def main(*argv):
#Setup to read from s3
aws_access_key = os.getenv('AWS_ACCESS_KEY_ID', 'default')
aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY', 'default')
conf = SparkConf().setAppName("taxishift1")
SparkContext.setSystemProperty('spark.executor.memory', '5g')
sc = SparkContext(conf=conf)
sc._jsc.hadoopConfiguration().set("fs.s3a.awsAccessKeyId",aws_access_key)
sc._jsc.hadoopConfiguration().set("fs.s3a.awsSecretAccessKey",aws_secret_access_key)
#Read from s3
for i in range(1,13):
raw_data = sc.hadoopFile('s3a://ddrum-s3/trip_data/trip_data_' + str(i) + '.csv',\
'org.apache.hadoop.mapred.TextInputFormat',\
'org.apache.hadoop.io.Text',\
'org.apache.hadoop.io.LongWritable')
#Call user defined function to map raw data into key-value pairs
new_data = raw_data.map(lambda x:splitit(x[1]))
#Combine data from multiple csv files
if i<2:
total_data = new_data
else:
total_data = total_data.union(new_data)
#Create total list of rides/shifts for each driver
total_data = total_data.reduceByKey(lambda x,y:x+y)\
.mapValues(lambda x:mergeit(x))\
#Create key-value for each shift, and set shifts greater than 10 hours to 10 hours (We want to know exactly when they go over)
ungrouped_data = total_data.flatMap(lambda x: [(x[0],r) for r in x[1]])\
.mapValues(lambda x: x if x[1]-x[0] < 36000 else [x[0],x[0]+36000])
#Extract only 10 hour shift offenders
offenders = ungrouped_data.filter(lambda x:x[1][1] - x[1][0] >= 36000)\
.map(lambda x:(x[1][1]))\
.collect()
#Plot number of offenders for every 30 minutes
offender_hist = plt.hist(offenders, bins=range(int(min(offenders)), int(max(offenders)) + 1800, 1800))
hist_min = int(min(offender_hist))
hist_min+=900
#Save to file
hist_csv = open('/home/ubuntu/offenders.csv','w')
for i in offender_hist[0]:
hist_csv.write(str(hist_min)+','+str(i)+'\n')
hist_min+=1800
hist_csv.close()
return
def main(*argv):
#Setup to read from s3
aws_access_key = os.getenv('AWS_ACCESS_KEY_ID', 'default')
aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY', 'default')
conf = SparkConf().setAppName("taxishift3")
SparkContext.setSystemProperty('spark.executor.memory', '5g')
sc = SparkContext(conf=conf)
sc._jsc.hadoopConfiguration().set("fs.s3a.awsAccessKeyId", aws_access_key)
sc._jsc.hadoopConfiguration().set("fs.s3a.awsSecretAccessKey",
aws_secret_access_key)
#Read from s3
for i in range(1, 13):
raw_data = sc.hadoopFile('s3a://ddrum-s3/trip_data/trip_data_' + str(i) + '.csv',\
'org.apache.hadoop.mapred.TextInputFormat',\
'org.apache.hadoop.io.Text',\
'org.apache.hadoop.io.LongWritable')
#Call user defined function to map raw data into key-value pairs
new_data = raw_data.map(lambda x: splitit(x[1]))
#Combine data from multiple csv files
if i < 2:
total_data = new_data
else:
total_data = total_data.union(new_data)
#Create total list of pickup chains and pickups for each coordinate
#If there are more than three times as many pickups as pickup chains, this might be a taxistand
total_data = total_data.reduceByKey(lambda x,y:x+y)\
.mapValues(lambda x:mergeit(x))\
.filter(lambda x:x[1][1]/x[1][0] > 3)\
.collect()
#Print results
total_data.sort()
for i in total_data:
print i
return
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName("spark_app_read_data_from_rcfile")
sc = SparkContext(conf=conf)
rowRDD = sc.hadoopFile(path="hdfs://dip.cdh5.dev:8020/user/yurun/rcfile",
inputFormatClass="org.apache.hadoop.hive.ql.io.RCFileInputFormat",
keyClass="org.apache.hadoop.io.LongWritable",
valueClass="org.apache.hadoop.hive.serde2.columnar.BytesRefArrayWritable",
valueConverter="com.sina.dip.spark.converter.BytesRefArrayWritableToObjectArrayConverter")
pairs = rowRDD.collect()
for pair in pairs:
print pair[0], pair[1]
sc.stop()
def main(cmd_line_args=None):
if cmd_line_args is None:
cmd_line_args = sys.argv[1:]
parser = _make_arg_parser()
args = parser.parse_args(cmd_line_args)
if args.num_reducers is not None and args.num_reducers <= 0:
raise ValueError(
'You can only configure num_reducers to positive number.')
# get job_class
job_module_name, job_class_name = args.job_class.rsplit('.', 1)
job_module = import_module(job_module_name)
job_class = getattr(job_module, job_class_name)
# load initial data
from pyspark import SparkContext
if args.job_args:
job_args = shlex_split(args.job_args)
else:
job_args = []
# get job steps. don't pass --steps, which is deprecated
job = job_class(job_args)
steps = job.steps()
# pick steps
start = args.first_step_num or 0
end = None if args.last_step_num is None else args.last_step_num + 1
steps_to_run = list(enumerate(steps))[start:end]
sc = SparkContext()
# keep track of one set of counters per job step
counter_accumulators = [
sc.accumulator(defaultdict(dict), CounterAccumulator())
for _ in steps_to_run
]
def make_increment_counter(step_num):
counter_accumulator = counter_accumulators[step_num - start]
def increment_counter(group, name, amount=1):
counter_accumulator.add({group: {name: amount}})
return increment_counter
def make_job(mrc, step_num):
j = job_class(job_args + ['--%s' % mrc, '--step-num=%d' % step_num])
j.sandbox() # so Spark doesn't try to serialize stdin
# patch increment_counter() to update the accumulator for this step
j.increment_counter = make_increment_counter(step_num)
return j
try:
if job.hadoop_input_format() is not None:
rdd = sc.hadoopFile(args.input_path,
inputFormatClass=job.hadoop_input_format(),
keyClass='org.apache.hadoop.io.Text',
valueClass='org.apache.hadoop.io.Text')
# hadoopFile loads each line as a key-value pair in which the contents
# of the line are the key and the value is an empty string. Convert to
# an rdd of just lines, encoded as bytes.
rdd = rdd.map(lambda kv: kv[0].encode('utf-8'))
else:
rdd = sc.textFile(args.input_path, use_unicode=False)
# run steps
for step_num, step in steps_to_run:
rdd = _run_step(step, step_num, rdd, make_job, args.num_reducers)
# write the results
if job.hadoop_output_format() is not None:
# saveAsHadoopFile takes an rdd of key-value pairs, so convert to that
# format
rdd = rdd.map(lambda line: tuple(
x.decode('utf-8') for x in line.split(b'\t', 1)))
rdd.saveAsHadoopFile(args.output_path,
outputFormatClass=job.hadoop_output_format(),
compressionCodecClass=args.compression_codec)
else:
rdd.saveAsTextFile(args.output_path,
compressionCodecClass=args.compression_codec)
finally:
if args.counter_output_dir is not None:
counters = [ca.value for ca in counter_accumulators]
sc.parallelize([json.dumps(counters)],
numSlices=1).saveAsTextFile(args.counter_output_dir)
if __name__ == '__main__':
if len(sys.argv) != 5:
print("Usage: spark_streaming.py <master> <begin> <end> <input>", file=sys.stderr)
exit(-1)
master, time_begin, time_end, input = sys.argv[1:]
input_path = input + '/' + time_begin + '.csv'
logger.info("--->" + master + " " + input_path)
sc = SparkContext(master, 'wxcity_userlogin_repeat_app')
sql_context = SQLContext(sc)
lines = sc.hadoopFile(input,
'org.apache.hadoop.mapred.TextInputFormat',
'org.apache.hadoop.io.LongWritable',
'org.apache.hadoop.io.Text'
)
rs_tuples = MysqlDao().findWithQuery(ConfigPortalSql.select_mysql_hos_gw_sup)
gwid_hosid_dict = {}
for r in rs_tuples:
hos_id = str(r[0])
gw_id = r[1]
gwid_hosid_dict[gw_id] = hos_id
logger.debug('-->gwid_hosid:' + str(gwid_hosid_dict.__len__()))
users = lines.map(lambda x: x[1].split(',')).filter(lambda x: len(x) == 17) \
.map(lambda p: (p[0].strip(), p[1].strip(), p[2].strip(), p[3].strip(), p[4].strip(), \
p[5].strip(), p[6].strip(), p[7].strip(), p[8].strip(), p[9].strip(), \
p[10].strip(), p[11].strip(), p[12].strip(), p[13].strip(), p[14].strip(), \
p[15].strip(), p[16].strip(), gwid_hosid_dict.get(p[1].strip(), "")))
def main(cmd_line_args=None):
if cmd_line_args is None:
cmd_line_args = sys.argv[1:]
parser = _make_arg_parser()
args = parser.parse_args(cmd_line_args)
if args.num_reducers is not None and args.num_reducers <= 0:
raise ValueError(
'You can only configure num_reducers to positive number.')
# get job_class
job_module_name, job_class_name = args.job_class.rsplit('.', 1)
job_module = import_module(job_module_name)
job_class = getattr(job_module, job_class_name)
# load initial data
from pyspark import SparkContext
if args.job_args:
job_args = shlex_split(args.job_args)
else:
job_args = []
# determine hadoop_*_format, steps
# try to avoid instantiating a job in the driver; see #2044
job = None
if args.hadoop_input_format is None:
job = job or job_class(job_args)
hadoop_input_format = job.hadoop_input_format()
else:
hadoop_input_format = args.hadoop_input_format or None
if args.hadoop_output_format is None:
job = job or job_class(job_args)
hadoop_output_format = job.hadoop_output_format()
else:
hadoop_output_format = args.hadoop_output_format or None
if args.sort_values is None:
job = job or job_class(job_args)
sort_values = job.sort_values()
else:
sort_values = args.sort_values
if args.steps_desc is None:
job = job or job_class(job_args)
steps = [
step.description(step_num)
for step_num, step in enumerate(job.steps())
]
else:
steps = json.loads(args.steps_desc)
# pick steps
start = args.first_step_num or 0
end = None if args.last_step_num is None else args.last_step_num + 1
steps_to_run = list(enumerate(steps))[start:end]
sc = SparkContext()
# keep track of one set of counters per job step
counter_accumulators = [
sc.accumulator(defaultdict(dict), CounterAccumulator())
for _ in steps_to_run
]
def make_increment_counter(step_num):
counter_accumulator = counter_accumulators[step_num - start]
def increment_counter(group, counter, amount=1):
counter_accumulator.add({group: {counter: amount}})
return increment_counter
def make_mrc_job(mrc, step_num):
j = job_class(job_args + ['--%s' % mrc, '--step-num=%d' % step_num])
# patch increment_counter() to update the accumulator for this step
j.increment_counter = make_increment_counter(step_num)
# if skip_internal_protocol is true, patch internal_protocol() to
# return an object whose *read* and *write* attributes are ``None``
if args.skip_internal_protocol:
j.internal_protocol = lambda: _NO_INTERNAL_PROTOCOL
return j
# --emulate-map-input-file doesn't work with hadoop_input_format
emulate_map_input_file = (args.emulate_map_input_file
and not hadoop_input_format)
try:
if emulate_map_input_file:
# load an rdd with pairs of (input_path, line). *path* here
# has to be a single path, not a comma-separated list
rdd = sc.union([
_text_file_with_path(sc, path)
for path in args.input_path.split(',')
])
elif hadoop_input_format:
rdd = sc.hadoopFile(args.input_path,
inputFormatClass=hadoop_input_format,
keyClass='org.apache.hadoop.io.Text',
valueClass='org.apache.hadoop.io.Text')
# hadoopFile loads each line as a key-value pair in which the
# contents of the line are the key and the value is an empty
# string. Convert to an rdd of just lines, encoded as bytes.
rdd = rdd.map(lambda kv: kv[0].encode('utf-8'))
else:
rdd = sc.textFile(args.input_path, use_unicode=False)
# run steps
for step_num, step in steps_to_run:
rdd = _run_step(step, step_num, rdd, make_mrc_job,
args.num_reducers, sort_values,
emulate_map_input_file,
args.skip_internal_protocol)
# max_output_files: limit number of partitions
if args.max_output_files:
rdd = rdd.coalesce(args.max_output_files)
# write the results
if hadoop_output_format:
# saveAsHadoopFile takes an rdd of key-value pairs, so convert to
# that format
rdd = rdd.map(lambda line: tuple(
x.decode('utf-8') for x in line.split(b'\t', 1)))
rdd.saveAsHadoopFile(args.output_path,
outputFormatClass=hadoop_output_format,
compressionCodecClass=args.compression_codec)
else:
rdd.saveAsTextFile(args.output_path,
compressionCodecClass=args.compression_codec)
finally:
if args.counter_output_dir is not None:
counters = [ca.value for ca in counter_accumulators]
# If the given path is an s3 path, use s3.parallelize,
# otherwise just write them directly to the local dir
if is_uri(args.counter_output_dir):
sc.parallelize([json.dumps(counters)],
numSlices=1).saveAsTextFile(
args.counter_output_dir)
else:
# Use regular python built-in file writer if the part-* file
# is not created
path = os.path.join(args.counter_output_dir, "part-00000")
if not os.path.exists(args.counter_output_dir):
os.mkdir(args.counter_output_dir)
with open(path, 'w') as wb:
wb.write(str(json.dumps(counters)))
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName("spark_app_read_data_from_seqfile")
sc = SparkContext(conf=conf)
lineRDD = sc.hadoopFile(path="hdfs://dip.cdh5.dev:8020/user/yurun/seqfile",
inputFormatClass="org.apache.hadoop.mapred.SequenceFileInputFormat",
keyClass="com.sina.dip.spark.converter.IntArrayWritable",
valueClass="org.apache.hadoop.io.NullWritable",
keyConverter="com.sina.dip.spark.converter.IntArrayWritableToObjectArrayConverter").map(lambda pair: pair[0])
lines = lineRDD.collect()
for line in lines:
print line
sc.stop()
def main():
conf = SparkConf().setAppName("first")
sc = SparkContext(conf=conf)
#print 40 * '-'
sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", aws_key_id)
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", aws_key)
config_dict = {
"fs.s3n.awsAccessKeyId": aws_key_id,
"fs.s3n.awsSecretAccessKey": aws_key
}
bucket = "project4capstones3"
prefix = "/2017/07/*/*/*"
filename = "s3n://{}/Trump/{}".format(bucket, prefix)
rdd = sc.hadoopFile(filename,
'org.apache.hadoop.mapred.TextInputFormat',
'org.apache.hadoop.io.Text',
'org.apache.hadoop.io.LongWritable',
conf=config_dict)
spark = SparkSession.builder.appName("PythonWordCount").config(
"spark.files.overwrite", "true").getOrCreate()
df = spark.read.json(rdd.map(lambda x: x[1]))
data_rm_na = df.filter(df["status_id"] != 'None')
features_of_interest = ["rt_status_user_followers_count",\
'rt_status_user_friends_count',\
'rt_status_user_statuses_count',\
'rt_status_retweet_count',\
'rt_status_user_listed_count',\
'rt_status_user_id',\
'rt_status_created_at',\
'status_created_at',\
'rt_status_user_name',\
'rt_status_num_user_mentions',\
'searched_names',\
'rt_status_sentMag',\
'rt_status_sentScore',\
'rt_status_favorite_count',\
'status_id']
df_reduce = data_rm_na.select(features_of_interest)
df_reduce = df_reduce.withColumn(
"rt_status_user_followers_count",
df_reduce["rt_status_user_followers_count"].cast(IntegerType()))
df_reduce = df_reduce.withColumn(
"rt_status_user_friends_count",
df_reduce["rt_status_user_friends_count"].cast(IntegerType()))
df_reduce = df_reduce.withColumn(
"rt_status_user_statuses_count",
df_reduce["rt_status_user_statuses_count"].cast(IntegerType()))
df_reduce = df_reduce.withColumn(
"rt_status_retweet_count",
df_reduce["rt_status_retweet_count"].cast(IntegerType()))
df_reduce = df_reduce.withColumn(
"rt_status_user_listed_count",
df_reduce["rt_status_user_listed_count"].cast(IntegerType()))
df_reduce = df_reduce.withColumn(
"rt_status_favorite_count",
df_reduce["rt_status_favorite_count"].cast(IntegerType()))
df_reduce = df_reduce.withColumn(
"rt_status_num_user_mentions",
df_reduce["rt_status_num_user_mentions"].cast(IntegerType()))
url_ = "jdbc:mysql://twittertalker1.csjkhjjygutf.us-east-1.rds.amazonaws.com:3306/innodb"
table_name_ = "retweet"
mode_ = "overwrite"
df_reduce.write.format("jdbc").option("url", url_)\
.option("dbtable", table_name_)\
.option("driver", "com.mysql.jdbc.Driver")\
.option("user", "XXXXXX")\
.option("password", "XXXXXXXX")\
.mode(mode_)\
.save()
""""""
# 读取sequenceFile
data = sc.sequenceFile(inputFile, "org.apache.hadoop.io.Text",
'org.apache.hadoop.io.IntWritable')
# 保存sequenceFile
data2 = sc.parallelize([('Panda', 3), ('Kay', 6), ('Snail', 2)])
data2.saveAsSequenceFile(outputFile)
# 5.2.5 对象文件
"""对象文件在 Python 中无法使用，不过 Python 中的 RDD 和 SparkContext 支持 saveAsPickleFile()
和 pickleFile() 方法作为替代。这使用了 Python 的 pickle 序列化库。不过，对象文件的
注意事项同样适用于 pickle 文件： pickle 库可能很慢，并且在修改类定义后，已经生产的
数据文件可能无法再读出来"""
# 5.2.6 Hadoop输入输出格式
# 1.读取其他Hadoop输入格式
input2 = sc.hadoopFile(inputFile).map(lambda x, y: (str(x), str(y)))
# 2.保存Hadoop输出格式
input2.saveAsNewAPIHadoopFile(inputFile)
# 3.to do
# 5.3文件系统
# 5.3.1 本地文件系统
rdd = sc.textFile(inputFile)
# 5.3.2 Amazon S3
# 5.3.3 HDFS:只需要将输入输出路径指定为hdfs://master:port/path即可
# 5.4 Spark SQL中的结构化数据,详见第九章
# 5.4.1Apache Hive
"""要把 Spark SQL 连接到已有的 Hive 上，你需要提供 Hive 的配置文件。你需要将 hive-site.
xml 文件复制到 Spark 的 ./conf/ 目录下"""
hiveCtx = HiveContext(sc)
rows = hiveCtx.sql('SELECT name, age FROM users')
firstRow = rows.first()
# sc.cancelJobGroup(groupId)
# sc.setJobGroup(groupId,"")
# 将概要信息转储到目录路径中
# sc.dump_profiles(path)
rdd = sc.emptyRDD()
# 创建一个没有分区或元素的RDD。
print(sc.getConf()) # 返回SparkConf对象
# getLocalProperty(key)
# Get a local property set in this thread, or null if it is missing. See setLocalProperty().
# classmethod getOrCreate(conf=None)
"""
sc.hadoopFile()
sc.hadoopRDD()
sheet = sc.newAPIHadoopFile(
'/user/me/sample.txt',
'org.apache.hadoop.mapreduce.lib.input.TextInputFormat',
'org.apache.hadoop.io.LongWritable',
'org.apache.hadoop.io.Text',
conf={'textinputformat.record.delimiter': 'Time\tMHist'}
)
parquet_rdd = sc.newAPIHadoopFile(
path,
'org.apache.parquet.avro.AvroParquetInputFormat',
'java.lang.Void',
'org.apache.avro.generic.IndexedRecord',
def main(cmd_line_args=None):
if cmd_line_args is None:
cmd_line_args = sys.argv[1:]
parser = _make_arg_parser()
args = parser.parse_args(cmd_line_args)
if args.num_reducers is not None and args.num_reducers <= 0:
raise ValueError(
'You can only configure num_reducers to positive number.')
# get job_class
job_module_name, job_class_name = args.job_class.rsplit('.', 1)
job_module = import_module(job_module_name)
job_class = getattr(job_module, job_class_name)
# load initial data
from pyspark import SparkContext
if args.job_args:
job_args = shlex_split(args.job_args)
else:
job_args = []
# determine hadoop_*_format, steps
# try to avoid instantiating a job in the driver; see #2044
job = None
if args.hadoop_input_format is None:
job = job or job_class(job_args)
hadoop_input_format = job.hadoop_input_format()
else:
hadoop_input_format = args.hadoop_input_format or None
if args.hadoop_output_format is None:
job = job or job_class(job_args)
hadoop_output_format = job.hadoop_output_format()
else:
hadoop_output_format = args.hadoop_output_format or None
if args.sort_values is None:
job = job or job_class(job_args)
sort_values = job.sort_values()
else:
sort_values = args.sort_values
if args.steps_desc is None:
job = job or job_class(job_args)
steps = [step.description(step_num)
for step_num, step in enumerate(job.steps())]
else:
steps = json.loads(args.steps_desc)
# pick steps
start = args.first_step_num or 0
end = None if args.last_step_num is None else args.last_step_num + 1
steps_to_run = list(enumerate(steps))[start:end]
sc = SparkContext()
# keep track of one set of counters per job step
counter_accumulators = [
sc.accumulator(defaultdict(dict), CounterAccumulator())
for _ in steps_to_run
]
def make_increment_counter(step_num):
counter_accumulator = counter_accumulators[step_num - start]
def increment_counter(group, name, amount=1):
counter_accumulator.add({group: {name: amount}})
return increment_counter
def make_mrc_job(mrc, step_num):
j = job_class(job_args + [
'--%s' % mrc, '--step-num=%d' % step_num
])
# patch increment_counter() to update the accumulator for this step
j.increment_counter = make_increment_counter(step_num)
return j
try:
if hadoop_input_format:
rdd = sc.hadoopFile(
args.input_path,
inputFormatClass=hadoop_input_format,
keyClass='org.apache.hadoop.io.Text',
valueClass='org.apache.hadoop.io.Text')
# hadoopFile loads each line as a key-value pair in which the
# contents of the line are the key and the value is an empty
# string. Convert to an rdd of just lines, encoded as bytes.
rdd = rdd.map(lambda kv: kv[0].encode('utf-8'))
else:
rdd = sc.textFile(args.input_path, use_unicode=False)
# run steps
for step_num, step in steps_to_run:
rdd = _run_step(step, step_num, rdd,
make_mrc_job, args.num_reducers, sort_values)
# max_output_files: limit number of partitions
if args.max_output_files:
rdd = rdd.coalesce(args.max_output_files)
# write the results
if hadoop_output_format:
# saveAsHadoopFile takes an rdd of key-value pairs, so convert to
# that format
rdd = rdd.map(lambda line: tuple(
x.decode('utf-8') for x in line.split(b'\t', 1)))
rdd.saveAsHadoopFile(
args.output_path,
outputFormatClass=hadoop_output_format,
compressionCodecClass=args.compression_codec)
else:
rdd.saveAsTextFile(
args.output_path, compressionCodecClass=args.compression_codec)
finally:
if args.counter_output_dir is not None:
counters = [ca.value for ca in counter_accumulators]
sc.parallelize(
[json.dumps(counters)],
numSlices=1
).saveAsTextFile(
args.counter_output_dir
)
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName("spark_app_read_data_from_seqfile")
sc = SparkContext(conf=conf)
lineRDD = sc.hadoopFile(
path="hdfs://dip.cdh5.dev:8020/user/yurun/seqfile",
inputFormatClass="org.apache.hadoop.mapred.SequenceFileInputFormat",
keyClass="com.sina.dip.spark.converter.IntArrayWritable",
valueClass="org.apache.hadoop.io.NullWritable",
keyConverter=
"com.sina.dip.spark.converter.IntArrayWritableToObjectArrayConverter").map(
lambda pair: pair[0])
lines = lineRDD.collect()
for line in lines:
print line
sc.stop()
from pyspark import SparkContext, SparkConf
"""
org.apache.hadoop.mapred
KeyValueTextInputFormat
SequenceFileAsBinaryInputFormat
SequenceFileAsTextInputFormat
SequenceFileInputFormat<K,V>
TextInputFormat
"""
sparkconf = SparkConf().setAppName('hadoop io read').setMaster('local')
sc = SparkContext(conf=sparkconf)
f = sc.hadoopFile('hdfs://172.19.0.2/newtextfile/part-00000',
'org.apache.hadoop.mapred.TextInputFormat',
'org.apache.hadoop.io.Text',
'org.apache.hadoop.io.IntWritable')
print f.collect()
