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A Python wrapper for the libhbase C API to HBase

Project Description


This is a Python C wrapper for MapRDB and HBase using the libhbase C API.

pychbase is modeled after the HappyBase API, but it does not use thrift, and is ideal for MapRDB.

pychbase is tested on Python 2.7 and MapR 5.1.


To compile as well as import pychbase, your LD_LIBRARY_PATH must have the directory with on it, normally in either:

  • $JAVA_HOME/lib/amd64/server
  • $JAVA_HOME/jre/lib/amd64/server

If you are using this with MapR, you must also have /opt/mapr/lib on your LD_LIBRARY_PATH


export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$JAVA_HOME/lib/amd64/server:/opt/mapr/lib


Installation on a MapR environment

Normally, the only environment variable to worry about on a MapR environment is $PYCHBASE_LIBJVM_DIR

export PYCHBASE_LIBJVM_DIR=/usr/lib/jvm/jre-1.7.0/lib/amd64/server
virtualenv pychbase
cd pychbase
source bin/activate
pip install pychbase

# Or build it from source
git clone
cd pychbase
python install

Installation on a Non-MapR environment

Please see the end of the readme for Cloudera intallation notes.

Run the tests

The file in the tests directory has two constants, ZOOKEEPER and TABLE_NAME, that probably won’t work if you run the tests without modification

Create a tests/ file like the following:

ZOOKEEPERS = 'localhost:7222'
TABLE_NAME = 'testpychbase'

To run the tests, make sure to be in the tests directory, or else you will face import problems:

cd tests

Currently nosetests will not work without facing an import issue.


I have attempted to mimic the great HappyBase API as closely as possible.

Make sure to set the LD_LIBRARY_PATH environment variable:

# MapR
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$JAVA_HOME/jre/lib/amd64/server:/opt/mapr/lib

# Non MapR
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/jvm/jre-1.7.0/lib/amd64/server::/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/native
export HBASE_LIB_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/
# I've only gotten it to work on CDH4. If you are on CDH5 you'll need to mess around with the classpath some more


from pychbase import Connection, Table, Batch

To create a connection:

# On MapR, you don't need to specify the CLDBS/zookeepers:
connection = Connection()

# On Non-MapR environments, you'll need to specify the zookeepers:
connection = Connection('zookeeper-one:2181",zookeeper-two:2181",zookeeper-three:2181"')

To create and delete tables:

# Create a table named 'test_table' with a single column family named 'f' with no additional attributes
connection.create_table('test_table', {'f': {}})

To get a table to operate on:

table = connection.table('test_table')

To put, delete, and get from a table:

# Put a row with the current timestamp
table.put('rowkey1', {'f:foo': 'bar', 'f:hai': 'bai'})
data = table.row('rowkey1')
assert data == {'f:foo': 'bar', 'f:hai': 'bai'}

# Get a row and only include a single column
data = table.row('rowkey1', columns=('f:foo',))
assert data == {'f:foo': 'bar'}

# Delete the row
data = table.row('rowkey1')
assert data == {}

# Put a row with a given timestamp
table.put('rowkey1', {'f:foo': 'bar'}, timestamp=1000)
table.put('rowkey1', {'f:foo': 'BAR'}, timestamp=10000)

# Get a row with a given timestamp and include its timestamp
data = table.row('rowkey1', timestamp=1000, include_timestamp=True)
assert data == {'f:foo': ('bar', 1000)}

To scan:

# Full table scan:
for row, data in table.scan():

# Scan with a start and stop:
for row, data in table.scan('foo', 'bar'):

# Scan with a row prefix:
for row, data in table.scan(row_prefix='baz'): # E.g., start='baz', stop='baz~'

# Scan with a filter: # Check out on how to use all of the filters
for row, data in table.scan(filter="SingleColumnValueFilter('f', 'foo', =, 'binary:foo')":

# Scan a table but return only row keys, no rows:
table.put('foo', {'f:foo': 'foo'}
table.put('foo1', {'f:foo': 'foo'}
table.put('foo2', {'f:foo': 'foo'}

rows = list(table.scan(only_rowkeys=True))
assert rows == ['foo', 'foo1', 'foo2']

To count the number of rows in a table:

# Full table count:
count = table.count()

# Count all rows with a start and stop
count = table.count('foo', 'bar')

# Count all rows whose row key starts with a row prefix:
count = table.count(row_prefix='baz') # E.g., start='baz', stop='baz~'

# Count all rows with a filter:
count = table.count(filter="SingleColumnValueFilter('f', 'foo', =, 'binary:foo')")

To batch put:

batch = table.batch()
datas = [
    ('foo', 'a', 'b'),
    ('foo1', 'a1', 'b1'),
    ('foo2', 'a2', 'b2'),

for data in datas:
    batch.put(data[0], {'f:foo': data[1], 'f:bar': data[2]})

errors = batch.send()

assert errors == 0

To batch delete:

batch = table.batch()
rows = ['foo', 'foo1', 'foo2']
for row in rows:

errors = batch.send()

assert errors == 0

Note that batch.send() returns the number of errors that occurred, if any. It is up to the client to ignore this or raise an exception.

batch = table.batch()
batch.put('foo', {'f:foo', 'bar'})
batch.put('foo', 'invalid')

errors = batch.send()
assert errors == 1

An additional helper method is table.delete_prefix(row_prefix), which deletes all rows containing starting with the prefix.

table.put('foo', {'f:foo', 'foo'}
table.put('foo1', {'f:foo', 'foo'}
table.put('foo2', {'f:foo', 'foo'}
table.put('foo3', {'f:foo', 'foo'}
table.put('bar', {'f:bar', 'bar'}

number_deleted = table.delete_prefix('foo')
assert number_deleted == 3

assert table.count() == 2

Note that attempting to batch/put unescaped null terminators will result in them being stripped. Attempting to use a row key with an unescaped null terminator will raise a TypeException. It is the user’s duty to escape null terminators before attempting to batch/put data.

table.put('foo', {'f:foo\0bar': 'baz\0bak'})
data = table.row('foo')
assert data == {'f:foo': 'baz'}

table.put('bar', {'f:foo\\0bar': 'baz\00bak'})
data = table.row('foo')
assert data == {'f:foo\\0bar': 'baz\00bak'}

HappyBase compatibility

One goal of this library is to maintain compatibility with the APIs in HappyBase.

Check out to understand which features of HappyBase I have not yet implemented.

In the future, I will force print warnings to stderr in the event a user uses an unimplemented feature.

Non-MapR Installation and Environment Variables Guide

I have not tested pychbase heavily on Cloudera. I couldn’t get it working on CDH5 due to a classpath issue with libhbase, and and while I was able to get it up and running with CDH4, some of the tests are failing. It seems to me that libhbase is not fully compatible outside of MapR.

export PYCHBASE_LIBJVM_DIR=/usr/lib/jvm/jre-1.7.0/lib/amd64/server
export PYCHBASE_INCLUDE_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/include
export PYCHBASE_LIBRARY_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/native
virtualenv pychbase
cd pychbase
source bin/activate
pip install pychbase

# Or build it from source
git clone
cd pychbase
python install

Please note that the following environment variables must be set in order to install pychbase correctly:



This defaults to TRUE. IF you are using Cloudera/etc, make sure to:



This is the directory that houses the file. Normally it is in either:

  • $JAVA_HOME/lib/amd64/server
  • $JAVA_HOME/jre/lib/amd64/server

If PYCHBASE_LIBJVM_DIR is not set, the installer will check if JAVA_HOME has been set, and then try each of the above directories. If JAVA_HOME is not set, it will attempt to default to /usr/lib/jvm/jre-1.7.0/.


export PYCHBASE_LIBJVM_DIR=/usr/lib/jvm/jre-1.7.0/lib/amd64/server


This houses the /hbase/hbase.h and other libhbase C header files.

If PYCHBASE_IS_MAPR is true, this defaults to /opt/mapr/include.

For Non-MapR environments, this must be set or the installation will fail.

Example on Cloudera:

export PYCHBASE_INCLUDE_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/include


This houses either the file on MapR environments, or the file on Non-MapR environments.

If PYCHBASE_IS_MAPR is true, this defaults to /opt/mapr/lib.

For Non-MapR environments, this must be set or the installation will fail.

Example on Cloudera:

export PYCHBASE_LIBRARY_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/native



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