MapReduce

One reducer in Custom Partitioner makes mapreduce jobs slower

由 好久不见. 提交于 2019-12-08 12:41:59
问题 Hi i have an application that reads records from HBase and writes into text files. Application is working as expected but when tested this for huge data it is taking 1.20 hour to complete the job . Here is the details of my application Size the data in the HBase is 400 GB approx 2 billions records . I have created 400 regions in the HBase tabl so 400 mappers . I have used custom Partitioner that puts records into 194 text files. I have lzo compression for map output and gzip for final output.

how to output first row as column qualifier names

由 眉间皱痕 提交于 2019-12-08 12:28:51
问题 I am able to process two nodes from an xml. And I am getting the output below: bin/hadoop fs -text /user/root/t-output1/part-r-00000 name:ST17925 currentgrade 1.02 name:ST17926 currentgrade 3.0 name:ST17927 currentgrade 3.0 but I need to have an output like: studentid curentgrade ST17925 1.02 ST17926 3.00 ST17927 3.00 How can I achieve this? My complete source code: https://github.com/studhadoop/xml/blob/master/XmlParser11.java EDIT: Solution protected void setup(Context context) throws

RM job was stuck when running with oozie

由 ♀尐吖头ヾ 提交于 2019-12-08 12:24:56
问题 I'm running a mapreduce wordcount job task on oozie. 2 jobs were submitted to the yarn, and then the monitoring tasks running upto 99% were stuck. Wordcount job has been 0%. When I kill off the monitor job, wordcount job runs smoothly. I use a cluster of 3 virtual machines, configuration is as follows: Profile per VM: cores=2 memory=2048MB reserved=0GB usableMem=0GB disks=1 Num Container=3 Container Ram=640MB Used Ram=1GB Unused Ram=0GB yarn.scheduler.minimum-allocation-mb=640 yarn.scheduler

Running a R script using hadoop streaming Job Failing : PipeMapRed.waitOutputThreads(): subprocess failed with code 1

由 社会主义新天地 提交于 2019-12-08 12:18:04
问题 I have a R script which works perfectly fine in R Colsole ,but when I am running in Hadoop streaming it is failing with the below error in Map phase .Find the Task attempts log The Hadoop Streaming Command I have : /home/Bibhu/hadoop-0.20.2/bin/hadoop jar \ /home/Bibhu/hadoop-0.20.2/contrib/streaming/*.jar \ -input hdfs://localhost:54310/user/Bibhu/BookTE1.csv \ -output outsid -mapper `pwd`/code1.sh stderr logs Loading required package: class Error in read.table(file = file, header = header,

Mapreduce in mongodb

由 ╄→尐↘猪︶ㄣ 提交于 2019-12-08 10:44:21
问题 I am wondering whether map reduce job in mongodb has anything to do with Hadoop. Mapreduce in Mongodb is a standalone and no dependency on any hadoop installation? If what I am guessing is correct, then the map reduce syntax is the same between the two or it just means that mongodb is supporting its own map reduce (with different syntax)? 回答1: Map Reduce is not fastest interface in mongodb to do ad hoc queries it is more designed for background jobs, creating reports etc. I wrote some time

Is CDH4 meant mainly for YARN?

由 三世轮回 提交于 2019-12-08 09:45:49
问题 I have several questions or rather confusions regarding CDH4. I am posting here since I did not get any concrete information regarding my questions. Is CDH4 meant to promote YARN? I tried setting up MapReduce1 using CDH4.3.0 using tarball. I finally did but it is round about and painful. Whereas YARN set up is strait forward. Is anyone using YARN in production at all? Apache clearly says that YARN is still in alpha version and not meant for production. In such cases why is Cloudera making

Cannot deserialize RDD with different number of items in pair

由 元气小坏坏 提交于 2019-12-08 09:29:00
问题 I have two RDD's which have key-value pairs. I want to join them by key (and according to the key, get cartesian product of all values), which I assume can be done with zip() function of pyspark. However, when I apply this, elemPairs = elems1.zip(elems2).reduceByKey(add) It gives me the error: Cannot deserialize RDD with different number of items in pair: (40, 10) And here are the 2 RDD's which I try to zip: elems1 => [((0, 0), ('A', 0, 90)), ((0, 1), ('A', 0, 90)), ((0, 2), ('A', 0, 90)), (

Loading more records than actual in HIve

由 痞子三分冷 提交于 2019-12-08 08:57:50
问题 While inserting from Hive table to HIve table, It is loading more records that actual records. Can anyone help in this weird behaviour of Hive ? My query would be looking like this: insert overwrite table_a select col1,col2,col3,... from table_b; My table_b consists of 6405465 records. After inserting from table_b to table_a, i found total records in table_a are 6406565. Can any one please help here ? 回答1: If hive.compute.query.using.stats=true; then optimizer is using statistics for query

couchdb map/reduce view: counting only the most recent items

由 ≯℡__Kan透↙ 提交于 2019-12-08 08:54:32
I have the following documents. Time stamped positions of keywords. { _id: willem-aap-1234, keyword:aap, position: 10, profile: { name: willem }, created_at: 1234 }, { _id: willem-aap-2345, keyword:aap, profile: { name: willem }, created_at: 2345 }, { _id: oliver-aap-1235, keyword:aap, profile: { name: oliver }, created_at: 1235 }, { _id: oliver-aap-2346, keyword:aap, profile: { name: oliver }, created_at: 2346 } Finding the most recent keywords per profile.name can be done by: map: function(doc) { if(doc.profile) emit( [doc.profile.name, doc.keyword, doc.created_at], { keyword : doc.keyword,

Why is CouchDB's reduce_limit enabled by default? (Is it better to approximate SQL JOINS in MapReduce views or List views?)

由 こ雲淡風輕ζ 提交于 2019-12-08 08:34:48
问题 I'm using CouchDB, and I want to make better use of MapReduce when querying data. My exact use case is the following: I have many surveys. Each survey has a meterNumber, meterReading, and meterReadingDate, for example: { meterNumber: 1, meterReading: 2050, meterReadingDate: 1480000000000 } I then use a Map function do produce readings by meterNumber. There are many keys that are repeated (reading the same meter on different dates). i.e. [ [meterNumber, {reading: xxx, readingDate: xxx}],
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