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from __future__ import annotations
import typing as t
from sqlglot import exp, transforms
from sqlglot.dialects.dialect import (
concat_to_dpipe_sql,
concat_ws_to_dpipe_sql,
rename_func,
ts_or_ds_to_date_sql,
)
from sqlglot.dialects.postgres import Postgres
from sqlglot.helper import seq_get
from sqlglot.tokens import TokenType
def _json_sql(self: Redshift.Generator, expression: exp.JSONExtract | exp.JSONExtractScalar) -> str:
return f'{self.sql(expression, "this")}."{expression.expression.name}"'
def _parse_date_add(args: t.List) -> exp.DateAdd:
return exp.DateAdd(
this=exp.TsOrDsToDate(this=seq_get(args, 2)),
expression=seq_get(args, 1),
unit=seq_get(args, 0),
)
class Redshift(Postgres):
# https://docs.aws.amazon.com/redshift/latest/dg/r_names.html
RESOLVES_IDENTIFIERS_AS_UPPERCASE = None
SUPPORTS_USER_DEFINED_TYPES = False
INDEX_OFFSET = 0
TIME_FORMAT = "'YYYY-MM-DD HH:MI:SS'"
TIME_MAPPING = {
**Postgres.TIME_MAPPING,
"MON": "%b",
"HH": "%H",
}
class Parser(Postgres.Parser):
FUNCTIONS = {
**Postgres.Parser.FUNCTIONS,
"ADD_MONTHS": lambda args: exp.DateAdd(
this=exp.TsOrDsToDate(this=seq_get(args, 0)),
expression=seq_get(args, 1),
unit=exp.var("month"),
),
"DATEADD": _parse_date_add,
"DATE_ADD": _parse_date_add,
"DATEDIFF": lambda args: exp.DateDiff(
this=exp.TsOrDsToDate(this=seq_get(args, 2)),
expression=exp.TsOrDsToDate(this=seq_get(args, 1)),
unit=seq_get(args, 0),
),
"STRTOL": exp.FromBase.from_arg_list,
}
def _parse_table(
self,
schema: bool = False,
joins: bool = False,
alias_tokens: t.Optional[t.Collection[TokenType]] = None,
parse_bracket: bool = False,
) -> t.Optional[exp.Expression]:
# Redshift supports UNPIVOTing SUPER objects, e.g. `UNPIVOT foo.obj[0] AS val AT attr`
unpivot = self._match(TokenType.UNPIVOT)
table = super()._parse_table(
schema=schema,
joins=joins,
alias_tokens=alias_tokens,
parse_bracket=parse_bracket,
)
return self.expression(exp.Pivot, this=table, unpivot=True) if unpivot else table
def _parse_types(
self, check_func: bool = False, schema: bool = False, allow_identifiers: bool = True
) -> t.Optional[exp.Expression]:
this = super()._parse_types(
check_func=check_func, schema=schema, allow_identifiers=allow_identifiers
)
if (
isinstance(this, exp.DataType)
and this.is_type("varchar")
and this.expressions
and this.expressions[0].this == exp.column("MAX")
):
this.set("expressions", [exp.var("MAX")])
return this
def _parse_convert(self, strict: bool) -> t.Optional[exp.Expression]:
to = self._parse_types()
self._match(TokenType.COMMA)
this = self._parse_bitwise()
return self.expression(exp.TryCast, this=this, to=to)
class Tokenizer(Postgres.Tokenizer):
BIT_STRINGS = []
HEX_STRINGS = []
STRING_ESCAPES = ["\\", "'"]
KEYWORDS = {
**Postgres.Tokenizer.KEYWORDS,
"HLLSKETCH": TokenType.HLLSKETCH,
"SUPER": TokenType.SUPER,
"SYSDATE": TokenType.CURRENT_TIMESTAMP,
"TOP": TokenType.TOP,
"UNLOAD": TokenType.COMMAND,
"VARBYTE": TokenType.VARBINARY,
}
# Redshift allows # to appear as a table identifier prefix
SINGLE_TOKENS = Postgres.Tokenizer.SINGLE_TOKENS.copy()
SINGLE_TOKENS.pop("#")
class Generator(Postgres.Generator):
LOCKING_READS_SUPPORTED = False
RENAME_TABLE_WITH_DB = False
QUERY_HINTS = False
VALUES_AS_TABLE = False
TZ_TO_WITH_TIME_ZONE = True
NVL2_SUPPORTED = True
TYPE_MAPPING = {
**Postgres.Generator.TYPE_MAPPING,
exp.DataType.Type.BINARY: "VARBYTE",
exp.DataType.Type.INT: "INTEGER",
exp.DataType.Type.TIMETZ: "TIME",
exp.DataType.Type.TIMESTAMPTZ: "TIMESTAMP",
exp.DataType.Type.VARBINARY: "VARBYTE",
}
PROPERTIES_LOCATION = {
**Postgres.Generator.PROPERTIES_LOCATION,
exp.LikeProperty: exp.Properties.Location.POST_WITH,
}
TRANSFORMS = {
**Postgres.Generator.TRANSFORMS,
exp.Concat: concat_to_dpipe_sql,
exp.ConcatWs: concat_ws_to_dpipe_sql,
exp.CurrentTimestamp: lambda self, e: "SYSDATE",
exp.DateAdd: lambda self, e: self.func(
"DATEADD", exp.var(e.text("unit") or "day"), e.expression, e.this
),
exp.DateDiff: lambda self, e: self.func(
"DATEDIFF", exp.var(e.text("unit") or "day"), e.expression, e.this
),
exp.DistKeyProperty: lambda self, e: f"DISTKEY({e.name})",
exp.DistStyleProperty: lambda self, e: self.naked_property(e),
exp.FromBase: rename_func("STRTOL"),
exp.JSONExtract: _json_sql,
exp.JSONExtractScalar: _json_sql,
exp.SafeConcat: concat_to_dpipe_sql,
exp.Select: transforms.preprocess(
[transforms.eliminate_distinct_on, transforms.eliminate_semi_and_anti_joins]
),
exp.SortKeyProperty: lambda self, e: f"{'COMPOUND ' if e.args['compound'] else ''}SORTKEY({self.format_args(*e.this)})",
exp.TsOrDsToDate: ts_or_ds_to_date_sql("redshift"),
}
# Postgres maps exp.Pivot to no_pivot_sql, but Redshift support pivots
TRANSFORMS.pop(exp.Pivot)
# Redshift uses the POW | POWER (expr1, expr2) syntax instead of expr1 ^ expr2 (postgres)
TRANSFORMS.pop(exp.Pow)
# Redshift supports ANY_VALUE(..)
TRANSFORMS.pop(exp.AnyValue)
RESERVED_KEYWORDS = {*Postgres.Generator.RESERVED_KEYWORDS, "snapshot", "type"}
def with_properties(self, properties: exp.Properties) -> str:
"""Redshift doesn't have `WITH` as part of their with_properties so we remove it"""
return self.properties(properties, prefix=" ", suffix="")
def datatype_sql(self, expression: exp.DataType) -> str:
"""
Redshift converts the `TEXT` data type to `VARCHAR(255)` by default when people more generally mean
VARCHAR of max length which is `VARCHAR(max)` in Redshift. Therefore if we get a `TEXT` data type
without precision we convert it to `VARCHAR(max)` and if it does have precision then we just convert
`TEXT` to `VARCHAR`.
"""
if expression.is_type("text"):
expression = expression.copy()
expression.set("this", exp.DataType.Type.VARCHAR)
precision = expression.args.get("expressions")
if not precision:
expression.append("expressions", exp.var("MAX"))
return super().datatype_sql(expression)
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