def setUp(self):
"""
Start Spark, define config and path to test data
"""
self.config = json.loads("""{"steps_per_floor": 21}""")
self.spark, *_ = start_spark(app_name='nlp_clause_test')
self.test_data_path = 'tests/test_data/segment_test'
def setUp(self):
"""Start Spark, define config and path to test data
"""
self.config = json.loads("""{"Max_Temp_": 21}""")
self.spark, *_ = start_spark()
self.test_data_path = (
'/Users/LRK/project-folder/GreenFlag/sparkjob/test_data/')
def main():
"""Main ETL script definition.
"""
# start Spark application and get Spark session, logger and config
spark, log, config, sc = start_spark(
app_name='analysis',
files=['configs/etl_config.json'])
log.warn('***analysis is up-and-running***')
# load data
df = load(spark, config["start_date"], config["stop_date"], config["folder"])
log.warn('***data loaded***')
# daily tasks
if config["daily"]:
df_visit_per_hour = visit_per_hour(df, config["stop_date"])
save(df_visit_per_hour, 'out/visit_per_hour', config["stop_date"])
df_visitor_per_hour = visitor_per_hour(df, config["stop_date"])
save(df_visitor_per_hour, 'out/visitor_per_hour', config["stop_date"])
df_referral_path = referral_path(df, sc, config["stop_date"])
save_json(df_referral_path, 'out/referral_path', config["stop_date"])
# monthly tasks
if config["monthly"]:
df_hourly_visit_pattern = hourly_visit_pattern(df, config["stop_date"])
save(df_hourly_visit_pattern, 'out/hourly_visit_pattern', config["stop_date"])
df_popular_os = popular_os(df, config["stop_date"])
save(df_popular_os, 'out/popular_os', config["stop_date"])
df_popular_browser = popular_browser(df, config["stop_date"])
save(df_popular_browser, 'out/popular_browser', config["stop_date"])
df_country_dist = country_dist(df, config["stop_date"])
save(df_country_dist, 'out/country_dist', config["stop_date"])
df_average_visit_duration = average_visit_duration(df, config["stop_date"])
save(df_average_visit_duration, 'out/average_visit_duration', config["stop_date"])
df_popular_page = popular_page(df, config["stop_date"])
save(df_popular_page, 'out/popular_page', config["stop_date"])
def main():
"""Main ETL script definition.
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config, environment = start_spark(
app_name='my_etl_job',
files=['configs/etl_config.json', 'configs/transformation.sql'])
# log that main ETL job is starting
log.warn('etl_job is up-and-running')
# Create ETL Components
try:
tasks = [
Extract(config['extract']),
Transform(config['transform']),
Load(config['load']),
Impala(config['impala'])
]
except KeyError as e:
print("Some component missing: " + repr(e))
Executor(spark, log, tasks, environment).run()
# log the success and terminate Spark application
log.warn('etl_job is finished')
spark.stop()
return None
def main():
"""Main ETL script definition.
:return: None
"""
job_name = sys.argv[1]
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name=job_name)
# log that main ETL job is starting
log.warn('%s is up-and-running' % job_name)
# execute ETL pipeline
data = extract_data(spark, config['data_source'])
#dynamically load transformations from settings
data = transform_data(data, config['transformations'], log)
#data_transformed = transform_data(data, config['steps_per_floor'])
load_data(data, config['data_output'])
# log the success and terminate Spark application
log.warn('%s is finished' % job_name)
spark.stop()
return None
def main():
input_path = 'E:\\tmp\\game_csv'
output_path = 'E:\\tmp\\output'
spark_session, log, config = start_spark(
app_name='nlp_tokenization', files=['./configs/file_list_config.json'])
data_frame = load_data(spark_session, input_path)
writer_csv(transform_data(data_frame), output_path)
def main(begin_date, end_date):
"""
品牌序章手百用户分析spark作业
"""
spark, log, config = start_spark(
app_name="brand_xuzhang_gen_pv_data_%s_%s" % (begin_date, end_date),
master='yarn',
spark_config= {
"spark.yarn.queue": "brand",
"spark.shuffle.dce.enable": "true",
"spark.executor.memory": "8g",
"spark.executor.cores": 1,
"spark.executor.instances": 500,
"spark.default.parallelism": 1000,
"spark.sql.shuffle.partitions": 1000,
})
output_path = "/app/ecom/brand/majian06/moirai/gen_pv_data/%s-%s" % (begin_date, end_date)
# execute ETL pipeline
log.warn('job etl is up-and-running')
data = extract_data(spark)
data_transformed = transform_data(spark, data)
load_data(data_transformed, output_path)
# log the success and terminate Spark application
log.warn('job etl is finished')
spark.stop()
return None
def main():
spark, config = start_spark(app_name='my_etl_job',
files=['Config/etl_config.json'])
path = config['file']['load']['path']
data = extract(spark, path)
transform_data = transform(data)
load(transform_data)
def main():
"""Main analysis script definition.
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(
app_name='bcg_case_study',
files=['configs/case_study_config.json'])
# log that main Analysis job is starting
log.warn('bcg_case_study_job started running')
# Execute config queries
primary_person_path = "..\\..\\Data\\Primary_Person_use.csv"
# primary_person_path = config['primary_person_csv_path']
primary_person_df = extract(spark, primary_person_path, log)
units_path = "..\\..\\Data\\Units_use.csv"
# units_path = config['units_csv_path']
units_df = extract(spark, units_path, log)
damages_path = "..\\..\\Data\\Damages_use.csv"
# damages_path = config['damages_csv_path']
damages_df = extract(spark, damages_path, log)
charges_path = "..\\..\\Data\\Charges_use.csv"
# charges_path = config['charges_csv_path']
charges_df = extract(spark, charges_path, log)
# ANALYSIS - 1
analysis_1(primary_person_df, log)
# ANALYSIS - 2
analysis_2(units_df, log)
# ANALYSIS - 3
analysis_3(units_df, primary_person_df, log)
# ANALYSIS - 4
analysis_4(units_df, log)
# ANALYSIS - 5
analysis_5(units_df, primary_person_df, log)
# ANALYSIS - 6
analysis_6(units_df, primary_person_df, log)
# ANALYSIS - 7
analysis_7(units_df, damages_df, log)
# ANALYSIS - 8
analysis_8(units_df, charges_df, primary_person_df, log)
# Log the success and terminate Spark application
log.warn('bcg_case_study job is finished')
spark.stop()
return None
def main():
"""Main ETL script definition.
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name='my_etl_job',
files=['configs/etl_config.json'])
# log that main ETL job is starting
log.warn('etl_job is up-and-running')
# execute Today_Load ETL
url = 'tests/test_data/energy/NOP_LOAD_FORECAST_20180214_04_input.csv'
df_NOP_0214_04 = extract_data_csv(spark, url)
groupbyList = ["CONGESTION_ZONE", "FORECAST_DT", "HOUR_NUM"]
targetColumn = "NOP"
resultColumnName = "TODAY_LOAD"
df_NOP_0214_04_GB = groupby_data(df_NOP_0214_04, groupbyList, targetColumn,
resultColumnName)
#df_NOP_0214_04_GB.show()
#execute Prev_Day_Load ETL
url = 'tests/test_data/energy/NOP_LOAD_FORECAST_20180213_11_input.csv'
df_NOP_0213_11 = extract_data_csv(spark, url)
groupbyList = ["CONGESTION_ZONE", "FORECAST_DT", "HOUR_NUM"]
targetColumn = "NOP"
resultColumnName = "PREV_DAY_LOAD"
df_NOP_0213_11_GB = groupby_data(df_NOP_0213_11, groupbyList, targetColumn,
resultColumnName)
#execute Hour_Load ETL
url = 'tests/test_data/energy/LFG_ST_Hourly_20180213_input.csv'
df_LFG_0213 = extract_data_csv(spark, url)
groupbyList = ["CONGESTION_ZONE", "FORECAST_DT", "HOUR_NUM"]
sumList = ["UNADJ_LOAD", "DISTRIB_LOSS_LOAD", "TRANSMISSION_LOSS_LOAD"]
resultColumnName = "ADJ_LOAD"
df_LFG_0213_GB = groupby_agg_data(df_LFG_0213, groupbyList, sumList,
resultColumnName)
#Join three DataFrames
joinList = ["CONGESTION_ZONE", "FORECAST_DT", "HOUR_NUM"]
df_join = join_data(df_NOP_0214_04_GB, df_NOP_0213_11_GB, joinList, 'left')
df_join_three = join_data(df_join, df_LFG_0213_GB, joinList, 'left')
output = order_data(df_join_three, joinList)
#Write output to output.csv
load_data(output)
# log the success and terminate Spark application
log.warn('test_etl_job is finished')
spark.stop()
return None
def setUp(self):
"""Start Spark, define config and path to test data
"""
print(os.getcwd())
print(os.listdir())
self.config = json.loads("""{"steps_per_floor": 21}""")
self.spark, *_ = start_spark()
self.test_data_path = 'tests/test_data/'
def run_test():
"""Running test function
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name='my_etl_test_job',
files=['configs/etl_config.json'])
create_test_data(spark)
spark.stop()
return None
def main():
spark_session, log, config = start_spark(
app_name='nlp_clause', files=['./configs/sentence_spilt_config.json'])
# 本地测试用
# input_file = 'E:/tmp/review_csv/output'
# output_file = 'E:/tmp/output_review'
# data = load_data(spark_session, input_file)
# data_transform = transform_data(data)
data = load_data(spark_session, config['input_path'])
data_transform = transform_data(data)
writer_csv(data_transform, config['output_path'])
def setUp(self):
"""Start Spark, define config and path to test data
"""
self.config = json.loads("""{
"start_date": "20160801",
"stop_date": "20160804",
"daily": true,
"monthly": true,
"folder": "tests/test_data/"
}""")
self.spark, _, _, self.sc = start_spark()
self.test_data_path = self.config["folder"]
self.input_data = load(self.spark, self.config["start_date"], self.config["stop_date"], self.test_data_path + "ga/")
def main():
"""Main ETL script definition.
:return: None
"""
parser = argparse.ArgumentParser()
parser.add_argument('--ftp_user', dest='ftp_user', help='FTP user name')
parser.add_argument('--ftp_password',
dest='ftp_password',
help='FTP Password')
parser.add_argument('--api_key',
dest='api_key',
help='Google Maps API Key')
known_args = parser.parse_args()
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name='my_etl_job',
files=['configs/etl_config.json'])
# log that main ETL job is starting
log.warn('etl_job is up-and-running')
# execute ETL pipeline
data = extract_data_module(spark) # extract_data(spark)
customer_data = read_from_postgres(spark, "localhost", "golang_user", "go",
"customer_price_list")
print(customer_data.show())
print(data.show())
data_transformed = transform_data(data, config['steps_per_floor'])
load_data(data_transformed)
"""Start the geocoding portion
"""
address1 = Row(id='123456', address='14 Maryland Blvd Toronto')
address2 = Row(id='789012',
address='Suite 2300 100 Wellington St West Toronto')
address3 = Row(id='345678', address='10 Bay Street Toronto')
address4 = Row(id='901234', address='373 Glebeholme Blvd Toronto')
addresses = [address1, address2, address3, address4]
address_df = spark.createDataFrame(addresses)
geo_enriched_data = address_df.withColumn(
"PlaceID", geocode_address_udf(col("address"),
lit(known_args.api_key)))
print(geo_enriched_data.show())
file_name = get_chd_file(known_args.ftp_user, known_args.ftp_password)
print(file_name)
# log the success and terminate Spark application
log.warn('test_etl_job is finished')
spark.stop()
return None
def main():
"""Main ETL script definition.
:return: None
"""
parser = argparse.ArgumentParser()
parser.add_argument('--sf_user', dest='sf_user', help='SF user')
parser.add_argument('--sf_password',
dest='sf_password',
help='SF password')
parser.add_argument('--sf_token', dest='sf_token', help='SF token')
known_args = parser.parse_args()
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name='my_etl_job',
files=['configs/etl_config.json'])
# log that main ETL job is starting
log.warn('etl_job is up-and-running')
sf_user = known_args.sf_user
sf_password = known_args.sf_password
salesforce = Salesforce(username=sf_user,
password=sf_password,
security_token='')
# query = "select id, name, annual_sales__c,average_check__c, chain_id__c, chain_name__c, chd_id__c, confidence_level__c, county__c,credit_rating__c,dayparts__c ,dma_name__c ,group_health_system__c ,hours__c ,location_name__c ,menu_items__c ,msa_name__c ,number_of_employees__c ,number_of_rooms__c ,number_of_seats__c ,operation_type__c ,parent_id__c,phone ,units__c ,website,years_in_business__c,yelp_url__c,chd_chain_id__c,Google_Place_ID__c,Qualification_Status__c,current_month_match__c,CustomerStatus__c, ShippingCity,ShippingLatitude,ShippingLongitude,ShippingPostalCode,ShippingState,ShippingStreet,market_segment_list__c,Current_Chd_Name__c, Data_Update_Case__c, exclude_from_chd_match__c, Current_Chd_Shipping_Street__c, Current_Chd_Shipping_City__c, Current_Chd_Shipping_State__c,Current_Chd_Shipping_Postal_Code__c from Account"
query = "SELECT id, chd_id__c, Google_Place_ID__c from Account" #Google_Place_ID__c
# accounts_spark = get_sf_df(query, salesforce, spark)
query_to_geocode = "SELECT id, Name, ShippingAddress from Account where chd_id__c = null AND google_place_id__c = null"
accounts_to_geocode_list = salesforce.query_all(query_to_geocode)
accounts_to_geocode_records = accounts_to_geocode_list['records']
accounts_to_geocode_pdf = pd.DataFrame(accounts_to_geocode_records)
# accounts_to_geocode_pdf = convert_simple_salesforce_ordered_dictionary_to_pandas_dataframe(accounts_to_geocode_records)
accounts_to_geocode_pdf['parsed_address'] = accounts_to_geocode_pdf[
'ShippingAddress'].apply(lambda x: json.dumps(x))
accounts_to_geocode_pdf = accounts_to_geocode_pdf.drop(
["attributes", "ShippingAddress"], axis=1)
accounts_to_geocode_spark = spark.createDataFrame(accounts_to_geocode_pdf)
accounts_to_geocode_spark.printSchema()
print(accounts_to_geocode_spark.count())
accounts_to_geocode_spark.write.parquet('tests/chd/sf_accounts_to_geocode',
mode='overwrite')
accounts = salesforce.query_all(query)
accounts_pandas = pd.DataFrame(accounts['records'])
accounts_spark = spark.createDataFrame(accounts_pandas)
accounts_spark.printSchema()
accounts_spark.write.parquet('tests/chd/sf_accounts', mode='overwrite')
def main():
# gameid = ['69698', '5151', '60187', '47330', '54928', '10497', '12492', '55307', '2301', '70056', '50500', '74870',
# '34768', '35141', '91972', '6922', '69383', '85118', '85452', '69411', '85552', '31074', '69405', '70215',
# '59520', '66187', '10056', '85846', '33973', '71417']
# input_path = 'E:\\tmp\\review_csv'
# input_path = 'E:\\tmp\\csv_test'
# output_path = 'E:\\tmp\\output_review'
spark_session, log, config = start_spark(app_name='nlp_tokenization',
files=['./configs/file_list_config.json'])
gameid = ('69698', '5151', '60187', '47330', '54928', '10497', '12492', '55307', '2301', '70056', '50500', '74870',
'34768', '35141', '91972', '6922', '69383', '85118', '85452', '69411', '62422', '31074', '69405', '70215',
'59520', '66187', '10056', '85846', '33973', '71417')
data_frame = load_data(spark_session, config[''], str(gameid))
writer_csv(data_frame, config[''], id.strip())
def main():
spark, log, config = start_spark(app_name='my_spark_app', files=[
'configs/etl_config.json'])
log.warn('etl job is up and running')
# execute ETL pipeline
data = extract_data(spark)
data_transformed = transform_data(data, config['steps_per_floor_'])
load_data(data_transformed)
log.warn('test etl job finished')
spark.stop()
return None
def main():
"""
:return:
"""
user_dict = 'user_dict.txt'
user_dict_path = resolve_filename(get_module_res(user_dict))
print(get_module_res(user_dict).name)
jieba.load_userdict(user_dict_path)
stop_path = resolve_filename(get_module_res('stop_word.txt'))
spark_session, log, config = start_spark(
app_name='nlp_tokenization', files=['configs/file_list_config.json'])
stop_words = stop_word(spark_session, stop_path)
# input_path=config['file_input'], out_put=config['file_output']
participle(session=spark_session, stop_words=stop_words)
def main():
spark, sc = start_spark(app_name="PySpark - AMRDEF", config='localhost')
# job-translate-amrdef
data = translator(
"file:////home/ivan/Documents/Primestone/Esquemas/AMRDEF_sample_modif_flags_actualdates.xml",
spark)
print("\n" * 10, "data translation done", "\n" * 10)
# job-enrich
data = enricher(data, spark)
print("\n" * 10, "data enrichment done", "\n" * 10)
# job-clean
data = cleaner(data, spark)
print("\n" * 10, "data cleaning done", "\n" * 10)
def main():
"""Main ETL script definition.
:return: None
"""
# start Spark application and get Spark session, logger, config and audit
spark, logger, config_dict, audit = start_spark(
app_name='PHM_alinity_i_205_results'
)
# log that main ETL job is starting
logger.info('etl_job is up-and-running')
# execute ETL pipeline
data = extract_data(spark, config_dict)
data_transformed = transform_data(data)
partition_list = data_transformed.agg(collect_set('transaction_date')).collect()[0][0]
logger.debug("Patitions to dedup: ")
logger.debug(str(partition_list))
cleansed_bucket = config_dict['etl_cleansed_bucket']
cleansed_key_prefix = config_dict['etl_cleansed_key'] + "/transaction_date="
s3_utils = S3Utils(spark, config_dict)
paths_list = []
for partition_suffix in partition_list:
if s3_utils.is_key_prefix_empty(cleansed_bucket, cleansed_key_prefix + str(partition_suffix)):
paths_list.extend(config_dict['s3a_prefix'] + cleansed_bucket + cleansed_key_prefix
+ str(partition_suffix) + "/*")
logger.debug("Patitions to dedup: " + str(paths_list))
if paths_list:
data_cleansed = spark.read.format("parquet").load(paths_list)
data_deduped = deduplicate_data(data_transformed, data_cleansed)
load_data(data_deduped, config_dict['s3a_prefix'] + config_dict['etl_cleansed_bucket']
+ "/" + config_dict['etl_cleansed_key'])
else:
load_data(data_transformed, config_dict['s3a_prefix'] + config_dict['etl_cleansed_bucket']
+ "/" + config_dict['etl_cleansed_key'])
# log the success and terminate Spark application
logger.info('test_etl_job is finished')
stop_spark(spark, config_dict, audit)
return None
def main():
spark, sql_context, log, config = start_spark(
app_name='radiography_analysis',
files=['configs/radiography_analysis_config.json'])
log.warn('Running radiography analysis...')
# extracting and transforming the dataset
[data_normal, data_covid19, data_lung_opacity,
data_viral_pneumonia] = extract_data(spark)
data_initial = transform_data(data_normal, data_covid19, data_lung_opacity,
data_viral_pneumonia, sql_context)
# percentage of samples (different categories)
data_transformed = transform_percentage_of_samples(data_initial)
load_data(data_transformed, "percentage_of_samples")
# take one sample of each group
data_transformed = transform_take_samples(data_initial)
load_data(data_transformed, "take_samples")
# colour distribution
data_transformed = transform_colour_distribution(data_initial)
load_data(data_transformed, "colour_distribution")
# ML classification (distributed)
data_transformed = transform_ml_classification(data_initial, spark)
load_data(data_transformed, "ml_classification")
# The trained model is available in -> /keras_model
# DL model compiling/training (not distributed)
# [data_transformed_matrix, data_transformed_acc] = transform_dl_classification(data_initial, spark)
# load_data(data_transformed_matrix, "dl_classification_matrix")
# load_data(data_transformed_acc, "dl_classification_accuracy")
# DL model inference (distributed)
data_transformed = transform_dl_model_inference(data_initial)
load_data(data_transformed, "dl_inference")
log.warn('Terminating radiography analysis...')
spark.stop()
return None
def setUp(self):
"""Start Spark, define config and path to test data
"""
self.spark, self.log, *_ = start_spark(app_name='unittest_etl_job')
self.config = json.loads("""{
"extract" : {"uri": "tests/test_data/udf_test_data/recipes_negative.json",
"clean": "True"},
"transform": {"udfs_required":["tominutes"],
"ingredient": "beef",
"ingredient": 30,
"ingredient": 60,
"ingredient": 60
},
"load" : {
"load_path": "output/report.csv"
}
}
""")
def setUp(self):
"""Start Spark, define config and path to test data
"""
self.spark, *_ = start_spark(app_name='my_etl_job')
self.config = json.loads("""{
"extract" : {"uri": "tests/test_data/udf_test_data/recipes_positive.json",
"clean": "True",
"temptable": "recipes"},
"transform": {"sql_path": "configs/transformation.sql",
"udfs_required":["tominutes"]},
"load" : {"database": "hellofresh",
"tablename": "recipes",
"load_path": "user/hive/warehouse/hellofresh.db/recipes",
"partition_cols": {"difficulty": "string"}
},
"impala" : {"impala_host": "localhost"}
}
""")
def main():
"""Main ETL script definition.
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(
app_name='my_etl_job',
files=['configs/etl_config.json'])
# log that main ETL job is starting
log.warn('etl_job is up-and-running')
# execute ETL pipeline
data = extract_data(spark)
data_transformed = transform_data(data, config['steps_per_floor'])
load_data(data_transformed)
# log the success and terminate Spark application
log.warn('test_etl_job is finished')
spark.stop()
return None
def main():
"""
Main ETL script definition.
:return: None
"""
platform = sys.argv[1] if len(sys.argv) > 1 else "local"
if platform not in ["local", "emr"]:
platform = "local"
config_path = "./configs/etl_config.json"
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name="spark-app", files=[config_path])
# log that main ETL job is starting
log.warn("spark-app is up-and-running")
if platform == "local":
spark.sparkContext.addPyFile("jobs/common.py")
spark.conf.set("spark.sql.crossJoin.enabled", "true")
# read config
config = config[platform]
# execute ETL pipeline
# extract
data_frames = extract_data(spark, log, config)
# transform
data_frames = transform_data(spark, log, config, data_frames)
# load
load_data(spark, log, config, data_frames)
# log the success and terminate Spark application
spark.stop()
return None
def main():
"""
:return:
"""
pattern = u'[\s+\.\!\/_,$%^*(+\"\')]+|[+——()?【】“”！，。？、~@#￥%……&*（）]+'
stop_words_file = './dependencies/stop_word.txt'
user_dict = './dependencies/user_dict.txt'
stop_words = stop_word(stop_words_file)
file_path = 'E:\\workspaces_learn\\taptap-spider'
spark_session, log, config = start_spark(app_name='nlp_tokenization',
files=['./configs/file_list_config.json'])
file_list = os.listdir(file_path)
for file in file_list:
if file.startswith('app_review'):
if file.endswith('.csv'):
file_split = file.split('.')[0]
file = file_path + '/' + file
sentences_list = load_data(spark_session, file)
word_split(sentences_list=sentences_list, stop_words=stop_words, user_dict=user_dict, pattern=pattern,
session=spark_session, file=file_split)
def main():
"""Main ETL script definition.
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(
app_name='process_text_job',
files=['configs/process_text_config.json'])
# log that main ETL job is starting
log.warn('process_text_job is up-and-running')
# execute ETL pipeline
data = extract_data(spark, config['input_path'])
data_transformed = transform_data(data)
load_data(data_transformed, config['output_path'])
# log the success and terminate Spark application
log.info('process_text_job is finished')
spark.stop()
return None
def main():
"""Main ETL script definition.
:return: None
"""
# start Spark application and get Spark session, logger and config
spark, log, config = start_spark(app_name='dpl_ecf',
files=['configs/etl_config.json'])
# log that main ETL job is starting
log.warn('dpl_ecf is up-and-running')
# execute ETL pipeline
data_frames = extract_data(spark, config)
data_frames_drop, data_frames_transpose = transform_data(
data_frames, config)
load_data(data_frames_drop, data_frames_transpose, config)
# log the success and terminate Spark application
log.warn('dpl_ecf is finished')
spark.stop()
return None
def __init__(self, app_name, files, jar_packages, dependencies = "packages.zip"):
# start Spark application and get Spark session, logger and config
self.app_name = app_name
spark, log, config = start_spark(
app_name=app_name,
files=[files],
master="192.168.122.3:7077",
jar_packages=jar_packages,
dependencies=dependencies
)
self.kafka_server = config["kafka_server"]
self.es_server = config["es_server"]
self.log = log
self.spark = spark
self.config = config
self.es_reader = (spark.read
.format("org.elasticsearch.spark.sql")
.option("es.nodes", self.es_server)
.option("es.net.http.auth.user","elastic")
.option("es.read.field.as.array.include", "winlog.keywords,etl_pipeline")
.option("es.read.field.exclude", "tags,user,z_original_message,z_logstash_pipeline")
)
