<!DOCTYPE html>
Standard Configs¶
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# My Standard Spark Session!
# Python libraries:
import os
import sys
import re
from dateutil import parser
# import datetime
from datetime import datetime
from datetime import date
import builtins
import json
import functools
import operator
from itertools import product
# Numpy & Pandas!
import numpy as np
import pandas as pd
pd.options.display.float_format = '{:20,.2f}'.format
pd.options.display.max_columns = None
# Spark!
from pyspark import SparkContext
from pyspark.sql.functions import *
from pyspark.sql.types import *
from pyspark.sql.window import *
from pyspark.sql import SparkSession, Row
spark = SparkSession.builder.appName("myapp").getOrCreate()
# spark = SparkSession.builder.master("yarn")\
# .config("spark.executor.instances", "32")\
# .config("spark.executor.cores", "4")\
# .config("spark.executor.memory", "4G")\
# .config("spark.driver.memory", "4G")\
# .config("spark.executor.memoryOverhead","4G")\
# .config("spark.yarn.queue","Medium")\
# .appName("myapp")\
# .getOrCreate()
sc = spark.sparkContext
spark.conf.set("spark.sql.sources.partitionColumnTypeInference.enabled", "false")
spark.conf.set("spark.debug.maxToStringFields","true")
My libraries and sparksession¶
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%load_ext autoreload
%autoreload 2
# The autoreload extension is already loaded. To reload it, use:
# %reload_ext autoreload
# mylib:
my_library = os.path.expanduser('~/.myconfigs')
my_spark = os.path.expanduser('~/spark2_dfanalysis')
sys.path.append(my_library)
sys.path.append(my_spark)
from shared.app_context import *
from builder.DataFrameBuild import *
ctx = ApplicationContext("Dev-Job")
DFB = DataFrameBuild(ctx.spark)
print("%16s %s" % ("Python Version:",sys.version))
print("%16s %s" % ("Python Path:",os.path.dirname(sys.executable)))
print("%16s %s" % ("My Python Libs:",my_library))
print("%16s %s" % ("My Spark Dir:",my_spark))
print("%16s %s" % ("My Spark Ctx:",ctx.spark))
# print(ctx.spark)
# print(os.listdir(my_spark))
# print(sys.path)
# print("\n")
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