import logging
from itertools import count
from sqlalchemy.sql import and_, expression
from sqlalchemy.schema import Column, Index
from dataset.persistence.util import guess_type
log = logging.getLogger(__name__)
class Table(object):
def __init__(self, database, table):
self.indexes = {}
self.database = database
self.table = table
@property
def columns(self):
"""
Get a listing of all columns that exist in the table.
>>> print 'age' in table.columns
True
"""
return set(self.table.columns.keys())
def drop(self):
"""
Drop the table from the database, deleting both the schema
and all the contents within it.
Note: the object will be in an unusable state after using this
command and should not be used again. If you want to re-create
the table, make sure to get a fresh instance from the
:py:class:`Database <dataset.Database>`.
"""
with self.database.lock:
self.database.tables.pop(self.table.name, None)
self.table.drop(engine)
def insert(self, row, ensure=True, types={}):
"""
Add a row (type: dict) by inserting it into the table.
If ``ensure`` is set, any of the keys of the row are not
table columns, they will be created automatically.
During column creation, ``types`` will be checked for a key
matching the name of a column to be created, and the given
SQLAlchemy column type will be used. Otherwise, the type is
guessed from the row's value, defaulting to a simple unicode
field.
::
data = dict(id=10, title='I am a banana!')
table.insert(data, ['id'])
"""
if ensure:
self._ensure_columns(row, types=types)
self.database.engine.execute(self.table.insert(row))
def update(self, row, keys, ensure=True, types={}):
"""
Update a row in the table. The update is managed via
the set of column names stated in ``keys``: they will be
used as filters for the data to be updated, using the values
in ``row``.
::
# update all entries with id matching 10, setting their title columns
data = dict(id=10, title='I am a banana!')
table.update(data, ['id'])
If keys in ``row`` update columns not present in the table,
they will be created based on the settings of ``ensure`` and
``types``, matching the behaviour of :py:meth:`insert() <dataset.Table.insert>`.
"""
if not len(keys):
return False
clause = [(u, row.get(u)) for u in keys]
if ensure:
self._ensure_columns(row, types=types)
try:
filters = self._args_to_clause(dict(clause))
stmt = self.table.update(filters, row)
rp = self.database.engine.execute(stmt)
return rp.rowcount > 0
except KeyError:
return False
def upsert(self, row, keys, ensure=True, types={}):
"""
An UPSERT is a smart combination of insert and update. If rows with matching ``keys`` exist
they will be updated, otherwise a new row is inserted in the table.
::
data = dict(id=10, title='I am a banana!')
table.upsert(data, ['id'])
"""
if ensure:
self.create_index(keys)
if not self.update(row, keys, ensure=ensure, types=types):
self.insert(row, ensure=ensure, types=types)
def delete(self, **filter):
"""
Delete rows matching the ``filter`` arguments.
::
table.delete(year=2010)
"""
q = self._args_to_clause(filter)
stmt = self.table.delete(q)
self.database.engine.execute(stmt)
def _ensure_columns(self, row, types={}):
for column in set(row.keys()) - set(self.table.columns.keys()):
if column in types:
_type = types[column]
else:
_type = guess_type(row[column])
log.debug("Creating column: %s (%s) on %r" % (column,
_type, self.table.name))
self.create_column(column, _type)
def _args_to_clause(self, args):
self._ensure_columns(args)
clauses = []
for k, v in args.items():
clauses.append(self.table.c[k] == v)
return and_(*clauses)
def create_column(self, name, type):
"""
Explicitely create a new column ``name`` of a specified type. ``type`` must be a `SQLAlchemy column type <http://docs.sqlalchemy.org/en/rel_0_8/core/types.html>`_.
::
table.create_column('person', sqlalchemy.String)
"""
with self.database.lock:
if name not in self.table.columns.keys():
col = Column(name, type)
col.create(self.table,
connection=self.database.engine)
def create_index(self, columns, name=None):
"""
Create an index to speed up queries on a table. If no ``name`` is given a random name is created.
::
table.create_index(['name', 'country'])
"""
with self.database.lock:
if not name:
sig = abs(hash('||'.join(columns)))
name = 'ix_%s_%s' % (self.table.name, sig)
if name in self.indexes:
return self.indexes[name]
try:
columns = [self.table.c[c] for c in columns]
idx = Index(name, *columns)
idx.create(self.database.engine)
except:
idx = None
self.indexes[name] = idx
return idx
def find_one(self, **filter):
"""
Works just like :py:meth:`find() <dataset.Table.find>` but returns only one result.
::
row = table.find_one(country='United States')
"""
res = list(self.find(_limit=1, **filter))
if not len(res):
return None
return res[0]
def _args_to_order_by(self, order_by):
if order_by[0] == '-':
return self.table.c[order_by[1:]].desc()
else:
return self.table.c[order_by].asc()
def find(self, _limit=None, _offset=0, _step=5000,
order_by='id', **filter):
"""
Performs a simple search on the table. Simply pass keyword arguments as ``filter``.
::
results = table.find(country='France')
results = table.find(country='France', year=1980)
Using ``_limit``::
# just return the first 10 rows
results = table.find(country='France', _limit=10)
You can sort the results by single or multiple columns. Append a minus sign
to the column name for descending order::
# sort results by a column 'year'
results = table.find(country='France', order_by='year')
# return all rows sorted by multiple columns (by year in descending order)
results = table.find(order_by=['country', '-year'])
For more complex queries, please use :py:meth:`db.query() <dataset.Database.query>` instead."""
if isinstance(order_by, (str, unicode)):
order_by = [order_by]
order_by = [self._args_to_order_by(o) for o in order_by]
args = self._args_to_clause(filter)
for i in count():
qoffset = _offset + (_step * i)
qlimit = _step
if _limit is not None:
qlimit = min(_limit - (_step * i), _step)
if qlimit <= 0:
break
q = self.table.select(whereclause=args, limit=qlimit,
offset=qoffset, order_by=order_by)
rows = list(self.database.query(q))
if not len(rows):
return
for row in rows:
yield row
def __len__(self):
"""
Returns the number of rows in the table.
"""
d = self.database.query(self.table.count()).next()
return d.values().pop()
def distinct(self, *columns, **filter):
"""
Returns all rows of a table, but removes rows in with duplicate values in ``columns``.
Interally this creates a `DISTINCT statement <http://www.w3schools.com/sql/sql_distinct.asp>`_.
::
# returns only one row per year, ignoring the rest
table.distinct('year')
# works with multiple columns, too
table.distinct('year', 'country')
# you can also combine this with a filter
table.distinct('year', country='China')
"""
qargs = []
try:
columns = [self.table.c[c] for c in columns]
for col, val in filter.items():
qargs.append(self.table.c[col] == val)
except KeyError:
return []
q = expression.select(columns, distinct=True,
whereclause=and_(*qargs),
order_by=[c.asc() for c in columns])
return self.database.query(q)
def all(self):
"""
Returns all rows of the table as simple dictionaries. This is simply a shortcut
to *find()* called with no arguments.
::
rows = table.all()"""
return self.find()
def __iter__(self):
"""
Allows for iterating over all rows in the table without explicetly
calling :py:meth:`all() <dataset.Table.all>`.
::
for row in table:
print row
"""
for row in self.all():
yield row
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