Hadoop streaming job create huge temp files
I was trying to run hadoop job to do the word shingling, and all my nodes soon get unhealthy state since the storage is used up.
Here is my mapper part:
shingle = 5
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
for i in range(0, len(line)-shingle+1):
print ('%st%s' % (line[i:i+shingle], 1))
For my understanding that 'print' will generate temp file on each node which occupy stroage space. If I took a txt file as an example:
cat README.txt |./shingle_mapper.py >> temp.txt
I can see the size of the original and temp file:
-rw-r--r-- 1 root root 1366 Nov 13 02:46 README.txt
-rw-r--r-- 1 root root 9744 Nov 14 01:43 temp.txt
The temp file size is over 7 times of the input file, so I guess this is the reason that each of my node is used up all storage.
My question is do I understand the temp file correctly? If so, is there any better way to reduce the size of temp files (adding additional storage is not an option for me)?
hadoop mapper
asked Nov 14 '18 at 6:49
pan
84
add a comment |
I was trying to run hadoop job to do the word shingling, and all my nodes soon get unhealthy state since the storage is used up.
Here is my mapper part:
shingle = 5
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
for i in range(0, len(line)-shingle+1):
print ('%st%s' % (line[i:i+shingle], 1))
For my understanding that 'print' will generate temp file on each node which occupy stroage space. If I took a txt file as an example:
cat README.txt |./shingle_mapper.py >> temp.txt
I can see the size of the original and temp file:
-rw-r--r-- 1 root root 1366 Nov 13 02:46 README.txt
-rw-r--r-- 1 root root 9744 Nov 14 01:43 temp.txt
The temp file size is over 7 times of the input file, so I guess this is the reason that each of my node is used up all storage.
My question is do I understand the temp file correctly? If so, is there any better way to reduce the size of temp files (adding additional storage is not an option for me)?
hadoop mapper
asked Nov 14 '18 at 6:49
pan
84
Try using combiner.
– gudok
Nov 15 '18 at 10:30
I tried combiner but got the same result. I think the temp files created by mapper job are still in huge size, my understanding is that the combiner can only reduce network traffic when sending data to reducers.
– pan
Nov 16 '18 at 0:35
add a comment |
I was trying to run hadoop job to do the word shingling, and all my nodes soon get unhealthy state since the storage is used up.
Here is my mapper part:
shingle = 5
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
for i in range(0, len(line)-shingle+1):
print ('%st%s' % (line[i:i+shingle], 1))
For my understanding that 'print' will generate temp file on each node which occupy stroage space. If I took a txt file as an example:
cat README.txt |./shingle_mapper.py >> temp.txt
I can see the size of the original and temp file:
-rw-r--r-- 1 root root 1366 Nov 13 02:46 README.txt
-rw-r--r-- 1 root root 9744 Nov 14 01:43 temp.txt
The temp file size is over 7 times of the input file, so I guess this is the reason that each of my node is used up all storage.
My question is do I understand the temp file correctly? If so, is there any better way to reduce the size of temp files (adding additional storage is not an option for me)?
hadoop mapper
asked Nov 14 '18 at 6:49
pan
84
I was trying to run hadoop job to do the word shingling, and all my nodes soon get unhealthy state since the storage is used up.
Here is my mapper part:
shingle = 5
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
for i in range(0, len(line)-shingle+1):
print ('%st%s' % (line[i:i+shingle], 1))
For my understanding that 'print' will generate temp file on each node which occupy stroage space. If I took a txt file as an example:
cat README.txt |./shingle_mapper.py >> temp.txt
I can see the size of the original and temp file:
-rw-r--r-- 1 root root 1366 Nov 13 02:46 README.txt
-rw-r--r-- 1 root root 9744 Nov 14 01:43 temp.txt
The temp file size is over 7 times of the input file, so I guess this is the reason that each of my node is used up all storage.
My question is do I understand the temp file correctly? If so, is there any better way to reduce the size of temp files (adding additional storage is not an option for me)?
hadoop mapper
hadoop mapper
asked Nov 14 '18 at 6:49
pan
84
asked Nov 14 '18 at 6:49
pan
84
asked Nov 14 '18 at 6:49
pan
84
asked Nov 14 '18 at 6:49
pan
84
asked Nov 14 '18 at 6:49
pan
84
84
Try using combiner.
– gudok
Nov 15 '18 at 10:30
I tried combiner but got the same result. I think the temp files created by mapper job are still in huge size, my understanding is that the combiner can only reduce network traffic when sending data to reducers.
– pan
Nov 16 '18 at 0:35
add a comment |
Try using combiner.
– gudok
Nov 15 '18 at 10:30
I tried combiner but got the same result. I think the temp files created by mapper job are still in huge size, my understanding is that the combiner can only reduce network traffic when sending data to reducers.
– pan
Nov 16 '18 at 0:35
Try using combiner.
– gudok
Nov 15 '18 at 10:30
Try using combiner.
– gudok
Nov 15 '18 at 10:30
I tried combiner but got the same result. I think the temp files created by mapper job are still in huge size, my understanding is that the combiner can only reduce network traffic when sending data to reducers.
– pan
Nov 16 '18 at 0:35
I tried combiner but got the same result. I think the temp files created by mapper job are still in huge size, my understanding is that the combiner can only reduce network traffic when sending data to reducers.
– pan
Nov 16 '18 at 0:35
add a comment |
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Try using combiner.
– gudok
Nov 15 '18 at 10:30
I tried combiner but got the same result. I think the temp files created by mapper job are still in huge size, my understanding is that the combiner can only reduce network traffic when sending data to reducers.
– pan
Nov 16 '18 at 0:35