mysql-connector-python
A self-contained Python driver for communicating with MySQL servers, using an API that is compliant with the Python Database API Specification v2.0 (PEP 249).
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Description
MySQL Connector/Python
======================
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MySQL Connector/Python enables Python programs to access MySQL databases, using
an API that is compliant with the `Python Database API Specification v2.0
(PEP 249) <https://www.python.org/dev/peps/pep-0249/>`__.
Features
--------
* `Asynchronous Connectivity <https://dev.mysql.com/doc/connector-python/en/connector-python-asyncio.html>`__
* `C-extension <https://dev.mysql.com/doc/connector-python/en/connector-python-cext.html>`__
* `Telemetry <https://dev.mysql.com/doc/connector-python/en/connector-python-opentelemetry.html>`__
Installation
------------
Connector/Python contains the Classic and X DevAPI connector APIs, which are
installed separately. Any of these can be installed from a binary
or source distribution.
Binaries are distributed in the following package formats:
* `RPM <https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/8/html/packaging_and_distributing_software/introduction-to-rpm_packaging-and-distributing-software>`__
* `WHEEL <https://packaging.python.org/en/latest/discussions/package-formats/#what-is-a-wheel>`__
On the other hand, the source code is distributed as a compressed file
from which a wheel package can be built.
The recommended way to install Connector/Python is via `pip <https://pip.pypa.io/>`__,
which relies on WHEEL packages. For such a reason, it is the installation procedure
that is going to be described moving forward.
Please, refer to the official MySQL documentation `Connector/Python Installation
<https://dev.mysql.com/doc/connector-python/en/connector-python-installation.html>`__ to
know more about installing from an RPM, or building and installing a WHEEL package from
a source distribution.
Before installing a package with `pip <https://pip.pypa.io/>`__, it is strongly suggested
to have the most recent ``pip`` version installed on your system.
If your system already has ``pip`` installed, you might need to update it. Or you can use
the `standalone pip installer <https://pip.pypa.io/en/latest/installation/>`__.
.. code-block:: bash
$ pip install mysql-connector-python
Installation Options
++++++++++++++++++++
Connector packages included in MySQL Connector/Python allow you to install
optional dependencies to unleash certain functionalities.
.. code-block:: bash
# 3rd party packages to unleash the telemetry functionality are installed
$ pip install mysql-connector-python[telemetry]
This installation option can be seen as a shortcut to install all the
dependencies needed by a particular feature. Mind that this is optional
and you are free to install the required dependencies by yourself.
Available options:
* dns-srv
* gssapi
* webauthn
* telemetry
Sample Code
-----------------------------------
.. code:: python
import mysql.connector
# Connect to server
cnx = mysql.connector.connect(
host="127.0.0.1",
port=3306,
user="mike",
password="s3cre3t!")
# Get a cursor
cur = cnx.cursor()
# Execute a query
cur.execute("SELECT CURDATE()")
# Fetch one result
row = cur.fetchone()
print("Current date is: {0}".format(row[0]))
# Close connection
cnx.close()
HeatWave GenAI and Machine Learning Support
-------------------------------------------
MySQL Connector/Python now includes an optional API for integrating directly with MySQL HeatWave's AI and Machine Learning capabilities. This new SDK is designed to reduce the time required to generate proofs-of-concept (POCs) by providing an intuitive Pythonic interface that automates the management of SQL tables and procedures.
The new ``mysql.ai`` module offers two primary components:
* **GenAI:** Provides implementations of LangChain's abstract ``LLM``, ``VectorStore``, and ``Embeddings`` classes (``MyLLM``, ``MyVectorStore``, ``MyEmbeddings``). This ensures full interoperability with existing LangChain pipelines, allowing developers to easily substitute existing components with HeatWave-backed versions.
* **AutoML:** Provides Scikit-Learn compatible estimators (``MyClassifier``, ``MyRegressor``, ``MyAnomalyDetector``, ``MyGenericTransformer``) that inherit from standard Scikit-Learn mixins. These components accept Pandas DataFrames and can be dropped directly into existing Scikit-Learn pipelines and grid searches.
**Note on Dependencies:** These features introduce dependencies on ``langchain``, ``pandas``, and ``scikit-learn``. To keep existing installations unchanged and the base connector lightweight, these dependencies are **not installed by default**. You must install them separately to use the ``mysql.ai`` features.
**Example: GenAI Chatbot with Memory**
This example demonstrates how to use ``MyLLM`` within a loop to create a simple chatbot that maintains conversation history.
.. code:: python
from collections import deque
from mysql import connector
from mysql.ai.genai import MyLLM
def run_chatbot(db_connection, chat_history_size=5):
# Initialize MyLLM with the database connection
my_llm = MyLLM(db_connection)
# Maintain a limited history for context
chat_history = deque(maxlen=chat_history_size)
system_msg = "System: You are a helpful AI assistant."
while True:
user_input = input("\nUser: ")
if user_input.lower() in ["exit", "quit"]:
break
# Format history and invoke the LLM
history = [system_msg] + list(chat_history) + [f"User: {user_input}"]
prompt = "\n".join(history)
# Invoke HeatWave GenAI
response = my_llm.invoke(prompt)
print(f"Bot: {response}")
# Update history
chat_history.append(f"User: {user_input}")
chat_history.append(f"Bot: {response}")
# Usage
with connector.connect(user='root', database='mlcorpus') as db_connection:
run_chatbot(db_connection)
**Example: HeatWave AutoML in a Scikit-Learn Pipeline**
This example shows how to use ``MyClassifier`` as a drop-in replacement within a standard Scikit-Learn pipeline.
.. code:: python
import pandas as pd
from mysql import connector
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from mysql.ai.ml import MyClassifier
# 1. Setup Data (Pandas DataFrame)
X = pd.DataFrame([[0.5, 0.1], [1.0, 0.8], [0.1, 0.2]], columns=["feat1", "feat2"])
y = pd.Series([0, 1, 0], name="target")
# 2. Connect and Train
with connector.connect(user='root', database='mlcorpus') as db_connection:
# Initialize the HeatWave classifier
clf = MyClassifier(db_connection)
# Create a standard Scikit-Learn pipeline
pipe = Pipeline([
("scaler", StandardScaler()),
("mysql_clf", clf)
])
# Fit the model (automates upload and training on HeatWave)
pipe.fit(X, y)
# Predict
preds = pipe.predict(X)
print(f"Predictions: {preds}")
# Score
score = pipe.score(X, y)
print(f"Accuracy: {score}")
Additional Resources
--------------------
- `MySQL Connector/Python Developer Guide <https://dev.mysql.com/doc/connector-python/en/>`__
- `MySQL Connector/Python Forum <http://forums.mysql.com/list.php?50>`__
- `MySQL Public Bug Tracker <https://bugs.mysql.com>`__
- `Slack <https://mysqlcommunity.slack.com>`__ (`Sign-up <https://lefred.be/mysql-community-on-slack/>`__ required if you do not have an Oracle account)
- `Stack Overflow <https://stackoverflow.com/questions/tagged/mysql-connector-python>`__
- `Oracle Blogs <https://blogs.oracle.com/search.html?q=connector-python>`__
Contributing
------------
There are a few ways to contribute to the Connector/Python code. Please refer
to the `contributing guidelines <https://github.com/mysql/mysql-connector-python/blob/trunk/CONTRIBUTING.md>`__ for additional information.
License
-------
Please refer to the `README.txt <https://github.com/mysql/mysql-connector-python/blob/trunk/README.txt>`__ and `LICENSE.txt <https://github.com/mysql/mysql-connector-python/blob/trunk/LICENSE.txt>`__
files, available in this repository, for further details.