import hashlib import pandas as pd from crc import session, app from crc.api.common import ApiError from crc.models.file import FileModel, FileDataModel from crc.models.workflow import WorkflowSpecModel, WorkflowSpecCategoryModel from crc.services.file_service import FileService from crc.services.workflow_sync import WorkflowSyncService from crc.api.workflow import get_workflow_specification def get_sync_workflow_specification(workflow_spec_id): return get_workflow_specification(workflow_spec_id) def join_uuids(uuids): """Joins a pandas Series of uuids and combines them in one hash""" combined_uuids = ''.join([str(uuid) for uuid in uuids.sort_values()]) # ensure that values are always # in the same order return hashlib.md5(combined_uuids.encode('utf8')).hexdigest() # make a hash of the hashes def verify_token(token, required_scopes): """ Part of the Swagger API permissions for the syncing API The env variable for this is defined in config/default.py If you are 'playing' with the swagger interface, you will want to copy the token that is defined there and use it to authenticate the API if you are emulating copying files between systems. """ if token == app.config['API_TOKEN']: return {'scope':['any']} else: raise ApiError("permission_denied", "API Token information is not correct") def get_changed_workflows(remote,as_df=False): """ gets a remote endpoint - gets the workflows and then determines what workflows are different from the remote endpoint """ remote_workflows_list = WorkflowSyncService.get_all_remote_workflows(remote) remote_workflows = pd.DataFrame(remote_workflows_list) # get the local thumbprints & make sure that 'workflow_spec_id' is a column, not an index local = get_all_spec_state_dataframe().reset_index() if local.empty: # return the list as a dict, let swagger convert it to json remote_workflows['new'] = True if as_df: return remote_workflows else: return remote_workflows.reset_index().to_dict(orient='records') # merge these on workflow spec id and hash - this will # make two different date columns date_x and date_y different = remote_workflows.merge(local, right_on=['workflow_spec_id','md5_hash'], left_on=['workflow_spec_id','md5_hash'], how = 'outer' , indicator=True).loc[lambda x : x['_merge']!='both'] # If there are no differences, then we can just return. if not different.empty: # each line has a tag on it - if was in the left or the right, # label it so we know if that was on the remote or local machine different.loc[different['_merge']=='left_only','location'] = 'remote' different.loc[different['_merge']=='right_only','location'] = 'local' # this takes the different date_created_x and date-created_y columns and # combines them back into one date_created column index = different['date_created_x'].isnull() different.loc[index,'date_created_x'] = different[index]['date_created_y'] different = different[['workflow_spec_id','date_created_x','location']].copy() different.columns=['workflow_spec_id','date_created','location'] # our different list will have multiple entries for a workflow if there is a version on either side # we want to grab the most recent one, so we sort and grab the most recent one for each workflow changedfiles = different.sort_values('date_created',ascending=False).groupby('workflow_spec_id').first() # get an exclusive or list of workflow ids - that is we want lists of files that are # on one machine or the other, but not both remote_spec_ids = remote_workflows[['workflow_spec_id']] local_spec_ids = local[['workflow_spec_id']] left = remote_spec_ids[~remote_spec_ids['workflow_spec_id'].isin(local_spec_ids['workflow_spec_id'])] right = local_spec_ids[~local_spec_ids['workflow_spec_id'].isin(remote_spec_ids['workflow_spec_id'])] # flag files as new that are only on the remote box and remove the files that are only on the local box changedfiles['new'] = False changedfiles.loc[changedfiles.index.isin(left['workflow_spec_id']), 'new'] = True output = changedfiles[~changedfiles.index.isin(right['workflow_spec_id'])] else: output = different # return the list as a dict, let swagger convert it to json if as_df: return output else: return output.reset_index().to_dict(orient='records') def sync_all_changed_workflows(remote): """ Does what it says, gets a list of all workflows that are different between two systems and pulls all of the workflows and files that are different on the remote system. The idea is that we can make the local system 'look' like the remote system for deployment or testing. """ workflowsdf = get_changed_workflows(remote,as_df=True) if len(workflowsdf) ==0: return [] workflows = workflowsdf.reset_index().to_dict(orient='records') for workflow in workflows: sync_changed_files(remote,workflow['workflow_spec_id']) sync_changed_files(remote,'REFERENCE_FILES') return [x['workflow_spec_id'] for x in workflows] def file_get(workflow_spec_id,filename): """ Helper function to take care of the special case where we are looking for files that are marked is_reference """ if workflow_spec_id == 'REFERENCE_FILES': currentfile = session.query(FileModel).filter(FileModel.is_reference == True, FileModel.name == filename).first() else: currentfile = session.query(FileModel).filter(FileModel.workflow_spec_id==workflow_spec_id, FileModel.name == filename).first() return currentfile def create_or_update_local_spec(remote,workflow_spec_id): specdict = WorkflowSyncService.get_remote_workflow_spec(remote, workflow_spec_id) # if we are updating from a master spec, then we want to make sure it is the only # master spec in our local system. if specdict['is_master_spec']: masterspecs = session.query(WorkflowSpecModel).filter(WorkflowSpecModel.is_master_spec == True).all() for masterspec in masterspecs: masterspec.is_master_spec = False session.add(masterspec) localspec = session.query(WorkflowSpecModel).filter(WorkflowSpecModel.id == workflow_spec_id).first() if localspec is None: localspec = WorkflowSpecModel() localspec.id = workflow_spec_id if specdict['category'] == None: localspec.category = None else: localcategory = session.query(WorkflowSpecCategoryModel).filter(WorkflowSpecCategoryModel.name == specdict['category']['name']).first() if localcategory == None: # category doesn't exist - lets make it localcategory = WorkflowSpecCategoryModel() localcategory.name = specdict['category']['name'] localcategory.display_name = specdict['category']['display_name'] localcategory.display_order = specdict['category']['display_order'] session.add(localcategory) localspec.category = localcategory localspec.display_order = specdict['display_order'] localspec.display_name = specdict['display_name'] localspec.name = specdict['name'] localspec.is_master_spec = specdict['is_master_spec'] localspec.description = specdict['description'] session.add(localspec) def update_or_create_current_file(remote,workflow_spec_id,updatefile): currentfile = file_get(workflow_spec_id, updatefile['filename']) if not currentfile: currentfile = FileModel() currentfile.name = updatefile['filename'] if workflow_spec_id == 'REFERENCE_FILES': currentfile.workflow_spec_id = None currentfile.is_reference = True else: currentfile.workflow_spec_id = workflow_spec_id currentfile.date_created = updatefile['date_created'] currentfile.type = updatefile['type'] currentfile.primary = updatefile['primary'] currentfile.content_type = updatefile['content_type'] currentfile.primary_process_id = updatefile['primary_process_id'] session.add(currentfile) content = WorkflowSyncService.get_remote_file_by_hash(remote, updatefile['md5_hash']) FileService.update_file(currentfile, content, updatefile['type']) def sync_changed_files(remote,workflow_spec_id): """ This grabs a list of all files for a workflow_spec that are different between systems, and gets the remote copy of any file that has changed We also have a special case for "REFERENCE_FILES" where there is not workflow_spec_id, but all of the files are marked in the database as is_reference - and they need to be handled slightly differently. """ # make sure that spec is local before syncing files if workflow_spec_id != 'REFERENCE_FILES': create_or_update_local_spec(remote,workflow_spec_id) changedfiles = get_changed_files(remote,workflow_spec_id,as_df=True) if len(changedfiles)==0: return [] updatefiles = changedfiles[~((changedfiles['new']==True) & (changedfiles['location']=='local'))] updatefiles = updatefiles.reset_index().to_dict(orient='records') deletefiles = changedfiles[((changedfiles['new']==True) & (changedfiles['location']=='local'))] deletefiles = deletefiles.reset_index().to_dict(orient='records') for delfile in deletefiles: currentfile = file_get(workflow_spec_id,delfile['filename']) # it is more appropriate to archive the file than delete # due to the fact that we might have workflows that are using the # file data currentfile.archived = True session.add(currentfile) for updatefile in updatefiles: update_or_create_current_file(remote,workflow_spec_id,updatefile) session.commit() return [x['filename'] for x in updatefiles] def get_changed_files(remote,workflow_spec_id,as_df=False): """ gets a remote endpoint - gets the files for a workflow_spec on both local and remote and determines what files have been change and returns a list of those files """ remote_file_list = WorkflowSyncService.get_remote_workflow_spec_files(remote,workflow_spec_id) remote_files = pd.DataFrame(remote_file_list) # get the local thumbprints & make sure that 'workflow_spec_id' is a column, not an index local = get_workflow_spec_files_dataframe(workflow_spec_id).reset_index() local['md5_hash'] = local['md5_hash'].astype('str') remote_files['md5_hash'] = remote_files['md5_hash'].astype('str') if len(local) == 0: remote_files['new'] = True remote_files['location'] = 'remote' if as_df: return remote_files else: return remote_files.reset_index().to_dict(orient='records') different = remote_files.merge(local, right_on=['filename','md5_hash'], left_on=['filename','md5_hash'], how = 'outer' , indicator=True).loc[lambda x : x['_merge']!='both'] if len(different) == 0: if as_df: return different else: return [] # each line has a tag on it - if was in the left or the right, # label it so we know if that was on the remote or local machine different.loc[different['_merge']=='left_only','location'] = 'remote' different.loc[different['_merge']=='right_only','location'] = 'local' # this takes the different date_created_x and date-created_y columns and # combines them back into one date_created column dualfields = ['date_created','type','primary','content_type','primary_process_id'] for merge in dualfields: index = different[merge+'_x'].isnull() different.loc[index,merge+'_x'] = different[index][merge+'_y'] fieldlist = [fld+'_x' for fld in dualfields] different = different[ fieldlist + ['md5_hash','filename','location']].copy() different.columns=dualfields+['md5_hash','filename','location'] # our different list will have multiple entries for a workflow if there is a version on either side # we want to grab the most recent one, so we sort and grab the most recent one for each workflow changedfiles = different.sort_values('date_created',ascending=False).groupby('filename').first() # get an exclusive or list of workflow ids - that is we want lists of files that are # on one machine or the other, but not both remote_spec_ids = remote_files[['filename']] local_spec_ids = local[['filename']] left = remote_spec_ids[~remote_spec_ids['filename'].isin(local_spec_ids['filename'])] right = local_spec_ids[~local_spec_ids['filename'].isin(remote_spec_ids['filename'])] changedfiles['new'] = False changedfiles.loc[changedfiles.index.isin(left['filename']), 'new'] = True changedfiles.loc[changedfiles.index.isin(right['filename']),'new'] = True changedfiles = changedfiles.replace({pd.np.nan: None}) # return the list as a dict, let swagger convert it to json if as_df: return changedfiles else: return changedfiles.reset_index().to_dict(orient='records') def get_all_spec_state(): """ Return a list of all workflow specs along with last updated date and a thumbprint of all of the files that are used for that workflow_spec Convert into a dict list from a dataframe """ df = get_all_spec_state_dataframe() return df.reset_index().to_dict(orient='records') def get_workflow_spec_files(workflow_spec_id): """ Return a list of all workflow specs along with last updated date and a thumbprint of all of the files that are used for that workflow_spec Convert into a dict list from a dataframe """ df = get_workflow_spec_files_dataframe(workflow_spec_id) return df.reset_index().to_dict(orient='records') def get_workflow_spec_files_dataframe(workflowid): """ Return a list of all files for a workflow_spec along with last updated date and a hash so we can determine file differences for a changed workflow on a box. Return a dataframe In the special case of "REFERENCE_FILES" we get all of the files that are marked as is_reference """ if workflowid == 'REFERENCE_FILES': x = session.query(FileDataModel).join(FileModel).filter(FileModel.is_reference == True) else: x = session.query(FileDataModel).join(FileModel).filter(FileModel.workflow_spec_id == workflowid) # there might be a cleaner way of getting a data frome from some of the # fields in the ORM - but this works OK filelist = [] for file in x: filelist.append({'file_model_id':file.file_model_id, 'workflow_spec_id': file.file_model.workflow_spec_id, 'md5_hash':file.md5_hash, 'filename':file.file_model.name, 'type':file.file_model.type.name, 'primary':file.file_model.primary, 'content_type':file.file_model.content_type, 'primary_process_id':file.file_model.primary_process_id, 'date_created':file.date_created}) if len(filelist) == 0: return pd.DataFrame(columns=['file_model_id', 'workflow_spec_id', 'md5_hash', 'filename', 'type', 'primary', 'content_type', 'primary_process_id', 'date_created']) df = pd.DataFrame(filelist).sort_values('date_created').groupby('file_model_id').last() df['date_created'] = df['date_created'].astype('str') return df def get_all_spec_state_dataframe(): """ Return a list of all workflow specs along with last updated date and a thumbprint of all of the files that are used for that workflow_spec Return a dataframe """ x = session.query(FileDataModel).join(FileModel) # there might be a cleaner way of getting a data frome from some of the # fields in the ORM - but this works OK filelist = [] for file in x: filelist.append({'file_model_id':file.file_model_id, 'workflow_spec_id': file.file_model.workflow_spec_id, 'md5_hash':file.md5_hash, 'filename':file.file_model.name, 'date_created':file.date_created}) if len(filelist) == 0: df = pd.DataFrame(columns=['file_model_id','workflow_spec_id','md5_hash','filename','date_created']) else: df = pd.DataFrame(filelist) # If the file list is empty, return an empty data frame if df.empty: return df # get a distinct list of file_model_id's with the most recent file_data retained df = df.sort_values('date_created').drop_duplicates(['file_model_id'],keep='last').copy() # take that list and then group by workflow_spec and retain the most recently touched file # and make a consolidated hash of the md5_checksums - this acts as a 'thumbprint' for each # workflow spec df = df.groupby('workflow_spec_id').agg({'date_created':'max', 'md5_hash':join_uuids}).copy() # get only the columns we are really interested in returning df = df[['date_created','md5_hash']].copy() # convert dates to string df['date_created'] = df['date_created'].astype('str') return df