# # Copyright 2015 LinkedIn Corp. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # import json import os import sys from com.ziclix.python.sql import zxJDBC from metadata.etl.dataset.hive import HiveViewDependency from org.slf4j import LoggerFactory from wherehows.common import Constant from wherehows.common.schemas import DatasetSchemaRecord, DatasetFieldRecord, HiveDependencyInstanceRecord, \ DatasetInstanceRecord from wherehows.common.writers import FileWriter import FileUtil from AvroColumnParser import AvroColumnParser from HiveColumnParser import HiveColumnParser from HiveExtract import TableInfo class HiveTransform: def __init__(self): self.logger = LoggerFactory.getLogger('jython script : ' + self.__class__.__name__) # connection username = args[Constant.HIVE_METASTORE_USERNAME] password = args[Constant.HIVE_METASTORE_PASSWORD] jdbc_driver = args[Constant.HIVE_METASTORE_JDBC_DRIVER] jdbc_url = args[Constant.HIVE_METASTORE_JDBC_URL] self.conn_hms = zxJDBC.connect(jdbc_url, username, password, jdbc_driver) self.curs = self.conn_hms.cursor() # variable self.dataset_dict = {} def transform(self, input, hive_instance, hive_metadata, hive_field_metadata, view_dependency): """ convert from json to csv :param input: input json file :param hive_instance: output data file for hive instance :param hive_metadata: output data file for hive table metadata :param hive_field_metadata: output data file for hive field metadata :return: """ all_data = [] with open(input) as input_file: for line in input_file: all_data.append(json.loads(line)) dataset_idx = -1 instance_file_writer = FileWriter(hive_instance) schema_file_writer = FileWriter(hive_metadata) field_file_writer = FileWriter(hive_field_metadata) dependency_file_writer = FileWriter(view_dependency) depends_sql = """ SELECT d.NAME DB_NAME, case when t.TBL_NAME regexp '_[0-9]+_[0-9]+_[0-9]+$' then concat(substring(t.TBL_NAME, 1, length(t.TBL_NAME) - length(substring_index(t.TBL_NAME, '_', -3)) - 1),'_{version}') else t.TBL_NAME end dataset_name, concat('/', d.NAME, '/', t.TBL_NAME) object_name, case when (d.NAME like '%\_mp' or d.NAME like '%\_mp\_versioned') and d.NAME not like 'dalitest%' and t.TBL_TYPE = 'VIRTUAL_VIEW' then 'dalids' else 'hive' end object_type, case when (d.NAME like '%\_mp' or d.NAME like '%\_mp\_versioned') and d.NAME not like 'dalitest%' and t.TBL_TYPE = 'VIRTUAL_VIEW' then 'View' else case when LOCATE('view', LOWER(t.TBL_TYPE)) > 0 then 'View' when LOCATE('index', LOWER(t.TBL_TYPE)) > 0 then 'Index' else 'Table' end end object_sub_type, case when (d.NAME like '%\_mp' or d.NAME like '%\_mp\_versioned') and t.TBL_TYPE = 'VIRTUAL_VIEW' then 'dalids' else 'hive' end prefix FROM TBLS t JOIN DBS d on t.DB_ID = d.DB_ID WHERE d.NAME = '{db_name}' and t.TBL_NAME = '{table_name}' """ # one db info : 'type', 'database', 'tables' # one table info : required : 'name' , 'type', 'serializationFormat' ,'createTime', 'DB_ID', 'TBL_ID', 'SD_ID' # optional : 'schemaLiteral', 'schemaUrl', 'fieldDelimiter', 'fieldList' for one_db_info in all_data: i = 0 for table in one_db_info['tables']: i += 1 schema_json = {} prop_json = {} # set the prop json for prop_name in TableInfo.optional_prop: if prop_name in table and table[prop_name] is not None: prop_json[prop_name] = table[prop_name] view_expanded_text = '' if TableInfo.view_expended_text in prop_json: view_expanded_text = prop_json[TableInfo.view_expended_text] text = prop_json[TableInfo.view_expended_text].replace('`', '') # this will be fixed after switching to Hive AST array = [] try: array = HiveViewDependency.getViewDependency(text) except: self.logger.error("HiveViewDependency.getViewDependency(%s) failed!" % (table['name'])) l = [] for a in array: l.append(a) names = str(a).split('.') if names and len(names) >= 2: db_name = names[0].lower() table_name = names[1].lower() if db_name and table_name: self.curs.execute(depends_sql.format(db_name=db_name, table_name=table_name, version='{version}')) rows = self.curs.fetchall() self.conn_hms.commit() if rows and len(rows) > 0: for row_index, row_value in enumerate(rows): dependent_record = HiveDependencyInstanceRecord( one_db_info['type'], table['type'], "/%s/%s" % (one_db_info['database'], table['name']), 'dalids:///' + one_db_info['database'] + '/' + table['dataset_name'] if one_db_info['type'].lower() == 'dalids' else 'hive:///' + one_db_info['database'] + '/' + table['dataset_name'], 'depends on', 'Y', row_value[3], row_value[4], row_value[2], row_value[5] + ':///' + row_value[0] + '/' + row_value[1], '') dependency_file_writer.append(dependent_record) prop_json['view_depends_on'] = l dependency_file_writer.flush() # process either schema flds = {} field_detail_list = [] if TableInfo.schema_literal in table and \ table[TableInfo.schema_literal] is not None and \ table[TableInfo.schema_literal].startswith('{'): sort_id = 0 urn = "hive:///%s/%s" % (one_db_info['database'], table['dataset_name']) self.logger.info("Getting schema literal for: %s" % (urn)) try: schema_data = json.loads(table[TableInfo.schema_literal]) schema_json = schema_data acp = AvroColumnParser(schema_data, urn = urn) result = acp.get_column_list_result() field_detail_list += result except ValueError: self.logger.error("Schema Literal JSON error for table: " + str(table)) elif TableInfo.field_list in table: # Convert to avro uri = "hive:///%s/%s" % (one_db_info['database'], table['dataset_name']) if one_db_info['type'].lower() == 'dalids': uri = "dalids:///%s/%s" % (one_db_info['database'], table['dataset_name']) else: uri = "hive:///%s/%s" % (one_db_info['database'], table['dataset_name']) self.logger.info("Getting column definition for: %s" % (uri)) try: hcp = HiveColumnParser(table, urn = uri) schema_json = {'fields' : hcp.column_type_dict['fields'], 'type' : 'record', 'name' : table['name'], 'uri' : uri} field_detail_list += hcp.column_type_list except: self.logger.error("HiveColumnParser(%s) failed!" % (uri)) schema_json = {'fields' : {}, 'type' : 'record', 'name' : table['name'], 'uri' : uri} if one_db_info['type'].lower() == 'dalids': dataset_urn = "dalids:///%s/%s" % (one_db_info['database'], table['dataset_name']) else: dataset_urn = "hive:///%s/%s" % (one_db_info['database'], table['dataset_name']) dataset_instance_record = DatasetInstanceRecord('dalids:///' + one_db_info['database'] + '/' + table['name'] if one_db_info['type'].lower() == 'dalids' else 'hive:///' + one_db_info['database'] + '/' + table['name'], 'grid', '', '', '*', True, table['native_name'], table['logical_name'], table['version'], table['create_time'], json.dumps(schema_json), json.dumps(view_expanded_text), dataset_urn) instance_file_writer.append(dataset_instance_record) if dataset_urn not in self.dataset_dict: dataset_scehma_record = DatasetSchemaRecord(table['dataset_name'], json.dumps(schema_json), json.dumps(prop_json), json.dumps(flds), dataset_urn, 'Hive', one_db_info['type'], table['type'], '', table.get(TableInfo.create_time), (int(table.get(TableInfo.source_modified_time,"0")))) schema_file_writer.append(dataset_scehma_record) dataset_idx += 1 self.dataset_dict[dataset_urn] = dataset_idx for fields in field_detail_list: field_record = DatasetFieldRecord(fields) field_file_writer.append(field_record) instance_file_writer.flush() schema_file_writer.flush() field_file_writer.flush() self.logger.info("%20s contains %6d tables" % (one_db_info['database'], i)) instance_file_writer.close() schema_file_writer.close() field_file_writer.close() dependency_file_writer.close() if __name__ == "__main__": args = sys.argv[1] t = HiveTransform() temp_dir = FileUtil.etl_temp_dir(args, "HIVE") schema_json_file = os.path.join(temp_dir, args[Constant.HIVE_SCHEMA_JSON_FILE_KEY]) instance_csv_file = os.path.join(temp_dir, args[Constant.HIVE_INSTANCE_CSV_FILE_KEY]) schema_csv_file = os.path.join(temp_dir, args[Constant.HIVE_SCHEMA_CSV_FILE_KEY]) field_csv_file = os.path.join(temp_dir, args[Constant.HIVE_FIELD_METADATA_KEY]) dependency_csv_file = os.path.join(temp_dir, args[Constant.HIVE_DEPENDENCY_CSV_FILE_KEY]) try: t.transform(schema_json_file, instance_csv_file, schema_csv_file, field_csv_file, dependency_csv_file) finally: t.curs.close() t.conn_hms.close()