I have a .txt
file as follows:
This is xyz
This is my home
This is my PC
This is my room
This is ubuntu PC xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxxxxxxxxxxxxxxxxxxx
(ignoring the blank line after each record)
I have set the block size as 64 bytes. What I am trying to check is, whether there exists a situation when a single record is broken into two blocks or not.
Now logically, since the block size is 64 bytes , after uploading the file to HDFS, it should create 3 blocks of size 64,64,27 bytes respectively, which it does. Also since the size of the first block is 64 bytes, it should contain the following data only :
This is xyz
This is my home
This is my PC
This is my room
Th
Now I want to see if the first block is like this or not, if I browse the HDFS via the browser and download the file, it downloads the entire file not a single block.
So I decided to run a map-reduce job which would only display the record values only.( Setting reducers=0
, and mapper output as context.write(null,record_value)
, also changing the default delimiter to ""
)
Now while running the job the job counters show 3 splits, which is obvious, but after completion when I check the output directory, it shows 3 mapper output files out of which 2 are empty and the first mapper output file has all the content of the file as it is.
Can anyone help me with this? Is there a possibility that the newer versions of hadoop handle incomplete records automatically?
We have already seen how input data is divided into input splits in Hadoop. Further each split is divided into records (key-value pair). Map tasks process each of these records. These input splits and records are logical, they don’t store or contain the actual data.
As you can see the InputSplit class has the length of input split in bytes and a set of storage locations where the actual data is stored. These storage locations help Hadoop framework to spawn map tasks as close to the data as possible in order to take advantage of data locality optimization.
Hadoop assigns a node for a split based on data locality principle. Hadoop will try to execute the mapper on the nodes where the block resides. Because of replication, there are multiple such nodes hosting the same block.
Input Split is basically used to control number of Mapper in MapReduce program. If you have not defined input split size in MapReduce program then default HDFS block split will be considered as input split during the data processing. Example:
Steps followed to reproduce the scenario
1) Created a file sample.txt
with the content with total size ~153B
cat sample.txt
This is xyz
This is my home
This is my PC
This is my room
This is ubuntu PC xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxxxxxxxxxxxxxxxxxxx
2) Added the property to hdfs-site.xml
<property>
<name>dfs.namenode.fs-limits.min-block-size</name>
<value>10</value>
</property>
and loaded into HDFS with block size as 64B
.
hdfs dfs -Ddfs.bytes-per-checksum=16 -Ddfs.blocksize=64 -put sample.txt /
This created three blocks of sizes 64B
, 64B
and 25B
.
Content in Block0
:
This is xyz
This is my home
This is my PC
This is my room
This i
Content in Block1
:
s ubuntu PC xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xx
Content in Block2
:
xx xxxxxxxxxxxxxxxxxxxxx
3) A simple mapper.py
#!/usr/bin/env python
import sys
for line in sys.stdin:
print line
4) Hadoop Streaming with 0
reducers:
yarn jar hadoop-streaming-2.7.1.jar -Dmapreduce.job.reduces=0 -file mapper.py -mapper mapper.py -input /sample.txt -output /splittest
Job ran with 3 input splits invoking 3 mappers and generated 3 output files with one file holding the entire content of sample.txt
and the rest 0B
files.
hdfs dfs -ls /splittest
-rw-r--r-- 3 user supergroup 0 2017-03-22 11:13 /splittest/_SUCCESS
-rw-r--r-- 3 user supergroup 168 2017-03-22 11:13 /splittest/part-00000
-rw-r--r-- 3 user supergroup 0 2017-03-22 11:13 /splittest/part-00001
-rw-r--r-- 3 user supergroup 0 2017-03-22 11:13 /splittest/part-00002
The file sample.txt
is split into 3 splits and these splits are assigned to each mapper as,
mapper1: start=0, length=64B
mapper2: start=64, length=64B
mapper3: start=128, length=25B
This only determines which portion of file has to be read by the mapper, not necessary that it has to be exact. The actual content that is read by a mapper is determined by the FileInputFormat and its boundaries, here TextFileInputFormat
.
This uses LineRecordReader
to read the content from each split and uses \n
as delimiter (line boundary). For a file that isn't compressed, the lines are read by each mapper as explained below.
For the mapper whose start index is 0, the line reading starts from the start of the split. If the split ends with \n
the reading ends at the split boundary else it looks for the first \n
post the length of the split assigned (here 64B
). Such that it does not end up processing a partial line.
For all the other mappers (start index != 0), it checks whether the preceding character from its start index (start - 1
) is \n
, if yes it reads the content from the start of the split else it skips the content that is present between its start index and the first \n
character encountered in that split (as this content is handled by other mapper) and starts to read from the first \n
.
Here, mapper1
(start index is 0) starts with Block0
whose split ends at the middle of a line. Thus, it continues to read the line which consumes the entire Block1
and since Block1
does not have a \n
character, mapper1
continues to read until it finds a \n
which ends with consuming of entire Block2
as well. That is how the entire content of sample.txt
ended up in single mapper output.
mapper2
(start index != 0), one character preceding to its start index is not a \n
, so skips the line and ends up with no content. Empty mapper output. mapper3
has the identical scenario as mapper2
.
sample.txt
like this to see different results
This is xyz
This is my home
This is my PC
This is my room
This is ubuntu PC xxxx xxxx xxxx xxxx
xxxx xxxx xxxx xxxx xxxx xxxx xxxx
xxxxxxxxxxxxxxxxxxxxx
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