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Python client for the Impala distributed query engine

Project description


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 2.7+ or 3.5+

  • six, bitarray

  • thrift==0.16.0

  • thrift_sasl==0.4.3


  • kerberos>=1.3.0 for Kerberos over HTTP support. This also requires Kerberos libraries to be installed on your system - see System Kerberos

  • 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

System Kerberos

Different systems require different packages to be installed to enable Kerberos support in Impyla. Some examples of how to install the packages on different distributions follow.


apt-get install libkrb5-dev krb5-user


yum install krb5-libs krb5-devel krb5-server krb5-workstation


Install the latest release 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 impala

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:

Furthermore the Cursor object returns you information about the columns returned in the query. This is useful to export your data as a csv file.

import csv

cursor.execute('SELECT * FROM mytable LIMIT 100')
columns = [datum[0] for datum in cursor.description]
targetfile = '/tmp/foo.csv'

with open(targetfile, 'w', newline='') as outcsv:
    writer = csv.writer(outcsv, delimiter=',', quotechar='"', quoting=csv.QUOTE_ALL, lineterminator='\n')
    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

How do I contribute code?

You need to first sign and return an ICLA and CCLA before we can accept and redistribute your contribution. Once these are submitted you are free to start contributing to impyla. Submit these to


We use Github issues to track bugs for this project. Find an issue that you would like to work on (or file one if you have discovered a new issue!). If no-one is working on it, assign it to yourself only if you intend to work on it shortly.

It's a good idea to discuss your intended approach on the issue. You are much more likely to have your patch reviewed and committed if you've already got buy-in from the impyla community before you start.


Now start coding! As you are writing your patch, please keep the following things in mind:

First, please include tests with your patch. If your patch adds a feature or fixes a bug and does not include tests, it will generally not be accepted. If you are unsure how to write tests for a particular component, please ask on the issue for guidance.

Second, please keep your patch narrowly targeted to the problem described by the issue. It's better for everyone if we maintain discipline about the scope of each patch. In general, if you find a bug while working on a specific feature, file a issue for the bug, check if you can assign it to yourself and fix it independently of the feature. This helps us to differentiate between bug fixes and features and allows us to build stable maintenance releases.

Finally, please write a good, clear commit message, with a short, descriptive title and a message that is exactly long enough to explain what the problem was, and how it was fixed.

Please create a pull request on github with your patch.

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