This repository has been archived by the owner. It is now read-only.
Permalink
Show file tree
Hide file tree
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Browse files
Merge pull request #47 from radibnia77/main
add rti transform module The module transforms the request table into the impression table. #44
- Loading branch information
Showing
3 changed files
with
225 additions
and
9 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| @@ -0,0 +1,199 @@ | ||
| # 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. | ||
|
|
||
| import yaml | ||
| import argparse | ||
|
|
||
| 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 | ||
|
|
||
|
|
||
| ''' | ||
| 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 | ||
| ''' | ||
|
|
||
|
|
||
| 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") | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
|
|
||
| def __save_as_table(df, table_name, hive_context, create_table): | ||
|
|
||
| if create_table: | ||
| command = """ | ||
| DROP TABLE IF EXISTS {} | ||
| """.format(table_name) | ||
|
|
||
| hive_context.sql(command) | ||
|
|
||
| command = """ | ||
| CREATE TABLE IF NOT EXISTS {} | ||
| ( | ||
| uckey string, count_array array<string>, hour int, day string | ||
| ) PARTITIONED BY (bucket_id int) | ||
| """.format(table_name) | ||
|
|
||
| hive_context.sql(command) | ||
|
|
||
| df.select('uckey', | ||
| 'count_array', | ||
| 'hour', | ||
| 'day', | ||
| 'bucket_id' | ||
| ).write.format('hive').option("header", "true").option("encoding", "UTF-8").mode('append').insertInto(table_name) | ||
|
|
||
|
|
||
| def run(hive_context, | ||
| input_table, output_table, | ||
| start_day, end_day, day_step, | ||
| new_bucket_size, | ||
| default_hour, default_price_cat): | ||
|
|
||
| _st = start_day | ||
| first_round = True | ||
|
|
||
| while True: | ||
|
|
||
| _end = min(end_day, advance_date(_st, day_step)) | ||
|
|
||
| if _st > _end: | ||
| break | ||
|
|
||
| # Read factdata table | ||
| command = """ | ||
| SELECT uckey, total, pt_d FROM {} WHERE pt_d BETWEEN '{}' AND '{}' | ||
| """.format(input_table, _st, _end) | ||
|
|
||
| _st = advance_date(_end, 1) | ||
|
|
||
| df = hive_context.sql(command) | ||
| print(command) | ||
|
|
||
| df = df.withColumnRenamed('pt_d', 'day') | ||
|
|
||
| # 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)) | ||
|
|
||
| # add hour | ||
| df = df.withColumn('hour', lit(default_hour)) | ||
|
|
||
| df = assign_new_bucket_id(df, new_bucket_size) | ||
|
|
||
| # we want to keep the paritions for a batch under 200 | ||
| df = df.repartition(200) | ||
|
|
||
| # Writing into partitions might throw some exceptions but does not impair data. | ||
| __save_as_table(df, output_table, hive_context, first_round) | ||
|
|
||
| first_round = False | ||
|
|
||
| return | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
|
|
||
| parser = argparse.ArgumentParser(description='Prepare data') | ||
| parser.add_argument('config_file') | ||
| args = parser.parse_args() | ||
|
|
||
| # Load config file | ||
| with open(args.config_file, 'r') as ymlfile: | ||
| cfg = yaml.load(ymlfile, Loader=yaml.FullLoader) | ||
| resolve_placeholder(cfg) | ||
|
|
||
| cfg_log = cfg['log'] | ||
| cfg_rti = cfg['pipeline']['rti_transform'] | ||
|
|
||
| sc = SparkContext() | ||
| hive_context = HiveContext(sc) | ||
| sc.setLogLevel(cfg_log['level']) | ||
|
|
||
| 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'] | ||
|
|
||
| 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) | ||
|
|
||
| sc.stop() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters