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Merge pull request #47 from radibnia77/main
add rti transform module The module transforms the request table into the impression table. #44
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| # Copyright 2019, Futurewei Technologies | ||
| # | ||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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. See the License for the | ||
| # specific language governing permissions and limitations | ||
| # under the License. | ||
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| import yaml | ||
| import argparse | ||
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| from pyspark import SparkContext | ||
| from pyspark.sql.functions import lit, udf | ||
| from pyspark.sql import HiveContext | ||
| from pyspark.sql.types import StringType, ArrayType | ||
| from datetime import datetime, timedelta | ||
| from util import resolve_placeholder | ||
| import hashlib | ||
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| ''' | ||
| This module transform | ||
| T1 : request based factdata | ||
| T2 : compatible factdata for main_ts.py | ||
| T1 | ||
| +-----------------------+---------+-------+ | ||
| |col_name |data_type|comment| | ||
| +-----------------------+---------+-------+ | ||
| |uckey |string |null | | ||
| |total |int |null | | ||
| |pt_d |string |null | | ||
| |# Partition Information| | | | ||
| |# col_name |data_type|comment| | ||
| |pt_d |string |null | | ||
| +-----------------------+---------+-------+ | ||
| CREATE TABLE table_name ( uckey string, total int) | ||
| PARTITIONED BY (pt_d string) | ||
| T2 | ||
| +-----------------------+-------------+-------+ | ||
| |col_name |data_type |comment| | ||
| +-----------------------+-------------+-------+ | ||
| |uckey |string |null | | ||
| |count_array |array<string>|null | | ||
| |hour |int |null | | ||
| |day |string |null | | ||
| |bucket_id |int |null | | ||
| |# Partition Information| | | | ||
| |# col_name |data_type |comment| | ||
| |day |string |null | | ||
| |bucket_id |int |null | | ||
| +-----------------------+-------------+-------+ | ||
| spark-submit --master yarn --num-executors 10 --executor-cores 5 --executor-memory 16G --driver-memory 16G --conf spark.driver.maxResultSize=5G --conf spark.hadoop.hive.exec.dynamic.partition=true --conf spark.hadoop.hive.exec.dynamic.partition.mode=nonstrict pipeline/main_rti_transform.py config.yml | ||
| ''' | ||
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| def advance_date(date, day_added): | ||
| _time = datetime.strptime(date, "%Y-%m-%d") | ||
| _time = _time + timedelta(days=day_added) | ||
| return _time.strftime("%Y-%m-%d") | ||
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| def assign_new_bucket_id(df, n): | ||
| def __hash_sha256(s): | ||
| hex_value = hashlib.sha256(s.encode('utf-8')).hexdigest() | ||
| return int(hex_value, 16) | ||
| _udf = udf(lambda x: __hash_sha256(x) % n) | ||
| df = df.withColumn('bucket_id', _udf(df.uckey)) | ||
| return df | ||
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| def __save_as_table(df, table_name, hive_context, create_table): | ||
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| if create_table: | ||
| command = """ | ||
| DROP TABLE IF EXISTS {} | ||
| """.format(table_name) | ||
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| hive_context.sql(command) | ||
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| command = """ | ||
| CREATE TABLE IF NOT EXISTS {} | ||
| ( | ||
| uckey string, count_array array<string>, hour int, day string | ||
| ) PARTITIONED BY (bucket_id int) | ||
| """.format(table_name) | ||
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| hive_context.sql(command) | ||
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| df.select('uckey', | ||
| 'count_array', | ||
| 'hour', | ||
| 'day', | ||
| 'bucket_id' | ||
| ).write.format('hive').option("header", "true").option("encoding", "UTF-8").mode('append').insertInto(table_name) | ||
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| def run(hive_context, | ||
| input_table, output_table, | ||
| start_day, end_day, day_step, | ||
| new_bucket_size, | ||
| default_hour, default_price_cat): | ||
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| _st = start_day | ||
| first_round = True | ||
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| while True: | ||
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| _end = min(end_day, advance_date(_st, day_step)) | ||
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| if _st > _end: | ||
| break | ||
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| # Read factdata table | ||
| command = """ | ||
| SELECT uckey, total, pt_d FROM {} WHERE pt_d BETWEEN '{}' AND '{}' | ||
| """.format(input_table, _st, _end) | ||
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| _st = advance_date(_end, 1) | ||
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| df = hive_context.sql(command) | ||
| print(command) | ||
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| df = df.withColumnRenamed('pt_d', 'day') | ||
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| # add count_array | ||
| # [Row(count_array=[u'0:0', u'1:0', u'2:0', u'3:0'], day=u'2018-03-09', hour=0, uckey=u'banner,1,3G,g_f,1,pt,1002,icc')] | ||
| df = df.withColumn('count_array', udf(lambda x: [default_price_cat + ':' + str(x)], ArrayType(StringType()))(df.total)) | ||
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| # add hour | ||
| df = df.withColumn('hour', lit(default_hour)) | ||
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| df = assign_new_bucket_id(df, new_bucket_size) | ||
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| # we want to keep the paritions for a batch under 200 | ||
| df = df.repartition(200) | ||
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| # Writing into partitions might throw some exceptions but does not impair data. | ||
| __save_as_table(df, output_table, hive_context, first_round) | ||
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| first_round = False | ||
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| return | ||
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| if __name__ == "__main__": | ||
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| parser = argparse.ArgumentParser(description='Prepare data') | ||
| parser.add_argument('config_file') | ||
| args = parser.parse_args() | ||
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| # Load config file | ||
| with open(args.config_file, 'r') as ymlfile: | ||
| cfg = yaml.load(ymlfile, Loader=yaml.FullLoader) | ||
| resolve_placeholder(cfg) | ||
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| cfg_log = cfg['log'] | ||
| cfg_rti = cfg['pipeline']['rti_transform'] | ||
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| sc = SparkContext() | ||
| hive_context = HiveContext(sc) | ||
| sc.setLogLevel(cfg_log['level']) | ||
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| default_hour = cfg_rti['default_hour'] | ||
| default_price_cat = cfg_rti['default_price_cat'] | ||
| day_step = cfg_rti['day_step'] | ||
| start_day = cfg_rti['start_day'] | ||
| end_day = cfg_rti['end_day'] | ||
| new_bucket_size = cfg_rti['new_bucket_size'] | ||
| input_table = cfg_rti['input_table'] | ||
| output_table = cfg['factdata_table_name'] | ||
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| run(hive_context=hive_context, | ||
| input_table=input_table, output_table=output_table, | ||
| start_day=start_day, end_day=end_day, day_step=day_step, | ||
| new_bucket_size=new_bucket_size, | ||
| default_hour=default_hour, default_price_cat=default_price_cat) | ||
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| sc.stop() |
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