Python DB API 2.0 client for Impala and Hive (HiveServer2 protocol)
Python Thrift Shell
Latest commit 3895a83 Oct 16, 2016 @schaffino schaffino committed with wesm Fixes for #219 and #211 and revert #216 (#220)
* Fetch error messages pre impala 2.7.0 and allow fetch to be turned off with fetch_error flag

* Use more up to date TCLIService thrift gen from hue project to allow logs to be retrieved


Python client for HiveServer2 implementations (e.g., Impala, Hive) for distributed query engines.

For higher-level Impala functionality, including a Pandas-like interface over distributed data sets, see the Ibis project.


  • HiveServer2 compliant; works with Impala and Hive, including nested data

  • Fully DB API 2.0 (PEP 249)-compliant Python client (similar to sqlite or MySQL clients) supporting Python 2.6+ and Python 3.3+.

  • Works with Kerberos, LDAP, SSL

  • SQLAlchemy connector

  • Converter to pandas DataFrame, allowing easy integration into the Python data stack (including scikit-learn and matplotlib); but see the Ibis project for a richer experience



  • Python 2.6+ or 3.3+

  • six, bit_array

  • thrift (on Python 2.x) or thriftpy (on Python 3.x)

For Hive and/or Kerberos support:

pip install thrift_sasl
pip install sasl


  • pandas for conversion to DataFrame objects; but see the Ibis project instead

  • sqlalchemy for the SQLAlchemy engine

  • pytest for running tests; unittest2 for testing on Python 2.6


Install the latest release (0.13.1) with pip:

pip install impyla

For the latest (dev) version, install directly from the repo:

pip install git+

or clone the repo:

git clone
cd impyla
python install

Running the tests

impyla uses the pytest toolchain, and depends on the following environment variables:

export IMPYLA_TEST_PORT=21050

To run the maximal set of tests, run

cd path/to/impyla
py.test --connect impyla

Leave out the --connect option to skip tests for DB API compliance.


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='', port=21050)
cursor = conn.cursor()
cursor.execute('SELECT * FROM mytable LIMIT 100')
print cursor.description  # prints the result set's schema
results = cursor.fetchall()

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

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

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