def main(argv):
infile = argv[1]
outfile1 = argv[2]
outfile2 = argv[3]
OUT1 = open(outfile1, 'w')
OUT2 = open(outfile2, 'w')
unicode_count = 0
period = 60
tag_dict = {}
tag_dq = collections.deque([], maxlen=period+1) ### maximum length of deque is 61. +1 because the issue of un-order tweet within 2s
last_time = ''
k = 0
N_v = 0
d_total = 0
escapes = ''.join([chr(char) for char in range(1, 32)])
temp_list = 31 * ' '
with open(infile) as IN:
for line in IN:
(content, time1, unicode_flag) = ut.tweets_cleaned_json(line, escapes, temp_list)
if not time1:
continue
if unicode_flag == 1: unicode_count += 1
OUT1.write(content + " (timestamp: " + time1 + ")\n")
tag_list = re.findall('#[^ ]*', content) ### find all potential tags in text
tag_list = [ut.clean_tag(x.strip('#')) for x in tag_list] ### remove all heading '#' in tags
tag_list = filter(None, [y.lower() for x in tag_list for y in x]) ### remove empty tags and change to lower case
tag_list = ['#'+x for x in tag_list] ### add '#' back in front of tags
temp_str = time1.split(" ")
temp_str1 = temp_str[3].split(":")
time_str = temp_str[1].strip() + ' ' + temp_str[2].strip() + ' ' + temp_str[5].strip() + ' ' + temp_str1[0].strip() + ' ' + temp_str1[1].strip() + ' ' + temp_str[2].strip()
time2 = datetime.strptime(time_str, '%b %d %Y %H %M %S')
N_v, d_total, last_time = ut.average_degree(tag_list, time2, N_v, d_total, tag_dict, tag_dq, last_time, period)
if N_v == 0:
av_degree = 0
else:
av_degree = float(d_total) / N_v
if k == 1: ### except for the first line, (newline + printing)
OUT2.write("\n%.2f" % av_degree)
else:
k = 1
OUT2.write("%.2f" % av_degree)
OUT1.write(str(unicode_count) + " tweets contained unicode.")
OUT1.close()
OUT2.close()
def main(argv):
if len(argv) != 4:
print "4 args required!"
print "Usage code_challenge_hadoop.py input.txt output1.txt output2.txt"
exit(1)
current_dir = os.getcwd()
infile = sys.argv[1].strip()
outfile1 = sys.argv[2].strip()
outfile2 = sys.argv[3].strip()
result_dir = current_dir + '/count_result'
hadoop_home = os.getenv('HADOOP_HOME').strip()
stream_jar = glob.glob(hadoop_home + '/contrib//streaming/hadoop-streaming*.jar')[0]
mapper_file = current_dir + '/count_mapper_hive.py'
reducer_file = current_dir + '/count_reducer_hive.py'
### clean tweets, output to ft1.txt and count for unicode
OUT1 = open(outfile1, 'w')
unicode_count = 0
k = 0
escapes = ''.join([chr(char) for char in range(1, 32)])
temp_list = 31 * ' '
with open(infile) as IN:
for line in IN:
(content, time1, unicode_flag) = ut.tweets_cleaned_json(line, escapes, temp_list)
if not time1:
continue
if unicode_flag == 1: unicode_count += 1
if k == 1:
OUT1.write("\n" + content + " (timestamp: " + time1 + ")" )
else:
k = 1
OUT1.write(content + " (timestamp: " + time1 + ")" )
OUT1.close()
OUT2 = open(outfile2, 'w') ### For convenience of hadoop streaming, we divide ft1.txt
OUT2.write(str(unicode_count) + " tweets contained unicode.") ### into ft1_hadoop.txt (contain only tweet), and unicode_count.txt
OUT2.close()
### Extract tag and combine same tag with same time (within 1 minute)
input_file = outfile1
call_str = 'hadoop jar ' + stream_jar + ' -mapper ' + mapper_file + ' -reducer ' + reducer_file + \
' -file ' + mapper_file + ' -file ' + reducer_file + ' -input ' + input_file + ' -output ' + \
result_dir + ' -mapper cat -reducer aggregate'
call_list = call_list = ['hadoop', 'jar', stream_jar, '-mapper', mapper_file, '-reducer', reducer_file,
'-file', mapper_file, '-file', reducer_file, '-input', input_file, '-output',
result_dir, '-mapper', 'cat', '-reducer', 'aggregate']
print call_str
subprocess.Popen(call_list).wait()
### Hive data warehouse building
from hive_service import ThriftHive
from thrift import Thrift
from thrift.transport import TSocket
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol
input_hive = result_dir + "/part-00000"
if os.path.isfile(result_dir+"/_SUCCESS") == True:
try:
transport = TSocket.TSocket('localhost', 10000)
transport = TTransport.TBufferedTransport(transport)
protocol = TBinaryProtocol.TBinaryProtocol(transport)
client = ThriftHive.Client(protocol)
transport.open()
print "Create table from thrift require change in MySQL as below:"
print "alter table SDS alter column IS_STOREDASSUBDIRECTORIES set default 0;"
client.execute("drop table if exists edge_source")
client.execute("""CREATE EXTERNAL TABLE if NOT EXISTS edge_source(year INT, month INT, day INT,
hour INT, minute INT, tag1 STRING, tag2 STRING, count INT)
ROW FORMAT DELIMITED FIELDS TERMINATED by ','""")
call_str = "LOAD DATA LOCAL INPATH '" + input_hive + "' INTO TABLE edge_source"
client.execute(call_str)
client.execute("drop table if exists edge")
client.execute("""CREATE TABLE if NOT EXISTS edge(tag1 STRING, tag2 STRING, count INT)
PARTITIONED by (year INT, month INT, day INT, hour INT, minute INT)
ROW FORMAT DELIMITED FIELDS TERMINATED by ','""")
client.execute("set hive.exec.dynamic.partition=true")
client.execute("set hive.exec.dynamic.partition.mode=nonstrict")
client.execute("""INSERT OVERWRITE TABLE edge PARTITION (year, month, day, hour, minute)
SELECT tag1, tag2, count, year, month, day, hour, minute FROM edge_source""")
#client.execute("select * from edge")
#print client.fetchAll()
except Thrift.TException, tx:
print "Error occurs!"
print '%s' % (tx.message)
def main(argv):
if len(argv) != 5:
print "5 args required!"
print "Usage code_challenge_hadoop.py input.txt output1.txt output2.txt output3.txt"
exit(1)
current_dir = os.getcwd()
infile = sys.argv[1].strip()
outfile1 = sys.argv[2].strip()
outfile2 = sys.argv[3].strip()
outfile3 = sys.argv[4].strip()
result_dir = current_dir + "/count_result"
hadoop_home = os.getenv("HADOOP_HOME").strip()
stream_jar = glob.glob(hadoop_home + "/contrib//streaming/hadoop-streaming*.jar")[0]
mapper_file = current_dir + "/count_mapper.py"
reducer_file = current_dir + "/count_reducer.py"
### clean tweets, output to ft1.txt and count for unicode
OUT1 = open(outfile1, "w")
unicode_count = 0
k = 0
escapes = "".join([chr(char) for char in range(1, 32)])
temp_list = 31 * " "
with open(infile) as IN:
for line in IN:
(content, time1, unicode_flag) = ut.tweets_cleaned_json(line, escapes, temp_list)
if not time1:
continue
if unicode_flag == 1:
unicode_count += 1
if k == 1:
OUT1.write("\n" + content + " (timestamp: " + time1 + ")")
else:
k = 1
OUT1.write(content + " (timestamp: " + time1 + ")")
OUT1.close()
OUT2 = open(outfile2, "w") ### For convenience of hadoop streaming, we divide ft1.txt
OUT2.write(
str(unicode_count) + " tweets contained unicode."
) ### into ft1_hadoop.txt (contain only tweet), and unicode_count.txt
OUT2.close()
### Extract tag and combine same tag with same time (within 1 minute)
input_file = outfile1
call_str = (
"hadoop jar "
+ stream_jar
+ " -mapper "
+ mapper_file
+ " -reducer "
+ reducer_file
+ " -file "
+ mapper_file
+ " -file "
+ reducer_file
+ " -input "
+ input_file
+ " -output "
+ result_dir
+ " -mapper cat -reducer aggregate"
)
call_list = call_list = [
"hadoop",
"jar",
stream_jar,
"-mapper",
mapper_file,
"-reducer",
reducer_file,
"-file",
mapper_file,
"-file",
reducer_file,
"-input",
input_file,
"-output",
result_dir,
"-mapper",
"cat",
"-reducer",
"aggregate",
]
print call_str
subprocess.Popen(call_list).wait()
if os.path.isfile(result_dir + "/_SUCCESS") == True:
period = 10 ### update average degree of last 10 min every 1 min
tag_dict = {}
tag_list = []
tag_dq = collections.deque([], maxlen=period)
last_time = ""
N_v, d_total, k = 0, 0, 0
OUT3 = open(outfile3, "w")
with open(result_dir + "/part-00000") as IN:
for line in IN:
temp_list = line.split("\\t")
time_str, tag1, tag2, count = temp_list[0], temp_list[1], temp_list[2], temp_list[3]
timestamp = datetime.strptime(time_str, "%Y-%m-%d %H:%M")
if k == 0:
k = 1
last_time = timestamp
if timestamp == last_time:
if tag1 and tag2:
tag_list.append((tag1, tag2, int(count))) ### if same time and not empty, add in tag list
else:
N_v, d_total = ut.average_degree_hadoop(tag_list, last_time, N_v, d_total, tag_dict, tag_dq, period)
if N_v == 0:
av_degree = 0
else:
av_degree = float(d_total) / N_v
tag_list = [(tag1, tag2, int(count))]
OUT3.write(last_time.strftime("%Y-%m-%d %H:%M") + "\t%.2f\n" % av_degree)
last_time = timestamp
IN.close()
N_v, d_total = ut.average_degree_hadoop(tag_list, last_time, N_v, d_total, tag_dict, tag_dq, period)
if N_v == 0:
av_degree = 0
else:
av_degree = float(d_total) / N_v
OUT3.write(last_time.strftime("%Y-%m-%d %H:%M") + "\t%.2f" % av_degree)
OUT3.close()
else:
print "Error occurs in map reduce!"
exit(1)
