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248 lines (203 loc) · 8.52 KB
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import math
from typing import Any, ClassVar, Dict, Sequence, Type
import logging
import attrs
from data_diff.abcs.database_types import (
Date,
Integer,
Float,
Decimal,
Timestamp,
Text,
TemporalType,
NumericType,
DbPath,
ColType,
UnknownColType,
Boolean,
)
from data_diff.databases.base import (
MD5_HEXDIGITS,
CHECKSUM_HEXDIGITS,
CHECKSUM_OFFSET,
BaseDialect,
ThreadedDatabase,
import_helper,
parse_table_name,
)
from data_diff.schema import RawColumnInfo
@import_helper(text="You can install it using 'pip install databricks-sql-connector'")
def import_databricks():
import databricks.sql
return databricks
@attrs.define(frozen=False)
class Dialect(BaseDialect):
name = "Databricks"
ROUNDS_ON_PREC_LOSS = True
TYPE_CLASSES = {
# Numbers
"INT": Integer,
"SMALLINT": Integer,
"TINYINT": Integer,
"BIGINT": Integer,
"FLOAT": Float,
"DOUBLE": Float,
"DECIMAL": Decimal,
# Timestamps
"TIMESTAMP": Timestamp,
"TIMESTAMP_NTZ": Timestamp,
"DATE": Date,
# Text
"STRING": Text,
"VARCHAR": Text,
# Boolean
"BOOLEAN": Boolean,
}
def type_repr(self, t) -> str:
try:
return {str: "STRING"}[t]
except KeyError:
return super().type_repr(t)
def quote(self, s: str) -> str:
return f"`{s}`"
def to_string(self, s: str) -> str:
return f"cast({s} as string)"
def _convert_db_precision_to_digits(self, p: int) -> int:
# Subtracting 2 due to wierd precision issues
return max(super()._convert_db_precision_to_digits(p) - 2, 0)
def set_timezone_to_utc(self) -> str:
return "SET TIME ZONE 'UTC'"
def parse_table_name(self, name: str) -> DbPath:
path = parse_table_name(name)
return tuple(i for i in path if i is not None)
def md5_as_int(self, s: str) -> str:
return f"cast(conv(substr(md5({s}), {1+MD5_HEXDIGITS-CHECKSUM_HEXDIGITS}), 16, 10) as decimal(38, 0)) - {CHECKSUM_OFFSET}"
def md5_as_hex(self, s: str) -> str:
return f"md5({s})"
def normalize_timestamp(self, value: str, coltype: TemporalType) -> str:
"""Databricks timestamp contains no more than 6 digits in precision"""
try:
is_date = coltype.is_date
except:
is_date = False
if isinstance(coltype, Date) or is_date:
return f"date_format({value}, 'yyyy-MM-dd')"
if coltype.rounds:
# cast to timestamp due to unix_micros() requiring timestamp
timestamp = f"cast(round(unix_micros(cast({value} as timestamp)) / 1000000, {coltype.precision}) * 1000000 as bigint)"
return f"date_format(timestamp_micros({timestamp}), 'yyyy-MM-dd HH:mm:ss.SSSSSS')"
precision_format = "S" * coltype.precision + "0" * (6 - coltype.precision)
return f"date_format({value}, 'yyyy-MM-dd HH:mm:ss.{precision_format}')"
def normalize_number(self, value: str, coltype: NumericType) -> str:
value = f"cast({value} as decimal(38, {coltype.precision}))"
if coltype.precision > 0:
value = f"format_number({value}, {coltype.precision})"
return f"replace({self.to_string(value)}, ',', '')"
def normalize_boolean(self, value: str, _coltype: Boolean) -> str:
return self.to_string(f"cast ({value} as int)")
@attrs.define(frozen=False, init=False, kw_only=True)
class Databricks(ThreadedDatabase):
DIALECT_CLASS: ClassVar[Type[BaseDialect]] = Dialect
CONNECT_URI_HELP = "databricks://:<access_token>@<server_hostname>/<http_path>"
CONNECT_URI_PARAMS = ["catalog", "schema"]
catalog: str
_args: Dict[str, Any]
def __init__(self, *, thread_count, **kw) -> None:
super().__init__(thread_count=thread_count)
logging.getLogger("databricks.sql").setLevel(logging.WARNING)
self._args = kw
self.default_schema = kw.get("schema", "default")
self.catalog = kw.get("catalog", "hive_metastore")
def create_connection(self):
databricks = import_databricks()
try:
return databricks.sql.connect(
server_hostname=self._args["server_hostname"],
http_path=self._args["http_path"],
access_token=self._args["access_token"],
catalog=self.catalog,
)
except databricks.sql.exc.Error as e:
raise ConnectionError(*e.args) from e
def query_table_schema(self, path: DbPath) -> Dict[str, RawColumnInfo]:
# Databricks has INFORMATION_SCHEMA only for Databricks Runtime, not for Databricks SQL.
# https://docs.databricks.com/spark/latest/spark-sql/language-manual/information-schema/columns.html
# So, to obtain information about schema, we should use another approach.
conn = self.create_connection()
catalog, schema, table = self._normalize_table_path(path)
with conn.cursor() as cursor:
cursor.columns(catalog_name=catalog, schema_name=schema, table_name=table)
try:
rows = cursor.fetchall()
finally:
conn.close()
if not rows:
raise RuntimeError(f"{self.name}: Table '{'.'.join(path)}' does not exist, or has no columns")
d = {
r.COLUMN_NAME: RawColumnInfo(
column_name=r.COLUMN_NAME, data_type=r.TYPE_NAME, numeric_precision=r.DECIMAL_DIGITS
)
for r in rows
}
assert len(d) == len(rows)
return d
# def select_table_schema(self, path: DbPath) -> str:
# """Provide SQL for selecting the table schema as (name, type, date_prec, num_prec)"""
# database, schema, name = self._normalize_table_path(path)
# info_schema_path = ["information_schema", "columns"]
# if database:
# info_schema_path.insert(0, database)
# return (
# "SELECT column_name, data_type, datetime_precision, numeric_precision, numeric_scale "
# f"FROM {'.'.join(info_schema_path)} "
# f"WHERE table_name = '{name}' AND table_schema = '{schema}'"
# )
def _process_table_schema(
self, path: DbPath, raw_schema: Dict[str, RawColumnInfo], filter_columns: Sequence[str], where: str = None
):
accept = {i.lower() for i in filter_columns}
col_infos = [row for name, row in raw_schema.items() if name.lower() in accept]
resulted_rows = []
for info in col_infos:
raw_data_type = info.data_type
row_type = info.data_type.split("(")[0]
info = attrs.evolve(info, data_type=row_type)
type_cls = self.dialect.TYPE_CLASSES.get(row_type, UnknownColType)
if issubclass(type_cls, Integer):
info = attrs.evolve(info, numeric_scale=0)
elif issubclass(type_cls, Float):
numeric_precision = math.ceil(info.numeric_precision / math.log(2, 10))
info = attrs.evolve(info, numeric_precision=numeric_precision)
elif issubclass(type_cls, Decimal):
items = raw_data_type[8:].rstrip(")").split(",")
numeric_precision, numeric_scale = int(items[0]), int(items[1])
info = attrs.evolve(
info,
numeric_precision=numeric_precision,
numeric_scale=numeric_scale,
)
elif issubclass(type_cls, Timestamp):
info = attrs.evolve(
info,
datetime_precision=info.numeric_precision,
numeric_precision=None,
)
else:
info = attrs.evolve(info, numeric_precision=None)
resulted_rows.append(info)
col_dict: Dict[str, ColType] = {info.column_name: self.dialect.parse_type(path, info) for info in resulted_rows}
self._refine_coltypes(path, col_dict, where)
return col_dict
@property
def is_autocommit(self) -> bool:
return True
def _normalize_table_path(self, path: DbPath) -> DbPath:
if len(path) == 1:
return self.catalog, self.default_schema, path[0]
elif len(path) == 2:
return self.catalog, path[0], path[1]
elif len(path) == 3:
return path
raise ValueError(
f"{self.name}: Bad table path for {self}: '{'.'.join(path)}'. Expected format: table, schema.table, or catalog.schema.table"
)
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