Crate Data Python client
Project description
CrateDB Python Client
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
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Client Usage
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.
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.
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
Here is another example, which provides the digest in the call:
>>> _ = f.seek(0) >>> blob_container.put(f, digest='6d46af79aa5113bd7e6a67fae9ab5228648d3f81') False
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
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