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from __future__ import annotations
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2025-02-13 15:51:35 +01:00
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import typing as t
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from sqlglot import exp
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from sqlglot.dialects.dialect import rename_func
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from sqlglot.dialects.hive import _parse_ignore_nulls
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from sqlglot.dialects.spark2 import Spark2, temporary_storage_provider
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from sqlglot.helper import seq_get
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from sqlglot.transforms import (
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ctas_with_tmp_tables_to_create_tmp_view,
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remove_unique_constraints,
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preprocess,
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move_partitioned_by_to_schema_columns,
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)
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2025-02-13 06:15:54 +01:00
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2025-02-13 15:56:32 +01:00
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def _parse_datediff(args: t.List) -> exp.Expression:
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"""
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Although Spark docs don't mention the "unit" argument, Spark3 added support for
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it at some point. Databricks also supports this variant (see below).
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For example, in spark-sql (v3.3.1):
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- SELECT DATEDIFF('2020-01-01', '2020-01-05') results in -4
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- SELECT DATEDIFF(day, '2020-01-01', '2020-01-05') results in 4
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See also:
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- https://docs.databricks.com/sql/language-manual/functions/datediff3.html
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- https://docs.databricks.com/sql/language-manual/functions/datediff.html
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"""
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unit = None
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this = seq_get(args, 0)
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expression = seq_get(args, 1)
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if len(args) == 3:
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unit = this
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this = args[2]
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return exp.DateDiff(
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this=exp.TsOrDsToDate(this=this), expression=exp.TsOrDsToDate(this=expression), unit=unit
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)
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def _normalize_partition(e: exp.Expression) -> exp.Expression:
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"""Normalize the expressions in PARTITION BY (<expression>, <expression>, ...)"""
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if isinstance(e, str):
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return exp.to_identifier(e)
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if isinstance(e, exp.Literal):
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return exp.to_identifier(e.name)
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return e
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class Spark(Spark2):
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class Tokenizer(Spark2.Tokenizer):
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RAW_STRINGS = [
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(prefix + q, q)
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for q in t.cast(t.List[str], Spark2.Tokenizer.QUOTES)
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for prefix in ("r", "R")
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]
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class Parser(Spark2.Parser):
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FUNCTIONS = {
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**Spark2.Parser.FUNCTIONS,
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"ANY_VALUE": _parse_ignore_nulls(exp.AnyValue),
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"DATEDIFF": _parse_datediff,
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}
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2025-02-13 21:04:14 +01:00
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def _parse_generated_as_identity(
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self,
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) -> (
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exp.GeneratedAsIdentityColumnConstraint
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| exp.ComputedColumnConstraint
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| exp.GeneratedAsRowColumnConstraint
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):
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this = super()._parse_generated_as_identity()
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if this.expression:
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return self.expression(exp.ComputedColumnConstraint, this=this.expression)
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return this
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class Generator(Spark2.Generator):
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TYPE_MAPPING = {
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**Spark2.Generator.TYPE_MAPPING,
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exp.DataType.Type.MONEY: "DECIMAL(15, 4)",
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exp.DataType.Type.SMALLMONEY: "DECIMAL(6, 4)",
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exp.DataType.Type.UNIQUEIDENTIFIER: "STRING",
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}
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TRANSFORMS = {
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**Spark2.Generator.TRANSFORMS,
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exp.Create: preprocess(
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[
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remove_unique_constraints,
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lambda e: ctas_with_tmp_tables_to_create_tmp_view(
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e, temporary_storage_provider
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),
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move_partitioned_by_to_schema_columns,
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]
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),
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exp.PartitionedByProperty: lambda self,
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e: f"PARTITIONED BY {self.wrap(self.expressions(sqls=[_normalize_partition(e) for e in e.this.expressions], skip_first=True))}",
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exp.StartsWith: rename_func("STARTSWITH"),
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exp.TimestampAdd: lambda self, e: self.func(
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"DATEADD", e.args.get("unit") or "DAY", e.expression, e.this
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),
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exp.TryCast: lambda self, e: (
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self.trycast_sql(e) if e.args.get("safe") else self.cast_sql(e)
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),
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}
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TRANSFORMS.pop(exp.AnyValue)
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TRANSFORMS.pop(exp.DateDiff)
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TRANSFORMS.pop(exp.Group)
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def computedcolumnconstraint_sql(self, expression: exp.ComputedColumnConstraint) -> str:
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return f"GENERATED ALWAYS AS ({self.sql(expression, 'this')})"
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def anyvalue_sql(self, expression: exp.AnyValue) -> str:
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return self.function_fallback_sql(expression)
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def datediff_sql(self, expression: exp.DateDiff) -> str:
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unit = self.sql(expression, "unit")
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end = self.sql(expression, "this")
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start = self.sql(expression, "expression")
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if unit:
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return self.func("DATEDIFF", unit, start, end)
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return self.func("DATEDIFF", end, start)
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