From 69ae68e2ec578072a06f8b5652174dfb9f28b003 Mon Sep 17 00:00:00 2001 From: Jacky Hu Date: Tue, 12 Nov 2024 19:18:37 -0800 Subject: [PATCH] Remove sqlalchemy and update README.md Signed-off-by: Jacky Hu --- CONTRIBUTING.md | 3 - README.md | 6 +- examples/sqlalchemy.py | 174 ------------ poetry.lock | 386 +------------------------- pyproject.toml | 10 +- src/databricks/sqlalchemy/__init__.py | 6 - 6 files changed, 12 insertions(+), 573 deletions(-) delete mode 100644 examples/sqlalchemy.py delete mode 100644 src/databricks/sqlalchemy/__init__.py diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ce0968d4..0cb25876 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -144,9 +144,6 @@ The `PySQLStagingIngestionTestSuite` namespace requires a cluster running DBR ve The suites marked `[not documented]` require additional configuration which will be documented at a later time. -#### SQLAlchemy dialect tests - -See README.tests.md for details. ### Code formatting diff --git a/README.md b/README.md index 54d4b178..156d914a 100644 --- a/README.md +++ b/README.md @@ -3,9 +3,9 @@ [![PyPI](https://img.shields.io/pypi/v/databricks-sql-connector?style=flat-square)](https://pypi.org/project/databricks-sql-connector/) [![Downloads](https://pepy.tech/badge/databricks-sql-connector)](https://pepy.tech/project/databricks-sql-connector) -The Databricks SQL Connector for Python allows you to develop Python applications that connect to Databricks clusters and SQL warehouses. It is a Thrift-based client with no dependencies on ODBC or JDBC. It conforms to the [Python DB API 2.0 specification](https://www.python.org/dev/peps/pep-0249/) and exposes a [SQLAlchemy](https://www.sqlalchemy.org/) dialect for use with tools like `pandas` and `alembic` which use SQLAlchemy to execute DDL. Use `pip install databricks-sql-connector[sqlalchemy]` to install with SQLAlchemy's dependencies. `pip install databricks-sql-connector[alembic]` will install alembic's dependencies. +The Databricks SQL Connector for Python allows you to develop Python applications that connect to Databricks clusters and SQL warehouses. It is a Thrift-based client with no dependencies on ODBC or JDBC. It conforms to the [Python DB API 2.0 specification](https://www.python.org/dev/peps/pep-0249/). -This connector uses Arrow as the data-exchange format, and supports APIs to directly fetch Arrow tables. Arrow tables are wrapped in the `ArrowQueue` class to provide a natural API to get several rows at a time. +This connector uses Arrow as the data-exchange format, and supports APIs (e.g. `fetchmany_arrow`) to directly fetch Arrow tables. Arrow tables are wrapped in the `ArrowQueue` class to provide a natural API to get several rows at a time. [PyArrow](https://arrow.apache.org/docs/python/index.html) is required to enable this and use these APIs, you can install it via `pip install pyarrow` or `pip install databricks-sql-connector[pyarrow]`. You are welcome to file an issue here for general use cases. You can also contact Databricks Support [here](help.databricks.com). @@ -22,7 +22,7 @@ For the latest documentation, see ## Quickstart -Install the library with `pip install databricks-sql-connector` +Install the library with `pip install databricks-sql-connector[pyarrow]` ```bash export DATABRICKS_HOST=********.databricks.com diff --git a/examples/sqlalchemy.py b/examples/sqlalchemy.py deleted file mode 100644 index 7492dc5a..00000000 --- a/examples/sqlalchemy.py +++ /dev/null @@ -1,174 +0,0 @@ -""" -databricks-sql-connector includes a SQLAlchemy 2.0 dialect compatible with Databricks SQL. To install -its dependencies you can run `pip install databricks-sql-connector[sqlalchemy]`. - -The expected connection string format which you can pass to create_engine() is: - -databricks://token:dapi***@***.cloud.databricks.com?http_path=/sql/***&catalog=**&schema=** - -Our dialect implements the majority of SQLAlchemy 2.0's API. Because of the extent of SQLAlchemy's -capabilities it isn't feasible to provide examples of every usage in a single script, so we only -provide a basic one here. Learn more about usage in README.sqlalchemy.md in this repo. -""" - -# fmt: off - -import os -from datetime import date, datetime, time, timedelta, timezone -from decimal import Decimal -from uuid import UUID - -# By convention, backend-specific SQLA types are defined in uppercase -# This dialect exposes Databricks SQL's TIMESTAMP and TINYINT types -# as these are not covered by the generic, camelcase types shown below -from databricks.sqlalchemy import TIMESTAMP, TINYINT - -# Beside the CamelCase types shown below, line comments reflect -# the underlying Databricks SQL / Delta table type -from sqlalchemy import ( - BigInteger, # BIGINT - Boolean, # BOOLEAN - Column, - Date, # DATE - DateTime, # TIMESTAMP_NTZ - Integer, # INTEGER - Numeric, # DECIMAL - String, # STRING - Time, # STRING - Uuid, # STRING - create_engine, - select, -) -from sqlalchemy.orm import DeclarativeBase, Session - -host = os.getenv("DATABRICKS_SERVER_HOSTNAME") -http_path = os.getenv("DATABRICKS_HTTP_PATH") -access_token = os.getenv("DATABRICKS_TOKEN") -catalog = os.getenv("DATABRICKS_CATALOG") -schema = os.getenv("DATABRICKS_SCHEMA") - - -# Extra arguments are passed untouched to databricks-sql-connector -# See src/databricks/sql/thrift_backend.py for complete list -extra_connect_args = { - "_tls_verify_hostname": True, - "_user_agent_entry": "PySQL Example Script", -} - - -engine = create_engine( - f"databricks://token:{access_token}@{host}?http_path={http_path}&catalog={catalog}&schema={schema}", - connect_args=extra_connect_args, echo=True, -) - - -class Base(DeclarativeBase): - pass - - -# This object gives a usage example for each supported type -# for more details on these, see README.sqlalchemy.md -class SampleObject(Base): - __tablename__ = "pysql_sqlalchemy_example_table" - - bigint_col = Column(BigInteger, primary_key=True) - string_col = Column(String) - tinyint_col = Column(TINYINT) - int_col = Column(Integer) - numeric_col = Column(Numeric(10, 2)) - boolean_col = Column(Boolean) - date_col = Column(Date) - datetime_col = Column(TIMESTAMP) - datetime_col_ntz = Column(DateTime) - time_col = Column(Time) - uuid_col = Column(Uuid) - -# This generates a CREATE TABLE statement against the catalog and schema -# specified in the connection string -Base.metadata.create_all(engine) - -# Output SQL is: -# CREATE TABLE pysql_sqlalchemy_example_table ( -# bigint_col BIGINT NOT NULL, -# string_col STRING, -# tinyint_col SMALLINT, -# int_col INT, -# numeric_col DECIMAL(10, 2), -# boolean_col BOOLEAN, -# date_col DATE, -# datetime_col TIMESTAMP, -# datetime_col_ntz TIMESTAMP_NTZ, -# time_col STRING, -# uuid_col STRING, -# PRIMARY KEY (bigint_col) -# ) USING DELTA - -# The code that follows will INSERT a record using SQLAlchemy ORM containing these values -# and then SELECT it back out. The output is compared to the input to demonstrate that -# all type information is preserved. -sample_object = { - "bigint_col": 1234567890123456789, - "string_col": "foo", - "tinyint_col": -100, - "int_col": 5280, - "numeric_col": Decimal("525600.01"), - "boolean_col": True, - "date_col": date(2020, 12, 25), - "datetime_col": datetime( - 1991, 8, 3, 21, 30, 5, tzinfo=timezone(timedelta(hours=-8)) - ), - "datetime_col_ntz": datetime(1990, 12, 4, 6, 33, 41), - "time_col": time(23, 59, 59), - "uuid_col": UUID(int=255), -} -sa_obj = SampleObject(**sample_object) - -session = Session(engine) -session.add(sa_obj) -session.commit() - -# Output SQL is: -# INSERT INTO -# pysql_sqlalchemy_example_table ( -# bigint_col, -# string_col, -# tinyint_col, -# int_col, -# numeric_col, -# boolean_col, -# date_col, -# datetime_col, -# datetime_col_ntz, -# time_col, -# uuid_col -# ) -# VALUES -# ( -# :bigint_col, -# :string_col, -# :tinyint_col, -# :int_col, -# :numeric_col, -# :boolean_col, -# :date_col, -# :datetime_col, -# :datetime_col_ntz, -# :time_col, -# :uuid_col -# ) - -# Here we build a SELECT query using ORM -stmt = select(SampleObject).where(SampleObject.int_col == 5280) - -# Then fetch one result with session.scalar() -result = session.scalar(stmt) - -# Finally, we read out the input data and compare it to the output -compare = {key: getattr(result, key) for key in sample_object.keys()} -assert compare == sample_object - -# Then we drop the demonstration table -Base.metadata.drop_all(engine) - -# Output SQL is: -# DROP TABLE pysql_sqlalchemy_example_table diff --git a/poetry.lock b/poetry.lock index 2031daa8..a1763098 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,25 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - -[[package]] -name = "alembic" -version = "1.14.0" -description = "A database migration tool for SQLAlchemy." -optional = true -python-versions = ">=3.8" -files = [ - {file = "alembic-1.14.0-py3-none-any.whl", hash = "sha256:99bd884ca390466db5e27ffccff1d179ec5c05c965cfefc0607e69f9e411cb25"}, - {file = "alembic-1.14.0.tar.gz", hash = "sha256:b00892b53b3642d0b8dbedba234dbf1924b69be83a9a769d5a624b01094e304b"}, -] - -[package.dependencies] -importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} -importlib-resources = {version = "*", markers = "python_version < \"3.9\""} -Mako = "*" -SQLAlchemy = ">=1.3.0" -typing-extensions = ">=4" - -[package.extras] -tz = ["backports.zoneinfo"] +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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[[package]] name = "thrift" version = "0.20.0" @@ -1190,13 +841,13 @@ twisted = ["twisted"] [[package]] name = "tomli" -version = "2.0.2" +version = "2.1.0" description = "A lil' TOML parser" optional = false python-versions = ">=3.8" files = [ - {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"}, - {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"}, + {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"}, + {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"}, ] [[package]] @@ -1249,31 +900,10 @@ h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] -[[package]] -name = "zipp" -version = "3.20.2" -description = "Backport of pathlib-compatible object wrapper for zip files" -optional = true -python-versions = ">=3.8" -files = [ - 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[tool.poetry.extras] -databricks-sqlalchemy = ["databricks-sqlalchemy"] -alembic = ["databricks-sqlalchemy", "alembic"] pyarrow = ["pyarrow"] [tool.poetry.dev-dependencies] @@ -45,9 +40,6 @@ 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"] build-backend = "poetry.core.masonry.api" @@ -64,5 +56,5 @@ 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"] env_files = ["test.env"] \ No newline at end of file diff --git a/src/databricks/sqlalchemy/__init__.py b/src/databricks/sqlalchemy/__init__.py deleted file mode 100644 index f79d4c20..00000000 --- a/src/databricks/sqlalchemy/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -try: - from databricks_sqlalchemy import * -except: - import warnings - - warnings.warn("Install databricks-sqlalchemy plugin before using this") \ No newline at end of file