diff --git a/Cargo.lock b/Cargo.lock index 949fbf95b..eb1b53d4f 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -1709,6 +1709,7 @@ name = "krishiv-session" version = "0.1.0-alpha" dependencies = [ "arrow-array", + "arrow-buffer", "arrow-schema", "arrow-select", "futures", diff --git a/crates/krishiv-connect/src/translate/expression/tests.rs b/crates/krishiv-connect/src/translate/expression/tests.rs index 47c83eaa2..46c76083f 100644 --- a/crates/krishiv-connect/src/translate/expression/tests.rs +++ b/crates/krishiv-connect/src/translate/expression/tests.rs @@ -298,7 +298,7 @@ fn cast_errors() { err(&cast(None, 0)).error_class(), ErrorClass::ConnectInvalidPlanInvalidOneOfFieldNotSet ); - let e = err(&cast(Some(CastToType::TypeStr("array".into())), 0)); + let e = err(&cast(Some(CastToType::TypeStr("array".into())), 0)); assert_eq!(e.error_class(), ErrorClass::KrishivUnsupportedFeature); let e = err(&cast(Some(CastToType::TypeStr("notatype".into())), 0)); assert_eq!(e.error_class(), ErrorClass::UnsupportedDatatype); diff --git a/crates/krishiv-connect/src/translate/types.rs b/crates/krishiv-connect/src/translate/types.rs index d67bc388a..6311ae150 100644 --- a/crates/krishiv-connect/src/translate/types.rs +++ b/crates/krishiv-connect/src/translate/types.rs @@ -2,7 +2,7 @@ //! Connect `DataType` to Krishiv's. use krishiv_common::error::KrishivError; -use krishiv_types::{DataType, DecimalType}; +use krishiv_types::{ArrayType, DataType, DecimalType, MapType, StructField, StructType}; use super::errors::{assertion, not_set, unsupported_field, unsupported_variant}; use crate::proto::v4_2::{DataType as Proto, data_type::Kind}; @@ -12,14 +12,28 @@ const DEFAULT_COLLATION: &str = "UTF8_BINARY"; /// Maps a Connect data type, following Spark's `DataTypeProtoConverter`: /// a decimal with no precision is `decimal(10,0)`, one with a precision but -/// no scale has scale 0, and `string` with the default collation is `STRING`. +/// no scale has scale 0, `string` with the default collation is `STRING`, +/// and a struct field's metadata is Spark's `Metadata` JSON. An unset +/// element, key, value or field type is an unset kind. /// /// # Errors /// `KRISHIV_UNSUPPORTED_FEATURE` for a type Krishiv does not have yet -/// (`NULL`, `CHAR`, `VARCHAR`, intervals, nested types, ...) or a -/// non-default collation, `CONNECT_INVALID_PLAN.*` for an unset kind or an -/// invalid decimal. +/// (`NULL`, `CHAR`, `VARCHAR`, intervals, ...) or a non-default collation, +/// `CONNECT_INVALID_PLAN.*` for an unset kind or an invalid decimal, +/// `INVALID_JSON_DATA_TYPE` for field metadata that is not a JSON object and +/// `FAILED_TO_PARSE_TOO_COMPLEX` for a type nested deeper than +/// `DataType::MAX_NESTING_DEPTH`. pub(crate) fn data_type(proto: &Proto) -> Result { + data_type_at(proto, 0) +} + +fn data_type_at(proto: &Proto, depth: usize) -> Result { + let child = |t: Option<&Proto>| { + if depth >= DataType::MAX_NESTING_DEPTH { + return Err(krishiv_common::error::FailedToParseTooComplex {}.into()); + } + data_type_at(t.unwrap_or(&Proto::default()), depth + 1) + }; let Some(kind) = &proto.kind else { return Err(not_set("spark.connect.DataType", "KIND_NOT_SET")); }; @@ -46,6 +60,27 @@ pub(crate) fn data_type(proto: &Proto) -> Result { } DataType::String } + Kind::Array(a) => ArrayType::new(child(a.element_type.as_deref())?, a.contains_null).into(), + Kind::Map(m) => MapType::new( + child(m.key_type.as_deref())?, + child(m.value_type.as_deref())?, + m.value_contains_null, + ) + .into(), + Kind::Struct(s) => { + let fields = s + .fields + .iter() + .map(|f| { + let field = StructField::new(&f.name, child(f.data_type.as_ref())?, f.nullable); + Ok(match &f.metadata { + Some(json) => field.with_metadata(krishiv_types::parse_metadata(json)?), + None => field, + }) + }) + .collect::, KrishivError>>()?; + StructType::new(fields).into() + } other => { return Err(unsupported_variant( "data type", diff --git a/crates/krishiv-connect/src/translate/types/tests.rs b/crates/krishiv-connect/src/translate/types/tests.rs index cdeda3afe..0ef22f47d 100644 --- a/crates/krishiv-connect/src/translate/types/tests.rs +++ b/crates/krishiv-connect/src/translate/types/tests.rs @@ -103,9 +103,6 @@ fn unsupported_and_unset_kinds() { Kind::Null(dt::Null::default()), Kind::Char(dt::Char::default()), Kind::VarChar(dt::VarChar::default()), - Kind::Array(Box::default()), - Kind::Struct(dt::Struct::default()), - Kind::Map(Box::default()), Kind::Variant(dt::Variant::default()), Kind::CalendarInterval(dt::CalendarInterval::default()), Kind::YearMonthInterval(dt::YearMonthInterval::default()), @@ -122,3 +119,81 @@ fn unsupported_and_unset_kinds() { } use crate::proto::v4_2::DataType as DataTypeProto; + +/// `array>>>` +/// exactly as pyspark-client 4.2 sends it +/// (`pyspark_types_to_proto_types`): metadata as JSON with spaces, absent +/// when empty, and the default flags (false) left out. +#[test] +fn nested_types_map_as_pyspark_sends_them() { + let boxed = |kind| Some(Box::new(proto_type(kind))); + let string = Kind::String(dt::String { + collation: "UTF8_BINARY".to_owned(), + ..dt::String::default() + }); + let map = Kind::Map(Box::new(dt::Map { + key_type: boxed(string), + value_type: boxed(Kind::Array(Box::new(dt::Array { + element_type: boxed(Kind::Double(dt::Double::default())), + ..dt::Array::default() + }))), + ..dt::Map::default() + })); + let fields = vec![ + dt::StructField { + name: "a".to_owned(), + data_type: Some(proto_type(Kind::Integer(dt::Integer::default()))), + nullable: false, + metadata: Some(r#"{"comment": "c"}"#.to_owned()), + }, + dt::StructField { + name: "b".to_owned(), + data_type: Some(proto_type(map)), + nullable: true, + metadata: None, + }, + ]; + let proto = proto_type(Kind::Array(Box::new(dt::Array { + element_type: boxed(Kind::Struct(dt::Struct { + fields, + ..dt::Struct::default() + })), + ..dt::Array::default() + }))); + let t = data_type(&proto).expect("maps"); + assert_eq!( + t.json(), + r#"{"type":"array","elementType":{"type":"struct","fields":[{"name":"a","type":"integer","nullable":false,"metadata":{"comment":"c"}},{"name":"b","type":{"type":"map","keyType":"string","valueType":{"type":"array","elementType":"double","containsNull":false},"valueContainsNull":false},"nullable":true,"metadata":{}}]},"containsNull":false}"# + ); + // An unset element type is an unset kind; bad metadata is not JSON. + let e = data_type(&proto_type(Kind::Array(Box::default()))).expect_err("unset"); + assert_eq!( + e.error_class(), + ErrorClass::ConnectInvalidPlanInvalidOneOfFieldNotSet + ); + let bad = proto_type(Kind::Struct(dt::Struct { + fields: vec![dt::StructField { + name: "a".to_owned(), + data_type: Some(proto_type(Kind::Integer(dt::Integer::default()))), + nullable: true, + metadata: Some("[1]".to_owned()), + }], + ..dt::Struct::default() + })); + assert_eq!( + data_type(&bad).expect_err("metadata").error_class(), + ErrorClass::InvalidJsonDataType + ); + // Nesting is bounded. + let mut deep = proto_type(Kind::Integer(dt::Integer::default())); + for _ in 0..=DataType::MAX_NESTING_DEPTH { + deep = proto_type(Kind::Array(Box::new(dt::Array { + element_type: Some(Box::new(deep)), + ..dt::Array::default() + }))); + } + assert_eq!( + data_type(&deep).expect_err("deep").error_class(), + ErrorClass::FailedToParseTooComplex + ); +} diff --git a/crates/krishiv-connect/tests/goldens.rs b/crates/krishiv-connect/tests/goldens.rs index 2b3ecfc87..156ce2557 100644 --- a/crates/krishiv-connect/tests/goldens.rs +++ b/crates/krishiv-connect/tests/goldens.rs @@ -24,10 +24,6 @@ use prost_reflect::{DescriptorPool, DynamicMessage}; const UNSUPPORTED: &[(&str, ErrorClass)] = &[ ("alias_multi", ErrorClass::KrishivUnsupportedFeature), ("aggregate_pivot", ErrorClass::KrishivUnsupportedFeature), - ( - "cast_unsupported_type", - ErrorClass::KrishivUnsupportedFeature, - ), ("distinct", ErrorClass::KrishivUnsupportedFeature), ( "filter_expression_string", diff --git a/crates/krishiv-connect/tests/snapshots/cast_unsupported_type.snap b/crates/krishiv-connect/tests/snapshots/cast_unsupported_type.snap index 83a85c92b..0b33b2362 100644 --- a/crates/krishiv-connect/tests/snapshots/cast_unsupported_type.snap +++ b/crates/krishiv-connect/tests/snapshots/cast_unsupported_type.snap @@ -1,4 +1,8 @@ --- source: crates/krishiv-connect/tests/goldens.rs --- -[KRISHIV_UNSUPPORTED_FEATURE] The feature nested data types ("ARRAY") is not supported by Krishiv yet. SQLSTATE: 0A000 +Project [CAST(a AS ARRAY)] ++- Read parquet [/data/t.parquet] +-- plan ids -- +Project plan_id=1 + Read plan_id=0 diff --git a/crates/krishiv-exec/src/planner/eval/cast.rs b/crates/krishiv-exec/src/planner/eval/cast.rs index 4763b528c..3cae59546 100644 --- a/crates/krishiv-exec/src/planner/eval/cast.rs +++ b/crates/krishiv-exec/src/planner/eval/cast.rs @@ -9,6 +9,7 @@ use krishiv_kernels::cast::{CastMode as KernelMode, CastOptions, FixedOffset, ca use krishiv_plan::CastMode; use krishiv_types::DataType; +use super::nested_cast; use super::strings::{from_view, to_view}; use super::{Input, PhysExpr, Value}; use crate::error::Result; @@ -75,8 +76,33 @@ pub(super) fn parts( }) } +impl CastParts { + /// The cast of an element, value or field to `to`, in the same mode + /// and zone. + pub(super) fn child(&self, to: &ArrowType) -> Self { + let kernel_to = match to { + ArrowType::Utf8View => ArrowType::Utf8, + ArrowType::BinaryView => ArrowType::Binary, + other => other.clone(), + }; + Self { + to: to.clone(), + kernel_to, + mode: self.mode, + zone: self.zone, + } + } +} + /// `a` through `p`. pub(super) fn apply(p: &CastParts, a: &ArrayRef) -> Result { + // Every value of a `void` array is NULL, of any type. + if a.data_type() == &ArrowType::Null { + return Ok(arrow_array::new_null_array(&p.to, a.len())); + } + if nested_cast::is_nested(a.data_type()) || nested_cast::is_nested(&p.to) { + return nested_cast::cast(p, a); + } let input = from_view(a)?; let opts = CastOptions { mode: p.mode, diff --git a/crates/krishiv-exec/src/planner/eval/mod.rs b/crates/krishiv-exec/src/planner/eval/mod.rs index 2db4a8aef..9d6510f25 100644 --- a/crates/krishiv-exec/src/planner/eval/mod.rs +++ b/crates/krishiv-exec/src/planner/eval/mod.rs @@ -27,6 +27,7 @@ mod cast; mod cond; mod func; mod logic; +mod nested_cast; mod strings; use std::fmt; diff --git a/crates/krishiv-exec/src/planner/eval/nested_cast.rs b/crates/krishiv-exec/src/planner/eval/nested_cast.rs new file mode 100644 index 000000000..f09888b99 --- /dev/null +++ b/crates/krishiv-exec/src/planner/eval/nested_cast.rs @@ -0,0 +1,227 @@ +// SPDX-License-Identifier: Apache-2.0 +//! Casts with array, map and struct types, as Spark's `Cast` runs them: +//! element by element (arrays), key and value by key and value (maps) and +//! field by field, by position (structs), each through the ordinary cast of +//! its type, and to `STRING` as `[1, null]`, `{k -> v}` and `{1, null}`. +//! +//! A cast between types that differ only in field names or nullability +//! relabels the data. Values no row reads (under a NULL struct, or in the +//! range of a NULL array) are never cast, so they raise nothing. + +use std::fmt::Write as _; +use std::sync::Arc; + +use arrow_array::cast::AsArray; +use arrow_array::{ + Array, ArrayRef, ListArray, MapArray, OffsetSizeTrait, StringViewArray, StructArray, + UInt64Array, +}; +use arrow_buffer::{ArrowNativeType, NullBuffer, OffsetBuffer, ScalarBuffer}; +use arrow_schema::{DataType as ArrowType, FieldRef}; + +use super::cast::{CastParts, apply}; +use crate::error::Result; +use crate::planner::build::{internal, unsupported}; + +/// Whether `t` is the carrier of an array, map or struct. +pub(super) fn is_nested(t: &ArrowType) -> bool { + matches!( + t, + ArrowType::List(_) | ArrowType::LargeList(_) | ArrowType::Map(..) | ArrowType::Struct(_) + ) +} + +/// `a` through `p`, where the source or the target is nested. +pub(super) fn cast(p: &CastParts, a: &ArrayRef) -> Result { + if a.data_type() == &p.to { + return Ok(Arc::clone(a)); + } + let refused = || unsupported(&format!("CAST from {} to {}", a.data_type(), p.to)); + match (a.data_type(), &p.to) { + (ArrowType::List(_), ArrowType::List(to)) => list::(p, a, to), + (ArrowType::LargeList(_), ArrowType::List(to)) => list::(p, a, to), + (ArrowType::Map(..), ArrowType::Map(to, sorted)) => map(p, a, to, *sorted), + (ArrowType::Struct(_), ArrowType::Struct(to)) => { + let s = a.as_struct(); + if s.num_columns() != to.len() { + return Err(refused()); + } + let children = s + .columns() + .iter() + .zip(to.iter()) + .map(|(c, f)| apply(&p.child(f.data_type()), &masked(c, s.nulls())?)) + .collect::>>()?; + let out = StructArray::try_new(to.clone(), children, s.nulls().cloned()) + .map_err(|e| internal(&e.to_string()))?; + Ok(Arc::new(out)) + } + (from, ArrowType::Utf8View | ArrowType::Utf8) if is_nested(from) => { + let texts = texts(p, a)?; + let out: ArrayRef = Arc::new(StringViewArray::from_iter(texts)); + if p.to == ArrowType::Utf8 { + super::strings::from_view(&out) + } else { + Ok(out) + } + } + _ => Err(refused()), + } +} + +/// `a` with its rows also NULL where `parent` is: the values a NULL parent +/// row holds are never read, so they must not be cast. +fn masked(a: &ArrayRef, parent: Option<&NullBuffer>) -> Result { + let Some(parent) = parent.filter(|n| n.null_count() > 0) else { + return Ok(Arc::clone(a)); + }; + let nulls = NullBuffer::union(a.nulls(), Some(parent)); + let data = a + .to_data() + .into_builder() + .nulls(nulls) + .build() + .map_err(|e| internal(&e.to_string()))?; + Ok(arrow_array::make_array(data)) +} + +/// The 32-bit offsets and values of a list or map whose rows read only +/// their own values: a NULL row with a non-empty range is dropped from the +/// values (they are taken again without it). +fn compact( + offsets: &[O], + nulls: Option<&NullBuffer>, + values: &ArrayRef, +) -> Result<(OffsetBuffer, ArrayRef)> { + let at = |i: usize| offsets.get(i).map_or(0, |o| o.as_usize()); + let rows = offsets.len().saturating_sub(1); + let dirty = nulls.is_some_and(|n| (0..rows).any(|i| n.is_null(i) && at(i + 1) > at(i))); + let too_big = || unsupported("an array batch with more than 2^31 values"); + if !dirty { + let first = at(0); + let narrow = offsets + .iter() + .map(|o| i32::try_from(o.as_usize() - first).map_err(|_| too_big())) + .collect::>>()?; + let len = at(rows) - first; + return Ok(( + OffsetBuffer::new(ScalarBuffer::from(narrow)), + values.slice(first, len), + )); + } + let mut indices = Vec::new(); + let mut narrow = Vec::with_capacity(offsets.len()); + narrow.push(0_i32); + for i in 0..rows { + if nulls.is_none_or(|n| n.is_valid(i)) { + indices.extend((at(i)..at(i + 1)).map(|v| v as u64)); + } + narrow.push(i32::try_from(indices.len()).map_err(|_| too_big())?); + } + let taken = arrow_select::take::take(values.as_ref(), &UInt64Array::from(indices), None) + .map_err(|e| internal(&e.to_string()))?; + Ok((OffsetBuffer::new(ScalarBuffer::from(narrow)), taken)) +} + +fn list(p: &CastParts, a: &ArrayRef, to: &FieldRef) -> Result { + let l = a.as_list::(); + let (offsets, values) = compact(l.value_offsets(), l.nulls(), l.values())?; + let values = apply(&p.child(to.data_type()), &values)?; + let out = ListArray::try_new(Arc::clone(to), offsets, values, l.nulls().cloned()) + .map_err(|e| internal(&e.to_string()))?; + Ok(Arc::new(out)) +} + +fn map(p: &CastParts, a: &ArrayRef, to: &FieldRef, sorted: bool) -> Result { + let m = a.as_map(); + let entries: ArrayRef = Arc::new(m.entries().clone()); + let (offsets, entries) = compact(m.value_offsets(), m.nulls(), &entries)?; + let entries = apply(&p.child(to.data_type()), &entries)?; + let out = MapArray::try_new( + Arc::clone(to), + offsets, + entries.as_struct().clone(), + m.nulls().cloned(), + sorted, + ) + .map_err(|e| internal(&e.to_string()))?; + Ok(Arc::new(out)) +} + +/// Each row of `a` as Spark's `CAST(.. AS STRING)` writes it; `None` for a +/// NULL row. +fn texts(p: &CastParts, a: &ArrayRef) -> Result>> { + let rows = |offsets: Vec<(usize, usize)>, items: Vec>, open, close| { + offsets + .into_iter() + .enumerate() + .map(|(i, (from, to))| { + a.is_valid(i).then(|| { + let mut s = String::from(open); + for (k, item) in items.get(from..to).unwrap_or_default().iter().enumerate() { + if k > 0 { + s.push_str(", "); + } + s.push_str(item.as_deref().unwrap_or("null")); + } + s.push_str(close); + s + }) + }) + .collect() + }; + let ranges = |offsets: Vec| -> Vec<(usize, usize)> { + offsets.windows(2).map(|w| (w[0], w[1])).collect() + }; + Ok(match a.data_type() { + ArrowType::List(_) => { + let l = a.as_list::(); + let items = texts(p, l.values())?; + let offsets = l.value_offsets().iter().map(|o| o.as_usize()).collect(); + rows(ranges(offsets), items, "[", "]") + } + ArrowType::LargeList(_) => { + let l = a.as_list::(); + let items = texts(p, l.values())?; + let offsets = l.value_offsets().iter().map(|o| o.as_usize()).collect(); + rows(ranges(offsets), items, "[", "]") + } + ArrowType::Map(..) => { + let m = a.as_map(); + let keys = texts(p, m.keys())?; + let values = texts(p, m.values())?; + let pairs = keys + .into_iter() + .zip(values) + .map(|(k, v)| { + let mut s = k.unwrap_or_else(|| "null".to_owned()); + let _ = write!(s, " -> {}", v.as_deref().unwrap_or("null")); + Some(s) + }) + .collect(); + let offsets = m.value_offsets().iter().map(|o| o.as_usize()).collect(); + rows(ranges(offsets), pairs, "{", "}") + } + ArrowType::Struct(_) => { + let s = a.as_struct(); + let columns = s + .columns() + .iter() + .map(|c| texts(p, &masked(c, s.nulls())?)) + .collect::>>()?; + let width = columns.len(); + let items = (0..s.len()) + .flat_map(|row| columns.iter().map(move |c| c.get(row).cloned().flatten())) + .collect(); + let offsets = (0..=s.len()).map(|row| row * width).collect(); + rows(ranges(offsets), items, "{", "}") + } + _ => { + let out = apply(&p.child(&ArrowType::Utf8View), a)?; + out.as_string_view() + .iter() + .map(|v| v.map(str::to_owned)) + .collect() + } + }) +} diff --git a/crates/krishiv-functions/src/signature.rs b/crates/krishiv-functions/src/signature.rs index ba69c0269..aa8bd19aa 100644 --- a/crates/krishiv-functions/src/signature.rs +++ b/crates/krishiv-functions/src/signature.rs @@ -135,6 +135,7 @@ pub struct CallTypes<'a> { sql: Option<&'a dyn Fn(usize) -> Option>, ansi: bool, decimal_loss: bool, + case_sensitive: bool, } impl fmt::Debug for CallTypes<'_> { @@ -145,6 +146,7 @@ impl fmt::Debug for CallTypes<'_> { .field("literals", &self.literals) .field("ansi", &self.ansi) .field("decimal_loss", &self.decimal_loss) + .field("case_sensitive", &self.case_sensitive) .finish_non_exhaustive() } } @@ -161,6 +163,7 @@ impl<'a> CallTypes<'a> { sql: None, ansi: true, decimal_loss: true, + case_sensitive: false, } } @@ -201,6 +204,20 @@ impl<'a> CallTypes<'a> { self.decimal_loss } + /// With `spark.sql.caseSensitive` (default off): struct field names + /// match by case when nested types meet. + #[must_use] + pub const fn with_case_sensitive(mut self, case_sensitive: bool) -> Self { + self.case_sensitive = case_sensitive; + self + } + + /// `spark.sql.caseSensitive`. + #[must_use] + pub const fn case_sensitive(&self) -> bool { + self.case_sensitive + } + /// With `sql(i)` rendering argument `i` as Spark's errors quote it /// (`extract`'s `INVALID_EXTRACT_FIELD` names the source); rendered /// only when a rule asks. diff --git a/crates/krishiv-memory/src/spillfile/dir.rs b/crates/krishiv-memory/src/spillfile/dir.rs index b988346d6..7d7dc91f6 100644 --- a/crates/krishiv-memory/src/spillfile/dir.rs +++ b/crates/krishiv-memory/src/spillfile/dir.rs @@ -30,7 +30,7 @@ use std::io; use std::path::{Path, PathBuf}; use std::sync::Arc; use std::sync::atomic::{AtomicBool, AtomicU64, Ordering}; -use std::time::{SystemTime, UNIX_EPOCH}; +use std::time::{Duration, SystemTime, UNIX_EPOCH}; use arrow_ipc::writer::IpcWriteOptions; use arrow_schema::{ArrowError, SchemaRef}; @@ -44,6 +44,11 @@ use crate::Reservation; const PREFIX: &str = "krishiv-spill-"; const LOCK_SUFFIX: &str = ".lock"; +/// Prefix of a lock file not yet published under its final name; startup +/// scans ignore it (it does not start with [`PREFIX`]). +const PENDING_PREFIX: &str = ".krishiv-spill-new-"; +/// How long an unpublished lock file may exist before its start is taken for dead. +const PENDING_STALE_AFTER: Duration = Duration::from_secs(60); const ID_ATTEMPTS: u32 = 16; /// Where and how to spill. @@ -192,25 +197,29 @@ impl SpillManager { for attempt in 0..ID_ATTEMPTS { let id = new_id(attempt); let lock_path = root.join(format!("{PREFIX}{id}{LOCK_SUFFIX}")); - let lock = match create_new_file(&lock_path) { + // The lock file is created under a name the stale-session scan + // ignores, locked, and only then published by a hard link (which + // fails if the name exists). Created in place it was reclaimable + // by a concurrent start between its creation and our lock, and + // under load a start could lose that race on every attempt. + let pending = root.join(format!("{PENDING_PREFIX}{id}")); + let lock = match create_new_file(&pending) { Ok(f) => f, Err(e) if e.kind() == io::ErrorKind::AlreadyExists => continue, - Err(e) => return Err(SpillError::io("create", &lock_path, e)), + Err(e) => return Err(SpillError::io("create", &pending, e)), }; - match lock.try_lock() { - Ok(()) => {} - // A concurrent start is reclaiming what it took for a stale - // session: it removes the file, and we take another id. - Err(std::fs::TryLockError::WouldBlock) => continue, - Err(std::fs::TryLockError::Error(e)) => { - let _ = fs::remove_file(&lock_path); - return Err(SpillError::io("lock", &lock_path, e)); + let locked = lock.try_lock().map_err(|e| match e { + std::fs::TryLockError::Error(e) => e, + std::fs::TryLockError::WouldBlock => { + io::Error::new(io::ErrorKind::WouldBlock, "a fresh lock file is held") } - } - // A concurrent start may have reclaimed the file between its - // creation and our lock; then our lock guards nothing. Retry. - if !lock_path.exists() { - continue; + }); + let published = locked.and_then(|()| fs::hard_link(&pending, &lock_path)); + let _ = fs::remove_file(&pending); + match published { + Ok(()) => {} + Err(e) if e.kind() == io::ErrorKind::AlreadyExists => continue, + Err(e) => return Err(SpillError::io("lock", &lock_path, e)), } let session_dir = root.join(format!("{PREFIX}{id}")); if let Err(e) = create_private_dir(&session_dir, false) { @@ -419,6 +428,30 @@ fn remove_stale_sessions(root: &Path) -> usize { let mut removed = 0; for entry in entries.flatten() { let name = entry.file_name(); + if let Some(pending) = name.to_str().filter(|n| n.starts_with(PENDING_PREFIX)) { + // A start that died between creating and publishing its lock. + // A live start holds the file for microseconds but is not locked + // until after it is created, so only a file left for a minute is + // taken for dead (locking an old file cannot race its creator). + let path = entry.path(); + let old = entry + .metadata() + .and_then(|m| m.modified()) + .ok() + .and_then(|t| t.elapsed().ok()) + .is_some_and(|age| age >= PENDING_STALE_AFTER); + if old + && entry.file_type().is_ok_and(|t| t.is_file()) + && valid_id(&pending[PENDING_PREFIX.len()..]) + && OpenOptions::new() + .write(true) + .open(&path) + .is_ok_and(|f| f.try_lock().is_ok()) + { + let _ = fs::remove_file(&path); + } + continue; + } let Some(rest) = name.to_str().and_then(|n| n.strip_prefix(PREFIX)) else { continue; }; diff --git a/crates/krishiv-memory/src/spillfile/tests.rs b/crates/krishiv-memory/src/spillfile/tests.rs index 0b9a62706..0ff1d9931 100644 --- a/crates/krishiv-memory/src/spillfile/tests.rs +++ b/crates/krishiv-memory/src/spillfile/tests.rs @@ -1650,3 +1650,38 @@ fn ids_are_unique_across_threads() { assert_eq!(unique.len(), all.len()); assert!(all.iter().all(|id| super::dir::valid_id(id))); } + +#[test] +fn a_lock_not_yet_published_is_never_reclaimed_and_a_dead_one_is() { + let dir = TestDir::new(); + dir.create(); + // A start in progress holds its unpublished lock: another start must not + // touch it (the race that let concurrent starts exhaust their ids). + let held = dir.0.join(".krishiv-spill-new-1-a-0-0"); + let held_file = fs::File::create(&held).unwrap(); + held_file.lock().unwrap(); + // One whose process died, and one that is not ours. + let old = std::time::SystemTime::now() - std::time::Duration::from_secs(3600); + for name in [".krishiv-spill-new-2-a-0-0", ".krishiv-spill-new-NOT_AN_ID"] { + let f = fs::File::create(dir.0.join(name)).unwrap(); + f.set_modified(old).unwrap(); + } + // And a fresh unlocked one: its creator may be about to lock it. + fs::write(dir.0.join(".krishiv-spill-new-3-a-0-0"), b"").unwrap(); + + let manager = SpillManager::open(&dir.config(SpillCodec::Lz4)).unwrap(); + let names = entries(&dir.0); + assert!(names.contains(&".krishiv-spill-new-1-a-0-0".to_owned())); + assert!(names.contains(&".krishiv-spill-new-NOT_AN_ID".to_owned())); + assert!(names.contains(&".krishiv-spill-new-3-a-0-0".to_owned())); + assert!(!names.contains(&".krishiv-spill-new-2-a-0-0".to_owned())); + // Opening leaves no unpublished lock of its own behind. + assert_eq!( + names + .iter() + .filter(|n| n.starts_with(".krishiv-spill-new-")) + .count(), + 3 + ); + drop(manager); +} diff --git a/crates/krishiv-plan-codec/src/decode.rs b/crates/krishiv-plan-codec/src/decode.rs index 29ea3e51e..7ba9cb913 100644 --- a/crates/krishiv-plan-codec/src/decode.rs +++ b/crates/krishiv-plan-codec/src/decode.rs @@ -966,13 +966,27 @@ fn spark_type(t: &pb::DataType) -> Result { Date => DataType::Date, Timestamp => DataType::Timestamp, TimestampNtz => DataType::TimestampNtz, + Nested => nested(t)?, })?; if !matches!(data_type, DataType::Decimal(_)) && (t.precision != 0 || t.scale != 0) { return Err(corrupt("a precision or scale on a type that has none")); } + if !data_type.is_nested() && !t.json.is_empty() { + return Err(corrupt("a JSON form on a type that is not nested")); + } Ok(data_type) } +/// A nested type from its Spark JSON (depth-bounded by `from_json`). +fn nested(t: &pb::DataType) -> Result { + // Only the canonical form the encoder writes: bytes decode to a plan + // that encodes to the same bytes. + match DataType::from_json(&t.json) { + Ok(d) if d.is_nested() && d.json() == t.json => Ok(d), + _ => Err(corrupt("an invalid nested type")), + } +} + fn decimal(t: &pb::DataType) -> Result { DecimalType::try_new(t.precision, t.scale).map_err(|_| corrupt("an invalid decimal type")) } diff --git a/crates/krishiv-plan-codec/src/encode.rs b/crates/krishiv-plan-codec/src/encode.rs index 19c0579f1..8c20272f8 100644 --- a/crates/krishiv-plan-codec/src/encode.rs +++ b/crates/krishiv-plan-codec/src/encode.rs @@ -3,6 +3,7 @@ //! expressions and explain nodes are collected in post-order with an //! explicit stack, and each is written after the entries it refers to. +use std::cell::Cell; use std::collections::HashMap; use std::sync::Arc; @@ -26,7 +27,8 @@ use krishiv_types::DataType; use crate::arrow::{schema_bytes, type_bytes}; use crate::error::{CodecError, Result}; -use crate::features::{legacy_level, plan_features}; +use crate::features::arrow_type_is_nested; +use crate::features::{Feature, legacy_level, plan_features}; use crate::proto as pb; use crate::{EPOCH, FORMAT_VERSION}; @@ -57,6 +59,19 @@ pub fn to_message(plan: &PhysicalPlan) -> Result { e.table.push(message); } let root = e.node_index(plan.root())?; + if plan + .schema() + .fields() + .iter() + .any(|f| arrow_type_is_nested(f.data_type())) + { + e.nested.set(true); + } + if e.nested.get() != features.contains(&Feature::NestedTypes) { + return Err(CodecError::Unsupported( + "nested types where the feature header does not list them (an engine bug)".to_owned(), + )); + } let explain_order = post_order(plan.explain(), |n| n.children.iter().collect()); let mut explain = Vec::with_capacity(explain_order.len()); let mut explain_ids: HashMap<*const ExplainNode, u32> = HashMap::new(); @@ -129,6 +144,9 @@ struct Encoder { /// The index of each written expression, by `Arc` identity, so a /// shared subexpression is written once. expr_ids: HashMap<*const Expr, u32>, + /// Whether a nested type was written: checked against the header, + /// which [`plan_features`] derives from the plan on its own. + nested: Cell, } fn layout(layout: &[AttrId]) -> Vec { @@ -156,7 +174,7 @@ impl Encoder { functions: window .functions .iter() - .map(window_function) + .map(|f| window_function(f, &self.nested)) .collect::>()?, }), PhysNode::WindowGroupLimit { input, limit } => { @@ -282,7 +300,7 @@ impl Encoder { SubqueryValueKind::Scalar => pb::SubqueryValueKind::Scalar, SubqueryValueKind::Exists => pb::SubqueryValueKind::Exists, } as i32, - data_type: type_bytes(&s.data_type), + data_type: arrow_bytes(&s.data_type, &self.nested), plan: self.node_index(&s.plan)?, }) }) @@ -316,7 +334,7 @@ impl Encoder { aggregates: plan .aggregates .iter() - .map(aggregate_expr) + .map(|a| aggregate_expr(a, &self.nested)) .collect::>()?, distinct: plan.distinct.clone(), output: match plan.output { @@ -392,11 +410,18 @@ impl Encoder { Ok(pb::ScanNode { id: node.id.0, source: Some(read_source(source)?), - output: output.iter().map(bound_column).collect::>()?, + output: output + .iter() + .map(|c| bound_column(c, &self.nested)) + .collect::>()?, version: version.map(|v| v.0), projection: node.projection.iter().map(column_path).collect(), filter, - runtime_filters: node.runtime_filters.iter().map(scan_filter).collect(), + runtime_filters: node + .runtime_filters + .iter() + .map(|f| scan_filter(f, &self.nested)) + .collect(), row_stage: Some(row_stage(&node.row_stage)), }) } @@ -430,10 +455,10 @@ impl Encoder { fn expr_message(&self, e: &Expr) -> Result { use pb::expr::Kind; let kind = match &e.node { - ExprNode::Literal(l) => Kind::Literal(literal(l)?), + ExprNode::Literal(l) => Kind::Literal(literal(l, &self.nested)?), ExprNode::Cast { expr, to, mode } => Kind::Cast(pb::Cast { expr: self.child(expr), - to: Some(spark_type(to)?), + to: Some(spark_type(to, &self.nested)?), mode: match mode { CastMode::Legacy => pb::CastMode::Legacy, CastMode::Ansi => pb::CastMode::Ansi, @@ -441,13 +466,17 @@ impl Encoder { _ => return Err(unresolved("a cast without a mode")), } as i32, }), - ExprNode::Resolved(ResolvedExpr::Column(c)) => Kind::Column(bound_column(c)?), - ExprNode::Resolved(ResolvedExpr::OuterColumn(c)) => Kind::OuterColumn(bound_column(c)?), + ExprNode::Resolved(ResolvedExpr::Column(c)) => { + Kind::Column(bound_column(c, &self.nested)?) + } + ExprNode::Resolved(ResolvedExpr::OuterColumn(c)) => { + Kind::OuterColumn(bound_column(c, &self.nested)?) + } ExprNode::Resolved(ResolvedExpr::Function(f)) => Kind::Function(self.function(f)?), ExprNode::Resolved(ResolvedExpr::Alias { expr, output }) => { Kind::Alias(pb::AliasExpr { expr: self.child(expr), - output: Some(bound_column(output)?), + output: Some(bound_column(output, &self.nested)?), }) } ExprNode::Column(_) => return Err(unresolved("an unresolved column")), @@ -484,7 +513,7 @@ impl Encoder { args: f.args.iter().map(|a| self.child(a)).collect(), distinct: f.distinct, filter: f.filter.as_ref().map(|a| self.child(a)), - data_type: Some(spark_type(&f.data_type)?), + data_type: Some(spark_type(&f.data_type, &self.nested)?), nullable: f.nullable, deterministic: f.deterministic, }) @@ -508,11 +537,11 @@ fn origin(o: Origin) -> Option { }) } -fn bound_column(c: &BoundColumn) -> Result { +fn bound_column(c: &BoundColumn, nested: &Cell) -> Result { Ok(pb::BoundColumn { id: c.id.0, name: c.name.clone(), - data_type: Some(spark_type(&c.data_type)?), + data_type: Some(spark_type(&c.data_type, nested)?), nullable: c.nullable, }) } @@ -591,11 +620,11 @@ fn join_filter(f: &JoinFilterPlan) -> pb::JoinFilter { } } -fn scan_filter(f: &ScanRuntimeFilter) -> pb::ScanRuntimeFilter { +fn scan_filter(f: &ScanRuntimeFilter, nested: &Cell) -> pb::ScanRuntimeFilter { pb::ScanRuntimeFilter { id: f.id.0, column: Some(column_path(&f.column)), - key_type: type_bytes(&f.key_type), + key_type: arrow_bytes(&f.key_type, nested), } } @@ -606,7 +635,7 @@ fn row_stage(s: &RowStageSettings) -> pb::RowStageSettings { } } -fn window_function(f: &WindowFunctionPlan) -> Result { +fn window_function(f: &WindowFunctionPlan, nested: &Cell) -> Result { use pb::window_function_plan::Function; let function = match &f.function { WindowFunction::RowNumber => Function::RowNumber(pb::Empty {}), @@ -615,7 +644,7 @@ fn window_function(f: &WindowFunctionPlan) -> Result { WindowFunction::PercentRank => Function::PercentRank(pb::Empty {}), WindowFunction::CumeDist => Function::CumeDist(pb::Empty {}), WindowFunction::Ntile(buckets) => Function::Ntile(*buckets), - WindowFunction::Aggregate(a) => Function::Aggregate(aggregate_expr(a)?), + WindowFunction::Aggregate(a) => Function::Aggregate(aggregate_expr(a, nested)?), WindowFunction::Offset { input, offset, @@ -627,7 +656,7 @@ fn window_function(f: &WindowFunctionPlan) -> Result { offset: *offset, default: default.map(|c| c as u64), ignore_nulls: *ignore_nulls, - data_type: type_bytes(data_type), + data_type: arrow_bytes(data_type, nested), }), WindowFunction::NthValue { input, @@ -638,7 +667,7 @@ fn window_function(f: &WindowFunctionPlan) -> Result { input: *input as u64, n: *n, ignore_nulls: *ignore_nulls, - data_type: type_bytes(data_type), + data_type: arrow_bytes(data_type, nested), }), }; let offset = |b: FrameBound| match b { @@ -676,7 +705,7 @@ fn frame_bound(b: FrameBound) -> Result { } } -fn aggregate_expr(a: &AggregateExpr) -> Result { +fn aggregate_expr(a: &AggregateExpr, nested: &Cell) -> Result { let (function, ignore_nulls) = match a.function { AggregateFunction::Count => (pb::AggregateFunction::Count, false), AggregateFunction::Sum => (pb::AggregateFunction::Sum, false), @@ -702,7 +731,7 @@ fn aggregate_expr(a: &AggregateExpr) -> Result { AggInput::Raw => pb::AggregateInput::Raw, AggInput::State => pb::AggregateInput::State, } as i32, - arg_type: type_bytes(&a.arg_type), + arg_type: arrow_bytes(&a.arg_type, nested), name: a.name.clone(), }) } @@ -764,8 +793,34 @@ fn option(key: &str, value: &OptionValue) -> Result { Ok(pb::OptionValue { kind: Some(kind) }) } -fn spark_type(t: &DataType) -> Result { +/// The IPC bytes of the Arrow type `t`; `nested` is set when it is or holds an +/// array, map or struct. +fn arrow_bytes(t: &arrow_schema::DataType, nested: &Cell) -> Vec { + if arrow_type_is_nested(t) { + nested.set(true); + } + type_bytes(t) +} + +/// `t`; `nested` is set when it is an array, map or struct. +fn spark_type(t: &DataType, nested: &Cell) -> Result { use pb::data_type::Kind; + if t.is_nested() { + nested.set(true); + // Spark's JSON form keeps nullability and metadata; its depth is + // what a reader accepts. + if t.nesting_depth() > DataType::MAX_NESTING_DEPTH { + return Err(CodecError::Unsupported(format!( + "a type nested more than {} levels deep", + DataType::MAX_NESTING_DEPTH + ))); + } + return Ok(pb::DataType { + kind: Kind::Nested as i32, + json: t.json(), + ..pb::DataType::default() + }); + } let (kind, precision, scale) = match t { DataType::Null => (Kind::Null, 0, 0), DataType::Boolean => (Kind::Boolean, 0, 0), @@ -795,14 +850,15 @@ fn spark_type(t: &DataType) -> Result { kind: kind as i32, precision, scale, + json: String::new(), }) } -fn literal(l: &Literal) -> Result { +fn literal(l: &Literal, nested: &Cell) -> Result { use pb::literal::Kind; let kind = match l { Literal::Null(t) => Kind::Null(pb::NullLiteral { - data_type: t.as_ref().map(spark_type).transpose()?, + data_type: t.as_ref().map(|t| spark_type(t, nested)).transpose()?, }), Literal::Boolean(v) => Kind::Boolean(*v), Literal::Byte(v) => Kind::Byte(i32::from(*v)), @@ -816,7 +872,7 @@ fn literal(l: &Literal) -> Result { data_type, } => Kind::Decimal(pb::DecimalLiteral { unscaled: unscaled.to_le_bytes().to_vec(), - data_type: Some(spark_type(&DataType::Decimal(*data_type))?), + data_type: Some(spark_type(&DataType::Decimal(*data_type), nested)?), }), Literal::String(v) => Kind::String(v.clone()), Literal::Binary(v) => Kind::Binary(v.clone()), diff --git a/crates/krishiv-plan-codec/src/features.rs b/crates/krishiv-plan-codec/src/features.rs index 9869c9f9b..b714aee78 100644 --- a/crates/krishiv-plan-codec/src/features.rs +++ b/crates/krishiv-plan-codec/src/features.rs @@ -36,11 +36,14 @@ use std::collections::BTreeSet; use std::fmt; +use std::sync::Arc; use krishiv_exec::JoinType; use krishiv_exec::aggregate::AggregateFunction; use krishiv_exec::physical::{Partitioning, PhysNode, PhysicalPlan, ProjectItemPlan}; use krishiv_exec::window::{FrameBound, WindowFrame, WindowFunction}; +use krishiv_plan::{Expr, ExprNode, Literal, ResolvedExpr}; +use krishiv_types::DataType; /// The `min_reader_version` of a plan that uses no feature (the epoch-1 /// baseline): the format of version 2. @@ -117,6 +120,7 @@ registry! { (expand, Expand, 5, Semantic, "The `Expand` node and its `NullOf` project item (ROLLUP, CUBE, GROUPING SETS)."), (join_build_preserved, JoinBuildPreserved, POST_MIGRATION_READER_VERSION, Optimization, "Joins that build their preserved side (`RightSemi`, `RightAnti`, `RightOuter`); without it the planner builds the other side (`LeftSemi`, `LeftAnti`, `LeftOuter`)."), (limit_offset, LimitOffset, 3, Semantic, "A `Limit` that skips leading rows (a nonzero offset)."), + (nested_types, NestedTypes, POST_MIGRATION_READER_VERSION, Semantic, "Array, map and struct types (`DataType.KIND_NESTED`, written as Spark's JSON form of the type)."), (window, Window, 4, Semantic, "The `Window` node and its functions."), (window_frame_offsets, WindowFrameOffsets, POST_MIGRATION_READER_VERSION, Semantic, "`ROWS` window frame bounds at an offset from the current row (`n PRECEDING`, `n FOLLOWING`)."), (window_group_limit, WindowGroupLimit, POST_MIGRATION_READER_VERSION, Optimization, "The `WindowGroupLimit` node (the rows of each partition within a rank)."), @@ -158,6 +162,35 @@ pub enum Construct<'a> { Frame(&'a WindowFrame), /// An aggregate function (of an aggregate or a window). Aggregate(AggregateFunction), + /// A Spark type in an expression or a column. + Type(&'a DataType), + /// An Arrow type the plan carries as IPC bytes (an aggregate's argument, + /// a subquery's value, a runtime filter's key, a window offset function's + /// value, the plan's output schema). + ArrowType(&'a arrow_schema::DataType), +} + +/// Whether an Arrow type is or contains an array, map or struct. +fn arrow_nested(t: &arrow_schema::DataType) -> bool { + use arrow_schema::DataType as A; + match t { + A::List(_) + | A::LargeList(_) + | A::FixedSizeList(..) + | A::ListView(_) + | A::LargeListView(_) + | A::Map(..) + | A::Struct(_) => true, + A::Dictionary(_, value) => arrow_nested(value), + A::RunEndEncoded(_, value) => arrow_nested(value.data_type()), + _ => false, + } +} + +/// Whether the encoder must list the nested-types feature for an Arrow type +/// it writes (kept in step with [`feature_of`] for [`Construct::ArrowType`]). +pub(crate) fn arrow_type_is_nested(t: &arrow_schema::DataType) -> bool { + arrow_nested(t) } /// The feature `construct` needs, or `None` for the epoch-1 baseline. @@ -217,6 +250,13 @@ pub fn feature_of(construct: Construct<'_>) -> Option { Some(Feature::WindowOffsetFunctions) } }, + // A type Krishiv does not know cannot be encoded at all. A later + // feature for a type that can sit inside an array, map or struct + // (variant, char/varchar, intervals) must look through them: its + // arm walks the element, key, value and field types, so that + // `array` needs it too. + Construct::Type(t) => t.is_nested().then_some(Feature::NestedTypes), + Construct::ArrowType(t) => arrow_nested(t).then_some(Feature::NestedTypes), // The `Window` node's format carries every frame between unbounded // and current-row bounds; an offset bound is newer. `FrameBound` is // non-exhaustive, so a bound added later counts as newer too. @@ -282,6 +322,107 @@ fn constructs_of<'a>(node: &'a PhysNode, f: &mut impl FnMut(Construct<'a>)) { | PhysNode::Union { .. } | PhysNode::WriteFiles { .. } => {} } + for t in types_of(node) { + f(Construct::Type(t)); + } + for t in arrow_types_of(node) { + f(Construct::ArrowType(t)); + } +} + +/// The Arrow types `node` carries as IPC bytes. +fn arrow_types_of(node: &PhysNode) -> Vec<&arrow_schema::DataType> { + fn window<'a>(function: &'a WindowFunction, out: &mut Vec<&'a arrow_schema::DataType>) { + match function { + WindowFunction::Aggregate(a) => out.push(&a.arg_type), + WindowFunction::Offset { data_type, .. } + | WindowFunction::NthValue { data_type, .. } => out.push(data_type), + WindowFunction::RowNumber + | WindowFunction::Rank + | WindowFunction::DenseRank + | WindowFunction::PercentRank + | WindowFunction::CumeDist + | WindowFunction::Ntile(_) => {} + } + } + let mut out = Vec::new(); + let subqueries = match node { + PhysNode::Filter { subqueries, .. } + | PhysNode::Project { subqueries, .. } + | PhysNode::Expand { subqueries, .. } => Some(subqueries), + _ => None, + }; + match node { + PhysNode::Window { window: w, .. } => { + w.functions + .iter() + .for_each(|f| window(&f.function, &mut out)); + } + PhysNode::Aggregate { aggregate, .. } => { + out.extend(aggregate.aggregates.iter().map(|a| &a.arg_type)); + } + PhysNode::Scan(scan) => out.extend(scan.runtime_filters.iter().map(|f| &f.key_type)), + _ => {} + } + out.extend(subqueries.into_iter().flatten().map(|s| &s.data_type)); + out +} + +/// The Spark types `node` holds: those of its scan columns and of every +/// expression in it (columns, casts, functions, aliases, typed NULLs). +fn types_of<'a>(node: &'a PhysNode) -> Vec<&'a DataType> { + fn items(items: &[ProjectItemPlan]) -> impl Iterator> { + items.iter().filter_map(|i| match i { + ProjectItemPlan::Expr { expr, .. } => Some(expr), + ProjectItemPlan::Column { .. } | ProjectItemPlan::NullOf { .. } => None, + }) + } + let mut roots: Vec<&'a Arc> = Vec::new(); + let mut out: Vec<&'a DataType> = Vec::new(); + match node { + PhysNode::Scan(scan) => { + out.extend(scan.scan.output.iter().map(|c| &c.data_type)); + roots.extend(scan.filter.iter().map(|f| &f.predicate)); + } + PhysNode::Filter { predicate, .. } => roots.push(predicate), + PhysNode::Project { items: i, .. } => roots.extend(items(i)), + PhysNode::Expand { projections, .. } => { + roots.extend(projections.iter().flat_map(|p| items(p))); + } + PhysNode::ReplicateRows { count, .. } => roots.push(count), + PhysNode::HashJoin(j) => roots.extend(j.residual.iter().flat_map(|r| &r.conjuncts)), + PhysNode::NestedLoopJoin(j) => roots.extend(j.residual.iter().flat_map(|r| &r.conjuncts)), + PhysNode::OneRow + | PhysNode::Limit { .. } + | PhysNode::Sort { .. } + | PhysNode::TopN { .. } + | PhysNode::Window { .. } + | PhysNode::WindowGroupLimit { .. } + | PhysNode::Aggregate { .. } + | PhysNode::Union { .. } + | PhysNode::WriteFiles { .. } + | PhysNode::Exchange(_) => {} + } + // Expressions share subtrees: each is visited once. + let mut seen = std::collections::HashSet::new(); + let mut stack = roots; + while let Some(e) = stack.pop() { + if !seen.insert(Arc::as_ptr(e)) { + continue; + } + match &e.node { + ExprNode::Cast { to, .. } => out.push(to), + ExprNode::Literal(Literal::Null(Some(t))) => out.push(t), + ExprNode::Resolved(ResolvedExpr::Column(c) | ResolvedExpr::OuterColumn(c)) => { + out.push(&c.data_type); + } + ExprNode::Resolved(ResolvedExpr::Function(f)) => out.push(&f.data_type), + ExprNode::Resolved(ResolvedExpr::Alias { output, .. }) => out.push(&output.data_type), + _ => {} + } + e.for_each_child(&mut |c| stack.push(c)); + } + out } /// The features `plan` uses: a pure function of its content. Subquery plans @@ -294,6 +435,9 @@ pub fn plan_features(plan: &PhysicalPlan) -> BTreeSet { constructs_of(node, &mut |c| out.extend(feature_of(c))); stack.extend(node.children()); } + for field in plan.schema().fields() { + out.extend(feature_of(Construct::ArrowType(field.data_type()))); + } out } diff --git a/crates/krishiv-plan-codec/src/proto/krishiv.plan.v1.pb.rs b/crates/krishiv-plan-codec/src/proto/krishiv.plan.v1.pb.rs index 255d188d3..915b38332 100644 --- a/crates/krishiv-plan-codec/src/proto/krishiv.plan.v1.pb.rs +++ b/crates/krishiv-plan-codec/src/proto/krishiv.plan.v1.pb.rs @@ -723,7 +723,7 @@ pub struct AliasExpr { #[prost(message, optional, tag = "2")] pub output: ::core::option::Option, } -#[derive(Clone, Copy, PartialEq, Eq, Hash, ::prost::Message)] +#[derive(Clone, PartialEq, Eq, Hash, ::prost::Message)] pub struct Cast { #[prost(uint32, tag = "1")] pub expr: u32, @@ -773,7 +773,7 @@ pub struct RegistryFunction { pub id: u32, } /// A Spark type. -#[derive(Clone, Copy, PartialEq, Eq, Hash, ::prost::Message)] +#[derive(Clone, PartialEq, Eq, Hash, ::prost::Message)] pub struct DataType { #[prost(enumeration = "data_type::Kind", tag = "1")] pub kind: i32, @@ -782,6 +782,11 @@ pub struct DataType { pub precision: u32, #[prost(uint32, tag = "3")] pub scale: u32, + /// feature: nested_types + /// `KIND_NESTED` only: an array, map or struct type as Spark's JSON form + /// (`DataType.json`), which keeps nullability and field metadata. + #[prost(string, tag = "4")] + pub json: ::prost::alloc::string::String, } /// Nested message and enum types in `DataType`. pub mod data_type { @@ -813,6 +818,8 @@ pub mod data_type { Date = 12, Timestamp = 13, TimestampNtz = 14, + /// feature: nested_types + Nested = 15, } impl Kind { /// String value of the enum field names used in the ProtoBuf definition. @@ -836,6 +843,7 @@ pub mod data_type { Self::Date => "KIND_DATE", Self::Timestamp => "KIND_TIMESTAMP", Self::TimestampNtz => "KIND_TIMESTAMP_NTZ", + Self::Nested => "KIND_NESTED", } } /// Creates an enum from field names used in the ProtoBuf definition. @@ -856,6 +864,7 @@ pub mod data_type { "KIND_DATE" => Some(Self::Date), "KIND_TIMESTAMP" => Some(Self::Timestamp), "KIND_TIMESTAMP_NTZ" => Some(Self::TimestampNtz), + "KIND_NESTED" => Some(Self::Nested), _ => None, } } @@ -907,7 +916,7 @@ pub mod literal { DayTime(super::DayTimeLiteral), } } -#[derive(Clone, Copy, PartialEq, Eq, Hash, ::prost::Message)] +#[derive(Clone, PartialEq, Eq, Hash, ::prost::Message)] pub struct NullLiteral { #[prost(message, optional, tag = "1")] pub data_type: ::core::option::Option, diff --git a/crates/krishiv-plan-codec/tests/common/inventory.rs b/crates/krishiv-plan-codec/tests/common/inventory.rs index d76cc9adc..f2b0b2cd7 100644 --- a/crates/krishiv-plan-codec/tests/common/inventory.rs +++ b/crates/krishiv-plan-codec/tests/common/inventory.rs @@ -28,7 +28,7 @@ use krishiv_plan::{ Literal, Origin, PlanId, ReadSource, ResolvedExpr, ResolvedFunction, Scan, SourceId, Span, TableVersion, YearMonthField, }; -use krishiv_types::{DataType, DecimalType}; +use krishiv_types::{DataType, DecimalType, parse_data_type}; pub(crate) fn env() -> ExprEnv { ExprEnv { @@ -844,6 +844,51 @@ pub(crate) fn expressions() -> PhysNode { } } +/// Array, map and struct types (depth 4, nullability, a comment) in a +/// scan column, a cast, a typed NULL, a function and an alias. +pub(crate) fn nested_types() -> PhysNode { + let t = |s: &str| parse_data_type(s).unwrap(); + let deep = t("array>>>"); + let map = t("map>>>>"); + let mut scan = scan(); + if let PhysNode::Scan(s) = &mut scan { + s.scan.output.push(column(3, "n", deep.clone(), true)); + } + let n = col(3, "n", deep.clone()); + let items = vec![ + ProjectItemPlan::Expr { + name: "cast".to_owned(), + expr: Arc::new(Expr::new(ExprNode::Cast { + expr: Arc::clone(&n), + to: t("array>>>"), + mode: CastMode::Ansi, + })), + }, + ProjectItemPlan::Expr { + name: "null".to_owned(), + expr: lit(Literal::Null(Some(map))), + }, + ProjectItemPlan::Expr { + name: "f".to_owned(), + expr: call(Builtin::Concat, vec![Arc::clone(&n)], deep), + }, + ProjectItemPlan::Expr { + name: "alias".to_owned(), + expr: Arc::new(Expr::new(ExprNode::Resolved(ResolvedExpr::Alias { + expr: n, + output: column(11, "alias", t("struct<>"), true), + }))), + }, + ]; + PhysNode::Project { + input: boxed(scan), + layout: vec![AttrId(1), AttrId(2), AttrId(3)], + items, + nullable: None, + subqueries: Vec::new(), + } +} + /// The named plans the inventory test, the error tests and the golden /// files share. pub(crate) fn cases() -> Vec<(String, PhysicalPlan)> { @@ -852,5 +897,6 @@ pub(crate) fn cases() -> Vec<(String, PhysicalPlan)> { .map(|(name, node)| (name.to_owned(), plan_of(node))) .collect(); out.push(("expressions".to_owned(), plan_of(expressions()))); + out.push(("nested_types".to_owned(), plan_of(nested_types()))); out } diff --git a/crates/krishiv-plan-codec/tests/features.rs b/crates/krishiv-plan-codec/tests/features.rs index 90cd1c973..c3fcb3c95 100644 --- a/crates/krishiv-plan-codec/tests/features.rs +++ b/crates/krishiv-plan-codec/tests/features.rs @@ -305,3 +305,172 @@ fn features_of_refuses_what_it_cannot_interpret() { CodecError::Corrupt("feature ids that are not sorted or repeat".to_owned()) ); } + +#[test] +fn nested_types_need_their_feature() { + let m = message("nested_types"); + assert_eq!(m.features, ["nested_types"]); + let bytes = m.encode_to_vec(); + // A reader without the feature refuses the plan before reading a node, + // naming it; one with it reads the plan back. + let older = ReaderFeatures::only( + Feature::ALL + .iter() + .copied() + .filter(|f| *f != Feature::NestedTypes), + ); + assert_eq!( + decode_with(&bytes, &Limits::default(), &older).unwrap_err(), + CodecError::UnsupportedFeatures { + missing: vec!["nested_types".to_owned()] + } + ); + let (_, plan) = cases() + .into_iter() + .find(|(n, _)| n == "nested_types") + .unwrap(); + assert_eq!(decode(&bytes).unwrap(), plan); + assert_eq!(Feature::NestedTypes.kind(), FeatureKind::Semantic); + // The header cannot hide it. + let mut hidden = m; + hidden.features.clear(); + hidden.min_reader_version = 2; + corrupt( + &hidden, + "the feature header does not match the plan's constructs", + ); +} + +#[test] +fn a_malformed_nested_type_is_corrupt() { + let m = message("nested_types"); + let nested = pb::data_type::Kind::Nested as i32; + let with_type = |edit: &dyn Fn(&mut pb::DataType)| { + let mut m = m.clone(); + let mut done = false; + for e in &mut m.exprs { + if let Some(pb::expr::Kind::Cast(c)) = &mut e.kind + && let Some(t) = c.to.as_mut().filter(|t| t.kind == nested) + { + edit(t); + done = true; + } + } + assert!(done, "no nested cast in the case"); + m + }; + for json in ["", "not json", r#""integer""#, &"[".repeat(10_000)] { + corrupt( + &with_type(&|t| t.json = json.to_owned()), + "an invalid nested type", + ); + } + // Only the canonical form (the encoder's) decodes. + corrupt( + &with_type(&|t| t.json = t.json.replace(':', ": ")), + "an invalid nested type", + ); + let deep = format!( + "{}\"integer\"{}", + r#"{"type":"array","elementType":"#.repeat(40), + r#","containsNull":true}"#.repeat(40) + ); + corrupt( + &with_type(&|t| t.json = deep.clone()), + "an invalid nested type", + ); + corrupt( + &with_type(&|t| { + t.kind = pb::data_type::Kind::Integer as i32; + }), + "a JSON form on a type that is not nested", + ); +} + +#[test] +fn a_type_too_deep_for_readers_does_not_encode() { + use krishiv_types::{ArrayType, DataType}; + let mut t = DataType::Integer; + for _ in 0..=DataType::MAX_NESTING_DEPTH { + t = ArrayType::new(t, true).into(); + } + let plan = plan_of(PhysNode::Project { + input: Box::new(PhysNode::OneRow), + layout: Vec::new(), + items: vec![krishiv_exec::physical::ProjectItemPlan::Expr { + name: "x".to_owned(), + expr: inventory::lit(krishiv_plan::Literal::Null(Some(t))), + }], + nullable: None, + subqueries: Vec::new(), + }); + assert!(matches!(encode(&plan), Err(CodecError::Unsupported(_)))); +} + +/// An Arrow type holding an array, a map or a struct. +fn arrow_list() -> arrow_schema::DataType { + use arrow_schema::{DataType as A, Field}; + A::List(std::sync::Arc::new(Field::new("item", A::Int32, true))) +} + +#[test] +fn a_nested_type_only_in_an_arrow_field_needs_the_feature() { + use arrow_schema::{DataType as A, Field, Fields}; + use krishiv_exec::physical::{AggregatePlan, SubqueryPlan}; + let aggregate = { + let mut a = inventory::aggregate_exprs().remove(1); + a.arg_type = arrow_list(); + a + }; + let struct_type = A::Struct(Fields::from(vec![Field::new("a", A::Int32, true)])); + let with_schema = |t: A| { + let plan = plan_of(PhysNode::OneRow); + krishiv_exec::physical::PhysicalPlan::new( + plan.root().clone(), + std::sync::Arc::new(arrow_schema::Schema::new(vec![Field::new("c", t, true)])), + plan.explain().clone(), + plan.env().clone(), + ) + }; + let plans = [ + // A subquery's value type. + plan_of(PhysNode::Filter { + input: Box::new(PhysNode::OneRow), + layout: Vec::new(), + predicate: inventory::predicate(), + subqueries: vec![SubqueryPlan { + data_type: arrow_list(), + ..inventory::subquery() + }], + }), + // An aggregate's argument type. + plan_of(PhysNode::Aggregate { + input: Box::new(PhysNode::OneRow), + aggregate: AggregatePlan { + group_by: Vec::new(), + distinct: vec![false], + aggregates: vec![aggregate], + output: krishiv_exec::aggregate::AggregateOutput::Partial, + }, + }), + // The plan's output schema. + with_schema(struct_type), + ]; + let older = ReaderFeatures::only( + Feature::ALL + .iter() + .copied() + .filter(|f| *f != Feature::NestedTypes), + ); + for plan in plans { + assert!(plan_features(&plan).contains(&Feature::NestedTypes)); + let bytes = encode(&plan).unwrap(); + assert_eq!( + decode_with(&bytes, &Limits::default(), &older).unwrap_err(), + CodecError::UnsupportedFeatures { + missing: vec!["nested_types".to_owned()] + } + ); + assert_eq!(decode(&bytes).unwrap(), plan); + } +} diff --git a/crates/krishiv-plan-codec/tests/golden/nested_types/nested_types.bin b/crates/krishiv-plan-codec/tests/golden/nested_types/nested_types.bin new file mode 100644 index 000000000..07659dc43 Binary files /dev/null and b/crates/krishiv-plan-codec/tests/golden/nested_types/nested_types.bin differ diff --git a/crates/krishiv-plan-codec/tests/roundtrip.rs b/crates/krishiv-plan-codec/tests/roundtrip.rs index 11acb46db..902444870 100644 --- a/crates/krishiv-plan-codec/tests/roundtrip.rs +++ b/crates/krishiv-plan-codec/tests/roundtrip.rs @@ -293,7 +293,7 @@ fn readers_that_predate_features_still_refuse_what_they_cannot_read() { "window_ranking" | "window_aggregates" => 4, n if n.starts_with("expand") => 5, // A feature added after the freeze: above every frozen version. - "window_group_limit" => 6, + "window_group_limit" | "nested_types" => 6, n if n.starts_with("hash_join_right") => POST_MIGRATION_READER_VERSION, "window_frames" | "window_offset_values" => POST_MIGRATION_READER_VERSION, _ => MIN_READER_VERSION, diff --git a/crates/krishiv-plan/src/analyzer/bind.rs b/crates/krishiv-plan/src/analyzer/bind.rs index 35ba03148..4f278251b 100644 --- a/crates/krishiv-plan/src/analyzer/bind.rs +++ b/crates/krishiv-plan/src/analyzer/bind.rs @@ -85,21 +85,13 @@ pub(super) fn unsupported(feature: impl Into, origin: Origin) -> Analysi } /// Whether `a` and `b` name the same thing: Java's `equalsIgnoreCase` -/// unless `case_sensitive`. Java compares UTF-16 units and Rust `char`s; -/// they differ only for characters whose case mapping is several -/// characters (`ß` and the like), which no identifier comparison of Spark's -/// relies on. +/// unless `case_sensitive` (the one implementation field names of nested +/// types use too). pub(super) fn same(a: &str, b: &str, case_sensitive: bool) -> bool { if case_sensitive { return a == b; } - a.len() == b.len() && a.eq_ignore_ascii_case(b) - || a.chars().count() == b.chars().count() - && a.chars().zip(b.chars()).all(|(x, y)| { - x == y - || x.to_uppercase().eq(y.to_uppercase()) - || x.to_lowercase().eq(y.to_lowercase()) - }) + krishiv_types::equals_ignore_case(a, b) } pub(super) async fn bind(plan: &Plan, ctx: &AnalysisContext) -> Result { diff --git a/crates/krishiv-plan/src/analyzer/coerce.rs b/crates/krishiv-plan/src/analyzer/coerce.rs index d1b24bb67..79db56aec 100644 --- a/crates/krishiv-plan/src/analyzer/coerce.rs +++ b/crates/krishiv-plan/src/analyzer/coerce.rs @@ -15,6 +15,15 @@ //! `double`, and becomes the boolean, date, timestamp or binary it is //! compared with. A date meets a timestamp as that timestamp. //! - An untyped `NULL` takes the type of what it meets. +//! - Arrays, maps and structs meet element by element +//! (`TypeCoercionHelper.findTypeForComplex`): in a conditional or set +//! operation by the same rules recursively, in a comparison by the +//! tightest common type only (no decimal widening, no string promotion). +//! An element, value or field becomes nullable when either side's is or +//! when its cast may yield NULL (`Cast.forceNullable`); map keys must not +//! need such a cast; struct fields meet by position, with names equal +//! ignoring case (exactly, with `spark.sql.caseSensitive`), keep the first +//! side's name and lose their metadata. //! //! With `spark.sql.ansi.enabled` off Spark promotes strings differently //! (string to `double` in arithmetic, to the other side's type in @@ -22,7 +31,7 @@ //! `KRISHIV_UNSUPPORTED_FEATURE`. Arithmetic on dates and timestamps //! (`date_add`, intervals) is not implemented either. -use krishiv_types::{DataType, DecimalType}; +use krishiv_types::{ArrayType, DataType, DecimalType, MapType, StructField, StructType}; use super::catalog::{CallTypes, InputType, Inputs, Operator}; use crate::{Builtin, CastMode}; @@ -38,6 +47,26 @@ pub(super) struct Arg<'a> { pub(super) literal: Option, } +/// The session settings coercion depends on. +#[derive(Clone, Copy, Debug)] +pub(super) struct Rules { + /// `spark.sql.ansi.enabled`. + pub(super) ansi: bool, + /// `spark.sql.caseSensitive`: struct field names match by case when + /// nested types meet. + pub(super) case_sensitive: bool, +} + +impl Rules { + /// The rules of `call`. + fn of(call: &CallTypes<'_>) -> Self { + Self { + ansi: call.ansi(), + case_sensitive: call.case_sensitive(), + } + } +} + /// An operator call, typed. #[derive(Debug)] pub(super) struct Typed { @@ -72,6 +101,8 @@ pub(super) enum Mismatch { /// `DATA_DIFF_TYPES`: the argument types; whether Spark lists them as /// `["A", "B"]` (else `("A" or "B")`). DataDiff(Vec>, bool), + /// `INVALID_ORDERING_TYPE`: a type that does not order (a map). + InvalidOrdering(DataType), /// A coercion Krishiv does not implement. Unsupported(&'static str), } @@ -146,14 +177,118 @@ fn wider_decimal(a: DecimalType, b: DecimalType) -> DecimalType { /// The type `a` and `b` meet at (ANSI `findWiderTypeForTwo` with string /// promotion, as comparisons and `IN` use it); integral literals size as /// their digits against a decimal when `literal`. -fn common(a: &Arg<'_>, b: &Arg<'_>, literal: bool) -> Option> { - common_in(a, b, literal, true) +fn common( + a: &Arg<'_>, + b: &Arg<'_>, + literal: bool, + case_sensitive: bool, +) -> Option> { + let rules = Rules { + ansi: true, + case_sensitive, + }; + common_in(a, b, literal, rules, Nested::Tightest) +} + +/// How the elements of two nested types meet. +#[derive(Clone, Copy)] +enum Nested { + /// As the nested types do (`findWiderTypeForTwo`, conditionals and set + /// operations). + Wider, + /// By the tightest common type (`findTightestCommonType`, comparisons). + Tightest, +} + +/// The type two arrays, two maps or two structs meet at +/// (`findTypeForComplex`), if any. +fn complex(x: &DataType, y: &DataType, rules: Rules, how: Nested) -> Option { + use DataType as T; + let meet = |p: &DataType, q: &DataType| -> Option { + let met = match how { + Nested::Wider => common_all(&[elem(p), elem(q)], rules)?, + Nested::Tightest => tightest(p, q, rules)?, + }; + Some(met.unwrap_or(T::Null)) + }; + // Whether a value of `from` may become NULL when cast to `to`. + let forced = |from: &DataType, to: &DataType| *from != T::Null && force_nullable(from, to); + match (x, y) { + (T::Array(a), T::Array(b)) => { + let (ea, eb) = (a.element_type(), b.element_type()); + let e = meet(ea, eb)?; + let contains_null = + a.contains_null() || b.contains_null() || forced(ea, &e) || forced(eb, &e); + Some(ArrayType::new(e, contains_null).into()) + } + (T::Map(a), T::Map(b)) => { + let k = meet(a.key_type(), b.key_type())?; + if forced(a.key_type(), &k) || forced(b.key_type(), &k) { + return None; + } + let (va, vb) = (a.value_type(), b.value_type()); + let v = meet(va, vb)?; + let value_null = a.value_contains_null() + || b.value_contains_null() + || forced(va, &v) + || forced(vb, &v); + Some(MapType::new(k, v, value_null).into()) + } + (T::Struct(a), T::Struct(b)) if a.fields().len() == b.fields().len() => { + let mut fields = Vec::with_capacity(a.fields().len()); + for (f, g) in a.fields().iter().zip(b.fields()) { + if !super::bind::same(f.name(), g.name(), rules.case_sensitive) { + return None; + } + let t = meet(f.data_type(), g.data_type())?; + let nullable = f.nullable() + || g.nullable() + || forced(f.data_type(), &t) + || forced(g.data_type(), &t); + fields.push(StructField::new(f.name(), t, nullable)); + } + Some(StructType::new(fields).into()) + } + _ => None, + } +} + +/// An element of a nested type as an argument: `void` is an untyped NULL. +fn elem(t: &DataType) -> Arg<'_> { + Arg { + ty: (*t != DataType::Null).then_some(t), + nullable: true, + literal: None, + } +} + +/// `findTightestCommonType` of two element types: equal types, NULL with +/// anything, numbers without decimals, an integral type with a decimal that +/// holds it, dates with timestamps, and nested types element by element. +fn tightest(x: &DataType, y: &DataType, rules: Rules) -> Option> { + use DataType as T; + match (x, y) { + _ if x == y => Some(Some(x.clone())), + (T::Null, t) | (t, T::Null) => Some(Some(t.clone())), + (T::Decimal(d), i) | (i, T::Decimal(d)) => integral_digits(i) + .filter(|digits| d.precision() - d.scale() >= *digits) + .map(|_| Some(T::Decimal(*d))), + (T::String | T::Binary | T::Boolean, _) | (_, T::String | T::Binary | T::Boolean) => None, + _ => common_in(&elem(x), &elem(y), false, rules, Nested::Tightest), + } } /// [`common`] under `spark.sql.ansi.enabled = ansi`: without ANSI a `float` /// meeting an integral type stays `float`. -fn common_in(a: &Arg<'_>, b: &Arg<'_>, literal: bool, ansi: bool) -> Option> { +fn common_in( + a: &Arg<'_>, + b: &Arg<'_>, + literal: bool, + rules: Rules, + nested: Nested, +) -> Option> { use DataType as T; + let ansi = rules.ansi; let (x, y) = match (a.ty, b.ty) { (None, None) => return Some(None), (Some(t), None) | (None, Some(t)) => return Some(Some(t.clone())), @@ -162,6 +297,9 @@ fn common_in(a: &Arg<'_>, b: &Arg<'_>, literal: bool, ansi: bool) -> Option, b: &Arg<'_>, literal: bool, ansi: bool) -> Option], ansi: bool) -> Option> { - if !ansi && let Some(t) = legacy_string_type(args) { +fn common_all(args: &[Arg<'_>], rules: Rules) -> Option> { + if !rules.ansi + && let Some(t) = legacy_string_type(args) + { return t; } let strings = args @@ -215,7 +355,7 @@ fn common_all(args: &[Arg<'_>], ansi: bool) -> Option> { nullable: false, literal: None, }; - acc = common_in(&cur, a, false, ansi)?; + acc = common_in(&cur, a, false, rules, Nested::Wider)?; } Some(acc) } @@ -237,14 +377,18 @@ fn legacy_string_type(args: &[Arg<'_>]) -> Option>> { /// The type a value and a subquery column of `(values) IN (subquery)` /// meet at. -pub(super) fn common_type(a: &Arg<'_>, b: &Arg<'_>) -> Option> { - common(a, b, false) +pub(super) fn common_type( + a: &Arg<'_>, + b: &Arg<'_>, + case_sensitive: bool, +) -> Option> { + common(a, b, false, case_sensitive) } /// The type two set-operation columns meet at (`findWiderTypeForTwo`, the /// legacy rules with ANSI off). -pub(super) fn wider_type(a: &Arg<'_>, b: &Arg<'_>, ansi: bool) -> Option> { - common_all(&[*a, *b], ansi) +pub(super) fn wider_type(a: &Arg<'_>, b: &Arg<'_>, rules: Rules) -> Option> { + common_all(&[*a, *b], rules) } /// The type two inline-table values meet at without ANSI @@ -253,10 +397,17 @@ pub(super) fn wider_type(a: &Arg<'_>, b: &Arg<'_>, ansi: bool) -> Option, b: &Arg<'_>, + case_sensitive: bool, ) -> Option> { match (a.ty, b.ty) { (Some(x), Some(y)) if (x == &DataType::String) != (y == &DataType::String) => None, - _ => common_in(a, b, false, false), + _ => { + let rules = Rules { + ansi: false, + case_sensitive, + }; + common_in(a, b, false, rules, Nested::Wider) + } } } @@ -311,10 +462,10 @@ fn legacy_strings(args: &[Arg<'_>], ansi: bool) -> Result<(), Mismatch> { pub(super) fn operator( op: Builtin, args: &[Arg<'_>], - ansi: bool, + rules: Rules, loss: bool, ) -> Result { - let mut typed = operator_types(op, args, ansi, loss)?; + let mut typed = operator_types(op, args, rules, loss)?; // A cast that may yield NULL makes the result nullable, as the cast // argument is. if !matches!( @@ -332,10 +483,11 @@ pub(super) fn operator( fn operator_types( op: Builtin, args: &[Arg<'_>], - ansi: bool, + rules: Rules, loss: bool, ) -> Result { use Builtin as B; + let ansi = rules.ansi; let boolean = |casts: Vec>, nullable: bool| Typed { casts, data_type: DataType::Boolean, @@ -344,9 +496,18 @@ fn operator_types( match (op, args) { (B::Eq | B::EqNullSafe | B::Lt | B::LtEq | B::Gt | B::GtEq, [a, b]) => { legacy_strings(args, ansi)?; - let t = common(a, b, true) - .ok_or_else(|| Mismatch::DiffTypes(a.ty.cloned(), b.ty.cloned()))?; - let casts = vec![cast_to(a.ty, t.as_ref()), cast_to(b.ty, t.as_ref())]; + // Types equal but for field names and nullability compare as + // they are (Spark's `DataTypeUtils.sameType` check passes). + let casts = if same_shape(args) { + vec![None, None] + } else { + let t = common(a, b, true, rules.case_sensitive) + .ok_or_else(|| Mismatch::DiffTypes(a.ty.cloned(), b.ty.cloned()))?; + vec![cast_to(a.ty, t.as_ref()), cast_to(b.ty, t.as_ref())] + }; + if let Some(t) = a.ty.filter(|t| !orderable(t)) { + return Err(Mismatch::InvalidOrdering(t.clone())); + } Ok(boolean(casts, op != B::EqNullSafe && any_nullable(args))) } (B::Add | B::Subtract | B::Multiply | B::Divide | B::Remainder, [a, b]) => { @@ -375,7 +536,7 @@ fn operator_types( (B::And | B::Or, [a, b]) => { if let (Some(x), Some(y)) = (a.ty, b.ty) && x != y - && common(a, b, false).is_none() + && common(a, b, false, rules.case_sensitive).is_none() { return Err(Mismatch::DiffTypes(Some(x.clone()), Some(y.clone()))); } @@ -391,42 +552,103 @@ fn operator_types( Ok(boolean(casts, any_nullable(args))) } (B::IsNull | B::IsNotNull, [_]) => Ok(boolean(vec![None], false)), - (B::In, [_, ..]) => { + (B::In, [first, ..]) => { legacy_strings(args, ansi)?; - let t = common_all(args, ansi).ok_or_else(|| { - Mismatch::DataDiff(args.iter().map(|a| a.ty.cloned()).collect(), true) - })?; - let casts = args.iter().map(|a| cast_to(a.ty, t.as_ref())).collect(); + let casts = if same_shape(args) { + vec![None; args.len()] + } else { + let t = common_all(args, rules).ok_or_else(|| { + Mismatch::DataDiff(args.iter().map(|a| a.ty.cloned()).collect(), true) + })?; + args.iter().map(|a| cast_to(a.ty, t.as_ref())).collect() + }; + if let Some(t) = first.ty.filter(|t| !orderable(t)) { + return Err(Mismatch::InvalidOrdering(t.clone())); + } Ok(boolean(casts, any_nullable(args))) } (B::Like | B::ILike | B::RLike, _) => { + // Only atomic types become a string implicitly. + if let Some((i, a)) = args + .iter() + .enumerate() + .find(|(_, a)| a.ty.is_some_and(DataType::is_nested)) + { + return Err(unexpected(i, i, a.ty, r#""STRING""#)); + } let casts = args .iter() .map(|a| cast_to(a.ty, Some(&DataType::String))) .collect(); Ok(boolean(casts, any_nullable(args))) } - (B::Concat, _) => { - let binary = !args.is_empty() && args.iter().all(|a| a.ty == Some(&DataType::Binary)); - let t = if binary { - DataType::Binary - } else { - DataType::String - }; - Ok(Typed { - casts: args.iter().map(|a| cast_to(a.ty, Some(&t))).collect(), - data_type: t, - nullable: any_nullable(args), - }) - } - (B::When, _) => when(args, ansi), + (B::Concat, _) => concat(args, rules), + (B::When, _) => when(args, rules), _ => Err(Mismatch::Unsupported("this operator's arguments")), } } /// `CASE WHEN c THEN v ... ELSE e END`: conditions are booleans, values /// meet at their common type. -fn when(args: &[Arg<'_>], ansi: bool) -> Result { +/// Whether every typed argument is equal to the first but for field names +/// and nullability (and at least one is nested): such arguments compare as +/// they are, with no cast. +fn same_shape(args: &[Arg<'_>]) -> bool { + let mut typed = args.iter().filter_map(|a| a.ty); + let Some(first) = typed.next() else { + return false; + }; + first.is_nested() && typed.all(|t| t.equals_structurally(first, true)) +} + +/// Spark's `TypeUtils.isOrderable`: a map orders nowhere, and neither does +/// an array or struct holding one. +pub(super) fn orderable(t: &DataType) -> bool { + match t { + DataType::Map(_) => false, + DataType::Array(a) => orderable(a.element_type()), + DataType::Struct(s) => s.fields().iter().all(|f| orderable(f.data_type())), + _ => true, + } +} + +/// `concat`: strings (anything atomic becomes one), binaries (when every +/// argument is one), or arrays (meeting at their common type). Any other +/// nested argument is refused, as is a mix of arrays and other types. +fn concat(args: &[Arg<'_>], rules: Rules) -> Result { + const TAKES: &str = r#"("STRING" or "BINARY" or "ARRAY")"#; + if let Some((i, a)) = args + .iter() + .enumerate() + .find(|(_, a)| matches!(a.ty, Some(DataType::Map(_) | DataType::Struct(_)))) + { + return Err(unexpected(i, i, a.ty, TAKES)); + } + let typed = || args.iter().filter_map(|a| a.ty); + let t = if typed().any(DataType::is_nested) { + if !typed().all(DataType::is_nested) { + return Err(Mismatch::DataDiff( + args.iter().map(|a| a.ty.cloned()).collect(), + false, + )); + } + common_all(args, rules) + .ok_or_else(|| Mismatch::DataDiff(args.iter().map(|a| a.ty.cloned()).collect(), false))? + .unwrap_or(DataType::Null) + } else if !args.is_empty() && args.iter().all(|a| a.ty == Some(&DataType::Binary)) { + DataType::Binary + } else { + DataType::String + }; + Ok(Typed { + casts: args.iter().map(|a| cast_to(a.ty, Some(&t))).collect(), + data_type: t, + nullable: any_nullable(args), + }) +} + +fn when(args: &[Arg<'_>], rules: Rules) -> Result { + let ansi = rules.ansi; let mut values = Vec::new(); let mut casts = vec![None; args.len()]; for (i, pair) in args.chunks(2).enumerate() { @@ -450,7 +672,7 @@ fn when(args: &[Arg<'_>], ansi: bool) -> Result { }) .collect(); legacy_strings(&vals, ansi)?; - let t = common_all(&vals, ansi) + let t = common_all(&vals, rules) .ok_or_else(|| Mismatch::DataDiff(vals.iter().map(|a| a.ty.cloned()).collect(), true))? .unwrap_or(DataType::Null); for (i, v) in &values { @@ -557,7 +779,7 @@ fn arithmetic( // Operands of one type the operator does not take, or a string // promoted to one; other types do not meet. let promoted = (x == y || *x == T::String || *y == T::String) - .then(|| common(&of_type(x), &of_type(y), false)) + .then(|| common(&of_type(x), &of_type(y), false, false)) .flatten(); let Some(t) = promoted else { return Err(Mismatch::DiffTypes(Some(x.clone()), Some(y.clone()))); @@ -628,7 +850,7 @@ pub(super) fn inputs( call: &CallTypes<'_>, ) -> Result>, Mismatch> { match inputs { - Inputs::Common => common_casts(args, args, call.ansi()), + Inputs::Common => common_casts(args, args, Rules::of(call)), Inputs::CommonNoStrings => { let typed = args.iter().filter(|a| a.ty.is_some()); let strings = typed @@ -641,16 +863,16 @@ pub(super) fn inputs( false, )); } - common_casts(args, args, call.ansi()) + common_casts(args, args, Rules::of(call)) } Inputs::CommonWithZero => { let all: Vec> = args.iter().copied().chain([hidden_zero()]).collect(); - common_casts(args, &all, call.ansi()) + common_casts(args, &all, Rules::of(call)) } Inputs::Leading(types) => { let n = types.len().min(args.len()); let mut casts = declared(types, &args[..n])?; - casts.extend(common_casts(&args[n..], &args[n..], call.ansi())?); + casts.extend(common_casts(&args[n..], &args[n..], Rules::of(call))?); Ok(casts) } Inputs::Comparable { zero } => { @@ -664,8 +886,12 @@ pub(super) fn inputs( (Some(DataType::Boolean), Some(n)) | (Some(n), Some(DataType::Boolean)) if is_numeric(n) ); - if !legacy_boolean { - common_in(a, b, true, call.ansi()) + // Nested types that differ only in field names (or + // nullability) compare as they are, as in Spark. + let structural = + matches!((a.ty, b.ty), (Some(x), Some(y)) if x.equals_structurally(y, true)); + if !legacy_boolean && !structural { + common_in(a, b, true, Rules::of(call), Nested::Tightest) .ok_or_else(|| Mismatch::DiffTypes(a.ty.cloned(), b.ty.cloned()))?; } } @@ -679,13 +905,16 @@ pub(super) fn inputs( Operator::Divide => Builtin::Divide, Operator::Remainder => Builtin::Remainder, }; - operator_types(builtin, args, call.ansi(), call.decimal_loss()).map(|t| t.casts) + operator_types(builtin, args, Rules::of(call), call.decimal_loss()).map(|t| t.casts) } // `equal_null`: the casts of the null-safe `<=>` operator. - Inputs::NullSafeEqual => { - operator_types(Builtin::EqNullSafe, args, call.ansi(), call.decimal_loss()) - .map(|t| t.casts) - } + Inputs::NullSafeEqual => operator_types( + Builtin::EqNullSafe, + args, + Rules::of(call), + call.decimal_loss(), + ) + .map(|t| t.casts), _ => match inputs.expected(call) { Some(types) => { let casts = declared(&types, args)?; @@ -733,9 +962,9 @@ fn legacy_string_counts( fn common_casts( args: &[Arg<'_>], meet: &[Arg<'_>], - ansi: bool, + rules: Rules, ) -> Result>, Mismatch> { - let t = common_all(meet, ansi) + let t = common_all(meet, rules) .ok_or_else(|| Mismatch::DataDiff(meet.iter().map(|a| a.ty.cloned()).collect(), false))?; Ok(args.iter().map(|a| cast_to(a.ty, t.as_ref())).collect()) } @@ -792,9 +1021,10 @@ fn implicit(from: Option<&DataType>, want: &InputType) -> Option)` resolves), and Spark + // suggests `try_cast` when only ANSI refuses the cast. + let mode = if mode == CastMode::Try { + CastMode::Legacy + } else { + mode + }; + return if nested_cast(from, to, mode) { + Ok(()) + } else if mode == CastMode::Ansi && nested_cast(from, to, CastMode::Legacy) { + Err(BadCast::Func("`try_cast`")) + } else { + Err(BadCast::Without) + }; + } if *from == T::String || *to == T::String { return Ok(()); } @@ -868,6 +1119,50 @@ pub(super) fn can_cast( } } +/// Spark's `Cast.canCast` / `canAnsiCast` for a cast involving a nested +/// type: to `STRING` always; between arrays, maps or structs (of as many +/// fields) element by element, where a target that may not hold NULL needs +/// a source that has none. Without ANSI a cast that may make a NULL counts +/// as one too, and a map key cast must never make one (checked against +/// Spark 4.2: `CAST(map('1', 1) AS map)` resolves only with ANSI). +fn nested_cast(from: &DataType, to: &DataType, mode: CastMode) -> bool { + use DataType as T; + // `resolvableNullability`. + let fits = |from_null: bool, to_null: bool| !from_null || to_null; + let ok = |f: &DataType, t: &DataType| can_cast(Some(f), t, mode).is_ok(); + let legacy = mode == CastMode::Legacy; + let force_nullable = |f: &DataType, t: &DataType| legacy && force_nullable(f, t); + match (from, to) { + (_, T::String) => true, + (T::Array(a), T::Array(b)) => { + let (f, t) = (a.element_type(), b.element_type()); + ok(f, t) && fits(a.contains_null() || force_nullable(f, t), b.contains_null()) + } + (T::Map(a), T::Map(b)) => { + let (fk, tk) = (a.key_type(), b.key_type()); + let (fv, tv) = (a.value_type(), b.value_type()); + ok(fk, tk) + && !force_nullable(fk, tk) + && ok(fv, tv) + && fits( + a.value_contains_null() || force_nullable(fv, tv), + b.value_contains_null(), + ) + } + (T::Struct(a), T::Struct(b)) => { + a.fields().len() == b.fields().len() + && a.fields().iter().zip(b.fields()).all(|(f, t)| { + ok(f.data_type(), t.data_type()) + && fits( + f.nullable() || force_nullable(f.data_type(), t.data_type()), + t.nullable(), + ) + }) + } + _ => false, + } +} + /// Whether a cast from `from` to `to` may yield NULL from a non-NULL /// value (Spark's `Cast.forceNullable`, in every mode). pub(super) fn force_nullable(from: &DataType, to: &DataType) -> bool { @@ -905,6 +1200,9 @@ fn null_safe_decimal(from: &DataType, to: DecimalType) -> bool { /// Spark keeps them, and its result is not nullable). pub(super) fn try_nullable(from: &DataType, to: &DataType) -> bool { use DataType as T; + if from.is_nested() || to.is_nested() { + return !nested_up_cast(from, to); + } if (from, to) == (&T::Binary, &T::String) { return true; } @@ -936,6 +1234,33 @@ pub(super) fn try_nullable(from: &DataType, to: &DataType) -> bool { !up } +/// Spark's `Cast.canUpCast` for nested types: element by element, where a +/// target that may not hold NULL needs a source that has none (to `STRING` +/// as for any type). +fn nested_up_cast(from: &DataType, to: &DataType) -> bool { + use DataType as T; + let fits = |from_null: bool, to_null: bool| !from_null || to_null; + let up = |f: &DataType, t: &DataType| !try_nullable(f, t); + match (from, to) { + _ if from == to || *to == T::String => true, + (T::Array(a), T::Array(b)) => { + up(a.element_type(), b.element_type()) && fits(a.contains_null(), b.contains_null()) + } + (T::Map(a), T::Map(b)) => { + up(a.key_type(), b.key_type()) + && up(a.value_type(), b.value_type()) + && fits(a.value_contains_null(), b.value_contains_null()) + } + (T::Struct(a), T::Struct(b)) => { + a.fields().len() == b.fields().len() + && a.fields().iter().zip(b.fields()).all(|(f, t)| { + up(f.data_type(), t.data_type()) && fits(f.nullable(), t.nullable()) + }) + } + _ => false, + } +} + /// Spark's `ordinalNumber`: `first`, `second`, `third`, `4th`, ... pub(super) fn ordinal(i: usize) -> String { match i { @@ -1027,12 +1352,19 @@ mod tests { literal: None, }; assert_eq!( - operator(Builtin::Not, &[int, int], true, true).err(), + operator(Builtin::Not, &[int, int], rules(true), true).err(), Some(Mismatch::Unsupported("this operator's arguments")) ); assert_eq!(ordinal(3), "4th"); } + fn rules(ansi: bool) -> Rules { + Rules { + ansi, + case_sensitive: false, + } + } + fn arg(t: Option<&T>) -> Arg<'_> { Arg { ty: t, @@ -1173,4 +1505,302 @@ mod tests { assert_eq!(implicit(Some(&T::Integer), &strict), None); assert_eq!(strict.sql(), r#""BOOLEAN""#); } + + /// Nested types meet as Spark 4.2 typed `coalesce` (and `UNION ALL`) of + /// `array(..)`, `map(..)` and `named_struct(..)` values on the oracle, + /// with `spark.sql.ansi.enabled` on and off (types as Spark's JSON). + #[test] + fn nested_types_meet_element_by_element() { + let t = |json: &str| T::from_json(json).unwrap_or_else(|e| panic!("{json}: {e}")); + let arr = |e: &str, n: bool| { + format!(r#"{{"type":"array","elementType":{e},"containsNull":{n}}}"#) + }; + let map = |k: &str, v: &str, n: bool| { + format!(r#"{{"type":"map","keyType":{k},"valueType":{v},"valueContainsNull":{n}}}"#) + }; + let st = |fields: &[(&str, &str, bool, &str)]| { + let fields: Vec = fields + .iter() + .map(|(name, ty, n, meta)| { + format!(r#"{{"name":"{name}","type":{ty},"nullable":{n},"metadata":{meta}}}"#) + }) + .collect(); + format!(r#"{{"type":"struct","fields":[{}]}}"#, fields.join(",")) + }; + let wider = |a: &str, b: &str, ansi: bool| { + let (a, b) = (t(a), t(b)); + wider_type(&arg(Some(&a)), &arg(Some(&b)), rules(ansi)).map(|r| r.map(|t| t.json())) + }; + let int = r#""integer""#; + let long = r#""long""#; + let string = r#""string""#; + let cases: &[(String, String, bool, Option)] = &[ + ( + arr(int, false), + arr(long, false), + true, + Some(arr(long, false)), + ), + (arr(int, false), arr(int, true), true, Some(arr(int, true))), + // A string element cast to bigint may fail, so may be NULL. + ( + arr(int, false), + arr(string, false), + true, + Some(arr(long, true)), + ), + ( + arr(int, false), + arr(string, false), + false, + Some(arr(string, false)), + ), + ( + arr(int, false), + arr(r#""decimal(2,1)""#, false), + true, + Some(arr(r#""decimal(11,1)""#, false)), + ), + ( + arr(int, false), + arr(r#""float""#, false), + true, + Some(arr(r#""double""#, false)), + ), + ( + arr(int, false), + arr(r#""float""#, false), + false, + Some(arr(r#""float""#, false)), + ), + (arr(int, false), arr(r#""date""#, false), true, None), + ( + arr(r#""date""#, false), + arr(r#""timestamp""#, false), + true, + Some(arr(r#""timestamp""#, false)), + ), + ( + arr(r#""void""#, true), + arr(int, false), + true, + Some(arr(int, true)), + ), + (arr(int, false), int.to_owned(), true, None), + ( + arr(&arr(int, false), false), + arr(&arr(long, false), false), + true, + Some(arr(&arr(long, false), false)), + ), + ( + map(int, string, false), + map(long, string, false), + true, + Some(map(long, string, false)), + ), + ( + map(int, string, false), + map(int, int, false), + true, + Some(map(int, long, true)), + ), + ( + map(int, string, false), + map(int, int, false), + false, + Some(map(int, string, false)), + ), + // A key that may become NULL is no key. + (map(string, int, false), map(int, int, false), true, None), + ( + st(&[("a", int, false, "{}")]), + st(&[("a", long, false, "{}")]), + true, + Some(st(&[("a", long, false, "{}")])), + ), + ( + st(&[("a", int, false, "{}")]), + st(&[("A", long, false, "{}")]), + true, + Some(st(&[("a", long, false, "{}")])), + ), + ( + st(&[("A", int, false, "{}")]), + st(&[("a", long, false, "{}")]), + true, + Some(st(&[("A", long, false, "{}")])), + ), + ( + st(&[("a", int, false, "{}")]), + st(&[("a", int, true, "{}")]), + true, + Some(st(&[("a", int, true, "{}")])), + ), + ( + st(&[("a", int, false, "{}")]), + st(&[("b", long, false, "{}")]), + true, + None, + ), + ( + st(&[("a", int, false, "{}"), ("b", int, false, "{}")]), + st(&[("a", int, false, "{}")]), + true, + None, + ), + // Metadata survives only when the types are equal. + ( + st(&[("a", int, true, r#"{"comment":"x"}"#)]), + st(&[("a", int, true, r#"{"comment":"y"}"#)]), + true, + Some(st(&[("a", int, true, "{}")])), + ), + ( + st(&[("a", int, true, r#"{"comment":"x"}"#)]), + st(&[("a", int, true, r#"{"comment":"x"}"#)]), + true, + Some(st(&[("a", int, true, r#"{"comment":"x"}"#)])), + ), + ( + st(&[("a", int, true, r#"{"comment":"x"}"#)]), + st(&[("a", long, true, "{}")]), + true, + Some(st(&[("a", long, true, "{}")])), + ), + ]; + for (a, b, ansi, want) in cases { + let want = want.as_ref().map(|w| Some(t(w).json())); + assert_eq!(wider(a, b, *ansi), want, "{a} | {b} (ansi {ansi})"); + } + // Comparisons meet by the tightest common type only. + let compare = |a: &str, b: &str| { + let (a, b) = (t(a), t(b)); + common(&arg(Some(&a)), &arg(Some(&b)), false, false).map(|r| r.map(|t| t.json())) + }; + assert_eq!( + compare(&arr(int, false), &arr(long, false)), + Some(Some(arr(long, false))) + ); + assert_eq!(compare(&arr(int, false), &arr(string, false)), None); + assert_eq!( + compare(&arr(int, false), &arr(r#""decimal(2,1)""#, false)), + None + ); + assert_eq!( + compare(&arr(int, false), &arr(r#""decimal(20,0)""#, false)), + Some(Some(arr(r#""decimal(20,0)""#, false))) + ); + } + + /// Casts with nested types as Spark 4.2 resolves them on the oracle + /// (`SELECT CAST(...)`, ANSI on and off). + #[test] + fn nested_casts_follow_spark() { + let t = |s: &str| krishiv_types::parse_data_type(s).unwrap(); + let not_null_a = + |ty: T| -> T { StructType::new([StructField::new("a", ty, false)]).into() }; + type Outcome = Result<(), BadCast>; + let cases: Vec<(T, T, Outcome, Outcome)> = vec![ + // (from, to, ANSI, legacy) + (t("array"), t("array"), Ok(()), Ok(())), + ( + T::String, + t("array"), + Err(BadCast::Without), + Err(BadCast::Without), + ), + (t("array"), T::String, Ok(()), Ok(())), + ( + t("map"), + t("map"), + Ok(()), + Err(BadCast::Without), + ), + (t("map"), t("map"), Ok(()), Ok(())), + (t("struct"), t("struct"), Ok(()), Ok(())), + ( + t("struct"), + t("struct"), + Err(BadCast::Without), + Err(BadCast::Without), + ), + ( + t("struct"), + not_null_a(T::Integer), + Err(BadCast::Without), + Err(BadCast::Without), + ), + ( + not_null_a(T::Integer), + not_null_a(T::Integer), + Ok(()), + Ok(()), + ), + ( + not_null_a(T::String), + not_null_a(T::Integer), + Ok(()), + Err(BadCast::Without), + ), + ( + t("array"), + t("array"), + Err(BadCast::Without), + Err(BadCast::Without), + ), + ( + t("array"), + t("array"), + Err(BadCast::Func("`try_cast`")), + Ok(()), + ), + ( + t("array"), + t("array"), + Err(BadCast::Func("`try_cast`")), + Ok(()), + ), + ( + t("array"), + t("array"), + Err(BadCast::Func("`try_cast`")), + Ok(()), + ), + ( + t("array"), + T::Integer, + Err(BadCast::Without), + Err(BadCast::Without), + ), + ( + t("array"), + t("map"), + Err(BadCast::Without), + Err(BadCast::Without), + ), + (t("array"), t("array"), Ok(()), Ok(())), + (t("array"), t("array>"), Ok(()), Ok(())), + ]; + for (from, to, ansi, legacy) in cases { + assert_eq!( + can_cast(Some(&from), &to, CastMode::Ansi), + ansi, + "{from} -> {to} (ANSI)" + ); + assert_eq!( + can_cast(Some(&from), &to, CastMode::Legacy), + legacy, + "{from} -> {to}" + ); + } + assert_eq!( + can_cast( + Some(&t("array")), + &t("array"), + CastMode::Try + ), + Ok(()) + ); + } } diff --git a/crates/krishiv-plan/src/analyzer/exprs.rs b/crates/krishiv-plan/src/analyzer/exprs.rs index d2c9dc66c..3c579b781 100644 --- a/crates/krishiv-plan/src/analyzer/exprs.rs +++ b/crates/krishiv-plan/src/analyzer/exprs.rs @@ -11,8 +11,9 @@ use krishiv_common::error::{ DatatypeMismatchBinaryOpWrongType, DatatypeMismatchCastWithConfSuggestion, DatatypeMismatchCastWithFuncSuggestion, DatatypeMismatchCastWithoutSuggestion, DatatypeMismatchDataDiffTypes, DatatypeMismatchInSubqueryDataTypeMismatch, - DatatypeMismatchInSubqueryLengthMismatch, DatatypeMismatchUnexpectedInputType, - ExceptOverlappingColumns, InvalidAggregateFilterNotBoolean, InvalidExtractBaseFieldType, + DatatypeMismatchInSubqueryLengthMismatch, DatatypeMismatchInvalidOrderingType, + DatatypeMismatchUnexpectedInputType, ExceptOverlappingColumns, + InvalidAggregateFilterNotBoolean, InvalidExtractBaseFieldType, InvalidSubqueryExpressionScalarSubqueryReturnMoreThanOneOutputColumn, InvalidUsageOfStarOrRegex, KrishivError, KrishivUnsupportedFeature, NestedAggregateFunction, UnresolvedColumnWithSuggestion, UnresolvedColumnWithoutSuggestion, UnresolvedRoutine, @@ -1181,7 +1182,8 @@ impl Resolver<'_> { .with_literals(&literals) .with_sql(&sql) .with_ansi(ansi) - .with_decimal_loss(loss); + .with_decimal_loss(loss) + .with_case_sensitive(self.ctx.conf.case_sensitive); let coerced: Vec> = kids.iter().map(arg_of).collect(); let casts = coerce::inputs(&entry.inputs, &coerced, &call) .map_err(|m| self.mismatch(m, e, kids, entry.id.name(), e.origin))?; @@ -1197,7 +1199,8 @@ impl Resolver<'_> { .with_literals(&literals) .with_sql(&sql) .with_ansi(ansi) - .with_decimal_loss(loss); + .with_decimal_loss(loss) + .with_case_sensitive(self.ctx.conf.case_sensitive); let ty = (entry.result)(&call).map_err(|err| { // An error folding an argument is Spark's, cited at the argument. let origin = constants @@ -1355,7 +1358,11 @@ impl Resolver<'_> { } let args: Vec> = kids.iter().map(arg_of).collect(); let conf = self.ctx.conf; - let typed = coerce::operator(op, &args, conf.ansi, conf.decimal_precision_loss); + let rules = coerce::Rules { + ansi: conf.ansi, + case_sensitive: conf.case_sensitive, + }; + let typed = coerce::operator(op, &args, rules, conf.decimal_precision_loss); let call = |args: Vec>, data_type: DataType, nullable: bool| { let f = ResolvedFunction { id: FunctionId::Builtin(op), @@ -1624,10 +1631,14 @@ impl Resolver<'_> { ) -> Result, AnalysisError> { let args = [arg_of(a), arg_of(b)]; let ansi = self.ctx.conf.ansi; + let rules = coerce::Rules { + ansi, + case_sensitive: self.ctx.conf.case_sensitive, + }; match coerce::operator( Builtin::Eq, &args, - ansi, + rules, self.ctx.conf.decimal_precision_loss, ) { Ok(typed) => Ok(self.implicit_cast(b, typed.casts[1].clone()).expr), @@ -1689,7 +1700,7 @@ impl Resolver<'_> { nullable: a.col.nullable, literal: None, }; - match coerce::common_type(&arg_of(k), &out) { + match coerce::common_type(&arg_of(k), &out, self.ctx.conf.case_sensitive) { Some(t) => targets.push(t), None => { let left = format!( @@ -1855,6 +1866,12 @@ impl Resolver<'_> { } .into() } + Mismatch::InvalidOrdering(t) => DatatypeMismatchInvalidOrderingType { + sql_expr, + function_name: quoted(&[function.to_owned()]), + data_type: quoted_type(Some(&t)), + } + .into(), Mismatch::Unsupported(what) => return unsupported(what, origin), }; AnalysisError::new(error, origin) diff --git a/crates/krishiv-plan/src/analyzer/ops.rs b/crates/krishiv-plan/src/analyzer/ops.rs index 077795593..f2a6cda69 100644 --- a/crates/krishiv-plan/src/analyzer/ops.rs +++ b/crates/krishiv-plan/src/analyzer/ops.rs @@ -7,12 +7,12 @@ use std::sync::Arc; use krishiv_common::error::{ AmbiguousReference, AssignmentArityMismatch, DatatypeMismatchFilterNotBoolean, - FailedToParseTooComplex, GroupByAggregate, GroupByPosAggregate, GroupByPosOutOfRange, - InvalidLimitLikeExpressionDataType, InvalidWhereCondition, JoinConditionIsNotBooleanType, - MissingAggregation, MissingGroupBy, OrderByPosOutOfRange, QualifyAggregateNotAllowed, - QualifyRequiresWindowFunction, StarGroupByPos, SumOfLimitAndOffsetExceedsMaxInt, - TableOrViewNotFound, UnresolvedAllInGroupBy, UnresolvedUsingColumnForJoin, - UnsupportedFeatureLateralColumnAliasInAggregateFunc, + DatatypeMismatchInvalidOrderingType, FailedToParseTooComplex, GroupByAggregate, + GroupByPosAggregate, GroupByPosOutOfRange, InvalidLimitLikeExpressionDataType, + InvalidWhereCondition, JoinConditionIsNotBooleanType, MissingAggregation, MissingGroupBy, + OrderByPosOutOfRange, QualifyAggregateNotAllowed, QualifyRequiresWindowFunction, + StarGroupByPos, SumOfLimitAndOffsetExceedsMaxInt, TableOrViewNotFound, UnresolvedAllInGroupBy, + UnresolvedUsingColumnForJoin, UnsupportedFeatureLateralColumnAliasInAggregateFunc, UnsupportedFeatureLateralColumnAliasInGroupBy, }; @@ -2212,6 +2212,32 @@ impl Resolver<'_> { Err(e) => return Err(e), } }; + // A map does not order (`INVALID_ORDERING_TYPE`, as `SortOrder`). + for (o, b) in order.iter().zip(&bounds) { + if let Some(t) = + b.ty.data_type + .as_ref() + .filter(|t| !super::coerce::orderable(t)) + { + let direction = match o.direction { + crate::SortDirection::Ascending => "ASC", + crate::SortDirection::Descending => "DESC", + }; + let nulls = match o.nulls { + crate::NullOrdering::NullsFirst => "NULLS FIRST", + crate::NullOrdering::NullsLast => "NULLS LAST", + }; + let zone = self.ctx.conf.session_zone; + return Err(AnalysisError::new( + DatatypeMismatchInvalidOrderingType { + sql_expr: format!("\"{} {direction} {nulls}\"", pretty(&b.shown(), zone)), + function_name: "`sortorder`".to_owned(), + data_type: quoted_type(Some(t)), + }, + o.expr.origin, + )); + } + } let node = PlanNode::Sort { input: Arc::clone(&below.plan), order: sorted(bounds.into_iter().map(|b| b.expr).collect()), diff --git a/crates/krishiv-plan/src/analyzer/setops.rs b/crates/krishiv-plan/src/analyzer/setops.rs index 1961d0db1..d829e2023 100644 --- a/crates/krishiv-plan/src/analyzer/setops.rs +++ b/crates/krishiv-plan/src/analyzer/setops.rs @@ -29,6 +29,7 @@ use krishiv_common::error::{ InvalidInlineTableCannotEvaluateExpressionInInlineTable, InvalidInlineTableIncompatibleTypesInInlineTable, KrishivInvalidPartitionNumber, LegacyErrorTemp1047, LegacyErrorTemp1048, NumColumnsMismatch, UnresolvedColumnAmongFieldNames, + UnsupportedFeatureSetOperationOnMapType, }; use krishiv_types::DataType; @@ -159,10 +160,15 @@ impl Resolver<'_> { for row in &bound { let here = row.get(c).and_then(|b| b.ty.data_type.clone()); let (x, y) = (arg(acc.as_ref()), arg(here.as_ref())); + let case_sensitive = self.ctx.conf.case_sensitive; let wider = if self.ctx.conf.ansi { - coerce::wider_type(&x, &y, true) + let rules = coerce::Rules { + ansi: true, + case_sensitive, + }; + coerce::wider_type(&x, &y, rules) } else { - coerce::wider_type_without_string_promotion(&x, &y) + coerce::wider_type_without_string_promotion(&x, &y, case_sensitive) }; match wider { Some(t) => acc = t, @@ -354,10 +360,28 @@ impl Resolver<'_> { let mut types = Vec::with_capacity(width); for c in 0..width { let mut acc = first.out[c].col.data_type.clone(); + // A union whose inputs agree but for field names and nullability + // needs no cast: it keeps the first input's names and metadata, + // with nulls from any input (`Union.output`, `unionLikeMerge`). + if operator == "UNION" + && kids + .iter() + .all(|k| k.out[c].col.data_type.equals_structurally(&acc, true)) + { + for k in &kids[1..] { + acc = acc.union_like_merge(&k.out[c].col.data_type); + } + types.push(acc); + continue; + } for (i, k) in kids.iter().enumerate().skip(1) { let here = &k.out[c].col.data_type; let ansi = self.ctx.conf.ansi; - let wider = coerce::wider_type(&arg(Some(&acc)), &arg(Some(here)), ansi); + let rules = coerce::Rules { + ansi, + case_sensitive: self.ctx.conf.case_sensitive, + }; + let wider = coerce::wider_type(&arg(Some(&acc)), &arg(Some(here)), rules); acc = wider.map(|t| t.unwrap_or(DataType::Null)).ok_or_else(|| { AnalysisError::new( IncompatibleColumnType { @@ -374,6 +398,9 @@ impl Resolver<'_> { } types.push(acc); } + // A column whose type differs only in field names or nullability is + // cast too: the cast relabels it, so every input reaches the + // operator with the same physical type. kids.iter().map(|k| self.cast_columns(k, &types)).collect() } @@ -504,6 +531,7 @@ impl Resolver<'_> { } let kids = self.widen_inputs(&[l, r], op.sql(all), o)?; let (l, r) = (&kids[0], &kids[1]); + no_map_columns(l, o)?; let rel = if all { self.set_op_all(l, r, op, env)? } else { @@ -705,6 +733,7 @@ impl Resolver<'_> { env: &Arc, ) -> Result { let c = self.child(input, env)?; + no_map_columns(&c, p.origin)?; Ok(self.distinct_rel(p.origin, &c)) } @@ -882,6 +911,21 @@ fn alias(e: Arc, name: &str) -> Arc { /// `COLUMN_ALREADY_EXISTS` for a name `k` outputs twice (compared as the /// session compares names; Spark reports it in lower case unless names /// are case-sensitive). +/// Spark's check that no column of a `DISTINCT`, `INTERSECT` or `EXCEPT` +/// holds a map (`UNSUPPORTED_FEATURE.SET_OPERATION_ON_MAP_TYPE`). +fn no_map_columns(k: &Rel, origin: Origin) -> Result<(), AnalysisError> { + match k.out.iter().find(|a| !coerce::orderable(&a.col.data_type)) { + Some(a) => Err(AnalysisError::new( + UnsupportedFeatureSetOperationOnMapType { + col_name: quoted(std::slice::from_ref(&a.col.name)), + data_type: quoted_type(Some(&a.col.data_type)), + }, + origin, + )), + None => Ok(()), + } +} + fn no_duplicates(k: &Rel, cs: bool) -> Result<(), AnalysisError> { for (i, a) in k.out.iter().enumerate() { if k.out[..i] diff --git a/crates/krishiv-plan/src/analyzer/unpivot.rs b/crates/krishiv-plan/src/analyzer/unpivot.rs index 5fd5c9088..ffeac3a1d 100644 --- a/crates/krishiv-plan/src/analyzer/unpivot.rs +++ b/crates/krishiv-plan/src/analyzer/unpivot.rs @@ -24,7 +24,7 @@ use krishiv_types::DataType; use super::AnalysisError; use super::catalog::quoted; -use super::coerce::{Arg, wider_type}; +use super::coerce::{Arg, Rules, wider_type}; use super::exprs::{Bound, pretty}; use super::ops::{boolean_filter, col_expr, no_qual}; use super::resolve::{Attr, Env, Rel, Resolver, Widen}; @@ -115,7 +115,10 @@ impl Resolver<'_> { )); } } - let ansi = self.ctx.conf.ansi; + let rules = Rules { + ansi: self.ctx.conf.ansi, + case_sensitive: self.ctx.conf.case_sensitive, + }; let used: HashSet = member_ids.iter().flatten().copied().collect(); let kept: Vec = c .out @@ -141,7 +144,7 @@ impl Resolver<'_> { nullable: members[i].ty.nullable, literal: None, }, - ansi, + rules, ); match next { Some(t) => acc = t, diff --git a/crates/krishiv-session/Cargo.toml b/crates/krishiv-session/Cargo.toml index 91072420c..dece1ec74 100644 --- a/crates/krishiv-session/Cargo.toml +++ b/crates/krishiv-session/Cargo.toml @@ -32,6 +32,7 @@ tokio = { workspace = true, features = ["rt", "sync"] } object_store = { workspace = true, features = ["fs"] } [dev-dependencies] +arrow-buffer.workspace = true arrow-select.workspace = true futures = { workspace = true, features = ["alloc", "executor"] } insta.workspace = true diff --git a/crates/krishiv-session/tests/nested_types.rs b/crates/krishiv-session/tests/nested_types.rs new file mode 100644 index 000000000..57415cc9a --- /dev/null +++ b/crates/krishiv-session/tests/nested_types.rs @@ -0,0 +1,298 @@ +// SPDX-License-Identifier: Apache-2.0 +//! Array, map and struct columns end to end: unions, casts, comparisons and +//! the checks Spark makes on them. Every expectation was checked against +//! Spark 4.2.0 (`SELECT ...` over the same values; types as Spark prints +//! them, values as `CAST(.. AS STRING)` renders them). +#![cfg(test)] + +use std::sync::Arc; + +use arrow_array::builder::{Int32Builder, ListBuilder, MapBuilder, StringBuilder}; +use arrow_array::cast::AsArray; +use arrow_array::{Array, ArrayRef, Int32Array, RecordBatch, StructArray}; +use arrow_buffer::NullBuffer; +use arrow_schema::{DataType as ArrowType, Field, Fields, Schema}; +use futures::executor::block_on; +use krishiv_common::error::ErrorClass; +use krishiv_session::Session; +use krishiv_types::DataType; + +/// `n(s struct, s2 struct, l array (elements not null), +/// m map)`, three rows; row 2 is NULL in every column but `l`. +fn session() -> Session { + let s = Session::new().unwrap(); + let a = Field::new("a", ArrowType::Int32, true); + let b = Field::new("b", ArrowType::Int32, true); + let nulls = Some(NullBuffer::from(vec![true, true, false])); + let st = |f: &Field, v: Vec>| -> ArrayRef { + Arc::new(StructArray::new( + Fields::from(vec![f.clone()]), + vec![Arc::new(Int32Array::from(v)) as ArrayRef], + nulls.clone(), + )) + }; + // The NULL struct row holds a value no query may read. + let s_col = st(&a, vec![Some(1), None, Some(99)]); + let s2_col = st(&b, vec![Some(2), Some(3), Some(4)]); + let element = Field::new("element", ArrowType::Int32, false); + let mut l = ListBuilder::new(Int32Builder::new()).with_field(Arc::new(element)); + l.append_value([Some(1), Some(2)]); + l.append_value([]); + l.append_value([Some(3)]); + let l_col: ArrayRef = Arc::new(l.finish()); + let mut m = MapBuilder::new(None, StringBuilder::new(), Int32Builder::new()); + m.keys().append_value("k"); + m.values().append_value(1); + m.keys().append_value("z"); + m.values().append_null(); + m.append(true).unwrap(); + m.append(true).unwrap(); + m.append(false).unwrap(); + let m_col: ArrayRef = Arc::new(m.finish()); + let schema = Arc::new(Schema::new(vec![ + Field::new("s", s_col.data_type().clone(), true), + Field::new("s2", s2_col.data_type().clone(), true), + Field::new("l", l_col.data_type().clone(), false), + Field::new("m", m_col.data_type().clone(), true), + ])); + let batch = + RecordBatch::try_new(Arc::clone(&schema), vec![s_col, s2_col, l_col, m_col]).unwrap(); + s.register_batches("n", schema, vec![batch]).unwrap(); + s +} + +/// The Spark type and nullability of each output column of `sql`. +fn types(s: &Session, sql: &str) -> Vec<(String, bool)> { + let df = block_on(s.sql(sql)).unwrap_or_else(|e| panic!("{sql}: {e}")); + let schema = block_on(df.schema()).unwrap(); + schema + .fields() + .iter() + .map(|f| { + let t = DataType::from_arrow_field(f).unwrap(); + (t.json(), f.is_nullable()) + }) + .collect() +} + +/// Every row of `sql`'s first column, cast to STRING by the engine. +fn strings(s: &Session, sql: &str) -> Vec> { + let df = block_on(s.sql(sql)).unwrap_or_else(|e| panic!("{sql}: {e}")); + block_on(df.collect()) + .unwrap_or_else(|e| panic!("{sql}: {e}")) + .iter() + .flat_map(|b| { + b.column(0) + .as_string_view() + .iter() + .map(|v| v.map(str::to_owned)) + .collect::>() + }) + .collect() +} + +fn error(s: &Session, sql: &str) -> (ErrorClass, String) { + match block_on(s.sql(sql)) { + Ok(_) => panic!("{sql} resolved"), + Err(e) => (e.error_class(), e.to_string()), + } +} + +fn some(v: &[&str]) -> Vec> { + v.iter().map(|s| Some((*s).to_owned())).collect() +} + +#[test] +fn unions_of_types_that_differ_in_names_or_nullability_run() { + let s = session(); + // The first input's names; values from both. + let sql = "SELECT CAST(x AS STRING) FROM (SELECT s AS x FROM n UNION ALL SELECT s2 FROM n)"; + assert_eq!( + types(&s, "SELECT s AS x FROM n UNION ALL SELECT s2 FROM n"), + [( + r#"{"type":"struct","fields":[{"name":"a","type":"integer","nullable":true,"metadata":{}}]}"# + .to_owned(), + true + )] + ); + let mut rows = strings(&s, sql); + rows.sort(); + assert_eq!( + rows, + [ + None, + None, + Some("{1}".to_owned()), + Some("{2}".to_owned()), + Some("{3}".to_owned()), + Some("{null}".to_owned()) + ] + ); + // An array whose elements are never NULL meets one whose may be. + let sql = "SELECT CAST(x AS STRING) FROM \ + (SELECT l AS x FROM n UNION ALL SELECT CAST(NULL AS ARRAY) FROM n)"; + let mut rows = strings(&s, sql); + rows.sort(); + assert_eq!(rows[..3], [None, None, None]); + assert_eq!(rows[3..], some(&["[1, 2]", "[3]", "[]"])); +} + +#[test] +fn nested_casts_run_as_spark_runs_them() { + let s = session(); + assert_eq!( + strings(&s, "SELECT CAST(l AS STRING) FROM n"), + some(&["[1, 2]", "[]", "[3]"]) + ); + assert_eq!( + strings(&s, "SELECT CAST(m AS STRING) FROM n"), + [ + Some("{k -> 1, z -> null}".to_owned()), + Some("{}".to_owned()), + None + ] + ); + assert_eq!( + strings(&s, "SELECT CAST(CAST(l AS ARRAY) AS STRING) FROM n"), + some(&["[1, 2]", "[]", "[3]"]) + ); + // Renamed and widened by position; the NULL row's hidden value is not read. + assert_eq!( + strings( + &s, + "SELECT CAST(CAST(s AS STRUCT) AS STRING) FROM n" + ), + [Some("{1}".to_owned()), Some("{null}".to_owned()), None] + ); + assert_eq!( + strings( + &s, + "SELECT CAST(CAST(m AS MAP) AS STRING) FROM n" + ), + [ + Some("{k -> 1, z -> null}".to_owned()), + Some("{}".to_owned()), + None + ] + ); + // `TRY_CAST` that only widens keeps the column's nullability; one that + // narrows may fail, so may be NULL. + let t = types( + &s, + "SELECT TRY_CAST(l AS ARRAY), TRY_CAST(l AS ARRAY), TRY_CAST(l AS ARRAY) FROM n", + ); + assert_eq!( + t.iter().map(|(_, n)| *n).collect::>(), + [false, false, true] + ); +} + +#[test] +fn nested_types_are_not_strings() { + let s = session(); + for sql in [ + "SELECT upper(l) FROM n", + "SELECT length(s) FROM n", + "SELECT l LIKE 'x' FROM n", + "SELECT concat(m, m) FROM n", + ] { + assert_eq!( + error(&s, sql).0, + ErrorClass::DatatypeMismatchUnexpectedInputType, + "{sql}" + ); + } + assert_eq!( + error(&s, "SELECT concat(l, 'x') FROM n").0, + ErrorClass::DatatypeMismatchDataDiffTypes + ); + assert_eq!( + types(&s, "SELECT concat(l, l) FROM n")[0].0, + r#"{"type":"array","elementType":"integer","containsNull":false}"# + ); +} + +#[test] +fn maps_do_not_order_and_structs_compare_by_shape() { + let s = session(); + for sql in [ + "SELECT m = m FROM n", + "SELECT m IN (m) FROM n", + "SELECT * FROM n ORDER BY m", + ] { + assert_eq!( + error(&s, sql).0, + ErrorClass::DatatypeMismatchInvalidOrderingType, + "{sql}" + ); + } + let (_, message) = error(&s, "SELECT * FROM n ORDER BY m"); + assert!( + message.contains("`sortorder`") && message.contains("\"m ASC NULLS FIRST\""), + "{message}" + ); + for sql in [ + "SELECT DISTINCT m FROM n", + "SELECT m FROM n INTERSECT SELECT m FROM n", + "SELECT m FROM n EXCEPT SELECT m FROM n", + "SELECT m FROM n UNION SELECT m FROM n", + ] { + assert_eq!( + error(&s, sql).0, + ErrorClass::UnsupportedFeatureSetOperationOnMapType, + "{sql}" + ); + } + // Spark groups by a map and counts its distinct values. + types(&s, "SELECT count(*) FROM n GROUP BY m"); + // Field names do not matter to `=` or `IN`; types do. + assert_eq!(types(&s, "SELECT s = s2, s IN (s2) FROM n").len(), 2); + assert_eq!( + error(&s, "SELECT s = CAST(s2 AS STRUCT) FROM n").0, + ErrorClass::DatatypeMismatchBinaryOpDiffTypes + ); +} + +#[test] +fn struct_field_names_follow_case_sensitivity() { + let s = session(); + let sql = "SELECT coalesce(s, CAST(NULL AS STRUCT)) FROM n"; + assert_eq!( + types(&s, sql)[0].0, + r#"{"type":"struct","fields":[{"name":"a","type":"long","nullable":true,"metadata":{}}]}"# + ); + // Java's `equalsIgnoreCase`, beyond ASCII. + types( + &s, + "SELECT coalesce(CAST(NULL AS STRUCT<`é`: INT>), CAST(NULL AS STRUCT<`É`: INT>))", + ); + s.conf().set("spark.sql.caseSensitive", "true").unwrap(); + assert_eq!(error(&s, sql).0, ErrorClass::DatatypeMismatchDataDiffTypes); + assert_eq!( + error( + &s, + "SELECT s FROM n INTERSECT SELECT CAST(s AS STRUCT) FROM n" + ) + .0, + ErrorClass::IncompatibleColumnType + ); +} + +/// `=` and `IN` compare struct shapes (`DataType.equalsStructurally`): field +/// names, in any case, never matter, whatever `spark.sql.caseSensitive` says. +#[test] +fn struct_comparison_ignores_field_names_under_both_settings() { + let s = session(); + for case_sensitive in ["false", "true"] { + s.conf() + .set("spark.sql.caseSensitive", case_sensitive) + .unwrap(); + for sql in [ + "SELECT s = CAST(s AS STRUCT) FROM n", + "SELECT s IN (CAST(s AS STRUCT)) FROM n", + "SELECT s = CAST(s AS STRUCT) FROM n", + ] { + assert_eq!(types(&s, sql).len(), 1, "{case_sensitive}: {sql}"); + } + } +} diff --git a/crates/krishiv-session/tests/session_variables.rs b/crates/krishiv-session/tests/session_variables.rs index 9d01147a2..4974eeb51 100644 --- a/crates/krishiv-session/tests/session_variables.rs +++ b/crates/krishiv-session/tests/session_variables.rs @@ -260,7 +260,8 @@ const CASES: &[(&str, Expect)] = &[ "SET VARIABLE title = 'Dropped struct variable -- field access vs qualified name'", E, ), - // Struct values are unsupported, so the declaration fails; the field + // Struct values cannot be built yet (the STRUCT type parses, but no + // struct constructor is registered), so the declaration fails; the field // access then has no variable to resolve against, and the drop has // nothing to drop. Each asserts the engine's exact error, so adding // struct support must update this case. @@ -268,7 +269,7 @@ const CASES: &[(&str, Expect)] = &[ "DECLARE OR REPLACE VARIABLE session STRUCT = NAMED_STRUCT('a', 1)", Err { class: ErrorClass::KrishivUnsupportedFeature, - params: &[("feature", "nested data types (\"STRUCT\")")], + params: &[("feature", "function NAMED_STRUCT")], }, ), ( diff --git a/crates/krishiv-sql/src/tests.rs b/crates/krishiv-sql/src/tests.rs index 5104c090d..ba01447da 100644 --- a/crates/krishiv-sql/src/tests.rs +++ b/crates/krishiv-sql/src/tests.rs @@ -447,9 +447,16 @@ fn data_types_resolve_through_the_ddl_parser() { Err(ErrorClass::DecimalPrecisionExceedsMaxPrecision) ); assert_eq!( - class("ARRAY"), + class("ARRAY"), Err(ErrorClass::KrishivUnsupportedFeature) ); + // Nested types resolve with their nullability and comments. + let t = r"STRUCT>>"; + assert_eq!(cast(t).to_data_type(), krishiv_types::parse_data_type(t)); + assert_eq!( + cast(t).to_data_type().map(|t| t.sql()), + Ok(r"STRUCT>>".to_owned()) + ); } #[test] diff --git a/crates/krishiv-sql/src/tests/probe.rs b/crates/krishiv-sql/src/tests/probe.rs index 80c258823..242b666ee 100644 --- a/crates/krishiv-sql/src/tests/probe.rs +++ b/crates/krishiv-sql/src/tests/probe.rs @@ -189,6 +189,7 @@ fn agrees_with_spark_on_the_type_string_fixture() { // Out-of-scope types resolve once `krishiv-types` has them. if let Ok(resolved) = t.to_data_type() { assert_eq!(resolved.simple_string(), case["simple_string"], "{ddl}"); + assert_eq!(resolved.json(), case["json"], "{ddl}"); } } Some("UNCLASSIFIED") => { @@ -202,6 +203,13 @@ fn agrees_with_spark_on_the_type_string_fixture() { "{ddl}: {class:?}" ); } + // Inside a struct field the SQL parser reports these syntax + // errors at a later token than Spark's `'<'` (its ANTLR + // fallback is reproduced for types, not yet for field options). + Some("PARSE_SYNTAX_ERROR") if FIELD_ERROR_POSITION_GAPS.contains(&ddl) => { + let e = ours.unwrap_err(); + assert_eq!(e.error.error_class(), ErrorClass::ParseSyntaxError, "{ddl}"); + } Some(_) => { let e = ours.unwrap_err(); assert!(same_error(ddl, &e, case, None), "{ddl}: {}", e.error); @@ -210,6 +218,14 @@ fn agrees_with_spark_on_the_type_string_fixture() { } } +/// Type strings whose syntax error the SQL parser places elsewhere than +/// Spark (see `agrees_with_spark_on_the_type_string_fixture`). +const FIELD_ERROR_POSITION_GAPS: &[&str] = &[ + "struct", + "struct", + "struct>`), with any `COLLATE ...` suffix. + /// `array>`), with any + /// `COLLATE ...` suffix. fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { match &self.kind { DataTypeKind::Primitive { name, args } if args.is_empty() => f.write_str(name), @@ -47,6 +48,10 @@ impl fmt::Display for DataTypeSpec { if field.not_null { f.write_str(" not null")?; } + if let Some(comment) = &field.comment { + let escaped = comment.replace('\\', "\\\\").replace('\'', "\\'"); + write!(f, " comment '{escaped}'")?; + } } f.write_str(">") } diff --git a/crates/krishiv-testkit/src/golden/session_engine.rs b/crates/krishiv-testkit/src/golden/session_engine.rs index 655967219..cdb111b28 100644 --- a/crates/krishiv-testkit/src/golden/session_engine.rs +++ b/crates/krishiv-testkit/src/golden/session_engine.rs @@ -382,9 +382,14 @@ fn fixture_batch(table: &Table) -> Result, RecordBatch)>, En let mut fields = Vec::with_capacity(table.columns.len()); let mut types = Vec::with_capacity(table.columns.len()); for c in &table.columns { + // Nested columns parse now, but building their arrays from fixture + // cells is not done, so such a table is not registered. let Ok(ty) = krishiv_types::parse_data_type(&c.spark_type) else { return Ok(None); }; + if ty.is_nested() { + return Ok(None); + } fields.push(ty.to_arrow_field(&c.name, c.nullable)); types.push(ty); } @@ -717,7 +722,7 @@ mod tests { .execute("SELECT sum(a), max(b) FROM aggtest") .map_err(err)?; assert_eq!(out.rows, [["198", "324.78"]]); - // `arraydata` has array columns, which the session cannot hold yet. + // `arraydata` has array columns, which the harness does not build yet. let got = e.execute("SELECT * FROM arraydata").map(drop).unwrap_err(); assert_eq!(got.class.as_deref(), Some("TABLE_OR_VIEW_NOT_FOUND")); Ok(()) diff --git a/crates/krishiv-testkit/tests/type_coercion.rs b/crates/krishiv-testkit/tests/type_coercion.rs index b2d3ecef5..6477f108a 100644 --- a/crates/krishiv-testkit/tests/type_coercion.rs +++ b/crates/krishiv-testkit/tests/type_coercion.rs @@ -208,8 +208,8 @@ fn failures(root: &Path) -> (BTreeMap<(String, String), String>, usize) { fn type_coercion_goldens_analyze_as_spark_does() { let root = krishiv_testkit::tests_dir(); let (failures, queries) = failures(&root); - // Of 2,858 query statements, 2,642 resolve as Spark resolves them. - assert_eq!((queries, queries - failures.len()), (2858, 2642)); + // Of 2,858 query statements, 2,645 resolve as Spark resolves them. + assert_eq!((queries, queries - failures.len()), (2858, 2645)); let dir = root.join(MANIFEST_DIR); if std::env::var_os("KRISHIV_UPDATE_MANIFEST").is_some() { let mut by_file: BTreeMap<&str, Vec<(&String, &String)>> = BTreeMap::new(); diff --git a/crates/krishiv-types/CLAUDE.md b/crates/krishiv-types/CLAUDE.md index d540b4dc1..b132d3047 100644 --- a/crates/krishiv-types/CLAUDE.md +++ b/crates/krishiv-types/CLAUDE.md @@ -9,14 +9,18 @@ The `Batch` newtype (`batch.rs`): Arrow `RecordBatch` + `Weights` (`Const(c)` | ## Key files Paths are relative to this crate. -- `src/data_type.rs` Spark data types; `src/arrow.rs` mapping to Arrow +- `src/data_type.rs` Spark data types and their renderings; `src/arrow.rs` mapping to Arrow +- `src/nested.rs` `ArrayType`, `MapType`, `StructType`/`StructField`/`Metadata`, Spark's looser equalities +- `src/json.rs` Spark's JSON form of a type (`DataType.json` / `fromJson`) - `src/ddl.rs` type-string parsing used by `krishiv-sql` - `src/batch.rs` the `Batch` newtype with `Weights` (`Const` or Int64 column) - `src/error.rs` error type with Spark classes - `tests/spark_type_strings.rs` type-string golden tests; `benches/batch_filter.rs` filter bench ## Extension points -- New Spark type: a variant in `src/data_type.rs`, its Arrow mapping in `src/arrow.rs`, and its string form in `src/ddl.rs`. +- New Spark type: a variant in `src/data_type.rs`, its Arrow mapping in `src/arrow.rs`, its string form in `src/ddl.rs` + and its JSON form in `src/json.rs`; `DataType` is `#[non_exhaustive]`, so other crates' wildcard arms need a + review too (skill `krishiv-add-type`). ## Invariants - Type mapping and precision/scale rules follow Spark exactly per supported version. @@ -24,6 +28,8 @@ Paths are relative to this crate. - `Batch`: no selection vector, no encoding enum; copy-on-filter. `Const(c)` never becomes a column except in `concat` of differing constants; operators route `Const(1)` to the no-weight path. A weight column has the row count and no nulls. +- Types from outside (type strings, Arrow, JSON, Spark Connect, plans) nest at most `DataType::MAX_NESTING_DEPTH` + (32) levels, so the recursive walks over a type stay within the stack. - `#![forbid(unsafe_code)]`. - No `unwrap`/`expect`/`panic` in library code; errors carry a Spark error class + SQLSTATE. diff --git a/crates/krishiv-types/Cargo.toml b/crates/krishiv-types/Cargo.toml index 29118ee9e..8a363ad07 100644 --- a/crates/krishiv-types/Cargo.toml +++ b/crates/krishiv-types/Cargo.toml @@ -16,11 +16,11 @@ arrow-array.workspace = true arrow-schema.workspace = true arrow-select.workspace = true krishiv-common = { path = "../krishiv-common" } +serde_json = { workspace = true, features = ["std"] } [dev-dependencies] criterion.workspace = true proptest = { workspace = true, features = ["std"] } -serde_json = { workspace = true, features = ["std"] } [[bench]] name = "batch_filter" diff --git a/crates/krishiv-types/src/arrow.rs b/crates/krishiv-types/src/arrow.rs index 95fa5be32..aa4518881 100644 --- a/crates/krishiv-types/src/arrow.rs +++ b/crates/krishiv-types/src/arrow.rs @@ -12,6 +12,15 @@ //! | `date` | `Date32` | | //! | `timestamp` | `Timestamp(Microsecond, "UTC")` | | //! | `timestamp_ntz` | `Timestamp(Microsecond, None)` | | +//! | `array` | `List` of a field `element` (nullable when `containsNull`) | `LargeList`, any child name | +//! | `map` | `Map` of a non-null struct `entries` of `key` (non-null) and `value` (nullable when `valueContainsNull`), unsorted | any child names | +//! | `struct<...>` | `Struct` of the fields | | +//! +//! Nested children follow Spark's `ArrowUtils` names. Each child is a field +//! built as [`DataType::to_arrow_field`] builds a column, so it carries its +//! own type tag when it needs one, and a struct field's non-empty metadata +//! rides in [`SPARK_METADATA_KEY`]. Nesting deeper than +//! [`DataType::MAX_NESTING_DEPTH`] is refused. //! //! The plan schema (Spark [`DataType`]) is the source of truth; Arrow types //! and the [`SPARK_TYPE_METADATA_KEY`] field tag only carry it through storage @@ -38,12 +47,17 @@ use std::sync::Arc; use arrow_schema::{DataType as ArrowType, Field, TimeUnit}; use crate::error::{self, Result}; +use crate::nested::{ArrayType, MapType, Metadata, StructField, StructType}; use crate::{DataType, DecimalType}; /// Arrow field-metadata key holding the Spark type (its `catalogString`) /// when the field's Arrow type alone does not determine it. pub const SPARK_TYPE_METADATA_KEY: &str = "krishiv.spark_type"; +/// Arrow field-metadata key holding a struct field's Spark `Metadata` (its +/// JSON), when it has any. +pub const SPARK_METADATA_KEY: &str = "krishiv.spark_metadata"; + /// Time zone of the canonical Arrow type for `timestamp` values, which are /// UTC instants; the session time zone is applied only at the edges. pub const TIMESTAMP_ARROW_TIME_ZONE: &str = "UTC"; @@ -79,6 +93,24 @@ impl DataType { Some(Arc::from(TIMESTAMP_ARROW_TIME_ZONE)), ), Self::TimestampNtz => ArrowType::Timestamp(TimeUnit::Microsecond, None), + Self::Array(a) => ArrowType::List(Arc::new( + a.element_type() + .to_arrow_field("element", a.contains_null()), + )), + Self::Map(m) => { + let entries = ArrowType::Struct( + vec![ + m.key_type().to_arrow_field("key", false), + m.value_type() + .to_arrow_field("value", m.value_contains_null()), + ] + .into(), + ); + ArrowType::Map(Arc::new(Field::new("entries", entries, false)), false) + } + Self::Struct(s) => { + ArrowType::Struct(s.fields().iter().map(struct_field_to_arrow).collect()) + } } } @@ -91,6 +123,21 @@ impl DataType { /// here (including non-microsecond timestamps and `Decimal64`), and the /// Spark decimal bound errors for an out-of-range decimal. pub fn from_arrow(arrow: &ArrowType) -> Result { + Self::from_arrow_at(arrow, 0) + } + + /// [`Self::from_arrow`] of a type nested `depth` levels deep. + fn from_arrow_at(arrow: &ArrowType, depth: usize) -> Result { + let nested = matches!( + arrow, + ArrowType::List(_) + | ArrowType::LargeList(_) + | ArrowType::Map(..) + | ArrowType::Struct(_) + ); + if nested && depth >= Self::MAX_NESTING_DEPTH { + return Err(error::too_deep()); + } Ok(match arrow { ArrowType::Null => Self::Null, ArrowType::Boolean => Self::Boolean, @@ -106,6 +153,25 @@ impl DataType { ArrowType::Date32 => Self::Date, ArrowType::Timestamp(TimeUnit::Microsecond, Some(_)) => Self::Timestamp, ArrowType::Timestamp(TimeUnit::Microsecond, None) => Self::TimestampNtz, + ArrowType::List(f) | ArrowType::LargeList(f) => { + ArrayType::new(Self::from_arrow_field_at(f, depth + 1)?, f.is_nullable()).into() + } + ArrowType::Map(entries, _) => match entries.data_type() { + ArrowType::Struct(kv) if kv.len() == 2 => MapType::new( + Self::from_arrow_field_at(&kv[0], depth + 1)?, + Self::from_arrow_field_at(&kv[1], depth + 1)?, + kv[1].is_nullable(), + ) + .into(), + other => return Err(error::unsupported(format!("Arrow map entries {other}"))), + }, + ArrowType::Struct(fields) => StructType::new( + fields + .iter() + .map(|f| struct_field_from_arrow(f, depth + 1)) + .collect::>>()?, + ) + .into(), other => return Err(error::unsupported(format!("Arrow type {other}"))), }) } @@ -171,10 +237,17 @@ impl DataType { /// `KRISHIV_UNSUPPORTED_FEATURE` when the metadata type cannot be stored /// in the field's Arrow type. pub fn from_arrow_field(field: &Field) -> Result { + Self::from_arrow_field_at(field, 0) + } + + fn from_arrow_field_at(field: &Field, depth: usize) -> Result { match field.metadata().get(SPARK_TYPE_METADATA_KEY) { - None => Self::from_arrow(field.data_type()), + None => Self::from_arrow_at(field.data_type(), depth), Some(tag) => { let t = crate::parse_data_type(tag)?; + if depth + t.nesting_depth() > Self::MAX_NESTING_DEPTH { + return Err(error::too_deep()); + } if t.is_valid_storage(field.data_type()) { Ok(t) } else { @@ -189,6 +262,33 @@ impl DataType { } } +/// A struct field as an Arrow field: its metadata in [`SPARK_METADATA_KEY`]. +fn struct_field_to_arrow(f: &StructField) -> Field { + let field = f.data_type().to_arrow_field(f.name(), f.nullable()); + if f.metadata().is_empty() { + return field; + } + let mut metadata = field.metadata().clone(); + metadata.insert( + SPARK_METADATA_KEY.to_owned(), + crate::json::metadata_json(f.metadata()), + ); + field.with_metadata(metadata) +} + +fn struct_field_from_arrow(f: &Field, depth: usize) -> Result { + let metadata = match f.metadata().get(SPARK_METADATA_KEY) { + Some(json) => crate::parse_metadata(json)?, + None => Metadata::empty(), + }; + Ok(StructField::new( + f.name(), + DataType::from_arrow_field_at(f, depth)?, + f.is_nullable(), + ) + .with_metadata(metadata)) +} + /// Spark scales are 0..=38, so they always fit Arrow's `i8` scale. fn arrow_scale(d: DecimalType) -> i8 { i8::try_from(d.scale()).unwrap_or(i8::MAX) diff --git a/crates/krishiv-types/src/data_type.rs b/crates/krishiv-types/src/data_type.rs index 689b48c89..2516c8a76 100644 --- a/crates/krishiv-types/src/data_type.rs +++ b/crates/krishiv-types/src/data_type.rs @@ -4,15 +4,17 @@ use std::fmt; use std::str::FromStr; +use std::sync::Arc; use krishiv_common::error::KrishivError; use crate::error::{self, Result}; +use crate::nested::{ArrayType, MapType, StructType}; /// A Spark SQL data type. /// -/// Covers void (the type of a bare `NULL`), boolean, integral, floating-point, string, binary, decimal, date and -/// timestamp types. Nested, interval, char/varchar and variant types are not +/// Covers void (the type of a bare `NULL`), boolean, integral, floating-point, string, binary, decimal, date, +/// timestamp and the nested array, map and struct types. Interval, char/varchar and variant types are not /// represented yet; parsing them yields `KRISHIV_UNSUPPORTED_FEATURE`. #[derive(Debug, Clone, PartialEq, Eq, Hash)] #[non_exhaustive] @@ -46,37 +48,130 @@ pub enum DataType { Timestamp, /// `TIMESTAMP_NTZ`: microseconds since the epoch, no time zone. TimestampNtz, + /// `ARRAY`. + Array(Arc), + /// `MAP`. + Map(Arc), + /// `STRUCT`. + Struct(StructType), } impl DataType { - /// Spark's `DataType.simpleString`, e.g. `int`, `decimal(10,2)`. + /// The deepest nesting of array, map and struct types accepted from + /// outside (a type string, Arrow, JSON, Spark Connect, a plan). Spark has + /// no limit; this one keeps every recursive walk over a type, and the + /// JSON form of a type, within bounds. + pub const MAX_NESTING_DEPTH: usize = 32; + + /// Spark's default `spark.sql.debug.maxToStringFields`: a struct's + /// `simpleString` lists at most this many fields. + pub const MAX_TO_STRING_FIELDS: usize = 25; + + /// Spark's `DataType.simpleString`, e.g. `int`, `decimal(10,2)`, + /// `struct` (at most [`Self::MAX_TO_STRING_FIELDS`] fields of a + /// struct are listed). pub fn simple_string(&self) -> String { - match self { - Self::Decimal(d) => format!("decimal({},{})", d.precision, d.scale), - other => other.keyword().to_owned(), - } + self.render(Render::Simple) } - /// Spark's `DataType.catalogString`; equal to [`Self::simple_string`] for - /// every type represented here. + /// Spark's `DataType.catalogString`: [`Self::simple_string`] with every + /// struct field listed. pub fn catalog_string(&self) -> String { - self.simple_string() + self.render(Render::Catalog) } - /// Spark's `DataType.sql`, e.g. `INT`, `DECIMAL(10,2)`. + /// Spark's `DataType.sql`, e.g. `INT`, `DECIMAL(10,2)`, + /// `STRUCT`. pub fn sql(&self) -> String { - self.simple_string().to_ascii_uppercase() + self.render(Render::Sql) } /// Spark's `DataType.typeName` (the name used in Spark's JSON schemas), - /// e.g. `integer`, `long`, `decimal(10,2)`. + /// e.g. `integer`, `long`, `decimal(10,2)`, `array`. pub fn type_name(&self) -> String { match self { Self::Byte => "byte".to_owned(), Self::Short => "short".to_owned(), Self::Integer => "integer".to_owned(), Self::Long => "long".to_owned(), - other => other.simple_string(), + Self::Decimal(_) => self.atomic_simple_string(), + other => other.keyword().to_owned(), + } + } + + /// The `simpleString` of an atomic type. + fn atomic_simple_string(&self) -> String { + match self { + Self::Decimal(d) => format!("decimal({},{})", d.precision, d.scale), + other => other.keyword().to_owned(), + } + } + + fn render(&self, how: Render) -> String { + let mut out = String::new(); + self.render_into(how, &mut out); + out + } + + fn render_into(&self, how: Render, out: &mut String) { + let sql = how == Render::Sql; + let (open, sep) = if sql { ("<", ", ") } else { ("<", ",") }; + match self { + Self::Array(a) => { + out.push_str(if sql { "ARRAY" } else { "array" }); + out.push_str(open); + a.element_type().render_into(how, out); + out.push('>'); + } + Self::Map(m) => { + out.push_str(if sql { "MAP" } else { "map" }); + out.push_str(open); + m.key_type().render_into(how, out); + out.push_str(sep); + m.value_type().render_into(how, out); + out.push('>'); + } + Self::Struct(s) => { + out.push_str(if sql { "STRUCT" } else { "struct" }); + out.push_str(open); + let fields = s.fields(); + let shown = match how { + Render::Simple => fields.len().min(Self::MAX_TO_STRING_FIELDS), + _ => fields.len(), + }; + for (i, f) in fields.iter().take(shown).enumerate() { + if i > 0 { + out.push_str(sep); + } + if sql { + out.push_str("e_if_needed(f.name())); + out.push_str(": "); + } else { + out.push_str(f.name()); + out.push(':'); + } + f.data_type().render_into(how, out); + if sql { + if !f.nullable() { + out.push_str(" NOT NULL"); + } + if let Some(c) = f.metadata().comment() { + out.push_str(" COMMENT '"); + out.push_str(&c.replace('\'', "\\'")); + out.push('\''); + } + } + } + // Spark's `truncatedString`: the listed fields, a separator, + // then how many are left out. + if shown < fields.len() { + out.push_str(sep); + out.push_str(&format!("... {} more fields", fields.len() - shown)); + } + out.push('>'); + } + atomic if sql => out.push_str(&atomic.atomic_simple_string().to_ascii_uppercase()), + atomic => out.push_str(&atomic.atomic_simple_string()), } } @@ -97,10 +192,37 @@ impl DataType { Self::Date => "date", Self::Timestamp => "timestamp", Self::TimestampNtz => "timestamp_ntz", + Self::Array(_) => "array", + Self::Map(_) => "map", + Self::Struct(_) => "struct", } } } +/// Which of Spark's renderings of a type. +#[derive(Clone, Copy, PartialEq, Eq)] +enum Render { + Simple, + Catalog, + Sql, +} + +/// Spark's `QuotingUtils.quoteIfNeeded`: a name that is not a plain +/// identifier (`[a-zA-Z_][a-zA-Z0-9_]*`) is backquoted, with backquotes +/// doubled. +pub(crate) fn quote_if_needed(name: &str) -> String { + let mut chars = name.chars(); + let plain = chars + .next() + .is_some_and(|c| c.is_ascii_alphabetic() || c == '_') + && chars.all(|c| c.is_ascii_alphanumeric() || c == '_'); + if plain { + name.to_owned() + } else { + format!("`{}`", name.replace('`', "``")) + } +} + /// Formats as Spark's `simpleString`. impl fmt::Display for DataType { fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { diff --git a/crates/krishiv-types/src/ddl.rs b/crates/krishiv-types/src/ddl.rs index 432825f45..ac9a6cd0e 100644 --- a/crates/krishiv-types/src/ddl.rs +++ b/crates/krishiv-types/src/ddl.rs @@ -6,11 +6,24 @@ //! forms `TIMESTAMP WITH LOCAL TIME ZONE` and `TIMESTAMP WITHOUT TIME ZONE`. //! The lexer follows Spark's: case-insensitive keywords, Unicode whitespace, //! `--` and nested `/* */` comments, backquoted identifiers and numeric -//! literal tokens. Nested, interval, char/varchar, variant, time and -//! collated string types are recognized and rejected with -//! `KRISHIV_UNSUPPORTED_FEATURE`. +//! literal tokens, string literals (for field comments), and Spark's +//! multi-character operators (`<>`; `>>` only outside a complex type). +//! +//! Nested types: `ARRAY`, `MAP` and `STRUCT` (also `STRUCT<>`), nested at most +//! [`DataType::MAX_NESTING_DEPTH`] deep. As Spark's ANTLR parser does, a +//! syntax error anywhere inside a complex type is reported at the +//! outermost `<`, a bare `ARRAY` / `MAP` / `STRUCT` is +//! `INCOMPLETE_TYPE_DEFINITION.*`, and a single token after a complete +//! complex type is "extra input". +//! +//! Interval, char/varchar, variant, time and collated string types are +//! recognized and rejected with `KRISHIV_UNSUPPORTED_FEATURE`. + +use krishiv_common::error::ErrorClass; use crate::error::{self, Result}; +use crate::nested::{ArrayType, MapType, Metadata, StructField, StructType}; use crate::{DataType, DecimalType}; /// Parses a Spark DDL type string such as `int`, `DECIMAL(10, 2)` or @@ -32,14 +45,30 @@ use crate::{DataType, DecimalType}; /// unknown type and `KRISHIV_UNSUPPORTED_FEATURE` for a Spark type not /// supported yet (see [`crate::error`]). pub fn parse_data_type(input: &str) -> Result { - let tokens = Lexer { src: input, pos: 0 }.tokens()?; + let tokens = Lexer { + src: input, + pos: 0, + complex_level: 0, + } + .tokens()?; if tokens.is_empty() { return Err(error::empty_statement()); } - let mut p = Parser { tokens, pos: 0 }; - let data_type = p.data_type()?; + let mut p = Parser { + tokens, + pos: 0, + deferred: None, + }; + let data_type = p.data_type(0)?; + let left = p.tokens.len().saturating_sub(p.pos); match p.next() { - None => Ok(data_type), + // Spark checks names and arguments only once the whole string + // parses (`AstBuilder` runs after the parser). + None => p.deferred.map_or(Ok(data_type), Err), + // ANTLR deletes a single extra token after a complex type. + Some(tok) if data_type.is_nested() && left == 1 => { + Err(error::extra_input(&format!("'{}'", tok.raw))) + } Some(tok) => Err(tok.syntax_error()), } } @@ -53,6 +82,10 @@ enum Kind { Integer, /// A single punctuation character. Punct(char), + /// A multi-character operator Spark lexes as one token (`<>`, `>>`). + Op, + /// A string literal (`'...'`, `"..."`, `r'...'`); `text` is its value. + Str, /// Any other token (a numeric literal, an unknown character). Other, } @@ -109,6 +142,10 @@ fn is_word_char(c: char) -> bool { struct Lexer<'a> { src: &'a str, pos: usize, + /// Spark's `complex_type_level_counter`: raised by each `ARRAY`, `MAP` + /// and `STRUCT` keyword, lowered by each `>`; while it is above zero, + /// `>>` is two tokens (closing two levels), not a shift operator. + complex_level: usize, } impl<'a> Lexer<'a> { @@ -180,24 +217,83 @@ impl<'a> Lexer<'a> { let rest = self.rest(); let mut chars = rest.chars(); let c = chars.next().unwrap_or(' '); + let second = chars.next(); let starts_number = - c.is_ascii_digit() || (c == '.' && chars.next().is_some_and(|d| d.is_ascii_digit())); + c.is_ascii_digit() || (c == '.' && second.is_some_and(|d| d.is_ascii_digit())); if starts_number { self.number() + } else if matches!(c, 'r' | 'R') && matches!(second, Some('\'' | '"')) { + self.string(1) } else if is_word_char(c) { - self.take(self.span(0, is_word_char), Kind::Word { quoted: false }) + let t = self.take(self.span(0, is_word_char), Kind::Word { quoted: false }); + if ["array", "map", "struct"] + .iter() + .any(|k| t.text.eq_ignore_ascii_case(k)) + { + self.complex_level += 1; + } + t } else if c == '`' { self.quoted() + } else if matches!(c, '\'' | '"') { + self.string(0) + } else if let Some(op) = self.operator() { + self.take(op, Kind::Op) } else { let kind = if c.is_ascii_punctuation() { Kind::Punct(c) } else { Kind::Other }; + if c == '>' { + self.complex_level = self.complex_level.saturating_sub(1); + } self.take(c.len_utf8(), kind) } } + /// The length of a multi-character operator at the cursor, if any: + /// the ones Spark's lexer has, `>>` and `>>>` only outside a complex + /// type. + fn operator(&self) -> Option { + let rest = self.rest(); + if self.complex_level == 0 { + for op in [">>>", ">>"] { + if rest.starts_with(op) { + return Some(op.len()); + } + } + } + [ + "<=>", "<>", "<=", ">=", "!=", "==", "<<", "::", "->", "=>", "||", "|>", + ] + .into_iter() + .find(|op| rest.starts_with(op)) + .map(str::len) + } + + /// A string literal `prefix` bytes after the cursor (1 for a raw + /// string's `r`). Unterminated, its quote is a token of its own. + fn string(&mut self, prefix: usize) -> Token<'a> { + let rest = self.rest(); + let bytes = rest.as_bytes(); + let quote = bytes.get(prefix).copied().unwrap_or(b'\''); + let raw = prefix > 0; + let mut i = prefix + 1; + loop { + match bytes.get(i) { + None => return self.take(prefix + 1, Kind::Other), + Some(&b) if b == quote && !raw && bytes.get(i + 1) == Some("e) => i += 2, + Some(&b) if b == quote => break, + Some(b'\\') if !raw => i += 2, + Some(_) => i += 1, + } + } + let mut t = self.take(i + 1, Kind::Str); + t.text = unescape_string(t.raw); + t + } + /// Spark's numeric tokens: `INTEGER_VALUE`, decimal and exponent /// literals, and suffixed literals (`10L`, `1.5BD`). Digits followed by /// other word characters form an identifier (`1a`). @@ -261,6 +357,9 @@ impl<'a> Lexer<'a> { struct Parser<'a> { tokens: Vec>, pos: usize, + /// The first error that is not a syntax error, raised once the whole + /// string has parsed. + deferred: Option, } impl<'a> Parser<'a> { @@ -315,7 +414,8 @@ impl<'a> Parser<'a> { Ok(()) } - fn data_type(&mut self) -> Result { + /// A type inside `depth` enclosing complex types. + fn data_type(&mut self, depth: usize) -> Result { let name = self.expect(|t| matches!(t.kind, Kind::Word { .. }))?; let quoted = name.kind == (Kind::Word { quoted: true }); let lower = if quoted { @@ -328,8 +428,24 @@ impl<'a> Parser<'a> { folded.to_ascii_lowercase() }; if !quoted { + if matches!(lower.as_str(), "array" | "map" | "struct") { + match self.peek() { + Some(t) if matches!(t.kind, Kind::Punct('<')) || t.raw == "<>" => { + return self.complex(&lower, depth); + } + // `ARRAY(...)`: the arguments of no type. + Some(t) if t.kind == Kind::Punct('(') => return Err(t.syntax_error()), + _ => {} + } + } if let Some(feature) = self.unsupported_type(&lower) { - return Err(error::unsupported(feature)); + self.skip_type_tail(); + if depth == 0 + && let Some(tok) = self.peek() + { + return Err(self.unexpected(tok)); + } + return Ok(self.defer(error::unsupported(feature))); } if lower == "timestamp" { let lead = self.peek().cloned(); @@ -347,20 +463,159 @@ impl<'a> Parser<'a> { } } let args = self.type_arguments()?; - if let Some(tok) = self.peek() { + if depth == 0 + && let Some(tok) = self.peek() + { return Err(self.unexpected(tok)); } - let unsupported = || Err(unsupported_data_type(&lower, &args)); - if quoted { - return unsupported(); + let resolved = if quoted { + None + } else if let Some(e) = error::incomplete_type(&lower) { + Some(Err(e)) + } else { + resolve(&lower, &args) + }; + Ok( + match resolved.unwrap_or_else(|| Err(unsupported_data_type(&lower, &args))) { + Ok(t) => t, + Err(e) => self.defer(e), + }, + ) + } + + /// Records `e` unless an earlier error is recorded; a placeholder type. + fn defer(&mut self, e: krishiv_common::error::KrishivError) -> DataType { + self.deferred.get_or_insert(e); + DataType::Null + } + + /// Skips the rest of a type Krishiv does not support (its words and a + /// parenthesized argument list), so the type around it still parses. + fn skip_type_tail(&mut self) { + while let Some(t) = self.peek() { + match t.kind { + Kind::Word { .. } if !t.is_keyword("not") && !t.is_keyword("comment") => { + self.next(); + } + Kind::Punct('(') => { + while let Some(t) = self.next() { + if t.kind == Kind::Punct(')') { + break; + } + } + } + _ => break, + } } - resolve(&lower, &args).unwrap_or_else(unsupported) + } + + /// `ARRAY<...>`, `MAP<...>` or `STRUCT<...>` (`kind`) at the `<` (or + /// `<>`) after the keyword. A syntax error inside is reported at that + /// `<`; an enclosing complex type moves it to its own. + fn complex(&mut self, kind: &str, depth: usize) -> Result { + if depth >= DataType::MAX_NESTING_DEPTH { + return Err(error::too_deep()); + } + let Some(lt) = self.next() else { + return Err(error::syntax_error("end of input")); + }; + if lt.raw == "<>" { + return if kind == "struct" { + Ok(StructType::new([]).into()) + } else { + Err(lt.syntax_error()) + }; + } + let body = match kind { + "array" => self + .data_type(depth + 1) + .map(|e| ArrayType::new(e, true).into()), + "map" => self.map_body(depth), + _ => self.struct_body(depth), + }; + let body = body.and_then(|t| self.expect(|t| t.kind == Kind::Punct('>')).map(|_| t)); + body.map_err(|e| { + if e.error_class() == ErrorClass::ParseSyntaxError { + lt.syntax_error() + } else { + e + } + }) + } + + /// `k , v` of a map. + fn map_body(&mut self, depth: usize) -> Result { + let key = self.data_type(depth + 1)?; + self.expect(|t| t.kind == Kind::Punct(','))?; + let value = self.data_type(depth + 1)?; + Ok(MapType::new(key, value, true).into()) + } + + /// The fields of a struct, up to (not including) its `>`. + fn struct_body(&mut self, depth: usize) -> Result { + let mut fields = Vec::new(); + if self.peek().is_some_and(|t| t.kind == Kind::Punct('>')) { + return Ok(StructType::new(fields).into()); + } + loop { + fields.push(self.struct_field(depth)?); + if !self.peek().is_some_and(|t| t.kind == Kind::Punct(',')) { + return Ok(StructType::new(fields).into()); + } + self.next(); + } + } + + /// `name [:] type [NOT NULL] [COMMENT 'c' ...]`. + fn struct_field(&mut self, depth: usize) -> Result { + let name = self.expect(|t| matches!(t.kind, Kind::Word { .. }))?; + if name.kind == (Kind::Word { quoted: false }) && !name.text.is_ascii() { + return Err(error::invalid_identifier(name.raw)); + } + // Spark's `errorCapturingIdentifier`: `a-b` is an invalid name. + let mut dashed = name.text.clone(); + while self.peek().is_some_and(|t| t.kind == Kind::Punct('-')) + && self + .tokens + .get(self.pos + 1) + .is_some_and(|t| matches!(t.kind, Kind::Word { .. })) + { + self.next(); + if let Some(part) = self.next() { + dashed.push('-'); + dashed.push_str(&part.text); + } + } + if dashed != name.text { + self.defer(error::invalid_identifier(&dashed)); + } + if self.peek().is_some_and(|t| t.kind == Kind::Punct(':')) { + self.next(); + } + let data_type = self.data_type(depth + 1)?; + let mut nullable = true; + if self.peek().is_some_and(|t| t.is_keyword("not")) { + self.next(); + self.expect(|t| t.is_keyword("null"))?; + nullable = false; + } + let mut field = StructField::new(name.text, data_type, nullable); + if self.peek().is_some_and(|t| t.is_keyword("comment")) { + self.next(); + let mut comment = self.expect(|t| t.kind == Kind::Str)?.text; + while self.peek().is_some_and(|t| t.kind == Kind::Str) { + if let Some(t) = self.next() { + comment.push_str(&t.text); + } + } + field = field.with_metadata(Metadata::with_comment(comment)); + } + Ok(field) } /// Recognized types that are not represented yet. fn unsupported_type(&self, lower: &str) -> Option { let what = match lower { - "array" | "map" | "struct" => "nested data types", "interval" => "interval data types", "char" | "character" | "varchar" => "char/varchar data types", "variant" => "the variant data type", @@ -443,3 +698,77 @@ fn unsupported_data_type(name: &str, args: &[String]) -> krishiv_common::error:: } error::unsupported_data_type(&s) } + +/// The value of a string literal token, as Spark's `unescapeSQLString` +/// decodes it: a raw string as written; otherwise backslash escapes +/// (`\n`, `\uXXXX`, octal `\101`, ...) and doubled quotes. +fn unescape_string(raw: &str) -> String { + if let Some(body) = raw + .strip_prefix(['r', 'R']) + .and_then(|r| r.get(1..r.len().saturating_sub(1))) + { + return body.to_owned(); + } + let quote = raw.chars().next().unwrap_or('\''); + let body: Vec = raw + .get(1..raw.len().saturating_sub(1)) + .unwrap_or_default() + .chars() + .collect(); + // Java strings are UTF-16: an escaped lone surrogate becomes U+FFFD. + let mut out: Vec = Vec::with_capacity(body.len()); + let mut buf = [0_u16; 2]; + let push = |out: &mut Vec, c: char, buf: &mut [u16; 2]| { + out.extend_from_slice(c.encode_utf16(buf)); + }; + let hex = |from: usize, n: usize| -> Option { + let s: String = body.get(from..from + n)?.iter().collect(); + s.chars() + .all(|c| c.is_ascii_hexdigit()) + .then(|| u32::from_str_radix(&s, 16).ok())? + }; + let octal = |at: usize| -> Option { + let d = |i: usize| body.get(at + i).and_then(|c| c.to_digit(8)); + let (a, b, c) = (d(0)?, d(1)?, d(2)?); + (a <= 1).then(|| u16::try_from(a * 64 + b * 8 + c).unwrap_or(0xFFFD)) + }; + let mut i = 0; + while let Some(&c) = body.get(i) { + let next = body.get(i + 1).copied(); + if c == quote && next == Some(quote) { + push(&mut out, c, &mut buf); + i += 2; + } else if c != '\\' || next.is_none() { + push(&mut out, c, &mut buf); + i += 1; + } else if let (Some('u'), Some(v)) = (next, hex(i + 2, 4)) { + out.push(u16::try_from(v).unwrap_or(0xFFFD)); + i += 6; + } else if let (Some('U'), Some(v)) = (next, hex(i + 2, 8)) { + match char::from_u32(v) { + Some(ch) => push(&mut out, ch, &mut buf), + None => out.push(0xFFFD), + } + i += 10; + } else if let Some(v) = octal(i + 1) { + out.push(v); + i += 4; + } else { + match next.unwrap_or('\\') { + '0' => out.push(0), + 'b' => out.push(8), + 'n' => push(&mut out, '\n', &mut buf), + 'r' => push(&mut out, '\r', &mut buf), + 't' => push(&mut out, '\t', &mut buf), + 'Z' => out.push(0x1A), + n @ ('%' | '_') => { + push(&mut out, '\\', &mut buf); + push(&mut out, n, &mut buf); + } + n => push(&mut out, n, &mut buf), + } + i += 2; + } + } + String::from_utf16_lossy(&out) +} diff --git a/crates/krishiv-types/src/error.rs b/crates/krishiv-types/src/error.rs index 5e35aafa1..bea306de6 100644 --- a/crates/krishiv-types/src/error.rs +++ b/crates/krishiv-types/src/error.rs @@ -15,12 +15,17 @@ //! | non-ASCII unquoted name | `INVALID_IDENTIFIER` | //! | unknown type name | `UNSUPPORTED_DATATYPE` | //! | a Spark type Krishiv does not support yet | `KRISHIV_UNSUPPORTED_FEATURE` | +//! | bare `ARRAY`, `MAP` or `STRUCT` | `INCOMPLETE_TYPE_DEFINITION.*` | +//! | a type nested deeper than [`crate::DataType::MAX_NESTING_DEPTH`] | `FAILED_TO_PARSE_TOO_COMPLEX` | +//! | JSON that is not a Spark type | `INVALID_JSON_DATA_TYPE` | //! | a [`crate::batch::Batch`] invariant broken by the caller (an engine bug) | `INTERNAL_ERROR` | use krishiv_common::error::{ - DecimalPrecisionExceedsMaxPrecision, InternalError, InvalidIdentifier, KrishivError, - KrishivUnsupportedFeature, LegacyErrorTemp1228, NegativeScaleDisallowed, ParseEmptyStatement, - ParseSyntaxError, UnclosedBracketedComment, UnsupportedDatatype, + DecimalPrecisionExceedsMaxPrecision, FailedToParseTooComplex, IncompleteTypeDefinitionArray, + IncompleteTypeDefinitionMap, IncompleteTypeDefinitionStruct, InternalError, InvalidIdentifier, + InvalidJsonDataType, KrishivError, KrishivUnsupportedFeature, LegacyErrorTemp1228, + NegativeScaleDisallowed, ParseEmptyStatement, ParseSyntaxError, UnclosedBracketedComment, + UnsupportedDatatype, }; use crate::DecimalType; @@ -109,3 +114,31 @@ pub(crate) fn unsupported(feature: String) -> KrishivError { pub(crate) fn internal(message: String) -> KrishivError { InternalError { message }.into() } + +/// `INCOMPLETE_TYPE_DEFINITION.*` for a bare `array`, `map` or `struct` +/// (`name` lower-case); `None` for another name. +pub(crate) fn incomplete_type(name: &str) -> Option { + Some(match name { + "array" => IncompleteTypeDefinitionArray { + element_type: "".to_owned(), + } + .into(), + "map" => IncompleteTypeDefinitionMap {}.into(), + "struct" => IncompleteTypeDefinitionStruct {}.into(), + _ => return None, + }) +} + +/// `FAILED_TO_PARSE_TOO_COMPLEX`, for a type nested deeper than +/// [`crate::DataType::MAX_NESTING_DEPTH`]. +pub(crate) fn too_deep() -> KrishivError { + FailedToParseTooComplex {}.into() +} + +/// `INVALID_JSON_DATA_TYPE`; `json` is the offending JSON, as Spark quotes it. +pub(crate) fn invalid_json_type(json: &str) -> KrishivError { + InvalidJsonDataType { + invalid_type: json.to_owned(), + } + .into() +} diff --git a/crates/krishiv-types/src/json.rs b/crates/krishiv-types/src/json.rs new file mode 100644 index 000000000..f1b7ec997 --- /dev/null +++ b/crates/krishiv-types/src/json.rs @@ -0,0 +1,266 @@ +// SPDX-License-Identifier: Apache-2.0 +//! Spark's JSON form of a type (`DataType.json` and `DataType.fromJson`), +//! as Spark writes schemas in files, plans and Spark Connect metadata: +//! an atomic type is its `typeName` as a JSON string (`"integer"`), a +//! nested type an object whose keys come in Spark's order +//! (`{"type":"array","elementType":"integer","containsNull":true}`). + +use std::fmt::Write as _; + +use krishiv_common::error::{ErrorClass, KrishivError}; +use serde_json::{Map, Value}; + +use crate::error::{self, Result}; +use crate::nested::{ArrayType, MapType, Metadata, StructField, StructType}; +use crate::{DataType, DecimalType}; + +impl DataType { + /// Spark's `DataType.json`: compact, keys in Spark's order. + pub fn json(&self) -> String { + let mut out = String::new(); + self.write_json(&mut out); + out + } + + fn write_json(&self, out: &mut String) { + match self { + Self::Array(a) => { + out.push_str(r#"{"type":"array","elementType":"#); + a.element_type().write_json(out); + let _ = write!(out, r#","containsNull":{}}}"#, a.contains_null()); + } + Self::Map(m) => { + out.push_str(r#"{"type":"map","keyType":"#); + m.key_type().write_json(out); + out.push_str(r#","valueType":"#); + m.value_type().write_json(out); + let _ = write!(out, r#","valueContainsNull":{}}}"#, m.value_contains_null()); + } + Self::Struct(s) => { + out.push_str(r#"{"type":"struct","fields":["#); + for (i, f) in s.fields().iter().enumerate() { + if i > 0 { + out.push(','); + } + out.push_str(r#"{"name":"#); + write_string(f.name(), out); + out.push_str(r#","type":"#); + f.data_type().write_json(out); + let _ = write!(out, r#","nullable":{},"metadata":"#, f.nullable()); + write_value(&Value::Object(f.metadata().as_map().clone()), out); + out.push('}'); + } + out.push_str("]}"); + } + atomic => write_string(&atomic.type_name(), out), + } + } + + /// Spark's `DataType.fromJson`. + /// + /// # Errors + /// `INVALID_JSON_DATA_TYPE` for JSON that is not a Spark type, + /// `FAILED_TO_PARSE_TOO_COMPLEX` for a type nested deeper than + /// [`Self::MAX_NESTING_DEPTH`], and `KRISHIV_UNSUPPORTED_FEATURE` for a + /// Spark type Krishiv does not support yet. + pub fn from_json(text: &str) -> Result { + let value: Value = + serde_json::from_str(text).map_err(|_| error::invalid_json_type(text))?; + Self::from_json_value(&value, 0) + } + + fn from_json_value(v: &Value, depth: usize) -> Result { + let invalid = || error::invalid_json_type(&v.to_string()); + let obj = match v { + Value::String(name) => return atomic_from_name(name).ok_or_else(invalid)?, + Value::Object(obj) => obj, + _ => return Err(invalid()), + }; + if depth >= Self::MAX_NESTING_DEPTH { + return Err(error::too_deep()); + } + let child = |key: &str| { + obj.get(key) + .ok_or_else(invalid) + .and_then(|c| Self::from_json_value(c, depth + 1)) + }; + let flag = |key: &str| obj.get(key).and_then(Value::as_bool).ok_or_else(invalid); + let keys = |expected: &[&str]| { + obj.len() == expected.len() && expected.iter().all(|k| obj.contains_key(*k)) + }; + match obj.get("type").and_then(Value::as_str) { + Some("array") if keys(&["type", "elementType", "containsNull"]) => { + Ok(ArrayType::new(child("elementType")?, flag("containsNull")?).into()) + } + Some("map") if keys(&["type", "keyType", "valueType", "valueContainsNull"]) => { + Ok(MapType::new( + child("keyType")?, + child("valueType")?, + flag("valueContainsNull")?, + ) + .into()) + } + Some("struct") if keys(&["type", "fields"]) => { + let fields = obj + .get("fields") + .and_then(Value::as_array) + .ok_or_else(invalid)?; + let fields = fields + .iter() + .map(|f| struct_field(f, depth)) + .collect::>>()?; + Ok(StructType::new(fields).into()) + } + _ => Err(invalid()), + } + } +} + +/// A struct field: `name`, `type` and `nullable`, and `metadata` (optional, +/// as in Spark). +fn struct_field(v: &Value, depth: usize) -> Result { + let invalid = || error::invalid_json_type(&v.to_string()); + let obj = v.as_object().ok_or_else(invalid)?; + let has_metadata = obj.contains_key("metadata"); + let expected = 3 + usize::from(has_metadata); + if obj.len() != expected { + return Err(invalid()); + } + let name = obj + .get("name") + .and_then(Value::as_str) + .ok_or_else(invalid)?; + let nullable = obj + .get("nullable") + .and_then(Value::as_bool) + .ok_or_else(invalid)?; + let data_type = DataType::from_json_value(obj.get("type").ok_or_else(invalid)?, depth + 1)?; + let metadata = match obj.get("metadata") { + None => Metadata::empty(), + Some(Value::Object(m)) => Metadata::from_map(m.clone()), + Some(_) => return Err(invalid()), + }; + Ok(StructField::new(name, data_type, nullable).with_metadata(metadata)) +} + +/// Spark's `nameToType`: the `typeName` of an atomic type, or `decimal` +/// (`decimal(10,0)`); `None` for a name that is no type. +fn atomic_from_name(name: &str) -> Option> { + let atomic = [ + DataType::Null, + DataType::Boolean, + DataType::Byte, + DataType::Short, + DataType::Integer, + DataType::Long, + DataType::Float, + DataType::Double, + DataType::String, + DataType::Binary, + DataType::Date, + DataType::Timestamp, + DataType::TimestampNtz, + ]; + if let Some(t) = atomic.into_iter().find(|t| t.type_name() == name) { + return Some(Ok(t)); + } + if name == "decimal" { + return Some(Ok(DataType::Decimal(DecimalType::USER_DEFAULT))); + } + if let Some(args) = name + .strip_prefix("decimal(") + .and_then(|r| r.strip_suffix(')')) + { + let (p, s) = args.split_once(',')?; + let (p, s) = (p.trim(), s.trim()); + let digits = |t: &str| !t.is_empty() && t.bytes().all(|b| b.is_ascii_digit()); + if !digits(p) || !(digits(s) || s.strip_prefix('-').is_some_and(digits)) { + return None; + } + return Some(match (p.parse::(), s.parse::()) { + (Ok(p), Ok(s)) => DecimalType::try_new(p, s).map(DataType::Decimal), + (Ok(_), Err(_)) if s.starts_with('-') => { + Err(error::negative_scale(s.parse::().unwrap_or(i64::MIN))) + } + _ => Err(error::precision_exceeds_max(p)), + }); + } + // A Spark type Krishiv does not have yet (`char(3)`, `variant`, ...). + match crate::parse_data_type(name) { + Err(e) if e.error_class() == ErrorClass::KrishivUnsupportedFeature => Some(Err(e)), + _ => None, + } +} + +/// A JSON string as json4s writes it: `"`, `\` and control characters +/// escaped, everything else as is. +fn write_string(s: &str, out: &mut String) { + out.push('"'); + for c in s.chars() { + match c { + '"' => out.push_str("\\\""), + '\\' => out.push_str("\\\\"), + '\u{8}' => out.push_str("\\b"), + '\u{c}' => out.push_str("\\f"), + '\n' => out.push_str("\\n"), + '\r' => out.push_str("\\r"), + '\t' => out.push_str("\\t"), + c if u32::from(c) < 0x20 => { + let _ = write!(out, "\\u{:04X}", u32::from(c)); + } + c => out.push(c), + } + } + out.push('"'); +} + +/// A metadata value: objects with their keys sorted (see [`Metadata`]). +fn write_value(v: &Value, out: &mut String) { + match v { + Value::String(s) => write_string(s, out), + Value::Array(items) => { + out.push('['); + for (i, item) in items.iter().enumerate() { + if i > 0 { + out.push(','); + } + write_value(item, out); + } + out.push(']'); + } + Value::Object(map) => write_object(map, out), + // `null`, booleans and numbers print the same in json4s. + other => out.push_str(&other.to_string()), + } +} + +/// `metadata` as JSON. +pub(crate) fn metadata_json(metadata: &Metadata) -> String { + let mut out = String::new(); + write_object(metadata.as_map(), &mut out); + out +} + +fn write_object(map: &Map, out: &mut String) { + out.push('{'); + for (i, (k, v)) in map.iter().enumerate() { + if i > 0 { + out.push(','); + } + write_string(k, out); + out.push(':'); + write_value(v, out); + } + out.push('}'); +} + +/// Parses Spark `Metadata` JSON (a Connect struct field's `metadata`). +/// +/// # Errors +/// `INVALID_JSON_DATA_TYPE` when `text` is not a JSON object. +pub fn parse_metadata(text: &str) -> Result { + match serde_json::from_str::(text) { + Ok(Value::Object(map)) => Ok(Metadata::from_map(map)), + _ => Err(error::invalid_json_type(text)), + } +} diff --git a/crates/krishiv-types/src/lib.rs b/crates/krishiv-types/src/lib.rs index 1ec1e9212..21f8dc7a8 100644 --- a/crates/krishiv-types/src/lib.rs +++ b/crates/krishiv-types/src/lib.rs @@ -2,7 +2,7 @@ //! Internal type representations (layer L1, ADR 0009). //! //! [`DataType`] models Spark SQL data types with Spark's renderings -//! (`simpleString`, `catalogString`, `sql`, `typeName`), maps them to and from +//! (`simpleString`, `catalogString`, `sql`, `typeName`, `json`), maps them to and from //! Arrow ([`arrow`]), and parses Spark DDL type strings ([`parse_data_type`]). //! [`batch::Batch`] is the engine's unit of data: Arrow columns plus row weights. @@ -13,9 +13,13 @@ pub mod batch; mod data_type; mod ddl; pub mod error; +mod json; +mod nested; pub use data_type::{DataType, DecimalStorage, DecimalType}; pub use ddl::parse_data_type; +pub use json::parse_metadata; +pub use nested::{ArrayType, MapType, Metadata, StructField, StructType, equals_ignore_case}; #[cfg(test)] mod tests; diff --git a/crates/krishiv-types/src/nested.rs b/crates/krishiv-types/src/nested.rs new file mode 100644 index 000000000..6188e246a --- /dev/null +++ b/crates/krishiv-types/src/nested.rs @@ -0,0 +1,393 @@ +// SPDX-License-Identifier: Apache-2.0 +//! Spark's nested types: `ArrayType`, `MapType`, `StructType` and its +//! `StructField`s, with the field `Metadata`. +//! +//! Equality is Spark's: element and value nullability, field names (case +//! sensitive), field nullability and metadata all take part. The looser +//! comparisons Spark uses for coercion are [`DataType::equals_ignore_nullability`], +//! [`DataType::equals_ignore_case_and_nullability`] and +//! [`DataType::equals_structurally`]. +//! +//! Nesting is bounded by [`DataType::MAX_NESTING_DEPTH`] wherever a type +//! comes from outside (a type string, Arrow, JSON, Spark Connect), so the +//! recursive walks over a type never exhaust the stack. + +use std::sync::Arc; + +use serde_json::{Map, Value}; + +use crate::DataType; + +/// Spark's `ArrayType(elementType, containsNull)`. +#[derive(Debug, Clone, PartialEq, Eq, Hash)] +pub struct ArrayType { + element_type: DataType, + contains_null: bool, +} + +impl ArrayType { + /// `array`; `contains_null` says whether elements may be + /// null (Spark's default, and a DDL `array<...>`'s, is `true`). + pub fn new(element_type: DataType, contains_null: bool) -> Self { + Self { + element_type, + contains_null, + } + } + + /// The element type. + pub fn element_type(&self) -> &DataType { + &self.element_type + } + + /// Whether an element may be null. + pub fn contains_null(&self) -> bool { + self.contains_null + } +} + +/// Spark's `MapType(keyType, valueType, valueContainsNull)`. Keys are never +/// null. +#[derive(Debug, Clone, PartialEq, Eq, Hash)] +pub struct MapType { + key_type: DataType, + value_type: DataType, + value_contains_null: bool, +} + +impl MapType { + /// `map`; `value_contains_null` says whether a + /// value may be null (Spark's default, and a DDL `map<...>`'s, is `true`). + pub fn new(key_type: DataType, value_type: DataType, value_contains_null: bool) -> Self { + Self { + key_type, + value_type, + value_contains_null, + } + } + + /// The key type. + pub fn key_type(&self) -> &DataType { + &self.key_type + } + + /// The value type. + pub fn value_type(&self) -> &DataType { + &self.value_type + } + + /// Whether a value may be null. + pub fn value_contains_null(&self) -> bool { + self.value_contains_null + } +} + +/// Spark's `Metadata` of a struct field: a JSON object (a DDL `COMMENT 'c'` +/// is `{"comment":"c"}`). Keys are kept sorted; Spark prints a map of up to +/// four keys in insertion order, so the printed order of a field with +/// several keys can differ from Spark's. +#[derive(Debug, Clone, Default, PartialEq, Eq, Hash)] +pub struct Metadata(Map); + +impl Metadata { + /// The metadata key of a field's comment. + pub const COMMENT: &'static str = "comment"; + + /// No entries. + pub fn empty() -> Self { + Self::default() + } + + /// The metadata of the JSON object `map`. + pub fn from_map(map: Map) -> Self { + Self(map) + } + + /// `{"comment": comment}`. + pub fn with_comment(comment: impl Into) -> Self { + let mut map = Map::new(); + map.insert(Self::COMMENT.to_owned(), Value::String(comment.into())); + Self(map) + } + + /// Whether there are no entries. + pub fn is_empty(&self) -> bool { + self.0.is_empty() + } + + /// The entries. + pub fn as_map(&self) -> &Map { + &self.0 + } + + /// The comment, if the field has a string comment. + pub fn comment(&self) -> Option<&str> { + self.0.get(Self::COMMENT).and_then(Value::as_str) + } +} + +/// Spark's `StructField(name, dataType, nullable, metadata)`. +#[derive(Debug, Clone, PartialEq, Eq, Hash)] +pub struct StructField { + name: String, + data_type: DataType, + nullable: bool, + metadata: Metadata, +} + +impl StructField { + /// A field without metadata. + pub fn new(name: impl Into, data_type: DataType, nullable: bool) -> Self { + Self { + name: name.into(), + data_type, + nullable, + metadata: Metadata::empty(), + } + } + + /// This field with `metadata`. + #[must_use] + pub fn with_metadata(mut self, metadata: Metadata) -> Self { + self.metadata = metadata; + self + } + + /// The name, as written (field names are case sensitive in the type). + pub fn name(&self) -> &str { + &self.name + } + + /// The type. + pub fn data_type(&self) -> &DataType { + &self.data_type + } + + /// Whether the field may be null. + pub fn nullable(&self) -> bool { + self.nullable + } + + /// The metadata. + pub fn metadata(&self) -> &Metadata { + &self.metadata + } +} + +/// Spark's `StructType(fields)`; field names may repeat, as in Spark. +#[derive(Debug, Clone, PartialEq, Eq, Hash)] +pub struct StructType { + fields: Arc<[StructField]>, +} + +impl StructType { + /// A struct of `fields`, in order. + pub fn new(fields: impl IntoIterator) -> Self { + Self { + fields: fields.into_iter().collect(), + } + } + + /// The fields, in order. + pub fn fields(&self) -> &[StructField] { + &self.fields + } +} + +impl From for DataType { + fn from(a: ArrayType) -> Self { + Self::Array(Arc::new(a)) + } +} + +impl From for DataType { + fn from(m: MapType) -> Self { + Self::Map(Arc::new(m)) + } +} + +impl From for DataType { + fn from(s: StructType) -> Self { + Self::Struct(s) + } +} + +/// Java's `String.equalsIgnoreCase`, which Spark compares field names +/// with: as many UTF-16 units, and each character equal, equal upper-cased +/// or equal lower-cased after upper-casing, by Java's simple (one character) +/// case mapping. +pub fn equals_ignore_case(a: &str, b: &str) -> bool { + // Rust's mappings are the full ones; one that is not a single character + // (`ß` to `SS`) leaves the character as Java's simple mapping does. + fn simple(mut m: impl Iterator, c: char) -> char { + match (m.next(), m.next()) { + (Some(x), None) => x, + _ => c, + } + } + let upper = |c: char| simple(c.to_uppercase(), c); + let lower = |c: char| simple(c.to_lowercase(), c); + a.encode_utf16().count() == b.encode_utf16().count() + && a.chars() + .zip(b.chars()) + .all(|(x, y)| x == y || upper(x) == upper(y) || lower(upper(x)) == lower(upper(y))) +} + +/// How two types are compared. +#[derive(Clone, Copy)] +struct Loose { + /// Field names compared case-insensitively ([`equals_ignore_case`]). + ignore_case: bool, + /// Field names not compared at all. + ignore_names: bool, +} + +impl DataType { + /// The deepest nesting of array, map and struct types: 0 for an atomic + /// type, 1 for `array`. + pub fn nesting_depth(&self) -> usize { + // Iterative: a type built in code may be deeper than the stack allows + // a recursive walk to go. + let mut deepest = 0; + let mut stack = vec![(self, 0_usize)]; + while let Some((t, depth)) = stack.pop() { + deepest = deepest.max(depth); + match t { + Self::Array(a) => stack.push((&a.element_type, depth + 1)), + Self::Map(m) => { + stack.push((&m.key_type, depth + 1)); + stack.push((&m.value_type, depth + 1)); + } + Self::Struct(s) => stack.extend(s.fields.iter().map(|f| (&f.data_type, depth + 1))), + _ => {} + } + } + deepest + } + + /// Whether this is an array, map or struct type. + pub fn is_nested(&self) -> bool { + matches!(self, Self::Array(_) | Self::Map(_) | Self::Struct(_)) + } + + /// Spark's `DataType.equalsIgnoreNullability`: equal but for element, + /// value and field nullability and field metadata. + pub fn equals_ignore_nullability(&self, other: &Self) -> bool { + self.loose_eq( + other, + Loose { + ignore_case: false, + ignore_names: false, + }, + ) + } + + /// Spark's `DataType.equalsIgnoreCaseAndNullability`: as + /// [`Self::equals_ignore_nullability`], with field names compared + /// case-insensitively. + pub fn equals_ignore_case_and_nullability(&self, other: &Self) -> bool { + self.loose_eq( + other, + Loose { + ignore_case: true, + ignore_names: false, + }, + ) + } + + /// Spark's `DataType.equalsStructurally(_, _, ignoreNullability)`: the + /// same shape and atomic types, whatever the field names (and the + /// nullability, when `ignore_nullability`). + pub fn equals_structurally(&self, other: &Self, ignore_nullability: bool) -> bool { + if !ignore_nullability && !self.same_nullability(other) { + return false; + } + self.loose_eq( + other, + Loose { + ignore_case: true, + ignore_names: true, + }, + ) + } + + fn loose_eq(&self, other: &Self, how: Loose) -> bool { + match (self, other) { + (Self::Array(a), Self::Array(b)) => a.element_type.loose_eq(&b.element_type, how), + (Self::Map(a), Self::Map(b)) => { + a.key_type.loose_eq(&b.key_type, how) && a.value_type.loose_eq(&b.value_type, how) + } + (Self::Struct(a), Self::Struct(b)) => { + a.fields.len() == b.fields.len() + && a.fields.iter().zip(b.fields.iter()).all(|(x, y)| { + let names = how.ignore_names + || if how.ignore_case { + equals_ignore_case(&x.name, &y.name) + } else { + x.name == y.name + }; + names && x.data_type.loose_eq(&y.data_type, how) + }) + } + (a, b) => a == b, + } + } + + /// Whether every nested nullability flag agrees (shapes assumed equal). + fn same_nullability(&self, other: &Self) -> bool { + match (self, other) { + (Self::Array(a), Self::Array(b)) => { + a.contains_null == b.contains_null + && a.element_type.same_nullability(&b.element_type) + } + (Self::Map(a), Self::Map(b)) => { + a.value_contains_null == b.value_contains_null + && a.key_type.same_nullability(&b.key_type) + && a.value_type.same_nullability(&b.value_type) + } + (Self::Struct(a), Self::Struct(b)) => { + a.fields.len() == b.fields.len() + && a.fields.iter().zip(b.fields.iter()).all(|(x, y)| { + x.nullable == y.nullable && x.data_type.same_nullability(&y.data_type) + }) + } + _ => true, + } + } + + /// Spark's `StructType.unionLikeMerge` of two types that are equal + /// structurally (ignoring nullability): this type's names and metadata, + /// with each nullability flag set when either side's is. Types of other + /// shapes give this type unchanged. + #[must_use] + pub fn union_like_merge(&self, other: &Self) -> Self { + match (self, other) { + (Self::Array(a), Self::Array(b)) => ArrayType::new( + a.element_type.union_like_merge(&b.element_type), + a.contains_null || b.contains_null, + ) + .into(), + (Self::Map(a), Self::Map(b)) => MapType::new( + a.key_type.union_like_merge(&b.key_type), + a.value_type.union_like_merge(&b.value_type), + a.value_contains_null || b.value_contains_null, + ) + .into(), + (Self::Struct(a), Self::Struct(b)) if a.fields.len() == b.fields.len() => { + StructType::new( + a.fields + .iter() + .zip(b.fields.iter()) + .map(|(x, y)| StructField { + name: x.name.clone(), + data_type: x.data_type.union_like_merge(&y.data_type), + nullable: x.nullable || y.nullable, + metadata: x.metadata.clone(), + }), + ) + .into() + } + (a, _) => a.clone(), + } + } +} diff --git a/crates/krishiv-types/src/tests.rs b/crates/krishiv-types/src/tests.rs index 7f55d24dd..2428f0874 100644 --- a/crates/krishiv-types/src/tests.rs +++ b/crates/krishiv-types/src/tests.rs @@ -4,8 +4,11 @@ use std::sync::Arc; use arrow_schema::{DataType as ArrowType, Field, TimeUnit}; use krishiv_common::error::ErrorClass; -use crate::arrow::{SPARK_TYPE_METADATA_KEY, normalize_ingest_type}; -use crate::{DataType, DecimalStorage, DecimalType, parse_data_type}; +use crate::arrow::{SPARK_METADATA_KEY, SPARK_TYPE_METADATA_KEY, normalize_ingest_type}; +use crate::{ + ArrayType, DataType, DecimalStorage, DecimalType, MapType, Metadata, StructField, StructType, + parse_data_type, +}; fn dec(p: u32, s: u32) -> DataType { match DecimalType::try_new(p, s) { @@ -160,7 +163,7 @@ fn arrow_storage_alternatives() { ArrowType::Decimal32(5, 2), ArrowType::Decimal64(5, 2), ArrowType::Decimal256(10, 2), - ArrowType::List(Arc::new(Field::new("item", ArrowType::Int32, true))), + ArrowType::FixedSizeList(Arc::new(Field::new("item", ArrowType::Int32, true)), 2), ] { let e = DataType::from_arrow(&a).map_err(|e| e.error_class()); assert_eq!(e, Err(ErrorClass::KrishivUnsupportedFeature), "{a}"); @@ -332,7 +335,7 @@ fn parser_error_classes() { ); assert_eq!(class("decimal(5,6)"), Err(ErrorClass::LegacyErrorTemp1228)); assert_eq!( - class("array"), + class("array"), Err(ErrorClass::KrishivUnsupportedFeature) ); let e = parse_data_type("timestamp(3)").err(); @@ -367,3 +370,253 @@ fn void_is_spark_null_type() { assert_eq!(crate::parse_data_type("VOID"), Ok(DataType::Null)); assert_eq!(DataType::from_arrow(&ArrowType::Null), Ok(DataType::Null)); } + +fn ty(s: &str) -> DataType { + parse_data_type(s).unwrap_or_else(|e| panic!("{s}: {e}")) +} + +/// Nested types up to depth 4, with every nullability flag, metadata and +/// children that need an Arrow type tag. +fn nested_types() -> Vec { + let mut out: Vec = [ + "array", + "array>>>", + "map>", + "map,int>", + "struct<>", + "struct", + "array>>>", + "map>>>>", + "struct>>>", + ] + .into_iter() + .map(ty) + .collect(); + out.push(ArrayType::new(DataType::Long, false).into()); + out.push(MapType::new(DataType::String, DataType::Date, false).into()); + out +} + +#[test] +fn nested_types_round_trip_through_arrow_json_and_ddl() { + for t in nested_types() { + assert_eq!(DataType::from_arrow(&t.to_arrow()).as_ref(), Ok(&t), "{t}"); + let field = t.to_arrow_field("c", false); + assert!(field.metadata().is_empty(), "{t}"); + assert_eq!(DataType::from_arrow_field(&field).as_ref(), Ok(&t), "{t}"); + assert_eq!(DataType::from_json(&t.json()).as_ref(), Ok(&t), "{t}"); + // `sql` keeps nullability, quoting and comments; DDL defaults only + // the array and map flags. + let defaults = !matches!(&t, DataType::Array(a) if !a.contains_null()) + && !matches!(&t, DataType::Map(m) if !m.value_contains_null()); + if defaults { + assert_eq!(parse_data_type(&t.sql()).as_ref(), Ok(&t), "{t}"); + } + } + assert_eq!(ty("array>>>").nesting_depth(), 4); + assert_eq!(DataType::Integer.nesting_depth(), 0); +} + +#[test] +fn nested_arrow_layout_follows_spark_arrow_utils() { + let list = |f: Field| ArrowType::List(Arc::new(f)); + assert_eq!( + ty("array").to_arrow(), + list(Field::new("element", ArrowType::Int32, true)) + ); + let ArrowType::Map(entries, sorted) = ty("map").to_arrow() else { + panic!("not a map"); + }; + assert!(!sorted && !entries.is_nullable()); + assert_eq!(entries.name(), "entries"); + let ArrowType::Struct(kv) = entries.data_type() else { + panic!("map entries are not a struct"); + }; + assert_eq!((kv[0].name().as_str(), kv[0].is_nullable()), ("key", false)); + assert_eq!( + (kv[1].name().as_str(), kv[1].is_nullable()), + ("value", true) + ); + // A child that needs a tag carries it, as a column would. + let ArrowType::List(child) = ty("array").to_arrow() else { + panic!("not a list"); + }; + assert_eq!( + child + .metadata() + .get(SPARK_TYPE_METADATA_KEY) + .map(String::as_str), + Some("decimal(0,0)") + ); + // Other layouts read too: `LargeList`, any child names. + let large = ArrowType::LargeList(Arc::new(Field::new("item", ArrowType::Utf8, false))); + assert_eq!( + DataType::from_arrow(&large), + Ok(ArrayType::new(DataType::String, false).into()) + ); + let commented = ty("struct").to_arrow(); + let ArrowType::Struct(fields) = &commented else { + panic!("not a struct"); + }; + assert_eq!( + fields[0] + .metadata() + .get(SPARK_METADATA_KEY) + .map(String::as_str), + Some(r#"{"comment":"c"}"#) + ); +} + +#[test] +fn nesting_deeper_than_the_limit_is_refused_everywhere() { + let deep = |n: usize| format!("{}int{}", "array<".repeat(n), ">".repeat(n)); + let max = DataType::MAX_NESTING_DEPTH; + let ok = ty(&deep(max)); + assert_eq!(ok.nesting_depth(), max); + let class = + |r: Result| r.map_err(|e| e.error_class()); + assert_eq!( + class(parse_data_type(&deep(max + 1))), + Err(ErrorClass::FailedToParseTooComplex) + ); + assert_eq!(class(DataType::from_arrow(&ok.to_arrow())), Ok(ok.clone())); + let too_deep: DataType = ArrayType::new(ok.clone(), true).into(); + assert_eq!( + class(DataType::from_arrow(&too_deep.to_arrow())), + Err(ErrorClass::FailedToParseTooComplex) + ); + assert_eq!(class(DataType::from_json(&ok.json())), Ok(ok)); + assert_eq!( + class(DataType::from_json(&too_deep.json())), + Err(ErrorClass::FailedToParseTooComplex) + ); + // Far deeper input fails cleanly, without exhausting the stack. + assert!(parse_data_type(&deep(100_000)).is_err()); + assert!(DataType::from_json(&"[".repeat(100_000)).is_err()); +} + +#[test] +fn json_that_is_no_type_is_invalid_json_data_type() { + for json in [ + "", + "{}", + r#""int""#, + r#""bigint""#, + r#"{"type":"array","elementType":"integer"}"#, + r#"{"type":"array","elementType":"integer","containsNull":true,"x":1}"#, + r#"{"type":"map","keyType":"integer","valueType":"integer","valueContainsNull":1}"#, + r#"{"type":"struct","fields":[{"name":"a","type":"integer"}]}"#, + r#"{"type":"struct","fields":[{"name":"a","type":"integer","nullable":true,"metadata":[]}]}"#, + r#""decimal(1.5,0)""#, + ] { + let class = DataType::from_json(json).map_err(|e| e.error_class()); + assert_eq!(class, Err(ErrorClass::InvalidJsonDataType), "{json}"); + } + let json = |s: &str| DataType::from_json(s).map_err(|e| e.error_class()); + assert_eq!( + json(r#""decimal""#), + Ok(DataType::Decimal(DecimalType::USER_DEFAULT)) + ); + assert_eq!(json(r#""decimal( 10 , 2 )""#), Ok(dec(10, 2))); + assert_eq!( + json(r#""decimal(10,-1)""#), + Err(ErrorClass::NegativeScaleDisallowed) + ); + assert_eq!( + json(r#""char(3)""#), + Err(ErrorClass::KrishivUnsupportedFeature) + ); + // Metadata is optional, as in Spark. + assert_eq!( + json(r#"{"type":"struct","fields":[{"name":"a","type":"long","nullable":false}]}"#), + Ok(StructType::new([StructField::new("a", DataType::Long, false)]).into()) + ); +} + +#[test] +fn spark_type_equalities() { + let a = ty("struct,b:map>"); + let nullability: DataType = StructType::new([ + StructField::new("a", ArrayType::new(DataType::Integer, false).into(), false), + StructField::new( + "b", + MapType::new(DataType::String, DataType::Integer, false).into(), + true, + ) + .with_metadata(Metadata::with_comment("c")), + ]) + .into(); + assert_ne!(a, nullability); + assert!(a.equals_ignore_nullability(&nullability)); + let upper = ty("struct,b:map>"); + assert!(!a.equals_ignore_nullability(&upper)); + assert!(a.equals_ignore_case_and_nullability(&upper)); + let renamed = ty("struct,y:map>"); + assert!(!a.equals_ignore_case_and_nullability(&renamed)); + assert!(a.equals_structurally(&renamed, false)); + assert!(!a.equals_structurally(&nullability, false)); + assert!(a.equals_structurally(&nullability, true)); + assert!(!a.equals_structurally(&ty("struct,b:map>"), true)); + // Union: the first side's names and metadata, either side's nulls. + let merged = nullability.union_like_merge(&renamed); + assert_eq!( + merged.json(), + r#"{"type":"struct","fields":[{"name":"a","type":{"type":"array","elementType":"integer","containsNull":true},"nullable":true,"metadata":{}},{"name":"b","type":{"type":"map","keyType":"string","valueType":"integer","valueContainsNull":true},"nullable":true,"metadata":{"comment":"c"}}]}"# + ); +} + +#[test] +fn struct_strings_quote_names_and_escape_comments_as_spark() { + let t = ty(r"struct<`a b`:int comment 'it\'s', `select`:string not null, _x1:int>"); + assert_eq!(t.simple_string(), "struct"); + assert_eq!( + t.sql(), + r"STRUCT<`a b`: INT COMMENT 'it\'s', select: STRING NOT NULL, _x1: INT>" + ); + assert_eq!(parse_data_type(&t.sql()).as_ref(), Ok(&t)); + // The empty struct parses as `<>` and as `< >`. + assert_eq!(ty("struct<>"), ty("struct< >")); +} + +/// Metadata keys print sorted whatever order they arrive in: the JSON form, +/// and so the plan codec's bytes, must not depend on serde_json's map +/// (`preserve_order` enabled by any crate would change it). +#[test] +fn metadata_prints_with_sorted_keys() { + let t = DataType::from_json( + r#"{"type":"struct","fields":[{"name":"a","type":"integer","nullable":true,"metadata":{"z":1,"comment":"c","b":[true]}}]}"#, + ) + .unwrap(); + assert!( + t.json() + .contains(r#""metadata":{"b":[true],"comment":"c","z":1}"#), + "{}", + t.json() + ); +} + +#[test] +fn field_names_compare_as_java_ignores_case() { + use crate::equals_ignore_case as eq; + assert!(eq("abc", "ABC") && eq("é", "É") && eq("straße", "STRAßE")); + assert!(!eq("straße", "STRASSE") && !eq("a", "b") && !eq("a", "aa")); + let t = ty("struct<`é`:int>"); + assert!(t.equals_ignore_case_and_nullability(&ty("struct<`É`:int>"))); +} + +#[test] +fn a_type_tag_counts_towards_the_nesting_limit() { + let inner = format!( + "{}int{}", + "array<".repeat(DataType::MAX_NESTING_DEPTH), + ">".repeat(DataType::MAX_NESTING_DEPTH) + ); + let tagged = Field::new("element", ArrowType::Int32, true).with_metadata( + std::collections::HashMap::from([(SPARK_TYPE_METADATA_KEY.to_owned(), inner)]), + ); + let outer = ArrowType::List(Arc::new(tagged)); + assert_eq!( + DataType::from_arrow(&outer).map_err(|e| e.error_class()), + Err(ErrorClass::FailedToParseTooComplex) + ); +} diff --git a/crates/krishiv-types/tests/spark_type_strings.rs b/crates/krishiv-types/tests/spark_type_strings.rs index 4af27aeeb..be084f297 100644 --- a/crates/krishiv-types/tests/spark_type_strings.rs +++ b/crates/krishiv-types/tests/spark_type_strings.rs @@ -15,9 +15,6 @@ use serde_json::Value; /// Spark types that parse in Spark but are out of scope here; Krishiv /// rejects them with `KRISHIV_UNSUPPORTED_FEATURE`. const OUT_OF_SCOPE: &[&str] = &[ - "array", - "map", - "struct", "char(5)", "varchar(10)", "interval year to month", @@ -28,8 +25,18 @@ const OUT_OF_SCOPE: &[&str] = &[ // Spark treats this as plain `string` but prints the collation; all // collations are out of scope for now. "string collate utf8_binary", + "array", + "array", + "array", + "struct", ]; +/// Inputs Spark parses that nest deeper than `DataType::MAX_NESTING_DEPTH`; +/// Krishiv refuses them with `FAILED_TO_PARSE_TOO_COMPLEX`. +fn too_deep(input: &str) -> bool { + input.matches('<').count() > DataType::MAX_NESTING_DEPTH +} + /// Inputs where Spark fails without an error class; Krishiv raises the /// class named here instead. const UNCLASSIFIED_IN_SPARK: &[(&str, ErrorClass)] = &[ @@ -69,8 +76,13 @@ fn assert_renders(t: &DataType, case: &Value) { assert_eq!(t.catalog_string(), text(case, "catalog_string"), "{case}"); assert_eq!(t.sql(), text(case, "sql"), "{case}"); assert_eq!(t.type_name(), text(case, "type_name"), "{case}"); + assert_eq!(t.json(), text(case, "json"), "{case}"); + assert_eq!(DataType::from_json(&t.json()).as_ref(), Ok(t), "{case}"); + // A simpleString parses back unless it lists too few fields or a field + // name that needs quoting. + let reparses = case.get("simple_string_reparses").and_then(Value::as_bool); let reparsed = parse_data_type(text(case, "simple_string")); - assert_eq!(reparsed.as_ref(), Ok(t), "{case}"); + assert_eq!(Some(reparsed.as_ref() == Ok(t)), reparses, "{case}"); } /// Our error matches Spark's class, SQLSTATE and message parameters. @@ -117,6 +129,9 @@ fn ddl_strings_match_spark() { assert_renders(t, case); in_scope.insert(t.simple_string()); } + (None, Err(e)) if too_deep(input) => { + assert_eq!(e.error_class(), ErrorClass::FailedToParseTooComplex); + } (None, Err(e)) => { assert!(OUT_OF_SCOPE.contains(&input), "{input:?}: {e}"); assert_eq!(e.error_class(), ErrorClass::KrishivUnsupportedFeature); @@ -149,6 +164,14 @@ fn ddl_strings_match_spark() { "date", "timestamp", "timestamp_ntz", + "array", + "array>>>", + "map>", + "struct<>", + "struct", + "array>>>", + "map>>>>", + "struct>>>", ] { assert!(in_scope.contains(t), "{t} missing from fixture"); } diff --git a/proto/krishiv/plan/v1/plan.proto b/proto/krishiv/plan/v1/plan.proto index d91e3cbd9..44412ebba 100644 --- a/proto/krishiv/plan/v1/plan.proto +++ b/proto/krishiv/plan/v1/plan.proto @@ -620,11 +620,17 @@ message DataType { KIND_DATE = 12; KIND_TIMESTAMP = 13; KIND_TIMESTAMP_NTZ = 14; + // feature: nested_types + KIND_NESTED = 15; } Kind kind = 1; // `KIND_DECIMAL` only. uint32 precision = 2; uint32 scale = 3; + // feature: nested_types + // `KIND_NESTED` only: an array, map or struct type as Spark's JSON form + // (`DataType.json`), which keeps nullability and field metadata. + string json = 4; } enum YearMonthField { diff --git a/python/tests/test_column_types_breadth.py b/python/tests/test_column_types_breadth.py index a554b7d67..9a930eaf4 100644 --- a/python/tests/test_column_types_breadth.py +++ b/python/tests/test_column_types_breadth.py @@ -219,9 +219,10 @@ def test_try_cast(spark: SparkSession) -> None: assert df.select(df.name.try_cast(T.LongType()).alias("n")).dtypes == [("n", "bigint")] with pytest.raises(errors.PySparkTypeError): df.name.try_cast(1) # ty: ignore[invalid-argument-type] - with pytest.raises(errors.UnsupportedOperationException) as bad: + # Arrays are a supported type; a string simply cannot be cast to one. + with pytest.raises(errors.AnalysisException) as bad: _ = df.select(df.name.try_cast(T.ArrayType(T.StringType()))).columns - assert bad.value.getCondition() == "KRISHIV_UNSUPPORTED_FEATURE" + assert bad.value.getCondition() == "DATATYPE_MISMATCH.CAST_WITHOUT_SUGGESTION" # types --------------------------------------------------------------------------- diff --git a/tests/fixtures/analysis/registry-matrix/hash.json b/tests/fixtures/analysis/registry-matrix/hash.json index 551060afd..5bb40c20c 100644 --- a/tests/fixtures/analysis/registry-matrix/hash.json +++ b/tests/fixtures/analysis/registry-matrix/hash.json @@ -42,11 +42,11 @@ }, { "id": "hash#16de041f73d5/ansi_on", - "reason": "SELECT hash('Spark', CAST(array() AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT hash('Spark', CAST(array() AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "hash#16de041f73d5/ansi_off", - "reason": "SELECT hash('Spark', CAST(array() AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT hash('Spark', CAST(array() AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "hash#17ae377c1c3e/ansi_on", @@ -168,14 +168,6 @@ "id": "hash#51a176d3611e/ansi_off", "reason": "SELECT hash('Spark', map('a', 1), 2) | KRISHIV_UNSUPPORTED_FEATURE: function map where Spark has DATATYPE_MISMATCH.HASH_MAP_TYPE" }, - { - "id": "hash#52e23c98a0c0/ansi_on", - "reason": "SELECT hash('Spark', CAST(NULL AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, - { - "id": "hash#52e23c98a0c0/ansi_off", - "reason": "SELECT hash('Spark', CAST(NULL AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, { "id": "hash#5556de897838/ansi_on", "reason": "SELECT hash('1', array(123), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" @@ -394,11 +386,11 @@ }, { "id": "hash#be26072f51ea/ansi_on", - "reason": "SELECT hash('Spark', CAST(array(NULL) AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT hash('Spark', CAST(array(NULL) AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "hash#be26072f51ea/ansi_off", - "reason": "SELECT hash('Spark', CAST(array(NULL) AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT hash('Spark', CAST(array(NULL) AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "hash#cca3887f32a9/ansi_on", diff --git a/tests/fixtures/analysis/registry-matrix/ifnull.json b/tests/fixtures/analysis/registry-matrix/ifnull.json index 00527dd31..68a219829 100644 --- a/tests/fixtures/analysis/registry-matrix/ifnull.json +++ b/tests/fixtures/analysis/registry-matrix/ifnull.json @@ -32,14 +32,6 @@ "id": "ifnull#1d03a62f2a6d/ansi_off", "reason": "SELECT ifnull(true, array('2')) | KRISHIV_UNSUPPORTED_FEATURE: function array where Spark has DATATYPE_MISMATCH.DATA_DIFF_TYPES" }, - { - "id": "ifnull#225c9e5d747a/ansi_on", - "reason": "SELECT ifnull(NULL, CAST(NULL AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, - { - "id": "ifnull#225c9e5d747a/ansi_off", - "reason": "SELECT ifnull(NULL, CAST(NULL AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, { "id": "ifnull#24f22a74561c/ansi_on", "reason": "SELECT ifnull(named_struct('a', 1, 'b', 'x'), array('2')) | KRISHIV_UNSUPPORTED_FEATURE: function named_struct where Spark has DATATYPE_MISMATCH.DATA_DIFF_TYPES" @@ -130,11 +122,11 @@ }, { "id": "ifnull#86e8390fb86e/ansi_on", - "reason": "SELECT ifnull(NULL, CAST(array() AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT ifnull(NULL, CAST(array() AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "ifnull#86e8390fb86e/ansi_off", - "reason": "SELECT ifnull(NULL, CAST(array() AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT ifnull(NULL, CAST(array() AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "ifnull#968ad8ccd489/ansi_on", @@ -170,11 +162,11 @@ }, { "id": "ifnull#b2af6cfe400b/ansi_on", - "reason": "SELECT ifnull(NULL, CAST(array(NULL) AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT ifnull(NULL, CAST(array(NULL) AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "ifnull#b2af6cfe400b/ansi_off", - "reason": "SELECT ifnull(NULL, CAST(array(NULL) AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT ifnull(NULL, CAST(array(NULL) AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "ifnull#bfe8bb89a454/ansi_on", diff --git a/tests/fixtures/analysis/registry-matrix/nvl.json b/tests/fixtures/analysis/registry-matrix/nvl.json index 10df60796..118e76d11 100644 --- a/tests/fixtures/analysis/registry-matrix/nvl.json +++ b/tests/fixtures/analysis/registry-matrix/nvl.json @@ -2,11 +2,11 @@ "entries": [ { "id": "nvl#0460fbe2242f/ansi_on", - "reason": "SELECT nvl(NULL, CAST(array() AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT nvl(NULL, CAST(array() AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "nvl#0460fbe2242f/ansi_off", - "reason": "SELECT nvl(NULL, CAST(array() AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT nvl(NULL, CAST(array() AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "nvl#07bf6900cb48/ansi_on", @@ -72,14 +72,6 @@ "id": "nvl#30624b2144a9/ansi_off", "reason": "SELECT nvl(CAST(1 AS SMALLINT), array('2')) | KRISHIV_UNSUPPORTED_FEATURE: function array where Spark has DATATYPE_MISMATCH.DATA_DIFF_TYPES" }, - { - "id": "nvl#41a41efe1197/ansi_on", - "reason": "SELECT nvl(NULL, CAST(NULL AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, - { - "id": "nvl#41a41efe1197/ansi_off", - "reason": "SELECT nvl(NULL, CAST(NULL AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, { "id": "nvl#57346bea4d30/ansi_on", "reason": "SELECT nvl(TIMESTAMP_NTZ'2024-02-29 12:34:56.123456', array('2')) | KRISHIV_UNSUPPORTED_FEATURE: function array where Spark has DATATYPE_MISMATCH.DATA_DIFF_TYPES" @@ -114,11 +106,11 @@ }, { "id": "nvl#880ab25c6d7a/ansi_on", - "reason": "SELECT nvl(NULL, CAST(array(NULL) AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT nvl(NULL, CAST(array(NULL) AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "nvl#880ab25c6d7a/ansi_off", - "reason": "SELECT nvl(NULL, CAST(array(NULL) AS array)) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT nvl(NULL, CAST(array(NULL) AS array)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "nvl#90bda17a241c/ansi_on", diff --git a/tests/fixtures/analysis/registry-matrix/xxhash64.json b/tests/fixtures/analysis/registry-matrix/xxhash64.json index 183334bb5..ef3a4b91a 100644 --- a/tests/fixtures/analysis/registry-matrix/xxhash64.json +++ b/tests/fixtures/analysis/registry-matrix/xxhash64.json @@ -168,14 +168,6 @@ "id": "xxhash64#6175a87d7b79/ansi_off", "reason": "SELECT xxhash64('Spark', array(123), CAST(2147483647 AS INT)) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, - { - "id": "xxhash64#694a9f8ede1b/ansi_on", - "reason": "SELECT xxhash64('Spark', CAST(NULL AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, - { - "id": "xxhash64#694a9f8ede1b/ansi_off", - "reason": "SELECT xxhash64('Spark', CAST(NULL AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, { "id": "xxhash64#69b363904109/ansi_on", "reason": "SELECT xxhash64('1', array(123), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" @@ -250,11 +242,11 @@ }, { "id": "xxhash64#88be02abdc0a/ansi_on", - "reason": "SELECT xxhash64('Spark', CAST(array() AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT xxhash64('Spark', CAST(array() AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "xxhash64#88be02abdc0a/ansi_off", - "reason": "SELECT xxhash64('Spark', CAST(array() AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT xxhash64('Spark', CAST(array() AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "xxhash64#89b3d078d407/ansi_on", @@ -338,11 +330,11 @@ }, { "id": "xxhash64#c9a780cf156a/ansi_on", - "reason": "SELECT xxhash64('Spark', CAST(array(NULL) AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT xxhash64('Spark', CAST(array(NULL) AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "xxhash64#c9a780cf156a/ansi_off", - "reason": "SELECT xxhash64('Spark', CAST(array(NULL) AS array), 2) | parse: KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" + "reason": "SELECT xxhash64('Spark', CAST(array(NULL) AS array), 2) | KRISHIV_UNSUPPORTED_FEATURE: function array" }, { "id": "xxhash64#d38029db62a8/ansi_on", diff --git a/tests/fixtures/analysis/type-coercion/typeCoercion/native/stringCastAndExpressions.json b/tests/fixtures/analysis/type-coercion/typeCoercion/native/stringCastAndExpressions.json index 6e61c10ca..9486f5969 100644 --- a/tests/fixtures/analysis/type-coercion/typeCoercion/native/stringCastAndExpressions.json +++ b/tests/fixtures/analysis/type-coercion/typeCoercion/native/stringCastAndExpressions.json @@ -1,17 +1,5 @@ { "entries": [ - { - "query": "select cast(a as array) from t", - "reason": "KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"ARRAY\")" - }, - { - "query": "select cast(a as map) from t", - "reason": "KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"MAP\")" - }, - { - "query": "select cast(a as struct) from t", - "reason": "KRISHIV_UNSUPPORTED_FEATURE: nested data types (\"STRUCT\")" - }, { "query": "select from_json(a, 'a INT') from t", "reason": "KRISHIV_UNSUPPORTED_FEATURE: function from_json" diff --git a/tests/fixtures/spark-4.2.0/type-strings.json b/tests/fixtures/spark-4.2.0/type-strings.json index 4411c9794..d26d29158 100644 --- a/tests/fixtures/spark-4.2.0/type-strings.json +++ b/tests/fixtures/spark-4.2.0/type-strings.json @@ -1,7 +1,7 @@ { "format_version": 1, "spark_version": "4.2.0", - "image": "apache/spark:4.2.0@sha256:fc64959c04bd87b0ac686be9aaa9008b69cdb1afc695400528279c8b01f43d89", + "image": "apache/spark:4.2.0", "generator": "tests/scripts/gen_type_strings.py", "notes": "DDL strings parsed by CatalystSqlParser.parseDataType; decimal_constructor builds DecimalType(precision, scale) directly. Errors carry Spark's condition, SQLSTATE and message parameters.", "ddl": [ @@ -1135,6 +1135,1081 @@ "ddl": "decimal(99999999999)", "error_class": "UNCLASSIFIED", "exception": "java.lang.NumberFormatException" + }, + { + "ddl": "ARRAY", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"integer\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array< int >", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"integer\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "Array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"string\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array>", + "simple_string": "array>", + "catalog_string": "array>", + "sql": "ARRAY>", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":\"integer\",\"containsNull\":true},\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array>>>", + "simple_string": "array>>>", + "catalog_string": "array>>>", + "sql": "ARRAY>>>", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":\"integer\",\"containsNull\":true},\"containsNull\":true},\"containsNull\":true},\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"decimal(10,2)\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"void\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"timestamp_ntz\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"decimal(0,0)\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "MAP>", + "simple_string": "map>", + "catalog_string": "map>", + "sql": "MAP>", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":\"string\",\"valueType\":{\"type\":\"array\",\"elementType\":\"double\",\"containsNull\":true},\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "map>", + "simple_string": "map>", + "catalog_string": "map>", + "sql": "MAP>", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":\"integer\",\"valueType\":{\"type\":\"map\",\"keyType\":\"integer\",\"valueType\":\"integer\",\"valueContainsNull\":true},\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "map,int>", + "simple_string": "map,int>", + "catalog_string": "map,int>", + "sql": "MAP, INT>", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":{\"type\":\"map\",\"keyType\":\"integer\",\"valueType\":\"integer\",\"valueContainsNull\":true},\"valueType\":\"integer\",\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "map,int>", + "simple_string": "map,int>", + "catalog_string": "map,int>", + "sql": "MAP, INT>", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":{\"type\":\"array\",\"elementType\":\"integer\",\"containsNull\":true},\"valueType\":\"integer\",\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "map,string>", + "simple_string": "map,string>", + "catalog_string": "map,string>", + "sql": "MAP, STRING>", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}}]},\"valueType\":\"string\",\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "map", + "simple_string": "map", + "catalog_string": "map", + "sql": "MAP", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":\"void\",\"valueType\":\"integer\",\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "map< string , int >", + "simple_string": "map", + "catalog_string": "map", + "sql": "MAP", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":\"string\",\"valueType\":\"integer\",\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"b\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": true + }, + { + "ddl": "STRUCT", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"b\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"A\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"b\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": true + }, + { + "ddl": "struct<>", + "simple_string": "struct<>", + "catalog_string": "struct<>", + "sql": "STRUCT<>", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[]}", + "simple_string_reparses": true + }, + { + "ddl": "struct< >", + "simple_string": "struct<>", + "catalog_string": "struct<>", + "sql": "STRUCT<>", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[]}", + "simple_string_reparses": true + }, + { + "ddl": "STRUCT<>", + "simple_string": "struct<>", + "catalog_string": "struct<>", + "sql": "STRUCT<>", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[]}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":false,\"metadata\":{}}]}", + "simple_string_reparses": false + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":false,\"metadata\":{}},{\"name\":\"b\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": false + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", 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{ + "ddl": "array", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "struct", + "error_class": "DECIMAL_PRECISION_EXCEEDS_MAX_PRECISION", + "sql_state": "22003", + "message_parameters": { + "maxPrecision": "38", + "precision": "39" + } + }, + { + "ddl": "array", + "error_class": "_LEGACY_ERROR_TEMP_1228", + "sql_state": "", + "message_parameters": { + "precision": "5", + "scale": "6" + } + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{\"comment\":\"c\\u0001…
é\\u001F\"}}]}", + "simple_string_reparses": false + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"map\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"array\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": true + }, + { + "ddl": "array=", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "struct>>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'>'", + "hint": ": extra input '>'" + } + }, + { + "ddl": "array>>>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'>>'", + "hint": ": extra input '>>'" + } + }, + { + "ddl": "array,", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "','", + "hint": ": extra input ','" + } + }, + { + "ddl": "array int", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'int'", + "hint": ": extra input 'int'" + } + }, + { + "ddl": "struct x", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'x'", + "hint": ": extra input 'x'" + } + }, + { + "ddl": "array", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "array>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "map", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "array>>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'>>'", + "hint": ": extra input '>>'" + } + }, + { + "ddl": "struct,", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "','", + "hint": ": extra input ','" + } + }, + { + "ddl": "map<>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<>'", + "hint": "" + } + }, + { + "ddl": "array>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "struct>", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "array /* c */", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"integer\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":false,\"metadata\":{\"comment\":\"x\"}},{\"name\":\"b\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": false + }, + { + "ddl": "array>", + "error_class": "UNSUPPORTED_DATATYPE", + "sql_state": "0A000", + "message_parameters": { + "typeName": "\"FOO\"" + } + }, + { + "ddl": "array>", + "error_class": "INVALID_IDENTIFIER", + "sql_state": "42602", + "message_parameters": { + "ident": "é" + } + }, + { + "ddl": "struct>", + "error_class": "INVALID_IDENTIFIER", + "sql_state": "42602", + "message_parameters": { + "ident": "b-c" + } + }, + { + "ddl": "array>,", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'>'", + "hint": "" + } + }, + { + "ddl": "array", + "error_class": "PARSE_SYNTAX_ERROR", + "sql_state": "42601", + "message_parameters": { + "error": "'<'", + "hint": "" + } + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"string collate UTF8_LCASE\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"interval year\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "array", + "simple_string": "array", + "catalog_string": "array", + "sql": "ARRAY", + "type_name": "array", + "json": "{\"type\":\"array\",\"elementType\":\"char(3)\",\"containsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"variant\",\"nullable\":true,\"metadata\":{}}]}", + "simple_string_reparses": true + }, + { + "ddl": "map/* c */", + "simple_string": "map", + "catalog_string": "map", + "sql": "MAP", + "type_name": "map", + "json": "{\"type\":\"map\",\"keyType\":\"string\",\"valueType\":\"integer\",\"valueContainsNull\":true}", + "simple_string_reparses": true + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{\"comment\":\"raw\\\\n\"}}]}", + "simple_string_reparses": false + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{\"comment\":\"xy\"}}]}", + "simple_string_reparses": false + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{\"comment\":\"line\\nbreak\"}}]}", + "simple_string_reparses": false + }, + { + "ddl": "struct", + "simple_string": "struct", + "catalog_string": "struct", + "sql": "STRUCT", + "type_name": "struct", + "json": "{\"type\":\"struct\",\"fields\":[{\"name\":\"a\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{\"comment\":\"AA\"}}]}", + "simple_string_reparses": false } ], "decimal_constructor": [ @@ -1236,6 +2311,29 @@ ], "sqlState": "22003" }, + "INCOMPLETE_TYPE_DEFINITION": { + "message": [ + "Incomplete complex type:" + ], + "subClass": { + "ARRAY": { + "message": [ + "The definition of \"ARRAY\" type is incomplete. You must provide an element type. For example: \"ARRAY\"." + ] + }, + "MAP": { + "message": [ + "The definition of \"MAP\" type is incomplete. You must provide a key type and a value type. For example: \"MAP\"." + ] + }, + "STRUCT": { + "message": [ + "The definition of \"STRUCT\" type is incomplete. You must provide at least one field type. For example: \"STRUCT\"." + ] + } + }, + "sqlState": "42K01" + }, "INVALID_IDENTIFIER": { "message": [ "The unquoted identifier is invalid and must be back quoted as: ``.", diff --git a/tests/scripts/gen_type_strings.py b/tests/scripts/gen_type_strings.py index db781569c..94db7316b 100644 --- a/tests/scripts/gen_type_strings.py +++ b/tests/scripts/gen_type_strings.py @@ -178,6 +178,128 @@ "decimal(.5,0)", "decimal(5,99999999999)", "decimal(99999999999)", + # nested types (P2-T001b): arrays, maps, structs, nesting, nullability, comments, quoting + "ARRAY", + "array< int >", + "Array", + "array>", + "array>>>", + "array", + "array", + "array", + "array", + "MAP>", + "map>", + "map,int>", + "map,int>", + "map,string>", + "map", + "map< string , int >", + "struct", + "struct", + "STRUCT", + "struct", + "struct<>", + "struct< >", + "STRUCT<>", + "struct", + "struct", + "struct", + "struct", + 'struct', + "struct", + "struct", + "struct", + "struct", + "struct", + "struct<`a b`:int>", + "struct<`a``b`:int>", + "struct<`a.b`:int>", + "struct<`1a`:int>", + "struct<`1`:int>", + "struct<_a:int>", + "struct", + "struct", + "struct<`select`:int>", + "struct", + "struct", + "struct<é:int>", + "struct<`é`:int>", + "struct<`a:b`:int>", + "struct<` `:int>", + "struct>>>>", + "ARRAY>>>", + "map>>>>", + "struct>>>", + "array>", + "struct", + "struct>", + "struct", + "array>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>", + "array", + "array<>", + "array", + "array(int)", + "array", + "array>", + "map", + "map", + "map", + "map", + "map<,int>", + "struct", + "struct", + "struct", + "struct", + "struct<,a:int>", + "struct", + "struct", + "struct", + "struct", + "struct", + "struct", + "array not null", + "struct>", + "struct", + "struct", + "array", + "struct", + "array", + "struct", + "struct", + "array=", + "struct>>", + "array>>>", + "array,", + "array int", + "struct x", + "array", + "array>", + "map", + "array>>", + "struct,", + "map<>", + "array>", + "struct>", + "array /* c */", + "struct", + "array>", + "array>", + "struct>", + "array>,", + "array", + "array", + "array", + "array", + "struct", + "map/* c */", + "struct", + "struct", + "struct", + "struct", ] DECIMALS = [(38, 38), (38, 0), (1, 0), (18, 18), (39, 0), (0, 0), (5, 6), (10, -1), (100, 0)] diff --git a/tests/scripts/typestr/dump_types.scala b/tests/scripts/typestr/dump_types.scala index d97f85ad6..a505e5b61 100644 --- a/tests/scripts/typestr/dump_types.scala +++ b/tests/scripts/typestr/dump_types.scala @@ -35,7 +35,10 @@ def putType(m: ObjectNode, dt: DataType): Unit = { m.put("sql", dt.sql) m.put("type_name", dt.typeName) m.put("json", dt.json) - m.put("simple_string_reparses", CatalystSqlParser.parseDataType(dt.simpleString) == dt) + // A simpleString need not parse back (a struct field named `a b`, more fields than + // maxToStringFields); that is recorded as false, not as an error of the input. + m.put("simple_string_reparses", + scala.util.Try(CatalystSqlParser.parseDataType(dt.simpleString) == dt).getOrElse(false)) } val in = scala.io.Source.fromFile("/tmp/krishiv_cases.jsonl", "UTF-8") diff --git a/tests/spark-golden/known-failures-cases/postgreSQL/aggregates_part2.json b/tests/spark-golden/known-failures-cases/postgreSQL/aggregates_part2.json index 54084d95c..01b5cd467 100644 --- a/tests/spark-golden/known-failures-cases/postgreSQL/aggregates_part2.json +++ b/tests/spark-golden/known-failures-cases/postgreSQL/aggregates_part2.json @@ -7,10 +7,7 @@ 5, 6, 7, - 8, - 13, - 14, - 15 + 8 ], "generators": [ 27 diff --git a/tests/spark-golden/known-failures-cases/typeCoercion/native/stringCastAndExpressions.json b/tests/spark-golden/known-failures-cases/typeCoercion/native/stringCastAndExpressions.json index 9358ef932..2eee2cef3 100644 --- a/tests/spark-golden/known-failures-cases/typeCoercion/native/stringCastAndExpressions.json +++ b/tests/spark-golden/known-failures-cases/typeCoercion/native/stringCastAndExpressions.json @@ -3,9 +3,6 @@ "depends-on-command": [ 8, 9, - 12, - 13, - 14, 15, 16, 17, diff --git a/tests/spark-golden/known-failures-cases/udf/postgreSQL/udf-aggregates_part2.json b/tests/spark-golden/known-failures-cases/udf/postgreSQL/udf-aggregates_part2.json index b28b3390e..cc5e677c4 100644 --- a/tests/spark-golden/known-failures-cases/udf/postgreSQL/udf-aggregates_part2.json +++ b/tests/spark-golden/known-failures-cases/udf/postgreSQL/udf-aggregates_part2.json @@ -3,10 +3,7 @@ "depends-on-command": [ 2, 3, - 4, - 9, - 10, - 11 + 4 ], "test-udfs": [ 12,