MapReduce

Job via Oozie HDP 2.1 not creating job.splitmetainfo

由 橙三吉。 提交于 2019-12-24 03:20:10
问题 When trying to execute a sqoop job which has my Hadoop program passed as a jar file in -jarFiles parameter, the execution blows off with below error. Any resolution seems to be not available. Other jobs with same Hadoop user is getting executed successfully. org.apache.hadoop.yarn.exceptions.YarnRuntimeException: java.io.FileNotFoundException: File does not exist: hdfs://sandbox.hortonworks.com:8020/user/root/.staging/job_1423050964699_0003/job.splitmetainfo at org.apache.hadoop.mapreduce.v2

Can we cascade multiple MapReduce jobs in Hadoop Streaming (lang: Python)

由 旧城冷巷雨未停 提交于 2019-12-24 02:47:06
问题 I am using Python and have to work on following scenario using Hadoop Streaming: a) Map1->Reduce1->Map2->Reduce2 b) I dont want to store intermediate files c) I dont want to install packages like Cascading, Yelp, Oozie. I have kept them as last option. I already went through the same kind of discussion on SO and elsewhere but could not find an answer wrt Python. Can you please suggest. 回答1: b) I dont want to store intermediate files c) I dont want to install packages like Cascading, Yelp,

How to use .jar in a pig file

由 安稳与你 提交于 2019-12-24 02:38:05
问题 I have two input files smt.txt and smo.txt. The jar file reads the text files and split the data according to some rule which is described in java file. And the pig file takes these data put into output files with doing mapreduce. register 'maprfs:///user/username/fl.jar'; DEFINE FixedLoader fl(); mt = load 'maprfs:///user/username/smt.txt' using FixedLoader('-30','30-33',...........) AS (.........); mo = load 'maprfs:///user/username/smo.txt*' using FixedLoader('-30','30-33',.....) AS (.....

Reduce function on Map Reduce showing incorrect results — why?

由 元气小坏坏 提交于 2019-12-24 02:18:09
问题 I have a data structure that keeps track of people in different cities: //in db.persons { name: "John", city: "Seattle }, { name: "Bill", city: "Portland" } I want to run a map reduce to get a list of how many people are in each city, so the result will look like this: { _id: "Seattle", value: 10 } My map reduce function looks like this: map = function(){ var city = this.city emit(city, 1); }; reduce = function(key, values){ var result = 0; values.forEach(function(value){ result += 1; });

Sqoop creating insert statements containing multiple records

由 旧时模样 提交于 2019-12-24 01:36:05
问题 we are trying to load the data from sqoop to netezza. And we are facing the following issue. java.io.IOException: org.netezza.error.NzSQLException: ERROR: Example Input dataset is as shown below: 1,2,3 1,3,4 sqoop command is as shown below: sqoop export --table <tablename> --export-dir <path> --input-fields-terminated-by '\t' --input-lines-terminated-by '\n' --connect 'jdbc:netezza://<host>/<db>' --driver org.netezza.Driver --username <username> --password <passwrd> The Sqoop is creating an

MapReduce的5个流程之InputFormat

由 佐手、 提交于 2019-12-24 00:19:18
MapReduce的5个流程 [input阶段]获取输入数据进行分片作为map的输入 [map阶段]过程对某种输入格式的一条记录解析成一条或多条记录 [shffle阶段]对中间数据的控制，作为reduce的输入 [reduce阶段]对相同key的数据进行合并 [output阶段]按照格式输出到指定目录 抽象类InputFormat 整个类结构： InputFormat作为一个抽象类，定义了两个功能： public abstract class InputFormat < K , V > { public abstract List < InputSplit > getSplits ( JobContext context ) throws IOException , InterruptedException ; public abstract RecordReader < K , V > createRecordReader ( InputSplit split , TaskAttemptContext context ) throws IOException , InterruptedException ; } getSplits()功能： 对于输出文件做逻辑上的分片工作，getSplits()方法将文件切分成InputSplit，InputSplit的个数对应map(

Impala和Hive的关系（详解）

由 我只是一个虾纸丫 提交于 2019-12-23 21:25:20
Impala和Hive的关系 　　 Impala是基于Hive的大数据实时分析查询引擎 ，直接使用Hive的元数据库Metadata,意味着impala元数据都存储在Hive的metastore中。并且impala兼容Hive的sql解析，实现了Hive的SQL语义的子集，功能还在不断的完善中。 与Hive的关系 　　Impala 与Hive都是构建在Hadoop之上的数据查询工具各有不同的侧重适应面，但从客户端使用来看Impala与Hive有很多的共同之处，如数据表元数 据、ODBC/JDBC驱动、SQL语法、灵活的文件格式、存储资源池等。 Impala与Hive在Hadoop中的关系如下图 所示。 Hive适合于长时间的批处理查询分析 ， 而Impala适合于实时交互式SQL查询 ，Impala给数据分析人员提供了快速实验、验证想法的大数 据分析工具。可以先使用hive进行数据转换处理，之后使用Impala在Hive处理后的结果数据集上进行快速的数据分析。 　　　　　　　　　　　　 Impala相对于Hive所使用的优化技术 1、没有使用 MapReduce进行并行计算，虽然MapReduce是非常好的并行计算框架，但它更多的面向批处理模式，而不是面向交互式的SQL执行。与 MapReduce相比：Impala把整个查询分成一执行计划树，而不是一连串的MapReduce任务

How to divide a big dataset into multiple small files in Hadoop in an efficient way

由 六月ゝ 毕业季﹏ 提交于 2019-12-23 21:10:04
问题 I have a big data set consisting of files with 1M records each and I'd like to divide it into some files with 1000 records each in Hadoop. I'm investigating different scenarios for achieving this goal. One is to make the split size small so that each mapper takes only a few records (~1000 records) and then output them. This requires running many mappers which is not efficient. The other solution is to consider one reducer and send all the records to it and them do the split there. This is

Unable to load OpenNLP sentence model in Hadoop map-reduce job

由 谁都会走 提交于 2019-12-23 18:25:57
问题 I'm trying to get OpenNLP integrated into a map-reduce job on Hadoop, starting with some basic sentence splitting. Within the map function, the following code is run: public AnalysisFile analyze(String content) { InputStream modelIn = null; String[] sentences = null; // references an absolute path to en-sent.bin logger.info("sentenceModelPath: " + sentenceModelPath); try { modelIn = getClass().getResourceAsStream(sentenceModelPath); SentenceModel model = new SentenceModel(modelIn);

How to properly create a Map/Reduce Index for RavenDB in C#

由 白昼怎懂夜的黑 提交于 2019-12-23 16:50:21
问题 I'm working on an app that uses RavenDB on the back end. It's my first time using Raven, and I'm struggling with Map/Reduce. I have been reading the doc's, but unfortunately I'm not getting anywhere in the process. Basically I have thousands of documents like this. { ..... "Severity": { "Code": 6, "Data": "Info" }, "Facility": { "Code": 16, "Data": "Local Use 0 (local0)" }, ..... } And out of it, I need to make a single query with output that looks like this. {"Severity": [ {"Emergency":0}, {
订阅 MapReduce