Metadata-Version: 1.2
Name: crate
Version: 0.21.3
Summary: Crate Data Python client
Home-page: https://github.com/crate/crate-python
Author: CRATE Technology GmbH
Author-email: office@crate.io
License: Apache License 2.0
Description: =====================
        CrateDB Python Client
        =====================
        
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        |
        
        A Python client library for CrateDB_.
        
        This library:
        
        - Implements the Python `DB API 2.0`_ specification
        - Includes support for SQLAlchemy_ (>= 1.0.0)
        
        Prerequisites
        =============
        
        Recent versions of this library require Python (>= 2.7) to run.
        
        Use library version 0.14 if you're running Python 2.6.
        
        Installation
        ============
        
        The CrateDB Python client is available as a pip_ package.
        
        To install, run::
        
            $ pip install crate
        
        To update, run::
        
            $ pip install -U crate
        
        Contributing
        ============
        
        This project is primarily maintained by Crate.io_, but we welcome community
        contributions!
        
        See the `developer docs`_ and the `contribution docs`_ for more information.
        
        Help
        ====
        
        Looking for more help?
        
        - Read `the project documentation`_
        - Check `StackOverflow`_ for common problems
        - Chat with us on `Slack`_
        - Get `paid support`_
        
        .. _contribution docs: CONTRIBUTING.rst
        .. _Crate.io: http://crate.io/
        .. _CrateDB: https://github.com/crate/crate
        .. _DB API 2.0: http://www.python.org/dev/peps/pep-0249/
        .. _developer docs: DEVELOP.rst
        .. _paid support: https://crate.io/pricing/
        .. _pip: https://pypi.python.org/pypi/pip
        .. _Slack: https://crate.io/docs/support/slackin/
        .. _SQLAlchemy: http://www.sqlalchemy.org
        .. _StackOverflow: https://stackoverflow.com/tags/crate
        .. _the project documentation: https://crate.io/docs/reference/python/
        
        ============
        Client Usage
        ============
        
        .. rubric:: Table of Contents
        
        .. contents::
           :local:
        
        Connect to a Database
        =====================
        
        Before we can start we have to import the client::
        
            >>> from crate import client
        
        The client provides a ``connect()`` function which is used to establish a
        connection, the first argument is the url of the server to connect to::
        
            >>> connection = client.connect(crate_host)
        
        CrateDB is a clustered database providing high availability through
        replication. In order for clients to make use of this property it is
        recommended to specify all hosts of the cluster. This way if a server does not
        respond, the request is automatically routed to the next server::
        
            >>> invalid_host = 'http://not_responding_host:4200'
            >>> connection = client.connect([invalid_host, crate_host])
        
        If no ``servers`` are given, the default one ``http://127.0.0.1:4200`` is used::
        
            >>> connection = client.connect()
            >>> connection.client._active_servers
            ['http://127.0.0.1:4200']
        
        
        If the option ``error_trace`` is set to ``True``, the client will print a whole traceback
        if a server error occurs::
        
            >>> connection = client.connect([crate_host], error_trace=True)
        
        It's possible to define a default timeout value in seconds for all servers
        using the optional parameter ``timeout``::
        
            >>> connection = client.connect([crate_host, invalid_host], timeout=5)
        
        Inserting Data
        ==============
        
        Before executing any statement a cursor has to be opened to perform
        database operations::
        
            >>> cursor = connection.cursor()
            >>> cursor.execute("""INSERT INTO locations
            ... (name, date, kind, position) VALUES (?, ?, ?, ?)""",
            ...                ('Einstein Cross', '2007-03-11', 'Quasar', 7))
        
        To bulk insert data you can use the `executemany` function::
        
            >>> cursor.executemany("""INSERT INTO locations
            ... (name, date, kind, position) VALUES (?, ?, ?, ?)""",
            ...                [('Cloverleaf', '2007-03-11', 'Quasar', 7),
            ...                 ('Old Faithful', '2007-03-11', 'Quasar', 7)])
            [{u'rowcount': 1}, {u'rowcount': 1}]
        
        `executemany` returns a list of results for every parameter. Each result
        contains a rowcount. If an error occures the rowcount is -2 and the result
        may contain an `error_message` depending on the error.
        
        .. note::
        
            If you are using a CrateDB server version older than 0.42.0 the client
            will execute a single sql statement for every parameter in the parameter
            sequence when you are using executemany. In this case, executemany doesn't
            return any value. To avoid that overhead you can
            use ``execute`` and make use of multiple rows in the INSERT
            statement and provide a list of arguments with the length of
            ``number of inserted records * number of columns``::
        
                >>> cursor.execute("""INSERT INTO locations
                ... (name, date, kind, position) VALUES (?, ?, ?, ?), (?, ?, ?, ?)""",
                ...                ('Creameries', '2007-03-11', 'Quasar', 7,
                ...                 'Double Quasar', '2007-03-11', 'Quasar', 7))
        
        .. Hidden: refresh locations
        
            >>> cursor.execute("REFRESH TABLE locations")
        
        Selecting Data
        ==============
        
        To perform the select operation simply execute the statement on the
        open cursor::
        
            >>> cursor.execute("SELECT name FROM locations where name = ?", ('Algol',))
        
        To retrieve a row we can use one of the cursor's fetch functions (described below).
        
        fetchone()
        ----------
        
        ``fetchone()`` with each call returns the next row from the results::
        
            >>> result = cursor.fetchone()
            >>> pprint(result)
            [u'Algol']
        
        If no more data is available, an empty result is returned::
        
            >>> while cursor.fetchone():
            ...     pass
            >>> cursor.fetchone()
        
        fetchmany()
        -----------
        
        ``fetch_many()`` returns a list of all remaining rows, containing no more than the specified
        size of rows::
        
            >>> cursor.execute("SELECT name FROM locations order by name")
            >>> result = cursor.fetchmany(2)
            >>> pprint(result)
            [[u'Aldebaran'], [u'Algol']]
        
        If a size is not given, the cursor's arraysize, which defaults to '1', determines the number
        of rows to be fetched::
        
            >>> cursor.fetchmany()
            [[u'Allosimanius Syneca']]
        
        It's also possible to change the cursors arraysize to an other value::
        
            >>> cursor.arraysize = 3
            >>> cursor.fetchmany()
            [[u'Alpha Centauri'], [u'Altair'], [u'Argabuthon']]
        
        fetchall()
        ----------
        
        ``fetchall()`` returns a list of all remaining rows::
        
            >>> cursor.execute("SELECT name FROM locations order by name")
            >>> result = cursor.fetchall()
            >>> pprint(result)
            [['Aldebaran'],
             ['Algol'],
             ['Allosimanius Syneca'],
             ['Alpha Centauri'],
             ['Altair'],
             ['Argabuthon'],
             ['Arkintoofle Minor'],
             ['Bartledan'],
             ['Cloverleaf'],
             ['Creameries'],
             ['Double Quasar'],
             ['Einstein Cross'],
             ['Folfanga'],
             ['Galactic Sector QQ7 Active J Gamma'],
             ['Galaxy'],
             ['North West Ripple'],
             ['Old Faithful'],
             ['Outer Eastern Rim']]
        
        Cursor Description
        ==================
        
        The ``description`` property of the cursor returns a sequence of 7-item sequences containing the
        column name as first parameter. Just the name field is supported, all other fields are 'None'::
        
            >>> cursor.execute("SELECT * FROM locations order by name")
            >>> result = cursor.fetchone()
            >>> pprint(result)
            [1373932800000,
             None,
             u'Max Quordlepleen claims that the only thing left ...',
             None,
             None,
             u'Star System',
             u'Aldebaran',
             None,
             None,
             1]
        
            >>> result = cursor.description
            >>> pprint(result)
            ((u'date', None, None, None, None, None, None),
             (u'datetime', None, None, None, None, None, None),
             (u'description', None, None, None, None, None, None),
             (u'details', None, None, None, None, None, None),
             (u'flag', None, None, None, None, None, None),
             (u'kind', None, None, None, None, None, None),
             (u'name', None, None, None, None, None, None),
             (u'nullable_date', None, None, None, None, None, None),
             (u'nullable_datetime', None, None, None, None, None, None),
             (u'position', None, None, None, None, None, None))
        
        Closing the Cursor
        ==================
        
        The following command closes the cursor::
        
            >>> cursor.close()
        
        If a cursor is closed, it will be unusable from this point forward.
        If any operation is attempted to a closed cursor an ``ProgrammingError`` will be raised.
        
            >>> cursor.execute("SELECT * FROM locations")
            Traceback (most recent call last):
            ...
            ProgrammingError: Cursor closed
        
        Closing the Connection
        ======================
        
        The following command closes the connection::
        
            >>> connection.close()
        
        If a connection is closed, it will be unusable from this point forward.
        If any operation using the connection is attempted to a closed connection an ``ProgrammingError``
        will be raised::
        
            >>> cursor.execute("SELECT * FROM locations")
            Traceback (most recent call last):
            ...
            ProgrammingError: Connection closed
        
            >>> cursor = connection.cursor()
            Traceback (most recent call last):
            ...
            ProgrammingError: Connection closed
        
        ================
        CrateDB BLOB API
        ================
        
        The CrateDB client library provides an API to access the powerful Blob storage
        capabilities of the CrateDB server.
        
        First, a connection object is required. This can be retrieved by importing the
        client module and then connecting to one or more CrateDB server::
        
            >>> from crate import client
            >>> connection = client.connect(crate_host)
        
        Every table which has Blob support enabled, may act as a container for
        Blobs. The ``BlobContainer`` object for a specific table can be
        retrieved like this::
        
            >>> blob_container = connection.get_blob_container('myfiles')
            >>> blob_container
            <BlobContainer 'myfiles'>
        
        The returned container object can now be used to manage the contained
        Blobs.
        
        .. rubric:: Table of Contents
        
        .. contents::
           :local:
        
        Uploading Blobs
        ===============
        
        To upload a Blob the ``put`` method can be used. This method takes a
        file like object and an optional SHA-1 digest as argument.
        
        In this example we upload a file without specifying the SHA-1 digest::
        
            >>> from tempfile import TemporaryFile
            >>> f = TemporaryFile()
            >>> _ = f.write(b"this is the content of the file")
            >>> f.flush()
        
        The actual ``put`` - it returns the computed SHA-1 digest upon completion::
        
            >>> print(blob_container.put(f))
            6d46af79aa5113bd7e6a67fae9ab5228648d3f81
        
        .. note::
        
          Omitting the SHA-1 digest results in one extra read of the file
          contents to compute the digest before the actual upload
          starts. Therefore, if the application already has a SHA-1 digest for
          the content, or is able to compute the digest on another read
          upfront, providing the digest will lead to better performance.
        
        Here is another example, which provides the digest in the call::
        
            >>> _ = f.seek(0)
            >>> blob_container.put(f, digest='6d46af79aa5113bd7e6a67fae9ab5228648d3f81')
            False
        
        .. note::
        
          The above call returned ``False`` because the object already
          existed. Since the digest is already known by the caller and it makes no
          sense to return it again, a boolean gets returned which indicates if
          the Blob was newly created or not.
        
        Retrieving Blobs
        ================
        
        Retrieving a blob can be done by using the ``get`` method like this::
        
            >>> res = blob_container.get('6d46af79aa5113bd7e6a67fae9ab5228648d3f81')
        
        The result is a generator object which returns one chunk per iteration::
        
            >>> print(next(res))
            this is the content of the file
        
        It is also possible to check if a blob exists like this::
        
            >>> blob_container.exists('6d46af79aa5113bd7e6a67fae9ab5228648d3f81')
            True
        
        Deleting Blobs
        ==============
        
        To delete a blob just call the ``delete`` method, the resulting boolean
        states whether a blob existed under the given digest or not::
        
            >>> blob_container.delete('6d46af79aa5113bd7e6a67fae9ab5228648d3f81')
            True
        
Keywords: crate db api sqlalchemy
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Database
Requires-Python: >=2.7
