how to process unstructured data in hadoop

process that represents the sending-receiving of messages. We can see that the original Map-reduce architecture is simple and straightforward, in the first few years, also received a number of successful cases, access to the industry wide support and affirmation, but as the size of the distributed system cluster and its workload growth, the original framework of the problem gradually surfaced, the main issues focused on the following: 1 Jobtracker

We know that hadoop will use inputformat to pre-process the data before processing the data to the map: Split the input data and generate a group of splits. One split is distributed to a mapper for processing. For each split, create a recordreader to read the

Hadoop shuffle stage Process Analysis mapreduce LongTeng 9 months ago (12-23) 399 browse 0 comments At the macro level, each hadoop job goes through two phases: MAP Phase and reduce phase. For MAP Phase, there are four sub-stages: read data from disk-Execute map function-combine result-to write the result to the local

DATA_FILE_NAMEBy observing its folder structure, we can see that mapfile consists of two parts, each of which is data and index.Index, which is a data-indexed file, mainly records the key value of each record and the offset of the record in the file.When Mapfile is interviewed, the index file is loaded into memory, and the index mapping relationship can quickly navigate to the file location where the recor

List of this document [-click here to close] First, the source Second, feedback 2.1 Overview 2.2 Optimization Summary 2.3 Configuration objects for Hadoop 2.4 Compression of intermediate results 2.5 serialization and deserialization of records becomes the most expensive operation in a Hadoop job! 2.6 Serialization of records is CPU sensitive, in contrast, I/O is nothing!

of two parts, data and index respectively.Index, which is a data-indexed file, primarily records the key value of each record and the position at which the record is offset in the file.When mapfile is accessed, the index file is loaded into memory, and the index mapping relationship quickly navigates to the location of the file where the specified record is located.Therefore, the retrieval efficiency of ma

2014-12-12 14:30two-way multifunctional hall of Fit building, Tsinghua Universitythe whole lecture lasted about one hours, about two and a half hours before Doug cutting a total of about 7 ppt, after half an hour of interaction. Doug Cutting a total of about 7 Zhang Ppt,ppt there is no content, each PPT only a title, the text is a picture, the content is mainly about their own open source business, Lucene, Hadoop and so on. PPTOne: Means for Change:h

This section is not much of a talk about what Hadoop is, or the basics of Hadoop because it has a lot of detailed information on the Web, and here's what to say about HDFs. Perhaps everyone knows that HDFs is the underlying Hadoop storage module dedicated to storing data, so how does HDFs work when uploading files? We

. But I can be sure that from this diagram you will not be able to understand the process of shuffle, because it is quite different from the facts, the details are also disordered. I'll describe the facts of shuffle in the following, so you just need to know the approximate range of shuffle-how to effectively transfer the output of the map task to the reduce side. It can also be understood that shuffle describes the

first, the sourceStreaming Hadoop performance optimization at scale, lessons learned at Twitter(Data planform @Twitter)Second, feedback2.1 OverviewThis paper introduces the core Data library team of Twitter, the performance analysis method used when using Hadoop to process o

Data management and fault tolerance in HDFs1. Placement of data blocksEach data block 3 copies, just like above database A, this is because the data in the transmission process of any node is likely to fail (no way, cheap machine is like this), in order to ensure that the

Hadoop, most map tasks and reduce Task execution is on a different node, of course, in many cases, reduce needs to cross the node to pull the map task results on other nodes, if the cluster is running a lot of jobs, then the normal execution of the task of the network resources within the cluster is very serious. This network consumption is normal, we cannot limit, can do is to maximize the reduction of unnecessary consumption. There is also a signif

third, the use of Oozie periodic automatic execution of ETL1. Oozie Introduction(1) What is Oozie?Oozie is a management Hadoop job, scalable, extensible, reliable workflow scheduling system, its workflow is composed of a series of actions made of a forward acyclic graph (DAGs), coordinator job is a time-frequency periodic trigger Oozie workflow job. The job types supported by Oozie are Java map-reduce, streaming map-reduce, Pig, Hive, Sqoop, and Distc

Presentation This step is simple, reading MySQL data, using highcharts tools such as various displays, you can also use crontab timed PHP script to send daily, weekly, etc.Subsequent updates Recently see some information and other people communicate found that cleaning data this step without PHP, can focus on HQL implementation of cleaning logic, the results are stored in