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    "info": {
        "author": "Matthew Moisen",
        "author_email": "mmoisen@cisco.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Database"
        ],
        "description": "pychbase\n========\n\nThis is a Python C wrapper for MapRDB and HBase using the `libhbase C\nAPI <https://github.com/mapr/libhbase>`__.\n\n``pychbase`` is modeled after the HappyBase API, but it does not use\n``thrift``, and is ideal for MapRDB.\n\n``pychbase`` is tested on Python 2.7 and MapR 5.1.\n\nLD\\_LIBRARY\\_PATH\n=================\n\nTo compile as well as import ``pychbase``, your ``LD_LIBRARY_PATH`` must\nhave the directory with ``libjvm.so`` on it, normally in either:\n\n-  $JAVA\\_HOME/lib/amd64/server\n-  $JAVA\\_HOME/jre/lib/amd64/server\n\nIf you are using this with MapR, you must also have ``/opt/mapr/lib`` on\nyour ``LD_LIBRARY_PATH``\n\nE.g,\n\n::\n\n    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$JAVA_HOME/lib/amd64/server:/opt/mapr/lib\n\nInstallation\n============\n\nInstallation on a MapR environment\n----------------------------------\n\nNormally, the only environment variable to worry about on a MapR\nenvironment is $PYCHBASE\\_LIBJVM\\_DIR\n\n::\n\n    export PYCHBASE_LIBJVM_DIR=/usr/lib/jvm/jre-1.7.0/lib/amd64/server\n    virtualenv pychbase\n    cd pychbase\n    source bin/activate\n    pip install pychbase\n\n    # Or build it from source\n    git clone https://github.com/mkmoisen/pychbase.git\n    cd pychbase\n    python setup.py install\n\nInstallation on a Non-MapR environment\n--------------------------------------\n\nPlease see the end of the readme for Cloudera intallation notes.\n\nRun the tests\n=============\n\nThe ``config.py`` file in the ``tests`` directory has two constants,\n``ZOOKEEPER`` and ``TABLE_NAME``, that probably won't work if you run\nthe tests without modification\n\nCreate a ``tests/local_config.py`` file like the following:\n\n::\n\n    ZOOKEEPERS = 'localhost:7222'\n    TABLE_NAME = 'testpychbase'\n\nTo run the tests, make sure to be in the ``tests`` directory, or else\nyou will face import problems:\n\n::\n\n    cd tests\n    python tests.py\n\nCurrently ``nosetests`` will not work without facing an import issue.\n\nUsage\n=====\n\nI have attempted to mimic the great HappyBase API as closely as\npossible.\n\nMake sure to set the LD\\_LIBRARY\\_PATH environment variable:\n\n::\n\n    # MapR\n    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$JAVA_HOME/jre/lib/amd64/server:/opt/mapr/lib\n\n    # Non MapR\n    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/jvm/jre-1.7.0/lib/amd64/server::/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/native\n    export HBASE_LIB_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/\n    # 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\n\nImports:\n\n::\n\n    from pychbase import Connection, Table, Batch\n\nTo create a connection:\n\n::\n\n    # On MapR, you don't need to specify the CLDBS/zookeepers:\n    connection = Connection()\n\n    # On Non-MapR environments, you'll need to specify the zookeepers:\n    connection = Connection('zookeeper-one:2181\",zookeeper-two:2181\",zookeeper-three:2181\"')\n\nTo create and delete tables:\n\n::\n\n    # Create a table named 'test_table' with a single column family named 'f' with no additional attributes\n    connection.create_table('test_table', {'f': {}})\n    connection.delete_table('test_table')\n\nTo get a table to operate on:\n\n::\n\n    table = connection.table('test_table')\n\nTo put, delete, and get from a table:\n\n::\n\n    # Put a row with the current timestamp\n    table.put('rowkey1', {'f:foo': 'bar', 'f:hai': 'bai'})\n    data = table.row('rowkey1')\n    assert data == {'f:foo': 'bar', 'f:hai': 'bai'}\n\n    # Get a row and only include a single column\n    data = table.row('rowkey1', columns=('f:foo',))\n    assert data == {'f:foo': 'bar'}\n\n    # Delete the row\n    table.delete('rowkey1')\n    data = table.row('rowkey1')\n    assert data == {}\n\n    # Put a row with a given timestamp\n    table.put('rowkey1', {'f:foo': 'bar'}, timestamp=1000)\n    table.put('rowkey1', {'f:foo': 'BAR'}, timestamp=10000)\n\n    # Get a row with a given timestamp and include its timestamp\n    data = table.row('rowkey1', timestamp=1000, include_timestamp=True)\n    assert data == {'f:foo': ('bar', 1000)}\n\nTo scan:\n\n::\n\n    # Full table scan:\n    for row, data in table.scan():\n        pass\n\n    # Scan with a start and stop:\n    for row, data in table.scan('foo', 'bar'):\n        pass\n\n    # Scan with a row prefix:\n    for row, data in table.scan(row_prefix='baz'): # E.g., start='baz', stop='baz~'\n        pass\n\n    # Scan with a filter: # Check out tests.py on how to use all of the filters\n    for row, data in table.scan(filter=\"SingleColumnValueFilter('f', 'foo', =, 'binary:foo')\":\n        pass\n\n    # Scan a table but return only row keys, no rows:\n    table.put('foo', {'f:foo': 'foo'}\n    table.put('foo1', {'f:foo': 'foo'}\n    table.put('foo2', {'f:foo': 'foo'}\n\n    rows = list(table.scan(only_rowkeys=True))\n    assert rows == ['foo', 'foo1', 'foo2']\n\nTo count the number of rows in a table:\n\n::\n\n    # Full table count:\n    count = table.count()\n\n    # Count all rows with a start and stop\n    count = table.count('foo', 'bar')\n\n    # Count all rows whose row key starts with a row prefix:\n    count = table.count(row_prefix='baz') # E.g., start='baz', stop='baz~'\n\n    # Count all rows with a filter:\n    count = table.count(filter=\"SingleColumnValueFilter('f', 'foo', =, 'binary:foo')\")\n\nTo batch put:\n\n::\n\n    batch = table.batch()\n    datas = [\n        ('foo', 'a', 'b'),\n        ('foo1', 'a1', 'b1'),\n        ('foo2', 'a2', 'b2'),\n    ]\n\n    for data in datas:\n        batch.put(data[0], {'f:foo': data[1], 'f:bar': data[2]})\n\n    errors = batch.send()\n\n    assert errors == 0\n\nTo batch delete:\n\n::\n\n    batch = table.batch()\n    rows = ['foo', 'foo1', 'foo2']\n    for row in rows:\n        batch.delete(row)\n\n    errors = batch.send()\n\n    assert errors == 0\n\nNote that ``batch.send()`` returns the number of errors that occurred,\nif any. It is up to the client to ignore this or raise an exception.\n\n::\n\n    batch = table.batch()\n    batch.put('foo', {'f:foo', 'bar'})\n    batch.put('foo', 'invalid')\n\n    errors = batch.send()\n    assert errors == 1\n\nAn additional helper method is ``table.delete_prefix(row_prefix)``,\nwhich deletes all rows containing starting with the prefix.\n\n::\n\n    table.put('foo', {'f:foo', 'foo'}\n    table.put('foo1', {'f:foo', 'foo'}\n    table.put('foo2', {'f:foo', 'foo'}\n    table.put('foo3', {'f:foo', 'foo'}\n    table.put('bar', {'f:bar', 'bar'}\n\n    number_deleted = table.delete_prefix('foo')\n    assert number_deleted == 3\n\n    assert table.count() == 2\n\nNote that attempting to batch/put unescaped null terminators will result\nin them being stripped. Attempting to use a row key with an unescaped\nnull terminator will raise a TypeException. It is the user's duty to\nescape null terminators before attempting to batch/put data.\n\n::\n\n    table.put('foo', {'f:foo\\0bar': 'baz\\0bak'})\n    data = table.row('foo')\n    assert data == {'f:foo': 'baz'}\n\n    table.put('bar', {'f:foo\\\\0bar': 'baz\\00bak'})\n    data = table.row('foo')\n    assert data == {'f:foo\\\\0bar': 'baz\\00bak'}\n\nHappyBase compatibility\n=======================\n\nOne goal of this library is to maintain compatibility with the APIs in\nHappyBase.\n\nCheck out ``__init__.py`` to understand which features of HappyBase I\nhave not yet implemented.\n\nIn the future, I will force print warnings to stderr in the event a user\nuses an unimplemented feature.\n\nNon-MapR Installation and Environment Variables Guide\n=====================================================\n\nI have not tested ``pychbase`` heavily on Cloudera. I couldn't get it\nworking on CDH5 due to a classpath issue with ``libhbase``, and and\nwhile I was able to get it up and running with CDH4, some of the tests\nare failing. It seems to me that ``libhbase`` is not fully compatible\noutside of MapR.\n\n::\n\n    export PYCHBASE_IS_MAPR=FALSE\n    export PYCHBASE_LIBJVM_DIR=/usr/lib/jvm/jre-1.7.0/lib/amd64/server\n    export PYCHBASE_INCLUDE_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/include\n    export PYCHBASE_LIBRARY_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/native\n    virtualenv pychbase\n    cd pychbase\n    source bin/activate\n    pip install pychbase\n\n    # Or build it from source\n    git clone https://github.com/mkmoisen/pychbase.git\n    cd pychbase\n    python setup.py install\n\nPlease note that the following environment variables must be set in\norder to install ``pychbase`` correctly:\n\n-  PYCHBASE\\_IS\\_MAPR\n-  PYCHBASE\\_LIBJVM\\_DIR\n-  PYCHBASE\\_INCLUDE\\_DIR\n-  PYCHBASE\\_LIBRARY\\_DIR\n\nPYCHBASE\\_IS\\_MAPR\n------------------\n\nThis defaults to ``TRUE``. IF you are using Cloudera/etc, make sure to:\n\n::\n\n    export PYCHBASE_IS_MAPR=FALSE\n\nPYCHBASE\\_LIBJVM\\_DIR\n---------------------\n\nThis is the directory that houses the ``libjvm.so`` file. Normally it is\nin either:\n\n-  $JAVA\\_HOME/lib/amd64/server\n-  $JAVA\\_HOME/jre/lib/amd64/server\n\nIf ``PYCHBASE_LIBJVM_DIR`` is not set, the installer will check if\n``JAVA_HOME`` has been set, and then try each of the above directories.\nIf ``JAVA_HOME`` is not set, it will attempt to default to\n``/usr/lib/jvm/jre-1.7.0/``.\n\nExample:\n\n::\n\n    export PYCHBASE_LIBJVM_DIR=/usr/lib/jvm/jre-1.7.0/lib/amd64/server\n\nPYCHBASE\\_INCLUDE\\_DIR\n----------------------\n\nThis houses the ``/hbase/hbase.h`` and other ``libhbase`` C header\nfiles.\n\nIf ``PYCHBASE_IS_MAPR`` is true, this defaults to ``/opt/mapr/include``.\n\nFor Non-MapR environments, this must be set or the installation will\nfail.\n\nExample on Cloudera:\n\n::\n\n    export PYCHBASE_INCLUDE_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/include\n\nPYCHBASE\\_LIBRARY\\_DIR\n----------------------\n\nThis houses either the ``libMapRClient.so`` file on MapR environments,\nor the ``libhbase.so`` file on Non-MapR environments.\n\nIf ``PYCHBASE_IS_MAPR`` is true, this defaults to ``/opt/mapr/lib``.\n\nFor Non-MapR environments, this must be set or the installation will\nfail.\n\nExample on Cloudera:\n\n::\n\n    export PYCHBASE_LIBRARY_DIR=/home/matthew/libhbase/target/libhbase-1.0-SNAPSHOT/lib/native\n\nAuthor\n======\n\n`Matthew Moisen <https://matthewmoisen.com>`__\n\nLicense\n=======\n\nMIT",
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