mrjob

1. Install mrjob Pip install mrjob For pip installation, see the previous article. 2. Code Testing After mrjob is installed, you can use it directly. If hadoop has been configured,No additional configuration required(The environment variable HADOOP_HOME must be configured.) mrjob-based programs can run directly on the

The most recent thing to do is write a mapreduce program with Mrjob and read the data from MONGO. My approach is easy and understood, because Mrjob can support Sys.stdin reading, so I consider using a Python program to read the data in MONGO, and then let the Mrjob script accept input, processing, and output.Specific ways:readinmongodb.py:#coding: UTF-8 "Created

1.1. Foreword Here we use the Python m/r framework mrjob to analyze.1.2. M/R Steps Mapper: The form of parsing the row data into Key=hh value=1Shuffle: The result of passing the Shuffle will generate a value iterator sorted with key valuesResults such as: 09 [1, 1, 1 ... 1, 1]Reduce: We're here to figure out 09 hours of traffic.Output such as: sum ([1, 1, 1 ...) 1, 1])1.3. Code Cat mr_pv_hour.py#-*-Coding:utf-8-*-From Mrjob.job import MrjobFrom Ng_

Recently, I joined Cloudera, and before that, I had been working on computational biology/genomics for almost 10 years. My analytical work is mainly done using the Python language and its great scientific stack of calculations. But I'm annoyed that most of the Apache Hadoop ecosystems are implemented in Java and are prepared for Java. So my top priority is to look for some Hadoop frameworks that Python can use. In this article, I will write down my personal views on some of the irrelevant scien

Hadoop I recently joined Cloudera, and before that, I have been working on computational biology/genomics for almost 10 years. My analytical work is mainly done using the Python language and its great scientific computing stack. But most of the Apache Hadoop ecosystem is implemented in Java and is prepared for Java, which makes me very annoyed. So, my first priority became the search for some of the Hadoop frameworks that Python can use. In this article, I will write down some of my personal v

makes me very annoyed. Therefore, my top priority is to find some Hadoop frameworks that can be used by Python. In this article, I will write down some of my personal views on these frameworks that are irrelevant to science. These frameworks include: Hadoop stream Mrjob Dumbo Hadoopy Pydoop Others In the end, in my opinion, Hadoop's data stream (streaming) is the fastest and most transparent option and most suitable for text processing.

containers were by the GPU blocked. The workaround is to kill the failed instance and let EMR to bring a new node and restore HDFS cluster. There were also problems resulting in failed jobs this HADOOP/EMR not could. Those included exceeding memory configuration limits, running out of disk spaces, S3 connectivity issues, bugs in the code And so on. This is why we needed ability to resume long running the jobs that failed. Our approach is to Writephoto_idinto Hadoop "s output for successfully

supported starting from hive0.ation. The from clause is allowed to join tables separated by commas. The join keyword is omitted, as shown below: SELECT *FROM table1t1, table2 t2, table3 t3WHERE t1.id = t2.id AND t2.id = t3.id AND t1.zipcode = '20140901 '; Version 0.ences +: Unqualified columnreferences Reference of unspecified fields is supported starting from Hive0.13.0, as follows: Create table a (k1 string, v1 string );Create table B (k2 string, v2 string ); SELECT k1, v1, k2, v2FROM a JOIN

Using Python to write MapReduce job mrjob allows you to use Python 2.5 + to write MapReduce jobs and run them on multiple different platforms. you can: Write multi-step MapReduce jobs using pure Python Test on local machine Run on Hadoop cluster Use Amazon Elastic MapReduce (EMR) to run on the cloud The installation method of pip is very simple. you do not need to configure it. run pip install mrjob

Mrjob allows you to write a MapReduce job with Python 2.5+ and run it on several different platforms, you can: Write a multi-step MapReduce job using pure Python Test on this machine Running on a Hadoop cluster Run on the cloud with Amazon Elastic MapReduce (EMR) Pip installation method is very simple, no configuration, direct operation: Pip install Mrjob code example: From mrjob.job import Mrjobcla

Code:# pv_day.py#!/usr/bin/env python#Coding=utf-8 fromMrjob.jobImportMrjob fromNginx_accesslog_parserImportNginxlineparserclassPvday (mrjob): Nginx_line_parser=Nginxlineparser ()defMapper (self, _, line): Self.nginx_line_parser.parse [Line] Day, _=str (self.nginx_line_parser.time_local). Split ()yieldDay, 1#every day of defreducer (self, Key, values):yieldkey, sum (values)defMain (): Pvday.run ()if __name__=='__main__': Main ()Code Explanation:Def

hive.optimize.skewjoin=true;--If it is a join process, the skew should be setting to truehive.groupby.skewindata=true: Load balanced when data is tilted, the selected item is set to True, and the resulting query plan will have two mrjob. In the first mrjob,The output set of the map is randomly distributed to reduce, with each reduce doing a partial aggregation operation and outputting the result, so the re

UserAgent:Code (does not contain spiders):#Cat top_10_useragent.py#!/usr/bin/env python#Coding=utf-8 fromMrjob.jobImportMrjob fromMrjob.stepImportMrstep fromNginx_accesslog_parserImportNginxlineparserImportHEAPQclassuseragent (mrjob): Nginx_line_parser=Nginxlineparser ()defMapper (self, _, line): Self.nginx_line_parser.parse (line) Field_item=self.nginx_line_parser.http_user_agentifField_item is notNone:yieldField_item, 1defreducer_sum (self, Key, va

, line in enumerate (f):Self.ng_line_parser.parse (line)Yield Self.ng_line_parser.to_dict ()def load_data (self, Path):"" "" "to load data generation Dataframe" "by the file path toSELF.DF = PD. Dataframe (Self._log_line_iter (path))def pv_day (self):"" Calculates PV for each day ""Group_by_cols = [' Access_time '] # need to group columns, only calculate and display the column# below we are grouped by Yyyy-mm-dd form, so we need to define the grouping policy:# Group Policy is: self.df[' access_t

option in the Model page.Note: Click on the End Date input box to select an incremental build of the cube's ending dates and submit the request.4. Click on the Monitor pageAfter the request is successfully submitted, you will see a new job created. Click the Job Details button to see the details displayed on the right. As shown below:Description: The job details provide each step of its record for tracking a job. You can dock the cursor over a step status icon to view the basic status and infor

Rjobconfig.default_speculativecap_running_tasks); this.proportiontotaltasksspeculatable = conf.getdouble (Mrjobconfig.speculativecap_total_tasks, MRJob Config.default_specUlativecap_total_tasks); This.minimumallowedspeculativetasks = Conf.getint (Mrjobconfig.speculative_minimum_allowed_tasks, MRJ Obconfig.default_speculative_minimum_allowed_tasks); }mapreduce.map.speculative: If True, the map task can presumably execute, that is, a map task can s

wait until the end of the map to start, not efficient use of network bandwidth2, typically a SQL will be parsed into multiple Mr Job,hadoop each job output is directly written HDFs, poor performance3, every job to start a task, spend a lot of time, can't do real-time4, the SQL functions performed by map,shuffle and reduce are different when SQL is converted to a mapreduce job. Then there is the need for map->mapreduce or mapreduce->reduce. This reduces the number of write HDFs, which can improv

= T_time_sk)Joindate_dim on (Ss_sold_date_sk = D_date_sk)where T_hour = 8 andd_year = 2002;Reading a small table into memory, if fact is read only once, rather than 2 times, can greatly reduce the execution time.Current and future optimizations current and future direction of tuning1) MERGEM*-MR patterns to a single MR. ----To turn multiple map-only job+mrjob patterns into a single Mr2) MERGEMJ->MJ into a single MJ when possible. -----as much as poss

job: To pass the job to Ooziec) define an abstract class Basejob, which defines two methods. These two methods are mainly used to do some preparatory work, that is, when you pass the job to Oozie using quartz, you need to find the directory where the job is stored in HDFs and copy it to the execution directory.D) Finally, there are two specific implementation classes, Mrjob and Sparkjob, which represent the mapreduce job and the spark job, respective