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 realize that we can't exactly dictate how many map tasks to use, we can only suggest. But still it doesn't make sense.
2016-01-07 07:19:25,117 INFO org.apache.hadoop.mapred.FileInputFormat (main): Total input paths to process : 1
2016-01-07 07:19:25,165 INFO org.apache.hadoop.mapreduce.JobSubmitter (main): number of splits:40
I have a single .txt file in my input which contains:
x,2,65
t,6,12
y,5,11
n,3,71
.
.
(8 lines)
I would expect 8 map tasks to be created, but instead I get 40 map tasks, from which 32 have nothing coming through stdin and hence don't do anything.
I'm running a separate executable through each map task with each line containing the parameters needed.
How does this all work?
I installed cloudera manager(CDH 5) and create own claster. Everything is good but when I run task that it run slowly(18 min). But the ruby's script is running about 5 seconds.
My task consists of:
#mapper.py
import sys
def do_map(doc):
for word in doc.split():
yield word.lower(), 1
for line in sys.stdin:
for key, value in do_map(line):
print(key + "\t" + str(value))
and
#reducer.py
import sys
def do_reduce(word, values):
return word, sum(values)
prev_key = None
values = []
for line in sys.stdin:
key, value = line.split("\t")
if key != prev_key and prev_key is not None:
result_key, result_value = do_reduce(prev_key, values)
print(result_key + "\t" + str(result_value))
values = []
prev_key = key
values.append(int(value))
if prev_key is not None:
result_key, result_value = do_reduce(prev_key, values)
print(result_key + "\t" + str(result_value))
I run my task this is command:
yarn jar hadoop-streaming.jar -input lenta_articles -output lenta_wordcount -file mapper.py -file reducer.py -mapper "python mapper.py" -reducer "python reducer.py"
log of run command:
15/11/17 10:14:27 WARN streaming.StreamJob: -file option is deprecated, please use generic option -files instead.
packageJobJar: [mapper.py, reducer.py] [/opt/cloudera/parcels/CDH-5.4.8-1.cdh5.4.8.p0.4/jars/hadoop-streaming-2.6.0-cdh5.4.8.jar] /tmp/streamjob8334226755199432389.jar tmpDir=null
15/11/17 10:14:29 INFO client.RMProxy: Connecting to ResourceManager at manager/10.128.181.136:8032
15/11/17 10:14:29 INFO client.RMProxy: Connecting to ResourceManager at manager/10.128.181.136:8032
15/11/17 10:14:31 INFO mapred.FileInputFormat: Total input paths to process : 909
15/11/17 10:14:32 INFO mapreduce.JobSubmitter: number of splits:909
15/11/17 10:14:32 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1447762910705_0010
15/11/17 10:14:32 INFO impl.YarnClientImpl: Submitted application application_1447762910705_0010
15/11/17 10:14:32 INFO mapreduce.Job: The url to track the job: http://manager:8088/proxy/application_1447762910705_0010/
15/11/17 10:14:32 INFO mapreduce.Job: Running job: job_1447762910705_0010
15/11/17 10:14:49 INFO mapreduce.Job: Job job_1447762910705_0010 running in uber mode : false
15/11/17 10:14:49 INFO mapreduce.Job: map 0% reduce 0%
15/11/17 10:16:04 INFO mapreduce.Job: map 1% reduce 0%
size of lenta_wordcount folder 2.5 mb. It consists of 909 files. Аverage file size 3КБ.
Ask questions if there is something you need to learn or perform any command
What am i doing wrong?
Hadoop is not efficient in handling large number of small files but it is efficient in processing small number of large files.
Since you have already using Cloudera, have a look at alternatives to improve performance with large number of small files with Hadoop as quoted in Cloudera article
Main reason for slow processing
Reading through small files normally causes lots of seeks and lots of hopping from datanode to datanode to retrieve each small file, all of which is an inefficient data access pattern.
If you have more number of files, you need more number of Mappers to read & process data. Thousands of Mappers processing small files & passing the output to Reducers over the Network will degrade the performance.
Passing input as sequential files with LZO compressions is one of the best alternatives to handle large number of small files. Have a look at SE Question 1 and Other Alternative
There are some other alternatives (some are not related to phtyon) but you should look at this article
Change the ingestion process/interval
Batch file consolidation
Sequence files
HBase
S3DistCp (If using Amazon EMR)
Using a CombineFileInputFormat
Hive configuration settings
Using Hadoop’s append capabilities
I've a file on hdfs in the path 'test/test.txt' which is 1.3G
output of ls and du commands is:
hadoop fs -du test/test.txt -> 1379081672 test/test.txt
hadoop fs -ls test/test.txt ->
Found 1 items
-rw-r--r-- 3 testuser supergroup 1379081672 2014-05-06 20:27 test/test.txt
I want to run a mapreduce job on this file but when i start the mapreduce job on this file the job fails with the following error:
hadoop jar myjar.jar test.TestMapReduceDriver test output
14/05/29 16:42:03 WARN mapred.JobClient: Use GenericOptionsParser for parsing the
arguments. Applications should implement Tool for the same.
14/05/29 16:42:03 INFO input.FileInputFormat: Total input paths to process : 1
14/05/29 16:42:03 INFO mapred.JobClient: Running job: job_201405271131_9661
14/05/29 16:42:04 INFO mapred.JobClient: map 0% reduce 0%
14/05/29 16:42:17 INFO mapred.JobClient: Task Id : attempt_201405271131_9661_m_000004_0, Status : FAILED
java.io.IOException: Cannot obtain block length for LocatedBlock{BP-428948818-namenode-1392736828725:blk_-6790192659948575136_8493225; getBlockSize()=36904392; corrupt=false; offset=1342177280; locs=[datanode4:50010, datanode3:50010, datanode1:50010]}
at org.apache.hadoop.hdfs.DFSInputStream.readBlockLength(DFSInputStream.java:319)
at org.apache.hadoop.hdfs.DFSInputStream.fetchLocatedBlocksAndGetLastBlockLength(DFSInputStream.java:263)
at org.apache.hadoop.hdfs.DFSInputStream.openInfo(DFSInputStream.java:205)
at org.apache.hadoop.hdfs.DFSInputStream.<init>(DFSInputStream.java:198)
at org.apache.hadoop.hdfs.DFSClient.open(DFSClient.java:1117)
at org.apache.hadoop.hdfs.DistributedFileSystem.open(DistributedFileSystem.java:249)
at org.apache.hadoop.hdfs.DistributedFileSystem.open(DistributedFileSystem.java:82)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:746)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.initialize(LineRecordReader.java:83)
at org.apache.hadoop.mapred.Ma`
i tried the following commands:
hadoop fs -cat test/test.txt gives the following error
cat: Cannot obtain block length for LocatedBlock{BP-428948818-10.17.56.16-1392736828725:blk_-6790192659948575136_8493225; getBlockSize()=36904392; corrupt=false; offset=1342177280; locs=[datanode3:50010, datanode1:50010, datanode4:50010]}
additionally i can't copy the file hadoop fs -cp test/test.txt tmp gives same error:
cp: Cannot obtain block length for LocatedBlock{BP-428948818-10.17.56.16-1392736828725:blk_-6790192659948575136_8493225; getBlockSize()=36904392; corrupt=false; offset=1342177280; locs=[datanode1:50010, datanode3:50010, datanode4:50010]}
output of the hdfs fsck /user/testuser/test/test.txt command:
Connecting to namenode via `http://namenode:50070`
FSCK started by testuser (auth:SIMPLE) from /10.17.56.16 for path
/user/testuser/test/test.txt at Thu May 29 17:00:44 EEST 2014
Status: HEALTHY
Total size: 0 B (Total open files size: 1379081672 B)
Total dirs: 0
Total files: 0 (Files currently being written: 1)
Total blocks (validated): 0 (Total open file blocks (not validated): 21)
Minimally replicated blocks: 0
Over-replicated blocks: 0
Under-replicated blocks: 0
Mis-replicated blocks: 0
Default replication factor: 3
Average block replication: 0.0
Corrupt blocks: 0
Missing replicas: 0
Number of data-nodes: 5
Number of racks: 1
FSCK ended at Thu May 29 17:00:44 EEST 2014 in 0 milliseconds
The filesystem under path /user/testuser/test/test.txt is HEALTHY
by the way i can see the content of the test.txt file from the web browser.
hadoop version is: Hadoop 2.0.0-cdh4.5.0
I got the same issue with you and I fixed it by the following steps.
There are some files that opened by flume but never closed (I am not sure about your reason).
You need to find the name of the opened files by the command:
hdfs fsck /directory/of/locked/files/ -files -openforwrite
You can try to recover files as command:
hdfs debug recoverLease -path <path-of-the-file> -retries 3
Or removing them by the command:
hdfs dfs -rmr <path-of-the-file>
I had the same error, but it was not due to the full disk problem, and I think the inverse, where there were files and blocks referenced by in the namenode that did not exist on any datanodes.
Thus, hdfs dfs -ls shows the files, but any operation on them fails, e.g. hdfs dfs -copyToLocal.
In my case, the hard part was isolating which files were listed but corrupted, as they existed in a tree having thousands of files. Oddly, hdfs fsck /path/to/files/ did not report any problems.
My solution was:
Isolate the location using copyToLocal which resulted in copyToLocal: Cannot obtain block length for LocatedBlock{BP-1918381527-10.74.2.77-1420822494740:blk_1120909039_47667041; getBlockSize()=1231; corrupt=false; offset=0; locs=[10.74.2.168:50010, 10.74.2.166:50010, 10.74.2.164:50010]} for several files
Get a list of the local directories using ls -1 > baddirs.out
get rid of the local files from the first copyToLocal
use for files incat baddirs.out;do echo $files; hdfs dfs -copyToLocal $files This will produce a list of directories checks, and errors where files are found.
get rid of the local files again, and now get lists of files from each affected subdirectory. Use that as input to a file-by-file copyToLocal, at which point you can echo each file as it's copied, then see where the error occurs.
use hdfs dfs -rm <file> for each file.
Confirm you got 'em all be removing all local files again, and using the original copyToLocal on the top level directory where you had problems.
A simple two hour process!
You are having some corrupted files with no blocks on datanode but an entry in namenode. Best to follow this:
https://stackoverflow.com/a/19216037/812906
According to this this may be produced by a full disk problem. I came across the same problem recently with an old file and checking my servers metrics it effectively was a full disk problem during the creation of that file. Most solutions just claim to delete the file and prey for it not happening again.
I am currently running a job I fixed the number of map task to 20 but and getting a higher number. I also set the reduce task to zero but I am still getting a number other than zero. The total time for the MapReduce job to complete is also not display. Can someone tell me what I am doing wrong.
I am using this command
hadoop jar Test_Parallel_for.jar Test_Parallel_for Matrix/test4.txt Result 3 \ -D mapred.map.tasks = 20 \ -D mapred.reduce.tasks =0
Output:
11/07/30 19:48:56 INFO mapred.JobClient: Job complete: job_201107291018_0164
11/07/30 19:48:56 INFO mapred.JobClient: Counters: 18
11/07/30 19:48:56 INFO mapred.JobClient: Job Counters
11/07/30 19:48:56 INFO mapred.JobClient: Launched reduce tasks=13
11/07/30 19:48:56 INFO mapred.JobClient: Rack-local map tasks=12
11/07/30 19:48:56 INFO mapred.JobClient: Launched map tasks=24
11/07/30 19:48:56 INFO mapred.JobClient: Data-local map tasks=12
11/07/30 19:48:56 INFO mapred.JobClient: FileSystemCounters
11/07/30 19:48:56 INFO mapred.JobClient: FILE_BYTES_READ=4020792636
11/07/30 19:48:56 INFO mapred.JobClient: HDFS_BYTES_READ=1556534680
11/07/30 19:48:56 INFO mapred.JobClient: FILE_BYTES_WRITTEN=6026699058
11/07/30 19:48:56 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=1928893942
11/07/30 19:48:56 INFO mapred.JobClient: Map-Reduce Framework
11/07/30 19:48:56 INFO mapred.JobClient: Reduce input groups=40000000
11/07/30 19:48:56 INFO mapred.JobClient: Combine output records=0
11/07/30 19:48:56 INFO mapred.JobClient: Map input records=40000000
11/07/30 19:48:56 INFO mapred.JobClient: Reduce shuffle bytes=1974162269
11/07/30 19:48:56 INFO mapred.JobClient: Reduce output records=40000000
11/07/30 19:48:56 INFO mapred.JobClient: Spilled Records=120000000
11/07/30 19:48:56 INFO mapred.JobClient: Map output bytes=1928893942
11/07/30 19:48:56 INFO mapred.JobClient: Combine input records=0
11/07/30 19:48:56 INFO mapred.JobClient: Map output records=40000000
11/07/30 19:48:56 INFO mapred.JobClient: Reduce input records=40000000
[hcrc1425n30]s0907855:
The number of map tasks for a given job is driven by the number of input splits and not by the mapred.map.tasks parameter. For each input split a map task is spawned. So, over the lifetime of a mapreduce job the number of map tasks is equal to the number of input splits. mapred.map.tasks is just a hint to the InputFormat for the number of maps.
In your example Hadoop has determined there are 24 input splits and will spawn 24 map tasks in total. But, you can control how many map tasks can be executed in parallel by each of the task tracker.
Also, removing a space after -D might solve the problem for reduce.
For more information on the number of map and reduce tasks, please look at the below url
https://cwiki.apache.org/confluence/display/HADOOP2/HowManyMapsAndReduces
As Praveen mentions above, when using the basic FileInputFormat classes is just the number of input splits that constitute the data. The number of reducers is controlled by mapred.reduce.tasks specified in the way you have it: -D mapred.reduce.tasks=10 would specify 10 reducers. Note that the space after -D is required; if you omit the space, the configuration property is passed along to the relevant JVM, not to Hadoop.
Are you specifying 0 because there is no reduce work to do? In that case, if you're having trouble with the run-time parameter, you can also set the value directly in code. Given a JobConf instance job, call
job.setNumReduceTasks(0);
inside, say, your implementation of Tool.run. That should produce output directly from the mappers. If your job actually produces no output whatsoever (because you're using the framework just for side-effects like network calls or image processing, or if the results are entirely accounted for in Counter values), you can disable output by also calling
job.setOutputFormat(NullOutputFormat.class);
It's important to keep in mind that the MapReduce framework in Hadoop allows us only to
suggest the number of Map tasks for a job
which like Praveen pointed out above will correspond to the number of input splits for the task. Unlike it's behavior for the number of reducers (which is directly related to the number of files output by the MapReduce job) where we can
demand that it provide n reducers.
To explain it with a example:
Assume your hadoop input file size is 2 GB and you set block size as 64 MB so 32 Mappers tasks are set to run while each mapper will process 64 MB block to complete the Mapper Job of your Hadoop Job.
==> Number of mappers set to run are completely dependent on 1) File Size and 2) Block Size
Assume you have running hadoop on a cluster size of 4:
Assume you set mapred.map.tasks and mapred.reduce.tasks parameters in your conf file to the nodes as follows:
Node 1: mapred.map.tasks = 4 and mapred.reduce.tasks = 4
Node 2: mapred.map.tasks = 2 and mapred.reduce.tasks = 2
Node 3: mapred.map.tasks = 4 and mapred.reduce.tasks = 4
Node 4: mapred.map.tasks = 1 and mapred.reduce.tasks = 1
Assume you set the above paramters for 4 of your nodes in this cluster. If you notice Node 2 has set only 2 and 2 respectively because the processing resources of the Node 2 might be less e.g(2 Processors, 2 Cores) and Node 4 is even set lower to just 1 and 1 respectively might be due to processing resources on that node is 1 processor, 2 cores so can't run more than 1 mapper and 1 reducer task.
So when you run the job Node 1, Node 2, Node 3, Node 4 are configured to run a max. total of (4+2+4+1)11 mapper tasks simultaneously out of 42 mapper tasks that needs to be completed by the Job. After each Node completes its map tasks it will take the remaining mapper tasks left in 42 mapper tasks.
Now comming to reducers, as you set mapred.reduce.tasks = 0 so we only get mapper output in to 42 files(1 file for each mapper task) and no reducer output.
In the newer version of Hadoop, there are much more granular mapreduce.job.running.map.limit and mapreduce.job.running.reduce.limit which allows you to set the mapper and reducer count irrespective of hdfs file split size. This is helpful if you are under constraint to not take up large resources in the cluster.
JIRA
From your log I understood that you have 12 input files as there are 12 local maps generated. Rack Local maps are spawned for the same file if some of the blocks of that file are in some other data node. How many data nodes you have?
In your example, the -D parts are not picked up:
hadoop jar Test_Parallel_for.jar Test_Parallel_for Matrix/test4.txt Result 3 \ -D mapred.map.tasks = 20 \ -D mapred.reduce.tasks =0
They should come after the classname part like this:
hadoop jar Test_Parallel_for.jar Test_Parallel_for -Dmapred.map.tasks=20 -Dmapred.reduce.tasks=0 Matrix/test4.txt Result 3
A space after -D is allowed though.
Also note that changing the number of mappers is probably a bad idea as other people have mentioned here.
Number of map tasks is directly defined by number of chunks your input is splitted. The size of data chunk (i.e. HDFS block size) is controllable and can be set for an individual file, set of files, directory(-s). So, setting specific number of map tasks in a job is possible but involves setting a corresponding HDFS block size for job's input data. mapred.map.tasks can be used for that too but only if its provided value is greater than number of splits for job's input data.
Controlling number of reducers via mapred.reduce.tasks is correct. However, setting it to zero is a rather special case: the job's output is an concatenation of mappers' outputs (non-sorted). In Matt's answer one can see more ways to set the number of reducers.
One way you can increase the number of mappers is to give your input in the form of split files [you can use linux split command]. Hadoop streaming usually assigns that many mappers as there are input files[if there are a large number of files] if not it will try to split the input into equal sized parts.
Use -D property=value rather than -D property = value (eliminate
extra whitespaces). Thus -D mapred.reduce.tasks=value would work
fine.
Setting number of map tasks doesnt always reflect the value you have
set since it depends on split size and InputFormat used.
Setting the number of reduces will definitely override the number of
reduces set on cluster/client-side configuration.
I agree the number mapp task depends upon the input split but in some of the scenario i could see its little different
case-1 I created a simple mapp task only it creates 2 duplicate out put file (data ia same)
command I gave below
bin/hadoop jar contrib/streaming/hadoop-streaming-1.2.1.jar -D mapred.reduce.tasks=0 -input /home/sample.csv -output /home/sample_csv112.txt -mapper /home/amitav/workpython/readcsv.py
Case-2 So I restrcted the mapp task to 1 the out put came correctly with one output file but one reducer also lunched in the UI screen although I restricted the reducer job. The command is given below.
bin/hadoop jar contrib/streaming/hadoop-streaming-1.2.1.jar -D mapred.map.tasks=1 mapred.reduce.tasks=0 -input /home/sample.csv -output /home/sample_csv115.txt -mapper /home/amitav/workpython/readcsv.py
The first part has already been answered, "just a suggestion"
The second part has also been answered, "remove extra spaces around ="
If both these didnt work, are you sure you have implemented ToolRunner ?
Number of map task depends on File size, If you want n number of Map, divide the file size by n as follows:
conf.set("mapred.max.split.size", "41943040"); // maximum split file size in bytes
conf.set("mapred.min.split.size", "20971520"); // minimum split file size in bytes
Folks from this theory it seems we cannot run map reduce jobs in parallel.
Lets say I configured total 5 mapper jobs to run on particular node.Also I want to use this in such a way that JOB1 can use 3 mappers and JOB2 can use 2 mappers so that job can run in parallel. But above properties are ignored then how can execute jobs in parallel.
From what I understand reading above, it depends on the input files. If Input Files are 100 means - Hadoop will create 100 map tasks.
However, it depends on the Node configuration on How Many can be run at one point of time.
If a node is configured to run 10 map tasks - only 10 map tasks will run in parallel by picking 10 different input files out of the 100 available.
Map tasks will continue to fetch more files as and when it completes processing of a file.