import logging import re from pandas import ExcelFile from sqlalchemy import func, desc from sqlalchemy.sql.functions import GenericFunction from crc import db from crc.api.common import ApiError from crc.models.api_models import Task from crc.models.file import FileDataModel, LookupFileModel, LookupDataModel from crc.models.workflow import WorkflowModel, WorkflowSpecDependencyFile from crc.services.file_service import FileService from crc.services.ldap_service import LdapService from crc.services.workflow_processor import WorkflowProcessor class TSRank(GenericFunction): package = 'full_text' name = 'ts_rank' class LookupService(object): """Provides tools for doing lookups for auto-complete fields. This can currently take two forms: 1) Lookup from spreadsheet data associated with a workflow specification. in which case we store the spreadsheet data in a lookup table with full text indexing enabled, and run searches against that table. 2) Lookup from LDAP records. In which case we call out to an external service to pull back detailed records and return them. I could imagine this growing to include other external services as tools to handle lookup fields. I could also imagine using some sort of local cache so we don't unnecessarily pound on external services for repeat searches for the same records. """ @staticmethod def get_lookup_model(spiff_task, field): workflow_id = spiff_task.workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY] workflow = db.session.query(WorkflowModel).filter(WorkflowModel.id == workflow_id).first() return LookupService.__get_lookup_model(workflow, field.id) @staticmethod def __get_lookup_model(workflow, field_id): lookup_model = db.session.query(LookupFileModel) \ .filter(LookupFileModel.workflow_spec_id == workflow.workflow_spec_id) \ .filter(LookupFileModel.field_id == field_id).first() # one more quick query, to see if the lookup file is still related to this workflow. # if not, we need to rebuild the lookup table. is_current = False if lookup_model: is_current = db.session.query(WorkflowSpecDependencyFile).\ filter(WorkflowSpecDependencyFile.file_data_id == lookup_model.file_data_model_id).count() if not is_current: if lookup_model: db.session.delete(lookup_model) # Very very very expensive, but we don't know need this till we do. lookup_model = LookupService.create_lookup_model(workflow, field_id) return lookup_model @staticmethod def lookup(workflow, field_id, query, limit): lookup_model = LookupService.__get_lookup_model(workflow, field_id) if lookup_model.is_ldap: return LookupService._run_ldap_query(query, limit) else: return LookupService._run_lookup_query(lookup_model, query, limit) @staticmethod def create_lookup_model(workflow_model, field_id): """ This is all really expensive, but should happen just once (per file change). Checks to see if the options are provided in a separate lookup table associated with the workflow, and if so, assures that data exists in the database, and return a model than can be used to locate that data. Returns: an array of LookupData, suitable for returning to the api. """ processor = WorkflowProcessor(workflow_model) # VERY expensive, Ludicrous for lookup / type ahead spiff_task, field = processor.find_task_and_field_by_field_id(field_id) if field.has_property(Task.PROP_OPTIONS_FILE): if not field.has_property(Task.PROP_OPTIONS_VALUE_COLUMN) or \ not field.has_property(Task.PROP_OPTIONS_LABEL_COL): raise ApiError.from_task("invalid_emum", "For enumerations based on an xls file, you must include 3 properties: %s, " "%s, and %s" % (Task.PROP_OPTIONS_FILE, Task.PROP_OPTIONS_VALUE_COLUMN, Task.PROP_OPTIONS_LABEL_COL), task=spiff_task) # Get the file data from the File Service file_name = field.get_property(Task.PROP_OPTIONS_FILE) value_column = field.get_property(Task.PROP_OPTIONS_VALUE_COLUMN) label_column = field.get_property(Task.PROP_OPTIONS_LABEL_COL) latest_files = FileService.get_spec_data_files(workflow_spec_id=workflow_model.workflow_spec_id, workflow_id=workflow_model.id, name=file_name) if len(latest_files) < 1: raise ApiError("invalid_enum", "Unable to locate the lookup data file '%s'" % file_name) else: data_model = latest_files[0] lookup_model = LookupService.build_lookup_table(data_model, value_column, label_column, workflow_model.workflow_spec_id, field_id) elif field.has_property(Task.PROP_LDAP_LOOKUP): lookup_model = LookupFileModel(workflow_spec_id=workflow_model.workflow_spec_id, field_id=field_id, is_ldap=True) else: raise ApiError("unknown_lookup_option", "Lookup supports using spreadsheet options or ldap options, and neither " "was provided.") db.session.add(lookup_model) db.session.commit() return lookup_model @staticmethod def build_lookup_table(data_model: FileDataModel, value_column, label_column, workflow_spec_id, field_id): """ In some cases the lookup table can be very large. This method will add all values to the database in a way that can be searched and returned via an api call - rather than sending the full set of options along with the form. It will only open the file and process the options if something has changed. """ xls = ExcelFile(data_model.data) df = xls.parse(xls.sheet_names[0]) # Currently we only look at the fist sheet. if value_column not in df: raise ApiError("invalid_emum", "The file %s does not contain a column named % s" % (data_model.file_model.name, value_column)) if label_column not in df: raise ApiError("invalid_emum", "The file %s does not contain a column named % s" % (data_model.file_model.name, label_column)) lookup_model = LookupFileModel(workflow_spec_id=workflow_spec_id, field_id=field_id, file_data_model_id=data_model.id, is_ldap=False) db.session.add(lookup_model) for index, row in df.iterrows(): lookup_data = LookupDataModel(lookup_file_model=lookup_model, value=row[value_column], label=row[label_column], data=row.to_json()) db.session.add(lookup_data) db.session.commit() return lookup_model @staticmethod def _run_lookup_query(lookup_file_model, query, limit): db_query = LookupDataModel.query.filter(LookupDataModel.lookup_file_model == lookup_file_model) query = re.sub('[^A-Za-z0-9 ]+', '', query) print("Query: " + query) query = query.strip() if len(query) > 0: if ' ' in query: terms = query.split(' ') new_terms = ["'%s'" % query] for t in terms: new_terms.append("%s:*" % t) new_query = ' | '.join(new_terms) else: new_query = "%s:*" % query # Run the full text query db_query = db_query.filter(LookupDataModel.label.match(new_query)) # But hackishly order by like, which does a good job of # pulling more relevant matches to the top. db_query = db_query.order_by(desc(LookupDataModel.label.like("%" + query + "%"))) #ORDER BY name LIKE concat('%', ticker, '%') desc, rank DESC # db_query = db_query.order_by(desc(func.full_text.ts_rank( # func.to_tsvector(LookupDataModel.label), # func.to_tsquery(query)))) from sqlalchemy.dialects import postgresql logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO) result = db_query.limit(limit).all() logging.getLogger('sqlalchemy.engine').setLevel(logging.ERROR) return result @staticmethod def _run_ldap_query(query, limit): users = LdapService().search_users(query, limit) """Converts the user models into something akin to the LookupModel in models/file.py, so this can be returned in the same way we return a lookup data model.""" user_list = [] for user in users: user_list.append( {"value": user['uid'], "label": user['display_name'] + " (" + user['uid'] + ")", "data": user }) return user_list