Python client for the Impala distributed query engine.

Fully supported:

• Lightweight, pip-installable package for connecting to Impala databases

• Fully DB API 2.0 (PEP 249)-compliant Python client (similar to sqlite or MySQL clients)

• Converter to pandas DataFrame, allowing easy integration into the Python data stack (including scikit-learn and matplotlib)

Alpha-quality:

• Wrapper for MADlib-style prediction, allowing for large-scale, distributed machine learning (see the Impala port of MADlib)

• Compiling UDFs written in Python into low-level machine code for execution by Impala (see the udf branch; powered by Numba/LLVM)

Required:

• python2.6 or python2.7

• thrift>=0.8 (Python package only; no need for code-gen)

Optional:

• pandas for the .as_pandas() function to work

This project is installed with setuptools>=2.

Install the latest release (0.8.1) with pip:

pip install impyla

For the latest (dev) version, clone the repo:

git clone https://github.com/cloudera/impyla.git
cd impyla
python setup.py install

Impyla implements the Python DB API v2.0 (PEP 249) database interface (refer to it for API details):

from impala.dbapi import connect
conn = connect(host='my.host.com', port=21050)
cursor = conn.cursor()
cursor.execute('SELECT * FROM mytable LIMIT 100')
print cursor.description # prints the result set's schema
results = cursor.fetchall()

Note: the specified port number should be for the HiveServer2 service (defaults to 21050 in CM), not Beeswax (defaults to 21000) which is what the Impala shell uses.

The Cursor object also supports the iterator interface, which is buffered (controlled by cursor.arraysize):

cursor.execute('SELECT * FROM mytable LIMIT 100')
for row in cursor:
    process(row)

You can also get back a pandas DataFrame object

from impala.util import as_pandas
df = as_pandas(cur)
# carry df through scikit-learn, for example