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from __future__ import annotations
import typing as t
from sqlglot import exp, parser
from sqlglot.dialects.dialect import create_with_partitions_sql, rename_func, trim_sql
from sqlglot.dialects.hive import Hive
from sqlglot.helper import seq_get
def _create_sql(self: Hive.Generator, e: exp.Create) -> str:
kind = e.args["kind"]
properties = e.args.get("properties")
if kind.upper() == "TABLE" and any(
isinstance(prop, exp.TemporaryProperty)
for prop in (properties.expressions if properties else [])
):
return f"CREATE TEMPORARY VIEW {self.sql(e, 'this')} AS {self.sql(e, 'expression')}"
return create_with_partitions_sql(self, e)
def _map_sql(self: Hive.Generator, expression: exp.Map) -> str:
keys = self.sql(expression.args["keys"])
values = self.sql(expression.args["values"])
return f"MAP_FROM_ARRAYS({keys}, {values})"
def _str_to_date(self: Hive.Generator, expression: exp.StrToDate) -> str:
this = self.sql(expression, "this")
time_format = self.format_time(expression)
if time_format == Hive.date_format:
return f"TO_DATE({this})"
return f"TO_DATE({this}, {time_format})"
def _unix_to_time_sql(self: Hive.Generator, expression: exp.UnixToTime) -> str:
scale = expression.args.get("scale")
timestamp = self.sql(expression, "this")
if scale is None:
return f"FROM_UNIXTIME({timestamp})"
if scale == exp.UnixToTime.SECONDS:
return f"TIMESTAMP_SECONDS({timestamp})"
if scale == exp.UnixToTime.MILLIS:
return f"TIMESTAMP_MILLIS({timestamp})"
if scale == exp.UnixToTime.MICROS:
return f"TIMESTAMP_MICROS({timestamp})"
raise ValueError("Improper scale for timestamp")
class Spark(Hive):
class Parser(Hive.Parser):
FUNCTIONS = {
**Hive.Parser.FUNCTIONS, # type: ignore
"MAP_FROM_ARRAYS": exp.Map.from_arg_list,
"TO_UNIX_TIMESTAMP": exp.StrToUnix.from_arg_list,
"LEFT": lambda args: exp.Substring(
this=seq_get(args, 0),
start=exp.Literal.number(1),
length=seq_get(args, 1),
),
"SHIFTLEFT": lambda args: exp.BitwiseLeftShift(
this=seq_get(args, 0),
expression=seq_get(args, 1),
),
"SHIFTRIGHT": lambda args: exp.BitwiseRightShift(
this=seq_get(args, 0),
expression=seq_get(args, 1),
),
"RIGHT": lambda args: exp.Substring(
this=seq_get(args, 0),
start=exp.Sub(
this=exp.Length(this=seq_get(args, 0)),
expression=exp.Add(this=seq_get(args, 1), expression=exp.Literal.number(1)),
),
length=seq_get(args, 1),
),
"APPROX_PERCENTILE": exp.ApproxQuantile.from_arg_list,
"BOOLEAN": lambda args: exp.Cast(
this=seq_get(args, 0), to=exp.DataType.build("boolean")
),
"IIF": exp.If.from_arg_list,
"INT": lambda args: exp.Cast(this=seq_get(args, 0), to=exp.DataType.build("int")),
"AGGREGATE": exp.Reduce.from_arg_list,
"DAYOFWEEK": lambda args: exp.DayOfWeek(
this=exp.TsOrDsToDate(this=seq_get(args, 0)),
),
"DAYOFMONTH": lambda args: exp.DayOfMonth(
this=exp.TsOrDsToDate(this=seq_get(args, 0)),
),
"DAYOFYEAR": lambda args: exp.DayOfYear(
this=exp.TsOrDsToDate(this=seq_get(args, 0)),
),
"WEEKOFYEAR": lambda args: exp.WeekOfYear(
this=exp.TsOrDsToDate(this=seq_get(args, 0)),
),
"DATE": lambda args: exp.Cast(this=seq_get(args, 0), to=exp.DataType.build("date")),
"DATE_TRUNC": lambda args: exp.TimestampTrunc(
this=seq_get(args, 1),
unit=exp.var(seq_get(args, 0)),
),
"STRING": lambda args: exp.Cast(this=seq_get(args, 0), to=exp.DataType.build("string")),
"TRUNC": lambda args: exp.DateTrunc(unit=seq_get(args, 1), this=seq_get(args, 0)),
"TIMESTAMP": lambda args: exp.Cast(
this=seq_get(args, 0), to=exp.DataType.build("timestamp")
),
}
FUNCTION_PARSERS = {
**parser.Parser.FUNCTION_PARSERS, # type: ignore
"BROADCAST": lambda self: self._parse_join_hint("BROADCAST"),
"BROADCASTJOIN": lambda self: self._parse_join_hint("BROADCASTJOIN"),
"MAPJOIN": lambda self: self._parse_join_hint("MAPJOIN"),
"MERGE": lambda self: self._parse_join_hint("MERGE"),
"SHUFFLEMERGE": lambda self: self._parse_join_hint("SHUFFLEMERGE"),
"MERGEJOIN": lambda self: self._parse_join_hint("MERGEJOIN"),
"SHUFFLE_HASH": lambda self: self._parse_join_hint("SHUFFLE_HASH"),
"SHUFFLE_REPLICATE_NL": lambda self: self._parse_join_hint("SHUFFLE_REPLICATE_NL"),
}
def _parse_add_column(self) -> t.Optional[exp.Expression]:
return self._match_text_seq("ADD", "COLUMNS") and self._parse_schema()
def _parse_drop_column(self) -> t.Optional[exp.Expression]:
return self._match_text_seq("DROP", "COLUMNS") and self.expression(
exp.Drop,
this=self._parse_schema(),
kind="COLUMNS",
)
def _pivot_column_names(self, pivot_columns: t.List[exp.Expression]) -> t.List[str]:
# Spark doesn't add a suffix to the pivot columns when there's a single aggregation
if len(pivot_columns) == 1:
return [""]
names = []
for agg in pivot_columns:
if isinstance(agg, exp.Alias):
names.append(agg.alias)
else:
"""
This case corresponds to aggregations without aliases being used as suffixes
(e.g. col_avg(foo)). We need to unquote identifiers because they're going to
be quoted in the base parser's `_parse_pivot` method, due to `to_identifier`.
Otherwise, we'd end up with `col_avg(`foo`)` (notice the double quotes).
Moreover, function names are lowercased in order to mimic Spark's naming scheme.
"""
agg_all_unquoted = agg.transform(
lambda node: exp.Identifier(this=node.name, quoted=False)
if isinstance(node, exp.Identifier)
else node
)
names.append(agg_all_unquoted.sql(dialect="spark", normalize_functions="lower"))
return names
class Generator(Hive.Generator):
TYPE_MAPPING = {
**Hive.Generator.TYPE_MAPPING, # type: ignore
exp.DataType.Type.TINYINT: "BYTE",
exp.DataType.Type.SMALLINT: "SHORT",
exp.DataType.Type.BIGINT: "LONG",
}
PROPERTIES_LOCATION = {
**Hive.Generator.PROPERTIES_LOCATION, # type: ignore
exp.EngineProperty: exp.Properties.Location.UNSUPPORTED,
exp.AutoIncrementProperty: exp.Properties.Location.UNSUPPORTED,
exp.CharacterSetProperty: exp.Properties.Location.UNSUPPORTED,
exp.CollateProperty: exp.Properties.Location.UNSUPPORTED,
}
TRANSFORMS = {
**Hive.Generator.TRANSFORMS, # type: ignore
exp.ApproxDistinct: rename_func("APPROX_COUNT_DISTINCT"),
exp.FileFormatProperty: lambda self, e: f"USING {e.name.upper()}",
exp.ArraySum: lambda self, e: f"AGGREGATE({self.sql(e, 'this')}, 0, (acc, x) -> acc + x, acc -> acc)",
exp.BitwiseLeftShift: rename_func("SHIFTLEFT"),
exp.BitwiseRightShift: rename_func("SHIFTRIGHT"),
exp.DateTrunc: lambda self, e: self.func("TRUNC", e.this, e.args.get("unit")),
exp.Hint: lambda self, e: f" /*+ {self.expressions(e).strip()} */",
exp.StrToDate: _str_to_date,
exp.StrToTime: lambda self, e: f"TO_TIMESTAMP({self.sql(e, 'this')}, {self.format_time(e)})",
exp.UnixToTime: _unix_to_time_sql,
exp.Create: _create_sql,
exp.Map: _map_sql,
exp.Reduce: rename_func("AGGREGATE"),
exp.StructKwarg: lambda self, e: f"{self.sql(e, 'this')}: {self.sql(e, 'expression')}",
exp.TimestampTrunc: lambda self, e: self.func(
"DATE_TRUNC", exp.Literal.string(e.text("unit")), e.this
),
exp.Trim: trim_sql,
exp.VariancePop: rename_func("VAR_POP"),
exp.DateFromParts: rename_func("MAKE_DATE"),
exp.LogicalOr: rename_func("BOOL_OR"),
exp.LogicalAnd: rename_func("BOOL_AND"),
exp.DayOfWeek: rename_func("DAYOFWEEK"),
exp.DayOfMonth: rename_func("DAYOFMONTH"),
exp.DayOfYear: rename_func("DAYOFYEAR"),
exp.WeekOfYear: rename_func("WEEKOFYEAR"),
exp.AtTimeZone: lambda self, e: f"FROM_UTC_TIMESTAMP({self.sql(e, 'this')}, {self.sql(e, 'zone')})",
}
TRANSFORMS.pop(exp.ArraySort)
TRANSFORMS.pop(exp.ILike)
WRAP_DERIVED_VALUES = False
CREATE_FUNCTION_RETURN_AS = False
def cast_sql(self, expression: exp.Cast) -> str:
if isinstance(expression.this, exp.Cast) and expression.this.is_type(
exp.DataType.Type.JSON
):
schema = f"'{self.sql(expression, 'to')}'"
return self.func("FROM_JSON", expression.this.this, schema)
if expression.to.is_type(exp.DataType.Type.JSON):
return self.func("TO_JSON", expression.this)
return super(Spark.Generator, self).cast_sql(expression)
class Tokenizer(Hive.Tokenizer):
HEX_STRINGS = [("X'", "'")]
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