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164 lines
6.0 KiB
Python
164 lines
6.0 KiB
Python
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# Copyright 2015 LinkedIn Corp. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#
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import sys
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from com.ziclix.python.sql import zxJDBC
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from wherehows.common import Constant
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class HiveLoad:
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def load_metadata(self):
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cursor = self.conn_mysql.cursor()
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load_cmd = """
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DELETE FROM stg_dict_dataset WHERE db_id = {db_id};
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LOAD DATA LOCAL INFILE '{source_file}'
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INTO TABLE stg_dict_dataset
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FIELDS TERMINATED BY '\Z' ESCAPED BY '\0'
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(`name`, `schema`, properties, fields, urn, source, @sample_partition_full_path, source_created_time, @source_modified_time)
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SET db_id = {db_id},
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storage_type = 'Table', dataset_type = 'hive',
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source_modified_time=nullif(@source_modified_time,''),
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sample_partition_full_path=nullif(@sample_partition_full_path,''),
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wh_etl_exec_id = {wh_etl_exec_id};
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-- SELECT COUNT(*) FROM stg_dict_dataset;
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-- clear
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DELETE FROM stg_dict_dataset where db_id = {db_id}
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AND (length(`name`)) = 0
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OR `name` like 'tmp\_%'
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OR `name` like 't\_%'
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;
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update stg_dict_dataset
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set location_prefix = substring_index(substring_index(urn, '/', 4), '/', -2) /* hive location_prefix is it's schema name*/
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WHERE db_id = {db_id} and location_prefix is null;
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update stg_dict_dataset
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set parent_name = substring_index(substring_index(urn, '/', 4), '/', -1) /* hive parent_name is it's schema name*/
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where db_id = {db_id} and parent_name is null;
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-- insert into final table
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INSERT INTO dict_dataset
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( `name`,
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`schema`,
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schema_type,
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fields,
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properties,
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urn,
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source,
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location_prefix,
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parent_name,
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storage_type,
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ref_dataset_id,
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status_id,
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dataset_type,
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hive_serdes_class,
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is_partitioned,
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partition_layout_pattern_id,
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sample_partition_full_path,
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source_created_time,
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source_modified_time,
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created_time,
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wh_etl_exec_id
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)
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select s.name, s.schema, s.schema_type, s.fields,
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s.properties, s.urn,
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s.source, s.location_prefix, s.parent_name,
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s.storage_type, s.ref_dataset_id, s.status_id,
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s.dataset_type, s.hive_serdes_class, s.is_partitioned,
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s.partition_layout_pattern_id, s.sample_partition_full_path,
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s.source_created_time, s.source_modified_time, UNIX_TIMESTAMP(now()),
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s.wh_etl_exec_id
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from stg_dict_dataset s
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where s.db_id = {db_id}
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on duplicate key update
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`name`=s.name, `schema`=s.schema, schema_type=s.schema_type, fields=s.fields,
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properties=s.properties, source=s.source, location_prefix=s.location_prefix, parent_name=s.parent_name,
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storage_type=s.storage_type, ref_dataset_id=s.ref_dataset_id, status_id=s.status_id,
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dataset_type=s.dataset_type, hive_serdes_class=s.hive_serdes_class, is_partitioned=s.is_partitioned,
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partition_layout_pattern_id=s.partition_layout_pattern_id, sample_partition_full_path=s.sample_partition_full_path,
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source_created_time=s.source_created_time, source_modified_time=s.source_modified_time,
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modified_time=UNIX_TIMESTAMP(now()), wh_etl_exec_id=s.wh_etl_exec_id
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;
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analyze table dict_dataset;
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""".format(source_file=self.input_schema_file, db_id=self.db_id, wh_etl_exec_id=self.wh_etl_exec_id)
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for state in load_cmd.split(";"):
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print state
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cursor.execute(state)
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self.conn_mysql.commit()
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cursor.close()
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def load_field(self):
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"""
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TODO: Load field is not used for now, as we need to open the nested structure type
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:return:
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"""
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cursor = self.conn_mysql.cursor()
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load_cmd = """
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DELETE FROM stg_dict_field_detail where db_id = {db_id};
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LOAD DATA LOCAL INFILE '{source_file}'
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INTO TABLE stg_dict_field_detail
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FIELDS TERMINATED BY '\Z'
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(urn, sort_id, parent_sort_id, @parent_path, field_name, field_label, data_type,
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@data_size, @precision, @scale, @is_nullable, @is_indexed, @is_partitioned, @default_value, @namespace, description,
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@dummy
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)
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set
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parent_path=nullif(@parent_path,'null')
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, data_size=nullif(@data_size,'null')
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, data_precision=nullif(@precision,'null')
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, data_scale=nullif(@scale,'null')
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, is_nullable=nullif(@is_nullable,'null')
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, is_indexed=nullif(@is_indexed,'null')
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, is_partitioned=nullif(@is_partitioned,'null')
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, default_value=nullif(@default_value,'null')
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, namespace=nullif(@namespace,'null')
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, db_id = {db_id}
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;
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analyze table stg_dict_field_detail;
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""".format(source_file=self.input_field_file, db_id=self.db_id)
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# didn't load into final table for now
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for state in load_cmd.split(";"):
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print state
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cursor.execute(state)
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self.conn_mysql.commit()
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cursor.close()
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if __name__ == "__main__":
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args = sys.argv[1]
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l = HiveLoad()
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# set up connection
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username = args[Constant.WH_DB_USERNAME_KEY]
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password = args[Constant.WH_DB_PASSWORD_KEY]
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JDBC_DRIVER = args[Constant.WH_DB_DRIVER_KEY]
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JDBC_URL = args[Constant.WH_DB_URL_KEY]
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l.input_schema_file = args[Constant.HIVE_SCHEMA_CSV_FILE_KEY]
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l.input_field_file = args[Constant.HIVE_FIELD_METADATA_KEY]
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l.db_id = args[Constant.DB_ID_KEY]
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l.wh_etl_exec_id = args[Constant.WH_EXEC_ID_KEY]
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l.conn_mysql = zxJDBC.connect(JDBC_URL, username, password, JDBC_DRIVER)
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l.load_metadata()
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# l.load_field()
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l.conn_mysql.close()
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