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Refactor datatables static methods
This commit is contained in:
JonnyWong16
parent
b32b1bae7c
commit
9b4f4b8ae4
1 changed files with 162 additions and 151 deletions
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@ -63,22 +63,22 @@ class DataTables(object):
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'named json_data.')
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'named json_data.')
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return None
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return None
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extracted_columns = self.extract_columns(columns=columns)
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extracted_columns = extract_columns(columns=columns)
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join = self.build_join(join_types, join_tables, join_evals)
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join = build_join(join_types, join_tables, join_evals)
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group = self.build_grouping(group_by)
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group = build_grouping(group_by)
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c_where, cw_args = self.build_custom_where(custom_where)
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c_where, cw_args = build_custom_where(custom_where)
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order = self.build_order(parameters['order'],
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order = build_order(parameters['order'],
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extracted_columns['column_named'],
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extracted_columns['column_named'],
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parameters['columns'])
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parameters['columns'])
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where, w_args = self.build_where(parameters['search']['value'],
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where, w_args = build_where(parameters['search']['value'],
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extracted_columns['column_named'],
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extracted_columns['column_named'],
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parameters['columns'])
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parameters['columns'])
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# Build union parameters
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# Build union parameters
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if table_name_union:
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if table_name_union:
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extracted_columns_union = self.extract_columns(columns=columns_union)
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extracted_columns_union = extract_columns(columns=columns_union)
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group_u = self.build_grouping(group_by_union)
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group_u = build_grouping(group_by_union)
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c_where_u, cwu_args = self.build_custom_where(custom_where_union)
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c_where_u, cwu_args = build_custom_where(custom_where_union)
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union = 'UNION SELECT %s FROM %s %s %s' % (extracted_columns_union['column_string'],
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union = 'UNION SELECT %s FROM %s %s %s' % (extracted_columns_union['column_string'],
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table_name_union,
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table_name_union,
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c_where_u,
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c_where_u,
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@ -117,172 +117,183 @@ class DataTables(object):
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return output
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return output
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def build_grouping(self, group_by=[]):
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# Build grouping
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group = ''
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for g in group_by:
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def build_grouping(group_by=[]):
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group += g + ', '
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# Build grouping
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if group:
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group = ''
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group = 'GROUP BY ' + group.rstrip(', ')
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return group
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for g in group_by:
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group += g + ', '
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if group:
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group = 'GROUP BY ' + group.rstrip(', ')
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def build_join(self, join_types=[], join_tables=[], join_evals=[]):
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return group
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# Build join parameters
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join = ''
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for i, join_type in enumerate(join_types):
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if join_type.upper() == 'LEFT OUTER JOIN':
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join += 'LEFT OUTER JOIN %s ON %s = %s ' % (join_tables[i], join_evals[i][0], join_evals[i][1])
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elif join_type.upper() == 'JOIN' or join_type.upper() == 'INNER JOIN':
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join += 'JOIN %s ON %s = %s ' % (join_tables[i], join_evals[i][0], join_evals[i][1])
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return join
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def build_join(join_types=[], join_tables=[], join_evals=[]):
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# Build join parameters
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join = ''
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def build_custom_where(self, custom_where=[]):
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for i, join_type in enumerate(join_types):
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# Build custom where parameters
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if join_type.upper() == 'LEFT OUTER JOIN':
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c_where = ''
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join += 'LEFT OUTER JOIN %s ON %s = %s ' % (join_tables[i], join_evals[i][0], join_evals[i][1])
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args = []
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elif join_type.upper() == 'JOIN' or join_type.upper() == 'INNER JOIN':
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join += 'JOIN %s ON %s = %s ' % (join_tables[i], join_evals[i][0], join_evals[i][1])
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for w in custom_where:
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return join
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if isinstance(w[1], (list, tuple)) and len(w[1]):
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c_where += '('
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for w_ in w[1]:
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def build_custom_where(custom_where=[]):
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if w_ == None:
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# Build custom where parameters
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c_where += w[0] + ' IS NULL OR '
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c_where = ''
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elif str(w_).startswith('LIKE '):
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args = []
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c_where += w[0] + ' LIKE ? OR '
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args.append(w_[5:])
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for w in custom_where:
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else:
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and_or = ' OR ' if w[0].endswith('OR') else ' AND '
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c_where += w[0] + ' = ? OR '
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w[0] = w[0].rstrip(' OR')
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args.append(w_)
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c_where = c_where.rstrip(' OR ') + ') AND '
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if isinstance(w[1], (list, tuple)) and len(w[1]):
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else:
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c_where += '('
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if w[1] == None:
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for w_ in w[1]:
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c_where += w[0] + ' IS NULL AND '
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if w_ is None:
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elif str(w[1]).startswith('LIKE '):
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c_where += w[0] + ' IS NULL'
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c_where += w[0] + ' LIKE ? AND '
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elif str(w_).startswith('LIKE '):
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args.append(w[1][5:])
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c_where += w[0] + ' LIKE ?'
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args.append(w_[5:])
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else:
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else:
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c_where += w[0] + ' = ? AND '
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c_where += w[0] + ' = ?'
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args.append(w[1])
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args.append(w_)
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c_where += ' OR '
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if c_where:
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c_where = c_where.rstrip(' OR ') + ')' + and_or
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c_where = 'WHERE ' + c_where.rstrip(' AND ')
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else:
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if w[1] is None:
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return c_where, args
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c_where += w[0] + ' IS NULL'
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elif str(w[1]).startswith('LIKE '):
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def build_order(self, order_param=[], columns=[], dt_columns=[]):
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c_where += w[0] + ' LIKE ?'
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# Build ordering
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args.append(w[1][5:])
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order = ''
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for o in order_param:
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sort_order = ' COLLATE NOCASE'
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if o['dir'] == 'desc':
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sort_order += ' DESC'
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# We first see if a name was sent though for the column sort.
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if dt_columns[int(o['column'])]['data']:
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# We have a name, now check if it's a valid column name for our query
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# so we don't just inject a random value
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if any(d.lower() == dt_columns[int(o['column'])]['data'].lower()
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for d in columns):
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order += dt_columns[int(o['column'])]['data'] + '%s, ' % sort_order
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else:
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# if we receive a bogus name, rather not sort at all.
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pass
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# If no name exists for the column, just use the column index to sort
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else:
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else:
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order += columns[int(o['column'])] + ', '
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c_where += w[0] + ' = ?'
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args.append(w[1])
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if order:
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c_where += and_or
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order = 'ORDER BY ' + order.rstrip(', ')
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return order
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if c_where:
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c_where = 'WHERE ' + c_where.rstrip(' AND ').rstrip(' OR ')
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def build_where(self, search_param='', columns=[], dt_columns=[]):
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return c_where, args
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# Build where parameters
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where = ''
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args = []
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if search_param:
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for i, s in enumerate(dt_columns):
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def build_order(order_param=[], columns=[], dt_columns=[]):
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if s['searchable']:
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# Build ordering
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# We first see if a name was sent though for the column search.
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order = ''
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if s['data']:
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# We have a name, now check if it's a valid column name for our query
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for o in order_param:
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# so we don't just inject a random value
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sort_order = ' COLLATE NOCASE'
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if any(d.lower() == s['data'].lower() for d in columns):
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if o['dir'] == 'desc':
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where += s['data'] + ' LIKE ? OR '
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sort_order += ' DESC'
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args.append('%' + search_param + '%')
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# We first see if a name was sent though for the column sort.
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else:
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if dt_columns[int(o['column'])]['data']:
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# if we receive a bogus name, rather not search at all.
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# We have a name, now check if it's a valid column name for our query
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pass
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# so we don't just inject a random value
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# If no name exists for the column, just use the column index to search
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if any(d.lower() == dt_columns[int(o['column'])]['data'].lower()
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else:
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for d in columns):
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where += columns[i] + ' LIKE ? OR '
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order += dt_columns[int(o['column'])]['data'] + '%s, ' % sort_order
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else:
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# if we receive a bogus name, rather not sort at all.
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pass
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# If no name exists for the column, just use the column index to sort
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else:
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order += columns[int(o['column'])] + ', '
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if order:
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order = 'ORDER BY ' + order.rstrip(', ')
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return order
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def build_where(search_param='', columns=[], dt_columns=[]):
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# Build where parameters
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where = ''
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args = []
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if search_param:
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for i, s in enumerate(dt_columns):
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if s['searchable']:
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# We first see if a name was sent though for the column search.
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if s['data']:
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# We have a name, now check if it's a valid column name for our query
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# so we don't just inject a random value
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if any(d.lower() == s['data'].lower() for d in columns):
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where += s['data'] + ' LIKE ? OR '
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args.append('%' + search_param + '%')
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args.append('%' + search_param + '%')
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if where:
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else:
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where = 'WHERE ' + where.rstrip(' OR ')
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# if we receive a bogus name, rather not search at all.
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pass
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return where, args
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# If no name exists for the column, just use the column index to search
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# This method extracts column data from our column list
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# The first parameter is required, the match_columns parameter is optional and will cause the function to
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# only return results if the value also exists in the match_columns 'data' field
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@staticmethod
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def extract_columns(columns=None, match_columns=None):
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columns_string = ''
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columns_literal = []
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columns_named = []
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columns_order = []
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for column in columns:
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# We allow using "as" in column names for more complex sql functions.
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# This function breaks up the column to get all it's parts.
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as_search = re.compile(' as ', re.IGNORECASE)
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if re.search(as_search, column):
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column_named = re.split(as_search, column)[1].rpartition('.')[-1]
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column_literal = re.split(as_search, column)[0]
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column_order = re.split(as_search, column)[1]
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if match_columns:
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if any(d['data'].lower() == column_named.lower() for d in match_columns):
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columns_string += column + ', '
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columns_literal.append(column_literal)
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columns_named.append(column_named)
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columns_order.append(column_order)
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else:
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else:
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where += columns[i] + ' LIKE ? OR '
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args.append('%' + search_param + '%')
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if where:
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where = 'WHERE ' + where.rstrip(' OR ')
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return where, args
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# This method extracts column data from our column list
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# The first parameter is required, the match_columns parameter is optional and will cause the function to
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# only return results if the value also exists in the match_columns 'data' field
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def extract_columns(columns=None, match_columns=None):
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columns_string = ''
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columns_literal = []
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columns_named = []
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columns_order = []
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for column in columns:
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# We allow using "as" in column names for more complex sql functions.
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# This function breaks up the column to get all it's parts.
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as_search = re.compile(' as ', re.IGNORECASE)
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if re.search(as_search, column):
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column_named = re.split(as_search, column)[1].rpartition('.')[-1]
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column_literal = re.split(as_search, column)[0]
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column_order = re.split(as_search, column)[1]
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if match_columns:
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if any(d['data'].lower() == column_named.lower() for d in match_columns):
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columns_string += column + ', '
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columns_string += column + ', '
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columns_literal.append(column_literal)
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columns_literal.append(column_literal)
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columns_named.append(column_named)
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columns_named.append(column_named)
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columns_order.append(column_order)
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columns_order.append(column_order)
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else:
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else:
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column_named = column.rpartition('.')[-1]
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columns_string += column + ', '
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if match_columns:
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columns_literal.append(column_literal)
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if any(d['data'].lower() == column_named.lower() for d in match_columns):
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columns_named.append(column_named)
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columns_string += column + ', '
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columns_order.append(column_order)
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columns_literal.append(column)
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else:
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columns_named.append(column_named)
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column_named = column.rpartition('.')[-1]
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columns_order.append(column)
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if match_columns:
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else:
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if any(d['data'].lower() == column_named.lower() for d in match_columns):
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columns_string += column + ', '
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columns_string += column + ', '
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columns_literal.append(column)
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columns_literal.append(column)
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columns_named.append(column_named)
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columns_named.append(column_named)
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columns_order.append(column)
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columns_order.append(column)
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else:
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columns_string += column + ', '
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columns_literal.append(column)
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columns_named.append(column_named)
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columns_order.append(column)
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columns_string = columns_string.rstrip(', ')
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columns_string = columns_string.rstrip(', ')
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# We return a dict of the column params
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# We return a dict of the column params
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# column_string is a comma seperated list of the exact column variables received.
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# column_string is a comma seperated list of the exact column variables received.
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# column_literal is the text before the "as" if we have an "as". Usually a function.
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# column_literal is the text before the "as" if we have an "as". Usually a function.
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# column_named is the text after the "as", if we have an "as". Any table prefix is also stripped off.
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# column_named is the text after the "as", if we have an "as". Any table prefix is also stripped off.
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# We use this to match with columns received from the Datatables request.
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# We use this to match with columns received from the Datatables request.
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# column_order is the text after the "as", if we have an "as". Any table prefix is left intact.
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# column_order is the text after the "as", if we have an "as". Any table prefix is left intact.
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column_data = {'column_string': columns_string,
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column_data = {'column_string': columns_string,
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'column_literal': columns_literal,
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'column_literal': columns_literal,
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'column_named': columns_named,
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'column_named': columns_named,
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'column_order': columns_order
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'column_order': columns_order
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}
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}
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return column_data
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return column_data
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