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persist_scripts.py
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persist_scripts.py
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from pyspark import SparkContext, SparkConf, StorageLevel
import sys
import string
import time
def mymapeo(x):
for y in string.punctuation:
x = x.replace(y,'').lower()
line = x.split(" ")
return line
def word_count_persist_mem_only(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app.py', 'config_scriptsDf.py'])
spark = SparkSession.builder.config(conf=conf).getOrCreate()
data = sc.textFile(data_file).flatMap(lambda x: x.split(" ")).collect()
paralData = sc.parallelize(data, 100)
print(paralData.getNumPartitions())
print(sc.getConf().get("spark.executor.instances"))
df = paralData.map(lambda r: Row(r)).toDF(["word"]).persist(StorageLevel(True, False, False, False, int(internal_param[7])))
df.show()
cleanDf = df.filter(col('word') != '').withColumn('word', regexp_replace(col('word'),'[^\sa-zA-Z0-9]', ''))
result = cleanDf.withColumn('count', lit(1)).groupBy('word').sum('count').withColumnRenamed('sum(count)','frequencies')
result.show()
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_sort_pesist_mem_only(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app.py', 'config_scriptsDf.py'])
spark = SparkSession.builder.config(conf=conf).getOrCreate()
data = sc.textFile(data_file).flatMap(lambda x: x.split(" ")).collect()
paralData = sc.parallelize(data, 200)
df = paralData.map(lambda r: Row(r)).toDF(["word"])
cleanDf = df.filter(col('word') != '').withColumn('word', regexp_replace(col('word'),'[^\sa-zA-Z0-9]', '')).persist(StorageLevel(True, False, False, False, int(internal_param[7])))
freqDf = cleanDf.withColumn('count', lit(1)).groupBy('word').sum('count').withColumnRenamed('sum(count)','frequencies')
topFreqsDf = freqDf.orderBy('frequencies').limit(5)
print('Number of words: ', cleanDf.count())
print('Top 5 frequencies:')
topFreqsDf.show()
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_plus_pesist_mem_only(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo)
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(True, False, False, False, int(internal_param[7])))
topFreqs = frequencies.sortBy(lambda x: x[1], ascending=False).persist(StorageLevel(True, False, False, False, int(internal_param[7])))
print('Top 5 frequencies:', topFreqs.take(5))
leastFreqs = frequencies.sortBy(lambda x: x[1], ascending=True)
print('Leats 5 frequencies:', leastFreqs.take(5))
topLenFreqs = topFreqs.sortBy(lambda x: len(x[0]), ascending=False).persist(StorageLevel(True, False, False, False, int(internal_param[7])))
print('Top 5 length:', topLenFreqs.take(5))
containsAwords = words.filter(lambda x: 'a' in x)
print('Number of words containing a:', containsAwords.count())
containsXwords = words.filter(lambda x: 'x' in x)
print('Number of words containing x:', containsXwords.count())
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_persist_mem_only_ser(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b)
frequencies.collect()
print(frequencies.take(5))
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_sort_pesist_mem_only_ser(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
numWords = data.count()
sortFreq = frequencies.sortBy(lambda x: x[1], ascending=False)
topFreqs = sortFreq.take(5)
print('Number of words: ', numWords)
print('Words frequencies:', sortFreq.collect())
print('Top 5 frequencies:', topFreqs)
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_plus_pesist_mem_only_ser(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo)
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
topFreqs = frequencies.sortBy(lambda x: x[1], ascending=False).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
print('Top 5 frequencies:', topFreqs.take(5))
leastFreqs = frequencies.sortBy(lambda x: x[1], ascending=True)
print('Leats 5 frequencies:', leastFreqs.take(5))
topLenFreqs = topFreqs.sortBy(lambda x: len(x[0]), ascending=False).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
print('Top 5 length:', topLenFreqs.take(5))
containsAwords = words.filter(lambda x: 'a' in x)
print('Number of words containing a:', containsAwords.count())
containsXwords = words.filter(lambda x: 'x' in x)
print('Number of words containing x:', containsXwords.count())
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_persist_mem_and_disk(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b)
frequencies.collect()
print(frequencies.take(5))
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_sort_pesist_mem_and_disk(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
numWords = data.count()
sortFreq = frequencies.sortBy(lambda x: x[1], ascending=False)
topFreqs = sortFreq.take(5)
print('Number of words: ', numWords)
print('Words frequencies:', sortFreq.collect())
print('Top 5 frequencies:', topFreqs)
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_plus_pesist_mem_and_disk(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo)
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
topFreqs = frequencies.sortBy(lambda x: x[1], ascending=False).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
print('Top 5 frequencies:', topFreqs.take(5))
leastFreqs = frequencies.sortBy(lambda x: x[1], ascending=True)
print('Leats 5 frequencies:', leastFreqs.take(5))
topLenFreqs = topFreqs.sortBy(lambda x: len(x[0]), ascending=False).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
print('Top 5 length:', topLenFreqs.take(5))
containsAwords = words.filter(lambda x: 'a' in x)
print('Number of words containing a:', containsAwords.count())
containsXwords = words.filter(lambda x: 'x' in x)
print('Number of words containing x:', containsXwords.count())
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_persist_mem_and_disk_ser(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b)
frequencies.collect()
print(frequencies.take(5))
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_sort_pesist_mem_and_disk_ser(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
numWords = data.count()
sortFreq = frequencies.sortBy(lambda x: x[1], ascending=False)
topFreqs = sortFreq.take(5)
print('Number of words: ', numWords)
print('Words frequencies:', sortFreq.collect())
print('Top 5 frequencies:', topFreqs)
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_plus_pesist_mem_and_disk_ser(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo)
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
topFreqs = frequencies.sortBy(lambda x: x[1], ascending=False).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
print('Top 5 frequencies:', topFreqs.take(5))
leastFreqs = frequencies.sortBy(lambda x: x[1], ascending=True)
print('Leats 5 frequencies:', leastFreqs.take(5))
topLenFreqs = topFreqs.sortBy(lambda x: len(x[0]), ascending=False).persist(StorageLevel(True, True, False, False, int(internal_param[7])))
print('Top 5 length:', topLenFreqs.take(5))
containsAwords = words.filter(lambda x: 'a' in x)
print('Number of words containing a:', containsAwords.count())
containsXwords = words.filter(lambda x: 'x' in x)
print('Number of words containing x:', containsXwords.count())
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_persist_disk_only(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b)
frequencies.collect()
print(frequencies.take(5))
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_sort_pesist_disk_only(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app.py', 'config_scriptsDf.py'])
spark = SparkSession.builder.config(conf=conf).getOrCreate()
data = sc.textFile(data_file).flatMap(lambda x: x.split(" ")).collect()
paralData = sc.parallelize(data, 100)
print(paralData.getNumPartitions())
print(sc.getConf().get("spark.executor.instances"))
df = paralData.map(lambda r: Row(r)).toDF(["word"])
df.show()
cleanDf = df.filter(col('word') != '').withColumn('word', regexp_replace(col('word'),'[^\sa-zA-Z0-9]', ''))
result = cleanDf.withColumn('count', lit(1)).groupBy('word').sum('count').withColumnRenamed('sum(count)','frequencies')
result.show()
numWords = data.count()
sortFreq = frequencies.sortBy(lambda x: x[1], ascending=False)
topFreqs = sortFreq.take(5)
print('Number of words: ', numWords)
print('Words frequencies:', sortFreq.collect())
print('Top 5 frequencies:', topFreqs)
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()
def word_count_plus_pesist_disk_only(internal_param, data_file):
try:
conf = SparkConf().setMaster("spark://dana:7077").setAppName(internal_param[1]).setAll([('spark.driver.cores', internal_param[2]), ('spark.driver.memory', internal_param[3]), ('spark.executor.instances', internal_param[4]), ('spark.executor.memory', internal_param[5]), ('spark.executor.cores', internal_param[6])])
sc = SparkContext(conf=conf, pyFiles=['run_app_small.py', 'run_app.py', 'sesgo_scripts.py', 'persist_scripts.py', 'repartition_scripts.py', 'config_scripts.py', 'wordCountConfig.py'])
data = sc.textFile(data_file)
words = data.flatMap(mymapeo)
frequencies= words.map(lambda x: (x, 1)).reduceByKey(lambda a,b: a+b).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
topFreqs = frequencies.sortBy(lambda x: x[1], ascending=False).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
print('Top 5 frequencies:', topFreqs.take(5))
leastFreqs = frequencies.sortBy(lambda x: x[1], ascending=True)
print('Leats 5 frequencies:', leastFreqs.take(5))
topLenFreqs = topFreqs.sortBy(lambda x: len(x[0]), ascending=False).persist(StorageLevel(False, True, False, False, int(internal_param[7])))
print('Top 5 length:', topLenFreqs.take(5))
containsAwords = words.filter(lambda x: 'a' in x)
print('Number of words containing a:', containsAwords.count())
containsXwords = words.filter(lambda x: 'x' in x)
print('Number of words containing x:', containsXwords.count())
app_id = sc.applicationId
sc.stop()
return app_id
except:
print("Configuration error: "+str(internal_param))
sc.stop()