I need some help in my map-reduce code.
The code run's perfectly in eclipse and in standalone mode, but when i package the code and try running it locally on pseudo distributed mode, the output is not as i expect.
Map input records = 11
Map input records = 11
Reduce input records = 11
Reduce output records = 0
These are the values i get.
where as when i run the same code in eclipse or in standalone mode with same config & input file
Map input records = 11
Map output records = 11
Reduce input records = 11
Reduce output records = 4
Can any one tell me whats wrong..??
i tried both the ways of building .jar file for eclipse -> export -> runable jar and form terminal as well(javac -classpath hadoop-core-1.0.4 -d classes mapredcode.java && jar -cvf mapredcode.jar -C classes/ .)
and how do i debug this..
Are you using a combiner() method?
And if yes. then is the o/p of combiner the same as that of the mapper?
Because in Hadoop, Combiner is run at the disposal of Hadoop itself and may not be running in the pseudo-disrtibuted mode in your case.
The combiner in itself is nothing but a reducer that is used to lower the network traffic.
And the code should be such that even if a Combiner is not running, the reducer should get the expected format from the mapper.
Hope it helps.
Related
I have the multiple text files.
The total size of them exceeds the largest disk size available to me (~1.5TB)
A spark program reads a single input text file from HDFS. So I need to combine those files into one. (I cannot re-write the program code. I am given only the *.jar file for execution)
Does HDFS have such a capability? How can I achieve this?
What I understood from your question is you want to Concatenate multiple files into one. Here is a solution which might not be the most efficient way of doing it but it works. suppose you have two files: file1 and file2 and you want to get a combined file as ConcatenatedFile
.Here is the script for that.
hadoop fs -cat /hadoop/path/to/file/file1.txt /hadoop/path/to/file/file2.txt | hadoop fs -put - /hadoop/path/to/file/Concatenate_file_Folder/ConcatenateFile.txt
Hope this helps.
HDFS by itself does not provide such capabilities. All out-of-the-box features (like hdfs dfs -text * with pipes or FileUtil's copy methods) use your client server to transfer all data.
In my experience we always used our own written MapReduce jobs to merge many small files in HDFS in distributed way.
So you have two solutions:
Write your own simple MapReduce/Spark job to combine text files with
your format.
Find already implemented solution for such kind of
purposes.
About solution #2: there is the simple project FileCrush for combining text or sequence files in HDFS. It might be suitable for you, check it.
Example of usage:
hadoop jar filecrush-2.0-SNAPSHOT.jar crush.Crush -Ddfs.block.size=134217728 \
--input-format=text \
--output-format=text \
--compress=none \
/input/dir /output/dir 20161228161647
I had a problem to run it without these options (especially -Ddfs.block.size and output file date prefix 20161228161647) so make sure you run it properly.
You can do a pig job:
A = LOAD '/path/to/inputFiles' as (SCHEMA);
STORE A into '/path/to/outputFile';
Doing a hdfs cat and then putting it back to hdfs means, all this data is processed in the client node and will degradate your network
I deployed a 5 node hadoop MR cluster in Azure. I am using a bash script to perform chaining. I am using Hadoop streaming API, as my implementation is in Python.
My input data is always in one file but the size of the file ranges from 1 mb to 2 gb.
I want to create multiple mappers to handle this. I tried running using this command:
yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-streaming.jar -D mapred.max.split.size=5000000 -files map2.py,red2.py -mapper map2.py -reducer red2.py -input wasb:///example/${ipfileA} -output wasb:///example/${opfile}
Here I have set my maximum split size to be 5mb.
I also tried to set my maximum block size to 5 mb.
However, when I run this, the number of input splits is always 2
mapreduce.JobSubmitter: number of splits:2
And number of map tasks launched are also always 2.
I want the number of map tasks to be dynamically set based on the size of the data. What should I do?
I am a newbie to Hadoop and Hive. I am using Hive integration with Hadoop to execute the queries. When I submit any query, following log messages appear on console:
Hive history
file=/tmp/root/hive_job_log_root_28058#hadoop2_201203062232_1076893031.txt Total MapReduce jobs = 1 Launching Job 1 out of 1 Number of reduce
tasks determined at compile time: 1 In order to change the average
load for a reducer (in bytes): set
hive.exec.reducers.bytes.per.reducer= In order to limit the
maximum number of reducers: set hive.exec.reducers.max= In
order to set a constant number of reducers: set
mapred.reduce.tasks= Starting Job = job_201203062223_0004,
Tracking URL =
http://:50030/jobdetails.jsp?jobid=job_201203062223_0004 Kill
Command = //opt/hadoop_installation/hadoop-0.20.2/bin/../bin/hadoop
job -kill job_201203062223_0004 Hadoop job information for Stage-1:
number of mappers: 1; number of reducers: 1 2012-03-06 22:32:26,707
Stage-1 map = 0%, reduce = 0% 2012-03-06 22:32:29,716 Stage-1 map =
100%, reduce = 0% 2012-03-06 22:32:38,748 Stage-1 map = 100%, reduce
= 100% Ended Job = job_201203062223_0004 MapReduce Jobs Launched: Job 0: Map: 1 Reduce: 1 HDFS Read: 8107686 HDFS Write: 4 SUCCESS Total
MapReduce CPU Time Spent: 0 msec OK
The text mentioned in bold starts a hadoop job (that's what I believe). It takes long time to start the job. Once this line gets executed, the map reduce operations execute swiftly. Following are my questions:
Is there any way to make the launch of hadoop job faster. Is it possible to skip this phase?
Where does the value of 'Kill command' come from (in the bold text)?
Please let me know if any inputs are required.
1) Starting Job = job_201203062223_0004, Tracking URL = http: :50030/jobdetails.jsp?jobid=job_201203062223_0004
ANS: your HQL query > translated to hadoop job > hadoop will do some background work (like planning resources,data locality,stages needed to process query,launch configs,job,taskids generation etc) > launch mappers > sort && shuffle > reduce (aggregation) > result to hdfs .
The above flow is part of hadoop job life cycle, so no skipping of any..
http://namenode:port/jobtracker.jsp --- you can see ur job status with job-id :job_201203062223_0004, (Monitering)
2) Kill Command = HADOOP_HOME/bin/hadoop job -kill job_201203062223_0004
Ans : before launching your mappers, you will be showed with these lines because, hadoop works on bigdata, which may take much or less time depends on your dataset size. so at any point of time if you want to kill the job, its a help line . For any hadoop-job this line will be shown, it won't take much time to show an info line like this.
some addons with respect to your comments :
Hive is not meant for low Latency jobs , i mean immediate in time results not possible.
(plz check the hive -purposes in apache.hive)
launching overhead(refer q1s - hadoop will do some background work) is there in Hive, it cant be avoided.
Even for datasets of small size, these launching over head is there in hadoop.
PS : if you are really expecting in time quick results ( plz refer shark )
first,Hive is the tool which replace your mr work by HQL.In the background,it has lost of predefined funcitions,mr programes.Run a HQL,HADOOP Cluster will do lost of things,find the data blocks,allocating taskļ¼and so on.
Second,you can kill a job by the hadoop shell command.
If you job id is AAAAA.
you can execute below command to kill it
$HADOOP_HOME/bin/hadoop job -kill AAAAA
Launch of hadoop job can get delayed due to unavailability of resources. If you use yarn you can see that the jobs are in accepted state but not yet running. This means there is some other ongoing job that has consumed all your executors and the new query is waiting to run.
You can kill the older job by using hadoop job -kill <job_id> command or wait for it to finish.
Does anyone know of a tool that can "crunch" the output files of Apache Hadoop into fewer files or one file. Currently I am downloading all the files to a local machine and the concatenate them in one file. So does anyone know of an API or a tool that does the same.
Thanks in advance.
Limiting the number of output files means you want to limit the number of reducers. You could do that with the help of mapred.reduce.tasks property from the Hive shell. Example :
hive> set mapred.reduce.tasks = 5;
But it might affect the performance of your query. Alternatively, you could use getmerge command from the HDFS shell once you are done with your query. This command takes a source directory and a destination file as input and concatenates files in src into the destination local file.
Usage :
bin/hadoop fs -getmerge <src> <localdst>
HTH
See https://community.cloudera.com/t5/Support-Questions/Hive-Multiple-Small-Files/td-p/204038
set hive.merge.mapfiles=true; -- Merge small files at the end of a map-only job.
set hive.merge.mapredfiles=true; -- Merge small files at the end of a map-reduce job.
set hive.merge.size.per.task=???; -- Size (bytes) of merged files at the end of the job.
set hive.merge.smallfiles.avgsize=??? -- File size (bytes) threshold
-- When the average output file size of a job is less than this number,
-- Hive will start an additional map-reduce job to merge the output files
-- into bigger files. This is only done for map-only jobs if hive.merge.mapfiles
-- is true, and for map-reduce jobs if hive.merge.mapredfiles is true.
I need fastest access to a single file, several copies of which are stored in many systems using Hadoop. I also need to finding the ping time for each file in a sorted manner.
How should I approach learning hadoop to accomplish this task?
Please help fast.I have very less time.
If you need faster access to a file just increase the replication factor to that file using setrep command. This might not increase the file throughput proportionally, because of your current hardware limitations.
The ls command is not giving the access time for the directories and the files, it's showing the modification time only. Use the Offline Image Viewer to dump the contents of hdfs fsimage files to human-readable formats. Below is the command using the Indented option.
bin/hdfs oiv -i fsimagedemo -p Indented -o fsimage.txt
A sample o/p from the fsimage.txt, look for the ACCESS_TIME column.
INODE
INODE_PATH = /user/praveensripati/input/sample.txt
REPLICATION = 1
MODIFICATION_TIME = 2011-10-03 12:53
ACCESS_TIME = 2011-10-03 16:26
BLOCK_SIZE = 67108864
BLOCKS [NUM_BLOCKS = 1]
BLOCK
BLOCK_ID = -5226219854944388285
NUM_BYTES = 529
GENERATION_STAMP = 1005
NS_QUOTA = -1
DS_QUOTA = -1
PERMISSIONS
USER_NAME = praveensripati
GROUP_NAME = supergroup
PERMISSION_STRING = rw-r--r--
To get the ping time in a sorted manner, you need to write a shell script or some other program to extract the INODE_PATH and ACCESS_TIME for each of the INODE section and then sort them based on the ACCESS_TIME. You can also use Pig as shown here.
How should I approach learning hadoop to accomplish this task? Please help fast.I have very less time.
If you want to learn Hadoop in a day or two it's not possible. Here are some videos and articles to start with.