Python client for the Impala distributed query engine
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
Python 2.6+ or 3.3+
thrift_sasl==0.2.1for hive and/or Kerberos support:
pandasfor conversion to
DataFrameobjects; but see the Ibis project instead
sqlalchemyfor the SQLAlchemy engine
pytestfor running tests;
unittest2for testing on Python 2.6
Install the latest release with
pip install impyla
For the latest (dev) version, install directly from the repo:
pip install git+https://github.com/cloudera/impyla.git
or clone the repo:
git clone https://github.com/cloudera/impyla.git cd impyla python setup.py install
Running the tests
impyla uses the pytest toolchain, and depends on the following environment variables:
export IMPYLA_TEST_HOST=your.impalad.com export IMPYLA_TEST_PORT=21050 export IMPYLA_TEST_AUTH_MECH=NOSASL
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='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()
Cursor object also exposes the iterator interface, which is buffered
cursor.execute('SELECT * FROM mytable LIMIT 100') for row in cursor: process(row)
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 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') writer.writerow(columns) for row in cursor: writer.writerow(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
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 CLA@cloudera.com.
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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