Download Hadoop: The Definitive Guide (3rd Edition) by Tom White PDF

By Tom White

Able to unencumber the facility of your info? With this finished advisor, you’ll how you can construct and keep trustworthy, scalable, disbursed platforms with Apache Hadoop. This booklet is perfect for programmers seeking to examine datasets of any dimension, and for directors who are looking to arrange and run Hadoop clusters.

You’ll locate illuminating case stories that exhibit how Hadoop is used to resolve particular difficulties. This 3rd variation covers contemporary alterations to Hadoop, together with fabric at the new MapReduce API, in addition to MapReduce 2 and its extra versatile execution version (YARN).
* shop huge datasets with the Hadoop disbursed dossier approach (HDFS)
* Run allotted computations with MapReduce
* Use Hadoop’s information and I/O construction blocks for compression, info integrity, serialization (including Avro), and patience
* become aware of universal pitfalls and complex positive factors for writing real-world MapReduce courses
* layout, construct, and administer a devoted Hadoop cluster—or run Hadoop within the cloud
* Load information from relational databases into HDFS, utilizing Sqoop
* practice large-scale facts processing with the Pig question language
* learn datasets with Hive, Hadoop’s information warehousing method
* benefit from HBase for dependent and semi-structured information, and ZooKeeper for construction dispensed platforms

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Extra info for Hadoop: The Definitive Guide (3rd Edition)

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The last section of the output, titled “Counters,” shows the statistics that Hadoop generates for each job it runs. These are very useful for checking whether the amount of data processed is what you expected. For example, we can follow the number of records that went through the system: five map inputs produced five map outputs, then five reduce inputs in two groups produced two reduce outputs. The output was written to the output directory, which contains one output file per reducer. The job had a single reducer, so we find a single file, named part-r-00000: % cat output/part-r-00000 1949 111 1950 22 This result is the same as when we went through it by hand earlier.

We will answer this first without using Hadoop, as this information will provide a performance baseline and a useful means to check our results. The classic tool for processing line-oriented data is awk. Example 2-2 is a small script to calculate the maximum temperature for each year. Example 2-2. =9999 && q ~ /[01459]/ && temp > max) max = temp } END { print max }' done The script loops through the compressed year files, first printing the year, and then processing each file using awk. The awk script extracts two fields from the data: the air temperature and the quality code.

Example 2-3 shows the implementation of our map method. Example 2-3. write(new Text(year), new IntWritable(airTemperature)); } The Mapper class is a generic type, with four formal type parameters that specify the input key, input value, output key, and output value types of the map function. For the present example, the input key is a long integer offset, the input value is a line of text, 22 | Chapter 2: MapReduce the output key is a year, and the output value is an air temperature (an integer).

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