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?
if use hive cli. the log is :
Total MapReduce jobs = 1
Stage-1 is selected by condition resolver.
Launching Job 1 out of 1
but in hive server or beeline. the log is :
INFO : Stage-1 is selected by condition resolver.
INFO : Number of reduce tasks not specified. Estimated from input data size: 1
how can I get the job number ?
I need calculate job progress and print it..
Is there a direct way to address the following error or overall a better way to use Hive to get the join that I need? Output to a stored table isn't a requirement as I can be content with an INSERT OVERWRITE LOCAL DIRECTORY to a csv.
I am trying to perform the following cross join. ipint is a 9GB table, and geoiplite is 270MB.
CREATE TABLE iplatlong_sample AS
SELECT ipintegers.networkinteger, geoiplite.latitude, geoiplite.longitude
FROM geoiplite
CROSS JOIN ipintegers
WHERE ipintegers.networkinteger >= geoiplite.network_start_integer AND ipintegers.networkinteger <= geoiplite.network_last_integer;
I use CROSS JOIN on ipintegers instead of geoiplite because I have read that the rule is for the smaller table to be on the left, larger on the right.
Map and Reduce stages complete to 100% according to HIVE, but then
2015-08-01 04:45:36,947 Stage-1 map = 100%, reduce = 100%, Cumulative
CPU 8767.09 sec
MapReduce Total cumulative CPU time: 0 days 2 hours 26
minutes 7 seconds 90 msec
Ended Job = job_201508010407_0001
Stage-8 is selected by condition resolver.
Execution log at: /tmp/myuser/.log
2015-08-01 04:45:38 Starting to launch local task to process map
join; maximum memory = 12221153280
Execution failed with exit status: 3
Obtaining error information
Task failed!
Task ID: Stage-8
Logs:
/tmp/myuser/hive.log
FAILED: Execution Error, return code 3 from
org.apache.hadoop.hive.ql.exec.mr.MapredLocalTask
MapReduce Jobs
Launched: Job 0: Map: 38 Reduce: 1 Cumulative CPU: 8767.09 sec
HDFS Read: 9438495086 HDFS Write: 8575548486 SUCCESS
My hive config:
SET hive.mapred.local.mem=40960;
SET hive.exec.parallel=true;
SET hive.exec.compress.output=true;
SET hive.exec.compress.intermediate = true;
SET hive.optimize.skewjoin = true;
SET mapred.compress.map.output=true;
SET hive.stats.autogather=false;
I have varied SET hive.auto.convert.join between true and false but with the same result.
Here are the errors in the output log from /tmp/myuser/hive.log
$ tail -12 -f tmp/mysyer/hive.log
2015-08-01 07:30:46,086 ERROR exec.Task (SessionState.java:printError(419)) - Execution failed with exit status: 3
2015-08-01 07:30:46,086 ERROR exec.Task (SessionState.java:printError(419)) - Obtaining error information
2015-08-01 07:30:46,087 ERROR exec.Task (SessionState.java:printError(419)) -
Task failed!
Task ID:
Stage-8
Logs:
2015-08-01 07:30:46,087 ERROR exec.Task (SessionState.java:printError(419)) - /tmp/myuser/hive.log
2015-08-01 07:30:46,087 ERROR mr.MapredLocalTask (MapredLocalTask.java:execute(268)) - Execution failed with exit status: 3
2015-08-01 07:30:46,094 ERROR ql.Driver (SessionState.java:printError(419)) - FAILED: Execution Error, return code 3 from org.apache.hadoop.hive.ql.exec.mr.MapredLocalTask
I am running the hive client on the Master, a Google Cloud Platform instance of type n1-highmem-8 type (8 CPU, 52GB) and workers are n1-highmem-4 (4CPU 26GB), but I suspect after MAP and REDUCE that a local join (as implied) takes place on the Master. Regardless, in bdutils I configured the JAVAOPTS for the worker nodes (n1-highmem-4) to: n1-highmem-4
SOLUTION EDIT: The solution is to organize the data the range data into a range tree.
I don't think it is possible to perform this kind of cross join brute force - just multiply the row numbers, it's a little out of hand. You need some optimizations, which I don't think hive is capable yet.
But is this problem can actually be solved in O(N1+N2) time providing you have your data sorted (which hive can do for you) - you just go through both lists simultaneously, on each step getting an ip integer, seeing if any intervals start on this integer, adding them, removing those that ended, emitting matching tuples, and so on. Pseudocode:
intervals=[]
ipintegers = iterator(ipintegers_sorted_file)
intervals = iterator(intervals_sorted_on_start_file)
for x in ipintegers:
intervals = [i for i in intervals if i.end >= x]
while(intervals.current.start<=x):
intervals.append(intervals.current)
intervals.next()
for i in intervals:
output_match(i, x)
Now, if you have an external script/UDF function that knows how to read the smaller table and gets ip integers as input and spits matching tuples as output, you can use hive and SELECT TRANSFORM to stream the inputs to it.
Or you can probably just run this algorithm on a local machine with two input files, because this is just O(N), and even 9 gb of data is very doable.
I am trying to monitor the progress of tasks within a job in Hadoop 1.2.1 MapReduce. I am able to start up my job with the following command:
RunningJob runningJob = JobClient.submitJob(conf);
I see that I can get the job status of all of the map and reduce tasks like this:
JobStatus jobStatus = runningJob.getJobStatus();
float mapProgress = jobStatus.mapProgress();
float reduceProgress = jobStatus.reduceProgress();
But I want to be able to get the progress of each task running within the job. Is there an API call to do this?
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.