[PECO-1803] Splitting the PySql connector into the core and the non core part

README.md

@@ -60,7 +60,7 @@ In the above example:
 - `server-hostname` is the Databricks instance host name.
 - `http-path` is the HTTP Path either to a Databricks SQL endpoint (e.g. /sql/1.0/endpoints/1234567890abcdef),
 or to a Databricks Runtime interactive cluster (e.g. /sql/protocolv1/o/1234567890123456/1234-123456-slid123)
-- `personal-access-token` is the Databricks Personal Access Token for the account that will execute commands and queries
+  `personal-access-token` is the Databricks Personal Access Token for the account that will execute commands and queries
 
 
 ## Contributing

databricks_sql_connector/pyproject.toml

@@ -0,0 +1,23 @@
+[tool.poetry]
+name = "databricks-sql-connector"
+version = "3.3.0"
+description = "Databricks SQL Connector for Python"
+authors = ["Databricks <[email protected]>"]
+license = "Apache-2.0"
+
+
+[tool.poetry.dependencies]
+databricks_sql_connector_core = { version = ">=1.0.0", extras=["all"]}
+databricks_sqlalchemy = { version = ">=1.0.0", optional = true }
+
+[tool.poetry.extras]
+databricks_sqlalchemy = ["databricks_sqlalchemy"]
+
+[tool.poetry.urls]
+"Homepage" = "https://github.com/databricks/databricks-sql-python"
+"Bug Tracker" = "https://github.com/databricks/databricks-sql-python/issues"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+

pyproject.toml → databricks_sql_connector_core/pyproject.toml

@@ -1,36 +1,26 @@
 [tool.poetry]
-name = "databricks-sql-connector"
-version = "3.3.0"
-description = "Databricks SQL Connector for Python"
+name = "databricks-sql-connector-core"
+version = "1.0.0"
+description = "Databricks SQL Connector core for Python"
 authors = ["Databricks <[email protected]>"]
-license = "Apache-2.0"
-readme = "README.md"
 packages = [{ include = "databricks", from = "src" }]
-include = ["CHANGELOG.md"]
 
 [tool.poetry.dependencies]
 python = "^3.8.0"
 thrift = ">=0.16.0,<0.21.0"
 pandas = [
     { version = ">=1.2.5,<2.2.0", python = ">=3.8" }
 ]
-pyarrow = ">=14.0.1,<17"
-
 lz4 = "^4.0.2"
 requests = "^2.18.1"
 oauthlib = "^3.1.0"
-numpy = [
-    { version = "^1.16.6", python = ">=3.8,<3.11" },
-    { version = "^1.23.4", python = ">=3.11" },
-]
-sqlalchemy = { version = ">=2.0.21", optional = true }
 openpyxl = "^3.0.10"
 alembic = { version = "^1.0.11", optional = true }
 urllib3 = ">=1.26"
+pyarrow = {version = ">=14.0.1,<17", optional = true}
 
 [tool.poetry.extras]
-sqlalchemy = ["sqlalchemy"]
-alembic = ["sqlalchemy", "alembic"]
+pyarrow = ["pyarrow"]
 
 [tool.poetry.dev-dependencies]
 pytest = "^7.1.2"
@@ -43,8 +33,6 @@ pytest-dotenv = "^0.5.2"
 "Homepage" = "https://github.com/databricks/databricks-sql-python"
 "Bug Tracker" = "https://github.com/databricks/databricks-sql-python/issues"
 
-[tool.poetry.plugins."sqlalchemy.dialects"]
-"databricks" = "databricks.sqlalchemy:DatabricksDialect"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
@@ -62,5 +50,5 @@ markers = {"reviewed" = "Test case has been reviewed by Databricks"}
 minversion = "6.0"
 log_cli = "false"
 log_cli_level = "INFO"
-testpaths = ["tests", "src/databricks/sqlalchemy/test_local"]
+testpaths = ["tests", "databricks_sql_connector_core/tests"]
 env_files = ["test.env"]

src/databricks/sql/client.py → databricks_sql_connector_core/src/databricks/sql/client.py

@@ -1,7 +1,6 @@
 from typing import Dict, Tuple, List, Optional, Any, Union, Sequence
 
 import pandas
-import pyarrow
 import requests
 import json
 import os
@@ -43,6 +42,10 @@
     TSparkParameter,
 )
 
+try:
+    import pyarrow
+except ImportError:
+    pyarrow = None
 
 logger = logging.getLogger(__name__)
 
@@ -977,14 +980,14 @@ def fetchmany(self, size: int) -> List[Row]:
         else:
             raise Error("There is no active result set")
 
-    def fetchall_arrow(self) -> pyarrow.Table:
+    def fetchall_arrow(self) -> "pyarrow.Table":
         self._check_not_closed()
         if self.active_result_set:
             return self.active_result_set.fetchall_arrow()
         else:
             raise Error("There is no active result set")
 
-    def fetchmany_arrow(self, size) -> pyarrow.Table:
+    def fetchmany_arrow(self, size) -> "pyarrow.Table":
         self._check_not_closed()
         if self.active_result_set:
             return self.active_result_set.fetchmany_arrow(size)
@@ -1164,14 +1167,14 @@ def _convert_arrow_table(self, table):
             timestamp_as_object=True,
         )
 
-        res = df.to_numpy(na_value=None)
+        res = df.to_numpy(na_value=None, dtype="object")
         return [ResultRow(*v) for v in res]
 
     @property
     def rownumber(self):
         return self._next_row_index
 
-    def fetchmany_arrow(self, size: int) -> pyarrow.Table:
+    def fetchmany_arrow(self, size: int) -> "pyarrow.Table":
         """
         Fetch the next set of rows of a query result, returning a PyArrow table.
 
@@ -1196,7 +1199,7 @@ def fetchmany_arrow(self, size: int) -> pyarrow.Table:
 
         return results
 
-    def fetchall_arrow(self) -> pyarrow.Table:
+    def fetchall_arrow(self) -> "pyarrow.Table":
         """Fetch all (remaining) rows of a query result, returning them as a PyArrow table."""
         results = self.results.remaining_rows()
         self._next_row_index += results.num_rows

src/databricks/sql/thrift_backend.py → databricks_sql_connector_core/src/databricks/sql/thrift_backend.py

@@ -8,7 +8,6 @@
 from ssl import CERT_NONE, CERT_REQUIRED, create_default_context
 from typing import List, Union
 
-import pyarrow
 import thrift.transport.THttpClient
 import thrift.protocol.TBinaryProtocol
 import thrift.transport.TSocket
@@ -37,6 +36,11 @@
     convert_column_based_set_to_arrow_table,
 )
 
+try:
+    import pyarrow
+except ImportError:
+    pyarrow = None
+
 logger = logging.getLogger(__name__)
 
 unsafe_logger = logging.getLogger("databricks.sql.unsafe")
@@ -652,6 +656,12 @@ def _get_metadata_resp(self, op_handle):
 
     @staticmethod
     def _hive_schema_to_arrow_schema(t_table_schema):
+
+        if pyarrow is None:
+            raise ImportError(
+                "pyarrow is required to convert Hive schema to Arrow schema"
+            )
+
         def map_type(t_type_entry):
             if t_type_entry.primitiveEntry:
                 return {
@@ -858,7 +868,7 @@ def execute_command(
             getDirectResults=ttypes.TSparkGetDirectResults(
                 maxRows=max_rows, maxBytes=max_bytes
             ),
-            canReadArrowResult=True,
+            canReadArrowResult=True if pyarrow else False,
             canDecompressLZ4Result=lz4_compression,
             canDownloadResult=use_cloud_fetch,
             confOverlay={

src/databricks/sql/utils.py → databricks_sql_connector_core/src/databricks/sql/utils.py

@@ -12,7 +12,6 @@
 from ssl import SSLContext
 
 import lz4.frame
-import pyarrow
 
 from databricks.sql import OperationalError, exc
 from databricks.sql.cloudfetch.download_manager import ResultFileDownloadManager
@@ -28,16 +27,21 @@
 
 import logging
 
+try:
+    import pyarrow
+except ImportError:
+    pyarrow = None
+
 logger = logging.getLogger(__name__)
 
 
 class ResultSetQueue(ABC):
     @abstractmethod
-    def next_n_rows(self, num_rows: int) -> pyarrow.Table:
+    def next_n_rows(self, num_rows: int):
         pass
 
     @abstractmethod
-    def remaining_rows(self) -> pyarrow.Table:
+    def remaining_rows(self):
         pass
 
 
@@ -100,7 +104,7 @@ def build_queue(
 class ArrowQueue(ResultSetQueue):
     def __init__(
         self,
-        arrow_table: pyarrow.Table,
+        arrow_table: "pyarrow.Table",
         n_valid_rows: int,
         start_row_index: int = 0,
     ):
@@ -115,7 +119,7 @@ def __init__(
         self.arrow_table = arrow_table
         self.n_valid_rows = n_valid_rows
 
-    def next_n_rows(self, num_rows: int) -> pyarrow.Table:
+    def next_n_rows(self, num_rows: int) -> "pyarrow.Table":
         """Get upto the next n rows of the Arrow dataframe"""
         length = min(num_rows, self.n_valid_rows - self.cur_row_index)
         # Note that the table.slice API is not the same as Python's slice
@@ -124,7 +128,7 @@ def next_n_rows(self, num_rows: int) -> pyarrow.Table:
         self.cur_row_index += slice.num_rows
         return slice
 
-    def remaining_rows(self) -> pyarrow.Table:
+    def remaining_rows(self) -> "pyarrow.Table":
         slice = self.arrow_table.slice(
             self.cur_row_index, self.n_valid_rows - self.cur_row_index
         )
@@ -184,7 +188,7 @@ def __init__(
         self.table = self._create_next_table()
         self.table_row_index = 0
 
-    def next_n_rows(self, num_rows: int) -> pyarrow.Table:
+    def next_n_rows(self, num_rows: int) -> "pyarrow.Table":
         """
         Get up to the next n rows of the cloud fetch Arrow dataframes.
 
@@ -216,7 +220,7 @@ def next_n_rows(self, num_rows: int) -> pyarrow.Table:
         logger.debug("CloudFetchQueue: collected {} next rows".format(results.num_rows))
         return results
 
-    def remaining_rows(self) -> pyarrow.Table:
+    def remaining_rows(self) -> "pyarrow.Table":
         """
         Get all remaining rows of the cloud fetch Arrow dataframes.
 
@@ -237,7 +241,7 @@ def remaining_rows(self) -> pyarrow.Table:
             self.table_row_index = 0
         return results
 
-    def _create_next_table(self) -> Union[pyarrow.Table, None]:
+    def _create_next_table(self) -> Union["pyarrow.Table", None]:
         logger.debug(
             "CloudFetchQueue: Trying to get downloaded file for row {}".format(
                 self.start_row_index
@@ -276,7 +280,7 @@ def _create_next_table(self) -> Union[pyarrow.Table, None]:
 
         return arrow_table
 
-    def _create_empty_table(self) -> pyarrow.Table:
+    def _create_empty_table(self) -> "pyarrow.Table":
         # Create a 0-row table with just the schema bytes
         return create_arrow_table_from_arrow_file(self.schema_bytes, self.description)
 
@@ -515,7 +519,7 @@ def transform_paramstyle(
     return output
 
 
-def create_arrow_table_from_arrow_file(file_bytes: bytes, description) -> pyarrow.Table:
+def create_arrow_table_from_arrow_file(file_bytes: bytes, description) -> "pyarrow.Table":
     arrow_table = convert_arrow_based_file_to_arrow_table(file_bytes)
     return convert_decimals_in_arrow_table(arrow_table, description)
 
@@ -542,7 +546,7 @@ def convert_arrow_based_set_to_arrow_table(arrow_batches, lz4_compressed, schema
     return arrow_table, n_rows
 
 
-def convert_decimals_in_arrow_table(table, description) -> pyarrow.Table:
+def convert_decimals_in_arrow_table(table, description) -> "pyarrow.Table":
     for i, col in enumerate(table.itercolumns()):
         if description[i][1] == "decimal":
             decimal_col = col.to_pandas().apply(

databricks_sql_connector_core/src/databricks/sqlalchemy/__init__.py

@@ -0,0 +1,6 @@
+try:
+	from databricks_sqlalchemy import *
+except:
+	import warnings
+
+	warnings.warn("Install databricks-sqlalchemy plugin before using this")

tests/e2e/common/decimal_tests.py → databricks_sql_connector_core/tests/e2e/common/decimal_tests.py

@@ -1,11 +1,20 @@
 from decimal import Decimal
 
-import pyarrow
 import pytest
 
+try:
+    import pyarrow
+except ImportError:
+    pyarrow = None
 
-class DecimalTestsMixin:
-    decimal_and_expected_results = [
+from tests.e2e.common.predicates import pysql_supports_arrow
+
+def decimal_and_expected_results():
+
+    if pyarrow is None:
+        return []
+
+    return [
         ("100.001 AS DECIMAL(6, 3)", Decimal("100.001"), pyarrow.decimal128(6, 3)),
         ("1000000.0000 AS DECIMAL(11, 4)", Decimal("1000000.0000"), pyarrow.decimal128(11, 4)),
         ("-10.2343 AS DECIMAL(10, 6)", Decimal("-10.234300"), pyarrow.decimal128(10, 6)),
@@ -17,7 +26,12 @@ class DecimalTestsMixin:
         ("1e-3 AS DECIMAL(38, 3)", Decimal("0.001"), pyarrow.decimal128(38, 3)),
     ]
 
-    multi_decimals_and_expected_results = [
+def multi_decimals_and_expected_results():
+
+    if pyarrow is None:
+        return []
+
+    return [
         (
             ["1 AS DECIMAL(6, 3)", "100.001 AS DECIMAL(6, 3)", "NULL AS DECIMAL(6, 3)"],
             [Decimal("1.00"), Decimal("100.001"), None],
@@ -30,7 +44,9 @@ class DecimalTestsMixin:
         ),
     ]
 
-    @pytest.mark.parametrize("decimal, expected_value, expected_type", decimal_and_expected_results)
+@pytest.mark.skipif(not pysql_supports_arrow(), reason="Skipping because pyarrow is not installed")
+class DecimalTestsMixin:
+    @pytest.mark.parametrize("decimal, expected_value, expected_type", decimal_and_expected_results())
     def test_decimals(self, decimal, expected_value, expected_type):
         with self.cursor({}) as cursor:
             query = "SELECT CAST ({})".format(decimal)
@@ -39,9 +55,7 @@ def test_decimals(self, decimal, expected_value, expected_type):
             assert table.field(0).type == expected_type
             assert table.to_pydict().popitem()[1][0] == expected_value
 
-    @pytest.mark.parametrize(
-        "decimals, expected_values, expected_type", multi_decimals_and_expected_results
-    )
+    @pytest.mark.parametrize("decimals, expected_values, expected_type", multi_decimals_and_expected_results())
     def test_multi_decimals(self, decimals, expected_values, expected_type):
         with self.cursor({}) as cursor:
             union_str = " UNION ".join(["(SELECT CAST ({}))".format(dec) for dec in decimals])

tests/e2e/common/large_queries_mixin.py → databricks_sql_connector_core/tests/e2e/common/large_queries_mixin.py

@@ -1,6 +1,10 @@
 import logging
 import math
 import time
+from unittest import skipUnless
+
+import pytest
+from tests.e2e.common.predicates import pysql_supports_arrow
 
 log = logging.getLogger(__name__)
 
@@ -40,6 +44,7 @@ def fetch_rows(self, cursor, row_count, fetchmany_size):
             + "assuming 10K fetch size."
         )
 
+    @skipUnless(pysql_supports_arrow(), "Without pyarrow lz4 compression is not supported")
     def test_query_with_large_wide_result_set(self):
         resultSize = 300 * 1000 * 1000  # 300 MB
         width = 8192  # B