Construir consultas¶
Una consulta es un valor: no ejecuta, no tiene conexión, y cada método devuelve una nueva. Eso es lo que permite que la misma consulta la ejecute la sesión síncrona o la asíncrona sin saber cuál — y lo que hace que apilar fragmentos funcione sin ninguna pieza extra.
Comparar una columna no da un bool. User.age > 18 es una SnakeCondition, y ahí está todo el
truco de la navegación profunda tipada.
De dónde sale este texto
Todo lo que hay bajo los títulos se genera desde los docstrings del propio paquete, en cada build.
Consultas¶
SnakeQuery(
model: type[T],
*,
registry: SnakeRegistry | None = None,
)
Bases: Generic[T]
Typed query over a model. Immutable: every method returns a new query.
The query and the REGISTRY it resolves against, which is the model's unless told otherwise.
It used to ask the global registry unconditionally, so @snake_model(registry=reg)
produced a model that could not be queried — and said so with a message that MISDIRECTED:
"is it missing @snake_model?", with the decorator right there. The answer was one getattr
away the whole time.
registry= is for the case where the model itself does not say: a SnakeRow, or a query
built before the decorator ran. Otherwise the model's own answer wins, which is what makes
navigation, the session and the emitter agree without anybody threading it through.
property
¶
The columns only()/defer() asked for, or None for every one of them.
The session asks for this rather than counting the row's width: two columns of one table can be projected two ways, and lining a plan up by counting is how a value lands on the wrong attribute without anybody noticing.
property
¶
The model being queried. The session uses it to map the rows back to T.
property
¶
registry: SnakeRegistry
The registry this query resolves against: the model's own unless one was passed.
Public because the SESSION has to ask it. Reaching for the global registry there is the same
defect one layer up: it works until two models share a class name, or until somebody uses
@snake_model(registry=...), and then it is wrong in silence.
property
¶
Tells whether the query asks for relationships to be loaded (it picks the session's route).
property
¶
Tells whether the query asks for rows to be locked (for_update). The compound looks at this.
property
¶
Tells whether the query carries limit/offset of its own. The compound looks at this.
property
¶
Tells whether the query carries order_by of its own. The compound looks at this.
The same question as has_bounds and for the same reason: both need the branch's
parentheses to stay inside the branch, and only one of the two was being asked.
property
¶
Whether the emitted SQL opens with a WITH. A plain query never does.
filter(*conditions: SnakeCondition) -> SnakeQuery[T]
Adds conditions (AND). Returns a NEW query; the current one is untouched.
order_by(
*keys: SnakeExpr[Any] | SnakeOrder,
) -> SnakeQuery[T]
Adds ordering keys. A bare column orders ascending.
limit(limit: int) -> SnakeQuery[T]
Sets the LIMIT (it replaces the previous one). Returns a new query.
offset(offset: int) -> SnakeQuery[T]
Sets the OFFSET (it replaces the previous one). Returns a new query.
group_by(*columns: SnakeValue[Any]) -> SnakeQuery[T]
Groups by the given columns. Returns a NEW query; the current one is untouched.
having(condition: SnakeCondition) -> SnakeQuery[T]
Filters over the groups (cumulative with AND, just like .filter()).
distinct() -> SnakeQuery[T]
Marks the query as SELECT DISTINCT. Returns a new query.
It applies to the full SELECT and to the projection. Standard DISTINCT only (no DISTINCT ON).
only(*columns: SnakeValue[Any]) -> SnakeQuery[T]
Brings ONLY these columns (plus the primary key). Returns a new query.
What it buys is bytes on a wide table; what it costs is an instance that is not whole, and
reading a column left out RAISES rather than handing back the column's default. That refusal
is the feature: without it a deferred name reads as None and the caller never learns.
The PRIMARY KEY comes back whether it was named or not, and that is not a convenience. An instance with no identity cannot be updated, deleted, or matched to the children of a prefetch — it would be a tuple with methods.
When what you want is the VALUES rather than the model, session.select(query, a, b) is the
better tool: typed tuples, no half-built instance, and nothing that can raise later.
defer(*columns: SnakeValue[Any]) -> SnakeQuery[T]
Brings everything EXCEPT these columns. Returns a new query.
The other side of only(), and the same warning applies to what comes back. Deferring the
primary key is refused: see only().
for_update(
*, nowait: bool = False, skip_locked: bool = False
) -> SnakeQuery[T]
Locks the selected rows until the end of the transaction (SELECT ... FOR UPDATE).
It is HALF of concurrency control (the other half is the isolation level). nowait fails if
the row is already locked; skip_locked ignores it (the queue pattern): they are opposites,
asking for both is an error.
join(
collection: SnakeCollection[M], how: SnakeJoin = INNER
) -> SnakeJoinedQuery[T, M]
EXPLICITLY joins a collection (to-many) to get THE CHILD'S ROWS into the projection.
It returns a SnakeJoinedQuery, a different type that only projects: a JOIN onto a
collection multiplies rows (a disaster for models), so it has no .all()/.first(). how
chooses whether childless parents show up.
as_scalar(column: SnakeExpr[V]) -> SnakeSubquery[V]
Wraps the query as a scalar subquery of ONE column, for .in_(...).
DIRECT column and flat WHERE only: navigating a relationship would demand a JOIN inside the subquery, which is not built yet, so it gets rejected in plain words.
include(
*relations: type[Any]
| SnakeCollection[Any]
| SnakePrefetch[Any],
) -> SnakeQuery[T]
Asks for relationships to be loaded (eagerly). Returns a new query.
To-one (User.car, proxies carrying a path) -> LEFT JOIN; to-many (SnakeCollection) ->
select-in. To nest to-many you pass a SnakePrefetch(...).then(...) (the collection does not
expose the child's relationships). Without .include(), touching the relationship blows up
with SnakeRelationshipNotLoaded (there is no silent N+1).
prefetches() -> tuple[SnakePrefetch[Any], ...]
Nested prefetch chains (deep to-many); the session resolves them level by level.
Includes that are to-one chains (loaded with a JOIN in the same query).
To-many relationships to load (resolved with a separate select-in, in the session).
Segments to load in order: root + each relationship prefix (parent before child).
Each segment is (prefix, model, table). It includes ALL the prefixes
(.include(User.car.brand) also loads car, so brand can be nested inside car).
to_include_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles the SELECT that loads the root + the included relationships (with their LEFT JOINs).
union(other: SnakeCompoundBranch[T]) -> SnakeCompound[T]
UNION: the rows of both, WITHOUT duplicates.
The type demands the SAME model, a guarantee SQL does not give (it is happy if the columns line up).
union_all(
other: SnakeCompoundBranch[T],
) -> SnakeCompound[T]
UNION ALL: the rows of both, KEEPING duplicates.
It is not an optimised UNION: a plain UNION deduplicates the whole result. Both exist so
the choice is made consciously.
except_(
other: SnakeCompoundBranch[T],
) -> SnakeCompound[T]
EXCEPT: the ones of this query that are NOT in the other. Trailing underscore: except is a reserved word.
intersect(
other: SnakeCompoundBranch[T],
) -> SnakeCompound[T]
INTERSECT: only the rows that are in BOTH queries.
recursive(
*,
on: tuple[SnakeExpr[Any], SnakeExpr[Any]],
distinct: bool = False,
) -> SnakeRecursive[T]
Expands this query by following a hop onto ITSELF (WITH RECURSIVE).
This query is the ANCHOR and on is the pair of columns that chains each level: first the
one pointing upwards, then the one identifying the row reached. Swapping it walks the
ANCESTORS (perfectly legitimate).
SnakeQuery(Category).filter(Category.id == 1).recursive(on=(Category.parent_id, Category.id))
distinct picks the operator joining each step: False (default) emits UNION ALL, what a
TREE wants; True emits UNION, so each step drops the rows already seen.
Pass distinct=True if the data may have CYCLES, which only the caller knows — a
self-referencing FK admits one. With UNION ALL a cyclic walk never ends: every lap yields
rows the engine counts as new, so it hangs instead of failing. limit() is NOT a
substitute — it bounds what comes back, not the walk. Measured on Postgres, a cyclic walk
with order_by() and LIMIT 3 never returns.
to_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles the query to (sql, params), generating JOINs if there is deep navigation.
to_count_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles a SELECT COUNT(*) with the same filters/JOINs (it ignores order and limit).
to_exists_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles a SELECT EXISTS(...) with the same filters/JOINs.
IT GUARDS THE SAME KNOBS AS THE COUNT, and it did not until this line was written. exists
was the one read path that called no guard at all, so it swallowed group_by, having,
distinct and lock in silence — an EXISTS over groups is a different question from an
EXISTS over rows, and the caller who asked got the second one with no word about it.
It is the same shape as entry #18 of the bug journal, which is about count() being fixed
and its two brothers being left. This is the third brother, found by adding a tenth knob and
noticing that one path did not refuse it.
What it honours is what a COUNT honours, for the same reasons: order, limit and offset do not change whether a row exists, and the includes are resolved afterwards by the session.
to_update_sql(
dialect: SnakeDialect, values: Mapping[str, object]
) -> tuple[str, tuple[object, ...]]
Compiles a BULK UPDATE with the query's WHERE (EXECUTION lives in the session).
The values arrive already as SQL column names. If the WHERE navigates a relationship it is
rewritten to <pk> IN (subquery) (no UPDATE ... FROM). The guards are put in place by
_guard_bulk_write.
to_delete_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles a BULK DELETE with the query's WHERE (EXECUTION lives in the session).
Like to_update_sql: a WHERE that navigates is rewritten to <pk> IN (subquery) (no DELETE ... FROM).
to_project_sql(
dialect: SnakeDialect,
columns: Sequence[SnakeValue[Any]],
explicit_joins: Sequence[
tuple[tuple[str, ...], bool]
] = (),
) -> tuple[str, tuple[object, ...]]
Compiles a SELECT <columns> (projection) with filters, GROUP BY/HAVING and their JOINs.
The paths of columns, WHERE, GROUP BY and HAVING all go into the JoinPlan (that is how a
group_by(Truck.maker.name) generates its JOIN); paths() of a COUNT(*) is empty.
explicit_joins are the .join()s onto collections: if there are any, the plan is ALWAYS
built (the JOIN already demands aliases).
to_annotate_sql(
dialect: SnakeDialect,
aggregates: Sequence[SnakeValue[Any]],
) -> tuple[str, tuple[object, ...]]
Compiles the SELECT of annotate(): base model columns + aggregates, grouping by the PK.
The remaining columns depend functionally on the PK (Postgres accepts that). An explicit
group_by is an error: to group by something else there is select() + group_by().
SnakeJoinedQuery(
query: SnakeQuery[T], joins: tuple[_JoinSpec, ...]
)
Bases: Generic[T, M]
Query with explicit JOIN(s) onto collections. Projectable only; immutable.
T is the ROOT model; M that of the last joined child (exposed by .right). It delegates the
normal state to a wrapped SnakeQuery[T] and adds the list of explicit JOINs.
property
¶
The ROOT model. The session uses it to qualify/coerce the projection.
property
¶
Right-hand side of the LAST JOIN, with the path already prefixed with the right alias.
Statically type[M] (joined.right.name re-triggers the class access -> SnakeExpr); at
runtime a SnakePathProxy whose path (("makers","name")) is qualified with the JOIN's
alias, not the root's.
filter(
*conditions: SnakeCondition,
) -> SnakeJoinedQuery[T, M]
Adds conditions (AND) to the WHERE. Returns a NEW query; the current one is untouched.
order_by(
*keys: SnakeExpr[Any] | SnakeOrder,
) -> SnakeJoinedQuery[T, M]
Adds ordering keys. It can order by the child's columns (joined.right.col).
limit(limit: int) -> SnakeJoinedQuery[T, M]
Sets the LIMIT (it replaces the previous one). Returns a new query.
offset(offset: int) -> SnakeJoinedQuery[T, M]
Sets the OFFSET (it replaces the previous one). Returns a new query.
distinct() -> SnakeJoinedQuery[T, M]
Marks the projection as SELECT DISTINCT. Returns a new query.
join(
collection: SnakeCollection[N], how: SnakeJoin = INNER
) -> SnakeJoinedQuery[T, N]
Chains ANOTHER explicit JOIN (starting from joined.right.<collection>).
The new child N becomes the right-hand side of .right, with its accumulated prefix.
to_project_sql(
dialect: SnakeDialect,
columns: Sequence[SnakeValue[Any]],
) -> tuple[str, tuple[object, ...]]
Compiles the projection combining the explicit JOINs with the column/WHERE/order paths.
Bases: Enum
How a collection is joined in an explicit .join() (projection only). INNER and LEFT only.
Both preserve the ROOT as the non-nullable side (every row has its parent). RIGHT/FULL would bring up rows with the root at NULL, impossible to hydrate: illegal states unrepresentable.
- INNER: only parents with at least one matching child (one row per child).
- LEFT: on top of that, each childless parent once with the child's columns at NULL.
dataclass
¶
SnakeCompound(
operator: SnakeSetOp,
left: SnakeCompoundBranch[T],
right: SnakeCompoundBranch[T],
order_by_keys: tuple[SnakeOrder, ...] = (),
limit_value: int | None = None,
offset_value: int | None = None,
)
Bases: Generic[T]
Two queries joined by a set operation, with ordering and bounding belonging to the SET.
order_by/limit/offset belong to the set, and are emitted after the last branch. Closed over
itself: a compound recomposes with no special cases.
property
¶
The model of the rows. Taken from the left one (both are of the same model, guaranteed by
the type and by _compose): SQL only demands that the columns line up, so without that
guarantee it would instantiate the rows wrong.
property
¶
The columns the branches PROJECT, or None when they bring whole rows.
The session maps by asking for this instead of counting the row's width, and a compound
that could not answer was read as whole rows: the values of a narrowed branch landed on the
wrong attributes. Both branches project the same set (_compose refuses anything else), so
the left one answers for the pair.
property
¶
Never: a compound loads no relationships. It is rejected at build time, not here.
property
¶
Never: a compound locks no rows. It is rejected at build time, not here.
property
¶
Tells whether the SET carries limit/offset of its own, so a compound nested in
another one answers the same question a query does.
property
¶
Whether a WITH RECURSIVE is hiding anywhere inside. It travels UP: a recursion nested
two compounds deep still ends up written inside a branch of the outer one.
order_by(
*keys: SnakeExpr[Any] | SnakeOrder,
) -> SnakeCompound[T]
Orders the SET. A bare column orders ascending, just like in SnakeQuery.
The key has to name a column the set HAS: refused here, where the caller still knows what they typed, rather than in the emitter where it turned into another column's name.
limit(limit: int) -> SnakeCompound[T]
Bounds the SET (it replaces the previous one). Returns a new compound.
offset(offset: int) -> SnakeCompound[T]
Skips rows of the SET (it replaces the previous one). Returns a new compound.
union(other: SnakeCompoundBranch[T]) -> SnakeCompound[T]
UNION: the rows of both, WITHOUT duplicates.
union_all(
other: SnakeCompoundBranch[T],
) -> SnakeCompound[T]
UNION ALL: the rows of both, KEEPING duplicates. It does not deduplicate, and is cheaper.
except_(
other: SnakeCompoundBranch[T],
) -> SnakeCompound[T]
EXCEPT: the left-hand ones that are NOT in the right-hand one. With a trailing underscore: it is a reserved word.
intersect(
other: SnakeCompoundBranch[T],
) -> SnakeCompound[T]
INTERSECT: only the ones that are in BOTH.
to_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles to (sql, params) concatenating the branches in textual order.
Each branch inside parentheses: without them a branch's LIMIT would read as the set's. The
ORDER BY goes UNqualified: the result is no table, its columns are the projection's.
Bases: Enum
Set operation (standard SQL, agnostic).
UNION deduplicates (which forces sorting/hashing everything) and UNION ALL does not:
different enum values, to force a CHOICE instead of inheriting a default.
dataclass
¶
SnakeRecursive(
anchor: SnakeQuery[T],
table: SnakeTableInfo,
child_column: str,
parent_column: str,
distinct: bool = False,
order_by_keys: tuple[SnakeOrder, ...] = (),
limit_value: int | None = None,
offset_value: int | None = None,
)
Bases: Generic[T]
An ANCHOR query that expands itself by following a hop onto itself.
child_column points upwards (parent_id) and parent_column identifies the accumulated row
(id): the direction is fixed by which one goes first (swapping them walks the ancestors, a
perfectly legitimate query).
distinct picks the set operator that joins each step to what has already been accumulated:
False (the default) emits UNION ALL, True emits UNION. It is the difference between a
walk that ends over cyclic data and one that does not.
property
¶
The model of the rows: the same as the anchor's. The session uses it to instantiate them.
property
¶
Never narrowed: the CTE's columns are the TABLE's, and a narrowed anchor is refused.
property
¶
Never: an anchor with includes is rejected at build time, not here.
property
¶
Never: a recursive CTE does not lock rows. It is rejected at build time.
property
¶
Tells whether it carries limit/offset of its own. The compound looks at this.
property
¶
Tells whether it carries order_by of its own. The compound looks at this.
property
¶
Always: this is the WITH RECURSIVE. The compound asks so it can refuse where a CTE
cannot be a branch, which is two of the three engines.
order_by(
*keys: SnakeExpr[Any] | SnakeOrder,
) -> SnakeRecursive[T]
Orders the RESULT (not the anchor nor the step). A bare column orders ascending.
The same guard the compound uses, and for the same reason: the SELECT ... FROM cte this
ordering hangs off has only the CTE's columns, so a key that navigates a relationship would
lose the hop and be written as another column's bare name.
limit(limit: int) -> SnakeRecursive[T]
Bounds the RESULT, and only the result: it does NOT bound the traversal.
It read as the safety net against a cycle until somebody measured it. Put an order_by()
in front of it —which is the normal way to ask for a hierarchy— and the engine has to
produce every row before it can sort them, so the bound never gets its turn and the query
hangs all the same. What ends a cyclic walk is recursive(..., distinct=True).
offset(offset: int) -> SnakeRecursive[T]
Skips rows of the result (it replaces the previous one).
to_sql(
dialect: SnakeDialect,
) -> tuple[str, tuple[object, ...]]
Compiles to (sql, params), with the ANCHOR's params first.
That order is mandatory: the anchor comes earlier in the string and Postgres' %s is positional.
Expresiones¶
Bases: SnakeValue[T]
Typed expression over ONE column.
path is the navigation down to the column (the last element is the column, the earlier ones
relationships: ("car", "brand", "name")). The only column leaf node.
python_type is the COMPILED type of the column, carried here so the emitter can reason about
it: the generic T is erased at runtime, and without this the SQL layer cannot tell an integer
division from a decimal one. It is None when nobody stamped it — a hand-built node in a test,
or a path the compiler never saw — and everything that reads it treats None as "no proof" and
changes nothing.
Boolean node of the query AST (WHERE, JOIN ON...).
It combines with & (AND), | (OR) and is negated with ~ (NOT).
dataclass
¶
SnakeOrder(
expr: SnakeValue[Any],
descending: bool,
nulls: SnakeNulls | None = None,
)
Ordering key: a value, its direction and where the NULLs go.
nulls=None leaves the engine default, which has a trap in it: Postgres puts the NULLs last in
ASC and first in DESC, so changing the direction moves the gaps around.
Bases: Generic[T]
Base of every VALUE expression: it knows how to compare itself and operate arithmetically.
With no SQL identity of its own (the subclasses provide that). The comparators produce a
SnakeCondition; the arithmetic ones another SnakeValue (SnakeArith), chainable.
Reads a key INSIDE a JSON column, as the declared type: meta.json_get("size", as_type=int).
as_type is required and it is not ceremony. What the engines give back from a document is
text, so without a declared type the comparison below would be a TEXT comparison and
'9' > '100' would be true — the same trap the capability catalogue documents for a
Decimal ordered as text. The type is what makes the ORM emit the cast.
Several keys walk a nested path in ONE access (json_get("owner", "name")), because every
engine takes a path and two accesses would be two trips through the document.
The key is validated rather than parametrised: it is emitted inside a literal, where no engine accepts a placeholder, so a key that is not a plain identifier is refused here.
in_(values: SnakeSubquery[T]) -> SnakeInSubquery
in_(
values: Iterable[T] | SnakeSubquery[T],
) -> SnakeInList | SnakeInSubquery
value IN (...): a set of values or a scalar SUBQUERY.
An iterable -> values typed to T, SnakeInList. A SnakeSubquery[T] (same T) -> value
IN (SELECT ...), SnakeInSubquery (the checker rejects a subquery of another type).
value LIKE pattern. Only over text expressions (self: SnakeValue[str]).
value ILIKE pattern: like like, but ignoring upper and lower case.
NOT (value IN (...)): the NEGATION of the IN, not a new node (one more node would be one more place to forget something).
value BETWEEN low AND high (inclusive), as an AND of two comparisons (no node of its own: equivalent SQL).
LIKE 'value%', with the wildcards of the VALUE escaped.
LIKE '%value', with the wildcards of the VALUE escaped.
LIKE '%value%', with the wildcards of the VALUE escaped.
Column paths contained in this node. Each concrete subclass defines it.
SnakeSubquery(
schema: str,
name: str,
column: str,
where: SnakeCondition | None = None,
)
Bases: SnakeValue[T]
Scalar subquery used as a VALUE: (SELECT <column> FROM <table> [WHERE ...]).
It is produced by SnakeQuery.as_scalar(column). Primitives only (agnostic; it does not import
SnakeQuery, which avoids a cycle). It has its own FROM: it contributes no paths and does not
correlate. It is used in .in_(...); its params are threaded into the outer numbering at emission.
A subquery has its own FROM: it references no columns of the outer query.
Bases: Enum
Supported scalar functions, with agnostic names that the dialect translates.
dataclass
¶
SnakeCase(
branches: tuple[SnakeCaseBranch[T], ...],
default: SnakeValue[T] | T | None = None,
has_default: bool = False,
)
Bases: SnakeValue[T]
CASE WHEN cond THEN value ... [ELSE default] END.
The branches are evaluated in order and the first match wins (the order is meaningful). Without
a default, no ELSE is emitted (in SQL that already means NULL).
Paths of the columns it mentions (conditions, results and default), for the JOIN planner.
dataclass
¶
SnakeCoalesce(arguments: tuple[SnakeValue[T] | T, ...])
dataclass
¶
SnakeNullIf(
value: SnakeValue[Any], sentinel: SnakeValue[Any] | Any
)
Bases: SnakeValue[T]
NULLIF(value, sentinel): NULL when the two are equal, and the value otherwise.
T is the type of the RESULT —which carries the | None this node introduces— and not that of
the operands, so the fields are stored untyped. Pinning them to T would demand a
SnakeValue[int | None] where the caller has a SnakeValue[int], and invariance refuses it.
Same shape as SnakeCoalesce, which stores its arguments the same way and for the same reason:
the constructor in this module is what pins the types down, which is where they belong.
Paths of the value and, if it is one, of the sentinel.
dataclass
¶
SnakeCast(source: SnakeValue[Any], as_type: type)
Bases: SnakeValue[T]
An EXPLICIT conversion: CAST(<source> AS <type>), with the type named at the call site.
It exists because the arithmetic operators are SnakeValue[T] | T -> SnakeArith[T] — one single
T — and that is right: promoting int to float behind the user's back would be the ORM
deciding. But strictness only holds up with an explicit door, and there was none: column * 1.0
does not type-check, so a real division between two integer columns could not be written at all.
The TYPE NAME is the dialect's, not this node's. Measured: CAST(x AS NUMERIC) answers 0.9 on
PostgreSQL and 0 on SQLite, whose NUMERIC affinity collapses an integral value back to an
integer. One spelling would be right on two engines and silently wrong on the third.
What it wraps: a cast adds no navigation, and swallowing these would drop the JOIN.
dataclass
¶
SnakeDateShift(
value: SnakeValue[Any], amount: int, unit: SnakeDatePart
)
Bases: SnakeValue[T]
Moving a date or a timestamp by a fixed amount: placed_on + 30 days.
ONE node for both directions: subtracting is adding a negative amount, which the three engines
accept and which keeps the sign in the VALUE instead of in a second node. T is the type of the
SOURCE — shifting a date gives a date.
The three spellings share nothing (+ INTERVAL, DATE_ADD, a modifier string), so the SQL is
the dialect's business. It is the clearest case in the whole ORM for that seam.
What it shifts: the amount is a literal and contributes no navigation of its own.
Bases: Enum
A part of a date, for DATE_TRUNC and EXTRACT. Standard SQL names.
Funciones de texto¶
Siete funciones escalares que traducen los tres motores. Lo que uno no puede hacer se DECLARA con motivo, nunca se omite — el silencio y «no soportada» serían indistinguibles.
from snakeorm import SnakeQuery
from snakeorm.expressions import snake_concat, snake_length, snake_lower, snake_substring
rows = session.select(
SnakeQuery(User).filter(snake_length(User.name) > 3),
snake_lower(User.name),
snake_concat(User.name, " <", User.email, ">"),
snake_substring(User.email, 1, 5),
)
Funciones de fecha¶
DATE_TRUNC y EXTRACT corren donde el motor las tiene y se RECHAZAN donde no, y a las dos no se las rechaza en los mismos sitios. DATE_TRUNC es solo de PostgreSQL: MySQL y SQLite declaran las dos que no la tienen, así que el plan se para en vez de emitir SQL que el motor iba a rechazar. EXTRACT la traducen PostgreSQL y MySQL, y solo SQLite la para. Nombrar a un motor como «el que no las tiene» habría sido la frase más limpia y la equivocada — el rechazo es por función, que es justo por lo que cada dialecto contesta por ella aparte.
La parte es un SnakeDatePart, no una cadena, y ahí está el sentido del enum: fija el vocabulario a las partes en las que los motores coinciden, así que la misma llamada significa lo mismo en los tres. Con un str, el emisor le lee un .value y muere de un AttributeError — un error de Python donde toca un rechazo legible, y por eso el tipo es lo que te mantiene fuera de ahí.
from snakeorm import SnakeDatePart, SnakeQuery
from snakeorm.expressions import snake_date_trunc, snake_extract
rows = session.select(
SnakeQuery(Visit),
snake_date_trunc(SnakeDatePart.MONTH, Visit.created_at), # PostgreSQL only
snake_extract(SnakeDatePart.YEAR, Visit.created_at), # PostgreSQL and MySQL
)
# On MySQL the first one stops the plan with a SnakeDialectError:
# MySQLDialect cannot translate DATE_TRUNC: MySQL has no DATE_TRUNC. Reach for it through
# `raw()` with the engine's own spelling.
# On SQLite BOTH stop, each naming its own function.
Redondeo y magnitud¶
ABS y ROUND vienen en todos los builds de todos los motores. Su ausencia de la tabla de SQLite fue el bug #34, y por eso justamente una función que falta hay que declararla en vez de dejarla en blanco.
Pedirle a ROUND decimales funciona en los tres, y llegar ahí requirió que un dialecto lo dijera: PostgreSQL tiene ROUND(double precision) y ROUND(numeric, int) y nada en medio, así que declara el tipo que quiere su forma de dos argumentos y el emisor castea. Tú escribes la misma llamada en todas partes.
from snakeorm import SnakeQuery
from snakeorm.expressions import snake_abs, snake_round
rows = session.select(
SnakeQuery(Reading),
snake_abs(Reading.delta), # the magnitude, sign dropped
snake_round(Reading.amount), # nearest whole number
snake_round(Reading.amount, 2), # to N places, on the three since bug #34 closed
)
Matemáticas que dependen del build¶
Los tres las traducen, y en SQLite son una opción de COMPILACIÓN (ENABLE_MATH_FUNCTIONS): un build sin ella contesta no such function: ceil en ejecución. Eso no puede ser una capacidad — una capacidad la contesta la clase del dialecto, que no sabe qué biblioteca se enlazó.
from snakeorm import SnakeQuery
from snakeorm.expressions import snake_ceil, snake_floor, snake_power, snake_sqrt
rows = session.select(
SnakeQuery(Reading),
snake_ceil(Reading.amount), # -8.76 -> -8, towards zero
snake_floor(Reading.amount), # -8.76 -> -9, away from zero
snake_sqrt(snake_power(Reading.delta, 2)), # the magnitude, the long way round
)
dataclass
¶
SnakeWindow(
func: str,
arg: SnakeValue[Any] | None = None,
extra_args: tuple[object, ...] = (),
partition_by: tuple[SnakeValue[Any], ...] = (),
order_by: tuple[SnakeOrder, ...] = (),
frame: SnakeFrame | None = None,
)
Bases: SnakeValue[T]
<func>(<arg>...) OVER (PARTITION BY ... ORDER BY ...).
func is the SQL NAME, kept as a string because two families live side by side (ranking, and
aggregates used as windows, which already have their own enum): a union enum would lie.
extra_args are literals of the function (the offset of LAG/LEAD), parametrised like every
other value.
over(
*,
partition_by: tuple[SnakeValue[Any], ...]
| list[SnakeValue[Any]] = (),
order_by: tuple[SnakeOrder, ...]
| list[SnakeOrder] = (),
frame: SnakeFrame | None = None,
) -> SnakeWindow[T]
The same function with its window defined. Without over(...), the window is ALL the rows.
frame is what turns a running total into a MOVING one. Without it the default frame runs
from the start of the partition to the current row, which is one useful answer out of many:
a trailing average, a centred window or a look-ahead all need the frame said out loud.
A FRAME WITHOUT AN ORDER IS REFUSED. 6 PRECEDING has to be preceding IN something, and with
no ORDER BY the engine picks an order of its own — so the same query answers differently on
two runs with nothing to show for it. That is the shape of failure this ORM does not ship.
It returns a NEW node (immutable AST): reusing a stored window holds no surprises.
Paths of everything the window touches (argument, partition, order): all three plan JOINs.
dataclass
¶
SnakeFrame(
mode: SnakeFrameMode,
start: SnakeFrameBound,
end: SnakeFrameBound,
)
<mode> BETWEEN <start> AND <end>: which neighbouring rows the function may look at.
dataclass
¶
One end of a frame. offset=None is UNBOUNDED; offset=0 is the current row.
The offset reaches the STATEMENT rather than params, and that is measured rather than lazy:
PostgreSQL and SQLite take a placeholder in a bound and MariaDB rejects it outright, so the only
portable spelling is the literal. It is safe for the same reason the JSON key path is — the value
is an int from Python's own type system and it is checked when the bound is BUILT, before any
SQL exists. An integer carries no injection.
Bases: Enum
ROWS counts ROWS, RANGE counts VALUES. With ties they answer differently.
Offering only ROWS would be the smaller API and the wrong one: somebody ordering by a day that
has several readings in it means RANGE, and handing them ROWS is a wrong answer with no error.
module-attribute
¶
SNAKE_CURRENT_ROW = SnakeFrameBound(
offset=0, following=False
)
snake_rows(
start: SnakeFrameBound, end: SnakeFrameBound
) -> SnakeFrame
ROWS BETWEEN start AND end: counts ROWS, so ties are separate rows.
snake_range(
start: SnakeFrameBound, end: SnakeFrameBound
) -> SnakeFrame
RANGE BETWEEN start AND end: counts VALUES, so tied rows come in together.
snake_preceding(rows: int | None = None) -> SnakeFrameBound
n PRECEDING, or UNBOUNDED PRECEDING with no argument.
snake_following(rows: int | None = None) -> SnakeFrameBound
n FOLLOWING, or UNBOUNDED FOLLOWING with no argument.
snake_case(*branches: SnakeCaseBranch[T]) -> SnakeCase[T]
snake_case(
*branches: SnakeCaseBranch[T], default: SnakeValue[T]
) -> SnakeCase[T]
snake_case(
*branches: SnakeCaseBranch[T], default: T
) -> SnakeCase[T]
snake_case(
*branches: SnakeCaseBranch[T],
default: SnakeValue[T] | T | object = _NO_DEFAULT,
) -> SnakeCase[T]
Builds a CASE WHEN. The branches are evaluated in order; the first match wins.
snake_case((User.age < 18, "minor"), (User.age < 65, "adult"), default="retired")
Without a default it returns NULL when none of them match (SQL semantics).
snake_coalesce(
first: SnakeValue[T], *rest: SnakeValue[T]
) -> SnakeCoalesce[T]
snake_coalesce(
first: SnakeValue[T | None], fallback: T
) -> SnakeCoalesce[T]
snake_coalesce(
first: SnakeValue[T], *rest: SnakeValue[T] | T
) -> SnakeCoalesce[T]
snake_coalesce(
first: SnakeValue[Any], *rest: SnakeValue[Any] | Any
) -> SnakeCoalesce[Any]
Builds a COALESCE(...): the first non-NULL argument.
The FIRST argument must be an expression: a literal there would never be NULL (it would always
return that literal) and it also anchors the type T.
THE MIDDLE OVERLOAD IS THE POINT: a COALESCE whose fallback is a LITERAL cannot be NULL, so it
drops the | None. COALESCE(SUM(x), 0) is an int, and that is the entire reason anybody
writes it — the value stops being nullable IN THE ENGINE, so it has to stop being nullable in the
type. Without it the declarator that exists to remove a None handed one back, and the caller
had to cast() in a project whose rule is zero Any.
The first overload is what keeps that from becoming a lie: with every argument an expression, nothing guarantees a value and the nullability survives. Order matters — it has to be tried before the literal one, or an expression fallback would be read as the literal.
snake_nullif(
value: SnakeValue[T], sentinel: SnakeValue[T] | T
) -> SnakeNullIf[T | None]
Builds a NULLIF(value, sentinel): turns a sentinel (the empty string) into NULL.
THE RESULT IS NULLABLE, and saying so is the whole point. This is the exact mirror of
snake_coalesce, which REMOVES a None and declares it: this one PUTS one in. It used to
return SnakeNullIf[T] — the same T it was given — so the declarator whose entire job is to
introduce a NULL was the one place the type did not mention it.
That is not a corner case. Guarding a division against zero is x / snake_nullif(y, 0), and the
NULL it produces IS the guard working. Typing that int told the caller the result could not be
None in a project that asks people to trust the checker over the engine.
snake_cast(
value: SnakeValue[Any], as_type: type[V]
) -> SnakeCast[V]
CAST(value AS <type>): an EXPLICIT change of type, named by whoever writes it.
THE ORM DOES NOT PROMOTE, and this is the door that makes that stance liveable. The arithmetic
operators carry one single T for both operands and the result, so Stock.reserved /
Stock.on_hand is an int — which is what SQL does, and sometimes what you want. When it is not,
you say so:
snake_cast(Stock.reserved, float) / Stock.on_hand -> SnakeArith[float]
as_type is what the RESULT is typed as, so the conversion travels through the type system
instead of around it. The SQL name of that type is the dialect's business: measured, SQLite needs
REAL where NUMERIC would answer 0.
The whitelist refuses by name at the CALL SITE rather than at emission, which is where a refusal is worth something: the alternative is SQL the engine rejects and a driver explaining a decision this ORM made.
snake_date_add(
value: SnakeValue[T], amount: int, unit: SnakeDatePart
) -> SnakeDateShift[T]
Moves a date FORWARD: snake_date_add(Order.placed_on, 30, SnakeDatePart.DAY).
CALENDAR UNITS ARE NOT PORTABLE and the ORM says so rather than hiding it. Measured, 2026-01-31
plus one month is 2026-02-28 on PostgreSQL and MySQL —both clamp— and 2026-03-03 on SQLite,
which overflows. That divergence is declared as Cap.CALENDAR_INTERVAL, so the session warns
once instead of letting whichever engine the developer happens to run be the one that gets
tested. DAY, HOUR, MINUTE, SECOND and WEEK are a fixed span and identical on all three.
Emulating the clamp was the alternative, and it is the wrong one: the ORM would be computing dates in Python behind an expression that claims to be SQL.
snake_date_sub(
value: SnakeValue[T], amount: int, unit: SnakeDatePart
) -> SnakeDateShift[T]
Moves a date BACKWARD. Same node with the sign flipped, because that is all it is.
snake_substring(
value: SnakeValue[str], start: int, length: int
) -> SnakeFuncCall[str]
SUBSTRING(value, start, length): a slice of text, counted from ONE like SQL does.
The bounds are VALUES and travel as parameters, which is not obvious enough to leave unsaid: a slice computed from user input with the numbers written into the statement is the shape an injection takes when nobody is looking at strings.
snake_replace(
value: SnakeValue[str], old: str, new: str
) -> SnakeFuncCall[str]
REPLACE(value, old, new): every occurrence, not the first. Both strings are parameters.
snake_ceil(value: SnakeValue[T]) -> SnakeFuncCall[T]
CEIL(value): rounds UP, keeping the type of its argument.
T -> T and not -> int, and that was measured rather than assumed: CEIL(1.2) answers 2 on
PostgreSQL and MySQL and 2.0 on SQLite. Declaring int would be false on one engine of three,
which is the exact family of bug the integer-division work removed. Keeping the argument's type
is true everywhere — a float in, a float back.
snake_floor(value: SnakeValue[T]) -> SnakeFuncCall[T]
FLOOR(value): rounds DOWN, keeping the type of its argument. Same measurement as CEIL.
snake_sqrt(value: SnakeValue[Any]) -> SnakeFuncCall[float]
SQRT(value): always a float. Measured double precision on PostgreSQL and a real on SQLite.
snake_power(
value: SnakeValue[Any], exponent: float
) -> SnakeFuncCall[float]
POWER(value, exponent): always a float, and the exponent travels as a parameter.
IN compuesto¶
snake_keys(model: type[M]) -> SnakeKeys[M]
Starts a composite IN over this model. Feed it the keys with .in_([...]).
snake_key(model: type[M]) -> SnakeKey[M]
Starts one row of a composite IN. Chain .set(column, value) once per column.
Bases: Generic[M]
The left-hand side: the tuple of columns, taken from the keys themselves.
It carries only the model, because the columns are whatever the keys declare — which is what lets one API serve any width. What it does is CHECK that they all declare the same ones.
in_(keys: Iterable[SnakeKey[M]]) -> SnakeCondition
(c1, c2, ...) IN ((v1a, v2a, ...), ...), refusing anything that would not be that.
Bases: Generic[M]
ONE row of the right-hand side: a column paired with its value, as many times as needed.
Immutable — set returns a new key. A shared prefix is a natural thing to write
(base.set(a, 1) and base.set(a, 2) off the same partial key), and with mutation the second
branch would either overwrite the first or trip the duplicate guard for no reason at all.
M is invariant, which is what makes a SnakeKey[Truck] inside a list of SnakeKey[Province]
a type error under both checkers. That is the guard that matters; the runtime one below is for
whoever runs no checker.
Pairs one column (or scalar expression) with the value this row compares it against.
T is bound by the SLOT, so the value has to match the column's type: this is the whole
reason the API is not a positional tuple.
Agregados¶
string_agg une los valores de un grupo en una cadena. El order_by de dentro de la llamada no es cosmético: sin él, el orden dentro del grupo es cosa del motor y los tres contestarían distinto por un motivo que no tiene que ver con tu consulta.
from snakeorm import SnakeQuery
from snakeorm.expressions import string_agg
rows = session.select(
SnakeQuery(Sale).group_by(Sale.region).order_by(Sale.region.asc()),
Sale.region,
string_agg(Sale.seller, ",", order_by=[Sale.seller.asc()]),
)
# postgres string_agg mysql GROUP_CONCAT sqlite group_concat -> one answer
dataclass
¶
SnakeStringAgg(
arg: SnakeValue[Any],
separator: str,
order_by: tuple[SnakeOrder, ...] = (),
)
Bases: SnakeValue[T]
STRING_AGG(arg, sep ORDER BY ...): a group's values joined into ONE string.
It gets its own node instead of a member of SnakeAggFunc because it is not shaped like the
others. SnakeAggregate emits FUNC(arg) uniformly, and this one has a second argument whose
POSITION differs per engine — and on MySQL is not an argument at all but the SEPARATOR keyword.
Squeezing it into the uniform node would mean the node knowing about engines, which is the one
thing the graph must never do.
order_by is not decoration: without it the concatenation comes back in whatever order the
engine chose, so a value somebody READS changes between runs.
The argument's and the order's: both of them plan JOINs.
string_agg(
value: SnakeValue[Any],
separator: str = ",",
*,
order_by: tuple[SnakeOrder, ...]
| list[SnakeOrder] = (),
) -> SnakeStringAgg[str | None]
Joins a group's values into one string: string_agg(Tag.name, ", ", order_by=[...]).
str | None because an aggregate over no rows is NULL on every engine, exactly like sum_.
THE SEPARATOR DOES NOT TRAVEL THE SAME WAY ON THE THREE, which is why the dialect writes this.
On PostgreSQL and SQLite it is a normal argument and goes in params; on MySQL it is the
SEPARATOR keyword, which was measured to reject a placeholder, so that dialect escapes it
through the same literal() the DDL defaults use.
order_by is worth passing whenever a person reads the result: without it the order is the
engine's business and can differ between two runs of the same query.
count(
arg: SnakeValue[Any] | None = None,
*,
distinct: bool = False,
) -> SnakeAggregate[int]
COUNT(*) with no argument; COUNT(col) or COUNT(DISTINCT col) with one. Always int.
sum_(arg: SnakeValue[T]) -> SnakeAggregate[T | None]
SUM(col). Preserves the column's type and adds None: with no rows to add up, it is NULL.
avg(arg: SnakeValue[Any]) -> SnakeAggregate[float | None]
AVG(col). The average is a real number (float), and it is NULL if there are no rows to average.
min_(arg: SnakeValue[T]) -> SnakeAggregate[T | None]
MIN(col). Preserves the column's type and adds None: with no rows, it is NULL.
max_(arg: SnakeValue[T]) -> SnakeAggregate[T | None]
MAX(col). Preserves the column's type and adds None: with no rows, it is NULL.
Funciones de ventana¶
row_number() -> SnakeWindow[int]
ROW_NUMBER(): position within the partition, with no ties (1, 2, 3, 4...).
dense_rank() -> SnakeWindow[int]
DENSE_RANK(): position with ties and NO gaps (1, 2, 2, 3...).
lag(
value: SnakeValue[T], offset: int = 1
) -> SnakeWindow[T | None]
LAG(value, n): the value of the row n positions BEFORE in the window.
Optional: on the first n rows there is no previous one and the result is NULL.
lead(
value: SnakeValue[T], offset: int = 1
) -> SnakeWindow[T | None]
LEAD(value, n): the value of the row n positions AFTER. Optional for the same reason.
Prefetch¶
SnakePrefetch(root: SnakeCollection[M])
Bases: Generic[M]
EXPLICIT chain of nested (eager) loading that starts at a to-many (Nation.makers).
A collection does not expose the child's relations, so the chain is not navigated: it is
declared with SnakePrefetch(Nation.makers).then(Maker.trucks). Generic in the model of the
LAST hop so that .then(...) only accepts relations of THAT child. The session resolves it
with ONE query per LEVEL (never N+1).
then(relation: SnakeCollection[N]) -> SnakePrefetch[N]
then(relation: type[N]) -> SnakePrefetch[N]
then(
relation: SnakeCollection[Any] | type[Any],
) -> SnakePrefetch[Any]
Chain one more hop onto the current child: to-many (SnakeCollection) or to-one
(type).
It returns a NEW SnakePrefetch (immutable). A column (SnakeExpr) matches no overload:
.then(Truck.model) does not compile.
filter(condition: SnakeCondition) -> SnakePrefetch[M]
Narrow WHICH CHILDREN get loaded at the CURRENT level (the last hop), WITHOUT dropping parents.
Different from query.filter() (which discards parents): it only narrows the select-in of
the level, a parent with no matching children gets [] but STILL comes back. It accumulates
with AND and returns a NEW SnakePrefetch (immutable) of the SAME type. The condition must
be over DIRECT columns of the model at THAT level; navigating or naming another column →
SnakeUnknownColumn.
hops() -> tuple[SnakePrefetchHop, ...]
The normalised hops in order (root first). The session walks them, one per level.
dataclass
¶
SnakePrefetchHop(
name: str,
kind: SnakeRelationshipKind,
parent_table: SnakeTableInfo,
child_model: type[object],
child_table: SnakeTableInfo,
relationship: SnakeRelationshipInfo,
child_filter: SnakeCondition | None = None,
)
A normalised hop of a prefetch chain (already resolved against the metadata graph).
SnakePrefetch produces it at construction time (with the relations already linked). The
session consumes it level by level: to_many with select-in (a list per parent), to_one with an
extra query (an object per parent). It carries the parent's table and the child's model+table
(to instantiate it).
instance-attribute
¶
kind: SnakeRelationshipKind
The same cardinality (and the SAME type) as SnakeRelationshipInfo, not a copy of the enum.