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accumulo-python3

Use this library to write Python 3 applications that integrate with Apache Accumulo.

Library features include:

  • Convenience classes for creating Accumulo objects, such as Mutations and Ranges
  • A blocking, synchronous client
  • A non-blocking, asynchronous client for applications using the asyncio module.
import accumulo
from accumulo import Mutation, RangePrefix, ScanOptions

connector = accumulo.AccumuloProxyConnectionContext().create_connector('user', 'secret')

# Create the table 'tmp' if it does not already exist.
if not connector.table_exists('tmp'):
    connector.create_table('tmp')

# Commit some mutations
with connector.create_writer('tmp') as writer:
    writer.add_mutations([
        Mutation('User.1', 'loc', 'x', value='34'),
        Mutation('User.1', 'loc', 'y', value='35'),
        Mutation('User.1', 'old_property', delete=True)
    ])

# Scan the table
with connector.create_scanner('tmp', ScanOptions(range=RangePrefix('User.1'))) as scanner:
    for r in scanner:
        print(r.row, r.cf, r.value_bytes)

Note. This library is a work in progress. It has been tested with Accumulo 1.9 and Python 3.8.

Installation

This library is not yet available on the Python Package Index.

Clone the repository and use pip to install locally into your environment.

git clone https://github.com/NationalSecurityAgency/accumulo-python3.git
cd accumulo-python3
pip install .

Optionally include the -e option with pip to install the library in edit mode, which is appropriate for local development.

pip install -e .

Background

Native integration with Accumulo is powered by Apache Thrift. This library embeds Thrift-generated Python 3 bindings for Accumulo in the accumulo.thrift submodule. The generated bindings are low-level and inconsistent with idiomatic Python 3 conventions. This library provides higher-level functionality around the generated bindings in order to support more practical development.

Accumulo Proxy is required to broker communications between Thrift clients (such as this library) and Accumulo.

Manual

Create a proxy connection

A proxy connection represents the connection to the Accumulo Proxy server.

Use the AccumuloProxyConnection and AccumuloProxyConnectionParams classes to create a proxy connection to Accumulo Proxy.

from accumulo import AccumuloProxyConnection, AccumuloProxyConnectionParams

# Note: These are the default settings.
proxy_connection = AccumuloProxyConnection(AccumuloProxyConnectionParams(hostname='127.0.0.1', port=42424))

# Alternatively, create a proxy connection using the default settings.
proxy_connection = AccumuloProxyConnection()

Alternatively, use the proxy connection instance as a context manager to automatically close it.

with proxy_connection:
    pass

Otherwise, use proxy_connection.close() to manually close the proxy connection instance.

Use the proxy connection to call the low-level Accumulo bindings

It may be necessary to use low-level Thrift-generated bindings to perform certain actions that are not supported by the higher-level functionality in this library. Use the client property of an AccumuloProxyConnection instance to access these bindings.

login = proxy_connection.client.login('user', {'password': 'secret'})
proxy_connection.client.changeUserAuthorizations(login, 'user', [b'ADMIN'])

Creating a blocking connector

A connector is an authenticated interface to Accumulo, and is used to perform actions that require authentication, such as creating tables or scanners. A context is used to create a connector.

Use the AccumuloProxyConnectionContext class to create a blocking connector instance.

from accumulo import AccumuloProxyConnectionContext

context = AccumuloProxyConnectionContext(proxy_connection)
connector = context.create_connector('user', 'secret')

Perform some basic table operations

In the example below, we create the table tmp if it does not already exist.

if not connector.table_exists('tmp'):
    connector.create_table('tmp')

Change user authorizations

In the example below, we add an authorization to the our user's authorizations.

from accumulo import AuthorizationSet

# Get the user's current set of authorizations
current_auths = connector.get_user_authorizations('tmp')
# AuthorizationSet behaves like a frozenset and supports set operators
new_auths = AuthorizationSet({'PRIVATE'}) | current_auths  # set union
connector.change_user_authorizations('user', new_auths)

Add some mutations

In the example below, we add mutations to a table.

from accumulo import Mutation, WriterOptions

# Use the writer as a context manager to automatically close it. The second parameter opts is optional.
with connector.create_writer('tmp', opts=WriterOptions()) as writer:
    writer.add_mutations([
        # Create a mutation with all parameters defined. 
        Mutation('row', b'CF', 'cq', 'visibility', 123, b'binaryvalue', False),
        # Create a mutation with keyword arguments
        Mutation('row', cq='cq', value='value'),
        Mutation('row', cf=b'cf', visibility=b'PRIVATE', delete=True),
        Mutation('row', timestamp=123)
    ])

Note that Mutation will automatically encode all parameters into binary values.

Scan the table

In the example below, we perform a full table scan.

from accumulo import ScanOptions

# Use the scanner as a context manager to automatically close it. The second parameter is optional.
with connector.create_scanner('tmp', ScanOptions()) as scanner:
    for r in scanner:
        # The scanner returns a facade that provides binary and non-binary accessors for the record properties.
        print(r.row, r.row_bytes, r.cf, r.cf_bytes, r.cq, r.cq_bytes, r.visibility, r.visibility_bytes, r.timestamp, 
              r.value, r.value_bytes)

We may alternatively create a batch scanner:

from accumulo import BatchScanOptions

# The second parameter is optional.
with connector.create_batch_scanner('tmp', BatchScanOptions()) as scanner:
    pass

The create_scanner and create_batch_scanner methods respectively accept a ScanOptions or BatchScanOptions object as a second parameter.

Scan with specific authorizations

ScanOptions and BatchScanOptions both support an authorizations keyword argument, which may be used to configure a scanner with specific authorizations.

Authorizations must be provided as an iterable of binary values. We may use the AuthorizationSet class to create a frozenset of binary values from binary and non-binary arguments.

from accumulo import AuthorizationSet

with connector.create_scanner(
    table='tmp', 
    opts=ScanOptions(
        authorizations=AuthorizationSet({'PRIVATE', 'PUBLIC'})
    )
) as scanner:
    pass

Scan specific columns

ScanOptions and BatchScanOptions both support a columns keyword argument, which may be used to only retrieve specific columns. Use the ScanColumn class to define column, which include a column family and an optional column qualifier.

from accumulo import ScanColumn

with connector.create_scanner(
    'tmp',
    ScanOptions(
        columns=[
            # Column family and column qualifier. Accepts binary and non-binary arguments. 
            ScanColumn(b'cf', 'cq'),
            # Column family only.
            ScanColumn('cf'),
        ]
    )
) as scanner:
    pass

Use scan ranges

ScanOptions and BatchScanOptions respectively accept an optional range or ranges keyword argument. Use the Key, Range, RangeExact, and RangePrefix classes to define ranges.

from accumulo import Key, Range, RangePrefix

with connector.create_scanner(
    'tmp',
    ScanOptions(
        # Binary and non-binary arguments are accepted
        range=Range(start_key=Key('sk', b'cf'))
    )
) as scanner:
    pass

with connector.create_scanner(
    'tmp',
    ScanOptions(
        range=Range(end_key=Key('ek', b'cf'), is_end_key_inclusive=True)
    )
) as scanner:
    pass

with connector.create_scanner(
    'tmp',
    ScanOptions(
        range=Range(start_key=Key('sk', b'cf'), end_key=Key('ek', 'cf', 'cq'))
    )
) as scanner:
    pass

with connector.create_batch_scanner(
    'tmp',
    BatchScanOptions(
        # batch scanner accepts multiple ranges
        ranges=[
            Range(start_key=Key(b'\xff')),
            RangePrefix('row', 'cf', b'cq'),
            RangePrefix(b'abc', 'cq')
        ]
    )
) as scanner:
    pass

Use an iterator

ScanOptions and BatchScanOptions both support an iterator_settings keyword argument.

from accumulo import IteratorSetting

with connector.create_scanner(
    'tmp',
    ScanOptions(
        iterator_settings=[
            IteratorSetting(priority=30, name='iter', iterator_class='my.iterator', properties={})
        ]
    )
) as scanner:
    pass

Writing asynchronous, non-blocking applications

The examples above are all examples of blocking code. This may be fine for scripts, but it is disadvantageous for applications such as web services that need to service client requests concurrently. Fortunately, this library includes an asynchronous connector that may be used to call the above methods in a non-blocking fashion using Python's async/await syntax.

Creating an asynchronous connector

Earlier, we used the AccumuloProxyConnectionContext class to create a blocking connector. To create an asynchronous connector, we will use the AccumuloProxyConnectionPoolContextAsync class.

from accumulo import AccumuloProxyConnectionPoolContextAsync

async_conn = await AccumuloProxyConnectionPoolContextAsync().create_connector('user', 'secret')

Unlike the blocking connector, the non-blocking connector uses a pool of proxy connection objects, and uses a thread pool executor to call the low-level bindings outside of the main event loop.

In the example below, we explore some more specific options for configuring an asynchronous connector.

from accumulo import (
    AccumuloProxyConnectionParams, AccumuloProxyConnectionFactory, AsyncAccumuloConnectorPoolExecutor, 
    AccumuloProxyConnectionPoolContextAsync
)

# The executor will generate new proxy connection instances on-demand, up to a limit.
executor = AsyncAccumuloConnectorPoolExecutor(
    proxy_connection_limit=4, 
    proxy_connection_factory=AccumuloProxyConnectionFactory(
        params=AccumuloProxyConnectionParams(hostname='127.0.0.1', port=42424)
    )
)
# A default executor is created if one is not provided.
context = AccumuloProxyConnectionPoolContextAsync(executor)
async_conn = await AccumuloProxyConnectionPoolContextAsync().create_connector('user', 'secret')

Using writers

async with await async_conn.create_writer('tmp') as writer:
    # Add mutations must be awaited
    await writer.add_mutations([Mutation('Row')])

Using scanners

async with await async_conn.create_scanner('tmp') as scanner:
    async for record in scanner:
        pass

Performing other operations

All other connector operations, such as create_table or table_exists, must similarly be called using the await syntax.

await async_conn.table_exists('tmp')

Asynchronously call low-level bindings

Use the executor to asynchronously call a low-level binding function. You must provide a getter function that returns the binding function from a proxy client instance.

# executor.run(gettern_fn, *args)
await executor.run(lambda c: c.tableExists, login, 'tmp')

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Build Python 3 applications that integrate with Apache Accumulo

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