MapReduce | 易学教程

Where Mapper output in Hadoop is saved?

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问题 I am interested in efficiently manage the Hadoop shuffling traffic and utilize the network bandwidth effectively. To do this I want to know how much shuffling traffic generated by each Datanodes ? Shuffling traffic is nothing but the output of mappers. So where this mapper output is saved ? How can i get the size of mapper output from each datanodes in a real time ? Appreciate your help. I have created a directory to store this mapper output as below. <property> <name>mapred.local.dir</name>

Running Hadoop MR jobs without Admin privilege on Windows

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问题 I have installed Hadoop 2.3.0 in windows and able to execute MR jobs successfully. But when I trying to execute MR jobs in normal privilege (without admin privilege) means job get fails with following exception. Here I tried with Pig Script sample. 2014-10-15 12:02:32,822 WARN [main] org.apache.hadoop.security.UserGroupInformation: PriviledgedActionException as:kaveen (auth:SIMPLE) cause:java.io.IOException: Split class org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigSplit not

Hadoop Java MapReduce parsing JSON with Jackson issues

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问题 I am using Jackson JSON parser (1.9.5) in Hadoop Java M/R program (0.20.205). Given the JSON example below: {"id":23423423, "name":"abc", "location":{"displayName":"Florida, Rosario","objectType":"place"}, "price":1234.55} Now, let say I just want to parse out id, location.displayName, and price so I created the following Java object and I am omitting unwanted fields. @JsonIgnoreProperties(ignoreUnknown = true) public class Transaction { private long id; private Location location; private

Hadoop to reduce from multiple input formats

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问题 I have two files with different data formats in HDFS. How would a job set up look like, if I needed to reduce across both data files? e.g. imagine the common word count problem, where in one file you have space as the world delimiter and in another file the underscore. In my approach I need different mappers for the various file formats, that than feed into a common reducer. How to do that? Or is there a better solution than mine? 回答1: Check out the MultipleInputs class that solves this exact

how to do distinct and group in mongodb?

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问题 how to do a mysql query SELECT COUNT(DISTINCT ip), COUNT(DISTINCT area) FROM visit_logs GROUP BY t_hour in mongodb without multi mapreduct? 回答1: You have to keep the list of "keys" in your objects, and compute your count as the count of the distinct keys; this can be done in the finalize method in MongoDb's map/reduce. Something like (untested): var mapFn = function() { emit(this.t_hour, { ips: [this.ip], areas: [this.area] ); }; var reduceFn = function(key, values) { var ret = { ips: {},

In Hadoop, where can i change default url ports 50070 and 50030 for namenode and jobtracker webpages

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问题 There must be a way to change the ports 50070 and 50030 so that the following urls display the clustr statuses on the ports i pick NameNode - http://localhost:50070/ JobTracker - http://localhost:50030/ 回答1: Define your choice of ports by setting properties dfs.http.address for Namenode and mapred.job.tracker.http.address for Jobtracker in conf/core-site.xml: <configuration> <property> <name>dfs.http.address</name> <value>50070</value> </property> <property> <name>mapred.job.tracker.http

How does one specify the input file for a runner from Python?

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问题 I am writing an external script to run a mapreduce job via the Python mrjob module on my laptop (not on Amazon Elastic Compute Cloud or any large cluster). I read from the mrjob documentation that I should use MRJob.make_runner() to run a mapreduce job from a separate python script as follows. mr_job = MRYourJob(args=['-r', 'emr']) with mr_job.make_runner() as runner: ... However, how do I specify which input file to use? I want to use a file "datalines.txt" in the same directory as my

MongoDB MapReduce update in place how to

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问题 *Basically I'm trying to order objects by their score over the last hour. I'm trying to generate an hourly votes sum for objects in my database. Votes are embedded into each object. The object schema looks like this: { _id: ObjectId score: int hourly-score: int <- need to update this value so I can order by it recently-voted: boolean votes: { "4e4634821dff6f103c040000": { <- Key is __toString of voter ObjectId "_id": ObjectId("4e4634821dff6f103c040000"), <- Voter ObjectId "a": 1, <- Vote

Where is the classpath set for hadoop

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问题 Where is the classpath for hadoop set? When I run the below command it gives me the classpath. Where is the classpath set? bin/hadoop classpath I'm using hadoop 2.6.0 回答1: As said by almas shaikh it's set in hadoop-config.sh , but you could add more jars to it in hadoop-env.sh Here is a relevant code from hadoop-env.sh which adds additional jars like capacity-scheduler and aws jar's. export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-"/etc/hadoop"} # Extra Java CLASSPATH elements. Automatically insert

Reduce连接(reduce-side joins)

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如果没有一个 map-side join 技术适合我们的数据集，那么就需要在 MapReduce 中使用 shuffle 来排序和连接两个数据集。这称为 reduce-side joins，也叫”重分区连接”。【例】基本的重分区连接(repartition join/reduce-side join) 重分区连接是 reduce 端连接。它利用 MapReduce 的排序-合并机制来分组数据。它只使用一个单独的 MapReduce 任务，并支持 N-way join，这里 N 指的是被连接的数据集的数量。 Map 阶段负责从多个数据集中读取数据，决定用于每条记录的连接值，并将连接值作为输出 key。输出 value 则包含在 reduce 阶段所合并的数据集的数据。 Reduce 阶段，一个 reduce 接收 map 函数传来的每一个 join key 的所有输出值，并将数据分为 N 个分区，这里 N 指的是被连接的数量。在该 reducer 接收到用于该 join value 的所有输入记录并在内存中对他们分区之后，它对所有分区执行一个笛卡尔积(Cartersian product)，并输出每个 join 的结果。下图演示了重分区 join: 要支持这个技术，MapReduce 代码需要满足以下条件: ■它需要支持多个 map 类，每个 map 处理一个不同的输入数据集