def component_analyses_post(): """Handle the POST REST API call. Component Analyses Batch is 4 Step Process: 1. Gather and clean Request. 2. Query GraphDB. 3. Build Stack Recommendation and Build Unknown Packages and Trigger componentApiFlow. 4. Handle Unknown Packages and Trigger bayesianApiFlow. """ input_json: Dict = request.get_json() ecosystem: str = input_json.get('ecosystem') if request.user_agent.string == "claircore/crda/RemoteMatcher": try: md5_hash = hashlib.md5( json.dumps(input_json, sort_keys=True).encode('utf-8')).hexdigest() logger.info("Ecosystem: %s => body md5 hash: %s", ecosystem, md5_hash) except Exception as e: logger.error("Exception %s", e) return jsonify({"message": "disabled"}), 404 try: # Step1: Gather and clean Request packages_list, normalised_input_pkgs = ca_validate_input( input_json, ecosystem) # Step2: Get aggregated CA data from Query GraphDB, graph_response = get_batch_ca_data(ecosystem, packages_list) # Step3: Build Unknown packages and Generates Stack Recommendation. stack_recommendation, unknown_pkgs = get_known_unknown_pkgs( ecosystem, graph_response, normalised_input_pkgs, input_json.get("ignore", {})) except BadRequest as br: logger.error(br) raise HTTPError(400, str(br)) from br except Exception as e: msg = "Internal Server Exception. Please contact us if problem persists." logger.error(e) raise HTTPError(400, msg) from e create_component_bookkeeping(ecosystem, packages_list, request.args, request.headers) # Step4: Handle Unknown Packages if unknown_pkgs: stack_recommendation = add_unknown_pkg_info(stack_recommendation, unknown_pkgs) pkgs_to_ingest = set( map( lambda pkg: ingestion_utils.Package(package=pkg.package, version=pkg.version), unknown_pkgs)) logger.debug("Unknown ingestion triggered for %s", pkgs_to_ingest) unknown_package_flow(ecosystem, pkgs_to_ingest) return jsonify(stack_recommendation), 202 return jsonify(stack_recommendation), 200
def post(): """Handle the POST REST API call. Component Analyses Batch is 4 Step Process: 1. Gather and clean Request. 2. Query GraphDB. 3. Build Stack Recommendation and Build Unknown Packages and Trigger componentApiFlow. 4. Handle Unknown Packages and Trigger bayesianApiFlow. """ response_template: Tuple = namedtuple("response_template", ["message", "status", "headers"]) input_json: Dict = request.get_json() ecosystem: str = input_json.get('ecosystem') user_agent = request.headers.get('User-Agent', None) manifest_hash = str(request.headers.get('manifest_hash', None)) request_id = request.headers.get('request_id', None) headers = {"uuid": request.headers.get('uuid', None)} try: # Step1: Gather and clean Request packages_list, normalised_input_pkgs = ca_validate_input( input_json, ecosystem) # Step2: Get aggregated CA data from Query GraphDB, graph_response = get_batch_ca_data(ecosystem, packages_list) # Step3: Build Unknown packages and Generates Stack Recommendation. stack_recommendation, unknown_pkgs = get_known_unknown_pkgs( ecosystem, graph_response, normalised_input_pkgs) except BadRequest as br: logger.error(br) raise HTTPError(400, str(br)) from br except Exception as e: msg = "Internal Server Exception. Please contact us if problem persists." logger.error(e) raise HTTPError(400, msg) from e create_component_bookkeeping(ecosystem, packages_list, headers.get("uuid"), user_agent, manifest_hash, request_id) # Step4: Handle Unknown Packages if unknown_pkgs: stack_recommendation = add_unknown_pkg_info( stack_recommendation, unknown_pkgs) pkgs_to_ingest = set( map( lambda pkg: ingestion_utils.Package(package=pkg.package, version=pkg.version), unknown_pkgs)) logger.debug("Unknown ingestion triggered for %s", pkgs_to_ingest) unknown_package_flow(ecosystem, pkgs_to_ingest) return response_template(stack_recommendation, 202, headers) return response_template(stack_recommendation, 200, headers)
def component_analyses_post(): """Handle the POST REST API call. Component Analyses Batch is 4 Step Process: 1. Gather and clean Request. 2. Query GraphDB. 3. Build Stack Recommendation and Build Unknown Packages and Trigger componentApiFlow. 4. Handle Unknown Packages and Trigger bayesianApiFlow. """ input_json: Dict = request.get_json() ecosystem: str = input_json.get('ecosystem') try: # Step1: Gather and clean Request packages_list, normalised_input_pkgs = ca_validate_input(input_json, ecosystem) # Step2: Get aggregated CA data from Query GraphDB, graph_response = get_batch_ca_data(ecosystem, packages_list) # Step3: Build Unknown packages and Generates Stack Recommendation. stack_recommendation, unknown_pkgs = get_known_unknown_pkgs( ecosystem, graph_response, normalised_input_pkgs) except BadRequest as br: logger.error(br) raise HTTPError(400, str(br)) from br except Exception as e: msg = "Internal Server Exception. Please contact us if problem persists." logger.error(e) raise HTTPError(400, msg) from e create_component_bookkeeping(ecosystem, packages_list, request.args, request.headers) # Step4: Handle Unknown Packages if unknown_pkgs: stack_recommendation = add_unknown_pkg_info(stack_recommendation, unknown_pkgs) pkgs_to_ingest = set(map(lambda pkg: ingestion_utils.Package(package=pkg.package, version=pkg.version), unknown_pkgs)) logger.debug("Unknown ingestion triggered for %s", pkgs_to_ingest) unknown_package_flow(ecosystem, pkgs_to_ingest) return jsonify(stack_recommendation), 202 return jsonify(stack_recommendation), 200