SQLAlchemy 2: reorganise dialect files into a single directory (#231)

Signed-off-by: Jesse Whitehouse <jesse.whitehouse@databricks.com>

Author: Jesse

CONTRIBUTING.md

@@ -148,10 +148,11 @@ The suites marked `[not documented]` require additional configuration which will
 
 SQLAlchemy provides reusable tests for testing dialect implementations.
 
-To run these tests, assuming the environment variables needed for e2e tests are set:
+To run these tests, assuming the environment variables needed for e2e tests are set, do the following:
 
 ```
-poetry run python -m pytest tests/sqlalchemy_dialect_compliance --dburi \
+cd src/databricks/sqlalchemy
+poetry run python -m pytest test/sqlalchemy_dialect_compliance.py --dburi \
   "databricks://token:$access_token@$host?http_path=$http_path&catalog=$catalog&schema=$schema" 
 ```
 

examples/sqlalchemy.py

@@ -34,7 +34,7 @@
 
 # Known Gaps
     - MAP, ARRAY, and STRUCT types: this dialect can read these types out as strings. But you cannot
-      define a SQLAlchemy model with databricks.sqlalchemy.dialect.types.DatabricksMap (e.g.) because
+      define a SQLAlchemy model with databricks.sqlalchemy.types.DatabricksMap (e.g.) because
       we haven't implemented them yet.
     - Constraints: with the addition of information_schema to Unity Catalog, Databricks SQL supports
       foreign key and primary key constraints. This dialect can write these constraints but the ability

poetry.lock

@@ -33,7 +33,7 @@ urllib3 = ">=1.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^7.1.2"
-mypy = "^0.950"
+mypy = "^0.981"
 pylint = ">=2.12.0"
 black = "^22.3.0"
 pytest-dotenv = "^0.5.2"
@@ -62,5 +62,3 @@ log_cli = "false"
 log_cli_level = "INFO"
 testpaths = ["tests"]
 env_files = ["test.env"]
-addopts = "--ignore=tests/sqlalchemy_dialect_compliance"
-

pyproject.toml

@@ -5,7 +5,7 @@
 # PEP 249 module globals
 apilevel = "2.0"
 threadsafety = 1  # Threads may share the module, but not connections.
-paramstyle = "pyformat"  # Python extended format codes, e.g. ...WHERE name=%(name)s
+paramstyle = "named"  # Python extended format codes, e.g. ...WHERE name=%(name)s
 
 
 class DBAPITypeObject(object):

setup.cfg

@@ -1 +1,341 @@
-from databricks.sqlalchemy.dialect import DatabricksDialect
+"""This module's layout loosely follows example of SQLAlchemy's postgres dialect
+"""
+
+import decimal, re, datetime
+from dateutil.parser import parse
+
+import sqlalchemy
+from sqlalchemy import types, event
+from sqlalchemy.engine import default, Engine
+from sqlalchemy.exc import DatabaseError, SQLAlchemyError
+from sqlalchemy.engine import reflection
+
+from databricks import sql
+
+
+from databricks.sqlalchemy.base import (
+    DatabricksDDLCompiler,
+    DatabricksIdentifierPreparer,
+)
+from databricks.sqlalchemy.compiler import DatabricksTypeCompiler
+
+try:
+    import alembic
+except ImportError:
+    pass
+else:
+    from alembic.ddl import DefaultImpl
+
+    class DatabricksImpl(DefaultImpl):
+        __dialect__ = "databricks"
+
+
+class DatabricksDecimal(types.TypeDecorator):
+    """Translates strings to decimals"""
+
+    impl = types.DECIMAL
+
+    def process_result_value(self, value, dialect):
+        if value is not None:
+            return decimal.Decimal(value)
+        else:
+            return None
+
+
+class DatabricksTimestamp(types.TypeDecorator):
+    """Translates timestamp strings to datetime objects"""
+
+    impl = types.TIMESTAMP
+
+    def process_result_value(self, value, dialect):
+        return value
+
+    def adapt(self, impltype, **kwargs):
+        return self.impl
+
+
+class DatabricksDate(types.TypeDecorator):
+    """Translates date strings to date objects"""
+
+    impl = types.DATE
+
+    def process_result_value(self, value, dialect):
+        return value
+
+    def adapt(self, impltype, **kwargs):
+        return self.impl
+
+
+class DatabricksDialect(default.DefaultDialect):
+    """This dialect implements only those methods required to pass our e2e tests"""
+
+    # Possible attributes are defined here: https://docs.sqlalchemy.org/en/14/core/internals.html#sqlalchemy.engine.Dialect
+    name: str = "databricks"
+    driver: str = "databricks"
+    default_schema_name: str = "default"
+
+    preparer = DatabricksIdentifierPreparer  # type: ignore
+    type_compiler = DatabricksTypeCompiler
+    ddl_compiler = DatabricksDDLCompiler
+    supports_statement_cache: bool = True
+    supports_multivalues_insert: bool = True
+    supports_native_decimal: bool = True
+    supports_sane_rowcount: bool = False
+    non_native_boolean_check_constraint: bool = False
+    paramstyle: str = "named"
+
+    @classmethod
+    def dbapi(cls):
+        return sql
+
+    def create_connect_args(self, url):
+        # TODO: can schema be provided after HOST?
+        # Expected URI format is: databricks+thrift://token:dapi***@***.cloud.databricks.com?http_path=/sql/***
+
+        kwargs = {
+            "server_hostname": url.host,
+            "access_token": url.password,
+            "http_path": url.query.get("http_path"),
+            "catalog": url.query.get("catalog"),
+            "schema": url.query.get("schema"),
+        }
+
+        self.schema = kwargs["schema"]
+        self.catalog = kwargs["catalog"]
+
+        return [], kwargs
+
+    def get_columns(self, connection, table_name, schema=None, **kwargs):
+        """Return information about columns in `table_name`.
+
+        Given a :class:`_engine.Connection`, a string
+        `table_name`, and an optional string `schema`, return column
+        information as a list of dictionaries with these keys:
+
+        name
+          the column's name
+
+        type
+          [sqlalchemy.types#TypeEngine]
+
+        nullable
+          boolean
+
+        default
+          the column's default value
+
+        autoincrement
+          boolean
+
+        sequence
+          a dictionary of the form
+              {'name' : str, 'start' :int, 'increment': int, 'minvalue': int,
+               'maxvalue': int, 'nominvalue': bool, 'nomaxvalue': bool,
+               'cycle': bool, 'cache': int, 'order': bool}
+
+        Additional column attributes may be present.
+        """
+
+        _type_map = {
+            "boolean": types.Boolean,
+            "smallint": types.SmallInteger,
+            "int": types.Integer,
+            "bigint": types.BigInteger,
+            "float": types.Float,
+            "double": types.Float,
+            "string": types.String,
+            "varchar": types.String,
+            "char": types.String,
+            "binary": types.String,
+            "array": types.String,
+            "map": types.String,
+            "struct": types.String,
+            "uniontype": types.String,
+            "decimal": DatabricksDecimal,
+            "timestamp": DatabricksTimestamp,
+            "date": DatabricksDate,
+        }
+
+        with self.get_connection_cursor(connection) as cur:
+            resp = cur.columns(
+                catalog_name=self.catalog,
+                schema_name=schema or self.schema,
+                table_name=table_name,
+            ).fetchall()
+
+        columns = []
+
+        for col in resp:
+
+            # Taken from PyHive. This removes added type info from decimals and maps
+            _col_type = re.search(r"^\w+", col.TYPE_NAME).group(0)
+            this_column = {
+                "name": col.COLUMN_NAME,
+                "type": _type_map[_col_type.lower()],
+                "nullable": bool(col.NULLABLE),
+                "default": col.COLUMN_DEF,
+                "autoincrement": False if col.IS_AUTO_INCREMENT == "NO" else True,
+            }
+            columns.append(this_column)
+
+        return columns
+
+    def get_pk_constraint(self, connection, table_name, schema=None, **kw):
+        """Return information about the primary key constraint on
+        table_name`.
+
+        Given a :class:`_engine.Connection`, a string
+        `table_name`, and an optional string `schema`, return primary
+        key information as a dictionary with these keys:
+
+        constrained_columns
+          a list of column names that make up the primary key
+
+        name
+          optional name of the primary key constraint.
+
+        """
+        # TODO: implement this behaviour
+        return {"constrained_columns": []}
+
+    def get_foreign_keys(self, connection, table_name, schema=None, **kw):
+        """Return information about foreign_keys in `table_name`.
+
+        Given a :class:`_engine.Connection`, a string
+        `table_name`, and an optional string `schema`, return foreign
+        key information as a list of dicts with these keys:
+
+        name
+          the constraint's name
+
+        constrained_columns
+          a list of column names that make up the foreign key
+
+        referred_schema
+          the name of the referred schema
+
+        referred_table
+          the name of the referred table
+
+        referred_columns
+          a list of column names in the referred table that correspond to
+          constrained_columns
+        """
+        # TODO: Implement this behaviour
+        return []
+
+    def get_indexes(self, connection, table_name, schema=None, **kw):
+        """Return information about indexes in `table_name`.
+
+        Given a :class:`_engine.Connection`, a string
+        `table_name` and an optional string `schema`, return index
+        information as a list of dictionaries with these keys:
+
+        name
+          the index's name
+
+        column_names
+          list of column names in order
+
+        unique
+          boolean
+        """
+        # TODO: Implement this behaviour
+        return []
+
+    def get_table_names(self, connection, schema=None, **kwargs):
+        TABLE_NAME = 1
+        with self.get_connection_cursor(connection) as cur:
+            sql_str = "SHOW TABLES FROM {}".format(
+                ".".join([self.catalog, schema or self.schema])
+            )
+            data = cur.execute(sql_str).fetchall()
+            _tables = [i[TABLE_NAME] for i in data]
+
+        return _tables
+
+    def get_view_names(self, connection, schema=None, **kwargs):
+        VIEW_NAME = 1
+        with self.get_connection_cursor(connection) as cur:
+            sql_str = "SHOW VIEWS FROM {}".format(
+                ".".join([self.catalog, schema or self.schema])
+            )
+            data = cur.execute(sql_str).fetchall()
+            _tables = [i[VIEW_NAME] for i in data]
+
+        return _tables
+
+    def do_rollback(self, dbapi_connection):
+        # Databricks SQL Does not support transactions
+        pass
+
+    def has_table(
+        self, connection, table_name, schema=None, catalog=None, **kwargs
+    ) -> bool:
+        """SQLAlchemy docstrings say dialect providers must implement this method"""
+
+        _schema = schema or self.schema
+        _catalog = catalog or self.catalog
+
+        # DBR >12.x uses underscores in error messages
+        DBR_LTE_12_NOT_FOUND_STRING = "Table or view not found"
+        DBR_GT_12_NOT_FOUND_STRING = "TABLE_OR_VIEW_NOT_FOUND"
+
+        try:
+            res = connection.execute(
+                f"DESCRIBE TABLE {_catalog}.{_schema}.{table_name}"
+            )
+            return True
+        except DatabaseError as e:
+            if DBR_GT_12_NOT_FOUND_STRING in str(
+                e
+            ) or DBR_LTE_12_NOT_FOUND_STRING in str(e):
+                return False
+            else:
+                raise e
+
+    def get_connection_cursor(self, connection):
+        """Added for backwards compatibility with 1.3.x"""
+        if hasattr(connection, "_dbapi_connection"):
+            return connection._dbapi_connection.dbapi_connection.cursor()
+        elif hasattr(connection, "raw_connection"):
+            return connection.raw_connection().cursor()
+        elif hasattr(connection, "connection"):
+            return connection.connection.cursor()
+
+        raise SQLAlchemyError(
+            "Databricks dialect can't obtain a cursor context manager from the dbapi"
+        )
+
+    @reflection.cache
+    def get_schema_names(self, connection, **kw):
+        # Equivalent to SHOW DATABASES
+
+        # TODO: replace with call to cursor.schemas() once its performance matches raw SQL
+        return [row[0] for row in connection.execute("SHOW SCHEMAS")]
+
+
+@event.listens_for(Engine, "do_connect")
+def receive_do_connect(dialect, conn_rec, cargs, cparams):
+    """Helpful for DS on traffic from clients using SQLAlchemy in particular"""
+
+    # Ignore connect invocations that don't use our dialect
+    if not dialect.name == "databricks":
+        return
+
+    if "_user_agent_entry" in cparams:
+        new_user_agent = f"sqlalchemy + {cparams['_user_agent_entry']}"
+    else:
+        new_user_agent = "sqlalchemy"
+
+    cparams["_user_agent_entry"] = new_user_agent
+
+    if sqlalchemy.__version__.startswith("1.3"):
+        # SQLAlchemy 1.3.x fails to parse the http_path, catalog, and schema from our connection string
+        # These should be passed in as connect_args when building the Engine
+
+        if "schema" in cparams:
+            dialect.schema = cparams["schema"]
+
+        if "catalog" in cparams:
+            dialect.catalog = cparams["catalog"]