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from sqlalchemy import create_engine
from sqlalchemy import types
from sqlalchemy.engine import reflection
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from gometa.metadata.com.linkedin.pegasus2avro.mxe import MetadataChangeEvent
from gometa.metadata.com.linkedin.pegasus2avro.metadata.snapshot import DatasetSnapshot
from gometa.metadata.com.linkedin.pegasus2avro.schema import SchemaMetadata, MySqlDDL
from gometa.metadata.com.linkedin.pegasus2avro.common import AuditStamp
from gometa.ingestion.api.source import WorkUnit
from pydantic import BaseModel
import logging
import time
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from typing import Optional
from dataclasses import dataclass
logger = logging.getLogger(__name__)
class SQLAlchemyConfig(BaseModel):
username: str
password: str
host_port: str
database: str = ""
scheme: str
options: Optional[dict] = {}
def get_sql_alchemy_url(self):
url=f'{self.scheme}://{self.username}:{self.password}@{self.host_port}/{self.database}'
logger.debug('sql_alchemy_url={url}')
return url
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@dataclass
class SqlWorkUnit(WorkUnit):
mce: MetadataChangeEvent
def get_metadata(self):
return {'mce': self.mce}
def get_schema_metadata(dataset_name, platform, columns) -> SchemaMetadata:
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canonical_schema = [ {
"fieldPath": column["name"],
"nativeDataType": repr(column["type"]),
"type": { "type":get_column_type(column["type"]) },
"description": column.get("comment", None)
} for column in columns ]
actor, sys_time = "urn:li:corpuser:etl", int(time.time()) * 1000
schema_metadata = SchemaMetadata(
schemaName=dataset_name,
platform=f'urn:li:dataPlatform:{platform}',
version=0,
hash="",
#TODO: this is bug-compatible with existing scripts. Will fix later
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platformSchema=MySqlDDL(tableSchema = ""),
created = AuditStamp(time=sys_time, actor=actor),
lastModified = AuditStamp(time=sys_time, actor=actor),
fields = canonical_schema
)
return schema_metadata
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def get_column_type(column_type):
"""
Maps SQLAlchemy types (https://docs.sqlalchemy.org/en/13/core/type_basics.html) to corresponding schema types
"""
if isinstance(column_type, (types.Integer, types.Numeric)):
return ("com.linkedin.pegasus2avro.schema.NumberType", {})
if isinstance(column_type, (types.Boolean)):
return ("com.linkedin.pegasus2avro.schema.BooleanType", {})
if isinstance(column_type, (types.Enum)):
return ("com.linkedin.pegasus2avro.schema.EnumType", {})
if isinstance(column_type, (types._Binary, types.PickleType)):
return ("com.linkedin.pegasus2avro.schema.BytesType", {})
if isinstance(column_type, (types.ARRAY)):
return ("com.linkedin.pegasus2avro.schema.ArrayType", {})
if isinstance(column_type, (types.String)):
return ("com.linkedin.pegasus2avro.schema.StringType", {})
return ("com.linkedin.pegasus2avro.schema.NullType", {})
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def get_sql_workunits(sql_config:SQLAlchemyConfig, platform: str, env: str = "PROD"):
url = sql_config.get_sql_alchemy_url()
engine = create_engine(url, **sql_config.options)
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inspector = reflection.Inspector.from_engine(engine)
database = sql_config.database
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for schema in inspector.get_schema_names():
for table in inspector.get_table_names(schema):
columns = inspector.get_columns(table, schema)
mce = MetadataChangeEvent()
if database != "":
dataset_name = f'{database}.{schema}.{table}'
else:
dataset_name = f'{schema}.{table}'
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dataset_snapshot = DatasetSnapshot()
dataset_snapshot.urn=(
f"urn:li:dataset:(urn:li:dataPlatform:{platform},{dataset_name},{env})"
)
schema_metadata = get_schema_metadata(dataset_name, platform, columns)
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dataset_snapshot.aspects.append(schema_metadata)
mce.proposedSnapshot = dataset_snapshot
yield SqlWorkUnit(id=dataset_name, mce = mce)
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