language we introduced the ability to encode the "kind" of primitive types. This can be used in a number of ways -- like "units" for natural things and like a "type" for synthetic keys and other such. It's easier to illustrate by example.

With the above in place, other expressions like p == j would also produce errors as these long values are no longer type compatible. This is a great way to add enforcement to your schema and procedures. Likewise you can use these annotations to add "units" to your data types. e.g.

Variables of type grams (e.g. g) are not compatible with variables of type meters (e.g. m) even though both are real.

Likewise, attemping to insert grams into a column that is typed to meters will give errors. Of course SQLite doesn't know about any of this so all the <> stuff is removed in the generated SQL. This is just about type enforcement at compile time.

Enumerations like:

enable this:

additionally in DML/DDL:

So the type kinds can be quite helpful when dealing with loose variables.

The notion of specific types was added to the language nearly two years ago to support the object type because there was a great desire to prevent object<dictionary> being assigned from object<list> but this "type kind", whether it's with units (e.g. "meters", "grams") or a type name (e.g. "job_id") adds a lot of high value type checking.

The kind can be added, stripped, or changed with a cast operation and the type system allows a constant or variable with no kind (e.g. "1") to mix and match with any kind so long as the base type is compatible as usual. So you get the most value by using the specific type consistently but you won't go insane adding test cases that use constants for instance.

As of this writing the expression kinds are checked for compatibility everywhere plus or minus bugs. There are extensive tests.

A common source of errors in stored procedures is incorrect typing in arguments. For instance, a particular key for an entity might need to be LONG or even always LONG NOT NULL or LONG NOT NULL @SENSITIVE and the only way to do this in the past was maybe with some #define thing. Otherwise you have to diligently get the type right in all the places, and should it ever change, again you have to visit all the places. To help with this situation, and to make code a little more self-describing we add named types to the language. This is a lot like typedef in the C language. They do not create different incompatible types but do let you name things well.

You can now write these sorts of forms:

Refer to the railroad diagram for the grammar details.

Additionally any enumerated type can be used as a type name. e.g.

Enumerations always get "not null" in addition to their base type.

This isn't a very complex feature but we hope that it will help create clearer code that is less likely to have type-related bugs.

Language support for virtual tables has lagged since I always thought they were of little interest to the language anyway. The CREATE TABLE forms in general are only declarations (except if you're doing the schema installer/upgrader output) and so you could just declare say a temp table that corresponds to the virtual table that you made in the same way that you might declare say sqlite_master if you wanted to use it. And since you have to register the module anyway, you may as well create the virtual table at the same time.

So there was no point in adding language support for the thing.

Furthermore the CREATE VIRTUAL TABLE form includes no information about the schema of the table so you'd need some kind of declaration anyway in order to tell the language what the columns are for the table you just created. So again you may as well just declare it like a normal table and not include that table in your schema upgrade file and be done with it.

And that was my thinking for the last 2 years. And then I learned something.

Virtual tables are durable.

Yeah, I always assumed that virtual tables were temp tables and they vanish and have to be redeclared every session but that is not the case. They are part of the durable schema so while you must pre-register the module associated with the virtual table, the virtual table is like other tables in that you only create it once and from then on it's part of the schema every time the database loads until you DROP it.

This changes everything.

With virtual tables being durable they belong in the schema upgrade process. And if they go there they also have to go into the JSON output. But we can't use the vanilla syntax that SQLite uses because that syntax is:

• not parseable, because the module arguments can be literally anything (or nothing), even a letter to your gramma.
• the arguments do not necessarily say anything about the table's schema at all

So in the CQL langauge I change the syntax a bit, the generalized form looks like this:

The part after the AS is used by CQL as a table declaration for the virtual table. The grammar for that is exactly the same as a normal CREATE TABLE statement. However that part is not transmitted to SQLite; when the table is created, SQLite sees only the part it cares about, the part before the AS.

Now this leaves the module arguments, they can be one of three things:

1. no arguments at all
2. a list of identifiers, constants, and parenthesized sublists just like in the @attribute form
3. the words arguments following

Case 1 Example#

becomes (to SQLite)

Note: empty arguments USING my_module() are not allowed in the SQLite docs but do seem to work in SQLite. We take the position that no args should be done with no parens, at least for now.

Case 2 Example#

This form allows for very flexible arguments but not totally arbitary arguments, so it can still be parsed and validated.

Case 3 Example#

This case recognizes the popular choice that the arguments are often the actual schema declaration for the table in question. So

becomes

The normalized text (keywords capitalized, whitespace normalized) of the table declaration in the as clause is used as the arguments.

Other details#

Virtual tables go into their own section in the JSON and they include the module and moduleArgs entries, they are additionally marked isVirtual in case you want to use the same processing code for virtual tables as normal tables. The JSON format is otherwise the same, although some things can't happen in virtual tables (e.g. there is no TEMP option so "isTemp" must be false in the JSON.

For purposes of schema processing, virtual tables are on the @recreate plan, just like indices, triggers, etc. This is the only option since the alter table form is not allowed on a virtual table.

Semantic validation enforces "no alter statements on virtual tables" as well as other things like, no indices, and no triggers, since SQLite does not support any of those things.

Finally, because virtual tables are on the @recreate plan, you may not have foreign keys that reference virtual tables. Such keys seem like a bad idea in any case.

There are many cases where stored procedures require complex arguments using data shapes well known to higher level languages or that come from the schema. There is already some affordance for this sort of thing in the form of this kind of pattern:

(I'll continue to use this simple example as I discuss the generalization below)

Then maybe something like this

The above expands into:

And I think we can all agree the sugared version is a lot easier to reason about and much less prone to errors as well.

Those features have been in the language for a long time and that's all fine and well but it isn't general enough to handle the usual mix of situations. For instance what if you need a procedure that works with two people? A hypothetical insert_two_people procedure cannot be written with the old form. This is where argument bundles come in. The idea here is to name the bundle which provides useful reference. To wit:

or alternatively

So what's going on here? Well, there are lots of reasons to keep the API to procedures simple and adding general purpose structured types would be at odds with that. It would require lots of knowledge about C structure layout and whatnot. And trying to call from java would require very complex JNI for any such procedure. So we avoid all that. We keep simple arguments. The above expands into:

Or course the types don't have to be the same, you can create and name shapes of your choice. The language allow you to use an argument bundle in all the places that a cursor was previously a valid source. That includes insert, fetch, update cursor, and procedure calls. You can refer to the arguments by their expanded name p1_address or alternatively p1.address means the same thing.

Here's another example showing a silly but illustrative thing you could do:

The above shows that you can use a bundle as the source of a shape elsewhere, and you can use a bundle as a source of data to load a cursor. After which you can do all the usual value cursor things like out statements and so forth.

In order to call procedures with argument bundles more readily from other languages, the JSON output now includes additional information about where procedure arguments originated; The field with this information is creatively called "argOrigin:" and it has 3 forms.

• "arg_name" -> the argument is not an expansion of anything
• "T arg_name" -> the argument came from like T
  • there will be one arg for each member of T
  • the formal argument name for this arg will be argname
  • if T is procedure arguments like p1 arguments then you'll get "p1[arguments] arg_name"
• "name T arg_name" -> the argument came from name like T (a named bundle)
  • there will be one arg for each member of T
  • the formal argument name for this arg will be T_arg_name
  • T could be procedure arguments as above
• If the source of an argument was a cursor or argument bundle name you get instead that thing's shape source name
  • this is always better because cursor names and bundle names are not globally unique.
• If the cursor had an anonymous source (e.g. like select 1 x) then you get the useless shape name "select"
  • this is an indicator that you should make some ad hoc struct for this procedure because there is no useful name for the arg bundle's type

None of this matters unless you're trying to make wrappers for a CQL procedure for some other language and you'd like to have your wrapper deal with structs rather than all loose arguments. the JSON basically tells you the structs.

Interestingly, argument bundles resulted in a significant reduction of code in the compiler. The argument bundle name has to be usable in the contexts where a cursor was previously usable. It is another source of shaped data. Getting that to work proved to be super simple as the two forms look almost identical to the compiler -- no coincidence there. So very little code was required to make from [cursor_name] work with from [any_shape_name] in the half dozen or so places that this construct is allowed (e.g. procedure call arguments, insert statements, etc.). However, there was as much code associated with from arguments as there was from cursor_name. And the code was nearly identical..

When argument bundles were introduced the natural thing to do was to create an artifical bundle called "arguments" which represents the bundle that is ALL the arguments. With that done, all the code for from arguments could be deleted because arguments itself was a valid shape name. Hence insert into T from arguments "just works". And so half the rewrites were deleted. The only cost was that the form from arguments like shape became the cursor form from arguments(like shape) which only adds mandatory parens to a form that was largely unused anyway (there were two cases in our entire codebase). The cursor form is more general as you can do from C(like A, like B) to get the fields that match A then those that match B. Arguments get this for free as well (well, at the cost of parens).

So overall, this feature was added, and the compiler got smaller and cleaner. Only the test suite had to grow.

Stay safe out there.

There is an unfortunate pattern of hard coding constants in SQL which I think comes from the fact that there's not an especially good way to encode constants in SQL. Things are a little better In CG/SQL's CQL language because it's normal to run things through the pre-processor first so you can do things like:

Having done so, you could write:

And at least you don't have to see these loose '2' values all over. An especially unfortunate form is the below, in which the auther is clearly crying for a symbol to use:

But if we use #define the language knows nothing of the names and it can't help you manage them or export them consistently or anything like that. I guess #define is pretty useful in several langauges (C and C++) so you could maybe #include the macros somehow but that doesn't seem especially great. And if you need them in Java you're getting no help at all.

So to this world we add enumerated constants. This is a bit short of enumerated types as we'll see later. You can now write something like this:

After this:

is the same as

And that is exactly what SQLite will see, the literal 14.

What's going on here? There's just a few rules:

• the enumeration can be any numeric type (bool, integer, long integer, real)
• the values of the enumeration start at 1 (i.e. if there is no = expression the first item will be 1, not 0)
• if you don't specify a value, the next value is the previous value + 1
• if you do specify a value it can be any constant expression and it will be cast to the type of the enumeration (even if thatis lossy)
• the enumeration can refer to previous values in itself with no qualification (big = 100.0, medium = big/2, small = medium/2)
• the enumeration can refer to previously defined enumerations as usual (code = business_type.restaurant)
• Once the enumeration is defined you refer to its members in a fully qualified fashion enum_name.member_name elsewhere

Why is this better than macros? Well for one thing the enum values can be checked at their declaration site, so if you have errors you will hear about them in a more reasonable place. But additionally since the structure is known to the compiler it can give you useful information in the outputs.

In the .h files you get:

In case of floating point values such as:

You get:

Which is unfortunately the best you can do since C has no floating point enums.

But in both cases the enums section of the JSON has the name of the enums and their members and values ready to go. With these values you can readily generate (with moustache or something) the language interfaces of your choice. This is a real help if you're trying to make helpers to call your CQL from say Java or something.

To do all this we needed to add some constant folding and general evaluation to the compiler. It's not much, just the normal numeric types and null. The supported operations include:

+, -, *, /, %, |, &, <<, >>, ~, and, or, not, ==, <=, >=, !=, <, >, the cast operator and the case forms. These are enough to make a lot of very interesting expressions, all of which are envaluated at compile time.

While the constant folding was added to allow for rich enum expressions, there is also the const() primitive in the language now which can appear anywhere a literal could appear. This allows you do things that were previously not allowed such as:

The const form is also very useful in macros:

This form ensures that the constant will be evaluated at compile time. Const can also also nest so you can build these kinds of macros from other macros or you can build enums this way. Anywhere you might need literals, you can use const.

Importantly, no enumerated data types were added to the language to do any of this. The values can help you to achieve some correctness by avoiding transcription mistakes but there is no additional type-safety provided here. Indeed given the rich mix between these types in SQLite, and with SQLite having no knowledge of enumerations at all it would be tricky to do a complete job. Still, this might happen in the future.

But for now, declaring constants that are really an intimate part of your schema is now possible and the addition of the constants to the .h files and the .json output should hopefully make these generally useful. At least we might see less of the hard-coded constant business with good values baked right into the schema declarations.

Happy Holidays.

Everyone knows the usual expression syntax x LIKE y to do a string match. But the CQL compiler also uses LIKE in a different way that's powerful and important. CQL has the notion of data shapes and you use LIKE to refer to them. The simplest source of a data shape, and maybe the most common, is a table. Maybe something like this:

Now suppose you want to write a procedure that can insert a row into that table, You could write

This is all fine and well but what if T had 50 columns? That gets old fast. And how can you be sure that you inserted the columns into T in the right order? This second example also compiles even though it's clearly wrong:

And of course you can imagine things get only more complicated with more columns in T.

We started adding the LIKE form to ease these issues and to ensure some consistency in the APIs while preventing simple transpostion errors. So you can instead write:

so here the like T in the argument list simply means "make arguments that are the same as the columns of table T" -- well, almost. It also adds an _ to the end of each name so you end up exactly the same declaration as the long form above. But you won't miss any arguments, and they'll be in the right order.

And notice that we used from arguments to indicate that we wanted the values to come from the arguments in order. Again this saves you from a lot of typing and a lot of error checking. You can't get the arguments in the wrong order.

These are the most basic patterns. But there are quite a few more.

Let's suppose you want to write a procedure that returns in row with the highest age in the above. Maybe you write something like this:

Here we made a cursor M that is the same as the cursor C and then we are going to generate a single row result from the cursor. Note that if you use a cursor name like M in an expression it refers to the hidden boolean that says if the cursor has a row in it or not. So M begins empty and we will load it if it's empty or if the age is higher than what we've already got.

Let's show a few more of the forms. Suppose we don't want to return name, just the id and the age. We can change things up a tiny bit.

So two things to notice. We used an ad hoc shape, making a fake select statement that returns the shape we want. This select doesn't run but it does define types and columns easily. Two not null integers in this case. Now M is not the same as C so we can't use the simplest form fetch M from C we have to use the more general form.

Fully expanded, what we wrote becomes:

But as you can see, we didn't have to type all those column names. And that's kind of the point of the LIKE construct.

So we've covered a bunch of the shape sources already:

• a table name
• a cursor name
• a select statement that gives the shape in an ad hoc fashion

There are three more

• a view name
• the return shape of a procedure that returns a result set
• the arguments of a procedure

View names are pretty simple, and they work the same as table names so we don't need to discuss those. Let's look at some of the other uses with procedures.

Suppose we have a procedure that can return a result set shape but we want to be able to mock its results so we can fake whatever result we need for testing.

We'll complicate this a bit adding a new table (keeping short table names for the sample to save typing)

And here's a procedure:

Now we want to be able to make any fake result we want, so maybe want a temp table. No problem:

The above is very generic and will maintain well. You can see we made a temp table that will have exactly the same shape as whatever my_proc returns. In this case it becomes:

And the rest are patterns we've seem before.

The last source of shapes are procedure arguments. There's lots of good cases for those, I wrote an entry on those previously but I'll give a simple example here too.

Suppose we have this weird procedure:

What if we wanted to log any errors that happen here? Maybe make a verison that logs. We can do it like this:

The nice thing about this logging wrapper procedure is that if delete_stuff changes, the wrapper will change with it.

That covers all of the shape sources and as we saw we can use them to create things like cursors, tables, and argument lists. We can use them to specify a subset of columns that might be of interest when fetching or updating cursors. And we can use them in one last way -- to restrict arguments to a particular shape. Let's see how that works by making the previous logger a little different. Here we added an argument which tells if we should look. And that might look like it would spoil the from arguments part of the forwarding, but there is the final way to use LIKE.

So this form lets you use some of your arguments, the ones that match a certain shape. And as we saw in the previous article you can also use from C to pass arguments where C is a cursor and in that case you can also specify that arguments be matched by name from C like shape. In both those cases the formal parameter names of the called procedure are matched against the names of the shape and passed in the order of the formals. So this is like "call by name", the fields of the cursor or the order of arguments in the argument list might be different than the formals but you'll get the correct items in the correct order regardless, because it matches by name.

These forms can save you a lot of typing... and are excellent at avoiding errors and improving maintainability. Where they appear in SQL statements, everything is expanded before it goes to SQLite so SQLite will see normal syntax forms. Which is good because obviously SQLite doesn't know anything about this enhanced LIKE business.

In the examples above there were only one or two columns with very short names, but in real world code there can easily be dozens of columns with very long names. In those cases, these forms really shine.

Following up on the last blog entry, I thought it would be useful to present a simple error tracing macro that you can use to see what kind of error flow is going on when you're having trouble understanding why a procedure is returning an error code. The idea is we want to create a macro that we can use like this:

We can do that with something like the below macros. These particular ones cause the output to go to stderr via fprintf but if that isn't what you need you can simply edit the macro. The macros looks like this:

So basically it's telling CQL to emit a #define into its output stream. In this case:

You could change that to any function you like, you can have it dump the errors where you like, or you can make it some dummy function you add so that you can set a breakpoint on it.

Whatever you do, do not leave your code with this sort of tracing enabled -- it's far too expensive in terms of code size. But it's perfect if you have this one procedure that is failing and it's hard for you to see where.

Obviously if you're making a custom trace thingy you don't need the macro at all, you can just emit your own #define with @echo as needed.

Note: @echo is quite a sledgehammer so don't use it lightly and not in production code but it is quite helpful for this sort of thing. CQL tests often use it to help make things visible to the tests. If you use @echo in weird ways you might not get working code when the codegen changes in the future.

The relevant state that is available to you inside a macro like this is:

• __FILE__ the current filename (comes from the C pre-processor, this is the .c file name not the .sql)
• __LINE__ the current line number (comes from the C pre-processor, this is the .c line number)
• _rc_ the current SQLite result code (always the current return code in every CQL procedure that uses SQLite)
• _db_ the current SQLite database pointer (always the current database in every CQL procedure that uses SQLite)
• _PROC_ the current procedure name (CQL has a #define for this for you)