Cloudera Docs

Kudu Python client

The Kudu Python client provides a Python friendly interface to the C++ client API. To install and use the Kudu Python client, you need to install the Kudu C++ client libraries and headers.

See Install Using Packages topic for installing the Kudu C++ client libraries.
  1. Update all the packages on your system by using the following command:
    yum -y update
  2. Install the extra packages for the Enterprise Linux distribution:
    sudo yum -y install epel-release
  3. Install the Python package manager:
    sudo yum -y install python-pip
  4. Verify the version of the PIP manager that you just installed:
    pip --version
  5. Install Cython:
    sudo pip install cython
  6. Download the following files using wget:
    • Kudu artifact: http://username:password@archive.cloudera.com/p/cdh7/RUNTIME_VERSION/redhat7/yum/kudu/KUDU_ARTIFACT
    • Kudu-client artifact: http://username:password@archive.cloudera.com/p/cdh7/RUNTIME_VERSION/redhat7/yum/kudu/KUDU-CLIENT_ARTIFACT
    • Kudu-client-devel artifact: http://username:password@archive.cloudera.com/p/cdh7/RUNTIME_VERSION/redhat7/yum/kudu/KUDU-CLIENT-DEVEL_ARTIFACT
  7. Install the kudu package from the local directory:
    sudo yum -y localinstall ./kudu-*
  8. Install the package used for developing Python extensions:
    sudo yum -y install python-devel
  9. Upgrade the setup tools:
    sudo pip install --upgrade pip setuptools
  10. Install the C++ compiler:
    sudo yum -y install gcc-c++
  11. Setup the Python package version:
    export PACKAGE_VERSION=<kudu-python-cloudera-version>
    For the 7.1.9 SP1 release, use the export PACKAGE_VERSION=7.1.9.1000 command.
  12. Install the Kudu-Python client:
    sudo pip install kudu-python-cloudera==$PACKAGE_VERSION
  13. The following sample demonstrates the use of part of the Python client: 
    import kudu
from kudu.client import Partitioning
from datetime import datetime

# Connect to Kudu master server
client = kudu.connect(host='kudu.master', port=7051)

# Define a schema for a new table
builder = kudu.schema_builder()
builder.add_column('key').type(kudu.int64).nullable(False).primary_key()
builder.add_column('ts_val', type_=kudu.unixtime_micros, nullable=False, compression='lz4')
schema = builder.build()

# Define partitioning schema
partitioning = Partitioning().add_hash_partitions(column_names=['key'], num_buckets=3)

# Create new table
client.create_table('python-example', schema, partitioning)

# Open a table
table = client.table('python-example')

# Create a new session so that we can apply write operations
session = client.new_session()

# Insert a row
op = table.new_insert({'key': 1, 'ts_val': datetime.utcnow()})
session.apply(op)

# Upsert a row
op = table.new_upsert({'key': 2, 'ts_val': "2016-01-01T00:00:00.000000"})
session.apply(op)

# Updating a row
op = table.new_update({'key': 1, 'ts_val': ("2017-01-01", "%Y-%m-%d")})
session.apply(op)

# Delete a row
op = table.new_delete({'key': 2})
session.apply(op)

# Flush write operations, if failures occur, capture print them.
try:
    session.flush()
except kudu.KuduBadStatus as e:
    print(session.get_pending_errors())

# Create a scanner and add a predicate
scanner = table.scanner()
scanner.add_predicate(table['ts_val'] == datetime(2017, 1, 1))

# Open Scanner and read all tuples
# Note: This doesn't scale for large scans
result = scanner.open().read_all_tuples()