MapReduce

问题 If the input files in XML format, I shouldn't be using TextInputFormat because TextInputFormat assumes each record is in each line of the input file and the Mapper class is called for each line to get a Key Value pair for that record/line. So I think we need a custom input format to scan the XML datasets. Being new to Hadoop mapreduce, is there any article/link/video that shows the steps to build a custom input format? thanks nath 回答1: Problem Working on a single XML file in parallel in

问题 I recently Setup 4 node Cassandra cluster for learning with one column family which hold time series data as. Key -> {column name: timeUUID, column value: csv log line, ttl: 1year}, I use Netflix Astyanax java client to load about 1 million log lines. I also configured Hadoop to run map-reduce jobs with 1 namenode and 4 datanode's to run some analytics on Cassandra data. All the available examples on internet uses column name as SlicePredicate for Hadoop Job Configuration, where as I have

问题 In netbeans with maven i have added third party dependency of org.apache.lucene lucene-core 4.2.0 because newer core versions do not contain OpenBitSet class. Here is the pom: <?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>org.apache.hadoop</groupId>

问题 This is a follow-up question from sqoop export local csv to MySQL error on mapreduce I was able to run the sqoop job and got the data into MySQL from local .csv file using below command: $ sqoop export -fs local -jt local -D 'mapreduce.application.framework.path=/usr/hdp/2.5.0.0-1245/hadoop/mapreduce.tar.gz' --connect jdbc:mysql://172.52.21.64:3306/cf_ae07c762_41a9_4b46_af6c_a29ecb050204 --username username --password password --table test3 --export-dir file:///home/username/folder/test3.csv

问题 I'm developing some code on hadoop with mapreduce that uses two mappers and two reducers. I've been told to use SequenceFileInputFormat and SequenceFileOutputFormat to make the output of the first reducer and the input of the second mapper to work together. The problem is that i'm recibing an error and after googleing a lot i don't know why. The error: java.lang.Exception: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io. IntWritable , received org.apache

问题 I'm testing a simple mapreduce application, but I'm getting a little stuck trying to understand what happen when I iterate over input values of a reduce call. This is the piece of code which behaves strangely.. public void reduce(Text key, Iterable<E> values, Context context) throws IOException, InterruptedException{ Iterator<E> iterator = values.iterator(); E first = (E)statesIter.next(); while(statesIter.hasNext()){ E state = statesIter.next(); System.out.println(first.toString()); // some

问题 I am a bit confused about How exactly the Mapreduce works . I have read some articles but didn't get the proper answer. Scenario: I stored a file of size 1 TB on top of HDFS (Let's say it is stored at a location /user/input/ ). Replication is 3 and the block size 128 MB. Now, I want to analyze this 1TB file using mapreduce. Since the block size is 128 MB, I will have 8192 blocks in total.Considering I have 100 machines in the cluster then Will 8192 map tasks will spawned on all the 100 nodes,

问题 I am getting JAVA Heap space error in my reducer phase .I have used 41 reducer in my application and also Custom Partitioner class . Below is my reducer code that throws below error . 17/02/12 05:26:45 INFO mapreduce.Job: map 98% reduce 0% 17/02/12 05:28:02 INFO mapreduce.Job: map 100% reduce 0% 17/02/12 05:28:09 INFO mapreduce.Job: map 100% reduce 17% 17/02/12 05:28:10 INFO mapreduce.Job: map 100% reduce 39% 17/02/12 05:28:11 INFO mapreduce.Job: map 100% reduce 46% 17/02/12 05:28:12 INFO

问题 I am implementing a left join functionality using map reduce. Left side is having around 600 million records and right side is having around 23 million records. In mapper I am making the keys using the columns used in left join condition and passing the key-value output from mapper to reducer. I am getting performance issue because of few mapper keys for which number of values in both the tables are high (eg. 456789 and 78960 respectively). Even though other reducers finish their job, these

问题 In Hadoop MapReduce no reducer starts before all mappers are finished. Can someone please explain me at which part/class/codeline is this logic implemented? I am talking about Hadoop MapReduce version 1 (NOT Yarn). I have searched the map reduce framework but there are so many classes and i don't understand much the method calls and their ordering. In other words i need (first for test purposes) to let the reducers start reducing even if there are still working mappers. I know that this way i