plexpy/plexpy/datatables.py
Tim ba1dfea9eb Fix bug on info pages for Shows.
Return error parameter to Datatables on exception.
Return draw counter to Datatables.
2015-07-18 23:27:20 +02:00

249 lines
10 KiB
Python

# This file is part of PlexPy.
#
# PlexPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# PlexPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with PlexPy. If not, see <http://www.gnu.org/licenses/>.
from plexpy import logger, helpers, database
import re
class DataTables(object):
"""
Server side processing for Datatables
"""
def __init__(self):
self.ssp_db = database.MonitorDatabase()
def ssp_query(self,
table_name=None,
columns=[],
custom_where=[],
group_by=[],
join_types=[],
join_tables=[],
join_evals=[],
kwargs=None):
if not table_name:
logger.error('PlexPy DataTables :: No table name received.')
return None
# Set default variable values
parameters = {}
args = []
group = ''
order = ''
where = ''
join = ''
c_where = ''
# Fetch all our parameters
if kwargs.get('json_data'):
parameters = helpers.process_json_kwargs(json_kwargs=kwargs.get('json_data'))
else:
logger.error('PlexPy DataTables :: Parameters for Datatables must be sent as a serialised json object '
'named json_data.')
return None
dt_columns = parameters['columns']
extracted_columns = self.extract_columns(columns=columns)
# Build grouping
if group_by:
for g in group_by:
group += g + ', '
if group:
grouping = True
group = 'GROUP BY ' + group.rstrip(', ')
else:
grouping = False
# Build ordering
for o in parameters['order']:
sort_order = ' COLLATE NOCASE'
if o['dir'] == 'desc':
sort_order = ' COLLATE NOCASE DESC'
# We first see if a name was sent though for the column sort.
if dt_columns[int(o['column'])]['data']:
# We have a name, now check if it's a valid column name for our query
# so we don't just inject a random value
if any(d.lower() == dt_columns[int(o['column'])]['data'].lower()
for d in extracted_columns['column_named']):
order += dt_columns[int(o['column'])]['data'] + '%s' % sort_order
else:
# if we receive a bogus name, rather not sort at all.
pass
# If no name exists for the column, just use the column index to sort
else:
order += extracted_columns['column_named'][int(o['column'])]
order += ', '
if order:
order = 'ORDER BY ' + order.rstrip(', ')
# Build where parameters
if parameters['search']['value']:
counter = 0
for s in parameters['columns']:
if s['searchable']:
# We first see if a name was sent though for the column search.
if s['data']:
# We have a name, now check if it's a valid column name for our query
# so we don't just inject a random value
if any(d.lower() == s['data'].lower() for d in extracted_columns['column_named']):
where += s['data'] + ' LIKE ? OR '
args.append('%' + parameters['search']['value'] + '%')
else:
# if we receive a bogus name, rather not search at all.
pass
# If no name exists for the column, just use the column index to search
else:
where += extracted_columns['column_named'][counter] + ' LIKE ? OR '
args.append('%' + parameters['search']['value'] + '%')
counter += 1
if where:
where = 'WHERE ' + where.rstrip(' OR ')
# Build join parameters
if join_types:
counter = 0
for join_type in join_types:
if join_type.upper() == 'LEFT OUTER JOIN':
join_item = 'LEFT OUTER JOIN %s ON %s = %s ' % \
(join_tables[counter], join_evals[counter][0], join_evals[counter][1])
elif join_type.upper() == 'JOIN' or join_type.upper() == 'INNER JOIN':
join_item = 'JOIN %s ON %s = %s ' % \
(join_tables[counter], join_evals[counter][0], join_evals[counter][1])
else:
join_item = ''
counter += 1
join += join_item
# Build custom where parameters
if custom_where:
for w in custom_where:
c_where += w[0] + ' = ? AND '
# The order of our args changes if we are grouping
if grouping:
args.insert(0, w[1])
else:
args.append(w[1])
if c_where:
c_where = 'WHERE ' + c_where.rstrip(' AND ')
# Build our queries
if grouping:
if c_where == '':
query = 'SELECT * FROM (SELECT %s FROM %s %s %s) %s %s' \
% (extracted_columns['column_string'], table_name, join, group,
where, order)
else:
query = 'SELECT * FROM (SELECT %s FROM %s %s %s %s) %s %s' \
% (extracted_columns['column_string'], table_name, join, c_where, group,
where, order)
else:
if c_where == '':
query = 'SELECT %s FROM %s %s %s %s' \
% (extracted_columns['column_string'], table_name, join, where,
order)
else:
query = 'SELECT * FROM (SELECT %s FROM %s %s %s %s) %s' \
% (extracted_columns['column_string'], table_name, join, where,
order, c_where)
# logger.debug(u"Query: %s" % query)
# Execute the query
filtered = self.ssp_db.select(query, args=args)
# Build grand totals
totalcount = self.ssp_db.select('SELECT COUNT(id) from %s' % table_name)[0][0]
# Get draw counter
draw_counter = int(parameters['draw'])
result = filtered[parameters['start']:(parameters['start'] + parameters['length'])]
output = {'result': result,
'draw': draw_counter,
'filteredCount': len(filtered),
'totalCount': totalcount}
return output
# This method extracts column data from our column list
# The first parameter is required, the match_columns parameter is optional and will cause the function to
# only return results if the value also exists in the match_columns 'data' field
@staticmethod
def extract_columns(columns=None, match_columns=None):
columns_string = ''
columns_literal = []
columns_named = []
columns_order = []
for column in columns:
# We allow using "as" in column names for more complex sql functions.
# This function breaks up the column to get all it's parts.
as_search = re.compile(' as ', re.IGNORECASE)
if re.search(as_search, column):
column_named = re.split(as_search, column)[1].rpartition('.')[-1]
column_literal = re.split(as_search, column)[0]
column_order = re.split(as_search, column)[1]
if match_columns:
if any(d['data'].lower() == column_named.lower() for d in match_columns):
columns_string += column + ', '
columns_literal.append(column_literal)
columns_named.append(column_named)
columns_order.append(column_order)
else:
columns_string += column + ', '
columns_literal.append(column_literal)
columns_named.append(column_named)
columns_order.append(column_order)
else:
column_named = column.rpartition('.')[-1]
if match_columns:
if any(d['data'].lower() == column_named.lower() for d in match_columns):
columns_string += column + ', '
columns_literal.append(column)
columns_named.append(column_named)
columns_order.append(column)
else:
columns_string += column + ', '
columns_literal.append(column)
columns_named.append(column_named)
columns_order.append(column)
columns_string = columns_string.rstrip(', ')
# We return a dict of the column params
# column_string is a comma seperated list of the exact column variables received.
# column_literal is the text before the "as" if we have an "as". Usually a function.
# column_named is the text after the "as", if we have an "as". Any table prefix is also stripped off.
# We use this to match with columns received from the Datatables request.
# column_order is the text after the "as", if we have an "as". Any table prefix is left intact.
column_data = {'column_string': columns_string,
'column_literal': columns_literal,
'column_named': columns_named,
'column_order': columns_order
}
return column_data