def interlinking_star_search(context, data_dict):
''' It searches lucene with a '*' wildcard. The wildcard is positioned at the end of \
the search string.
'''
schema = context.get('schema', dsschema.interlinking_star_search_schema())
data_dict, errors = _validate(data_dict, schema, context)
if errors:
raise p.toolkit.ValidationError(errors)
term = data_dict.get('term')
reference_resource = data_dict.get('reference_resource')
terms = lucene_access.search(term, reference_resource, 'like')
if isinstance(terms, int):
return ''
return terms
def _interlink_column(context, res, col_name, original_ds, new_ds, reference, ref_fields):
res_id = original_ds.get('resource_id')
total = original_ds.get('total')
columns = json.loads(res.get('interlinking_columns_status','{}'))
# The interlinked column is marked with the reference resource with which it is interlinked.
for k,v in columns.iteritems():
if k == col_name:
columns.update({k:reference})
columns = json.dumps(columns)
original_res = p.toolkit.get_action('resource_show')(context, {'id': res.get('interlinking_parent_id')})
original_res['interlinked_column'] = col_name
original_res = p.toolkit.get_action('resource_update')(context, original_res)
res = p.toolkit.get_action('resource_show')(context, res)
res['interlinking_resource'] = True
res['interlinking_columns_status'] = columns
res['interlinking_status'] = 'undergoing'
res['reference_fields'] = json.dumps(ref_fields)
res = p.toolkit.get_action('resource_update')(context, res)
STEP = 100
offset = 0
for k in range(0,int(ceil(total/float(STEP)))):
offset = k*STEP
recs = p.toolkit.get_action('datastore_search')(context, {
'resource_id':res_id,
'offset': offset,
'limit': STEP,
'sort':'_id'}).get('records')
nrecs = []
for rec in recs:
original_term = rec.get(col_name)
suggestions = lucene_access.search(original_term, reference, 'search')
if isinstance(suggestions, int):
return -1
# If any suggestions were returned
if len(suggestions['records']) > 0:
# The first field is the field on which the search was run
search_field = suggestions['fields'][0]
if len(suggestions['records']) > 0:
best_suggestion = suggestions['records'][0]
for suggestion in suggestions['records']:
if suggestion['scoreField'] > best_suggestion['scoreField']:
best_suggestion = suggestion
nrec = {'_id': rec.get('_id'),
search_field: best_suggestion[search_field],
'int__score': best_suggestion['scoreField'],
'int__checked_flag': False,
'int__all_results': json.dumps(suggestions)}
for field in suggestions['fields']:
if field != search_field and field != 'scoreField':
nrec[field] = best_suggestion[field]
nrecs.append(nrec)
# No suggestions were returned
else:
real_fields = lucene_access.getFields(reference, False)
if isinstance(real_fields, list):
suggestions = { "fields": real_fields,
"records": [],
}
search_field = real_fields[0]
nrec = {'_id': rec.get('_id'),
search_field: "",
'int__score': "",
'int__checked_flag': False,
'int__all_results': json.dumps(suggestions)}
for field in suggestions['fields']:
if field != search_field and field != 'scoreField':
nrec[field] = ""
nrecs.append(nrec)
else:
return -1
ds = p.toolkit.get_action('datastore_upsert')(context,
{
'resource_id': new_ds.get('resource_id'),
'allow_update_with_id':True,
'force': True,
'records': nrecs
})
offset=offset+STEP
return new_ds